Sensor Networks - Balancing Energy Use and Quality of Service Lawrence A. Bush, Christopher D. Carothers and Boleslaw K. Szymanski Department of Computer Science Rensselaer Polytechnic Institute Troy, NY 12180, U.S.A. {BushL2,chrisc,szymansk}@cs.rpi.edu July 30, 2003 Abstract Sensor Networks will change the way computers interface with our world and each other.This transformation will be shaped by the network centric paradigm demonstrated in Sensor Net- works. Sensor Networks require a data-centric networking paradigm to efficiently and effectively share data. Directed Diffusion is a data-centric networking paradigm which has been established and studied. Energy efficient routing algo- rithms have been developed, however, these methods can be improved. Routing algorithms to improve these methods are presented here.This paper presents computer simulation re- sults which verify the effectiveness of previously extablishe- drouting algorithms and compare them to the new and im- proved routing algorithms. The results show significant im- provement in energy efficiency and resilience.Ultimately, this paper incorporates effective modeling of Sensor Networks to demonstrate the usefulness of these new optimization strate- gies. 1 Introduction A Sensor Network is a distributed sensing technology that can be used to monitor physical phenomenon. A Sensor Network is easily deployed and is therefore useful for many applica- tions. A Sensor Network is made up of many distributed sen- sor nodes. This topology requires an Ad-hoc routing paradigm. Sensor nodes are battery operated. Many Sensor Network ap- plications require thousands of sensor nodes which will be de- ployed in remote locations. This makes battery replacement impractical. Therefore, energy conservation is very important for Sensor Networks. Traditional Ad-hoc routing algorithms are not optimized for energy conservation. For this reason, en- ergy efficient Ad-hoc routing paradigms are an area of active research. Directed Diffusion is an example of an energy efficient routing paradigm which is central to this paper. Directed Dif- fusion algorithms consist of a flooding phase followed by a path reinforcement phase and a routing phase. The flood- ing phase of the Directed Diffusion paradigm is very costly. Therefore, avoiding this phase is desirable. Algorithms have been developed with the objective of extending the duration of the routing phase between floodings. This effectively avoids some flooding. The complicating factor is that sensor nodes are prone to failure. Therefore, in order to extend the routing phase, the employed paths must be resilient to node failure. The means to accomplish this is by setting up multiple routing paths between the source which senses the physical phenom- ena and the sink. These paths can be set up in many configu- rations. However, setting up and maintaining extra paths also requires extra energy. It is important for this act to be energy efficient. An example of such an algorithm is presented in [1]. An algorithm which reduces the energy used to maintain mul- tiple paths or improves the resilience to path failure would en- able a Sensor Network to operate more efficiently which will extend the life of the network. In our paper, we build upon the Braided Multi-path algo- rithm presented in [1]by providing a new algorithm that si- multaneously reduces the maintainence overhead associated with these multiple paths and increases their resilience to node failure. Computer simulation results which verify the claims made in [1] are presented here. The simulation model was then used to compare the new algorithm to the Braided Multi- path algorithm. The new algorithm was shown to improve re- silience to path failure by up to 39% while using as little as 25% of the energy for a given epoch. These significant im- provements in energy efficiency and resilience encourage us to further develop the Sensor Network simulation capabilities of Rensselaer’s Optimistic Simulation System (ROSS). 2 Related Work 2.1 What is a Sensor Network? Sensor Networks have been described and surveyed in many papers including [2]. A Sensor Network is a multi-hop self- configuring wireless network consisting of many sensor nodes.A diagram of a sensor network is shown in Figure 1. A sensor 1
Transcript
Sensor Networks - Balancing Energy Use and Quality of Service
Lawrence A. Bush,Christopher D. Carothers and
Boleslaw K. Szymanski
Department of Computer ScienceRensselaer Polytechnic Institute
Troy, NY 12180, U.S.A.{BushL2,chrisc,szymansk }@cs.rpi.edu
July 30, 2003
Abstract
Sensor Networks will change the way computers interface withour world and each other.This transformation will be shapedby the network centric paradigm demonstrated in Sensor Net-works. Sensor Networks require a data-centric networkingparadigm to efficiently and effectively share data. DirectedDiffusion is a data-centric networking paradigm which hasbeen established and studied. Energy efficient routing algo-rithms have been developed, however, these methods can beimproved. Routing algorithms to improve these methods arepresented here.This paper presents computer simulation re-sults which verify the effectiveness of previously extablishe-drouting algorithms and compare them to the new and im-proved routing algorithms. The results show significant im-provement in energy efficiency and resilience.Ultimately, thispaper incorporates effective modeling of Sensor Networks todemonstrate the usefulness of these new optimization strate-gies.
1 Introduction
A Sensor Network is a distributed sensing technology that canbe used to monitor physical phenomenon. A Sensor Networkis easily deployed and is therefore useful for many applica-tions. A Sensor Network is made up of many distributed sen-sor nodes. This topology requires an Ad-hoc routing paradigm.Sensor nodes are battery operated. Many Sensor Network ap-plications require thousands of sensor nodes which will be de-ployed in remote locations. This makes battery replacementimpractical. Therefore, energy conservation is very importantfor Sensor Networks. Traditional Ad-hoc routing algorithmsare not optimized for energy conservation. For this reason, en-ergy efficient Ad-hoc routing paradigms are an area of activeresearch.
Directed Diffusion is an example of an energy efficientrouting paradigm which is central to this paper. Directed Dif-fusion algorithms consist of a flooding phase followed by apath reinforcement phase and a routing phase. The flood-
ing phase of the Directed Diffusion paradigm is very costly.Therefore, avoiding this phase is desirable. Algorithms havebeen developed with the objective of extending the duration ofthe routing phase between floodings. This effectively avoidssome flooding. The complicating factor is that sensor nodesare prone to failure. Therefore, in order to extend the routingphase, the employed paths must be resilient to node failure.The means to accomplish this is by setting up multiple routingpaths between the source which senses the physical phenom-ena and the sink. These paths can be set up in many configu-rations. However, setting up and maintaining extra paths alsorequires extra energy. It is important for this act to be energyefficient. An example of such an algorithm is presented in [1].An algorithm which reduces the energy used to maintain mul-tiple paths or improves the resilience to path failure would en-able a Sensor Network to operate more efficiently which willextend the life of the network.
In our paper, we build upon the Braided Multi-path algo-rithm presented in [1]by providing a new algorithm that si-multaneously reduces the maintainence overhead associatedwith these multiple paths and increases their resilience to nodefailure. Computer simulation results which verify the claimsmade in [1] are presented here. The simulation model wasthen used to compare the new algorithm to the Braided Multi-path algorithm. The new algorithm was shown to improve re-silience to path failure by up to 39% while using as little as25% of the energy for a given epoch. These significant im-provements in energy efficiency and resilience encourage usto further develop the Sensor Network simulation capabilitiesof Rensselaer’s Optimistic Simulation System (ROSS).
2 Related Work
2.1 What is a Sensor Network?
Sensor Networks have been described and surveyed in manypapers including [2]. A Sensor Network is a multi-hop self-configuring wireless network consisting of many sensor nodes.Adiagram of a sensor network is shown in Figure 1. A sensor
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Figure 1: A diagram of a Sensor Network.
node is the fundamental electronic building block of a SensorNetwork. It is a self contained modular low-cost electronicsystem that incorporates three functions (sensing, computa-tion and communication) into a small package (for exampleone inch in diameter). The sensing component can be Seis-mic, Magnetic, Thermal, Visual Spectrum, Infrared, Acousticor Radar. The computation component can include data anal-ysis such as beam forming or aggregation of related data. Itcan also include routing computation overhead. The commu-nication component involves RF transmission and receptionbetween multiple nodes within the transmission vicinity.
Characteristics: Sensor Networks need to be scalable. Scal-able means that the network will perform properly when thereare many nodes in the network. A forest fire detection Sen-sor Network would have tens of thousands of sensor nodes.Therefore, it needs a scalable Open System Interconnection(OSI) model. Sensor Networks must also work properly undervarying densities with irregular and changing topologies. Ac-cording to [3] a requirement of a good routing procedure is that“[i]t should adapt to changes in the network topology result-ing from nodal and channel failures.”Since a Sensor Networkis typically deployed in an unorganized manner, the densityof the network would not be uniform. An additional causeof non-uniformity is sensor failure. Sensor nodes are fail-ure prone due to malfunction, destruction (for example dueto fire) and extinguished battery stores. A network that canoperate under these conditions is said to be fault tolerant. Sen-sor Networks may also use a massive redundancy strategy toenhance system lifetime. Sensor nodes are power limited be-cause non-replaceable batteries power them. In most cases,it is impractical to change the batteries because the nodes arenumerous and in remote locations. Therefore, the nodes maybe deployed in numbers that are larger than necessary in orderto extend the life of the system. The system must be able tooperate in this dense node environment and take advantage ofits characteristics. Sensor Networks are generally broadcastbased and data-centric because the nodes have no global iden-tification. Sensor Networks need a paradigm such as DirectedDiffusion in order to accommodate this data-centric character-istic. It should efficiently enable Sensor Querying and Data
Dissemination in a network in which nodes have no globalidentifiers (i.e. Data Aggregation using Attribute Based Nam-ing). Above all, Sensor Networks need to conserve their lim-ited battery power. Therefore, they need to be energy aware.To accommodate these energy use demands, a Sensor Networkrequires a communication paradigm that enables Ad-hoc orga-nization yet gives consideration to energy use and applicationrequirements for quality of service. Directed Diffusion is sucha paradigm and is discussed in detail in Section 2.2. Gener-ally, the routing protocol must be energy-efficient in order tomaximize the system lifetime. Energy efficiency can be de-fined in terms of the percentage of nodes that are still alive,connectivity or total energy use for a particular task. Energyefficiency is primarily accomplished by using the best rout-ing path and by turning off unneeded components of the node.Therefore, the sensor nodes need control features to managethe node hardware energy use characteristics. It is widely ac-cepted that computation is inexpensive relative to communi-cation. Therefore, it is wise to employ computations whichreduce communication energy. In summary, Sensor Networkalgorithms need to be energy aware.
Applications: A Sensor Network is used to discover or mon-itor phenomena. For example, it may detect the presence andmovement of objects or monitor temperature. A Sensor Net-work may be deployed to monitor temperature in a forest inorder to identify and locate forest fires as discussed in [4].This would require dense deployment within or near the phe-nomena. In addition to forest fire detection, there are manyother useful and interesting applications of Sensor Networks.Some examples are earthquake measurement [5], C4ISRT [6]and Bio-sensing [7] [8]. The deployment of sensor nodes isnot organized; therefore, the exact and relative position of thenodes is not predetermined. For example, a network of tem-perature sensing nodes may be airdropped into a forest. Thisrequires that the network self configure in an Ad-hoc manner.These applications have varying requirements for energy use,latency, quality of service and fault tolerance.
2.2 Directed Diffusion
Current Sensor Network research is focused on algorithms andprotocols for managing energy consumption using protocolssuch as Directed Diffusion. There are essentially two types ofDirected Diffusion: Sink Initiated and Source Initiated.
Sink Initiated: In Directed Diffusion, the sink will querythe network by disseminating throughout the Sensor Networka description of the data that it is interested in. This descriptionis known as an interest because it is expressing an interest in aparticular named data type.
The dissemination of the interest is essentially a floodingmechanism. As the dissemination of the interest takes place,the network sets up routing gradients which are like a trail ofbread crumbs from that spot in the network back to the sink.If a sensor node has data that matches the interest, it will sendits data back to the source (typically) using the best path avail-able. The gradients indicate the best path available. A diagramof this process is shown in Figure 2.
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Figure 2: A diagram of the sink initiated Directed Diffusionprocess.
An interest expression may not only indicate the type ofdata that it is interested, but also the range of values of that dataas well as the periodicity of updates that it wants. For example,if the network is monitoring a forest for fires, the query mayask for temperature data that is above a certain degree. If anode is sensing temperature in that range, then it should sendthat data to the sink at the defined interval.
Source Initiated: It is important to distinguish the abovemechanism as receiver-initiated routing protocol. This pro-tocol can also work as a sender initiated routing protocol. Therelative benefits are application specific. The monitoring ver-sus the surveillance tasks epitomize the application specifictradeoffs. To understand this, the applications should be fur-ther explained. The objective of a monitoring task may beto query the system when the operator wants to check on thestatus of the situation “out there.” This application is betterserved by sink initiated Directed Diffusion method because thesource nodes should remain “silent” to conserve energy untilthe user (and subsequently the sink) expresses interest in thenode’s data.On the other hand, the objective of the surveillancetask is to monitor an areafor the presence of some phenom-ena.This application is better served by the source initiated Di-rected Diffusion method becausethe source should notify thenetwork when it detects the presence of the phenomena.ThisSource Initiated protocol is described in [1] which is an ex-tension of [9]. The flooding phase in this version of DirectedDiffusion advertises the availability of the source data.This isfollowed by a path reinforcement phase which identifies thebest path(s) and initiatesthe sending of data packets. A dia-gram of this process is shown in Figure 3. In [1], different pathconstruction techniques are tested to increase the time periodbetween the source-initiated flooding phases. Full networkflooding is then only needed when all the paths connectingthe source-sink pair fail. Specifically, the paper [1] presents aBraided Multi-path reinforcement strategy for Directed Diffu-sion routing in Sensor Networks.
SPIN: A pure source initiated Directed Diffusion scheme isalso identified in the literature as Sensor Protocols for Infor-mation via Negotiation (SPIN) [10]. SPIN, developed at theMassachusetts Institute of Technology (MIT), is similar to Di-rected Diffusion. In the SPIN protocol, the source advertisesnew data. SPIN incorporates data aggregation, local neighborinteractions and decision making in order to reduce redundantrequests for the data. These optimizations help to avoid implo-sion and overlap as well as reduce the overhead of adaptation
Figure 3: A diagram of the source initiated Directed Diffusionprocess.
to topology changes. Implosion happens when duplicate mes-sages are sent to the same node. Overlap is when two nodesobserve the same region. Protocols that organize and optimizethese interactions are very beneficial.
Attributes: Directed Diffusion is a reactive, on-demand rout-ing protocol, which addresses scalability, energy efficiencyand robustness to topology changes. Unlike Internet protocol,it accomplishes this without using global identifiers.Sensor Net-works are Ad-hoc, therefore the nodes have no identificationmechanism. In order for Directed Diffusion to work, the dataitself has to be named. For example, if a monitoring net-work is monitoring temperature, a data type may be “temper-ature.” Directed Diffusion only utilizes local routing informa-tion.This avoids much of the routing table updates requiredfor path based connectivity. Specifically, Directed Diffusiononly stores the next hop routing information rather than usingthe connectivity abstraction. This also reduces the amount ofrouting information stored at each node and improves routingefficiency and resiliency by allowing topology changes to behandled locally. These attributes enable data delivery with-out total network connectivity which would inhibit scalabil-ity. The on-demand path construction also enables robustnesswhile saving energy. While adaptations of Directed Diffusionhave been studied, more studies are needed. Directed Dif-fusion is a sound paradigm, which continues to evoke newgeneric and application specific ideas.
2.3 Braided Multi-path Routing
The paper [1] presents a Braided Multi-path routing algorithmwhich improves upon the Directed Diffusion concepts pre-sented in [9]. The objective of the Braided Multi-path Rout-ing algorithm is to extend the life of the network by conserv-ing energy. Braided Multi-path conserves energy by prudentlyavoiding the costly flooding phase of Directed Diffusion. Thisis accomplished by extending the routing phase.
The routing phase ends primarily due to node failure. If anode on the routing path fails, the source can no longer senddata to the sink. To recover from this situation, the floodingphase must be initiated. One strategy to avoid this is to extendthe period of time between flooding phases.
The above description assumes that the routing algorithmreinforces a single path as shown in Figure 4. The singlepath algorithm works as follows. In the first phase, the sourcefloods the network. Each flooding packet tracks its hop-countwhich is the distance in hops from the source. Each node
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Figure 4: A diagram of the single-path routing algorithm.
Figure 5: A diagram of the Disjoint Multi-path routing algo-rithm.
records the minimum hop-count to the source and the affil-iated link.When the flooding phase is completed, each nodehas local knowledge of the shortest path back to the souce.Collectively, these hop-count records are known as gradients.In the second phase, the sink reinforces the preferred path (theone with the lowest hop-count) by sending a reinforcementpacket up that path. Each node will set this as its primarypath. This continues until the reinforcement packet reachesthe source.Once the primary path is established, data packetswill be routed along the primary path at regular intervals. Asingle node failure on the primary path will cause total pathfailure. However, a multi-path routing strategy can be em-ployed to increase resilience to path failure. For example, aDisjoint Multi-path routing algorithm may be employed forthis purpose. The paper [1] describes a Disjoint Multi-path al-gorithm where a primary path is reinforced, followed by a sec-ond alternate path as shown in Figure 5. The Disjoint Multi-path algorithm works like the single path algorithm except thatan alternate path is reinforced in addition to the primary path.This path must enforce the disjoint property which means thatit does not intersect the primary path. The paper shows thatthis algorithm improved path resilience, but at a significantcost. It is important to note that the maintenance of alternatepaths requires some additional energy. This is primarily in theform of Keep-Alive packets. The paper explains that
“the source periodically floods low-rate data overall alternate paths in the multipath in order to keepalive those paths, thereby permitting fast recoveryfrom failures on the primary path.”
Essentially, the Keep-Alive packets are sent from the sourcedown the alternate paths to keep themfrom going into sleepmode. The Keep-Alive packets also serve to maintain whichpaths are still valid. In the paper [1], Braided Multi-path (shown
Figure 6: A diagram of the Braided Multi-path routing algo-rithm.
in Figure 6) is presented as an alternative to Disjoint Multi-path. In the Braided Multi-path algorithm, the sink reinforcesthe primary path as was done in the single path routing al-gorithm. Additionally, at each node on the primary path, analternate reinforcement packet is also sent down the penulti-mate path of that node. That reinforcement marks that nodeas the alternate path. The reinforcement continues from therefollowing the shortest path to the source, reinforcing all theway. If the reinforcement happens upon the primary path, itrejoins it and terminates. This process continues until the rein-forcement packet reaches the source. The sum of these createsa braid-like path set consisting of a primary path and a seriesof alternate paths which each serve to circumnavigate a par-ticular node of the primary path. Braided Multi-path increasesthe resilience of the path, but at a lower path maintenance cost.This algorithm is the primary result presented in [1].
3 Solution Description
This section describes three improvements on the Braided Multi-path algorithm described in [1]. The first improvement is DealerRouting. Dealer Routing reduces the energy use to maintainalternate paths.Dealer routing is then integrated with the BraidedMulti-path algorithm to demonstrate that the energy use is re-duced.The second improvement is N-Braided Dealer Routing.N-Braided Dealer Routing improves the resilience by increas-ing the number of alternate paths in the routing braid.The rout-ing braid is the interconnected set of routing paths. The addi-tional alternate paths improve the network’s ability to main-tain a path from the source to the sink in the presence of nodefailures which will extend the time period between floodingphases andfurther reduces energy consumption.The third im-provement is Log N-Braided Dealer Routing. Log N-BraidedDealer Routing increases the bushyness of the routing braid.Theincreased bushyness further improves resilience with minima-lincrease in energy consumption. Essentially, the increasedbushyness provides more interconnections between the pathswhich increases the number of possible ways for the packetsto be routed around failed nodes.
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Figure 7: This diagram depicts four steps of Braided DealerRouting. The squares represent data packets.
3.1 Dealer Routing
Dealer Routing implements an alternative to using Keep-Alivepackets. Keep-Alive packets are used to keep the alternatepaths in the set of routing paths in a ready state. Keep-Alivepackets can be implemented in various ways. This algorithmis an optimized variant of the concepts presented in [1]. Thefollowing quote from [1] describes the nature of Keep-Alivepackets.
“Specifically, when a primary path is set up, thenetwork also sets up multipaths along which datais sent at a low-rate. This low-rate data representsthe energy expended for maintaining multipaths.We use the term maintenance overhead to denotethis energy. The low-rate data thus constitutes“Keep-Alives” on the alternate paths. As soon asa failure is detected on the primary path, nodescan quickly reinforce an alternate path without theneed for network-wide flooding to initiate discov-ery.”
Dealer Routing takes an alternate approach which elim-inates the need for Keep-Alive packets. Dealer Routing al-ternates which path it sends a packet down. Since the pathsare braided, each node may have multiple paths. If there aremore than two reinforcedpaths from the given node, it willsend the first data packet down one of them, the second datapacket down another and so on, in an established arbitrary or-der until each reinforced path has recieved a data packet asshown in Figure 7.This sequence is analogous to dealing cards.Each of these nodes behaves by the same rules. By doing so,each path is kept alive but the maintenance overhead associ-ated with sending the low-rate data is eliminated.
3.2 Braided Multi-path Dealer Routing
In order to test the effectiveness of the Dealer Routing strat-egy, we integrated it into the Braided Multi-path routing.Theflooding and path reinforcement phases of Braided Multi-pathDealer Routingoperate the same as they did in the BraidedMulti-pathalgorithm presented in [1]. However, the routingphase uses the Dealer Routingstrategy instead of primary path
Figure 8: This diagram shows and example of reinforced pathsin Log 5-Braided Dealer Routing.
routing to avoid the use of Keep-Alive packets. The objectiveis to extend the lifetime of the network by reducing the energyuse in the routing phase while maintaining a similar level ofresilience to node failures.
3.3 N-Braided-Multi-path Dealer Routing
N-Braided Multi-path Dealer routing is a variation on BraidedDealer Routing.The flooding and routing phases of N-BraidedMulti-path Dealer Routingoperate the same as they did in theBraided Multi-path Dealer Routing algorithm.However, the pathconstruction phase differs in that it enables the use of a greaterthan binary alternate path splitting. In this algorithm, anynumber of N-ary path splitting rates can be used. As in theBraided Routing algorithm, an alternate reinforcement packetis sent down the penultimate pathof each primary path nodeand marks that node as the alternate path.However, it doesnot stop there, rather an alternate reinforcement packet is sentdown N-1 alternate (next best) paths of each primary pathnode, marking them as alternate paths.Each of these reinforce-ment packets subsequently reinforces the shortest path to thesource. The sum of these creates a braid-like path set consist-ing of a primary path and a series of alternate paths. However,in this case, it has a wider swath.The paths in the swath inter-mingle in interesting ways which may increase resilience.Thisalgorithm also uses the Dealer Routing instead of primary pathrouting with Keep-Alive packets.
3.4 Log N-Braided-Multi-path Dealer Routing
This algorithm is a variation on the N-Braided-Multi-path DealerRouting algorithm.The flooding and routing phases of N-BraidedMulti-path Dealer Routingoperate just as they did in the Braided
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Multi-path Dealer Routing algorithm.However, the alternatepath construction phase differs in that the alternatepath rein-forcement packets do not simply reinforce the shortest pathto the source. In addition to doing that, the alternate nodeswill subsequentlysend an alternate reinforcement packet downthe N − 1 next best pathsfrom each alternate node.N , how-ever, is not the originalN , but is equal to(N − 1)/2. Inother words, ifN = 5, the primary path will reinforceN − 1(4) alternatepaths. Each of these alternate paths will reinforce(N−1)/2 (2) paths.Subsequently, each of these alternate pathswill reinforce (N − 1)/2/2 (1) paths. Each alternate patheminating from the primary pathsplits forlog2(N − 1) lev-els.Subsequently, they reinforce the shortest path to the source.The sum of these creates a braid-like path set consisting ofa primary path and a wider, very intertwined series of alter-nate paths as shown in Figure 8.The objective is to further in-crease resilience by creating more ways toroute around fail-ures. This method uses more nodes and therefore more en-ergy.However, the reinforcements generally converge towardsthe primary path, reducing the number of extra nodes thatare used while greatly increasingthe number of interconnec-tions.This algorithm also uses Dealer Routing instead of pri-mary path routing with Keep-Alive packets.
4 Solution Analysis
4.1 Model Validation
The purpose of the initial simulation runs was to verify the re-sults of the paper [1] and establish a model to use as a baselinefrom which to make improvements. These simulations com-pared single, multi-path and braided routing algorithms. Therelative results were in line with the results taken from the pa-per. The simulation runs have a prescribed duration. The timeperiod simulated measures the energy used during the adver-tisement flooding phase as well as the route reinforcement anddata routing phases. The relative length of the data routingphase changes the results. This is because the advertisementphase uses a large amount of energy. The primary energy sav-ings takes place during the data routing phase. If the data rout-ing phase is extended, then the energy savings is greater. Thesimulation duration used was long enough to accrue substan-tive energy savings in this phase. However, the exact percent-age saved is dependent on the time elapsed between epochs,where an epoch is comprised of the advertisement, reinforce-ment, and data routing phases.
With that said, the simulation results support the resultsfrom the paper [1] because the Disjoint Multi-path and BraidedMulti-path algorithms result in greater resilience than the sin-gle path routing algorithm. Also, the Braided Multi-path usesless energy than the Disjoint Multi-path method.
4.2 Performance Results
Figure 9 shows the simulation results which compare the per-formance of the routing algorithms. They were compared invarious configurations. The network is represented as a 32 by32 regular mesh. All of the simulations model isloated fail-ures as described in [1]. We also model pattern failures as
described in [1]. Half of the simulations use pattern failuresand half do not. Half of the algorithms were simulated us-ing a path length of 6 and half were simulated using a pathlength of 20. Our model assumes a fixed transmission ra-dius as described in [1]. Therefore, the path loss was notmodeled. Consequently, transmission and reception energy ismodeled as a uniform 100 micro-Joules. These simulationsare not intended to test MAC protocols. In these simulations,the MAC layer was abstracted away. The paper [11] notesthat this has been said to skew simulation results, however,for certain nodes such as the Pico Radio where every nodehas a locally unique channel, there are no transmission col-lision losses.The Braided Dealer Routing showed some posi-tive and interesting performance characteristics.For example,it demonstrated significantly and universally lower energy usethan the baseline Braided Multi-path routing algorithm.We ex-pected the resilience of the baseline Braided Multi-path andthe new Braided Dealer routing algorithm to be equal.The sim-ulation results for a path length of 6 concur. However fora path length of 20, the simulation results measured the re-silience of the new Braided Dealer routing algorithm, as rep-resented in Figures 12 and 13, to be similar but slightly lowerthan the resilience of the baseline Braided Multipath routingalgorithm. The reason for this is not known.
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Algorithm Isolated Pattern Number of Path Resilience EnergyFailure % Failures Paths Length Use
The N-Braided Dealer routing algorithm was not the best per-forming routing algorithm in our simulations. However, itdemonstrated that increasing the airity of braided routing wasa viable variation when combined with Dealer Routing. Thesimulations showed that the energy use of N-Braided DealerRouting was stilllower than the baseline Braided Multi-pathdespite the higher airity. However, the effect on resiliencewas mixed.Specifically, the simulation results measured theenergy use of N-Braided Dealer Routingto be approximately12% and 70% lower than the baseline Braided Multi-path for
path lengths of 6 and 20 respectively.The simulation resultsalso showed the resilience of the N-Braided Dealer Routing tobe better than the resilience of the baseline Braided Multi-pathrouting algorithm for a path length of 6,but worse for a pathlenth of 20. The simulation results with pattern failures weresimilar but not as pronounced.The probable cause of the poorresults for the longer path length is that some of the nodes inthe routing path set went to sleep because they did not recievea data packet often enough.These nodes will not wake backup unless they recieve a wake-up call.Therefore, they simplystop routing packets.While we could wake them back up, weinfer from [1] that it is not a viable recovery option to do sobe-
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cause enabling this systemic problem would preclude fast re-covery from failures on the primary path. Interestingly, thisphenomenon may be beneficial in Log Braided Dealer Rout-ing.That algorithm has a very bushy path set. Due to the dif-ficulty in keeping all nodes alive, some may inadvertently goto sleep. This causes the algorithm to reinforce the remainingpaths in the swath which still offer adequate redundancy butare easier to keep alive.
4.4 Log N-Braided Dealer Routing
The Log N-Braided Dealer Routing algorithm is the best per-forming routing algorithm in our simulations. It demonstratedthe highest resilience and lowest energy use in most cases.There were a few cases in which another algorithm showedbetter resilience than the Log-Braided Dealer Routing algo-rithm.However, those algorithms used far more energy thanthe Log N-Braided Dealer Routing algorithm, makingthemless desirable. The Log-Braided Dealer Routing algorithmused significantly less energy than the other algorithms, withthe exception of the less resilient single path algorithm. Specif-ically, the Log-Braided Dealer Routing, using an airity of 5,was more resilient and used less energy than the Braided Multi-path algorithm presented in [1]. As stated above, this algo-rithm provided the baseline for this project, from which toimprove upon. Most importantly, the energy use of the Log-Braided Dealer Routing algorithm is more scalable than theBraided Multi-path algorithm. This is clear from the results
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Figure 12: Results of the comparison of Braided Multi-pathand Braided Dealer Routing
using a path length of 20 with no pattern failures. The Log-Braided Dealer Routing algorithm was significantly more re-silient while using only approximately 30% as much energy ascompared to the Braided Multi-path algorithm. All in all, theLog N-Braided Dealer Routing algorithm demonstrated thebest cost-benefittradeoff between energy use and resilience tonode failure.
4.5 Pattern Failure Impacts
One interesting effect of modeling pattern failures is that theylevel the playing field. In other words, when pattern fail-ures are simulated, the positive gains of the resilient routingschemes are lessened. The result is that the performance ofthe “better” algorithms is not notably better in the presence ofpattern failures. Pattern failures appear to be difficult to re-cover from without flooding the network.
4.6 Reinforcing Disjoint Paths
While conducting simulation runs and adjusting various algo-rithm parameters we observed that it often took a long timefor the network to construct alternate disjoint paths. This isbecause the alternate path often converges back to the primarypath causing a negative reinforcement. This action enforcesthe disjoint property in the algorithm. If the paths convergefrequently, it will take much longer to construct the alternate
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Figure 13: Results of the comparison of Braided Multi-pathand Braided Dealer Routing
path than the primary path. Depending on the operation ofthe algorithm, this may affect recovery latency or cause Keep-Alive complications. This makes braided paths more desirablefor this reason alone. Since Braided paths do not enforce thedisjoint property, they take less time to construct.
5 Conclusion
In this paper we have reported on our studies of Directed diffu-sion routing algorithms. Our studies include the constructionof computer simulations, validating our simulation model byconfirming previous research results and simulating and mea-suring the behavior of new algorithms given certain networkassumptions. While our ultimate objective was to extend net-work life, the supporting objectives are as follows:
• optimize energy conservation
• extend the duration of the routing phase
• improve resilience to node failures
• reduce the energy required to enable resilience
We have constructed algorithms to help achieve these goals.The results are very encouraging and interesting. Some of ourfindings are as follows:
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Figure 14: Results of the comparison of Braided Multi-pathand Braided Dealer Routing
• Dealer Routing is useful because it avoids the use ofKeep-Alive packets which saves a significant amount ofenergy.
• Increasing the number of braided paths appears to im-prove resilience in some cases, but requires careful adap-tations to be effective.
• Increasing the number of braided paths combined with astrategy to increas the interconnectedness of these pathsimproves resilience to node failure with a managable in-crease in overhead.
• Multi-path routing in general does not solve the pat-tern failure problem. We believe that it is well suitedto handle isolated failures. Challenging the encompass-ing nature of pattern failures would probably require ahuge amount of overhead. Consequently, re-floodingthe whole network remains the better solution for han-dling pattern failures.
In summary, these strategies and concepts, while tested inan idealized setting under particular assumtions, would help toimprove sensor network routing issues.
Future Work: An interesting area of future work is to testthe load balancing effect of these algorithms; in particular,their energy conservation effect on the overall system consist-ing of multiple sources. A side effect of the Dealer Routing
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Figure 15: Results of the comparison of Braided Multi-pathand Braided Dealer Routing
algorithms presented in this paper is that the network load isdistributed among multiple paths, speading out the energy use.While not explicitly tested, this should have the effect of ex-tending the life of the network. This effect depends on theoverall traffic in the network.
References
[1] D. Ganesan, R. Govindan, S. Shenker, and D. Estrin.“Highly-Resilient, Energy-Efficient Multi-path Routingin Wireless Sensor Networks.”Mobile Computing andCommunications Review,Vol. 4, No. 5, October 2001.
[2] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E.Cayirci. “Wireless Sensor Networks: A Survey.”Com-puter Networks, Volume 38, Issue 4, 15 March 2002,Pages 393-422.
[3] L. Kleinrock. “Queueing Systems, Volume 2: ComputerApplications, Section 6.1 Simulation and Routing.”Wi-ley Interscience, Page 424, New York, 1976.
[4] R. Min, M. Bhardwaj, S. Cho, N. Ickes, E. Shih, A.Sinha, A. Wang, and A. Chandrakasan. “Energy-CentricEnabling Technologies for Wireless Sensor Networks.”IEEE Wireless Communications (formerly IEEE Per-sonal Communications), Vol. 9, No. 4, August 2002, pp.28-39.
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Figure 16: Results of the comparison of Braided Multi-pathand Braided Dealer Routing
[5] P. Davis, M. Kohler, J. Wallace. “Seismic Monitor-ing.” http://www.cens.ucla.edu/Research/Technology/seismic_monitor.htm .
[6] H. Hasenauer. “Reaching the Army Vision Warrior Ex-tended Battlespace sensors, WEB.”Soldiers, Volume 55,No. 6, Page 23, June 2000.
[7] Alan Mainwaring, Joseph Polastre, Robert Szewczyk,David Culler, and John Anderson, “Wireless Sensor Net-works for Habitat Monitoring,”WSNA’02, Atlanta, Geor-gia, September 2002.
[8] H. Wang, J. Elson, L. Girod, D. Estrin and K. Yao. “Tar-get Classification and Localization in a Habitat Monitor-ing Applicaton.” In Proceedings of the IEEE ICASSP2003, Hong Kong, April 2003. To appear.
[9] C. Intanagonwiwat, R. Govindan, and D. Estrin. “Di-rected Diffusion: A Scalable and Robust Communica-tion Paradigm for Sensor Networks.”In Proceedings of6th ACM/IEEE Mobicom Conference, 2000.
[10] J. Kulik, W. R. Heinzelman, H. Balakrishnan.“Negotiation-based protocols for disseminating in-formation in wireless Sensor Networks.”ACM/IEEE Int.Conf. on Mobile Computing and Networking, Seattle,WA, Aug. 1999.
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Figure 17: Results of the comparison of Braided Multi-pathand Braided Dealer Routing
[11] R. Shah and J. Rabaey. “Energy Aware Routing forLow Energy Ad Hoc Sensor Networks.”In Proc. IEEEWireless Communications and Networking Conference(WCNC), Orlando, FL, March 2002.
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Figure 18: Results of the comparison of Braided Multi-pathand Braided Dealer Routing
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Figure 21: Results of the comparison of Braided Multi-pathand Braided Dealer Routing
