SELF DEPLOYMENT OF MOBILE
SENSOR NETWORK FOR OPTIMAL
COVERAGE
S.INDU* and Prof. Asok Bhattacharyya
ECE Department, Delhi Technological University, Bawana Road, Delhi, 110042, India†
s.indu@rediffmail‡
Venu Kesham and prof. Santhanu Chaudhury
Electrical Engineering Department
Multimedia Laboratory, Indian Institute of Technology Delhi, India
Abstract:
The effectiveness of a distributed self organizing Mobile Wireless sensor Network depends to a large extent on coverage provided by the sensor deployment. Mainly this paper considers the fundamental problems of a dynamic sensor network, THE COVERAGE. Localization of mobile sensors is a challenging issue, and the same is solved by developing a spatial addressing scheme for mobile sensor networks for locating mobile sensors relative to the field. The hexagonal Cell based management model [8] for localization is used, as the hexagon gives a better representation of circular field sensor nodes, which maximizes the coverage with minimum overlapping. A Self Configuring Distributed Deployment Algorithm, which assures complete coverage of a given sensing field, is proposed in this paper. Finally the proposed algorithm has been simulated on JAVA simulator and then validated by implementing the same on Cricket motes.
Keywords: self-organizing; wireless sensor network; coverage.
1. Introduction
Sensor networks are self-sustaining systems of nodes that co-ordinate amongst themselves autonomously but, their development is hindered by the constraints of the devices used. Firstly, they are power constrained which causes device failure and make energy efficient communication essential. They also have limited computing power, preventing sophisticated network protocols from being run, and limited bandwidth which constraints the amount of communication. Human intervention to keep the network up and running, in such conditions, is at the least a tedious job and mostly infeasible [16],[3],[9]. It is for this reason that there is a continued effort to make sensor networks as autonomous as possible. It is envisaged that in near future, very large scale Sensor Network consisting of both mobile and static nodes will be deployed for applications ranging from environment monitoring to emergency search and rescue operation [10]. In this paper organization of a sensor network when nodes are deployed randomly at the sensor field is addressed.
Sensor deployment has received considerable attention recently. When the environment is unknown or hostile such as remote harsh field, disaster area and toxic urban regions, sensors cannot be deployed manually. To scatter sensor by aircraft is one possible solution. However, using this technique, the actual landing position of sensors cannot be controlled due to the existence of wind and obstacles such as tree and buildings. Consequently, the coverage may be inferior to the application requirements no matter how many sensors are dropped [2],[12],[13]. Moreover in many cases such as during in building toxic-leaks detection, chemical sensors must be placed inside a building from the entrance of the building. In such cases, it is necessary to make use of mobile sensors, which can move to the correct place to provide the required coverage. For the deployment of Mobile sensor network, location of each node should be determined dynamically. For calculating the location of nodes relative to the field spatial addressing scheme for Mobile sensor Network is developed using hexagonal cell based management model. A hexagon represents a better coverage model for an Omni-directional sensor node with a circular field which also maximizes the coverage with minimum overlapping. In this model the coverage space is geographically partitioned into several disjoint and equal sized hexagonal cell regions. Each cell is then given a cell-ID and the same can be calculated from the coordinates of sensor node.
2. State of the art
Random placement of sensors may not satisfy the requirement due to the hostile deployment enviroment. Two methods can be used to enhance the coverage: Incremental sensor deployment and Movement assisted sensor deployment. In incremental sensor deployment [2], nodes are deployed one by one, using the location information of previously deployed nodes. This algorithm is not scalable and is computationally expensive.
A robot works in coordination with a sensor network [6]. The sensor network assists the robot in navigation and the robot deploys the additional sensors to maximize the sensor coverage of the network. Potential field technique is applied in a centralized way [17]. One powerful leader is used to calculate the field and generates the location for all other nodes. This algorithm assumes that the environment is static and known before deployment.
A novel distributed self-deployed protocol for mobile sensor was proposed by G.Wang [11] They used Voronoi diagrams [5] to find the coverage holes in sensor network, and proposed three algorithms, VEC (Vector-based), VOR(Voronoi based), and Mini max,to guide sensor movements towards coverage hole. When applied to randomly deployed sensors, these algorithms can provide high coverage within a short time and limited moving distance. If the initial distribution of sensors is extremely uneven, disconnection may occur, thus, the Voronoi polygon constructed may not be accurate enough, which results in more moves and larger moving distance.
[18] Uses the idea of ‘social potential’ where the potential for a robot are constructed with respect to other robots. The author describes heuristics to design social potential for achieving a variety of behaviors like clustering, patrolling, etc. This method does not aim at maximizing the area coverage.
The algorithm proposed by Bin Zhang and et.al.,[19] randomly partitions space into sufficientlysmall neighborhoods at each iteration. Within each neighborhood a redistribution process directed by a cluster head is enacted. In the proposed approach whole space is divided into fixed hexagonal cells which provide maximum coverage withminimum overlapping. The algorithm selects a head node automatically and then it will distribute the nodes to the neighboring void cells. The main advantage of proposed algorithm compared to Bin Zhang’s algorithm [19] is that this approach deploys sensor nodes without forming cluster heads after each iterations, which has high message complexity.
In the algorithm proposed by G.Wang and et.al [11], every node determines tits final location according to its initial location and the event distribution; hence the motion of each node can be predicted by any other node. However the approach discussed by Z. Bulter and D.Rus [7] assumes that the initial distribution is uniform and depends on every sensor node predicting the motion of other nodes, which may be expensive. To predict the motion, each node also needs to know the initial location of all other nodes and all nodes need to know and agree on the location of all events. Such information must be flooded across the whole network. The proposed approach makes no assumptions on initial distribution of the sensor nodes and no information flooding is needed. Also Voronoi needs a very high computational resource which is not needed in proposed algorithm.
Most of the existing movement-assisted sensor deployment protocols rely on the notion of virtual force to move existing sensor form an initial unbalanced state to a balanced state. Sensors are involved in a sequence of computation (for a new position) and movement. A centralized virtual force based mobile sensor deployment algorithm (VFA) proposed by Y. Zou, combines the idea of potential field and disk packing [20]. This algorithm uses a powerful cluster head, which will communicate with all the other sensor, collect sensor position information, and calculate forced and utilizes these forces to position each sensor. More over this method needs costlier laser beams to detect the presence of sensor.
The proposed algorithm is distributed which can be applied to a system consisting of large number of nodes, no assumptions on the initial distribution of sensor nodes and the deploying environment and doesn’t involve complex computational geometry. Short range transmitter and receivers are used for detecting presence of sensors. The simulation of proposed approach is done by varying the size of hexagon, initial distribution and probability of a node being head. Finally the algorithm is validated experimentally using Crossbrow Cricket motes.
3. Localization
3.1. Cellular based management model
Modeling of SOSN (self organizing sensor netwoks) can be addressed from various aspects, such as sensing coverage, node placement, connectivity, energy consumption, etc. The present works aims at modeling
the sensor network from the coverage point of view. A distributed deployment algorithm, which ensures the uniform distribution of sensor nodes throughout the sensing field, i.e., each cell in the region must be occupied by at least one sensor node, is proposed. In the proposed framework, Cellular Based management model for Mobile AdHoc-Sensor Network[8] is used. The proposed algorithm needs the area of the sensing field as reference for localization of sensor nodes. This can be given as the range of latitude and longitude field in case of an open field or as coordinates in case of a closed field. Firstly the coverage space is geographically partitioned into several disjoint and equal sized cellular regions. Each cell is then assigned a unique Cell-id ( Cx, Cy) relative to the field as shown in fig. 1. For the given (x,y) co-ordinates ((x,y) coordinates of the sensor node) the corresponding cell id(Cx.Cy) of the node is calculated by comparing the node coordinates with the coordinates of the central point of each cell. The coordinates of each node can be calculated either from satellite (GPS) or from 3 Beacons placed locally as shown in fig 2. The sensor node in each cell will then select its master/head node and all other nodes will become slave. It is assumed that master present in one cell can directly communicating with master (head) of the neighboring cells. Let the single radius of each host be Rc. The size of hexagon is calculated from the maximum communication range of sensor.
4. Self Configuring Deployment Algorithm for Maximum Coverage
In this section, a distributed algorithm for sensor deployment is described, considering that the space is divided into several disjoint and equal-sized hexagonal cellular regions. Each node first elects itself as a cell head with a pre-defined probability head ( Phead ). If one node elects itself as a cell head, it broadcasts its location to its neighbors. Otherwise, the node listens to the message from cell head and the node will send its location to its corresponding cell head. If no head is elected with in a cell then all the nodes will be in the normal state and will respond to the message transmitted by the head node. While learning step of the deployment process, each
cell head is capable of communicating with neighboring cell head/Normal nodes. If once a head node is elected then all the other nodes in that cell will become slaves. Once the head node is elected then, it will execute the deployment algorithm which will uniformly spread the nodes along the given sensor field. Basically the head node will find out the position of the neighboring cells and then move redundant sensors in it’s cell to the unoccupied cell and this node (the node which is currently moved) will become the head node of the cell. This will be repeated till all the cells are covered.
4.1. Assumptions
The key assumptions are
No details of the space to be sensed is known
Each node has the ability to determine its positional co-ordinates with respect to a reference axis Each node can autonomously navigate from its current location to a commanded goal location All nodes communicate within a short range Re
The sensor network contains large number of nodes.
4.2. Deployment algorithm
Hi k
represents the head node of kthcell at ith iteration and f(p) is the set of current node locations. Ai k
is the adjacent cell information of the head cell Hi
k and Vi
k
are the victim nodes that are targeted to the empty neighboring cells of the cell Ck by the head node Hik
1. Partition the sensing field into small sub cells Ck having a regular pattern
2. i=0;
3. While termination conditions are not satisfied do
4. Select a cell head from Hik the nodes which are moved to a cell Ck
5. Each node n within cell sends position P[n] to the cell head
6. Each cell head Hi
k
learn the neighboring cells information for all and constructs the adjacency
list Aik . Each cell head Hik selects the victim nodes Vik that are to be sent to the neighboring cells
7. Assign each victim node a new position
8. Notify the adjacent cells with positions of the victim nodes
9. All the Victim nodes will move to the new cells
10. i=i+1;
11. end
As a part of analysis it is shown that the proposed deployment algorithm will converge and will attain a stable state after certain number of iterations. For this a term called occupancy is defined and then it is shown that the occupancy will increase or will remain constant as time progress by taking some constraints on the properties of some sensor node. Occupancy is defined as the total number of cells occupied at a given iteration. At first the case in which two nodes are targeted to a single void cell, which leads to decrease in the occupancy is considered and then the case in which the occupancy is continuously increasing with every k iterations on an average is considered
.
4.3. Analysis
Let n be the total number of cells in the given area whose side is given by ”S” Oi be the occupancy at a iteration i with a given probability of electing head be Phead
Oi = ∑
n
k=1Ci
k (1)
Ci
k
= 1 if cell is occupied (2) 0 if cell is not occupied
Oi0 is the initial occupancy
The occupancy may be decreased on due to the following reasons
All the adjacent cell of a void cell is occupied with a single sensor node and if more than one head node send node to the void cell, the occupancy reduces
If all the adjacent cells of a void cell is occupied with normal node, the algorithm will skip step 5-9 and there will not be any change in occupancy and the occupancy reduces
The reduction in occupancy due to the first reason is avoided by notifying the victim node to all adjacent nodes. The reduction in occupancy due to the second reason will be solved by itself which is explained as given below
Given probability of the node being head is Phead. In the cell the probability of the node to in normal state is Pnormal. Hence
Phead +Pnormal =1 (4) Phead =1- Phead (5)
After k iterations the probability of the node being the head at least one node will become head, then that head node will go through the steps 5-9 which will fill the empty cell hence increasing the cell occupancy.
If the number of nodes is less than number of cells, the occupancy Oi will remain constant and it will not satisfy(2) the algorithm will not converge. The nodes will always be in motion, providing dynamic coverage. 5. Results
5.1. Simulation
The important criteria for the proposed framework are the mobility models used by the sensor nodes. In NS-2 it is possible to have only predefined mobility models, whereas this work needs to calculate the node location dynamically. Hence JAVA based simulator is developed [14] based on Shimla Simulator for validating this work. For simulation, 140 nodes in an area of 1025*850 sq.units are used. The side of hexagon is varied from 70 units to 100 units. For each case 3 different initial configurations are considered.
The simulation results are as shown in Fig. 3. The uniform distribution of nodes gives 100% initial coverage and hence no node should change its position even after execution of the algorithm. Hence this case can be considered as a test case. Simulation converges after 40 to 50 iterations for random distribution. For uniform distribution of nodes it is converged at the first iteration itself. Fig. 4 shows the result on Java Simulator when all nodes are placed in a single cell. Fig. 5 and Fig. 6 shows intermediate distribution and final distribution of all sensor nodes.
5.2. Experimental evaluation
For experimental evaluation a sensor node, a range of sensors for monitoring various phenomenon and a mobility platform are used. The first two have been purchased from vendors where as the mobility platform was developed in the lab. The cricket motes from crossbrow are used, as it can be programmed for both sensing computational task as well as location awareness. MPR410 Sensor Nodes with MIB510 programming board as shown in Fig: 7 are used for validation.
Three different softwares were used to implement the system. The firmware is uploaded on three motes which served as Beacons. This firmware was slightly modified and used as a Listener. Finally a single node was programmed as a packet forwarder for the coverage panel application.
The BeeBots are used (as shown in fig 8) to provide mobility for nodes. BeeBots have been inspired from CotsBot [1] and have been similarly built by modifying toy car. The Beebots are configured using tinyOs [4] for interfacing with PIC microcontroller for providing mobility.
mobile nodes are used. The initial positions of both static and mobile nodes are as shown in Fig. 9. Coverage panel was built in Java for displaying, in real time, the ongoing activity as shown in Fig.11. Both static and mobile nodes are used. In fig-11 the pink ones represent static nodes and the red ones used represent mobile nodes. 3 cricket beacons were used which are represented as black dots in Fig.11. Fig. 10 gives the final positions of static and mobile node after implementation of Self Configuring Deployment Algorithm. As the number of nodes are less than the number of cells, this process will continue and as a result, this will provide a dynamic coverage
6. Conclusion
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