Experimental Evaluation of the Performance ofMulti-hop Wireless Sensor Networks ∗

Ioannis Chatzigiannakis, Sotiris Nikoletseas and Andreas Strikos†

Department of Computer Engineering and Informatics,University of Patras, 26500, Patras, Greece and

Research Academic Computer Technology Institute (CTI){ichatz,nikole}@cti.gr, strikos@ceid.upatras.gr

Abstract

This paper presents experimental measurements of bulkdata transfer in a wireless multi-hop sensor network envi-ronment. We investigate the effect of the number of the hopsand the conditions of the surrounding environment on theperformance of the network in terms of achieved transferrates. Our findings validate the theoretically establishedresults on the relation between the throughput and the net-work diameter, i.e. is inversely proportional to the networkdiameter and in particular the number of hops needed fordata to reach its destination. Furthermore, we indicate howthroughput is (significantly) affected by the type of the phys-ical environment, i.e. it drops as the harshness of the ambi-ent conditions increases.

1 Introduction

Wireless Sensor Networks are considered as very largesystems comprised of small sized, low-power, low-cost sen-sor devices that collect detailed information about the physi-cal environment. Each device has one or more sensors, em-bedded processors and low-power radios, and is normallybattery operated. Examining each such single device in-dividually, might appear to have small utility. The real-ization of Sensor networks however, lies in using and co-coordinating a vast number of such devices and allows theimplementation of very large sensing tasks.

In a usual scenario, these networks are largely deployedin areas of interest (such as inaccessible terrains or disas-ter places) for fine grained monitoring in various classes of

∗This work has been partially supported by the IST Programme ofthe European Union under contract number IST-2005-15964 (AEOLUS)and the PYTHAGORAS Programme under the European Social Fund(ESF) and Operational Program for Educational and Vocational TrainingII (EPEAEK II).

†Currently an undergraduate student

applications [1]. The flexibility and self-organization, faulttolerance, high sensing fidelity, low-cost and rapid deploy-ment characteristics of sensor networks create many newand exciting application areas for remote sensing. In thenear future, this wide range of application areas will makesensor networks an integral part of life [6].

Multi-hop wireless sensor networks present many chal-lenges, as end-to-end reliable delivery of data requires eachpacket (segment) to traverse one or more intermediate hopsen route to the destination. In addition to the unreliablewireless transmission at each hop, contention problemsfrom “hidden nodes” and “exposed nodes” in the wire-less network limit the number of data packets that can bein flight concurrently from source to destination. Thesephysical-layer properties constrain the throughput achiev-able over a multi-hop path [3].

As radios that are sufficiently distant can transmit con-currently, the total amount of data that can be simultane-ously transmitted for one hop increases linearly with thetotal area of the ad hoc network. [5, 7] study the capac-ity of static wireless networks and provide estimates on theper node capacity expected. If the node density is constant,this means that the total one-hop capacity is O(u), whereu is the total number of nodes. However, as the networkgrows larger, the number of hops between each source anddestination may also grow larger, depending on communi-cation patterns. One might expect the average path lengthto grow with the spatial diameter of the network, or equiv-alently the square root of the area size, or O

(√u). Based

on this assumption, the total end-to-end capacity is roughlyO

(u√u

), and the end-to-end throughput available to each

node is O(

1√u

).

The analysis provided in [5, 7] indicates that in this ap-proach (of using a path of intermediate hosts to propagatemessages) capacity is also a limiting factor; it is not encour-aging that the throughput available to each node approaches

zero as the number of nodes increases. These fundamen-tal results suggest that this approach of achieving commu-nication in ad-hoc networks is fundamentally non-scalablebut could be improved under the assumption that in largenetworks, nodes may communicate mostly with physicallynearby nodes. If local communication predominates, pathlengths could remain nearly constant as the network grows,leading to constant per node available throughput. How-ever, even in such cases where the formation of a shortpath (a few intermediate nodes) is feasible, [7] observe thatthe capacity of a chain of nodes when using an ideal MACprotocol can achieve a chain utilization of at most 1

3 . Fur-thermore, if one assumes that radios can interfere with eachother beyond the range at which they can communicate suc-cessfully, the expected maximum utilization of a chain ofad-hoc nodes may be as high as 1

4 .

2 Our Experimental Framework

We have created an experimental environment that con-sists of 8 MICA sensor nodes [8], a second generation“mote” module used for research and development of lowpower, wireless, sensor networks. The device is equippedwith a low-power Atmega128L microcontroller [2] runningat 4MHz. It is an 8-bit microcontroller with 128 Kbytesof flash program memory, from which it runs its operationsystem, and 4 Kbytes of system RAM.

The node’s radio system enables wireless communica-tion with neighboring nodes and the outside world. Thecommunication is implemented with an RF MonolithicsTR1000 transceiver [10], which can be externally controlledto have a transmission radius ranging from centimeters totens of meters and can operate at communication rates upto 40Kbps. During our tests we used the digital poten-tiometer (DS1804) contained in the transceiver in order todynamically adjust the transmission strength of the radio.The radio transceiver of our motes operates at 916.57 MHz,although these devices come in one more operational fre-quency (413MHz). The component does not allow trans-mission and reception at the same time.

The microcontroller has a built in 8-channel analog todigital converter which can be coupled to a number of dif-ferent sensor boards sensing light, temperature, acceler-ation, magnetic field, voltage, sound volume, barometricpressure, humidity and solar radiation. Additionally we canconnect a programming board and install programs or ag-gregate data onto a PC from the sensors network. Finallythe power system operate on 2 AA inexpensive batteries.

The MICA hardware platform has been designed to sup-port the TinyOS [11] execution model presented. TinyOSaccomplishes the desirable conditions for embedded sys-tems OS. It has small footprint, low system overheadand low power consumption. Developed at UC Berkeley,

TinyOS, is an event-based operating system where all sys-tem functions are broken down into individual componentsthat interact through narrow command and event interfaces.The component-based structure of TinyOS allows for anapplication designer to select from a variety of system com-ponents in order to achieve application specific goals.

TinyOS has no kernel, no process management and novirtual memory. It uses the Matchbox [4] filing systemfor file management and active messages for communica-tion. There is no blocking or waiting in system componentsand there is no mechanism for suspending and preemptinga command. Because of this commands and events exe-cute quickly and run to completion and there are no long-running threads in the system. However in order to allowlong-running operation the concept of a task is introduced.Tasks can be posted by components to a system schedulerfor background process. At most one task is active in thesystem at any time. TinyOS implements B-MAC[9] pro-tocol, which MICA motes use for MAC protocol. In addi-tion we used a very simple routing protocol that establisheda single, static route connecting each mote with the baseboard.

Figure 1. A chain of MICA motes

3 Throughput Findings

Our first experiment took place outdoors. Our goal wasto measure the throughput of a sensors network that consistsof 2-8 MICA motes. The network topology was a chain. Weplaced the motes in a line having a distance of about 40cmbetween them, so that only adjacent motes were within thetransmission range of each other (see Figure 1). As we men-tioned above, we used the digital potentiometer contained inthe transceiver in order to decrease the transmission strengthof the radio. We raise the motes 10-15cm from the groundin order to have more accurate results with the communica-tion part. We must mention that the batteries of every motewere always charged to avoid power problem.

Figure 2. Throughput versus Number of hops

The primary goal was to repeat exactly same tests withdifferent number of motes. In our chain topology the firstmote was always connected with the base board and there-fore with a laptop, so we could record every single incomingmessage. The last mote in chain (line) was always sendingbulk messages, containing only an increasing integer. Thein between motes were only forwarding the messages to theadjacent mote towards the destination. So the message sentby the last mote would finally be received by the first moteconnected with the laptop. The last mote was transmittingmessages using the following 9 intervals between each con-secutive transmission: 18msec, 37msec, 50msec, 75msec,100msec, 150msec, 200msec, 300msec, 600msec. In thefirst phase of the experiment we had 2 motes, the one send-ing and the other receiving the messages. So we had 1 hop.We took 8 values for the total incoming messages one forevery transmit rate. The number of the total incoming mes-sages refers to the messages the first mote received for asingle minute. To ensure the validity of the experiment’s re-sults we determine the value after a 7-minute test and thentook the average value of these 7 values. In the second phasewe did exactly the same with 3 motes and 2 hops. The in be-tween mote, as we mentioned earlier, was only forwardingthe messages to the neighbor mote. Each time we increasedthe number of the motes and consequently of the hops. Wekept on until 8 motes and 7 hops.

These experiments study the sensitivity of throughput tothe number of hops, to the transmit rate and to the envi-ronment of the wireless sensor network as well. Figure 2depicts the relation between the delivery rate (as measuredat the data destination) and the packet injection rate. The

Figure 3. Nodal Point versus Number of Hops

delivery rate initially increases like linearly with the injec-tion rate; however, when a certain injection rate is reached,the wireless medium is unable to further accommodate theadditional packets (due to collisions) and no further increaseof the delivery rate is measured. We call this threshold valueof the delivery rate a nodal point. Figure 2 shows the differ-ence in throughput for 4 chain topologies; for 1 hop, for 3hops, for 7 hops and the ideal, where we receive every sin-gle message that was sent. As shown in 1 hop curve we hadto send messages with transmit rate 1 message/18msecondin order to be able to view the emergence of the nodal point.Additionally this nodal point decreases in every phase. Thatmeans that the more we increase the hops the more thethroughput point decreases. This finding is in line with thetheoretical results of [5, 7] since the end to end through-put available to the nodes is inversely proportional to thenetwork diameter. Interestingly, even for a 7 hops networkthe throughput becomes very small (i.e. about 1 Kbps). Fig-ure 3 shows how the nodal point decreases when the numberof hops increases.

In addition with the above experiments we choose thespecific chain topology of the 3 hops and we measurethe throughput in two more environments. One indoors,where very limited ambient noise was present, and one out-doors, where the ground and the environment was harshand more inhospitable. We use 4 different transmit rates( 1message/37, 100, 300, 600msec), which eventually wereenough in order to reach interesting results. All the otherprocedures we followed were exactly the same as the onesof the above experiments.

The second experiment confirms the relation that ex-

Figure 4. Effect of Surrounding Environment

ists between the environment and the throughput. Figure 4shows the throughput for the three environments we havetried in contrast with the ideal curve.

Apart from the throughput point the results show thatthe throughput itself decreases as well, when we increasetransmit rate. The maximum throughput we measured wasin the first phase where we have 2 motes and 1 hop dis-tance. This value was around 6Kbs. As we mentioned inthe beginning of this paper the maximum achievable datarate of MICA motes is 40Kbps. This of course is idealbecause the MAC protocol that TinyOS provides for theMICA motes has throughput limit of 13Kbps according to[9]. But the gap between the actual throughput and the the-oretically possible is still big. One intuitive explanation forthis gap is the experiment’s environment. As we said, theexperiments took place outdoors, where many interferencesexist, such as noise, dust, obstacles, etc. Except that, whenthe hops are increased, it is reasonable to experience morelosses because the probability for a message to miss is in-creased since the transmission are increased as well. Ad-ditionally, we confirmed that the environment is critical forthe throughput as shown in figure 4. The more harsh it isthe less throughput we can get.

4 Conclusions & Future Work

We have presented the results of three empirical mea-surements on the performance of wireless sensor networks,which demonstrate the behavior of throughput when wechange the transfer rate, the number of hops and the typeof the measurement’s environment. Our results indicate

that the performance achieved in the real environment (i)is much lower than the theoretical maximum, (ii) is heavilyaffected by the number of hops that a packet needs to travelto reach the destination and (iii) is directly affected by thesurrounding environment. We believe that our work givesuseful insights on the actual performance achieved by cur-rent technology when operating under real conditions. Weplan to continue our experiments with other technologies,different MAC layer protocol implementations and networktopologies (e.g. star, grid) in order to provide a more com-plete view on the capabilities of real wireless sensor sys-tems.

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