dynamic power management in new architecture of wireless sensor networks

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMSInt. J. Commun. Syst. 2009; 22:671–693Published online 23 December 2008 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/dac.989

Dynamic power management in new architecture of wirelesssensor networks

Chuan Lin1, Naixue Xiong2,∗,†,‡, Jong Hyuk Park3,§ and Tai-hoon Kim4,§

1School of Mathematics and Statistics, Wuhan University, Wuhan, China2Department of Computer Science, Georgia State University, Atlanta, GA, U.S.A.3Department of Computer Science and Engineering, Kyungnam University, Korea

4Division of Multimedia Engineering, Hannam University, Daedeuk-gu, Daejeon, Korea

SUMMARY

Dynamic power management (DPM) technology has been widely used in sensor networks. Though manyspecific technical challenges remain and deserve much further study, the primary factor currently limitingprogress in sensor networks is not these challenges but is instead the lack of an overall sensor networkarchitecture. In this paper, we first develop a new architecture of sensor networks. Then we modify thesleep state policy developed by Sinha and Chandrakasan in (IEEE Design Test Comput. 2001; 18(2):62–74)and deduce that a new threshold satisfies the sleep-state transition policy. Under this new architecture,nodes in deeper sleep states consume lower energy while asleep, but require longer delays and higherlatency costs to awaken. Implementing DPM with considering the battery status and probability of eventgeneration will reduce the energy consumption and prolong the whole lifetime of the sensor networks.We also propose a new energy-efficient DPM, which is a modified sleep state policy and combined withoptimal geographical density control (OGDC) (Wireless Ad Hoc Sensor Networks 2005; 1(1–2):89–123)to keep a minimal number of sensor nodes in the active mode in wireless sensor networks. Implementingdynamic power management with considering the battery status, probability of event generation and OGDCwill reduce the energy consumption and prolong the whole lifetime of the sensor networks. Copyright q2008 John Wiley & Sons, Ltd.

Received 20 August 2008; Revised 29 October 2008; Accepted 31 October 2008

KEY WORDS: wireless sensor networks; cubic and cross-layer architecture; dynamic power management;optimal geographical density control (OGDC); data fusion

∗Correspondence to: Naixue Xiong, Department of Computer Science, Georgia State University, Atlanta, GA, U.S.A.†E-mail: [email protected]‡Research Scientist.§Professor.

Contract/grant sponsor: Foundation of ubiquitous computing and networking project (UCN) ProjectContract/grant sponsor: The Ministry of Knowledge Economy (MKE) 21st Century Frontier R&D Program in KoreaContract/grant sponsor: US National Science Foundation CAREER Award; contract/grant number: CCF-0545667

Copyright q 2008 John Wiley & Sons, Ltd.

672 C. LIN ET AL.

1. INTRODUCTION

Wireless sensor networks (WSNs) have emerged as an attractive technology for their wide-rangeapplications in battlefield communication, homeland security, environment monitoring, traffic moni-toring, and biology detection. A sensor network is comprised of a large number of minisculedevices equipped with one or more sensors, some processing circuits, and a wireless transceiver.Unlike conventional networks, the main goals are prolonging the life of the network and preventingconnectivity degradation through aggressive energy management as most of these devices havelimited battery life and it is infeasible to replenish energy via replacing batteries on up to tens ofthousands of sensors in most of the applications.

Research on WSNs has progressed dramatically in the past decade. The hardware, particularlyradio technology, is improving rapidly, leading to cheaper, faster, smaller, and longer-lasting nodes.Many system challenges, such as robust multihop routing, effective power management, precisetime synchronization, and efficient in-network query processing, have been tackled.

The primary factor currently limiting progress in WSNs is not any specific technical challenge(though many remain and deserve much further study) but is instead the lack of an overall sensornetwork architecture [1]. Hence, there need an architecture suited for WSNs. Such architectureshould identify the essential components and their conceptual relationships, so that it would becomepossible to compose components in a manner that allows innovation, promotes interoperability,and promises robustness and scalability.

One goal of an architecture is to allow components developed for one system to be used inmany other different systems. As the same challenge in building large software systems, the needto build composable components is hardly unique to WSNs. However, an architecture is morethan just the design of reusable software components. It is a set of principles that guide wherefunctionality should be implemented. Functional units, protocols, and physical hardware roughlyfollow those guidelines along with a set of interfaces [1].

In sensor networks, energy is a critical resource, while applications exhibit a limited set ofcharacteristics. The main goals of WSNs are prolonging the lifetime of the network and preventconnectivity degradation through aggressive energy management. It is quite different from Internetand other conventional wireless networks like cellular networks and MANETs. Thus, the require-ments and limitations of sensor networks make their architecture and protocols both challengingand divergent from the needs of traditional Internet architecture. Many applications, such as large-scale collaborative sensing, distributed signal processing, and distributed data assimilation, requiresensor data to be available at multiple resolutions, or to allow fidelity to be traded-off for energyefficiency.

There is both a need and an opportunity to provide an architecture suited for the applications-specific sensor networks. This is our motivation to characterized the existed sensor network andpropose CCL architecture for WSNs.

Sensor networks are distributed event-based systems that differ from traditional communicationnetworks in several ways: sensor networks have severe energy constraints, redundant low-rate data,and many-to-one flows. The end-to-end routing schemes that have been proposed in the literaturefor mobile ad hoc networks are not appropriate under these settings. Data-centric technologies areneeded that perform in-network aggregation of data to yield energy-efficient dissemination.

To achieve satisfactory network lifetime the energy efficiency of WSNs needs to be tackledon all levels of the entire network. Many researchers are devoted to reducing power consumptionin various aspects of hardware design, data processing, network protocols, and operating system

Copyright q 2008 John Wiley & Sons, Ltd. Int. J. Commun. Syst. 2009; 22:671–693DOI: 10.1002/dac

DYNAMIC POWER MANAGEMENT 673

[2–5]. Otherwise once the system has been designed, additional energy savings can be achieved byusing dynamic power management (DPM), which shuts down the sensor nodes if no events occur[6]. The basic idea of this policy is to shutdown devices when not needed and wake them up whennecessary. This shutdown yields good savings. But while this power saving method seeminglyprovides significant energy gains, it is important to remember that sensor nodes communicateusing short data packets. The shorter the packets, the more the dominance of startup energy [7].Hence, we have to carefully use DPM to get the maximum life of sensor node.

In fact, for example, if we blindly turn the radio off during each idling slot, over a period oftime we might end up expending more energy than if the radio had been left on. As a result,there should need other smarter scheme to turn the nodes on/off. In another words, operationin a power-saving mode is energy-efficient only if the time spent in that mode is greater than acertain threshold. There can be a number of such useful modes of operation for a wireless sensornode, depending on the number of the states of the microprocessor, memory, A/D converter, andtransceiver. It is also important to consider the state of computation when system turns componentson/off to reduce energy. The state of the computation in each period of time represents the stateof the application and its restrictions in an instant of time, which can have a direct influence ondecisions taken by a power manager.

Another important issue that arises in a high-density sensor networks is density control—thefunction that controls the density of the working sensor set to a certain level. Specifically, densitycontrol ensures that only a subset of sensor nodes operates in the active mode, while fulfillingthe following two requirements: (i) coverage: the area that can be monitored is not smaller thanthat which can be monitored by a full set of sensors and (ii) connectivity: the sensor networkremains connected so that information collected by sensor nodes can be relayed back to data sinksor controllers. In addition to the above two requirements, it is desirable to choose a minimal set ofworking sensors in order to reduce power consumption and prolong network lifetime. Finally, dueto the distributed nature of sensor networks, a practical density control algorithm should be notonly distributed but also completely localized (i.e. relies on and makes use of local informationonly) [8].

One of the best way to guarantee the maximum lifetime of sensor network thus to make betteruse of sensor network is to perform data fusion (aggregation) of data packets for overcomingthe resource-constrained. The idea of data fusion is to combine the data coming from differentsources, eliminate redundancy, minimize the number of transmissions and thus save energy andreduce collision. Lots of factors affect the performance of data fusion, such as the type of thesources, the density of the sensors, the placement of aggregation points, the aggregation function,and the communication network topology.

Based on these concepts, we have modified the system model and algorithm proposed in [9]by considering with more factors such as the battery status and the energy waste of wakening up.In [9], the energy saving equation does not consider that during the wake up interval, the nodecannot be working until it enters the active state completely. Moreover, a DPM policy must takeinto account the extra energy consumption needed for awakening the node back to active state andshould be able to foresee how long it will be remain idle. All these factors decide the sleep stateand sleep period of a single node. Therefore, the time threshold was revised and then we proposeda new sleep–awaken policy in this paper. In addition, a new energy-efficient DPM combing withOptimal Geographical Density Control (OGDC) [10] was proposed.

The remainder of this paper is organized as follows. In Section 2 we introduce some relatedwork and public literature. The common properties of a WSN architecture, a sample of DPM,


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and Data Fusion in sensor networks are also discussed. In Section 3, a cubic and cross-layerarchitecture is proposed. Next, in Section 4 we derive the sleep time threshold corresponding tothe sleep state and propose a new DPM with OGDC to maintain coverage as well as connectivityusing a minimal number of sensor nodes. In Section 5, we present the simulation results. Finally,Section 6 presents our conclusion and some future work.

2. RELATED WORK

2.1. Sensor network architecture (SNA)

OSI reference model provides the basic framework for development of standards for interconnectingtwo or more systems. TCP/IP stack has similar motivation and structure as the OSI model. Eachlayer in OSI provides a set of services to the subsystems in the layer above. In providing theseservices, a layer implements a set of functionalities using the services made available by the layer(s)below. The Internet architecture demonstrated how a properly chosen set of guiding principlescan shape the evolution of a complex system over vast changes in technology, scale, and usage[11, 12].

The layered model of the TCP/IP stack and the OSI reference model encapsulate the issuesappropriate to the related tasks within each layer. In a standardized way, this layering allowstransparent access to all lower-level functions, and makes it possible to upgrade any given layerwithout the redesign of other layers. The strength of the layered approach lies in its modularity.Every layer now can be supported by different vendors, or implemented for different hardwareplatforms, and yet the overall stack can be realized by simply combining the appropriate protocolsfor every layer. This modularity leads to simplicity by hiding the complexities of the lower layers.

The power of the Internet is revealed not so much in the elegance or efficiency of its individualcomponents, but in the overall ability to encompass tremendous growth in scale and in diversityas usage and technology rapidly evolved.

Similarly, research for providing Quality of Service (QoS)-based services has sought cross-layer collaboration in-network stack [13]. But these cross-layering efforts have only increased thecomplexity of the protocol stack, with the layers being much more dependent upon one other, andless modular than the original stack. Application-specific network protocol design has been exploredby some projects. Plexus [14] allows applications to achieve high performance with customizedprotocols, where the application-specific protocols are installed dynamically into the operatingsystem kernel. But, Plexus needs the protocols to be written in Modula-2, a type safe language,and it runs only in the context of the SPIN extensible operating system [1]. Exokernel [15] presentsan architecture to permit the application-specific customization of operating system abstractions,including network resources. While Exokernel provides a suitable architecture for supportingapplication-specific protocol stack, it is unclear what set of network abstractions will be suitableand/or sufficient for the evolving WASN environment and WASN applications. Berkeley motesuse TinyOS [16] operating system. TinyOS uses active message-based networking, and providesonly minimal networking support to applications because of the extreme hardware constraints ofthe motes. This limits the types of applications that can be efficiently supported. The network stackused by PicoNodes at Berkeley identifies the need for many WASN specific situations (such askeeping the transport layer off the stack), but it still does not support application-specific adaptationand other WASN design goals.



Srisathapornphat et al., proposed Sensor information networking architecture (SINA) [13]. Thismodel assumes the sensor nodes as a massive collection of distributed objects in which SINA actsas a middleware. It facilitates adaptive organization of sensor information by allowing applicationsto issue queries into, collect replies from, and monitor changes in the sensor networks. WithSINA, organization and information provision are done at the lower layers, whereas queries andmonitoring tasks are done at higher layers. In this architecture, sensor nodes autonomously formgroups called clusters. The cluster will be based on the power level and proximity. This clusteringprocess is applied recursively to form a hierarchy of clusters. Hence, it is known as hierarchicalclustering. This is expected to increase the lifetime of sensor nodes by decreasing the powerrequired for information exchange. It also uses attribute-based Naming queries to increase theoverall network efficiency.

Many application-based physical architectures had been proposed for specific applications, suchas a three-tiered sensor network architecture for traffic information monitoring and processingproposed by Zhang et al. [17], a Two-tiered WSN architecture for structural health monitoringproposed by Kottapalli et al. [18], and Santashil in his thesis developed an adaptive cross-layeredsensor network architecture that enables multi-scale collaboration and communication [19]. Tatianaet al., had proposed a self-healing hybrid sensor network architecture called SASHA [20].

In [21], Anis et al., proposed a decentralized two-tiered architecture, where an upper layerWireless Local Area Network serves as a backbone to a WSN. This adapted sensor networks canbe used for real-time communication.

Frank Golatowski et al., described a new concept of service-oriented software architecture formobile sensor networks [22]. By the use of this architecture, a flexible, scalable programming ofapplications based on adaptive middleware is possible. The middleware supports mechanisms forcooperative data mining, self-organization, networking, and energy optimization to build higher-level service structures.

David C. et al., proposed an abstractive sensor networks architecture (SNA) which loweringthe waistline [1]. They defined a unifying abstraction of the narrow waist, sensornets protocols(SP), which bridges network and application protocols to underlying data link and physical layers.Being SP is unifying abstraction with common format and semantics across many physical layers,functionality divides across the packet boundaries are a problem. David C. et al., examine how arich set of network layer protocols found in sensor networks might be organized relative to SP, andlook at concerns that naturally cut across layers, which allow aspects of several services fallinginto different layers to cooperate without bypassing it. (We will extend this cross-layering thoughtwhich will be discussed in Section 3.)

2.2. Dynamic power management

The lifetime of a sensor network depends highly on the power consumption performed at eachsensor node. A more efficient power management results in a longer network lifetime. Severalmethodologies have been proposed, at hardware and system levels, to design energy-efficientcommunication process, sensor node operating system and sensor node circuits. In addition, avariety of DPM techniques have been proposed to reduce the power consumption in sensor nodesand in general battery-powered embedded systems [9, 23–26] by selectively shutting down thecomponents. Much work has been done exploiting sleep state and active power management [9, 27],dynamic voltage scaling [9, 25, 28] and dynamic voltage and frequency scaling [25], sentry-basedpower management [29], an application-driven approach [30], software and operating system power


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management and battery state awareness power management. Depending on the approach that isused, DPM policies are classified as predictive or stochastic policies.

A widely-used predictive technique consists in turning OFF the system components if an idletime greater than or equal to a timeout threshold value T is detected. This approach is based onthe assumption that if the idle time is greater than T , the system is likely to remain idle for atime period long enough to save energy. A more accurate method is proposed in [31], where theupcoming idle time is predicted by using an exponential-average approach. If the predicted idletime is sufficiently long, the system component is switched OFF at once. In [9], the authors proposean OS-directed power management technique to improve the energy efficiency of sensor nodes.The node would update the probability of even generation. It is event-based power managementpolicy for a single node, but not an effective policy for the whole system. First, it may causeevent-missed situation due to the operation system isolates the node in the deepest sleep state andit is awakened until a specific sleeping interval goes by. Second, the authors only consider that anevent occur can wake up the sleeping node. Third, it is inefficient if the density of nodes is overdense in some region [32]. In addition, predictive techniques have a few limitations: they cannotprovide an accurate tradeoff between energy saving and performance degradation, and they do notdeal with a generic system architecture where service requests can be queued.

A stochastic policy has been proposed in [33] to overcome these limitations. The consideredsystem is composed of a service provider, a service requester, a power manager, and a requestqueue. The service provider and requester are represented as Markov processes, and the powermanager determines the device state of operation by issuing commands to the service provider. Inthis case, the optimal policy strictly depends on how the system is modeled and on the abstractionsthat have been made. Moreover, the amount of energy consumed by the power manager remainsto be accounted for.

Several centralized and distributed algorithms have been proposed for sensing coverage in sensornetworks [34–40]. Slijepcevic and Potkonjak [37] address the problem of finding the maximalnumber of covers in a sensor network, where a cover is defined as a set of nodes that can completelycover the monitored area. They proved the NP completeness of this problem, and provided acentralized heuristic solution. Ye et al. [39] present PEAS, a distributed, probing-based densitycontrol algorithm for robust sensing coverage. The algorithm guarantees that the distance betweenany pair of working nodes is at least the probing range, but does not ensure that the coverage area ofa sleeping node is completely covered by other nodes, i.e. it does not guarantee complete coverage.Cerpa and Estrin [38] present ASCENT, to automatically configure sensor network topologies.In ASCENT, each node measures the number of active neighbors and the per-link data loss ratethrough data traffic. Tian and Georganas [40] provide an algorithm that provides complete coverageusing the concept of ‘sponsored area’. In [35], Zhang and Hou proved that if the radio range isat least twice of the sensing range, a complete coverage of a convex area implies connectivityamong the working set of nodes, and propose a fully decentralized and localized algorithm, calledOGDC, for density control in large-scale sensor networks.

ZigBee [41] is the name of a specification for a suite of high-level communication protocolsusing small, low-power digital radios based on the IEEE 802.15.4 standard for wireless personalarea networks (WPANs). The technology is intended to be simpler and cheaper than other WPANs,such as Bluetooth. ZigBee is targeted at radio-frequency applications that require a low data rate,long battery life, and secure networking. Recently ZigBee-based technology has been widely usedin low cost, low-power WSNs. For example, RCM4510W [42] is a ZigBee/802.15.4-based modulethat taps into the exciting, high growth ZigBee market for low cost, low-power wireless sensor



networks. These modules add to the powerful list of embedded control features that have madeRabbitCore modules so appealing to design engineers around the world.

2.3. Data fusion

Data fusion has emerged as a basic approach in order to reduce the number of transmissions ofsensor nodes, and hence minimize the overall power consumption and the probability of collisionin the network. Lots of factors affect the performance of data fusion, such as the type of thesources of interest, the density of the sensors, the placement of aggregation points, the aggregationfunction, and the communication network topology.

Many researchers have been currently engaged in developing various approaches to improve theperformance of data fusion aimed at these aspects, separately. All of the effort is trying to solvethe two problems: self-organization and in-network processing.

Self-organization: As the sensors need not be connected with any infrastructure because ofon-board radio and battery, sensor node’s main utility lies in being rapidly deployed by randomlystrewing them over a region of interest . This means that the devices and the wireless links will notbe laid out to achieve a planned topology. During the operation, sensors would be difficult or evenimpossible to access and hence their network needs to operate autonomously. Moreover, with timeit is possible that sensors fail (one reason is battery drain) and cannot be replaced. It is, therefore,essential that sensors learn about each other and organize into a network on their own, whichis called self-organization. There are mainly two aspects to study the self-organization of WSNcurrently, one is data dissemination paradigm and the other is network topology, both of which arecritical factors that determine the communication throughput. In addition, as an important item ofself-organization, the synchronization of sensor network in Section 3.2.

In-network processing: Based on the data dissemination paradigm and the network topology,we are able to construct a network topology and plan the routing. However, each sensor acts asa ‘plain sensor’ in a network to sense the environment and a router to replay traffic for others.The next problem is the number of data generated by even very few sensors (active) can be largeenough to block the whole network. And a large part of these data is useless to the end user.Actually, the data are supposed to be processed before/when they are transmitted. This is referredto as in-network processing. In-network processing can significantly improve the scalability andlifetime of sensor networks, during which redundant, useless, and spurious data are deleted andpartial observations from different sensors are combined and aggregated.

3. CCL—A CUBIC AND CROSS-LAYER ARCHITECTURE

Conventional network architectures are designed according to a layering approach, such as theOSI reference model of Internet [12]. Here each layer of the system is designed separately andis independent of the application. A layered approach allows the system design to be broken intosmaller pieces that can be developed independently. We also adapt this thought to SNA.

A cross-layer architecture that exploits features of the application can achieve greater perfor-mance than general-purpose protocols. The application-specific nature of sensor networks is moreconducive to cross-layer and cross-application customization.

In our architecture, we defined two communication channels—wireless channel and sensorchannel. Sensor nodes detect the stimuli (signals) generated by events or other nodes over a sensor


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Sink

Cluster head

Sensor node

Sensor channel

Wireless channel

Cluster B

Cluster C

Cluster A

Figure 1. The model of a typical wireless sensor network (WSN).

channel and forward the detected information to the cluster head, the cluster head then forward thefused data to the other cluster head or to the sink nodes directly over a wireless channel. Figure 1depicts the model of a typical WSN environment. It should be noted that the nature of signalpropagation between cluster heads and cluster heads to sink over the wireless channel is inherentlydifferent from that between sensor nodes over the sensor channel.

In cluster hierarchical architecture, the ordinary node can be elected to be a cluster head usingan appropriate predetermined mechanism, for example, TDMA. Hence, a sensor node must beequipped with (1) a sensor protocol stack, which enables it to detect signals generated by eventsand to receive the information from other sensor nodes over the sensor channel and (2) a wirelessprotocol stack, which enables it to send reports to the other sensor nodes (and eventually to sinknodes) over the wireless channel. On the other hand, a sensor node also has a power model thatembodies the energy-producing components (e.g. battery) and the energy-consuming components(e.g. radio and CPU). Other models, such as mobility model, can also be included.

Figures 2 and 3 depict, respectively, the vertical view and overview of a WSN architecture. Asshown in Figures 2 and 3, we claim that sensor networks can also have a common abstraction—thesensor service protocol (SSP), which is similar to the ‘narrow waist-SP’ in [1].

Physical layer addresses the needs of simple but robust modulation, transmission, and receivingtechniques. It is responsible for frequency selection, carrier frequency generation, signal detection,modulation, and data encryption. Thus far, the 915MHz ISM band has been widely suggested forsensor networks.

3.1. Sensor service protocols (SSP)

The data link layer is responsible for the multiplexing of data streams, data frame detection, mediumaccess, and error control. It ensures reliable point-to-point and point-to-multi-point connections ina communication network.



Application layer

Transport Layer

Networking layer

Data Link LayerData Link Layer

Physical Layer

Networking layer

Wireless Channel Sensor Channel

Sensor Service Protocols (SSP)

Physical Layer

Sink NodeSensor Node

Figure 2. The vertical view of CCL architecture.

Application Layer

Sensor Service Layer (SSL)

Physical Layer

Pow

er Managem

entS

ynchronization

Localization

Security

Figure 3. An overview of CCL architecture.

There are two challenges in designing SSP: providing services rich enough for applicationsand keeping that interface platform independent. First, the architecture should provide servicesrich enough for applications. Besides the native network functions, such as routing and packetforwarding, future service architectures are required enabling location and utilization of services.A service is a program that can be accessed for standardized functions over a network. Servicesallow cascading without previous knowledge of each other, and thus enable the solution of complextasks. A typical service used during the initialization of a node is the localization of a data sinkfor sensor data. Gateways or neighboring nodes can provide this service. To find this service, thenode uses a service discovery protocol.

The second is keeping the service platform independent. The functionality of SSP is similar toa distributed middleware layer of service-based architecture in [1]. The nodes can be contactedonly through services of the middleware layers. They do not perform any individual tasks. TheSSP coordinates the cooperation of the services within the network. It is logically located in the


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network layer, but it exists physically in the nodes. All layers together, in conjunction with theirconfigurations, compose the sensor network application.

Transport layer is especially needed when the system is going to be accessed through the Internetor other external networks.

3.2. Cross-layer consideration

One of the principle challenges to a sensor network architecture is the defining of the interfacesto services that cannot be efficiently encapsulated in a single layer, such as power management,synchronization, localization, and security etc., as shown in Figure 3 that depicts the overview ofCCL architecture. Sometimes we need to consider other services, for example, mobility of sensornetworks.

Power management: The power management plane manages how a sensor node uses its power.For example, the sensor node may turn off its receiver after receiving a message from one of itsneighbors. This is to avoid getting duplicate messages. In addition, when the power level of asensor node is low, the sensor node broadcasts to its neighbors that it is low in power and cannotparticipate in routing messages. The remaining power is reserved for sensing.

Synchronization: For guaranteeing the quality (e.g. accuracy and freshness) of the data thatultimately reaches information sinks, we should define when to receive data from its childrenas a node for keeping aggregated data in current period from next period. This is referred astiming problem. Clock synchronization is a critical piece of infrastructure in WSNs. In particular,distributed WSNs make extensive use of synchronized time in many contexts, for example, informing TDMA schedules, integrating a time series of proximity detections into a velocity estimate,in detecting redundant detections of a phenomenon from multiple sensors, and in distributed beam-forming systems. The varieties of uses lead to highly varied and non-standard requirement in thescope.

Some of the communication algorithms make an inherent assumption that there exist somemechanisms through which local clocks of all the sensor nodes are synchronized. Though thisassumption is valid, we need to have an explicit way of synchronizing local clocks of all sensornodes. Clock synchronization is also required for accurate time stamps in cryptographic schemes.This is for recognizing duplicate detection of the same event from different sensor nodes, fordata aggregation algorithms like beam forming, for ordering of logged events, and for many othersimilar applications.

Localization: In localization panel, there are two different conceptions: the first is finding thelocation of the sensor nodes with respect to each other. The second is finding the location of anexternal object, such as an intruder entering into a sensing area.

Security: The selection of proper security mechanisms for WSNs depends on the networkapplication and the environmental conditions. Additionally, the resources of sensor nodes (processorperformance, memory capacity, and energy) have to be taken into account. There exist somestandard security requirements, like availability, confidentiality, integrity, authentication, and non-repudiation. Additionally, we need to consider some special security requirements for wirelesssensor networks like message freshness, intrusion detection, intrusion tolerance, containment, etc.

Owing to the application-specific properties of sensor networks, this block may be adapted tospecific applications. In mobile sensor networks, a mobility management plane would be consid-ered. The mobility management plane detects and registers the movement of sensor nodes; hence,a route back to the user is always maintained, and the sensor nodes can keep track of who their



neighbor sensor nodes are. By knowing who the neighbor sensor nodes are, the sensor nodes canbalance their power.

In a sensor network, not all sensor nodes in a region are required to perform the sensing task atthe same time. As a result, some sensor nodes perform a task more than others, depending on theirpower level. Hence, a task assignment plane is also needed. The task assignment plane balancesand schedules the sensing tasks given to a specific region, so that sensor nodes can work together ina power-efficient way, route data in a mobile sensor network, and share resources between sensornodes. Without them, each sensor node will just work individually.

4. POWER-AWARE SENSOR NODE MODEL

This model describes the power consumption in different levels of node-sleep states. Every compo-nent in a node can be in different states. The processor can be in active, idle, or sleep mode, so canthe radio, memory, and A/D converter. Each node sleep state corresponds to a particular combina-tion of component power modes. In general, if there are N components labeled (1,2, . . . ,N ), eachwith ki sleep states, the total number of node sleep states is �ki . Every component power modehas a latency overhead associated with transitioning to that mode. Therefore, each sleep mode ischaracterized by power consumption and latency overhead. The deeper the sleep state of the node,the lesser the power it consumes and more the latency it spends. However, from a practical pointof view not all sleep states are useful.

Let us assume that all sensor nodes will have components such as processor, memory, sensingwith A/D converter, radio. Hence, a sensor node will have the following sleep states, as listed inTable I.

Table I describes the component power modes corresponding to five different useful sleep statesfor the sensor node. These sleep states are chosen based on actual working conditions of thesensor node, for example, s0 is the active state and s4 is the deepest sleep state. Each sleep state ischaracterized by latency and power consumption. The deeper the sleep state, the lesser the powerconsumption, and more the latency. However we can see from the above table that not all combina-tion of the states are useful, for example, it does not make sense to have memory active and every-thing else completely off. The design problem is to formulate a policy for transitioning betweenstates based on observed events and the battery status, so as to maximize lifetime of the sensor.

This model is called Power-Aware Sensor mode. It is similar to the system power model inthe Advanced Configuration and Power Interface standard, which is an open industry specificationco-developed by Hewlett-Packard, Intel, Microsoft, Phoenix, and Toshiba that defines a flexibleand extensible interface for power management in PCs and related hardware [43].

Table I. Sensor node sleep states (Tx=Trasmit, Rx=Receive).

States Processor Memory Sensor Radio

S0 Active Active On Tx/RxS1 Idle Sleep On RxS2 Sleep Sleep On RxS3 Sleep Sleep On OffS4 Sleep Sleep Off Off


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Active

j ,0

i ,0

τ

P0

Pi

Pj

t1 t2

ti

s0

si

sj

τ0, j

0, iτ

τ

t

Active Idle

Figure 4. State transition latency and power.

4.1. Sleep-state transition policy

Let us assume that an event is detected by node k at some time. An event occurred at t1, and thenext event occurred at t2= t1+ ti . At time t1, node k decides to transit to sleep state sk from theactive state s0. We also assume that the process of transiting to sleep states is gradual, not direct,for example, the node is first transited to sleep state s1 and then to state s2 and so on. Each statessk has power consumption Pk , and the time transiting to it from the active states and back is givenby �0,k and �k,0, as shown in Figure 4. By our definition of node sleep states there are severalconditions for any i< j ,

Pi>Pj , �0, j >�0,i , � j,0>�i,0 (1)

Every transition from state si to state s j has a cost in terms of power consumption, denoted byPi, j , and of delay overhead, denoted by �i, j =�0, j −�0,i . From a practical point of view, the costassociated with transitions from state i to state j (i< j) is usually much lower than that associatedwith the reverse transition and for the sake of simplicity is neglected [26].

We now derive a set of sleep time thresholds Tth,k , in which the node sleep states sk should stay.Transiting to sleep state si from state s0 and awakening up back to the active state will result in anet energy loss if idle time ti<Tth,i because of the transition energy overhead. This assumes thatno productive work can be done in the transition period, which is invariably true. But in [9], theenergy saving equation does not consider that during the interval �i,0, the node cannot be workinguntil it enters the active state completely. Moreover, a DPM policy must take into account the extraenergy consumption needed for awakening the node back to active state s0 and should be able toforesee how long it will be remain idle. In our assumption, the active state is directly transited tosleep state sk and the cost is neglected for reduced computation complexity. Therefore, the energysaving from a state transition to a sleep state and back is given by

Esave,k = 1

2(P0−Pk)(ti −�0,k+ ti +�k,0)

= (P0−Pk)

(ti − �0,k−�k,0

2

)(2)



Actually the saving energy consumption is the trapezoid area in the figure. Such a transition isonly useful when Esave,k��Ek,0, where �Ek,0 is defined as the additional energy consumptiondue to awakening the sensor node back to state s0. It is clear that the node should be in sleepstate only when its idle period can be long enough so that the saved energy compensates for theexpended transition energy. This implies that the saving energy must be not less than the energywasted by awakening the node. This leads to the threshold value,

Tth,k = 1

2(�0,k−�k,0)+ �Ek,0

P0−Pk(3)

where �0,k =�0,1+�1,2+·· ·+�k−1,k . This equation implies that the longer the delay overhead oftransition S0→ Sk or the shorter the overhead transition of awakening back to state s0, the higherthe energy-gain threshold. In [9], the author did not consider that awakening a sensor node alsoneed lots of energy and extra time. It is clear that �Ei,0< P0�i,0, therefore, our threshold will besmaller than that in [9] and there is a greater probability that the node will be in the sleep state.Hence, it will increase energy consumption saving and prolong the whole lifetime of the sensornetworks. The simulation shown in Figure 8 will prove this.

In the deepest sleep state s4 the sensor node cannot detect an event or receive a message from theother nodes. When system is in state s4, there is a chance that some events will get lost. Therefore,whether or not transit to the deepest sleep state and how to determine the deepest sleeping periodbecomes an important issue. It should be noted that in clustering protocol, the cluster head cannotbe allowed to enter in the deepest sleep state while the other normal nodes can do so. We can obtainthe deepest sleeping period according to the battery status and the parameter � defined in [32],

T =�eVs/Vp (4)

where Vs denotes the standard working voltage and Vp represents the present voltage of the battery.Therefore, we can define the deepest sleep state period T using any � as a time counter for state s4.

To determine the sleep states to which the node will transit, we can refer to the event generationmodel presented in [9] and the hybrid automata theory. The node in the shallower sleep state woulddetermine its sleep state sk(k=1,2,3,4), according to event generation probability �k using theformula [9]

Pk(Tth,0)=e−�kTth (5)

where Pk(Tth,0) denotes the probability of no events occurring in a node sensing area Ck overthreshold interval Tth. Let Pth,k(Tth) be the probability that at least one event occurs in time t atnode k,

Pth,k(Tth)=1−Pk(Tth,0)=1−e−�kTth (6)

The probability of at least one event occurring is an exponential distribution characterized by aspatially weighted event arrival �k , which indicates the mean rate of event generation (time elapseddivided by the total number of events registered by node k), and the value of �k , may change withtime. This is an important parameter used to determine which sleep state will the node enter. IfPk(Tth,0) is bigger than a fixed value P , which is equal to 0.5 in our scheme, the node will entersleep state si .

Because the hybrid automata is the formal representation of a hybrid system, as a finite-statemachine, where the states are represented as a finite set of control states [44], we can use thehybrid automata to represent our sleep state transition policy as shown in Figure 5.


684 C. LIN ET AL.

s0

s1

s2s3

s4

PTp th >)0,(1

1,2 &&)0,( thth TtPTp >>

3,3 &&)0,( thth TtPTp >>

1,1 thTt <=

Tt <=

PTp th <=)0,(3

PTpTt th <=> )0,(|| 3

PTp th <=)0,(2

2,3 &&)0,( th2,th th TtPTp >>

PTp th <=)0,(1

3,thTt <=

Tt <= T is the time counter

Figure 5. Graphic representation of a hybrid automation for sleep states transition policy.

In our method we can awaken the idle sensor node in several ways, including event driven,message driven policies, and a time counter. An event-driven policy works under the shallowersleep state. When a critical event such as violent changes in temperature or a signal gener-ated by a moving object occurs, the sensor produces an interrupt and awakens the CPU. CPUprocesses the signal with data fusion algorithm or transmitting to other nodes, then goes to sleepagain.

A message-driven policy works under k=1 or 2, because in these sleep states the receiver isstill on. When a node j sends a waken-up message to its adjacent nodes, node i is in sleep statesk1 and node k is in state sk2 for transmitting data. Upon receiving this message, node i will checkto see if its sleeping time has been more than Tth(k1), if that is true, the node will wake up tostate s0 and send an acknowledgement message to node j , otherwise, it will wait until t>Tth(k1).Does the same node k. Node j will send data to the first one to respond. This method can avoidthe huge energy consumption caused by packet transmission failure. We propose a message-drivenalgorithm that is shown in Figure 6.

The time counter technique can only be used to awaken the node in the deepest sleep state s4.

4.2. DPM with OGDC

It is desirable to choose a minimal set of working sensors in order to reduce power consumption andprolong network lifetime. Otherwise, due to the distributed nature of sensor networks, a practicaldensity control algorithm should be not only distributed but also completely localized (i.e. relieson and makes use of local information only) [45].

We use a completely localized density control algorithm, called OGDC [10] with DPM basedon the CCL architecture. DPM–OGDC algorithm one side plays a power management plane roleto manages how a sensor node uses its power, for example, the sensor node may turn off it receiver



Node j

(sink)

Send a message

ack

ki tt <=yes no

receive a message

Node i

1ks

)( 1kTt thi >

receive a packet

0S

Wait for it

no

receive a message

Node k

2ks

)( 2kTt thk >

receive a packet

0S

Wait for kt

noyes yes

Receive ackin Max(ti,tk)

yes

Choose another node

noack

Return to sleep state

Return to sleep state

Figure 6. Diagram of the message-driven policy.

after receiving a message from its neighbors. This is to avoid getting duplicate messages. It alsomanages the sensor which power level is low and broadcasts to its neighbors. On the other hand,DPM–OGDC algorithm can maintain sensing coverage and connectivity by keeping a minimalnumber of sensor nodes in the active mode in WSNs. It has been proved that if the radio rangeis at least twice of the sensing range, a complete coverage of a convex area implies connectivityamong the working set of nodes [10]. Hence, in high-density homogenous sensor networks, fullcoverage means full connectivity. With such a proof, we can then focus only on the coverageproblem.

DPM–OGDC algorithm attempts to select sensor nodes that are as close to optimal locations aspossible to be the working nodes. At any time, a node is in one of the five states si (i=0,1, . . . ,4),while in OGDC is three states: ‘UNDECIDED’, ‘ON’, and ‘OFF’. Time is divided into rounds.At the beginning of each round, all the nodes wake up to be set as s0, and carry out the operationof selecting working nodes. By the end of the execution, all the nodes change their states tobe set as each of the five states si (i=0,1, . . . ,4) based on the event arrival rate and remain inthat state until the beginning of the next round. The length of each round is so chosen that it ismuch longer than the time it takes to execute OGDC but much shorter than the average sensorlifetime.

For the sake of simplicity, the latency impact factor was neglected in our DPM–OGDC algorithm.Nodes in deeper sleep states consume lower energy while asleep, but incur a longer delay andhigher energy cost while awaken. It is more practical to consider the node and network latency, forexample, the latency for sleeping node to wake up. The problem of optimally trading-off betweenpower saving and QoS provisioning for uplink traffic has to be addressed in future DPM–OGDC.


686 C. LIN ET AL.

5. SIMULATION AND ANALYSIS

We suppose a 100 nodes system distributed uniformly and randomly over a 50×50m area. Wealso assume that sensor nodes are capable of transmitting directly to the sink node, while do notconsider the multihop operation.

Figure 7 shows a node’s sleep states based on the event arrival rates. If �k<17.3, transition tostate S4 is always possible expect for cluster head, it will enter state S3 if 17.3��k<34.7, S2 for34.7��k<46.2, S1 for 46.2��k<99 and come back to active state S0 if �k�99. Figure 8 showsthe normalized energy consumption according to the event arrival rate �k (time elapsed divided bythe total number of events registered by node k). It can be seen from that, with a low event arrivalrate, the energy consumption with different sleep states is much less than that with higher eventrate. As the event arrival rate increasing, the more nodes will awaken up to the active state thatneeds more energy. The modified threshold consumes less energy than the original one in [9].

Next we will demonstrate that node energy consumption tracks event probability. Figure 9 showsthe event frequency with a Gaussian spatial distribution centered around (25,25), and shows thenormalized energy consumption of overall spatial nodes. Figure 9(a) is the spatial distribution ofevent arrival rate with Gaussian distribution and Figure 9(b) shows the configuration of spatialpower consumption in the sensor node.

In the scenario without power management, there is uniform energy consumption at all nodes.In order to compare the energy consumption using modified threshold with the original ones

in [9], we suppose a 100 nodes system distributed uniformly and randomly over 50×50m area.We also assume that sensor nodes are capable of transmitting directly to the sink node, while donot consider the multihop operation. Owing to this fact, the monitoring node is positioned in themiddle of the field, at position (25,25). We also assume that all nodes are fixed and each sensornode has an initial energy of 100 J (Joules).

In the coverage area, coverage is measured as follows: we divide the area into 50×50m squaregrids. A grid is considered covered if the center of the grid is covered, and coverage is defined as

Figure 7. Sleep states based on the event generation rates.



Figure 8. Normalized energy consumption corresponding to the event arrival rate (modifiedthreshold versus the original in [9]).

the ratio of the number of grids that are covered by at least one sensor to the total number of grids.For this 50×50m area, 45 hexagon cells are required to cover the entire area if the hexagon-basedGAF-like algorithm is used as shown in Figure 10. Hence, the hexagon-based algorithm ensures100% coverage if at least 45 sensors operate in the active mode in each round, one for each cell[10]. Similarly, at least 47 nodes are required to operate in the active mode under the ‘sponsoredarea’ algorithm proposed in [40] to ensure the complete coverage. When the number of sensornodes in the sensor network increases, the sponsored area algorithm requires more nodes to coverthe entire area. It is obvious that the number of active nodes increases with the amount of totalnodes increasing, but the increasing slop is more and more flat (see in Figure 11).

Figure 12 gives the dynamics of the coverage over the time in a typical simulation run for asensor network of 300 sensor nodes in a 50×50m area. OGDC can provide over 95% coverage forappropriately 10 times of the lifetime of a single sensor node and the total power of the networkdecreases smoothly [10].

Figure 13 shows the lifetime comparison of three approaches in our simulation. We refer to thelength of the time that the network operates prior to becoming unusable as the network lifetime.From that we can see using DPM with OGDC may prolong the sensor network lifetime than thatonly using DPM. In the scenario without power management, there is uniform energy consumptionat all nodes and the lifetime will be a constant.

6. CONCLUSION AND FUTURE WORK

In this paper, a cubic and cross-layer architecture for wireless sensor networks was proposed.We argue for use of the sensor service protocol (SSP) as a middleware layer. The nodes can becontacted only through services of the middleware layers. They do not perform any individualtasks. The SSP coordinates the cooperation of the services within the network. In addition, all


688 C. LIN ET AL.

Figure 9. Simulation of DPM in a sensor network: (a) spatial distribution of event arrival rates (Gaussian)and (b) shows the configuration of spatial power consumption in the sensor node.

these services are considered or accessible to be in a cross-layer manner instead of being fullencapsulated at one layer. Hence, we consider power management, synchronization, localization,and security as the cross-layer abstractions to our architecture.

Under this architecture, we discuss the DPM technology and data fusion method. The purposeof DPM and data fusion is overcoming the limit energy supply and bandwidth constraints commu-nication capability. Then we modified the DPM sleep state policy of Sinha and Chandrakasan in[9], and derived the sleep threshold with respect to the extra energy cost for returning the sleepnode to an active state and the battery status. We also proposed an energy-efficient DPM withOGDC in sensor networks.



Figure 10. Hexagons are required to cover a 50×50m area.

Figure 11. The number of active nodes under different amounts of total nodes.

However, there are many questions that need to be answered before our architecture becomes areality. Our future work will focus on the exact services and functionalities provided by the SSP,and the interaction between SSP and cross-layers. Moreover, the ultimate purpose of DPM anddata fusion is to guarantee the maximum lifetime of sensor network. There is still some roomfor potential improvement in this field. First, one drawback to our scheme is that we do notanalysis the latency, which is very important parameter in a sensor network. Tradeoffs among


690 C. LIN ET AL.

Figure 12. Dynamics of the sensing coverage versus time in a sensor network of 300 sensor nodes.

Figure 13. The lifetime comparison of three approaches in our simulation.

energy consumption, delay as well as overhead, should be considered in our future work. Second,we need to address the question of how to avoid event missing effectively when the nodes arein sleep states. Third, in OGDC with DPM, each node needs to know its own location. Finally,we need to derive the upper bound of the network lifetime in large areas and also consider themultihop operation in our DPM sensor network. Another future work that attracts more attentionis hierarchical data fusion and the more efficiently collaborative signal processing system and theconnecting of the physical world with pervasive networks.



ACKNOWLEDGEMENTS

This research is supported by Foundation of ubiquitous computing and networking project (UCN) Project,the Ministry of Knowledge Economy (MKE) 21st Century Frontier R&D Program in Korea and a result ofsubproject UCN 08B3-S1-10M. In addition, this research has been supported by the US National ScienceFoundation CAREER Award under Grant No. CCF-0545667.

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AUTHORS’ BIOGRAPHIES

Chuan Lin received his MS degree and PhD in computer science from the HubeiUniversity and Wuhan University, respectively, China. Since 1999, he has been with thefaculty of information and computation science, school of mathematics and statistics,Wuhan University. His research interests include wireless sensor networks and DSP,Computer Networks, Ubiquitous and Pervasive computing, etc.

Naixue Xiong is a research scientist in Department of Computer Science, Georgia StateUniversity, U.S.A. His research interests include Communication Protocols, NetworkArchitecture and Design, Network Technologies, and Distributed and parallel Systems.Until now, Dr Xiong published about 90 research articles (including about 30 inter-national journal articles). Some of his works were published or submitted in IEEE orACM transactions and IEEE INFOCOM. He has been a Program Chair, General Chair,Publicity Chair, PC member, and OC member of over 40 international conferences,and was invited to serve as a reviewer for over 20 international journals. Now, he isserving as an Associate Editor, Editorial Board Member, and Guest Editor for abouteight international journals.

Jong Hyuk Park received his PhD degree in the Graduate School of InformationSecurity from Korea University, Korea. He is now a professor at the Department ofComputer Science and Engineering, Kyungnam University, Korea. Dr Park has publishedmany research papers in international journals and conferences. Dr Park has been servedas chairs, program committee, or organizing committee chair for many internationalconferences and workshops. Dr Park is the founder of the International Conference onMultimedia and Ubiquitous Engineering (MUE), International Conference on IntelligentPervasive Computing (IPC), and the International Symposium on Smart Home (SH).Dr Park is an editor-in-chief of the International Journal of Multimedia and UbiquitousEngineering (IJMUE), the managing editor of the International Journal of Smart Home(IJSH). In addition, he has been served as a guest editor for international journalsby some publishers. Dr Park’s research interests include Digital Forensics, Security,

Ubiquitous and Pervasive Computing, Context Awareness, Multimedia Services, etc.

Tai-Hoon Kim received his MS degrees and PhD in Electrics and Electronics andComputer Engineering from the Sungkyunkwan University, Korea. Now, he is currently aprofessor of Hannam University. He wrote 16 books about the software development, OSsuch as Linux and Windows 2000, and computer hacking and security. And he publishedabout 100 papers by 2006. He was a chair and program committee of internationalconferences and workshops. He was a guest editor of AJIT and FGCS Journal, and nowhe is an editor-in-chief of JSE and IJSIA Journal. He researched security engineering,the evaluation of information security products, or systems with Common Criteria andthe process improvement for security enhancement. In these days, he researches alsosome approaches and methods making IT systems more secure.


dynamic power management in new architecture of wireless sensor networks

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