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

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,

Abstract
A hybrid wireless sensor network consists of both static dimensions have yet to be created. The cost of sensor
sensor and mobile sensor nodes. Static sensors monitor the nodes is similarly, ranging from hundreds of dollars to a
environment and report events occurring in the sensing few cents, depending on the size of the sensor network and
field. Mobile sensors are then dispatched to visit these the complexity required of individual sensor nodes. Size
event locations to conduct more advanced analysis. The and cost constraints on sensor nodes result in
sensing 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].Sensors
without colliding with any obstacles and mobile sensor typically have very limited power, memory and processing
should reach the event location in a shortest path. resources. Therefore interactions between sensors are
Therefore the objective of the paper is to dispatch the limited to short distances and low data-rates. Sensor node
mobile sensor to the event location in a shortest collision- energy efficiency and sensor network data-transfer
free path. This paper gives a modified dijkstra’s algorithm reliability are the primary design parameters. A WSN is
to dispatch mobile sensor from its position to the event usually deployed with static sensor nodes to perform
location. Our solution proposes a simple way to dispatch monitoring missions in the region of interest. However due
the mobile sensor to the event location in the presence of to the dynamic changes of events, a pure static WSN will
obstacle with modified dijkstra’s algorithm. This paper face more severe problems [3]. By introducing mobility to
contributes in defining a more general and easiest dispatch some or all nodes i.e., by deploying mobile sensor nodes in
solution in the presence of obstacles. a WSN, its capability can be enhanced. Hybrid Sensor
Networks with static and mobile nodes open a new frontier
Index terms– Modified Dijkstra’s Algorithm, Collision- of research in Wireless Sensor Networks (WSNs).Static
Free Path, Mobile Sensor, Hybrid WSN, Global sensors support environmental sensing and network
Positioning System, Dispatch, Static WSN. communication. They serve as the backbone to identify
where suspicious events may appear and report such events
I. Introduction to mobile sensors. These Mobile sensors are more
A Wireless Sensor Network is a collection of spatially resource-rich in sensing [5] and computing capabilities and
deployed wireless sensors by which to monitor various can move to particular locations to conduct more
changes 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 collect
collaborative manner without relying on any underlying the sensed information about the event, mobile sensors are
infrastructure support [1]. The development of WSN was then dispatched to visit these event locations to conduct
originally motivated by military applications such as more in depth analysis about the events. Applications of
battlefield 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 the
environment and habitat monitoring, healthcare event locations should be visited, not even single location
applications, home automation and traffic control. In should not be left un-visited, because that may cause
addition to one or more sensors each node in a sensor harmful effect to the sensor network. Therefore balancing
network is typically equipped with a radio transceiver or energy consumption is very important in case of mobile
other 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 an
envisaged size of a single sensor node can vary from energy balanced way is discussed in [6]. The sensing field
shoebox-sized nodes to devices the size of grain of dust may contain obstacle but the mobile sensor should be
although 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


shortest path. The paper [18] gives dispatch solution in the space). So that the disc can be processed as a point. Chew
presence of obstacles with traditional dijkstra’s algorithm. [10] extended the idea of the visibility graph to a path
In this paper we focus on the problem of dispatching graph (called tangent graph in [11], [l2]), which registers
mobile sensor to the event location in shortest collision- circular arcs on the boundary of the configuration obstacles
free 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 to
free motion among obstacles if its center always keeps at a a non rotating convex body, and pruned the path graph so
distance of r and preventing the mobile sensor from that the logarithmic factor is removed. Storer and Reif also
moving into these expanded areas. This expanded area is showed the usefulness of their algorithm for a disc. The
treated as configuration-space (C-space)[18]. The shortest Voronoi diagram [14] is obviously one answer because a
path of the center consists of a set of circular arcs of circles point on the Voronoi graph is collision-free for a disc if its
called vertex circles (v-circles)[18] of radius r centered at shortest distance to the obstacles is larger than the radius of
obstacles, and common tangents of the arcs. In this the the disc. However, it does not take care of the issue of
tangent points and v-circles are determined by the value of shortest paths. The work in [1] addresses modified
the radius r. dijkstra’s algorithm for route selection in car navigation
After finding the C-space and v-circles of all the obstacles system. Where it shows how the data structure that
we need to find shortest path from mobile sensor to the represents routes can be modified to assist Dijkstra’s
target 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 the
problem we adopted modified dijkstra’s algorithm. The shortest path is not always what the user wants; what the
rest of the paper is organized as follows: Section 2 reviews user really wants to have is the most comfortable route for
related work. Section 3 proposes a method to compute him or her to drive. Therefore the paper [1] gives solution
shortest 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 factors
future research topics are drawn in Section 5. in path planning of transportation and navigation systems.
Most researches only take the issue of shortest distance
II. 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 in
improve a WSN’s topology. In [7], static sensors detecting [2] proposes two algorithms: the Least-Turn Path
events will ask mobile sensors to move to their locations to Algorithm and the Minimum-Cost Path Algorithm to
conduct more in-depth analysis. The mobile sensor that has balance both the path length and turns. Where the authors
a shorter moving distance and more energy, and whose introduced modified dijkstra’s algorithm for the proposed
leaving 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 problem
to the event locations in an energy balanced way, where such as finding the efficient route by the user. Where the
dispatch problem is considered for a single round. In that paper proposes a new shortest path algorithm. The
we 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 a
number of event locations and the number of mobile rectangle or a static and dynamic hexagon.
sensors. One mobile sensor will be dispatched to one event
location (when, mobile sensors >= event locations), one III Computing the Shortest Collision Free Path
mobile sensor will be dispatched to one cluster of event Sensors are aware of their own locations, which can be
locations (when, mobile sensors < event locations) But achieved by Global Positioning System (GPS). In Hybrid
they made an assumption that the sensing field does not Wireless Sensor Network, once static sensor identifies the
contain any obstacles. The assumption that was made in event, Mobile sensor will be dispatched to the event
the 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 bipartite
problem, but they do not consider energy balancing, graph G = (SUL, SXL). Each mobile sensor and event
dispatching mobile sensor in the presence of obstacles and location is converted into a vertex. Edges only connect
only optimize energy consumption in one round. There are vertices between S and L. For each si belong to S and each
several 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-


d(si,lj). Where emove is the energy required to move unit A. Proposed Schema
distance and d (si,lj) is the distance between si and lj. Once the static sensor identifies the event location, mobile
Algorithm to find M (matching between mobile sensor to sensor will be dispatched to the event location. While
event location) is as follows: moving mobile sensor towards the event location, it should
1. 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 from
2. 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 authors
a. 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 as
b. 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 this
c. 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, is
3. 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 certain
to step 4. In [6], authors have calculated the distance point, algorithm compares the point with the destination
between 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 to
obstacles and reaching the target in shortest path. We store lengths of paths from start point to other destination
making use of the above mentioned algorithm to dispatch points in Dijkstra Algorithm. In modified Dijkstra
the mobile sensor. Algorithm, element structure of array Dist was extended as
below:

typedef shuct DistRecorddouble
{



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