© The Author 2009. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.For Permissions, please email: journals.permissions@oxfordjournals.org

doi:10.1093/comjnl/bxp112

Saving Energy in Wireless Local AreaSensor Networks

Jaime Lloret∗, Sandra Sendra, Hugo Coll and Miguel Garcia

Instituto de Investigacion para la Gestión Integrada de Zonas Costeras, Universidad Politécnica de Valencia,Camino Vera s/n, 46022 Valencia, Spain

∗Corresponding author: jlloret@dcom.upv.es

Many wireless sensor network developers have adopted Bluetooth and Zigbee technologies for sensorcommunication. However, it has a big problem: the radio coverage is very short; so many sensors areneeded to cover wide areas and sensors are expensive. On the other hand, these technologies lack ahigh bandwidth. IEEE 802.11a/b/g technologies have higher coverage areas and higher bandwidth,but their energy consumption is significantly higher. This paper discusses the energy consumptionanalytically and gives the considerations that should be taken into account in developing a wirelesslocal area sensor network. Then, we propose a new protocol based on group collaboration to beimplemented in these types of networks in order to decrease the energy consumption and save theenergy wasted by the whole system. Finally, the protocol proposed is simulated and compared with

other protocols available in the Ns2 simulator in order to prove that our solution is better.

Keywords: WLASN; energy consumption; energy saving; IEEE 802.11

Received 29 August 2009; revised 12 November 2009Handling editor: Yu-Chee Tseng

1. INTRODUCTION

A wireless sensor network (WSN) can be defined as a network ofsmall embedded systems located strategically inside a physicalmedium (following an ad hoc configuration and where aphysical medium as a cable does not exist) that are capableof interacting with this and providing information on itsconditions. When the sensor nodes are installed, we should keepin mind that they should be autonomous and, frequently, theywill be disregarded.

Because of it, the nodes should be small-size devices inorder to avoid causing any visual impact on the ecosystem,and they not should consume too much energy. We shouldalso keep in mind that the nodes have limited data processingand storage capacity. We should not forget that it shouldimplement a security system in order to avoid intrusions, byany other network. The typical structure of a WSN usuallyhas sensor nodes: they are radio processors that take thesensor data (physical magnitude values of the medium) andsend the information to the base station. The base station isthe data gatherer, which is usually a common computer orembedded system with higher process capacity. The gatewayis the interconnection between the sensor network and the datanetwork.

Bluetooth is an open specification for wireless networksbased on radiofrequency that operates in the industrial scientificmedical frequency band (2.4 GHz) forming wireless personalarea networks (WPANs) [1]. It has low energy consumptionand its cost is quite low. Bluetooth controls its interferenceand susceptibility to interference by using spread spectrummodulation. It uses a frequency hopping spread spectrumsignaling method. Bluetooth was formalized in the IEEE802.15.1 standard in its version 1.2. Bluetooth technology usesa small area network without infrastructure (piconets). Nodesshare a physical channel with a clock and a unique sequenceof jumps in the same piconet. In Bluetooth, different channelscan coexist. While a master can belong only to one piconet, anyother device can belong at the same time to several piconets.This overlap is a denominated scatternet (dispersed network),although there are no defined routing capacities among them.This technology has been developed to be used for deviceinterconnection such as in computers, mp3, personal digitalassistants etc. at a distance of about 10 m, although the latestproducts in the market achieve 100 m of coverage radius.Nevertheless, this coverage range can be greater having higherantenna gain, and bigger distances can be obtained using signalrepeaters.

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ZigBee is an alliance, without spirit of lucre, of more than100 companies, where most of them are semiconductor makers.ZigBee and IEEE 802.15.4 are standards-based protocolsthat provide the network infrastructure required for WSNapplications [2]. 802.15.4 defines the physical and MediaAccess Control (MAC) layers, and ZigBee defines the networkand application layers. This technology is focused to createlow-rate personal area networks (LR-WPAN). Their objectivesare to develop and to implant a wireless technology of lowenergy consumption and low cost. It is presented as a veryeconomic solution for WSNs. It is also destined toward energymanagement, automation and remote control applications.ZigBee products work in a band of frequencies that includes2.4 GHz, using the modulation orthogonal QPSK (QuadraturePhase Shift Keying) with expansion direct sequence spreadspectrum (DSSS), and 868 MHz in Europe or 915 MHz inNorth America with the modulation Binary Phase Shift Keyingwith expansion DSSS. Its energy consumption is very low; itconsumes 20 times less energy than the IEEE 802.11 standardand their implementation is relatively simple. On the contrary,their typical coverage range is around 50 m, although this valuecould increase, depending on the environment.

Inside the IEEE 802.11 standard [3], standard variantslike IEEE 802.11a, IEEE 802.11b, IEEE 802.11g and IEEE802.11n are included among others. The original standard alsodefines the Carrier Sense Multiple Access/Collision Avoidance(CSMA/CA) protocol as the access method. CSMA/CA is acarrier sense and multiple access method used to avoid collisionsamong the data frames. The 2.4000–2.4835 GHz band is dividedinto 13 channels each of width 22 MHz, but spaced only5 MHz apart, with channel 1 centered on 2.412 GHz and 13on 2.472 GHz to which Japan adds a 14th channel 12 MHzabove Channel 13. The availability of channels is regulatedby the country, constrained in part by the allocated radiospectrum to various services. IEEE 802.11b data are encodedusing DSSS signaling technology. It uses complementary codekeying (CCK) modulated with the QPSK technology to achievea maximum raw data rate of 11 Mbps. IEEE 802.11a/g usesorthogonal frequency division multiplexing (OFDM) methods

with maximum of 52 sub-carriers, achieving a maximum rawdata rate of 54 Mbps. IEEE 802.11n is a new standard forwireless communications that presents a significant increasein the maximum raw data to a maximum theoretical valueof 600 Mbps. The current state of the art supports a physicalrate of 450 Mbps, with the use of three spatial streams at achannel width of 40 MHz. Furthermore, IEEE 802.11n usesmultiple-input multiple-output (MIMO); this is the utilizationof multiple transmitter and receiver antennas to improvethe system performance. MIMO technology provides spatialdivision multiplexing where within one spectral channel ofbandwidth, spatially multiplexes, multiple independent datastreams are transferred simultaneously. This causes the data rateand coverage area increases, in contrast to the other variants.Basically, 802.11 can work in two ways: the ad hoc mode,when the stations communicate to each other directly; and theinfrastructure mode, in which the stations access the networkthrough one or several access points. Its coverage can reachmore than 300 m. The effective overall range of 802.11a is lessthan that of 802.11b/g. In theory, signals of 802.11a are absorbedmore readily by walls and other solid objects in their path dueto their smaller wavelength and, as a result, cannot penetrateas far as those of 802.11b. The main differences between theintroduced variants are shown in Table 1.

The main problem of the Bluetooth and IEEE 802.11technology in order to become a wearable technology at thelevel of a sensor network is the complexity of the protocoland the topology rigidity of the networks, as well as the highconsumption of power of the devices.

Depending on the case, in our implementation we could needa higher coverage range than the one offered by Bluetoothor Zigbee, while very small energy consumption is required.In this paper, we propose a new protocol based on IEEE802.11a/b/g that allows energy saving, while the proposedalgorithm decreases the energy consumption of the wholesystem. We can see their differences in Table 2.

Although many WSN developers have adopted Bluetooth andZigbee technologies for sensors communication, we will provein this paper that IEEE 802.11a/b/g/n are feasible technologies

TABLE 1. Comparison of 802.11 variants.

802.11a 802.11b 802.11g 802.11n

Max data rate (Mbps) 54 11 54 500Max throughput (Mbps) 25 7 25 144Frequency (GHz) 5 2.4 2.4 2.5/5Modulation OFDM CCK modulated

with QPSKOFDM OFDM

Typical indoor coverage 30 m (54 Mbps) 30 m (11 Mbps) 30 m (54 Mbps) 75 m (500 Mbps)90 m (6 MBps) 90 m (1 MBps) 90 m (1 MBps) 90 m (70 MBps)

Typical outdoor coverage 120 m (54 Mbps) 30 m (11 Mbps) 120 m (54 Mbps) 300 m (500 Mbps)460 m (1 MBps) 300 m (1 MBps) 460 m (1 MBps) 1150 m (1 MBps)

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Saving Energy in WLASNs 3

TABLE 2. Comparison of wireless technologies.

WLAN (IEEE 802.11) Bluetooth (802.15.1) Zigbee (802.15.4)

Frequency bands 5 GHz (a/n) 2.4 GHz 2.4 GHz2.4 GHz (b/g/n) 868/915 MHz

Raw data rate 11 Mbps (2.4 GHz in b) 1 Mbps 250 Kbps (2.4 GHz)54 Mbps (2.4 GHz in g or 5 GHz in a) 40 Kbps (915 MHz)500 Mbps (2.4 and 5 GHz in n) 20 Kbps (868 MHz)

Number of channels 11–14 (depends on the country in b/g)52 channels in a

79 16 (2.4 GHz)

10 (915 MHz)1 (868 MHz)

Data types Digital Digital, Audio Digital, Key-value pairsCoverage range 100–300–900 m 10–100 m 30–50 mBattery autonomy Hours Days YearsArchitecture Star Star Star, tree, clusterBest applications Internet inside building Computer and phone

peripheralsLow-cost control and

monitoring

for wireless local area sensor networks (WLASNs) when it isneeded to cover higher areas with higher bandwidth. We willalso propose a group-based protocol to decrease the powerconsumption in several application scenarios.

The rest of the paper is structured as follows. Section 2presents some related works about energy consumptionin WSNs. Section 3 shows the chipsets in the marketthat accomplish IEEE 802.11 a/b/g variants and gives theconsumption comparison between them. The applicationenvironment where WLASNs can be used and the initialconsiderations that should be taken into account are shownin Section 4. Section 5 tackles the problem analytically. Theprotocol proposal is presented in Section 6. Section 7 simulatesour proposal and compares the results with other protocols.Finally, Section 8 gives the conclusions and future work.

2. RELATED WORK

Several papers have been published related with the analysisof energy consumption of different wireless technologies andrelated with power-saving mechanisms in WLANs from theMAC level perspective.

2.1. Analysis and comparatives of energy consumptionof different wireless technologies

In [4], a comparative study of the Bluetooth (IEEE 802.15)and IEEE 802.11 technologies is shown. This paper makes anexhaustive analysis of the main characteristics of both tech-nologies and in particular it analyses three integrated modelsthat are employed in devices that use these communicationtechnologies. These are, Bluecore01 and Bluecore02, manufac-tured by Cambridge Silicon radio and used by Bluetooth and

Prism, mounted in IEEE 802.11 devices. The paper comparesthe devices taking into account several parameters such as thespectrum used, the modulation characteristics and interferenceproblems, power requirements, characteristics of the networktopology, the ability to create an efficient network, the creationspeed of the networks, the characteristics of the links amongthe devices of a single basic cell and the maximum attainablethroughput, security systems and the ability to offer a givenQoS. As we can see in this study, each device has been man-ufactured for a purpose; the first ones are developed to workinside a room, therefore it does not need so much transmis-sion/reception power and it consumes less; while the IEEE802.11 system has higher transmission/reception power and itsconsumption, consequently, is higher.

In [5], Feeney and Nilsson present an exhaustive study ofLucent WaveLAN IEEE 802.11 device. Their paper showsa series of experiments giving detailed measurements ofthe energy consumption of this device operating in an adhoc networking environment. The data are presented as asmall mathematical development that models, by means ofa collection of linear equations, the energy consumption insending, receiving and discarding broadcast and point-to-pointdata packets of various sizes. Their results suggest that energyconsumption and bandwidth utilization are not related, becauseit is necessary to consider the cost of transmitting, receivingand discarding packets. However, the proportions of broadcastand point-to-point traffic used by the protocol should also beconsidered. Furthermore, the number of hops required in amulti-hop routing environment usually increases due to thereduced transmission range associated with higher data ratesand due to the high fixed overhead.

In [6], the authors propose three asynchronous power-saving protocols that are directly applicable to IEEE

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802.11-based MANETs. These protocols are nameddominating-awake-interval, periodically-fully-awake-intervaland quorum-based protocols. They focus their study from thefollowing two points of view: the first one enforces the hosts tosend more beacon packets than the original IEEE 802.11 stan-dard does and the second one establishes a wake-up and sleeppatterns for the hosts. Their protocols are designed according tothe following rules: they seek to send higher number of beaconsin order to prevent the inaccurate-neighbor problem; a mobilehost in a power-save mode should insist more on sending bea-cons. Their protocol does not count on clock synchronizationand the wake-up patterns of two hosts must overlap with eachother, no matter how much time their clocks drift away. Finally,in order to predict the wake-up moment, if a host hears anotherhost’s beacon, it should be able to derive that host’s wake-uppattern based on their time difference. They demonstrated withsimulations that their power-saving protocols can save muchenergy with reasonable route establishment probability.

Because the traditional ad hoc wireless MAC protocols aredesigned without considering the battery state, Jayashree et al.[7] show an energy-efficient battery-aware MAC protocol, themain aims of which are minimal power consumption, longerbattery life, fairness and higher throughput.A battery consists ofan array of electro-chemical cells. BAMAC models the batterybehavior, using a stochastic process named Markov Chain. Init, each node maintains a battery table that contains informationabout the remaining battery charge of each of its one-hopneighbors. The entries in the table are arranged in the non-increasing order of the remaining battery charges. The RTS,CTS, Data and ACK packets carry the remaining battery chargeof the node that originated the packet. A node listening to thesepackets makes a corresponding entry in its battery table. Theirproposed protocol is implemented using GloMoSim simulatorand it has been compared with the DWOP protocol. In thesimulation, we can see that the life of the batteries is extendedaround 70% and it reduces the consumption of the nominalcapacity of the battery for package transmission by 21%,compared with the IEEE 802.11 and DWOP MAC protocols.

On the other hand, other works propose new protocols basedon the existing ones. They do not make distinction of the typesof technology used in the background. We can point out [8],where the authors propose new protocols that can be applied toWSNs. The paper proposes a new energy-efficient approach forclustering nodes in ad hoc sensor networks. Hybrid energy-efficient distributed (HEED) clustering periodically selectscluster heads (CHs) according to a hybrid of their residualenergy and a secondary parameter. HEED also seeks prolongingnetwork lifetime by distributing energy consumption. It triesto reduce the high control on the network. The paper showsthe clustering process and determines that the CH positioncan be uniform. HEED is compared with LEACH, WCAand GC protocols, among others. HEED optimizes resourceusage according to the network density and applicationrequirements.

We want to highlight that there is no protocol such as theone that is presented in this paper. It is based on the formationof groups of sensors instead of clusters of sensors. Moreover,there is cooperation between nodes in order to minimize theenergy consumed in the network. Cluster-based networks area subset of the group-based networks. A group-based networkis capable of having any type of topology inside any group,not only a cluster. Furthermore, in a group-based network,each group could be using a different type of routing protocol.A cluster-based network is made of a CH, Gateways andCluster Members (CM). CH nodes fully control the cluster.The size of the cluster is usually about one or two hopsfrom the CH node. In the proposed group-based network, thecentral node performs only the creation and maintenance ofthe boundary of the group without any other responsibility.In cluster-based networks, the gateways could be located inthe boundary or in an overlapping region between clusters andmanage communication with adjacent clusters, while the bordernodes in a group-based network are in the boundary and onlybelong to one group. A very nice explanation about cluster-based networks is presented in [9]. Group-based topologies arealso explained in [10]. A summary of the differences betweencluster-based and group-based networks can be seen in Table 3.

2.2. Power-saving mechanisms in WLANs from theMAC-level perspective

Razzano and Pietrabissa [11] describe a new control architecturesolution that is adopted for a WLAN developed in theframework of the WIND-FLEX (wireless indoor flexible highbit rate modem architecture) project. It considers the powerconsumption due to the wireless modem and presents a novelcontrol entity based in fuzzy logic that has been alreadyapplied for the determination of the transmission power incode-division multiple-access scheme. The consumed poweris greatly influenced by the choice of the bit rate used for thepacket transmission and the reduction of power consumptionis obtained by defining the most appropriate bit rate for datatransmission, with respect to the traffic situation.

In [12], Meiyappan et al. describe several techniques toreduce the dynamic power consumption in WLAN systemsto help to increase the battery life in mobile systems. Theysuggest techniques that can be used to reduce dynamic powerconsumption for the 802.11n system level and for the baseband.They propose to reduce the device’s consumption is its initialdesign, because a small device consumes less energy. Butthe reduction of the manufacture technology could suppose aconsiderable rise in the price on the final product. The intentionof reducing the device’s consumption should not suppose anaccused ascent of the final product price.

In [13], we can see a focus on the design and evaluationof an enhancement of the distributed WLAN power-savingmechanisms defined in the IEEE 802.11 and 802.11e standards.The main objective of the no data acknowledgment (NDACK)

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mechanism is to increase the power saving by reducing theduration of the awake periods when the stations have no pendingdata stored at theAccess Point. They use this method to increasethe battery lifetime of WLAN mobile devices while providingthe required QoS. Their results demonstrate that the powerconsumption due to the channel congestion can be reduced withNDACK and QoS.

Jung and Vaidya present in [14] an optimization of the power-saving mechanism in the distributed coordination function inIEEE 802.11 standard, which is divided into the so-calledbeacon intervals. At the start of each beacon interval, each nodein the power-saving mode periodically wakes up for a durationcalled the ATIM Window. During this time period, the nodesexchange control packets to determine whether they need tostay awake for the rest of the beacon interval. The energy savingand throughput achieved by the nodes are directly affectedby the size of the ATIM window. They propose an adaptivemechanism to dynamically choose a suitableATIM window sizeaccording to observed network conditions. Their new protocolhas been called dynamic power-saving mechanism and it allowsthat a node also can power off its wireless network interfacewhenever it finishes packet transmission for the announcedpackets. Initially, each node begins with ATIM window sizeequal to ATIMmin. A node will increase its ATIM window sizeto the next higher value, if any of the above rules are satisfied,at the beginning of the next beacon interval. Finally, we cansee that the proposed scheme can improve energy consumptionwithout degrading throughput.

3. IEEE 802.11A/B/G VARIANTS HARDWAREAVAILABILITY AND CONSUMPTIONCOMPARISON

In order to deploy a WSN working with IEEE 802.11 standard,we should pay attention to two main issues: the hardwareavailability (including its consumption and capacity) and selectthe proper IEEE 802.11 variant. In this discussion we leaveout IEEE 802.11n because, although the devices found in themarket that accomplish an IEEE 802.11n draft consume justa little bit more than the IEEE 802.11a/b/g devices, nowadaysthere are no hardware available in the market that satisfies ourmain requirements (serial interfaces embedded and low-costdevices).

3.1. Hardware availability

We can find in the market several embedded systems thatincorporate the IEEE 802.11 standard. They are low energyconsumption solutions.At the same time, these devices integratea great variety of resources and functions. Because their sizesare quite small, they can be included inside portable devices.Furthermore, due to their low power consumption, they canalso be fed by batteries. All these modules are also designed to

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enable the communication between wireless devices and a serialconnection, and thus a sensor can be connected to the serialinterface. In this subsection, we analyze only the chipsets thathave their power consumption specifications published. Thereare other IEEE 802.11 compliant chipsets in the market, butthey do not have embedded serial interfaces.

Lantronix®, Inc. presents three dedicated products to IEEE802.11 wireless communication. The first of them is the WiPort[15]. It is a compact, integrated hardware and software modulethat enables to build wireless networking into any electronicdevice with serial or Ethernet capability. MatchPort b/g [16]and MatchPort b/g Pro [17] are dedicated co-processor modulesthat manage wireless and network activity. They use a dualprocessor design; one of them converts wired serial data intoTCP/IP packets, providing web server capabilities; the otheris an 802.11 b/g baseband and radio chipset. MatchPort b/gand MatchPort b/g Pro enable 802.11 b/g wireless connectivityand web services on any device with a serial interface on itsmicrocontroller.

RF Solutions Ltd. is another manufacturer that works onembedded devices. It presents a device called S103 WLANCompact Serial Module [18]. This module is compatiblewith 802.11b standard protocol. It supports 64/128-bits WEPsecurity and can operate in an ad hoc or infrastructure mode. Itis compact and has a small size (40 × 60 mm) and low powerconsumption.

ConnectOne Ltd. has two embedded devices NanoWiReach [19] and Mini Socket iWiFi [20]. Both devices area secure serial-to-WLAN device server module and they canact as a bridge to connect serial devices to 802.11b/g WLANs.Mini Socket iWiFi includes the iChip CO2128 IP Communica-tion Controller chip, whereas Nano WiReach includes the iChipCO2144 IP Communication Controller chip. Both use a Marvell88W8686 WiFi chipset.

Roving Networks, Inc. has several products. The first of themis the RN-134 ‘SuRF’ board [21]. It enables the connectiondirectly from a WLAN to a standard RS232 interface or throughthe TTL UART interface. This product is compatible with IEEE802.11 b/g and it is prepared to control remote and industrialsensors, home automation and telemetry measurements. Italso has other products such as WiFly GSX (RN-131G) [22]and WiFly (RN-111B) modules [23]. They have a smallform factor and extremely low power consumption. Theyare indicated for mobile wireless applications such as assetmonitoring, global positioning system (GPS) tracking andbattery sensors. Furthermore, the WiFly (RN-111B) allows theRS485 connection.

Another manufacturer is RABBITTM. Their embeddedsystem, called RCM4400W RabbitCore module [24], adds Wi-Fi/802.11b functionality and it also allows creating systemsbased in Wi-Fi technology in order to be used in industrialcontrol, building automation or in a remote terminal unit.

Finally, we present some products of Digi International®.It has, among other products, Connect Wi-ME [25] and

ConnectCore Wi-9C [26]. These three products are fullycustomizable and secure 802.11 wireless-embedded modules.They can also be used to create a fully web-enabled andintelligent data station, in order to gather some informationcoming to an industrial sensor.

Table 4 shows a comparison of the devices previouslydescribed. The typical values of consumed current, the operatingvoltage and their standard wireless specification have beengathered from the datasheet of each device. In some devices,we have also specified the CPU type that is integrated in thesystem. There are some parameters that are not provided by themanufacturer (we have written them in the table as not available,n/a). In the table, we can see that the ones that consume lesscurrent in the standby mode are Nano WiReach and Mini SocketiWiFi, but the one that consumes less current transmitting andreceiving is ‘WiFly GSX’ 802.11G (RN-131G) module.

For a regulated 3.3V power line (almost all of them use thisvoltage value), the LTC3440 Synchronous Bock-Boost DC/DCConverter can be used. It is rated to supply up to 600 mA ofcurrent [27], which will be enough to support even the worstcase current draw of any of the devices.

3.2. Discussion on IEEE 802.11 variants

An IEEE 802.11b variant has lower throughput modulationscheme than IEEE 802.11a/g variants, which should imply lowbattery consumption if we examine the consumption of thedevice per bite or byte while it is transmitting or receiving.Taking the analysis given in [28], IEEE 802.11b consumes 30times more than IEEE 802.11a/g when it is transmitting orreceiving. Nevertheless, in an IEEE 802.11 variant, the devicemust remain in an active mode 3 to 4 times more than an IEEE802.11 a/g variant to transmit or receive a certain amount of data.IEEE 802.11b requires that the header appended to every datapacket is 5 to 10 times longer than the header on an 802.11a/gpacket. Taking into consideration the aforementioned devices,working with an IEEE 802.11b variant consumes two to threetimes more power than IEEE 802.11a/g devices.

Figure 1 shows the modulation rate at various distances andthe power consumption for IEEE 802.11a/b/g variants. The802.11a/g clients can select from the widest possible range ofOFDM data rates of 54, 48, 36, 24, 18, 12, 9 and 6 Mbps, and802.11b can select the CCK rates of 11, 5.5, 2 and 1 Mbps. Thisselection depends on the distance between the transmitter andthe receiver. The figure shows better power efficiency of IEEE802.11a/g over IEEE 802.11b (between 2.5 and 3 times more,depending on the type of packets). On the other hand, IEEE802.11g has larger coverage range than IEEE 802,11a.

However, although this is the theory and we can find several802.11b/g products in the market that follow this theory, suchas the Atheros AR5001X+ 802.11a/b [29] and the Cisco®Aironet® 802.11a/b/g Wireless PCI Adapter [30], where it isfollowed only by the receiving mode, not by the transmittingmode, there are others that give the same power consumption

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7TABLE 4. Comparison of device consumption.

Current Current Currentconsumption consumption consumption Operating IEEE

Device Manufacturer in TX (mA) in Rx (mA) in Standby (mA) Core CPU voltage (V) specifications

Nano WiReach Connect One Ltd 250 190 8 32-bit RISC ARM7TDMI,low-leakage, 0.13 µm, running at48 MHz

3.3 802.11 b

802.11 gMini Socket iWiFi Connect One Ltd 250 190 8 32-bit RISC ARM7TDMI,

low-leakage, 0.13 µm, running at48 MHz

3.3 802.11 b

802.11 gS103 WLAN compact

serial moduleRf solutions 325 210 80 n/a 5 802.11 b

‘WiFly’ 802.11BModule (RN-111B)

Roving networks 110 40 35 n/a 3.3 802.11 b

‘WiFly GSX’ 802.11GModule (RN-131G)

Roving networks 210 40 15 n/a 3.3 802.11 b

802.11 gWiFly GSX Super

Module ‘SuRFBoard’ (RN-134)

Roving networks 212 50 15 n/a 3.3 802.11 b

802.11 gMatchPort b/g Lantronix 360 225 76 Lantronix DSTni-EX x86 CPU,

on-chip 256 KB zero wait staticSRAM, 2048 KB Flash, 16 KBBoot ROM, 8 GPIO

3.3 802.11 b

802.11 gWiPort Lantronix 650 395 91 Lantronix DSTni-EX 186 CPU,

on-chip 256 KB zero wait staticSRAM, 2048 KB Flash, 16 KBBoot ROM

3.3 802.11 b

802.11 gMatchPort® b/g Pro Lantronix 350 260 160 Lantronix 32-bit processor, 166 MHz

(159 MIPS—Dhrystone 2.1)3.3 802.11 b

802.11 gRCM4400W

RabbitCoreRABBIT 450 450 80 Rabbit 4000 Microprocessors

(60 MHz)3.3 802.11 b

Connect Wi-ME DigiBoard 346 186 34 32-bit, 55 MHz NS7520 processorARM7TDMI ARM core

3.3 802.11 b

ConnectCore Wi-9C DigiBoard 900 700 10 32-bit, 155 MHz NS9360 processorARM926EJ-S ARM core

3.3 802.11 b

802.11 g

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IEEE 802.11 b IEEE 802.11 g IEEE 802.11 a

FIGURE 1. Energy consumed at various distances for different data rates of IEEE 2802.11a/b/g.

(such as the test performed in [31]) or even the contrary (in refs[32] and [33], it can be seen that the devices consume less powerin the IEEE 802.11b mode than in the IEEE 802.11g mode). Itindicates that 802.11a/g is between two and three times moreeffective than 802.11b.

4. APPLICATION ENVIRONMENT AND INITIALCONSIDERATIONS

In this section, we introduce in which application environmentswe are considering applying WLASNs. We base the applicationenvironment on the area to cover, but the bandwidth could alsobe taken into account as a factor to force the selection of theIEEE 802.11 standard for the WSN.

We propose a group-based network where the network issplit into smaller zones to support a large number of nodes andlarger areas. Several application environments are the followingones:

(i) Let us suppose a job where all human resources needto be split into groups to achieve a purpose (such asfirefighter squads for putting out the fire). Now, let ussuppose that all people involved in that activity needa device that has to be connected with other devicesin the same group to receive information from themembers within the group, and closer groups haveto be connected to coordinate their efforts. Currently,coordination between groups is done through a wirelessconnection to the command center or using satellitecommunications. However, sometimes neither of thosesolutions can be used because a free of obstacles line ofsight is needed, because there are too many wall lossesor because more gain or power is needed to reach thedestination.

(ii) For battle field communication, it is especially usefulfor inter-squad communication to collaborate when anobjective is targeted by position detectors where thereare larger distances than several tens of meters.

(iii) Rural and agricultural environments. A group-basedtopology in this kind of environment could be usefulto detect plagues or fire and to propagate an alarm toneighboring lands. It will facilitate easier managementand control in detecting fires and plagues over largeareas while also allowing scalability.

(iv) Health monitoring.A patient could need to be monitoredin several locations while doing his activity. Every roomor place could have a group of sensors (and even eachgroup with a different type of topology inside) andneighbor groups must be communicated to keep trackof the patient.

(v) It could be used in any kind of system in which anevent or alarm is based on what is happening in aspecific zone, but conditioned to the events that arehappening in neighbor zones. One example is a group-based system to measure the environmental impact ofa place (forest, marine reef etc.). It could be bettermeasured if the measurements are taken from differentgroups of sensors, but those groups of sensors have to beconnected in order to estimate the whole environmentalimpact.

Let us suppose an initial condition of a relatively big area(such as the ones aforementioned) that has to be covered by asensor network. When the area has several square kilometers,only the IEEE 802.11 standard can satisfy the requirements.Figure 2 shows the number of sensors needed to cover differentareas according to the coverage radius of the used sensors foreach technology studied. We can observe that the number of

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Saving Energy in WLASNs 9

0

2000

4000

6000

8000

10 000

12 000

001011Surface (km2)

Num

ber

of s

enso

rs

Zigbee Bluetooth IEEE 802.11b/g IEEE 802.11n

FIGURE 2. Number of sensors needed by each technology to cover several surface areas.

sensors using Zigbee technology will be too much when largeareas have to be covered. The best technologies for these typesof environments are IEEE 802.b/g and IEEE 802.11n.

Taking into account the values shown in Fig. 2, we have toachieve a balance between the power consumed by the sensor,the number of sensors needed to cover the zone and the costof the sensors. Although Zigbee is the cheapest technology(followed by Bluetooth), this technology is not useful for largeareas.

Taking into account the economic issues, IEEE 802.11ntechnology is more expensive than 802.11b/g, and hence,nowadays, it is better to choose 802.11b/g.

5. ANALYTICAL MODEL

Denote by E(t) the energy consumption for the sensor. E(t) canbe broken down into five main components: the circuit energyconsumption Ec(t), the data processing energy consumptionEp(t), the energy when it is reading and writing in the memory,the energy consumed by the physical sensor and the IEEE802.11 chip energy consumption Echip(t), which includes theenergy consumed by the transmitter and receiver amplifierbecause it is included in the chip in all cases shown in Table 4.The energy consumption is shown as

E(t) = E(t)s + Ep(t) + Em(t) + Ec(t) + Echip(t) (1)

The energy consumed because of data processing (Ep(t)) isgiven by the expression provided by Wang and Chandrakasan[34]. It depends on the following two main parts: the activeenergy dissipation because of the switching and the energydissipated by the processor because of the leakage current. Itcan be seen in the following expression:

Ep(t) = bN

∫ t

0

(Cv2(t) + I0

K· v(t) · e(v(t)/n·VT )

(v(t) − g)

)dt (2)

where b is the number of bits processed, Kand g are processor-dependent variables, N is the number of clock cycles per task,C is the average capacitance switched per cycle and I0 is thecurrent consumed.

Halgamuge et al. [35] propose that sensor logging energyconsumption Em(t) depends on the current consumed in readingthe memory and writing in the memory. It can be formulated asshown in the following expression:

Em(t) = b · vdd

8

(∫ tread

0iread(t) dt +

∫ twrite

0iwrite(t) dt

)(3)

Physical sensors are usually formed by A/D circuits. We willbe able to measure the energy consumed by this part of thecircuit knowing the equivalent impedance of the sensor (Zth)

and the voltage consumed during the interval as shown by thefollowing expression:

Es(t) = Zth

∫ t

0v2(t) dt (4)

Ec depends on the number of resistances, condensers andcoils, and on the integrated circuits used in the circuit. It can beexpressed as is shown in the following expression:

Ec(t) = 1

Zin

∫ t

0i2(t) dt (5)

where Zin is the entrance impedance of the circuit, i(t) is thecurrent consumed by the circuit and t is the time when we wantto finish measuring the circuit.

The chip could be in a standby mode, transmitting frames orreceiving frames. If we group all the time that it has been in astandby mode from t = 0 to t = t0, all the time that it has beentransmitting from t = t0 to t = t1 and all the time that it has beentransmitting from t = t1 to t = t2, the energy consumption of

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0

50 000

100 000

150 000

200 000

250 000

0 50 100 150 200 250 300Time (h)

Ene

rgy

cons

umpt

ion

(mW

)

(1) ConnectCore Wi-9C (2) WiPort(3) MatchPort® b/g Pro (4) S103 WLAN Compact(5) RCM4400W RabbitCore (6) MatchPort b/g(7) Connect Wi-EM (8) Nano WiReach(9) Mini Socket iWiFi (10) WiFly 802.11B (RN-111B)(11) WiFly GSX Super (RN-134) (12) WiFly GSX 802.11G (RN-131G)

FIGURE 3. Power consumption comparison.

the chip can be measured as shown in the following expression:

Echip(t) =∫ t0

0Esm(t) dt +

∫ t1

t0

Et(t) dt +∫ t2

t1

Er(t) dt (6)

where Esm(t) is the energy consumption in the standby mode,Et(t) is the energy consumption when the chip is sendingpackets and Er(t) is the energy consumption when the chipis receiving packets.

In order to compare the energy consumed by the chipsetsshown in Table 4, we consider that the circuit used to regulatethe battery needed for all the chips will consume the sameenergy and it will be quite low compared with the energyconsumed by the chips. The energy consumed by the sensorwill also be negligible compared with the energy wasted by thechips. Taking into account the measurements taken in [36], wesuppose that a wireless link spends 70% of the time idle (inthe standby mode), 20% of the time receiving and 10% of thetime transmitting. Figure 3 shows the comparison of the energyconsumed over time by the chipsets shown in Table 4. Theyhave been ordered and numbered in the figure from the mostenergy consumption to the lowest one. The chip that consumesless energy is ‘WiFly’ 802.11B (RN-111B) module, while thechip that consumes most energy (six times more than the lowestone) is ConnectCore Wi-9C.

6. PROTOCOL PROPOSAL

We propose a group-based sensor network that uses thecooperation between groups using the IEEE 802.11b/gtechnology. When a group detects an event, it alerts, jointly withthe parameters measured, its neighboring groups. Cooperationwith other groups could change the direction of the alertpropagation and the level of the alert. According to thiscooperation, the sensor network will be efficient and the sensorswill have a longer lifetime.

This proposal of cooperative sensor networks is based on thegroup-based idea presented in [37] by Lloret et al. (some ofthe authors of this paper). It works in a similar manner, buthere we have introduced cooperation issues in order to decreasethe messages sent. Fig. 4 shows an example of a group-basedtopology.

Group-based network formation is as follows. Every nodehas three parameters as follows: nodeID, which is unique in itsgroup; groupID, which is given manually, using GPS, or using awireless location system; and λ, which gives its capacity. Theseparameters characterize the node and select its neighbors usingthe proximity and its λ (see [38] for more details).

When a new node joins the network (it could be the first),it sends a hello message (called helloGroup) in order to join a

Group-based sensornetwork

FIGURE 4. Group-based sensor network.

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group. If there is no response from any node for 3 s, the nodeconsiders itself as a central node of a group in the network,and it will take the value groupID = 1 and nodeID = 1.When the node receives helloGroup ACK messages from severalcandidate neighbors, first it puts a timestamp on their reply andchooses the best nodes with which to have a link (this election istaken based on the λ parameter which comes in the helloGroupACK message). Responses received after 3 s will be discarded.In case of receiving replies from nodes of different groups, itwill choose the group whose replies have the highest averageλ parameter, and thus it will take into account replies only forthat group. Then, the node will send an okGroup message tothe selected neighbors and the neighbors will reply with theokGroup ACK message to the assigned nodeID, indicating thatthe link has been established. Neighbor nodes will send thenewNode message (that contains the nodeID) to the rest of thenodes in the same group in order to inform that there is a newnode in the group. In order to update all nodes in the group,the newNode message is distributed using the Reverse PathForwarding algorithm [39]. Nodes will send keepalive messagesperiodically to their neighbors. If a node does not receive akeepalive message from a neighbor before the dead time, it willremove this entry from its neighbor’s database and will start thegroup update process. As the groupID is in the helloGroup ACKmessage, the new node will know which group it has joined. Theprocess is shown in Fig. 5.

Links between border nodes from different groups areestablished as a function of their proximity and the λ parameterof the replying nodes, but it could be changed by an algorithmusing the node’s position or delay in replying. If we base ourproposal on the λ parameter, we distribute the load of thenetwork between groups, but if we base our proposal on a node’sposition or choose the neighbor with the lowest distance to thecentral node, we balance the number of nodes in the groups.We have proposed the following two choices to establish theboundaries of the group:

(i) When the boundaries of the group are the same as thearea that is to be covered, border nodes are known usingGPS.

Newnode

Neighbornodes

Nodes in the same group

helloGroup

helloGroup AC

okGroup

okGroup ACK

newNode

Keepalive

Keepalive

FIGURE 5. Procedure when a new node joins the network.

(ii) When the boundary of the group is limited by thediameter of the group, the maximum number of hopsfrom the central node must be known. When it achievesthe maximum number of hops, the node is marked as aborder node, and it will inform new joining nodes thatthey must create a new group.

In order to have an efficient group-based sensor network,the groups will communicate with their neighboring groups.When a group detects an event, it sounds the alert, jointlywith the parameters measured, routing the information to itsneighboring groups (not to all groups) based on the positionof each group. The position of the sensors (it could be enteredmanually or using GPS) and a position-based routing algorithm[40] are used to know the group situation. Neighboring groupswill reply to the first group if any of the parameters is changedin the place of that group, in order to take appropriate actions.Cooperation with other groups could change the direction of thealert propagation and the level of the alert.

In a group-based network, all groups will be aware of anevent produced inside the group. The network efficiency wouldbe even higher than in a regular topology [10]. Nevertheless,in cooperative group-based networks, this efficiency is greater,because only the neighboring groups will be aware of it. Theother groups could be in a sleep mode. The sleep groups willbe saving energy and they would not transmit unnecessaryinformation.

We defined three alert levels. Level_1 means the maximumalert level and Level_3 the lowest. This cooperative group-basedarchitecture runs the following steps:

(i) A sensor detects a warning event.(ii) It senses other parameters (it could be done using a

multi-sensor).(iii) The node assigns an alert level for each neighboring

group and routes the information for each group usinga position-based routing algorithm [40].

(iv) The destination group senses the same parameters whenit receives the information.

(v) If the parameters are close, the neighboring groups willaccept the alert level; if they are not close, they changethe level according to the measured parameters and sendthe new parameters to the source group.

(vi) When the source group receives the information, itanalyses the level that it should give to the neighboringgroups and sends them the new level. Then go to step(iv).

Figure 6 shows the flow chart of the steps explained.Taking into account the group-based sensor network shown

in Fig. 4, if there is an alert in a node placed in Group 1, themessages sent because of the operation shown in Fig. 6 are seenin Fig. 7.

Let us imagine that a plague of insects produces an event inGroup 1, and the other parameters sensed are the wind direction

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FIGURE 6. Cooperative architecture operation.

and speed (now called pIni). Nodei calculates the level for eachgroup and sends the alert and pIni to all nodes in its group.

FIGURE 7. Messages exchanged because of an alert in a node in theGroup 1.

Each message will also be routed to the appropriate neighboringgroups. The area where the alert will be forwarded is shown inFig. 8. Thus, the information will reach Groups 2, 8 and 6.

In order to provide a dynamic network, we have alsoconsidered changes in the parameters sensed. Let us supposethat the wind comes from east, and hence Group 2 has level_1,Group 8 has level_2 and the Group 6 has level_3. The p

process is executed in each group when the information arrives.If the parameters sensed are close to the initial parameters,the alert level does not vary. Let us suppose that Group 6detects changes in the wind direction (pNew). This groupwill send the new wind direction to Group 1, and then thisgroup will estimate the new alert level. Figure 9 shows thealgorithm.

Cooperative group-basedsensor network

FIGURE 8. Area where the alert will be forwarded.

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Saving Energy in WLASNs 13

If (pNew ≤ pIni*1.1 || pNew ≥ pIni*0.9) Send ack to source group Alert level assigned = OK Else Send ack + pNew Calculate Alert level End

FIGURE 9. Process p operation.

7. SIMULATIONS

In this section, we compare our proposal with other ad hocrouting protocols in order to show that it consumes less energythan the others.

7.1. Test bench

In order to simulate our proposal, we have used the topologyshown in Fig. 4 for several protocols using Ns2 Simulator [41].It has 68 nodes split in eight groups. The number of nodes ineach group varies from 5 to 10 nodes. As shown in Fig. 2, it willcover around 20 km2 in an open environment.

Each node has an omni-directional antenna centered on it.The MAC protocol was CSMA/CA. The standard implementedis IEEE 802.11b with a radio channel of 11 Mbps. The operationfrequency is 2.4 GHz and the propagation model used is the two-ray ground propagation model.

After an event in a node of the Group 1, all the nodes inits group and in its neighboring groups are advised of thatalert. We simulated a transmission during 30 s between the nodepositioned on the Group 1 and the nodes that should be reached(only 36 of them). Figure 10a shows the entire network withoutany event and Fig. 10b shows the groups that will be advised ofthe alarm.

The packet size follows an exponential distribution with amean value of 1024 bits and a traffic load of 10% of thetime.

The data transmission has been tested using destination-sequenced distance-vector routing (DSDV) protocol [42], adhoc on-demand distance vector (AODV) protocol [43], dynamicsource routing protocol (DSR) protocol [44] (all of themavailable in the Ns2 Simulator) and our protocol proposal.

7.2. Simulations

Figure 11 shows the number of packets sent during the 30 s forDSDV, AODV, DSR and our proposal. It shows that the peaks inDSDV are between 1450 and 1500 packets, the AODV protocolhas peaks higher than 5000 packets and the peaks in DSR arebetween 1500 and 1600 (except the peak of 2240 packets). Ourproposal is significantly more stable. It has a maximum value of640 packets and a minimum value of 138 packets. The averagevalue is 491 packets.

If we measure the number of packets sent during the test, wecan observe the difference between them. Figure 12 shows thatduring the simulation DSR has sent 361 000 packets,AODV hassent close to 237 500 packets (34.21% less messages than DSR),DSDV has sent more than 168 600 packets (29% less messagesthan AODV and 53.28% less than DSR) and our proposal hassent more than 147 850 messages (12.32% less messages thanDSDV; 37.74% messages less than AODV; and 59.04% lessthan DSR).

Now, we can take into account the considerations and valuesprovided in [45] by Heinzelman et al. The transmission and thereception energy are given by the following expressions:

ET x(k, r) = (ET x + Eampr2) · k (7)

ERx(k) = ERx · k (8)

(b)(a)Cooperative group-basedsensor network when thereis an event

Group-based sensornetwork

FIGURE 10. (a) Group topology without any event. (b) Groups that will be advised by Group 1.

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0

1000

2000

3000

4000

5000

6000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Time (s)

Pac

kets

Our Proposal DSDV AODV DSR

FIGURE 11. Protocol comparison.

0

50 000

100 000

150 000

200 000

250 000

300 000

350 000

400 000

Protocols

Num

ber

of p

acke

ts

Our Proposal DSDV AODV DSR

FIGURE 12. Number of packets sent.

where k is the number of bits sent in the packet; ET x = ERx =50 nJ/bit; and Eamp = 100 pJ/b/m2.

Remembering that, in our simulation, the packet size fol-lowed a mean value of 1024 bits, we can now estimate thepower consumption because of the packets transmitted andreceived by all nodes during the simulation. Figure 13 showsthe energy consumption in the network. We have consideredclose to zero the circuit energy consumption, the data process-ing energy consumption, the energy when it is reading and

0

10 0000

20 0000

30 0000

40 0000

50 0000

0 5 10 15 20 25 30Time (s)

Pow

er c

onsu

mpt

ion

(mW

)

Our proposalDSDVAODVDSR

FIGURE 13. Power consumption in the network because of thepackets transmitted and received.

writing in the memory and the energy consumed by the phys-ical sensor because it is very less compared with the energyconsumed by the nodes due to all the packets sent and received.Our proposal is the one that has less power consumption andDSR is the one that consumes more power, although AODV isthe worst in the first 9 s.

Taking into account these measurements, we can calculate theWLASN lifetime for all the studied protocols. In order to knowhow much power is in the network initially, we can consider alithium battery with 700 Wh/kg [46] for each one of the nodesinvolved (36 nodes). Table 5 shows the lifetime for a node whenit is running each protocol. AODV has improved over DSR by52% of its lifetime. DSDV has improved by 114% of the lifetimeof DSR and 41% the lifetime of AODV. Furthermore, finally,our protocol has improved by 144% the lifetime of DSR, 61%the lifetime of AODV and 14% the lifetime of DSDV.

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TABLE 5. Lifetime of the WLASN for each protocol.

Protocol Lifetime (approximately)

Our protocol 4 months and 25 daysDSDV 4 months and 7 daysAODV 3 monthsDSR 1 month and 29 days

8. CONCLUSIONS

In this paper, we have presented the technologies used in WSNs.Although Zigbee and Bluetooth are the most used becausetheir energy consumption is quite low, they have very shortradio coverage and hence many sensors are needed to coverwide areas. We have shown the IEEE 802.11a/b/g chipsets thatsatisfy the requirements needed to be used in WSNs and can bebought in the market. Although IEEE 802.11a/b/g technologieshave higher coverage areas and higher bandwidth, their energyconsumption is quite higher, and therefore we have discussedtheir consumption and how it can be decreased. Finally, wehave elected the IEEE 802.11b/g variants because IEEE 802.11nis too expensive nowadays and IEEE 802.11a has lower radiocoverage than the others.

We have modeled the energy consumption of WSN nodesin a manner different from other works (taking into account thetime wasted in each process (transmitting, receiving, processingetc.)).

In this paper, we have also presented a cooperative group-based sensor network for several application environments suchas fire detection, and monitoring rural and agricultural andnatural crises. When a sensor detects a new event, an alert issent to its group and it is distributed to neighboring groups. Thecooperation between groups is used to send messages betweengroups in order to obtain the right alert level.

We have simulated the proposed protocol using the Ns2simulator and we have obtained better energy conservationmeasurements compared with the ones obtained by otherprotocols available in this simulator. We have also proved thatour protocol allows a larger lifetime for the nodes in a WLASNthan the other protocols.

Due to the excellent features of IEEE 802.11n, when itis ratified and the cost of the devices decreased, it will bestudied in depth to be used in the application environmentsaforementioned.

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