Ultimate Guide to Power Efficiency

Power Efficiency Guide

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Section Vii Energy Management

28 Design Challenges in Energy-Efficient Medium Access Control for Wireless Sensor Networks Duminda Dewasurendra, Amitabh Mishra 29.2 Overview of Node-Level Energy Management 29.3 Overview of Energy-Efficient Communication 29.4 Node-Level Processor-Oriented Energy Management 29.5 Node-Level I O-Device-Oriented Energy Management

Energy Consumption in WSNs

Other components will consume energy when fulfilling their tasks. Extensive study and analysis of energy consumption in WSNs are available 69, 80, 83, 87 . Energy consumed for sensing includes (1) physical signal sampling and conversion to electrical signal (2) signal conditioning and (3) analog to digital conversion. It varies with the nature of hardware as well as applications. For example, interval sensing consumes less energy than continuous monitoring therefore, in addition to designing low-power hardware, interval sensing can be used as a power-saving approach to reduce unnecessary sensing by turning the nodes off in the inactive duty cycles. However, there is an added overhead whenever transiting from an inactive state to the active state. This leads to undesirable latency as well as extra energy consumption. However, sensing energy represents only a small percentage of the total power consumption in a WSN. The majority of the consumed power is in computing and communication,...

Overview of Node Level Energy Management

One approach to reduce energy consumption is to employ low-power hardware design techniques 7, 14, 33 . These design approaches are static in that they can only be used during system design and synthesis. Thus, these optimization techniques do not fully exploit the potential for node-level power reduction under changing workload conditions and their ability to trade off performance with power reduction is thus inherently limited. An alternative and more effective approach to reducing energy in embedded systems and sensor networks is based on dynamic power management (DPM), in which the operating system (OS) is responsible for managing the power consumption of the system.

Energy Consumption for Data Encryption and Decryption

Data encryption and decryption play vital roles in secure DSNs they are performed in pre- and postprocessing stages in a sensor node, respectively. Therefore it is useful to measure energy consumption for different data encryption decryption algorithms on different embedded microprocessors. Assume a scenario in which the security requirement is high, messages are in small size, and public key algorithms are used to encrypt and decrypt messages. Table 38.2 gives information for computing a 128-b multiply function, the basic building block for most public key algorithms, at the reference 3.3 V. P represents power consumption f represents clock frequency t represents CPU time for the execution and E represents energy consumption. A processor's energy consumption for encryption and decryption operations is directly related to the costs of performing the basic modular arithmetic functions widely used in security protocols. For example, in RSA encryption, the modular operation (Me mod n)...

Model of Sensor Node and Energy Consumption in DSNs

Energy dissipation through the sensor network is the sum of the energy consumed by all the sensor nodes in the network. This includes three parts (1) energy dissipation on the sensor transducer (2) energy dissipation for communication among sensor nodes and (3) energy consumed by the microprocessor in computation. The amount of energy consumed by the sensor transducers depends on the sensitivity of the sensor and is normally a minor part of the entire sensor node's energy consumption.* Radio transmission may contribute more than half of the peak power. It consumes more power in transmit mode than in receive mode because the transmit amplifier must be active at the transmitter's end. Radio transceivers are relatively complex circuits and it is difficult to reduce the communication energy consumption. A simplified model for communication energy can be found in Heinzelman et al. 10 and techniques to minimize this part of the energy consumption have been developed 2, 16, 33, 36 however,...

Energy Management and Topology Maintenance

A number of alternative power minimization methods act above the MAC layer powering off redundant nodes' radios in order to expand the battery lifetimes. For example, AFECA 19 trades off energy consumption and the quality of the message delivery services based on the application requirements. GAF 20 is another power-saving scheme that saves energy by powering off the redundant nodes. GAF identifies the redundant nodes by using the geographic location and a conservative estimate of the radio ranges. It superimposes a virtual grid proportional to the communication radius of the nodes onto the network. Because the nodes in one grid are equal from the routing perspective, the radios of the redundant nodes within a grid can be turned off. The nodes awake within a grid rotate to balance their energy. SPAN is a power-saving, distributed, randomized coordination approach 1 that preserves connectivity in wireless networks. The work presented in Koushanfar and colleagues 9 has proved the...

Energy Management in PMP Mode

Listening Interval Sleeping Interval

Figure 13.5 Energy management scheme in IEEE 802.16. Figure 13.5 Energy management scheme in IEEE 802.16. Figure 13.6 shows the energy consumption in terms of different Ag Ac ratio with fixed A 0.05. The comparison shows that the consumed energy varies with the different frame directions. This is because, with more outgoing frames (or external operations), the sleep mode is terminated instantaneously by such frames with higher probability,

Background Research Motivation

Besides the research on natural, flexible HCI, various research areas and technologies would benefit from efforts to model human perception of affective feedback computationally. For instance, automatic recognition of human affective states is an important research topic for video surveillance as well. Automatic assessment ofboredom, inattention, and stress will be highly valuable in situations where firm attention to a crucial but perhaps tedious task is essential, such as aircraft control, air traffic control, nuclear power plant surveillance, or simply driving a ground vehicle like a truck, train, or car. An automated tool could provide prompts for better performance, based on the sensed user's affective states.

Management Challenges

Energy is a critical resource in WSNs. Thus, all operations performed in the network should be energy efficient. Topology is dynamic because sensor nodes can become out of service temporarily or permanently (nodes can be discarded, lost, destroyed, or even run out of energy). In this scenario, faults are a common fact, which is not expected in a traditional network.

Functional Architecture for Sensor Networks

Compared to conventional distributed databases in which information is distributed across several sites, the number of sites in a sensor network equals the number of sensor nodes, and the information collected by each node (e.g., sensor readings) becomes an inherent attribute of that node 9 . To support energy-efficient and scalable operations, sensor nodes could be autonomously clustered. Furthermore, the data-centric nature of sensor information makes it more effectively accessible via an attribute-based naming approach instead of explicit addresses 10 . In addition, as these sensors are integrated into and extract information from physical environments, many applications also require the location information to be passed along with their sensor data. As a result, a generic functional architecture for sensor networks consists of the following components

Practical use of Game Theory Example

These consuming costs contain the costs of operation (energy consumption, training costs, wages, foreign conferences costs, software, licence fees, and so on), marketing costs (training and foreign trip costs, advertisement costs, market research costs, cost of polls, mailing costs and so on).

System Architecture Protocols and Algorithms 9521 Sensor Deployment Strategies

Energy consumption at sensor node level has been described in Raghunathan et al. 81 , Shih et al. 92 , and Sinha and Chandrakasan 95 . From a functionality perspective, energy is consumed for sensing, computation, and communications. Power conservation can be achieved in any of these functions. Second, adaptively adjusting the operating voltage and frequency to meet the dynamically changing workload without degrading performance is a method of energy saving on computation. The rationale behind this technique is that the computational workload of MCU in WSNs is usually time varying and peak system performance is not always demanded. Dynamic voltage scaling (DVS) 14, 39, 73, 81 is an example of this approach. However, this scheme needs to predict the microprocessor's workload so as to adjust the power supply and operating frequency. A workload prediction strategy in WSNs is described in Chakrabarty et al. 14 . More accurate prediction can lead to higher power efficiency with less...

Evaluating Consumption Levels

As discussed earlier, the Bluetooth low power modes have different characteristics and are suited to different classes of applications. Each low power mode also has a different cost in terms of energy consumption.The power consumption of a device is influenced by the hardware used, the low power parameters negotiated, and the type of application it is running. This section will aim to give a very general indication of the relative power consumption characteristics of the Bluetooth low power modes. Absolute values for the average current consumption in each mode are meaningless since it is highly dependent on the underlying hardware. This section will therefore concentrate on the relative power consumption of some of the Bluetooth low power modes.

Applications of the ISDN Interface

This feature has never been well defined. The concept is that many household devices can be connected to the data channel. This can include an energy management system that would let the power company selectively turn off the refrigerator or air conditioner for an hour or so at peak usage time. The concept also includes connecting the utility meters to permit remote monitoring and billing. Although several proof-of-concept trials of this technology have been conducted, apparently the cost of implementation outweighed the potential savings.

Individual Components of SN Nodes

SN nodes generally are composed of six components processor storage unit power supply sensors and or actuators and, finally, communication (radio) subsystems. It is apparent that standard processors, possibly augmented with DSP, and other coprocessors and some ASIC units will provide adequate processing capabilities at acceptable low-energy rates. Also the state of the art of the actuators is such that they are still not used in the current generation of SN nodes. Therefore, the focus is on the other five components. For the sake of completeness, the discussion begins by presenting a processor specifically designed for sensor networks.

Task Decomposition and Allocation

A major characteristic of sensor networks is that all nodes in the network collaborate toward a common application. An important design issue is how to achieve good application performance in a cost-effective and energy-efficient way. Leveraging the wide hardware spectrum, a designer should decompose a complex application into different tasks and assign them to appropriate hardware in the tiered network. The goal is to match different task requirements with different node capabilities.

Tradeoffs And Constraints

By and large, even though some general design guidelines should be carefully followed, there is no one-size-fits-all solution that can meet all design requirements. Furthermore, different design objectives may need to be addressed in different scenarios. For example, in sensor networks, energy saving may be even more important than improving throughput.

Modeling of Dynamic Sensor Networks

The definition and development of models in order to analyze and evaluate sensor networks can help not only to study the network behavior and predict the evolvement of the system systematically, but also to direct deployment and implementation of these networks. This section introduces and highlights the performance metrics involved in the modeling process of dynamic sensor networks. Then the modeling of sensor networks is addressed from various aspects, such as sensing coverage, node placement, connectivity, energy consumption, etc.

Time Synchronization Protocols

Instead of time synchronization between the sender and receiver during an application, such as in the Internet, the sensor nodes in the sensor field must maintain a similar time within a certain tolerance throughout the lifetime of the network. Combining with the criteria that sensor nodes must be energy efficient, low cost, and small in a multihop environment as described in Section 16.1, this requirement offers a challenging problem. In addition, the sensor nodes may be left unattended for a long period of time, e.g., in deep space or on an ocean floor. For short-distance multihop broadcast, data processing time and the variation of data processing time may contribute the most in time fluctuations and differences in path delays. Also, the time difference between two sensor nodes is significant over time due to the wandering effect of the local clocks.

Necessity of Resource Efficiency

The limited physical size of sensor nodes has the inherent problem of severe resource limitation. Therefore, in WSNs, resource efficiency is extremely critical despite its complexity. Above all, energy-efficient protocols are in high demand in order to extend the lifetime of the system. Because a WSN often operates in a human-unattended manner, the power supply (which is usually an attached battery) cannot be

Energy Efficient MAC Protocols

The functions of the data link layer include framing and link access, reliable delivery, flow control, error detection, and retransmission. Because nodes share a common wireless medium for communication, MAC sublayer protocols are critical to providing coordination among nodes. These protocols attempt to provide reliable communication and achieve high throughput with bounded latency, while at the same time minimizing collisions and energy dissipation 50, 89, 90 . The following discussion covers sources influencing energy consumption at the MAC layer, which may lead to directions to improve energy efficiency. Different kinds of energy-efficient MAC layer approaches will also be discussed and some comparisons made in terms of energy efficiency.

Classification of Network Layer Protocols

To reduce communication's energy consumption, network layer protocols have drawn considerable attention. Many factors influence the design of network layer protocols, and a wide range of schemes have been proposed. Table 18.1 presents a classification of energy-efficient (E2) network layer protocols. Note that the purpose of such classification is to aid with the study of energy-efficient network layer protocols. Other researchers may opt to use different, and possibly more elaborate, classifications. Energy-efficiency objectives Minimizing energy consumption in forwarding each individual Minimizing in-network total energy consumption the sensed values. The second kind is reactive transmission of sensed values is triggered by some specific conditions. They can be driven by a phenomenon or query. The last kind is hybrid, which is a combination of the proactive and reactive methods. In order to realize energy efficiency, different communication protocols have been proposed to fit each...

Energy Efficient Data Delivery Protocols

One of the critical responsibilities of network layer protocols is to provide data delivery between desired source and destination. In WSNs, data delivery protocols should take energy efficiency into consideration. A number of protocols target E2 data forwarding. These can be classified into distinct groups according 18.5.2.1 Energy-Efficient Information Collection (E2IC) Protocols Flat multihop E2IC protocols. The geographical adaptive fidelity (GAF) algorithm 96 and a protocol called SPAN 14 are proposed for wireless ad hoc networks. Due to their scalability, they are also applicable in WSNs. Taking advantage of redundant deployment of sensor nodes and low duty cycle, both protocols designate to rotate switching nodes between active and inactive states without losing the connectivity of the system. In GAF, equivalent nodes are identified based on geographic locations on a virtual grid, so they can substitute each other directly and transparently without affecting the routing...

Signal and Data Processing

Several data aggregation algorithms have been reported in the literature. The most straightforward is duplicate suppression, i.e., if multiple sources send the same data, the intermediate node will only forward one of them. Using a maximum or minimum function is also possible. Heinzelman and colleagues 31 and Julik and colleagues 48 proposed SPIN (see Section 18.5.2.2) to realize traffic reduction for information dissemination using metadata negotiations between sensors to avoid redundant and or unnecessary data propagation through the network. The greedy aggregation approach 43 can improve path sharing and attain significant energy savings when the network has higher node densities compared with the opportunistic approach. Krishnamachari and colleagues 47 described the impact of source-destination placement on the energy costs and delay associated with data aggregation. They also investigated the complexity of optimal data aggregation. In Reference 111, a polynomial-time algorithm...

Barrier Coverage Model

The best coverage problem is further explored and formalized by Li and colleagues 14 , who proposed a distributed algorithm for MSP computation using the relative neighborhood graph. The authors also considered two extensions MSP with least energy consumption and MSP with smallest path distance.

On Distributed Sensor Networks

34 aims to develop low-power, low-cost, wireless MEMS-based microsensors that can sense, actuate, and communicate. Power efficiency is provided by power management over the network, low-power mixed signal circuits, and low-power radio frequency (RF) receivers 2 . Berkeley's Smart Dust project 35 uses optical, instead of RF, transmission techniques to make communication with reference to energy inexpensive 16 . More recently, researchers in MIT launched the Ultra Low-Power Wireless Sensor project, which targets design and fabrication of sensor systems capable of wirelessly transmitting data at 1 b s 1 Mb s with average transmission power of 10 mW to 10 mW 36 . They have focused on developing energy-efficient communication protocols, in particular, energy-scalable algorithms 10, 29 . At a network level, Estrin et al. 9 discuss the scalable coordination problem and argue that a localized algorithm, in which sensors only interact with other sensors in a restricted vicinity, is promising...

Simulation Results Effect of Mobility

Significantly higher packet delivery fraction than tworay and shadowing channel models by about 50 and 80 percent respectively, as can be seen in Figure 3a. Figure (3b) shows that as the speed increases, the NRL, (routing overhead), increases too. It also shows that shadowing channel model has higher effect on NRL when the speed is increased compared with other channel models. As it can be seen from Figure 3c, the shadowing channel model has lower energy efficiency compared with other channels. Also it shows that as the speed of mobile nodes increases, the energy efficiency decreases for all wireless channel modes. channel models decreases. It is clear from Figure (4c) that shadowing channel model has a longer delay than other channel models. The delay in the shadowing model decreases very fast as the pause time increases. The energy efficiency of shadowing channel model is low but it increases as the pause time increases. Thus, shadowing channel model can deliver traffic data per...

Moteto Mote Communication

The procedures for establishing and operating a network require the motes to communicate with one another. The task of routing packets from a source to a destination can be broken down into discovering the position of the destination and the actual forwarding of packets 20 . Furthermore, channel access can be implemented by two different methods contention or explicit organization 21 . The contention-based approach is not suitable for DSNs because of its requirement to monitor the channel for a long span of time. Because the reception and transmission have almost the same energy cost, the organized channel access is characterized with better energy efficiency. At the same time, the process of establishing time division multiple access (TDMA) slots or frequency bands also consumes energy. In an attempt to alleviate this problem, some protocols employ a hierarchical structure that requires partitioning the network. A subtle effect of multihop communication is that energy consumption is...

Impact of Network Load

Model demonstrates significantly higher packet delivery ratio than tworay and shadowing channel models by about 50 and 90 percent respectively, as it can be seen in Figure (5a). NRL of freespace and tworay channel models is very low compared to shadowing channel model, see Figure (5b). In Figure (5 c), shadowing channel model shows a longer delay than other channel models. Energy efficiency of freespace channel model is almost stable for all network load levels, and outperforms tworay and shadowing channel models by (0 45) and (160 500) percent respectively, as it is shown in Figure (5d). In all cases, the performance of DSR protocol with shadowing channel model demonstrates significantly lower performance than other channel models. While with freespace, DSR protocol performs better as it is clear from Figure 5.

Dynamic Voltage Scaling on Sensor Nodes

Figure 38.5 gives an overview of how a microprocessor achieves energy efficiency by switching supply voltages. The encrypted data packets received by the radio transceivers are passed to the microprocessor, which decrypts and authenticates the data at the current voltage.* The microprocessor then checks whether the packet contains a message header if not, it continues message decryption for the following packets. Otherwise, the microprocessor obtains information about the size of the message, estimated processing load, and size of the result from the message header. TABLE 38.3 Energy Consumption for Public Key Algorithms at 3.3 V Computational Energy Consumption (mJ) TABLE 38.3 Energy Consumption for Public Key Algorithms at 3.3 V Computational Energy Consumption (mJ) The goal of preprocessing is to decrypt the message using the most energy-efficient voltage and determine the voltage for data processing. The decrypting voltage is decided based on the information provided by the...

Conclusions And Discussions

Multiple antenna techniques play important roles in physical layer techniques. This chapter provides a survey of multiple antenna techniques for the WMNs. By using multiple-antenna techniques, the capacity and throughput of the WMNs could be remarkably enlarged, and the energy efficiency and routing performance of the WMNs could be greatly improved. Furthermore, implementing the multiple antenna techniques could provide better connectivity and more accurate location estimation for the WMNs.

Overview of Energy Efficient Communication

Energy-efficient communication in sensor networks is crucial because most sensor nodes are battery driven and therefore severely energy constrained. Considerable research has been recently carried out in an effort to make communication in sensor networks energy efficient 5, 12, 13, 19, 23, 24, 27, 29, 31, 32, 36, 38, 39 . The focus here is on reducing energy consumption in wireless sensor networks for target localization and data communication. The transmission of detailed target information consumes a significant amount of energy because of the large volume of raw data. Contention for the limited bandwidth among the shared wireless communication channels causes additional delay in relaying detailed target information to the cluster head.

Gathering Information in Wireless Sensor Networks

One of the salient features of WSNs is information gathering, the ultimate goal of which is to group and collect the information sensed by the sensor nodes. This section deals with a protocol to assemble and retrieve such information. More specifically, time- and energy-efficient protocols are proposed to compute the sum over any commutative and associative binary operator for the values stored in the sensor nodes. The binary operators can be addition multiplication logical AND OR finding the maximum minimum etc. Consider a single-hop WSN comprising m sensor nodes, in which each sensor has a unique ID in 1,m . Let Si denote a sensor node with ID i, (1 i m), which has a value xi stored in it. In this scenario, the sum problem can be solved in m - 1 time slots as follows for each time slot i, (1 i m - 1), the sensor node Si broadcasts xi on the channel and sensor node Si+1 monitors the channel to receive xi. Then, Si+1 computes xi+1 xi xi+1. After m - 1 iterations, node Sm holds the...

Protocol Fault Tolerant WSNSUM

After partitioning the grid, step 2.1 computes the sum on each sub-block using the energy-efficient and fault-tolerant protocol of Lemma 23.3. Because step 2.1 can be computed in parallel for neighboring blocks, it takes O(r2) time slots to compute the sum on each sub-block and no sensor needs to wake for more than O(log r) time slots. Let S' , (1 i, j 9), be the sensor node that holds the sum of sub-block i,j at the end of step 2.1. The sum of each block in step 2.2 is computed in a snake-like fashion by combining the partial results of step 2.1. Because there are 81 sub-blocks, the sum on blocks can be computed in O(1) time slots and no sensor node needs to wake for more than two time slots. Step 4 and step 5 are performed as in the previous protocol. Theorem 23.2. On a WSN in which the sensor nodes are arranged in cells of grid size -Jn x-Jn , one sensor node per cell, the sum of n numbers can be computed by a fault-tolerant and energy-efficient protocol in

Energy Driven System Configuration

For a given set of messages with certain statistical information, it will be interesting to see what can guide selection of the right combination of microprocessor and public key algorithm to implement the secure DSN in the most energy-efficient way. For this purpose, the following simulations in which, for each different setting of messages, the energy consumption was stimulated on all possible system configurations, were conducted. The interarrival rate m takes value from the set of 0.125,0.1, 0.05,0.025, 0.01 the message size will be within one of the following ranges 200,20000 200, 4000 200, 10000 or 10000,20000 . The processing time falls into one of the following 500,4000 or 100,1000 , TABLE 38.5 Run-Time and Energy Consumption Breakdown for Simulation on MIPS R4000 with TABLE 38.5 Run-Time and Energy Consumption Breakdown for Simulation on MIPS R4000 with The preliminary results elicit several interesting observations. For example, if the microprocessor is not fast enough to...

On Dynamic Voltage Scaling

The preceding technology gives a DVS system the flexibility of operating at different voltages and clock frequencies to conserve energy. At the system level, there has been research on task-scheduling strategies for adjusting CPU speed so as to reduce energy consumption of DVS systems, particularly from the realtime system and operating system societies. Most of this work, based on a scheduling model suggested by Yao and coworkers 30 , assumes that the CPU speed can be changed arbitrarily as a result of voltage scaling. Most research (particularly early research) work on DNS is on multiple DVS systems in which multiple voltages are simultaneously available on the chip. For practical reasons, this is also the DVS system that will be used in this chapter for the energy-efficient DSN design. The most important and relevant energy-reduction techniques on such multiple voltage DVS systems will now be surveyed. The study of a multiple DVS system at a high level focuses on how to assign...

Motivational Example

A secure DSN is used as an example to explain how DVS, with the help of a message header, can reduce energy consumption. Each sensor receives an encrypted message from other nodes every 5 s. The sensor must decrypt the message, process the data, and encrypt and send out the result before the arrival of the next message. A message contains a certain number of packets of fixed size. Suppose the RSA algorithm is used as the encryption function, which requires 110 and 5 ms to decrypt and encrypt a single packet, respectively.* Now consider two messages, t and t2, both with 10 packets. Assume that t requires 2 s for data processing and needs 20 packets for the (encrypted) processing result, and that t2 demands a forward therefore no data processing is needed and the encryption results in a 10-packet message. The microprocessor will be on for data decryption encryption and processing with a power consumption of 230 mW at the 3.3-V reference voltage. It stays in the idle state from the...

Performance Evaluation

The main objective of the protocol is gradually to balance energy consumption across the network. To evaluate performance of this protocol, the following metrics, which reflect dispersion or concentration of energy consumption across a network, are used. Variance of energy level. The variance of the energy levels of all the nodes is the primary measure of dispersion. A high variance indicates higher energy consumption at some of the nodes compared to others.

Energy Evaluation Model for Target Localization in Wireless Sensor Networks

Consider the energy consumption for a sensor network that is actively detecting a target in the sensor field. Assume that sensor nodes are homogeneous and therefore the energy consumption for sensing is the same for each sensor node. Because the focus here is on energy minimization of communication traffic due to target activities or events, energy consumed by sensor nodes when they are in the idle state is not considered. This does not imply, however, that the energy consumption of idle sensor nodes can always be ignored. To simplify the energy analysis, first consider a primitive sensor model that focuses on the energy consumption of the wireless sensor network due to the target activities or events. Suppose the sensor node has three basic energy consumption types sensing, transmitting and receiving and these power values (energy per unit time) are Es, Et, and Er, respectively. If all sensors that reported the target for querying are selected, the total energy consumed for the event...

Data Transmission Phase

During this phase, data packets are transmitted from one leader node to the other through the optimal path (with least energy weakness). Each data packet also potentially collects information about the energy consumption en route by keeping track of residual energy levels of nodes on the path. When energy levels of a given critical number of nodes fall below a certain threshold, the data transmission phase ends and the new optimal path determination phase begins. The fundamental steps of the data transmission phase are

Simulation on Different System Configurations

For one set of messages generated from a 1-h simulation (described previously), further simulations are conducted on different combinations of microprocessors and public key algorithms. For each system, simulations are performed on the fixed voltage (3.3 V) core and the core with multiple voltages (3.3, 2.4, and 1.2 V). Figure 38.8 and Figure 38.9 report energy consumption and nonidle time for five representative systems (the ElGamal algorithm is excluded because of its high encryption cost) Figure 38.8 indicates significant energy reduction in all systems, from 58 in the M-Core system to 73 in the StrongARM core, with an average of 64 energy savings. Considering that the microprocessor is responsible for 30 of the sensor node's total energy consumption, this means an energy savings of 20 , which is still significant. Although a constant 55 energy reduction from data processing occurs, energy savings from data decryption and data encryption are very different. For RSA, in which...

Merits Drawbacks and Implications for WSNs

Energy consumption is reduced in EC-MAC due to the use of a centralized scheduler, as in Bluetooth. Therefore, collisions over the wireless channel are avoided, thus reducing the number of retransmissions. Additionally, mobile receivers are not required to monitor the transmission channel as a result of communication schedules. The centralized scheduler may also optimize the transmission schedule so that individual mobiles transmit and receive within contiguous transmission slots. This scheme highlights the fact that scheduling algorithms that consider mobile battery power level in addition to packet priority may improve performance for low-power mobiles. Techniques used to minimize the energy consumption and performance of EC-MAC in this regard are discussed in detail in Sivalingam et al. 6 .

Mobile Agent Routing Using the Genetic Algorithm

The processing element dispatches a mobile agent that visits a subset of sensors within the cluster to fuse data collected in the coverage area. Generally speaking, the more sensors that are visited, the higher the detection accuracy will be achieved using any reasonable data fusion algorithm 15 . However, visiting more sensors often incurs more communication and computing costs. The routing objective is to find a path for a mobile agent that satisfies the desired detection accuracy while minimizing energy consumption and path loss. An approximate solution based on a genetic algorithm proposed by Wu and coworkers 6 is briefly described next. To facilitate the optimization process using genetic algorithm, an objective function of path P that considers the trade-off among energy consumption EC(P), path loss PL(P), and detected signal energy FIGURE 25.2 Performance comparison (a) node sizes vs. hop numbers (b) node sizes vs. path losses (c) node sizes vs. energy consumptions (d) node...

Online Scheduling for Multistate Devices Algorithm Muscles

For a precomputed task schedule, MUSCLES generates a sequence of power states for every device so that energy is minimized. It operates as follows (also see Figure 29.10) Let device ki be in state pSj at scheduling instant sm. MUSCLES finds the next task, tl, that uses k-t (line 1). A check is then performed to test whether ki can be switched down to a lower powered state. This is done by ensuring that at least j + 1 valid scheduling instants are between the current scheduling instant and tl's start time. The presence of j + 1 valid scheduling instants implies that device k-t can be switched down from state pSj to pSy+j (line 3). The absence of j + 1 valid scheduling instants precludes the shutting down of k-t to a lower powered state a check is then performed to test whether the device must be switched up. If exactly j instants are present, then the device must be switched up in order to ensure timeliness (line 4). At the completion of a task, Tm, the same process is repeated....

Energy Aware Communication

This section describes a novel target localization approach based on a two-step communication protocol between the cluster head and the sensors within the cluster. Because the energy consumption in wireless sensor networks increases significantly during periods of activity, which may be triggered, for example, by a moving target 5 , an energy-reduction method is proposed for target localization

Data Communication Phase

Data funneling creates clusters within the sensor network, but does so in a fluid fashion, which makes the approach a lot less brittle. There is no single cluster head whose failure can be devastating to the functionality of the network. Instead, the border nodes take turns acting as the cluster head, spreading out the responsibility and the load (i.e., energy consumption) among them. Also, the controller can redefine the regions into which its area of interest is divided, thereby forcing the nodes to divide into new clusters and elect new sets of border nodes. The controller can redefine the regions based on the data received from the nodes and or the energy remaining in the nodes so as to ensure that nodes with the greatest energy reserves act as border nodes. of the sensor readings is done at the aggregation points. Performing compression on the sensor readings at the aggregation points within a region would result in even greater energy savings.

Design Challenges for Wireless Sensor Networks

Energy consumption of a WSN occurs in three domains sensing data processing and communications among these, radio communication is the major consumer of energy. As highlighted in Pottie and Kaiser 12 , energy for transmitting 1kb over a distance of 100 m is estimated as 3 J. With the same amount of energy, a general purpose processor with 100 MIPS W power could execute 3 million instructions. The sensing circuitry consumes less power than the processor board in a typical WSN platform such as MICA 13, 14 . However, the radio consumes two to three times the power of the processor during packet transmission. Power consumption of the radio during listening to the channel for reception is also higher than the processor at full operation, but relatively lower than the transmitting power. The MICA sensor network platform defines four modes of operation, and Table 28.3 shows the typical current draw and power consumption of each node. Thus, it is clear that the research focus should be on...

Reliable Routing in Geographically Routed Sensor Networks

Let G be an arbitrary sensor network following geographic routing, with sensor success probabilities P, communication energy costs C, and data of value vr to be routed from leader node sr to the sink leader node sq, where vi 0Vi * r. Although the RQR problem is NP-hard for general sensor networks, it becomes surprisingly easy when the additional constraint of path length 7 is added. Lemma 34.1. Let Li be the longest geographically routed path from si to sq in G. Then, si can determine its optimal RQR neighbor under the reliability payoff model in Li steps.

Why Are MAC Layer Design Issues Important

In the context of WSNs, this requirement is extremely critical. According to the characteristics highlighted previously, nodes of a WSN carry extremely low energy resources and remain unattended after deployment therefore, the node lifetime depends entirely on how energy is conserved during communication. Although some exhausted nodes could be compensated using redundant neighboring nodes, certain situations may arise rendering a part of the network completely inactive due to low connectivity and insufficient coverage, or making that part of the network inaccessible and isolated from the other parts. Such scenarios could be averted by avoiding unnecessary transmissions and longer listening periods activities that consume the highest amount of power in nodes.

Simulation Results

First, the messages that the sensor receives in the simulated communications will be described. Then, for different microprocessors coupled with different public key algorithms, the average energy consumed by the traditional fixed voltage processor and the multiple voltage processor for the same sets of messages will be reported. To analyze where the energy saving comes from better, detailed time and energy data are given for the case of an MIPS R4000 processor with RSA. Several other case studies with questions such as the role of different public key algorithms and, eventually, how to guide system configurations are also conducted.

Models and Abstractions

The current standard assumption is that each node is only aware of its own neighborhood, i.e., nodes to which it can directly communicate. Sometimes this definition is enhanced to k-hop neighbors. In the future, schemes that explicitly state what is stored at each node will emerge. Essentially, as data structures play a crucial rule in the development of standard computer algorithms, data placement plays a crucial rule in localized algorithms. It is also important to note that as storage technology rapidly emerges, assuming that each node has only information about its own neighborhood is unrealistic. However, although information in static networks can be easily stored in each node, it would be expensive for each node to inform too many nodes about its status when the network is mobile or when an energy-saving procedure is conducted using sleeping mode. A number of energy consumption models exist. A specific example of an energy consumption model for wireless radio is given by...

Summary and Open Questions

Wireless sensor networks have attracted considerable attention recently due to potential wide applications in various areas and the ubiquitous computing. Much excellent research has been conducted to study the electronic and the networking parts of wireless sensor networks. Networking also has many interesting topics, such as topology control routing energy conservation QoS mobility management and so on. This chapter presented an overview of recent progress in applying computational geometry techniques to solve questions such as topology construction and localized routing in wireless sensor networks.

Power Attenuation Model

Energy conservation is a critical issue in sensor networks for the node and network life because the nodes are powered by batteries only. Each sensor node typically has a portable set with transmission and reception processing capabilities. To transmit a signal from a node to another node, the power consumed by these two nodes consists of the following three parts. First, the source node needs to consume some power to prepare the signal. Second, in the most common power-attenuation model, the power needed to support a link uv is I Uv I I5, where I Uv I I is the Euclidean distance between u and v, and is a real constant between 2 and 5 dependent on the transmission environment. This power consumption is typically called path loss. Finally, when a node receives the signal, it needs to consume some power to receive, store, and then process that signal. For simplicity, this overhead cost can be integrated into one cost, which is almost the same for all nodes. Thus, c will be used to...

Gateway Load Balancing In Wireless Mesh Networks

Gateway nodes connect a WMN to the wired network, generally to the Internet. Therefore, traffic aggregation happens at gateway nodes which essentially limits the WMN's capacity. In addition to the limited capacity, the gateway node particularly expends much more energy for handling large number of packets it forwards, and this high energy consumption leads to quicker failure of gateway nodes in energy constrained WMNs. Therefore, gateway load balancing assumes significance in order to achieve the following

Management Dimensions

QoS architectures can only be effective and provide guaranteed services if QoS elements can be adequately configured and monitored mechanisms can be defined to help managers to deal with these elements. Also, such mechanisms must allow replacement of the current device-oriented management approach by a network-oriented or cluster-oriented approach. Thus, in addition to the management of elements (physical and logical resources), management applications must also manage QoS aspects. Components involved in QoS support to WSNs include QoS models, QoS sensing, processing, and QoS dissemination 22 . The larger the number of monitored QoS parameters is, the larger the energy consumption and the lower the network lifetime are. On the other hand, this will lead to a large number of collisions and potentially to congestion situations, increasing latency and reducing energy efficiency. Congestion control must be based not only on the capacity of the network, but also on the...

Overview Of Wireless Mesh Networks

The mesh routers normally have more powerful energy supplies than those of mesh clients. Since the infrastructure backbone and hybrid WMNs are equipped with mesh routers, the more complex but effective algorithms could be implemented in the WMNs. These algorithms improve the performance of the system at different layers and from different aspects. The energy consumption is highly reduced in the WMNs or the ad hoc networks due to the application of multihop-related techniques. However, the limited energy problem is still considered as a bottleneck for the performance of the ad hoc or the sensor networks, because powerful digital signal processing (DSP) could not be conducted in the nodes. 4. The energy consumption is a critical problem for the WMNs, especially for the mesh clients. Multihop techniques on the one hand reduce the transmitted power of the mesh clients. On the other hand, however, they also increase the duration and the frequency of the data transmission at the mesh...

Multiple Antenna Techniques For Wireless Mesh Networks

As seen from Section 11.3, the employment of multiple antennas could improve the performance of the WMNs from different aspects. Generally, in order to meet the challenges in the WMNs, the functions of the multiple antenna techniques could be classified into many different parts increase the capacity and throughput, improve the routing performance, increase energy efficiency, and many other performance improvements. Section 11.4.1 through Section 11.4.4 illustrates the above-mentioned functions of multiple antenna techniques for the WMNs. However, the multiple antenna techniques could also be exploited in the areas such as broadcasting, antijamming, and so on. Energy-efficient routing protocol for the ad hoc networks with the directional antennas is studied 32 . Since the directional antennas consume more energy than that of the omni-directional antennas, the energy efficiency is also a big challenge for the mesh client with the directional antennas. Fortunately, the energy...

Motivation and Design Issues in WSN Routing

One of the main design goals of WSNs is to prolong the lifetime of the network and prevent connectivity degradation by employing aggressive energy management techniques. This is motivated by the fact that energy sources in WSNs are irreplaceable and their lifetime is limited. However, the positions of the sensor nodes are usually not engineered or predetermined and thus allow random deployment in inaccessible terrain or disaster relief operations. This implies that the nodes are expected to perform sensing and communication with no continual maintenance or human attendance and battery replenishment, which limits the amount of energy available to the sensor nodes. Therefore, extensive collaboration between sensor nodes is required to perform high-quality sensing and to behave as fault-tolerant systems. Current routing protocols designed for traditional networks cannot be used directly in a sensor network because Data collected by many sensors in WSNs are based on common phenomena there...

Directional Source Aware Routing Protocol DSAP

In order to resolve the problems of power efficiency, a unique identification system has been developed for the networks used. The idea behind this identification system is to identify the location of each node in the network that will help in routing the packets. The system has the following properties This is the basic scheme developed for routing messages. However, the objective is to incorporate energy efficiency as well. This is achieved by considering the maximum available power and minimal directional value when picking which node route to take. Instead of simply picking the node with the lowest directional value, the directional value is divided by the power available at that node. The smaller value of this power-constrained directional value is the path chosen. This allows for a least-transmission path that is also cognizant of power resources, although in some cases a longer path may be chosen if the available power dictates that choice. Salhieh and Schwiebert 10 have...

Energy Efficient Random Coverage

This subection presents several energy-efficient coverage mechanisms because energy efficiency, caused by limited battery resources, is an important issue in WASN. Mechanisms that conserve energy resources are highly desirable because they have a direct impact on network lifetime. Network lifetime is in general defined as the time interval in which the network can perform the sensing functions and transmit data to the sink. During the network lifetime, some nodes may become unavailable (e.g., physical damage, lack of power resources) or additional nodes might be deployed. An efficient, frequently used mechanism is to schedule the sensor node activity and allow redundant nodes to enter the sleep mode as often and for as long as possible. To design such a mechanism, the following questions must be answered Slijepcevic and Potkonjak 21 and Cardei et al. 2 consider a large population of sensors, deployed randomly for area monitoring. The goal is to achieve an energy-efficient design that...

Routing Protocols For Manet

Wireless devices are often powered by batteries that have a finite amount of energy. In some ad hoc networks such as sensor networks deployed in a hostile zone, it may not be possible to change a battery once it runs out of energy. As a consequence, the conservation of energy is of foremost concern for those networks. A good ad hoc routing protocol should therefore be energy efficient.

Classification and Comparison of MAC Protocols

Classification Mac Protocols

Centralized MAC protocols include polling algorithms and controlled multiplexing (or channel partitioning) algorithms 50, 70 . A centralized controller is needed to coordinate channel access among the different nodes and collision-free operation can be achieved. Thus, energy wasted due to collisions can be eliminated. However, because of the high overhead and long delay, pure polling mechanisms are not suitable in large-scale WSNs. Depending on how bandwidth is assigned, controlled multiplexing mechanisms can be frequency division multiplexing access (FDMA), code division multiplexing (CDMA), or time division multiplexing access (TDMA). This class of protocols is preferable in WSNs 65 , not only because it is collision free, but also because nodes can be turned off in unassigned slots, thus saving energy expenditure due to idle sensing and overhearing. Centralized multiplexing access, therefore, lacks flexibility and scalability to adapt to the variation of WSN applications. Some...

Pointto Multipoint and Mesh Networking Modes

In this chapter, we will comprehensively describe the mesh networking mode in the IEEE 802.16 WiMAX and explain the fundamental operation mechanisms. Section 13.2 introduces the PHY fundamentals. The WMAN-OFDM modulation scheme is deliberately explained since this air interface is used in both PMP and mesh networking modes. Following the PHY overview, the Medium Access Control (MAC) layer overview is also provided. Subsequently, the frame structure, energy management, and security management in the PMP mode is presented. In Section 13.4, following the explanation of the mesh frame structure and the functionalities of each subframe, we elaborate the entry process of a new node before it is entitled to transmit data in the mesh network. In addition, the scheduling algorithms for determining the transmission opportunities of the control messages and the data subframe are presented. In Section 13.5, a new priority scheme is proposed to differentiate diverse QoS. Section 13.6 presents the...

Specific Analytic Solutions

As a simple application of the moment or variational method, consider first the case of a low-energy pulse propagating in a constant-dispersion fiber with negligible nonlinear effects. Recalling that (1 +C2) T2 is related to the spectral width of the pulse that does not change in a linear medium, we can replace this quantity with its initial value (1 +Cq) Tq, where 7o and Co are input values at z 0. Since the second term is negligible in Eq. (4.6.7), it can be integrated easily and provides the solution

Conclusion

Monitoring applications based on wireless sensor networks represent a new important class of applications that can provide data to different kinds of observers. Furthermore, WSNs must deliver the data of interest according to different parameters, such as power efficiency and latency.

Flat Routing

Node to the destination nodes (i.e., the set of base stations) is built. The paths of the tree are built while avoiding nodes with low energy or QoS guarantees. At the end of this process, each sensor node will be part of the multipath tree. For each node, two metrics are associated with each path an additive QoS metric, i.e., delay, and a measure of the energy usage for routing on that path. The energy is measured with respect to how many packets will traverse that path. SAR will calculate a weighted QoS metric as the product of the additive QoS metric and a weight coefficient associated with the priority level of the packet. The objective of the SAR algorithm is to minimize the average weighted QoS metric throughout the lifetime of the network. If topology changes due to node failures, a path recomputation is needed. As a preventive measure, a periodic recomputation of paths is triggered by the base station to account for any changes in the topology. A handshake procedure based on a...

Hierarchical Routing

Hierarchical or cluster-based routing, originally proposed in wireline networks, comprises well-known techniques with special advantages related to scalability and efficient communication. As such, the concept of hierarchical routing is also utilized to perform energy-efficient routing in WSNs. In a hierarchical architecture, higher energy nodes can be used to process and send the information while low energy nodes can be used to perform the sensing in the proximity of the target. This means that creation of clusters and assigning special tasks to cluster heads can greatly contribute to overall system scalability, lifetime, and energy efficiency. Heinzelman et al. 1 introduced a hierarchical clustering algorithm for sensor networks called low energy adaptive clustering hierarchy (LEACH). LEACH is a cluster-based protocol that includes distributed cluster formation. The authors allowed for a randomized rotation of the cluster head's role in the objective of reducing energy consumption...

Multipath Routing

The resilience of a protocol is measured by the likelihood that an alternate path exists between a source and a sink when the primary path fails. This can be increased by maintaining multiple paths between the source and the sink at the expense of increased energy consumption, and keeping these alternate paths alive by sending periodic messages. Thus, the resilience of the network should be increased while keeping the maintenance overhead of these paths low. This subsection discusses routing protocols that use multiple paths rather than a single path in order to enhance network performance. Ganesan and coworkers 22 have proposed an energy-efficient multipath routing protocol that uses braided multipaths instead of completely disjoint multipaths so as to keep the cost of maintenance low. The costs of such alternate paths are also comparable to the primary path because they tend to be much closer to the primary path. Chang and Tassiulas 23 proposed an algorithm to route data through a...

Query Based Routing

All the nodes have tables consisting of the sensing task queries received, and hence they send data that match these queries when they receive them. Directed diffusion (described in Subsection 6.2.1.2) is an example of this type of routing. In directed diffusion, the sink node sends out interest messages to sensors. As the interest is propagated throughout the sensor network, the gradients from the source back to the sink are set up. When the source has data for the interest, the source sends the data along the interest's gradient path. To lower energy consumption, data aggregation (e.g., duplicate suppression) is performed en route.

Energy and Power

Let us consider the principle of energy conservation in volume V, which is enclosed by a surface S. The medium filling the volume V is characterized According to the energy conservation principle, the power delivered by the sources in the volume V is equal to the sum of the power transmitted through the surface S and power dissipated in the volume, plus 2m times the net reactive energy stored in the volume. This principle is called Poynting's theorem, which can be written as

Software Development

Because of severe resource constraints, the software environment of WSNs is very different from those other distributed and parallel computing systems. Issues such as energy efficiency, scalability, and reliability are fundamental factors in software development for WSNs 13, 47, 49, 67, 81, 94, 99 .

Park Mode

Once the devices have paired and are ready to connect to each other there are two power-saving strategies to be adopted. The first is saving energy while the devices are attempting to establish an RFCOMM connection, and the second is once the RFCOMM connection has been established an RFCOMM connection must be established in order for AT commands to be exchanged so that the audio link (through the use of a SCO connection) can be set up. This is achieved by placing one device into connectable mode (i.e., into page scan mode and letting the other initiate the creation of the connection. According to the Headset profile, either the headset or the Audio Gateway can initiate the connection attempt. If the headset is in slave mode (waiting for the Audio Gateway to connect to it), then it can employ the same technique used in pairing. It can save power by reducing the time it spends scanning (i.e., with its radio transceiver powered on).

Processor

Berkeley BWRC research group has designed and implemented a prototype processor its main target areas include voice processing and related applications for wireless devices. For example, the processor can be used in museums to provide better interaction between visitors and displayed items. The Maia processor 63 is built around an ARM8 core with 21 coprocessors. These 21 processors include two MACs two ALUs eight address generators eight embedded memories and an embedded low-energy FPGA 24 . The goal is to provide enough parallelism at low energy levels. ARM8 core configures the memory-mapped satellites using a 32b configurable bus and also communicates data with the satellite coprocessors using two pairs of I O interface ports by applying direct memory reads writes. The interactions between the ARM8 and coprocessor satellites are carried out through an interface control unit. A two-level, hierarchical, mesh-structured, reconfigurable interconnect network is used to establish the...

Sensor Network Node

It is difficult to anticipate technological trends, but one can easily identify at least some high-impact trends and required solutions. For example, it is apparent that overall energy consumption-balanced architectures are needed. Another high-impact research topic concerns sensor organization and development of the interface between components. Finally, due to privacy, security, and authentication needs, techniques such as unique ID for CPU and other components that facilitate privacy will be in high demand.

Longevity

Kumar et al. consider the suitability of two hardware platforms for various tasks, given their respective power consumption 30 . They consider the Mica mote, which uses very little power but performs complex calculations slowly, and the iPAQ, which consumes significantly more power but performs computations relatively quickly. Their results indicate that when significant computation is required, a faster processor can be more energy efficient than a slower one, due to the short time required to perform the calculation. However, for sensing tasks that require operation over a long period of time, a low-power node that meets the minimum processing requirements is more effective. Thus, a tiered architecture that partitions network functions among hardware designed for each function may increase network lifetime.

Communication

To enable collaborative processing, nodes must be able to communicate with each other. In a tiered network, nodes are often organized into clusters. If a large node exists in a cluster, it is normally selected as a cluster head. No matter what size they are, these nodes must use the same radio to communicate. They also need to run the same low-level protocols, such as the link and MAC protocols. An example is LEACH 21 , in which a cluster runs a TDMA protocol. Within a cluster, nodes only send their data to the cluster head. The cluster head sends aggregate data to a base station using a long-range radio. The role of cluster head will typically rotate among cluster members in order to distribute energy consumption evenly. Task decomposition and allocation are important issues in designing a tiered network. Appropriate task allocation is able to improve sensing reliability, reduce network cost, reduce energy consumption in computation and communication, and utilize special resources...

Background

Much of the related research addresses WSNs that are mobile and battery powered. Because of these requirements, most of the literature is concentrated on finding solutions at various levels of the communication protocol, including being extremely energy efficient. Energy efficiency is often gained by accepting a reduction in network performance 7 . Although one does not wish to waste energy, this system does have a constant, renewable energy source. However, a very low-power dissipation allowance offers constraint, which fits nicely with an energy-efficient scheme. Popular power-saving ideas include specialized nodes, negotiation, and data fusion. Low-energy adaptive clustering hierarchy (LEACH) 2, 13 is a new communication protocol that tries to distribute the energy load evenly among the network nodes by randomly rotating the cluster head among the sensors. This assumes a finite amount of power and aims at conserving as much as possible despite a dynamic network. LEACH uses...

Summary

Because the number of neighbors differs with different topologies, one expects different topologies to have different power usage rates. Even simulations of the contention-free case show that different topologies have different levels of power efficiency. The results show that the total power consumption is reduced for topologies with fewer neighbors although the topologies with more neighbors require fewer hops, the power expended by many nodes to receive these messages increases the power usage. Among the two-dimensional topologies, the best power efficiency is achieved with two dimensions with four neighbors. The three-dimensional topology performs even better, although this topology may not be feasible for some applications.

Concluding Remarks

Due to its energy-efficiency considerations, multihop communication is used as the main communication mode in sensor networks, while the hierarchical, cluster-based multihop networking mode is described as the operational mode to address issues associated with scalability problems, especially in large-scale sensor systems. Because a sensor network is more data oriented than traditional wireless ad hoc networks are, the data fusion strategy plays an important role in the network design. Several data fusion dissemination strategies were discussed that ranged from centralized to local distributed methods and provide various trade-offs among accuracy, communication cost, and computing processing cost.

Error Control

Therefore, the link reliability can be achieved by increasing the output transmit power or the use of suitable FEC scheme. Due to energy constraints of the sensor nodes, increasing the transmit power is not a feasible option. Therefore, using FEC is still the most efficient solution, given the constraints of the sensor nodes. Although the FEC can achieve significant reduction in the BER for any given value of the transmit power, the additional processing power consumed during encoding and decoding must be considered when designing an FEC scheme. If this additional power is greater than the coding gain, the whole process is not energy efficient and thus the system is better without coding. On the other hand, the FEC is a valuable asset to the sensor networks if the additional processing power is less than the transmission power savings. Thus, the trade-off between this additional processing power and the associated coding gain should be optimized in order to have powerful,...

Related Work

Recent research on system software for sensor networks has seen the introduction of distributed virtual machines designed to provide convenient high-level abstractions to application programmers, while implementing low-level distributed protocols transparently in an efficient manner 26 . This approach is taken in MagnetOS 12 , which exports the illusion of a single Java virtual machine on top of a distributed sensor network. The application programmer writes a single Java program the run-time system is responsible for code partitioning, placement, and automatic migration so that total energy consumption is minimized. Mat 20 is another example of a virtual machine developed for sensor networks. It implements its own bytecode interpreter, built on top of TinyOS 16 .

Sinr

Since mobility is one of the main motivations for using wireless communications, the practicality of many of the techniques described in this chapter depends heavily on the ability to implement them in portable, battery-operated handsets. Thus, the issue of energy consumption is of considerable importance in the development of interference suppression algorithms for wireless systems. In cellular systems, there is an asymmetry with respect to this issue, in that the base station (i.e., the uplink transceiver) is relatively unconstrained by energy consumption, whereas the mobiles (i.e., the downlink transceivers) are severely constrained. So, the use of sophisticated signal processing, such as multiuser detection, at the base station does not pose a serious energy-consumption problem. Since these techniques allow better performance for a given level of received signal energy than do conventional methods, the use of such methods in the base station can reduce required transmitter power...

Series Voltage

The net terminal voltage is another key factor in determining the power dissipation and overall power efficiency as well as the device temperature rise. For the right ordinate in Fig. 6.12, we assumed that the voltage could be modeled by a constant offset plus a linear term in current that is,

Computation

Motes incorporate a processor to carry out computations locally. Functionality typically requires the processor to run in outbursts separated by idle periods. Within the idle period, the processor may enter a power reduction mode to save energy 9 . The battery lifetime is influenced by the power efficiency of a running processor and the balance between active and idle periods.

Discrete Devices

Although the advantages of VCSELs for array applications have been widely touted, their potential for low-cost LED-like production coupled to their superiority to LEDs for advanced applications may provide a large LED-upgrade market for discrete VCSELs. VCSELs can be modulated at multigigahertz rates, their output beams are well collimated, and their overall power efficiency can be much higher than that of an analogous LED. Thus, for higher capacity data links, efficient coupling to fibers, low-cross-talk free-space links, or minimizing drive power requirements, VCSELs may offer an attractive evolutionary path to systems designers who want to improve the performance of existing LED-based links.

Open Issues

How much sensor node energy to spend on a particular task entrusted on a WSN depends on how critical current application objectives are. As explained in Section 28.2 and Section 28.4.2, the same network used for a low-frequency continuous monitoring application may be employed for mission-critical tracking or emergency threat alert in the next instance. In such a scenario, less critical goals of a sensor network become highly critical and the energy saving requirements become secondary as compared to latency and throughput. A challenging and open issue is to develop medium access schemes for WSNs that have changing missions. SMAC 15 and TRAMA 18 attempt to achieve this to a certain extent nevertheless, more work must be done in this area. Optimal schemes depending on WSN type. Certain applications such as habitat monitoring may have stationary traffic patterns mostly over the total lifespan of the WSN employed. For these types of applications,...

The Message Header

To enable power control for energy efficiency, a sensor node at the receiving end must have additional information about the upcoming message to avoid selecting inappropriate voltages.* The earlier this information is available, the better decision can be made and the more energy can be saved by switching to the proper supply voltage. The complete knowledge of the message will not be available to the receiver sensor until it is completely revealed at the end of preprocessing. However, this may already be late for energy reduction because preprocessing can consume a nontrivial amount of energy in certain cases for example, in some secure DSNs the asymmetric public key cryptographic algorithms (such as RSA) used to provide security consume a significant amount of energy in decryption. *If the selected voltage is higher than necessary, further energy reduction is still possible if the selected voltage is lower, then the sensor is in danger of missing the deadline or must raise the...

Physical Layer

The physical layer is responsible for establishing communication in a given medium between two nodes. Typical tasks at this level include modulation-demodulation and encoding-decoding. Traditionally, fully hardwired solutions have been used in order to minimize cost and maximize energy efficiency. A software radio is a wireless communication device in which parts or all of the physical layer functions are realized in software 33 .

Interference

In addition to spanner (which means connectivity and energy efficiency) and other properties discussed previously, it is desirable to have a topology with high capacity or throughput, so that it can route as much traffic as in the topology. One of the important issues affecting the throughput is interference. Modeling interference in a wireless environment is a complex task. The wireless medium is susceptible to path loss, noise, interference, and blockages due to physical obstructions. Rajaraman 133 reviewed several models from path loss, bit-error rate to interference. Gupta and Kumar 62 analyzed the throughput of ad hoc networks under the physical and protocol models of interference. For detailed definitions of these models, refer to Gupta and Kumar 62 and Rajaraman 133 .

Bluetooth

Compared to contention-based MAC schemes, TDMA schemes have a natural advantage of energy conservation because the duty cycle of the radio is reduced and there are no contention-introduced overheads or collisions. Nodes can be put to sleep to save energy during the off intervals of the duty cycle, thereby making this an obvious candidate for WSNs. This particular MAC protocol is briefly described here because of its significant contribution toward minimizing the power consumption of nodes in wireless and mobile ATM networks. Goals of this access protocol are to conserve battery power to support multiple traffic classes and to provide different levels of service quality through bandwidth allocation. Although the IEEE 802.11 and Bluetooth standards address energy efficiency, this was not one of the central design issues in developing these protocols. The EC-MAC protocol 6 , on the other hand, was developed with the issue of energy efficiency as a primary design goal.

CPUCentric DPM

It is well known that the power consumption of a CMOS circuit exhibits a cubic dependence on the supply voltage Vdd. However, the execution time of an application task is proportional to the sum of the gate delays on the critical path in a CMOS processor. Because gate delay is inversely proportional to Vdd, the execution time of a task increases with decreasing supply voltage. The energy consumption of the CMOS circuit, which is the product of the power and the delay, therefore exhibits a quadratic dependence on Vdd. In embedded sensor nodes, where peak processor performance is not always necessary, a drop in the operating speed (due to a reduction in operating voltage) can be tolerated in order to obtain quadratic reductions in energy consumption. This forms the basis for DVS the quadratic dependence of energy on Vdd has made it one of the most commonly used power reduction techniques in sensor nodes and other embedded systems. When processor workload is low, the OS can reduce the...

Cooling

The energy dissipated by the equipment may lead to such a temperature rise that endangers the normal, standard operation. Therefore, construction of a reliable cooling system is an important criterion, which influences the availability. The energy consumption of the cooling system is considerable, therefore consideration shall be given to the economical construction of the cooling system, too. If the conditions of installation, accommodation are known, it shall be required that the cooling system of the equipment is adapted to the cooling system of the whole building.

Simulations

The simulations show that energy-aware routing reduces the average energy consumption per node from 14.99 to 11.76 mJ, an improvement of 21.5 (Table 30.1). This is primarily due to the very low overhead of the protocol. At the same time, it reduces the energy differences between different nodes. Figure 30.4 shows energy consumption for the linear programming formulation. In such an optimal scenario, the controller nodes clearly are the bottleneck because they must process all the data packets traversing the network. In another performance run, the network was simulated till a node ran out of energy. For diffusion routing, this occurred after 150 min it took 216 min for the energy-aware routed network to fail. This is an increase in network lifetime of 44 , which agrees with the results of the previous simulation. In that simulation, the maximum energy use among all nodes was 57.44 mJ for diffusion and 41.11 mJ for energy-aware routing. This means that diffusion had a maximum energy...

Score Based Ranking

Energy consumption and needs more bandwidth. Therefore, the cluster head cannot afford to query all the sensors for detailed reports. Sensor detection information also has an inherent redundancy, so it is not necessary to query all sensors. The scoring approach is able to select the most suitable sensors for this purpose.

Results

With DVS, minimum energy consumption results when the processing rate variation is minimized because of the convexity of the energy workload model. Figure 27.9 plots the relative battery life improvement as a function of the variance in workload. Each workload profile is Gaussian with a fixed average workload. Although the average workload might be constant, the battery life improvement from DVS will degrade as the fluctuations in workload increase. Actual energy savings in the field depend significantly on processing rate requirements and event statistics. To estimate the energy savings from active mode power consumption, one would need an estimate of the workload variation on the system. If it is assumed that the average workload requirement is 50 , with slow variation, the estimated energy savings are about 30 . Idle mode energy savings, on the other hand, can be significant. If it is assumed that the operational duty cycle is 1 , the estimated energy savings are about 96 . This...

Merits and Drawbacks

A significant portion of nodes will belong to two or more virtual clusters under this scheme. The energy consumption of such nodes would be higher compared to nodes within a single virtual cluster. Hence, the portion of such nodes and its effect on performance should be analyzed under real application scenarios. Also, the performance of this MAC scheme should be studied along with different routing schemes in order to assess its performance of intercluster communication, especially for multihop unicast and multicast messages. Data routing across virtual clusters needs to be studied further for its latency and throughput.

Sensing

A common method for analog-to-digital conversion is the successive approximation 9 . Because the ADC determines one bit of the result in each cycle, it would be possible to apply selective resolution. Consequently, different samples will have different numbers of bits and different energy costs. Finally, one may only want to test if the input value belongs to a certain range. In this case, a microcontroller with an on-chip analog comparator can be a power-efficient solution. Microcontrollers such as the Atmel ATmega161L are capable of turning off the comparator to reduce the power consumption 10 .

Energy Aware Routing

The previous section specified optimal routing policy if centralized computation were possible in reality the protocols need to work in a decentralized fashion. Thus, routes need to be selected based on some metric without full knowledge of the network. Even though sensor networks are energy limited, finding the lowest energy route and using that for every communication is not the best thing to do for network lifetime. The reason is that using a low-energy path frequently leads to energy depletion of the nodes along that path and, in the worst case, may lead to network partition. Setup phase or interest propagation. Directional flooding occurs to find all the routes from source to destination and their energy costs. This is when routing (interest) tables are built up. Data propagation phase. Data are sent from source to destination, using the information from the earlier phase. This is when paths are chosen probabilistically according to the energy costs calculated earlier.

Denial of Service

In a WSN, however, general interactive computing facilities are not likely to exist in any of the devices. The large number of devices, their relative inaccessibility, and low energy supplies are more manageable when localized autonomy and coordination are present. In such an environment, in-network services (such as localization, routing, and power management) are of more direct benefit to the sensor nodes that interact with them than to the human operators. Rather than treat only the human deployers, owners, or monitors of the network as users, it may be more useful to consider individual sensor nodes as users with respect to in-network services.

Analysis

The primary goal of localized algorithms is to minimize the amount of energy spent on communication. This does not necessarily mean minimization of the number of packets. The current technology indicates that the most effective way of saving energy is through placing the radios of as many nodes as possible into sleeping mode. Also, note that in future applications, energy minimization will not be necessarily equivalent in the first approximation the minimization of energy devoted to communication. Depending on the technology, and even more on the targeted applications, computation or some other components may have the dominant role.

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