Chapter 10Location Service, Information Disseminationand Object Tracking in Wireless SensorNetworks by Using Quorum Methods

Dan-Dan Liu1 and Xiao-Hua Jia21

1 Computer SchoolWuhan University, Wuhan, China 430072liudd2004@hotmail.com

2 Department of Computer ScienceCity University of HongKong, HongKongjia@cs.cityu.edu.hk

1 Introduction

1.1 Quorum System

Quorum methods were originally used in consistency control for data replicasin distributed database systems. Replication of data is the key technology forhigh availability and fault tolerance in a distributed system. Upon receivinga request to perform an operation on a particular datum, the server is re-sponsible for cooperating with other servers in the group that have copies ofthe data. Different replication schemes involve different numbers of servers forthe successful accomplishment of an operation. For example, in the traditionalread-any/write-all scheme, a writing request must be finished by all servers inthe system, so that a read operation can be performed by any single server.However, this scheme is not reliable because failure any one of the serverswill cause failure of the write operation, and finally failure of data accessing.The quorum consensus method aims at reducing the number of servers tak-ing part in a data operation. A quorum is a subgroup of servers whose sizegives it the right to make decisions. For example, if having a majority is thecriterion, a quorum must consist of at least n/2 servers cooperating to carryout operations, where n is the total number of servers in the system.

The quorum method was used early on in [1]. Gifford in his paper devel-oped a file replication scheme in which a number of ‘votes’ is assigned to eachphysical copy at a replica manager of a single logical file. In each transaction,a read quorum of R votes must be collected to proceed with a read opera-

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tion, and a write quorum of W votes must be collected to perform a writeoperation. The R and W are set as:

W > half the total votes,R+W > total number of votes for the group.This ensures that there is at least one common copy involved in an oper-

ation between any pair of a read quorum and a write quorum or two writequorums. There is always a subset of the representatives of a file whose votestotal to W that are current. Thus, any read quorum is guaranteed to get acurrent copy, which is determined by the version numbers.

The quorum method has also been used in preventing transactions in dif-ferent partitions of networks from producing inconsistent results [2]. It requiresthat an update operation on a logical object should be completed successfullywithin a quorum. Without enough members to form a quorum, operationswill not be performed thus avoiding conflicting results.

The reliability and performance characteristics of the quorum method canbe controlled by appropriately adjusting the R and W. In the general situation,comparative to the file replication scheme in [1], each physical copy at a singleserver can be regarded as being assigned the same votes, say 1. Thus, W andR determine the number of nodes composing each quorum. There is a tradeoffbetween overhead consumption in write operations and read operations. Thecharacteristics of writes improve as W decreases, and similarly for reads bydecreasing R. The choice of R and W will depend on an application’s read-to-write ratio, the cost of reading and writing, and the desired reliability andperformance.

In [3], the number of nodes within a quorum has possibly been reducedto approximately

√n. That is, n servers in the system can be partitioned

conceptually into√

n rows and√

n columns. Each column and row serves asa quorum; all of the quorums form a quorum system. There is always a serverlocated in the intersection of each pair of row and column. The overhead of thesystem is controlled at O(

√n). Compared with the O(n) servers involved for

accessing data in traditional read-any/write-all or read-all/write-one replicaschemes, to O(n/2 ) in those having majority criterion schemes, and to O(

√n),

the quorum method has significantly reduced traffic in the system which canbe translated into significant energy savings.

1.2 Problems in Wireless Sensor Networks

In recent years, the Wireless Sensor Network (WSN) [4] has received signifi-cant interest from the research community. A sensor network usually consistsof hundreds or thousands of tiny and inexpensive sensor nodes, which are de-ployed on the ground, in the air, in vehicles, on bodies, under water and insidebuildings without any sensing infrastructure a priori, providing functions ofdata sensing, information processing and communication. Because of its spa-tial coverage and multiplicity in sensing aspect and modality, a sensor networkcan be used for many military and civil applications, such as environment or

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habitat monitoring, target surveillance and object tracking, traffic monitoringand traffic control, and so on [5].

A WSN can be treated as a distributed database system. In such a systemeach sensor node operates autonomously with no central node of control. Itcan be a data source (producing data) as well as a data sink (consuming data).Each node will update sensed data to the networks in an efficient way (writeoperation) so that interested querying nodes are able to get the current copyof the new data (read operation) as soon as possible. Each of these operationsis based on the node’s local information.

Several challenges are in front of us for the design and implementation ofsuch a distributed information system for resource constrained WSNs. First,the system is limited by the energy capacity of WSNs. Sensors are poweredby batteries and it may not be possible to recharge or replace them in manyapplications. The system operations must be energy efficient. Second, thesystem is limited by the bandwidth capacity of sensor nodes. The interactions(i.e., communications) among the sensors must be kept minimal. Third, sensornodes do not have global topology information and they, sometimes, evendo not have global identifiers. Each sensor node only knows its own and itsneighbors’ positions, and its operations must be based on its local information.

There are three important problems in such a distributed WSN, namelylocation service, information dissemination and object tracking.

A. Location ServiceLocation service is a general obstacle for distributed networks. Without a

central node of control, updated location information about nodes should betimely obtained before data can be exchanged among nodes correctly. How-ever, due to the limitation of their power and hardware capability, sensors willbe exhausted if they maintain tables for the location of all the others. More-over, if mobility is available, sensors are even harder to trace than other nodes.Thus, location both in time and space has been identified as a key technologyfor the successful deployment and operation of context-aware sensor networkservices.

B. Information DisseminationThe main goal for WSNs is to provide distributed information services on

sensed data efficiently. When a node senses data, it will process the data andadvertise the data to others that are interested in it. If other users need toaccess certain data, they make a query to the network, and whoever is holdingthe data will pass it to them. However, the challenge is that the queryingnode generally has no idea which sensor owns such data, and the sensingnode doesn’t know which sensor will be interested in new data either. Thus,many current researches focus on disseminating sensed information throughthe network in a distributed and efficient way, which can guarantee the currentone to be captured quickly.

C. Object TrackingObject tracking in a military battlefield situation is one of the most im-

portant applications of WSNs. A sensor node deployed in a battlefield can

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detect vehicles or soldiers intruding into its sensing scope. It will generate areliable report, containing the location or ID of the intruder, and advertise tothe network through established routes and nodes. Individuals who need thecurrent location of certain vehicles or troops later can send a request to thenetwork and get proper information from sensor nodes inside the network. Ithas strict requirements on energy consumption, since sensors are impossibleto recharge or replace during a battle.

Because of its flexibility, interrelated to energy efficiency as introducedabove, the quorum method can be considered as a good means to deal withthese three important problems in wireless sensor networks. The rest of thischapter is organized as follows: we overview the location service problem anddifferent protocols to solve it from the view of the quorum method in Section2; Section 3 discusses the information dissemination problem and focuses onthe employment of quorum-based method protocols combined with negotia-tion. Its performance is shown through mathematical analysis and simulationcomparison. One of the major applications of wireless sensor networks—objecttracking is discussed in Section 4. If we combine the frequency of object de-tection and request with the consideration of different quorum size, minormodification of the quorum-based method can receive even better results.Conclusion about this chapter is made in Section 5.

2 Location Service

There are two classes of protocols that deal with the routings among nodes,topology-based and position-based [6]. Since nodes may easily die out by run-ning out of energy or moving out of their original place, the topology of sensornetworks is changed frequently. Therefore position-based routing is introducedto eliminate these deficiencies. The first issue to address in the position-basedrouting is location service. A location tracking mechanism is essential for up-dating and retrieving location information about nodes to establish connec-tions before data is routed. Nevertheless, because of the limitation of powerand hardware capability, sensors will be exhausted if they maintain tables forthe locations of all the other nodes. Moreover, if mobility is available, sensorsare even harder to trace than others. Thus how to facilitate location servicein a distributed way is not a trivial task.

Location service includes two major tasks. First is the location update orlocation registration, where nodes periodically disseminate their up-to-dateposition information. Second is the destination search. The current locationof a node can be obtained from some servers when communication to thatnode is initiated. There exist many interesting issues in the management oflocation information for these nodes which is similar to many mobile networks[7], [8]:

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1) Frequency of location updates. There are basically three alternatives,namely time-based, movement-based and distance-based strategies to deter-mine update intervals [9].

2) Information organization. There are several distributed strategies forhierarchical organization of location information proposed in [10], [11].

3) Information update and retrieval. There can be multiple location serversin the networks. It is the tradeoff between performance of the write/readquorum and the system overhead.

4) Load balance of servers. In distributed database architecture, some lo-cation servers may be overloaded with location updates and query requestswhile other servers are relatively idle. We should distribute those servers, bal-ance the load, and make full use of those available resources to prolong thelife of networks.

So far, there have been several protocols proposed to solve this location ser-vice problem. They can be typically divided into two main categories: proac-tive and reactive approaches. They both take ideas from the quorum method.

2.1 Proactive Protocols

In many proactive protocols, one node has to maintain locations of all theother nodes. They periodically propagate up-to-date location informationthroughout the network. The method is like the write-all/read-one scheme ina distributed database system. Updates take place in all nodes, and whichevernode receives a command can start data retrieval immediately. However, thiswill cause high overheads affecting bandwidth utilization, throughput as wellas power usage [12]. It has low scalability. For a large scale wireless sensornetwork, or a network composed of nodes with high mobility, large numberof updating interchanges among nodes will consume considerable energy andeven cause many flooding problems.

For example, within the Distance Routing Effect Algorithm for Mobil-ity (DREAM) framework [13], each node maintains a position database thatstores position information about other nodes that are parts of the network.Locations of nodes will be disseminated throughout the network periodically.More specific metrics are given to control the frequency of the update to bal-ance the trade-off between overhead in full updating and accurate informationacquisition.

2.2 Reactive Protocols

In reactive (on demand) protocols, a path discovery mechanism is initiatedonly when a source node tries to find a route to a destination node that ismoving around. The path information is maintained as long as it is neededby the source. Generally, reactive protocols consume fewer overheads thanproactive protocols. When there is no significant movement, there is neither

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Dan-Dan Liu and Xiao-Hua Jia

a need to periodically send advertisements, nor to receive them. However, thedelay caused by searching a new route is inevitable.

The Location Aided Routing proposal [14], for example, proposes the use ofposition information to enhance the route discovery phase of reactive routingapproaches. A source broadcasts a route request message to all its neighbors,searching for the destination. Neighbors that are within the request zone willforward them further. After the destination node, which should be within thezone, receives such a query, it will send back a route reply message as well asits current location. Such protocol works as the read-some/write-one scheme.Nodes don’t need to update their location to others immediately after themovements. They reply only when requests are received.

2.3 Rendezvous-Based Protocols

Recently, many protocols have been proposed in a novel fashion, called ren-dezvous based protocols. The basic idea in this kind of protocols is that allnodes in the network agree with a mapping. When nodes are going to sendout location updates, they choose a subset of available nodes. When nodesneed to find some other nodes, they send out location queries to a potentiallydifferent subset of nodes. Different protocols should be designd to make surethat the intersection of two subsets is not empty. These rendezvous nodes actas location servers where location updates are stored and location queries arelooked up. From the terms of the quorum method, it ensures that a read quo-rum can get the current copy of latest location data. Note that the traditionalscheme requires to satisfy the inequality of R+W>n (n is the total number ofnodes), while the rendezvous based method never needs all nodes cooperatingfor each data accessing.

Two different approaches to performing the mapping, hashing-based andquorum-based, have been proposed [15].

A. Hashing-Based ProtocolsIn hashing-based protocols, each node chooses its location server via a

hashing function, either in the node identifier space or in the location space.All location servers form a quorum. The size of quorum is closely relatedto hierarchical division and location servers’ selection. Recall that either inthe traditional read-one/write-all scheme, or one with a majority criterion,the size R of the read quorum and size W of the write quorum should abideby R+W> n (n is the total number of nodes), no matter what adjustment ismade between them. More efficiently, in this kind of method, updates only takeplace on location servers, a subset of N nodes inside the network, which areselected through mapping with a well-known hashing function. Later, locationis retrieved from the same group of location servers. The availability of thismethod lies in the common knowledge for producing a quorum intersection.

Hashing-based protocols can be further divided into hierarchical hashingand flat hashing. In the hierarchical hashing-based protocols (for example [16],[17]), networks are divided into several hierarchical grids. One or more nodes

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in each level are designated as location servers for each node, so that locationupdates and queries only need to traverse up or down the hierarchy to findthem. Because the height of the hierarchy is O(log n), effectively each node’slocation is disseminated to O(log n) location servers.

Another kind of hashing is flat hashing (for example [18], [19], [20]). In suchprotocols, each node calculates for itself a home region, which may consist ofone or more nodes within a fixed area in the network, through a uniform andwell-known hash function. This home region, acting as location server, willstore the location update and reply to corresponding queries for that node.The number of location servers for each node is independent of the totalnumber of nodes n, but the energy consumption on these O(1) servers is com-paratively high, which may cause some other problems such as a bottleneckin a network.

B. Quorum-Based ProtocolsFor the same purpose, to reduce the number of nodes involved in forming a

read quorum and write quorum as in the hashing-based method, the quorum-based method also never needs all nodes to collaborate in each data accessing.The number involved in each quorum is significantly reduced to O(

√n), where

n is the total number of nodes residing in the network. Moreover, there is nopreliminary information about the hash function in each node. Authors in [21]have discussed several methods for generating quorum systems.

In [22], the author applied a similar idea, as in [3] with some modification,to form a simple and efficient quorum system to wireless networks. Recallthat in [3], n servers in the system are evenly partitioned into

√n rows and√

n columns, so that intersections occur between rows and columns. However,there are no strict rows or columns inside real networks. Nevertheless, withsome modification, the protocol works well as follows: nodes exchange loca-tion information with immediate neighbors whenever a link associated withthe nodes is broken or created. After the number of such link changes reachesa certain threshold value, the node broadcasts a location update packet toits neighbors. Then the packet is retransmitted along the north-south direc-tion of this node (forming a column) by the northernmost and southernmostneighbor of each iteration until it reaches the end of this ‘column’. A destina-tion search packet from the source is forwarded in a similar fashion. It is firstbroadcasted to the easternmost and westernmost neighbors of the source, andretransmitted along its east-west direction (forming a row) until it reachesthe end. The location update and destination search process can form a crossshape inside the network (Figure 1).

Figure 1 (a) is the best situation that quorum method is used in a regulargrid system. Obviously, a row and a column are bound to intersect. Figure 1(b) shows the situation that the quorum method is applied to common wirelesssensor networks. Sensors along the north-south direction and the east-westdirection form the column and row respectively. Intuitively, queries can beanswered by at least one rendezvous.

241Chapter 10 Location Service, Info. Dissemination and Object Tracking

Dan-Dan Liu and Xiao-Hua Jia

D

S

D

(a) (b)

S

Fig. 1. Illustration of the quorum-based location management.

In a comparatively sparse network, rows and columns will be more likelyformed in a diagonal or zigzag way, not vertically or horizontally as desired.Thus the probability of intersection will decrease as the density decreases. Toincrease the reliability of quorum intersection, each ‘column’ and ‘row’ can beexpanded to a thicker quorum. Face routing on the perimeter of the networkcan be added as well, which will be discussed specifically in the following sub-section, to make sure the intersection between read and write quorums exists.Figure 2 (all data are acquired from [22]) shows the success rate and floodingrate (number of transmissions divided by n) for quorum-based schemes forn=100 nodes in the network, respectively. The thickness of each column (p)and row (s) is varied from 1 to 2. The average number of neighbors of eachnode (k) is varied from 4 to 12. As we can see, a quorum-based scheme canachieve high success rate with low energy cost. It is an energy efficient methodto deal with the transaction of data accessing.

Fig. 2. Performance of the quorum method.

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0.6

0.8

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4 5 6 7 8 9 10 11 12 degree k

succ

ess

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p=s=1 p=s=2

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The quorum method has another advantage when all networks move dy-namically together in a given direction, such as a rescue team or army. Becausethe correlation among nodes within each quorum is almost stable, less trafficis needed to maintain neighbor information than other methods.

3 Information Dissemination

WSNs aim at disseminating information to querying nodes as soon as possible.Since WSNs are designed in a distributed fashion, sensors have to make deci-sions based on its local information. They have no idea about which node willask to consume produced information in the future. Obviously, if all recentlysensed data are broadcasted throughout the whole network, all consumers aresure to get one copy. However, this will cause energy waste. Alternately, moreefficient quorums should be selected to cut down overhead while maintaininghigh quality performance of both updates and requests.

Generally, the sensing scopes by different nodes are more likely to overlapdue to the unattended nature of sensor infrastructure, and the sensed infor-mation is much larger in volume. There is no need to refresh each piece of inte-gral produced information to others every time. This is different from locationservice. Therefore negotiation can be combined with. By negotiation, integraldata are updated in the source (sensed sensor) only. A meta description aboutthe data regarded as advertisement is disseminated to its write quorum. Later,when destination search is transmitted within a read quorum, the rendezvouscan help match the request with recorded advertisements. With the locationprovided in advertisement, request packets can correctly reach the source. Fi-nally, the complete information can be obtained from the source. Negotiationhelps reduce the traffic in disseminating redundant or duplicate data.

3.1 Traditional Protocols

There are many previous protocols to solve this information disseminationproblem. The simplest one is by flooding. Such a straightforward protocol isstateless and it disseminates information quickly in a network. However, itincurs heavy cost and a message implosion problem.

To overcome the deficiencies of the classic flooding, Heinzelman et al[23] proposed a family of adaptive protocols for information disseminationin WSNs, called Sensor Protocols for Information via Negotiation (SPIN).They introduced an important mechanism of negotiation. Meta-data that de-scribes the observed data are broadcast to neighbors through advertisement(ADV). Negotiation helps ensure that only useful information is transmittedso as to save energy.

SPIN is an event-driven method, which pushes new data to the networkregardless of whether the data will be consumed. Another important data-centric routing, called directed diffusion [24], was proposed to diffuse data

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queries in an on-demand fashion. An interest message is broadcasted by asink node through its neighbors when it needs to access data. The sink nodethen starts to report back after receiving such an interest message.

The common disadvantage of these three typical protocols is that eachnode is required to play a role in every transaction of data accessing, joiningeither the write quorum or the read quorum. Lots of problems exist due tothe large amount of traffic among nodes.

Gossiping [25] is an alternative to the classic flooding that avoids theimploding problem and achieves energy efficiency through randomization [26],[27]. However, it disseminates information slowly and cannot even guaranteethe eventual delivery of the data to querying nodes.

3.2 Quorum-Based Method

In [28], the authors discuss a distributed processing system of WSNs thatprovides distributed information services on sensed data efficiently. In sucha system each sensor node operates autonomously with no central node ofcontrol in the network. Each node can be a data source (producing data) aswell as a data sink (consuming data). In such an environment, the system isrobust and fault tolerant. It can continue to function properly in the presenceof failures of individual sensor nodes.

A. Quorum-Based Method DescriptionSpecifically, the quorum method is used in conjunction with a negotiation

mechanism that was first introduced in SPIN.Transmission of ADV message -When a node s has sensed new data, it

broadcasts an ADV to its neighbors in northward and southward directions.As for the northward direction, the neighbor with the largest y-coordinatewill broadcast the message further to its neighbors. The ADV is propagatednorthward hop-by-hop until it reaches the node at the north boundary of thenetwork. At the same time, the same operation is taken to propagate the ADVin the southward direction. Figure 3 illustrates the transmission of ADV/REQ,where s is the source node, t the querying node and u the rendezvous node.

Transmission of REQ message -When a node intends to acquire thedata that it is interested in, it broadcasts a REQ to its neighbors. Similarlyto ADV propagation, the REQ is propagated in both eastward and westwarddirections until it reaches the west and east boundaries. Figure 3(a) also il-lustrates the propagation of the REQ. The most important fact is that whenthe row and column intersect, some nodes (in the case of Figure 3(a), node uand two other nodes in the small square) must have received both ADV andREQ, which are rendezvous nodes.

Transmission of DATA message -The first rendezvous, in which a pairof ADV and REQ matches, continues to send the REQ to the source nodealong the route where the matched ADV was transmitted. Upon receivingthe REQ, the source node transmits DATA message to the querying nodealong the same route as the REQ. This can avoid extra routing for DATA

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messages, which could save significant routing overhead in sensor networks.An alternative method used after ADV and REQ are matched is geometricrouting, such as Greedy Perimeter Stateless Routing (GPSR) [29], GeometricAdaptive Face Routing (GOAFR) [30], or Greedy-Face-Greedy (GFG) [31].Such geometric routing will help build the shortest path between source nodeand querying node.

t u

ADV

REQ

s

(a) (b) (c)

t u

s

t u

s

REQ DATA

Fig. 3. Illustration of the quorum-based information dissemination.

Note that there is no need of ordering ADV and REQ for the matcheddata. Arrived messages will be stored for future matching until expirationtime is reached.

In this scheme, the quorum system is constructed in a similar way as itis applied to location service. The difference is that the negotiation message,ADV, is forwarded to the write quorum, while new data only updates in thesensing node. ADV contains description information, which is much shorterthan complete data. Thus write and read operations take much less consump-tion than the previous quorum method without negotiation. They achievefurther energy efficiency.

B. Recovery SchemeHowever, forming quorums only by row and column is not sufficient for

their intersection. There are a couple of technical problems that should beaddressed.

Now, suppose that a node v receives an ADV from node w that shouldbe forwarded northward, but all its neighbors have smaller y-coordinates thanitself. In this case Figure 4(a), the ADV reaches a local maximum with respectto the direction to the north (i.e., a node without any better neighbors). Thissituation will occur when the deployment of sensor nodes has some “voidareas” (i.e., the small areas that do not have any sensor nodes).

This failure of further forwarding is due to the inherence of greedy algo-rithms. Greedy algorithms work in phases. In each phase, a decision is madethat appears to be the best, without regard for future consequences. There

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are three basic greedy algorithms, known as: DIR [13], [14], [30], GEDIR [34],[35] and MFR [36].

In order to direct the message to leave out of the local maximum v, theprotocol should execute a recovery scheme. So it applies the right-hand rule offace routing [30] on the Gabriel graph [32]. The Gabriel graph is a long-knownplanar graph that contains edges between nodes u and v if and only if no othernodes are located within the circle centered in the middle of edge (u,v) andwith diameter ‖uv‖. It has some good properties when used for routing inwireless networks, such as localized computing and an energy efficient pathfinding [33].

The process is as follows: applying the face routing, when node v findsthat no neighbor is further north than itself, it forwards the message to itsneighbor v1 that has the largest angle α formed from wv to vv1 in clockwisedirection (see Figure 4(a)). After v1 receives the message, it will perform thesame operation as node v. Eventually, the message will bypass the void region,and be received by node u.

The handling of “void areas” in a sensor network would introduce extraoverhead to the quorum system. Since nodes don’t have the global topologyof the entire network, the message received by the nodes truly located onthe boundary of the network will be propagated along the boundary until ittraverses back to either node v as shown in Figure 4(b), or meets some nodeat its traversed route as shown in Figure 4(c). In either case, the messagetravels a lot of unnecessary distance. However, this overhead is inevitable ifthe case of “void areas” has to be handled.

(a) (b) (c)

v 1

v u

v

v

w

Fig. 4. Protocol Implementation.

C. Performance Analysis of Quorum SystemAuthors in [28] also studied the efficiency of the quorum method through

some mathematical analysis in two special network topologies, the (m×m)-grid and (m×m)-hexagonal grid. Both topologies are very popular in wirelesscommunications, where many ad hoc networks or randomly deployed sensor

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networks can be approximated by grid or hexagonal topologies. Two perfor-mance metrics are evaluated: 1) The number of nodes that are required toforward an ADV/REQ, denoted by fQ; 2) The hop counts of the route be-tween the source node and querying node, denoted by hQ. The hop countsindicate the distance data would travel. Each node in the network is supposedto have the same independent possibility to disseminate and inquire aboutthe information.

i. Numbers of Forwarding NodesWe first consider the case where the network graph is an (m×m)-grid, and

for easy presentation, let m=2k+1.

S

Fig. 5. Performances in a grid.

Theorem 3.1 The expected value of fQ is E[fQ] < 5m.

Proof. The message is transmitted to both north and south directions in par-allel and stops after traversing along the boundary under non-duplicated facerouting (Figure 5). It is easy to find that when the source node s is locatedinside the grid, the number of nodes required to forward the message is

fQ = (2k − 2) + 2(2k − 1) + 2(2k + 1) = 10k − 2.When the source node s is located at the boundary of the grid, the number

required isfQ = 2(2k − 1) + 2(2k + 1)− 1 = 8k − 1.Therefore, we haveE[fQ] = (2k−1)2

m2 (10k − 2) + 8km2 (8k − 1) < 5m.

The proof is then finished.We now consider the case where the network graph is an (m×m) hexagonal

grid.

Theorem 3.2 The expected value of fQ is E[fQ] < 68m/9.

247Chapter 10 Location Service, Info. Dissemination and Object Tracking

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S

Fig. 6. Performances in a hexagonal grid.

Proof. Note that there are (2m−1) rows. The total number of nodes in thenetwork is (Figure 6):∑m

i=2(2m− i) + (2m− 1) = 3m2 − 3m + 1.It is easy to find that when the source node s is located inside the grid and

at the column having (2m− i) nodes, the number of nodes that are requiredto forward the message is

fQ = 6(m− 1) + (2m− i− 1) = 8m− i− 7, for i =1, 2,. . ., m;and when the source node s is located at the boundary of the grid, the

number required is fQ = 6m− 7. Thus we haveE[fQ] = 1

3m2−3m+1 [6(m− 1)(6m− 7)+2

∑m−1i=1 (2m− i− 2)(8m− i− 7)− (2m− 3)(8m− 8)] < 68m/9.

The proof is then finished.From the above theorems, we can see that the quorum-based protocol

needs only O(√

n) nodes to forward messages in grids, where n is the totalnumber of nodes inside the network. They achieve significant energy savingswhen compared with the classic flooding, which requires O(n) nodes.

ii. Hop Counts of Generated PathsSimilarly, let’s first consider the case where the network graph is the

(m×m)-grid (Figure 5), and for the easy presentation, let m=2k+1.

Theorem 3.3 The expected value of hQ is E[hQ] < 4m/3.

Proof. Clearly, there are a total of m2(m2−1)/2 possible pairs of source andquerying nodes. Note that given any, the quorum-based protocol produces ashortest path between them. Let f(m) be the sum of hop counts of the shortestpaths between all node pairs in an (m×m)-grid. By solving a set of six linearequations, we can obtain

f(m) = (2m5 − 16m4 + 94m3 − 620m2 + 1404m− 864)/3.Therefore, we have E[hQ] = 2f(m)/m2(m2 − 1) < 4m/3.

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The proof is then finished.Now consider the case where the network graph is an (m×m) hexagonal

grid (Figure 6). Using the same analysis method of Theorem 3, we can provethe following theorem.

Theorem 3.4 The expected value of hQ is E[hQ] < 41m/45.

Proof. Clearly, there are a total of 3m(m− 1)(3m2− 3m + 1)/2 possible pairsof source and querying nodes. f(m) is defined as above. We then can obtain

f(m) = m(82m4 − 205m3 + 200m2 − 95m− 18)/20.Therefore we have E[hQ] < 41m/45.The proof is then finished.

3.3 Other Quorum Methods

Aydin and Shen [37] proposed a match-making method in ad hoc and WSNsfor producers and consumers to forward their ADV/SUB without flooding(SUB for data SUBscription, which is similar to REQ). The quorum system isconstructed in a different way. Each ADV and SUB spreads along four mainsections (east, west, north and south) as defined by a threshold angle. The sizeof each quorum is related to the density of networks and the threshold angle.In fact, this match-making method only performs well when the network isdense enough or source and destination nodes are close to each other.

Figure 7 (from [28]) compares the success rate and forwarding rate (num-ber of forwarding nodes divided by n) for quorum-based and match-makingmethods for n=100 nodes in the network, respectively. The transmission ra-dius of node varies from 13 to 40. According to the comparison, the quorumbased scheme can achieve a much higher success rate and requires about 30percent of its nodes to forward messages. It is an energy efficient method todeal with data accessing transactions. Although the match-making protocolrequires fewer nodes to forward packets, it is at the sacrifice of a much lowersuccess rate.

4 Object Tracking

Object tracking is one of the most important applications of WSNs in oursociety, especially for military usage. Assume existence of a battlefield, withinwhich some tanks and soldiers are patrolling around. Thousands of sensornodes are deployed in an unattended fashion. They should collect informationabout intruders and sound alarms to the military in good time. On one hand,once sensor nodes detect a new object moving into its surveillant scope, theyshould generate robust and reliable sensing reports, and forward the reportquickly and efficiently to the multiple data sinks (moving soldiers or staticcommand centers) that have interest in it [38]. Alternately, the data report

249Chapter 10 Location Service, Info. Dissemination and Object Tracking

Dan-Dan Liu and Xiao-Hua Jia

Fig. 7. Quorum method vs Match-making method.

can be saved locally waiting for queries from other nodes [39]. On the otherhand, soldiers need the up-to-date location information of other tanks or sol-diers from time to time. They should send their request to the appropriatesensors that can provide desired information or initiate detection as soon aspossible. Note that moving objects are free to traverse around within a largearea. Such a tracking requires non-local collaboration among sensors. It canreduce the bandwidth consumption thus preserve the energy efficiency, andalleviate the risk of individual node/link failures. In addition, adding negoti-ation before integral data transmission helps reduce redundant and duplicatedata dissemination. Therefore, object tracking combines the problems exist-ing in location service and information dissemination and can be dealt withby similar methods.

4.1 Traditional Protocols

If we apply traditional methods to object tracking, some similar deficienciesappear as in location service and information dissemination. For example un-der SPIN [23] or Directed Diffusion [24], all nodes in the network (i.e. n) arerequired to participate in data accessing. This is very costly especially whenthe frequency of event occurrence and interest is not equivalent. In addition,both of them use some sort of flooding which suffers many drawbacks in wire-less networks, such as excessive message redundancy, contention, collision [40],and unreliability [41].

To reduce the energy cost in disseminating queries through all nodes, likeDirected Diffusion, authors in [42] introduce a hierarchical data queryingmethod. Each registration points, leaders of data source and sensor nodesmeeting multi-resolution needs construct a hierarchy of three levels. New de-tections are updated locally, while advertisements are registered to registra-tion points. Any requests only need to traverse down the hierarchy to find

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answers. Simulation shows it outperforms the directed diffusion by achievinghigher delivery rate with lower energy cost.

More recently, many researches concentrate on dynamic sensor collabora-tion and aggregation with constrained energy. Data are aggregated to a certainleader or root, on which full update operation is carried out. Zhao et al. [43],[44], [45] apply information driven approaches to deal with this issue. A sen-sor node/leader is selected to detect the object, and the object moves, theresponsibility of tracking will be handed off to another node/leader accordingto some criterion measuring information gain. Cerpa et al. [46] suggested col-laborating those multiple sensor nodes surrounding the object so that the dataprovided could be more complete, reliable and accurate. Zhang et al. in [47]proposed a dynamic convoy tree-based collaboration (DCTC) framework forobject tracking. Sensor nodes surrounding the moving object will constructan initial convoy tree. The root will be the final recipient of reports from eachnode and will process them using certain algorithms [48], [49] to generate aconsolidated sensing report that will be saved locally, waiting for query, orsent to the sinks. An improvement on selecting a new root to replace the oldone as an object moves far away is introduced in [50].

4.2 Quorum-Based Method

A. Method DescriptionTo further improve the performance of write and read operation, we can

also apply a quorum-based method to track moving objects. The basic ideais that when any sensor node has detected e.g., a new tank, it updates thisevent locally, and advertises this information by propagating an ADV in bothnorth and south directions until reaching the north and south boundaries ofthe network, to form a write quorum; and when any soldier needs to accesssome information (this node is called a querying node), it sends out certainREQ in both east and west directions. Nodes on this east-west direction arecollected as a read quorum. The REQ is bound to meet corresponding ADVat a rendezvous node if the DATA was advertised before. This rendezvousnode will provide the matched description about DATA, and forward theREQ to the sensing node along the route that was traversed by ADV before.After correctly arriving at the sensing node, the integral data of the eventwill be read and forwarded to the soldier along the reverse route that theREQ traversed. On the whole, the network has been divided conceptuallyinto different rows and columns. By using this quorum system, the messagecost for new data dissemination is greatly reduced to O(

√n).

Consider the situation, three tanks move into the sensing region of nodeA, B, and C, respectively (see Figure 8). Each of these nodes broadcast theadvertisement on detecting tanks in north-south directions (formed by blacknodes), which form three columns with certain thickness. Thus every node ineach column takes a record of the advertisement. Later on, when a soldierneeds to take control of the overall situation on the battlefield, he sends out a

251Chapter 10 Location Service, Info. Dissemination and Object Tracking

Dan-Dan Liu and Xiao-Hua Jia

request entering the sensor network at node S. This request will be transmit-ted in the east-west direction (formed by gray nodes). For example, he maysimply ask: “Where is the tank, whose ID is 5?” Obviously, there are three dif-ferent groups of rendezvous nodes that havevreceived both request and newadvertisement, respectively. They will take a comparison and only the onethat has the matched ADV will respond and continue to forward the REQtowards the sensing source. In another case the soldier may ask: “How manytanks are there in the battlefield at the time 4:00 pm?” As a result, none ofthe single rendezvous nodes can reply with a complete answer. Collaborationamong the groups is needed. Rendezvous nodes can combine receiving replieswith its own data, and proceed with aggregation before sending out furtherreplies . This will help reduce bandwidth consumption. Alternately, raw dataare directly forwarded to the soldier to minimize delay.

s

A B

C ADV

REQ

Fig. 8. Illustration of the quorum-based object tracking.

Similarly, if one tank moves from place A to place C via place B (Figure 8),the quorum based method still works well. For example, the soldier requires:“Report back the location of the tank every 30 seconds.” Sensor nodes willdisseminate information detection whenever a new tank moves into its range.But rendezvous nodes in the row will take an aggregation, and only reply withthe location of the tank every 30 seconds. Alternately, a mechanism adjustingthe frequency of new location information dissemination is added, so that thenumber of columns is limited.

B. Frequency ConsiderationRecall that due to the “void areas” mentioned in Subsection 3.2.2, face

routing should be added to guarantee that any update information couldarrive at an extreme point of the network. However, the negative affect isthat nodes on the perimeter of a network will also execute face routing dueto its lack of global information. Specifically, a write quorum should collect

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nodes on a “column” as well as on a face of local maxima to perform updates,so that read quorum collecting nodes on a “row” as well as on the face aresure to get current data.

However, the frequency of new events detected by sensors and that ofdata really needed by a soldier is not equivalent most of the time. The newevent sensed by a node is distributed in a large domain, while sometimessoldiers are really interested in the data within some specific area. As a result,collecting nodes in such a symmetrical way for both write and read quorumswill cost considerable system overhead, although only advertisement insteadof complete DATA is updated through write quorums by negotiation. Theway to reduce this consumption is let only one of the quorums execute facerouting while the other one will stop forwarding packets when they arriveat local maxima. This mechanism should combine with the frequency of newevent advertisement and soldier requests. The more frequent an operation,the fewer nodes the corresponding quorum must collect.

Thus, the quorum-based method can be enhanced by adding a minor mod-ification as follow: if sensors are assigned to disseminate whenever new infor-mation is detected, then ADV will be only propagated to the end of each“column”. It will stop if no further neighbor in the given direction can befound. On the other hand, REQ have to finish the whole quorum. It is firstforwarded along the east and west directions, then bypasses the void area ifany, and finally stops after traversing the outer boundary of the network. Onthe contrary, if request is more frequent than detection, we should let thetraversal of REQ be shorter than that of ADV.

Note that there is a tradeoff between system overhead and data deliveryrate. Decreasing nodes in constructing either quorum will reduce the pos-sibility of successful intersection. Thickness of each “column”, “row”, and“boundary” can be increased to reduce delay and increase reliability. In addi-tion, sensor nodes often detach from the original quorum it resided in withoutwarning due to mobility or depletion of energy. If those nodes that were sup-posed to be in the intersected places are unavailable, the required informationwill not be accessible. Therefore, a mechanism is needed to transfer the infor-mation database to other nodes remaining in or moving into the same places,which substitutes as the new database. Thus nodes in the quorums can trans-mit a database with information being picked up by all neighboring nodeswhen necessary.

4.3 Other Quorum-Based Protocols

In a recent work, Liu et al [51] proposed a comb-needle query support model,which forms different quorum systems (Figure 9). Each sensor node advertisesits DATA message to form a vertical needle with length 2l (write quorum).A querying node disseminates its REQ to form a vertical comb with gap sbetween the teeth (read quorum). When s ≤ 2l, the REQ message is boundto meet the DATA message. The advantage is that parameters s and l can

253Chapter 10 Location Service, Info. Dissemination and Object Tracking

Dan-Dan Liu and Xiao-Hua Jia

be adjusted depending on frequencies of events and queries in the system,dynamically. However, in an irregular network, the REQ messages can hardlyfollow a comb shape, and the comb may miss the needle (i.e., the success rateof an REQ finding its matched data is poor). This problem will be even moresevere in the presence of “void areas” in the network.

S

D

Fig. 9. Comb-needle model.

5 Conclusion

In this chapter, we tried to present an overview of the quorum method ap-plied to three main problems in wireless networks. They are: location service,information dissemination and object tracking. Location service is the generalobstacle for distributed networks, since the current location of a destinationnode should be known before any data can be delivered correctly. The maingoal of wireless sensor networks is to provide required and latest data sensedat a source node to any other nodes that are interested in the data. There-fore, the central issue that needs to be addressed is to develop energy efficientprotocols to disseminate information. Object tracking in a military battlefieldsituation is one of the most important applications of sensor networks. It isa combination of the other two problems and can be dealt with by similarmethods.

The challenge of these problems is that there is no central control node.Source nodes can only disseminate data to the networks from their local in-formation, and querying nodes have to forward requests to nodes that canprovide them with desired data or initiate detection as soon as possible. Quo-rum methods are designed to facilitate these operations. A subset of nodesis collected to form a write quorum, in which update operations take place.

254

A potential different subset of nodes is collected to form a read quorum, sothat retrieval processes are executed. The availability of this quorum methodlies in that each quorum proceed with enough nodes to make sure intersectionbetween write and read quorums exists.

Traditional protocols solving these problems prepare each node to partic-ipate in data accessing, either in forming write quorums or read quorums.The system overhead is O(n). Later on, networks can be divided into severalhierarchies. One or more nodes in each level are selected to be members ofeach quorum. Thus the size of quorum becomes proportional to the height ofthe hierarchy. Another significant improvement in reducing system overhead isthat by conceptually and evenly dividing the networks into rows and columns,the size of quorums has decreased to O(

√n). Moreover, negotiation and fre-

quency control can be combined in constructing quorum systems. Negotiationhelps avoid redundant data dissemination. Selecting nodes to form quorumsaccording to the frequency of operation occurrence improves the performanceof quorums even more.

Quorum methodology is one sort of efficient way to deal with transactionof data accessing. A wireless sensor network can be treated as a databasesystem. The performance of WSN can be improved by appropriately adjust-ing the parameters of the quorum method. As technology has developed andknowledge has accumulated, WSN has been applied to many applications forhuman beings. More challenging work is coming.

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