International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5   Dispatch of mobile sensors...
International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5shortest path. The paper [18]...
International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5d(si,lj). Where emove is the ...
International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5Value; //record length of pat...
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IJCER (www.ijceronline.com) International Journal of computational Engineering research

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IJCER (www.ijceronline.com) International Journal of computational Engineering research

  1. 1. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5 Dispatch of mobile sensors in the presence of Obstacles Using Modified Dijkstra Algorithm Shalini Kumari H A, Shivanna K, Department of Computer Science and Engineering, Shridevi Institute of Engineering and Technology, Tumkur,AbstractA hybrid wireless sensor network consists of both static dimensions have yet to be created. The cost of sensorsensor and mobile sensor nodes. Static sensors monitor the nodes is similarly, ranging from hundreds of dollars to aenvironment and report events occurring in the sensing few cents, depending on the size of the sensor network andfield. Mobile sensors are then dispatched to visit these the complexity required of individual sensor nodes. Sizeevent locations to conduct more advanced analysis. The and cost constraints on sensor nodes result insensing field may contain obstacles. A big challenge is corresponding constraints on resources such as energy,how to dispatch the mobile sensor to the event location memory, computational speed and bandwidth [2].Sensorswithout colliding with any obstacles and mobile sensor typically have very limited power, memory and processingshould reach the event location in a shortest path. resources. Therefore interactions between sensors areTherefore the objective of the paper is to dispatch the limited to short distances and low data-rates. Sensor nodemobile sensor to the event location in a shortest collision- energy efficiency and sensor network data-transferfree path. This paper gives a modified dijkstra’s algorithm reliability are the primary design parameters. A WSN isto dispatch mobile sensor from its position to the event usually deployed with static sensor nodes to performlocation. Our solution proposes a simple way to dispatch monitoring missions in the region of interest. However duethe mobile sensor to the event location in the presence of to the dynamic changes of events, a pure static WSN willobstacle with modified dijkstra’s algorithm. This paper face more severe problems [3]. By introducing mobility tocontributes in defining a more general and easiest dispatch some or all nodes i.e., by deploying mobile sensor nodes insolution in the presence of obstacles. a WSN, its capability can be enhanced. Hybrid Sensor Networks with static and mobile nodes open a new frontierIndex terms– Modified Dijkstra’s Algorithm, Collision- of research in Wireless Sensor Networks (WSNs).StaticFree Path, Mobile Sensor, Hybrid WSN, Global sensors support environmental sensing and networkPositioning System, Dispatch, Static WSN. communication. They serve as the backbone to identify where suspicious events may appear and report such eventsI. Introduction to mobile sensors. These Mobile sensors are moreA Wireless Sensor Network is a collection of spatially resource-rich in sensing [5] and computing capabilities anddeployed wireless sensors by which to monitor various can move to particular locations to conduct morechanges of environmental conditions such as temperature, complicated missions such as providing in-depth analysis,sound, vibration, pressure, motion, pollutants in a repairing the network etc [3]. Once static sensors collectcollaborative manner without relying on any underlying the sensed information about the event, mobile sensors areinfrastructure support [1]. The development of WSN was then dispatched to visit these event locations to conductoriginally motivated by military applications such as more in depth analysis about the events. Applications ofbattlefield surveillance. However wireless sensor networks wireless sensor networks have been studied in [4],are now used in many civilian application areas, including [5].Mobile sensors have less moving energy and all theenvironment and habitat monitoring, healthcare event locations should be visited, not even single locationapplications, home automation and traffic control. In should not be left un-visited, because that may causeaddition to one or more sensors each node in a sensor harmful effect to the sensor network. Therefore balancingnetwork is typically equipped with a radio transceiver or energy consumption is very important in case of mobileother wireless communication devices, a small sensors, so that all the event locations can be served.microcontroller and an energy source, usually battery. The Dispatching mobile sensors to the event locations in anenvisaged size of a single sensor node can vary from energy balanced way is discussed in [6]. The sensing fieldshoebox-sized nodes to devices the size of grain of dust may contain obstacle but the mobile sensor should bealthough functioning motes’ of genuine microscopy dispatched without colliding with any obstacle, and it should reach event location with minimum distance or Issn 2250-3005(online) September| 2012 Page 1458
  2. 2. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5shortest path. The paper [18] gives dispatch solution in the space). So that the disc can be processed as a point. Chewpresence of obstacles with traditional dijkstra’s algorithm. [10] extended the idea of the visibility graph to a pathIn this paper we focus on the problem of dispatching graph (called tangent graph in [11], [l2]), which registersmobile sensor to the event location in shortest collision- circular arcs on the boundary of the configuration obstaclesfree path with modified dijkstra’s algorithm. A mobile (C-obstacle) and common tangents of the circular arcs.sensor of radius r (non-negative integer) has a collision- Hershberger and Guibas [13] further developed this idea tofree motion among obstacles if its center always keeps at a a non rotating convex body, and pruned the path graph sodistance of r and preventing the mobile sensor from that the logarithmic factor is removed. Storer and Reif alsomoving into these expanded areas. This expanded area is showed the usefulness of their algorithm for a disc. Thetreated as configuration-space (C-space)[18]. The shortest Voronoi diagram [14] is obviously one answer because apath of the center consists of a set of circular arcs of circles point on the Voronoi graph is collision-free for a disc if itscalled vertex circles (v-circles)[18] of radius r centered at shortest distance to the obstacles is larger than the radius ofobstacles, and common tangents of the arcs. In this the the disc. However, it does not take care of the issue oftangent points and v-circles are determined by the value of shortest paths. The work in [1] addresses modifiedthe radius r. dijkstra’s algorithm for route selection in car navigationAfter finding the C-space and v-circles of all the obstacles system. Where it shows how the data structure thatwe need to find shortest path from mobile sensor to the represents routes can be modified to assist Dijkstra’starget location. Paper [18] used dijkstra’s algorithm to find algorithm to find subjectively optimal driving route.the shortest path. But it has a drawback. To overcome that Selecting the shortest path is not a hard problem, but theproblem we adopted modified dijkstra’s algorithm. The shortest path is not always what the user wants; what therest of the paper is organized as follows: Section 2 reviews user really wants to have is the most comfortable route forrelated work. Section 3 proposes a method to compute him or her to drive. Therefore the paper [1] gives solutionshortest collision free path for a mobile sensor. Section 4 to select the most comfortable route that the user wants.proposes modified dijkstra’s algorithm. Conclusions and The path length and number of turns are the major factorsfuture research topics are drawn in Section 5. in path planning of transportation and navigation systems. Most researches only take the issue of shortest distanceII. Related Work into account, and the impact of turns are rarely mentioned,Mobile sensors have been intensively researched to that is, the shortest path may not be the fastest. The work inimprove a WSN’s topology. In [7], static sensors detecting [2] proposes two algorithms: the Least-Turn Pathevents will ask mobile sensors to move to their locations to Algorithm and the Minimum-Cost Path Algorithm toconduct more in-depth analysis. The mobile sensor that has balance both the path length and turns. Where the authorsa shorter moving distance and more energy, and whose introduced modified dijkstra’s algorithm for the proposedleaving will generate a smaller uncovered hole, is invited. minimum-cost path algorithm.The work in [6] addresses how to dispatch mobile sensors The work in [3] also addresses car navigation problemto the event locations in an energy balanced way, where such as finding the efficient route by the user. Where thedispatch problem is considered for a single round. In that paper proposes a new shortest path algorithm. Thewe mainly considered centralized dispatch algorithm, in suggested algorithm is a modified version of dijkstra’s,that they discussed two cases based on the values of which states that search space is restricted by the use of anumber of event locations and the number of mobile rectangle or a static and dynamic hexagon.sensors. One mobile sensor will be dispatched to one eventlocation (when, mobile sensors >= event locations), one III Computing the Shortest Collision Free Pathmobile sensor will be dispatched to one cluster of event Sensors are aware of their own locations, which can belocations (when, mobile sensors < event locations) But achieved by Global Positioning System (GPS). In Hybridthey made an assumption that the sensing field does not Wireless Sensor Network, once static sensor identifies thecontain any obstacles. The assumption that was made in event, Mobile sensor will be dispatched to the eventthe previous paper is relaxed in this paper. The event location to collect in detail information about the event.location or cluster of event locations is considered as The work in [6] gives the solution to the dispatch problem.target. The studies [8], [9] also address the sensor dispatch The authors constructed a weighted complete bipartiteproblem, but they do not consider energy balancing, graph G = (SUL, SXL). Each mobile sensor and eventdispatching mobile sensor in the presence of obstacles and location is converted into a vertex. Edges only connectonly optimize energy consumption in one round. There are vertices between S and L. For each si belong to S and eachseveral works treating the shortest path of a disc. These lj belongs to L, its weight is defined as w (si,lj) = emove*approaches use the concept of configuration space (C-Issn 2250-3005(online) September| 2012 Page 1459
  3. 3. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5d(si,lj). Where emove is the energy required to move unit A. Proposed Schemadistance and d (si,lj) is the distance between si and lj. Once the static sensor identifies the event location, mobileAlgorithm to find M (matching between mobile sensor to sensor will be dispatched to the event location. Whileevent location) is as follows: moving mobile sensor towards the event location, it should1. For each location lj, associate with it a preference listPj, not collide with any obstacles in the network. Sensor nodes which contains all mobile sensors ranked by their are battery oriented, energy minimization is very important weights in correspondence with li in an ascending order. in WSN and mobile sensors have less moving energy it In case of tie, sensors IDs are used to break the tie. should reach the event location in a minimum distance. Construct a queue Q containing all locations in L. The path that allows mobile sensor to move without Create a bound Bj for each location lj belongs to L to colliding with any obstacle is called as restrict the mobile sensors that lj can match with. Initially, set Bj =w (si, lj) such that si is the βth element Collision Free Path. in lj’s preference list Pj, where β is a system parameter. Our goal is to find the shortest collision-free path from2. Dequeue an event location, say, lj from Q. Mobile Sensor si’s current position to event location’s Select the first candidate mobile sensor, say, si from position (xj, yj) which is treated as the destination or target, Pj, and try to match si with lj. If si is also unmatched, we considering the existence of obstacles. Specifically, the add the match (si, lj) into M and remove si from Pj. movement of si should not collide with any obstacle. Otherwise, si must have matched with another location, Several studies have addressed this issue [15], [16], [17]. say, lo. Then, lj and lo will compete by their bounds Bj Here, we propose a modified approach of that in [16]. and Bo. Location lj wins the competition if one of these Work in [18] addresses how to dispatching mobile sensor conditions is true: to event location in the presence of obstacles. The authorsa. Bj > Bo Since lj has raised to a higher bound, we match used dijkstra’s algorithm to find the shortest path from si with lj. mobile sensor to the event location, which is treated asb. Bj = Bo and w (si, lj) < w (si, lO) since moving si to lj is target. But this method is having a drawback. The goal of more energy efficient, match si with lj. the paper is to overcome this drawback. To overcome thisc. Bj = Bo, si is the only candidate of lj, and lo has more drawback modified dijkstra’s algorithm is proposed. than one candidate: In this case, if si is not matched with lj, lj has to increase its bound Bj. However, lo may A. Modified Dijkstra’s Algorithm not increase its bound Bo if si is not matched with lo. The classical Dijkstra’s Algorithm is used to solve shortest Thus, we match si with lj. path planning problem from one start point to other If lj wins the competition, were place the pair (si, lo) in destination points in connected-graph, and only lengths of M by (si, lj), remove si from Pj, enqueue lo into Q, and paths from start point to all other destination points are go to step 7. Otherwise, remove si from Pj (since lj will provided, but those middle points, which the final path not consider si any more) and go to step 6. from start point to each destination point passes through, is3. If lj still has candidates in Pj (under bound Bj), go to not provided, this is treated as the drawback in the classical step 5 directly. Otherwise, increase lj’s bound to Bj = w Dijkstra’s algorithm. (sk, lj) such that sk is the βth element in the current Pj and So, this paper uses modified Dijkstra Algorithm solve then go to step 5. (Note that since Pj is sorted in an dispatch problem in the presence of obstacles. There are ascending order and the first mobile sensor si is always two aspects of Dijkstra Algorithm, which are mainly removed from Pj after step 5, obtain a new larger bound modified. One aspect, which was modified, is the finishing Bj =w (sk, lj) > w (si, lj) condition of the algorithm. In modified Dijkstra Algorithm,4. If Q is empty, the algorithm terminates; otherwise, go when algorithm obtains a path from start point to a certainto step 4. In [6], authors have calculated the distance point, algorithm compares the point with the destinationbetween the mobile sensor and the event location without point Vd, if it is the very Vd, algorithm can stop, otherwise,the presence of obstacles. In this paper we are showing the must continue. The other aspect, which was also modified,method to calculate the distance in the presence of is the array, which array is named Dist, and it is used toobstacles and reaching the target in shortest path. We store lengths of paths from start point to other destinationmaking use of the above mentioned algorithm to dispatch points in Dijkstra Algorithm. In modified Dijkstrathe mobile sensor. Algorithm, element structure of array Dist was extended as below: typedef shuct DistRecorddouble {Issn 2250-3005(online) September| 2012 Page 1460
  4. 4. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5Value; //record length of path from VS to current point Sensor System,” Computer, vol. 40, no. 6, pp. 60-66, Juneint Prior; //record No of prior point of current point in final 2007.path [6.] Y.C. Wang, W.C. Peng, and Y.C. Tseng, “Energy-} DIST; balanced Dispatch of Mobile Sensors in a Hybrid Wireless Sensor Network,” IEEE Trans. On parallel and distributed systems, vol. 21, no. 12, December. 2010.Having that Dist array, when the destination point Vd is [7.] A. Verma, H. Sawant, and J. Tan, “Selection andsearched during executing algorithm, we may use the Navigation of Mobile Sensor Nodes Using a Sensorinformation of the Prior field of Dist to make tracks Network,” Pervasive and Mobile Computing, vol. 2, no. 1,forward for the start point. Finally, all vertices that the final pp. 65-84, 2006.path from Vs to Vd passes through can be obtained, [8.] Y.C. Wang and Y.C. Tseng, “Distributed Deploymentthereby, the final path can he protracted.Executing the Schemes for Mobile Wireless Sensor Networks to Ensureabove modified Dijkstra Algorithm, we can get a series of Multilevel Coverage,” IEEE Trans. Parallel andcontrol points of path, and store control points into an Distributed Systems, vol. 19, no. 9, pp. 1280-1294, Sept. 2008.array. In order to get smooth moving path, we can use [9.] Y.C. Wang, C.C. Hu, and Y.C. Tseng, “Efficientthose control points as interpolation points to do spline Placement and Dispatch of Sensors in a Wireless Sensorapproximation to path. As far as interpolation problem is Network,” IEEE Trans. Mobile Computing, vol. 7, no. 2,cconsidered, it has been solved very well today. pp. 262-274, Feb. 2008. [10.] L. P. Chew, “Planning the shortest path for a disc in O (n2IV. Conclusion and Future Work log N) time,” in Proc. ACM Symp. ComputationalIn this paper, we presented a method to find shortest and Geometry, 1985, pp. 214-219.collision-free path for a mobile sensor to reach to event [11.] Y. H. Liu and S. Arimoto, “Proposal of tangent graph andlocation which will be treated as target. This method is the extended tangent graph for path planning of mobileeasiest, simple and efficient way to find the path for a robots,” in Proc. IEEE Inr. Con$ Robot. Automat., 1991,mobile sensor. To dispatch the mobile sensor to the event pp. 312-317. [12.] “Path planning using tangent graph for mobile robotslocation use proposed Modified Dijkstra’s Algorithm. This among polygonal and curved obstacles,” Int. J. Robot.algorithm overcomes the drawback of the previously Res., MlT Press, vol. 11, no. 5, pp. 376-382, 1992.proposed algorithm. [13.] J. Hershberger and L. Guibas, “An O(n2) shortest pathIn our work we have treated mobile sensors to be in algorithm for a non-rotating convex body,’’ J. Algorithms,circular shape. But they may also be in different shapes. As vol. 9, pp. 1846, 1988.a future research, we extend our work to find shortest, [14.] 0.Takahashi and R.J. Schiling, “Motion planning in acollision-free path for a mobile sensor which is in any plane using generalized Voronoi diagrams,” IEEE Trans.shape. Robot. Automat., vol. 6, no. 2, pp. 143-150, 1989. [15.] Yasushi Kambayashi , Hidemi Yamachi, YasuhiroREFERENCES Tsujimura, Hisashi Yamamoto “Dijkstra Beats Genetic[1.] IanF.Akyildiz,WeilianSu,YogeshSankarasubramanian, Algorithm: Integrating Uncomfortable Intersection-Turns and Erdal Cayirci Georgia Institute of Technology “A to Subjectively Optimal Route Selection”, ICCC2009 Survey on Sensor Networks” IEEE Communication IEEE 7th International Conference on Computational Magazine August 2002. Cybernetics, November 26-29, 2009.[2.] Yong Wang, Garhan Attebury and Byrav Ramamurthy “A [16.] Gene Eu Jan, Ming Che Lee, S. G. 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