The Effect of Network Topology on Geographic Routing Performance in Localized Networks
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The Effect of Network Topology on Geographic Routing Performance in Localized Networks

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In this paper, we examine the role network ...

In this paper, we examine the role network
topology play in the geographic routing decision and its
performance. Much of the work carried out on geographic
routing in current decade to navigate data in localized
networks. In ideal environment, it has been verified to provide
significant performance improvement over stringently
address-centric routing approaches. Geographic routing
protocol’s great benefit is its dependence only on information
of the forwarding node’s immediate neighbors. The global
view required is negligible and reliant on the density of nodes
in the localized network, not the network size or number of
destination nodes in the network. Our work is distinguished
from most previous studies of geographic routing in this we
consider the degree of on intermediate nodes in a path chosen
by routing decision process, not just the network density. We
examine several geographic properties including the
possibility of deciding specific geographic path along a specific
topology and effect of degree of a node in a path. Our analysis
shows that routing performance depends on the network
topology, and tends to be better when path traverse from
medium node degree path.

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The Effect of Network Topology on Geographic Routing Performance in Localized Networks Document Transcript

  • 1. ACEEE Int. J. on Network Security, Vol. 01, No. 03, Dec 2010 The Effect of Network Topology on Geographic Routing Performance in Localized Networks Alok Kumar, and Shirshu Varma Indian Institute of information Technology, Allahabad, India Email: {alokkumar, shirshu}@iiita.ac.inAbstract—In this paper, we examine the role network considered to continue making move toward the destinationtopology play in the geographic routing decision and its [8]. In this case, the fluctuation in the forwarding mode i.e.performance. Much of the work carried out on geographic greedy and supplement mode, could cause much delay androuting in current decade to navigate data in localized degrade overall performance. Usually, geometric routingnetworks. In ideal environment, it has been verified to providesignificant performance improvement over stringently utilizes GPS (Global Positioning System) locationaddress-centric routing approaches. Geographic routing information or other localization techniques [5] to decideprotocol’s great benefit is its dependence only on information the locations of the nodes. Due to its simple forwardingof the forwarding node’s immediate neighbors. The global mechanism, geographic routing almost perfectly finds theview required is negligible and reliant on the density of nodes route in dense networks where the possibility of finding ain the localized network, not the network size or number of forwarding node is comparatively high. However,destination nodes in the network. Our work is distinguished geographic routing experiences degraded performance infrom most previous studies of geographic routing in this we sparse networks where the possibility of finding route isconsider the degree of on intermediate nodes in a path chosen comparatively low [4]. Since network topology describedby routing decision process, not just the network density. Weexamine several geographic properties including the with node degree in WSNs but in case of geographicpossibility of deciding specific geographic path along a specific routing, specific path select by it is more significant thantopology and effect of degree of a node in a path. Our analysis entire topology of the network. To define geographicshows that routing performance depends on the network routing path, we characterize it with an attribute, pathtopology, and tends to be better when path traverse from degree. It is define as the ratio of the summation of degreemedium node degree path. of intermediate nodes to the number of intermediate nodesIndex Terms—Routing, network protocols, performance present between the source-destination of the route. Theanalysis, wireless sensor networks path degree reflects the possibilities to choose different route per intermediate node. I. INTRODUCTION In this paper, we present analysis of topological properties on a simplified, abstract model of geographic Localized distributed Wireless sensor networks (WSNs) routing interconnectivity and circuitousness of the routeare increasingly becoming vital to the development of determined by it. Our results indicate that the higher pathsmart environments. These networks play a crucial role in degree route is an important contributor to circuitousmodern day systems, as they aid in the mechanization of routing. Our study of circuitousness of geographic routingtransport systems, architectural constructions, industrial routes provides some insight into the routing decisionsprocesses, as well as in home appliances [1]. based on geometric structures. Although circuitousness Geographic routing has been introduced in localized may not forever relate to routing performance, it can oftennetworks i.e. network in which nodes aware about own be a view of a routing problem with geometric structurelocation, and mostly applicable in wireless ad hoc and that deserves more careful examination.sensor networks. Geographical routing [2], [3], [4] has The structure of rest paper as follows. In Section 2, webeen popular in current decade for localized networks with present a geographic routing network model and describeadvantage that the nodes are not necessary to maintain the modified greedy-compass routing scheme which westorage for finding route, and can make simple forwarding used for analytical study. In Section 3, we provide thedecisions for traffic based on the locations of its reasoning of topological changes in localized WSNs thatneighboring nodes without much of communication affect the geographic routing performance. Section 4overhead. Because geographic routing does not need a provide in depth analysis of circuitousness of routes androute management procedure, it carries minimum provide understanding of relation between path degree andcommunication and computing overhead compared to other distance. We also find the correlation between delay andoff-line routing schemes such as proactive, reactive, and location of nodes in a network. Lastly, we conclude ourhybrid routing protocols. In general, geographic routing simulation analysis work.forwards a packet in greedily manner wherever possible.Each packet is moved with the location of its destination II. BACKGROUNDand assumes that all nodes know their own locations in thenetwork space. A node forwards a packet to it’s a neighbor A. Connectivity and Topology Dynamicsthat is geographically nearest to the destination node. Local In this Section, we provide the reasoning for theminimum may exist where packet forwarding node is topology changes due to connectivity properties andnearer to the destination than its neighbors. In such cases,greedy approach fails and a supplement strategy must be© 2010 ACEEE 53DOI: 01.IJNS.01.03.257
  • 2. ACEEE Int. J. on Network Security, Vol. 01, No. 03, Dec 2010 vertices V = {v1 , v 2 ,..., v n } and edges E = {e1 , e 2 ,..., e n } that illustrate the network topology by way of a graph G (V , E ) . In the case of ranges associated with a certain edge, therefore we assigning a weight based on distance, to every edge e = (v p , vq ) connecting the vertices v p and v q . We consider location of nodes in the traditional way, position are usually viewed as three-dimensional coordinates ( x, y, z ) in a Cartesian reference coordinate space. Of course, many other transformations to other Figure 1. Different radio link models for Node’s neighborhood: coordinate systems (e.g. polar coordinates) are equivalent,(a) perfect unit disk connectivity, (b) switched links (irregularity), but the Cartesian system will be considered here. In a (c) unreliable links how the caption is centered in the column. three-dimensional system, the Euclidean distance between two point v p and v q in our space is defined by: dist (vp , vq ) = (xp − xq ) + (yp − yq ) + (z p − zq ) . protocol-level properties of WSNs. We broadly classify 2 2 2the reasons of changes in the topology in following G If dist (v p , v q ) ≤ R than node v p and v q communicatecategories:B. Irregular radio range: directly and consider as neighbor of each other. We also Connectivity is an important feature for WSNs to modeled some WSNs constraints like low energy that is aprovide the nodes with the competence of communicating major reason of topological changes. Each node in thewith one or many sinks. In most of the literature, radio network has own batteries as energy source. Each sensorlinks are considered as ideal, that is, without transmission node can have three different states; active, sleep, and dead.errors. To maintain this assumption, the reception threshold Our main focus active and sleep states because these statesshould be sufficiently high to assurance that radio links affect the topology of the network and fluctuate thehave a small transmission error possibility. As an effect, all connection between nodes. We assume that all nodes knowunreliable links are dismissed in this scenario. This own location by some localization algorithms [4] or GPSapproach is sub-optimal concerning power consumption for device. This assumption is realistic in a sensor network duethe reason that unreliable links should allow to decrease to its application nature, nodes need to be know their owneither the transmission energy or the number of active locations when reporting sensed data; the packets aresensor nodes. generally sent back to a known sink position, or to a position specified in a broadcast query message, generallyC. Sensor node state: destination of maximum packets in the localized sensor The routing path failure may happen during packet network. We also modeled some WSNs constraints liketransmission because of node dying out (no battery), low energy that is a major reason of topological changes.collision, node busy, node sleep mode, or other accidents. Each node in the network has own batteries as energyIn general, sensor nodes are static; although may be some source.sensor nodes are mobile according to the application’snature. Even if of all nodes are static, the network topology B. Geographic Routingchanges over time, because nodes usually perform The communication overhead to gather routingfunctioning in duty-cycle, with sleeping and awake phase information is considered one of the main serious scalingto reduce consumption of energy. Thus, the network limitations of our major communication technologiestopology formed by active sensors changes as they including wireless ad hoc and sensor networks. In Jontransform their state over time period. Kleinberg model [9], each node resides in a coordinate space, in addition of being part of the global network III. GEOGRAPHIC ROUTING MODEL topology. Within this coordinate space each node has abstract information about the destination to navigate In this section, we discuss the details of our localized information into a network. This abstract information alsonetwork model and the prominent aspects of geographic views in geometric routing system. The geometricalrouting schemes. We also provide a geographic routing properties of the wireless sensor networks permitscheme related to the discussion in this paper. This is a navigation of information with the help of some geometricnode-disjoint multipath geographic routing scheme. structures and local network topology information. InA. Localized Network Model geometric routing protocol, the decision on to which node We use a location-aware model for network in which to route a packet is based only on: (a) Own locationfinite number of nodes are placed in a finite dimensional information, (b) Destination node location that mention interrain and all nodes have identical radio transceiver and the header of the packet. This includes the source-communicate within a range R . For the geometric destination information of the packet, and (c) The localabstractions to be used, assume the network, a set of topology knowledge information collected by the node from one-hop neighbors of the node.© 2010 ACEEE 54DOI: 01.IJNS.01.03.257
  • 3. ACEEE Int. J. on Network Security, Vol. 01, No. 03, Dec 2010 This subsection summarizes some localized geometricrouting protocols presented in the networking andcomputational geometry literature. Compass Routing: Let d be the destination node.Current node u finds the next relay node v such that the Figure 2. Geographic path for calculation of path degree. .angle ∠vud is the smallest among all neighbors of u in agiven topology [12]. IV.METHODOLOGY AND SIMULATION RESULTS Greedy Routing: Let d be the destination node. Current The aim of this simulation work is to study the effect ofnode u finds the next relay node v such that the distance path degree on geographic routing performance. This study vd is the smallest among all neighbors of u in a given is focused on the results based on the gathered networktopology [7]. path data using a simulated network environment. We are Greedy-Compass Routing: Current node u first finds investigating the dynamic properties of sensor network (e.g., how routes change over time due dead mode or sleepthe neighbors v1 and v 2 such that v1 forms the smallest mode by some energy saving scheme), so we only trace acounterclockwise angle ∠duv1 and v 2 forms the smallest single snapshot of the localized network path between a specific pair of nodes. Geographic routing makes decisionclockwise angle ∠duv2 among all neighbors of u with the of next forwarding node on basis of local topology atsegment ud . The packet is forwarded to the node of particular time (online nature); therefore it is not probable{ v1 , v 2 } with minimum distance to d [10]. that several of the routes in collected data are dead paths at In any real-time network phenomena, a node requires the time of our measurement.some metrics to navigate the information. These metrics Since network topology characterize with average nodecan be measured by either using knowledge of entire degree in the network but in case of geographic routing,network or using local knowledge of particular node. For specific path select by it, is more significant than entireour study, we consider a geographic routing protocol, i.e. network topology. To evaluate topology impact ongeographic node-disjoint path routing protocol (GNPR) [6]. performance, we characterize the geographic route withThis protocol is multipath in nature and it uses two path degree. It is define as the ratio of the summation ofattributes for routing decision, i.e. direction and distance degree of intermediate nodes to the number of intermediatesimultaneous. These attributes used in compass and greedy nodes present between the source-destination of the route.routing, respectively. The path degree reflects the possibilities to choose different Given three location information; own, neighbors and route per intermediate node. We characterize a metric, distance fraction for analysis of the network topology. It isdestination, the node can find two nodes v1 and v 2 with a fraction of the Euclidian distance of a route to thesmallest angle ∠duv1 and ∠duv2 and route greedily by geographic distance between the source-destination pair ofchoosing either v1 or v 2 , which is nearest in means of the route. The distance fraction reflects the degree to which the network route between two sensor nodes deviates fromEuclidian distance, to the destination in the coordinate the straight geographic route between the nodes. A fractionspace. When the procedure fail to determine route while of one would demonstrate a perfect match while a largevoid condition arise, it revert back to previous hop node fraction would show a circuitous route.and start route discovery with other next better option andsummarized node where void condition occur for furtherpath discovery. After discover sufficient node disjointpaths, it optimizes the path with some parameter likeminimum path length or minimum end-to-end delay. Theprocedure of one iteration of GNPR is:GNPR (source_location, neighbor_list, destination_location)0. Begin at source node s and start to explore the path (path_identifier) to destination t.1. CR: Select two nodes (u, v) that minimize the angle ∠sut and ∠svt .2. GR: Proceed to the neighbor in (u, v) that closest to t.3. If no neighbor is available other than previous hop node Figure 3. CDF of distance fraction to different path degree routes. w at node x: a. Revert back to node w and summarized node x as block node. b. Select next greedy-compass choice rather than x.4. Repeat step 1-3 till path s to t discover.© 2010 ACEEE 55DOI: 01.IJNS.01.03.257
  • 4. ACEEE Int. J. on Network Security, Vol. 01, No. 03, Dec 2010 ACKNOWLEDGMENT The authors gratefully acknowledge the infrastructural and financial support from Indian Institute of information Technology, Allahabad, India. REFERENCES [1] I. Akyildiz, ”Wireless Sensor Networks: A Survey,” Computer Networks, vol. 38, no. 4, pp. 393-422, March 2002. [2] B. Karp and H. T. Kung, ”GPSR: Greedy Perimeter Stateless Routing for Wireless Networks,” in MobiCom ’00: Figure 4. CDF of minimum end-to-end RTT to different path Proceedings of the 6th annual international conference on degree routes. Mobile computing and networking. New York, NY, USA: ACM, 2000, pp. 243-254. Using GNPR protocol, we find out all node disjoint [3] F. Kuhn, R. Wattenhofer, Y. Zhang, and A. Zollinger,paths for every source to destination. These node-disjoint ”Geometric adhoc routing: of theory and practice,” inpaths classified by the path degree. This difference between PODC ’03: Proceedings of the twenty-second annualthe three groups of source nodes is imitated in the symposium on Principles of distributed computing. Newcumulative distribution function of the distance fraction for York, NY, USA: ACM, 2003, pp. 63-72.the three cases of path degree selection. As shown in Fig. 1, [4] B. Leong, B. Liskov, and R. Morris, ”Geographic routingthe distance fraction tends to be the smallest for routes that without planarization,” in NSDI ’06: Proceedings 3rd Symposium on Networked Systems Design andhave higher path degree and the largest for routes that have Implementation, San Jose, CA, May 2006.medium path degree because of path degree route with 5 to [5] Madhulika, A. Kumar, and S. Varma, ”Iterative and10 has less concavity. Distributed Rangefree Localization Algorithm for Wireless Finally, we analyze the correlation between path degree Sensor Networks”. in IMPACT ’09: Proceedingsand the end-to-end delay along a route. Though path degree International Conference on Multimedia, Signal Processingby itself cannot present any information about several and Communication Technologies, Aligarh, India, Marchperformance characteristics like congestion along a path, 2009.bandwidth, the linearized distance of a route does impose a [6] A. Kumar, and S. Varma, ”Geographic Node-Disjoint Pathminimum delay along a route. Fig. 2 illustrates the Routing for Wireless Sensor Networks,” IEEE Sensors Journal, vol. 10, no. 6, pp. 1138-1139, June, 2010.relationship of the minimum RTT along a route to the path [7] P. Bose, P. Morin, I. Stojmenovic’, and J. Urrutia, ”Routingdegree of a route and the geographic distance between the with guaranteed delivery in ad hoc wireless networks,” inend-nodes. We make two vital observations. First, at small Wireless Networks, vol. 7, 1999, pp. 48-55.values of the path degree there exists correlation between [8] D. Chen and P. K. Varshney, ”A survey of void handlingthe delay and path degree for a large fraction of end-nodes techniques for geographic routing in wireless networks,”especially for small values of path degree. Second, Communications Surveys and Tutorials, IEEE, vol. 9, no. 1,linearized distance along a route does impose a minimum pp. 50-67, 2007.end-to-end RTT that is an important performance metric [9] J. M. Kleinberg, ”Navigation in a small world,” Nature, vol.for real-time latency sensitive applications. 406, no. 6798, August 2000. [10] P. Morin, ”Online Routing in Geometric Graphs”. PhD thesis, Carleton University School of Computer Science, CONCLUSIONS 2001. In this paper, we have discussed a simulation study for [11] S. M. Ross, ”Introduction to Probability and Statistics for Engineers and Scientists,” Second Edition, 2nd ed.,examining the impact of network connectivity and Academic Press, January 2000.topology on geographic routing performance. Under this, [12] E. Kranakis, H. Singh, and J. Urrutia, ”Compass routing onthe effect of the geographic locations and network topology geometric networks,” in Proc. 11 th Canadian Conference onof end-nodes on the circuitousness of route is studied. The Computational Geometry, Vancouver, August 1999, pp. 51-results provide the understanding of circuitousness of 54.routes discovered by geographic node- disjoint path routingprotocol in simulated network with different path degree.Finally, we find the correlation between the minimum end-to-end delay and the path degree along their route.© 2010 ACEEE 56DOI: 01.IJNS.01.03.257