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Optimization of self-directed target coverage in wireless multimedia sensor network.

1. Introduction

Wireless multimedia sensor network (WMSN) [1], an advancing form of wireless sensor network (WSN) [2], is a multihop self-organizing distributed sensing network which is constituted by battery of multimedia sensor nodes with ability of calculation, storage, and wireless communication. Through multimedia sensor in nodes, this advanced network can find, collect, and handle many kinds of media information, such as audio, video, and image, in surrounding environment. Then the multimedia data will be sent to information gathering center by multihop trunking scheme. After data analysis in information gathering center, overall and effective environment monitoring will be accomplished [1,3].

One of the most important issues in WSN is sensing coverage, which is also measurement for service quality in WSN [4]. In traditional sensor network, the sensor coverage is usually hypothesized as omnidirectional and predefined as a crude round [1]. But, in WMSN, the coverage method is very different with its traditional sensor network. The nodes in WMSN, such as video and image sensor nodes, catch a directed visual field, named as field of view (FoV) [5]. Usually the hypothesis is that direction of multimedia sensor node is adjustable [6]. When the node is randomly deployed into malicious environment, it can cover the target and the area self-directly through cooperation with neighbor nodes.

In this paper, the coverage expectation value from node to target is delineated through probabilistic models of FoV disk, which is the ground for self-direction wireless multimedia sensor node and better coverage of target. The optimization algorithms for single-node single-target, multinode single-target, and single-node multitarget are given to solve the coverage problem. Also, the self-directed direction of target covered by FoV disk for every node is set as candidate sensor direction. Genetic algorithm is used to discriminate the self-directed coverage optimization in multinode multitarget circumstance and to discover the minimum set of nodes for coverage of all targets.

2. Relevant Work

Coverage issue of randomly deployed sensor node is the hotspot in this field. In traditional WSN, most researches hypothesise that the omnidirection sensor node which covers area is a circle with its center in node. But, in WMSN, the sensor cover area is usually hypothesized as sector, which is not applicable for traditional coverage algorithm [6,7]. Coverage issue can be divided into two categories, the area coverage which ensures that the whole area is covered by sensor node and the target coverage which ensures that every interested target in district is covered by at least one sensor node.

New distributed algorithm has been raised after research in self-directed WMSN [6]. This algorithm realizes node optimal sensing direction, minimal node coverage redundancy in sensing field, and maximal multimedia coverage. Directional sensor network deployment with connectivity and coverage guarantee has been considered in recent study [8]. In this study, minimal directional sensor network was deployed to form a connected network covering interested area. For the target coverage in directional sensor network, different priority was assigned to target [7]. Taking into account the distance between the target and node, a problem of target coverage based on priority has been suggested and a minimal node set was tried to be chosen for all targets monitoring.

In target coverage problem of directional sensor network, the direction and rotation of angle in node directional compensation were not considered [9]. Also, for resolving multinode multitarget coverage optimization, a battery of fixed sensing direction was set up for every sensor node, which was related to node angle of sensing 9]. But rotatable self-directed sensor node has 360[degrees] sensing direction. In this research, the self-directed direction and angle of rotation are inducted into self-direction process of multimedia sensor networknode, which could decrease the adjustment of sensor node to save energy while optimizing target coverage.

3. Network Hypothesis and Multimedia Coverage FoV Model

The network field in our research is two-dimensional Euclidean field with randomly distributed multimedia sensor nodes and a certain number of interested targets, which means that the sensing direction and position of all nodes are random and independent. The same as the hypothesis in recent study [6], the image senor is equipped with multimedia sensor node to provide FoV with angle value [THETA], which could bilaterally rotate to redefine sensing direction in two-dimensional Euclidean area. Simultaneously, it is hypothesized that monitoring target and multimedia sensor node can achieve their position information through lightweight positioning technology of WSN [10]. Also the nodes in network are hypothesized to have same sensing model, which means that they have identical sensing radius and angle. Since the WMSN is self-oriented, the sensor nodes constituting network can regulate their sensing angle, which means that inexpensive multimedia node can rotate around its vertical axis [6].

In a WMSN, S = {[s.sub.1], [s.sub.2], ..., [S.sub.N]} is the set of multimedia sensor nodes, [R.sub.s] is sensing radius of node, and interested target battery is T = {[t.sub.1], [t.sub.2], [t.sub.m]}. With regard to sensing model of multimedia sensor node, the definition is provided as follows.

Definition 1. 2D FoV: 2D FoV is a directional sensing area of multimedia sensor node, which is hypothesized as a proximate sector in two-dimensional space (Figure 1). 2D FoV of node [s.sub.i] is defined as tetrad ([s.sub.i], [R.sub.s], [theta], [s.sub.i]C), with [s.sub.i] as sensor, [R.sub.s] as sensing radius, [theta] as vertex angle of sector, and [s.sub.i]C as current sensing direction of node [s.sub.i] which divide sector into two halves.

Definition 2. 2D FoV disk: the 2D FoV disk of multimedia sensor node is defined as a set of all possible 2D FoV of node, which should be a round area with radius as [R.sub.s] (Figure 2).

Definition 3. Target coverage of multimedia sensor node [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]: equation (1) shows the coverage [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] of node [s.sub.i] to target [T.sub.k]:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (1)

ALGORITHM 1: Single-node single-interested target self-direction
coverage algorithm.

Input: multimedia sensor node [s.sub.i], interested target [T.sub.k],
  sensing radius [R.sub.s], FoV vertex angel [theta], sensing
  direction [s.sub.i]C
Output: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
Define Rotate Direction; // rotated direction of node, value as
  CLOCKWISE or ANTICLOCKWISE
Define Rotate Angle; // rotated angel of node
Calculate d([s.sub.i], [T.sub.k]); // distance between multimedia
  sensor node [s.sub.i] and interested target [T.sub.k]
IF d([s.sub.i], [T.sub.k]) > [R.sub.s] THEN
     [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII];
     RETURN [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
END
IF d([s.sub.i], [T.sub.k]) < [R.sub.S] THEN
    Calculate [??][T.sub.k][s.sub.i]C; // the angel between sensing
      direction [s.sub.i] to interested target [T.sub.k]
    IF [??][T.sub.k][s.sub.i]C = 0 THEN
          [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
          RETURN [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
    END
    IF 0 < [??] [T.sub.k][s.sub.i]C [less than or equal to] [pi] THEN
         RotateDirection = anticlockwise;
         RotateAngle = [??][T.sub.k][s.sub.i]C;
    END
    IF [pi] < [??] [T.sub.k][S.sub.i]C < 2[pi] THEN
            RotateDirection = clockwise;
            RotateAngle = 2[pi] - [??][T.sub.k][s.sub.i]C;
    END
  ROTATE([s.sub.i], RotateDirection, RotateAngle); // [s.sub.i]
    rotated RotateAngle to RotateDirection
 [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
RETURN [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]


If the interested target was located in one FoV disk of some node and not covered by this node in present, there is the possibility that this target could be covered by this node when proceeding with its self-direction. Taking into account decline of monitoring quality followed by increasing distance between interested target and node, along with the deflected angle between the interested target and current sensing direction of node ([??][T.sub.k][s.sub.i]C as shown in Figure 2), the coverage expectation value of node to interested target is defined.

Definition 4. Expectation value of multimedia sensor node to target [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]: equation (2) shows coverage expectation value of node [s.sub.i] to target [T.sub.k]:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)

where [??][T.sub.k][s.sub.i]C means current sensing direction to interested target anticlockwise, with value between [0, 2[pi]) and parameter 0 [less than or equal to] [lambda] [less than or equal to] 1, 0 [less than or equal to][beta] [less than or equal to] 1, which defined the adjustable weight of diverted angle and distance between target and node in coverage expectation.

4. Self-Directed Target Coverage Optimization

4.1. Single-Node Single-Target Self-Direction. At first, single-node single interested target coverage, the simplest circumstance, is discussed. The hypothesis is that interested target was located in FoV disk of node but not covered by node, which needs self-directed adjustment to be covered (Algorithm 1). As long as the target was located in FoV disk, it does not need to regard the distance between target and node in single-node single-target self-direction model. The purpose is the minimal rotated angle of node to realize optimal coverage to interested target which is located in [s.sub.i]C, sensing direction of node.

4.2. Multinode Single-Target Self-Directed Coverage. The problem of multinode single-target self-direction is existence of multiple multimedia sensor nodes. The hypothesis is that interested target is not covered by any node while all nodes can cover the interested target through self-direction. According to the coverage expectation value [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] of multimedia sensor node to target, the node with maximal coverage expectation value is chosen to proceed with self-direction and cover interested target, which is located in sensing direction of node, in optimal angle. The distance between interested target and node and the angel adjusted during self-direction must be considered (see Algorithm 2).

ALGORITHM 2: The algorithm of multinode single-target self-directed
coverage.

Input: set of multimedia sensor nodes S, interested target
 [T.sub.k], sensing radius [R.sub.s], FoV vertex
 angel [theta], sensing direction SC corresponding to node set S
Output: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

Define [s.sub.i]; //node selected to cover interested target,
  [s.sub.i] [member of] S
Define RotateDirection; //rotated direction of node, value as
  CLOCKWISE or ANTICLOCKWISE
Define RotateAngle; //rotated angel of node
Let B = [empty set]; //for node set of FoV disk coverage target
  [T.sub.k]
Let [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] //for
  coverage expectation of node to interested target in B;
FOR each s [member of] S
    Calculate d(s, [T.sub.k])
    IF d(s, [T.sub.k]) [less than or equal to] [R.sub.s] THEN
       B = Bu R};
       Calculate [??][T.sub.k]sC
       Calculate [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
       [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
    END
END
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII];
[s.sub.i] = B[[absolute value of i];
IF 0 < [??][T.sub.k][s.sub.i]C [less than or equal to] [pi] THEN
      RotateDirection = anticlockwise;
      RotateAngle = [??][T.sub.k][s.sub.i]C;
END
IF [pi] < [??][T.sub.k][s.sub.i]C < 2[pi] THEN
        RotateDirection = clockwise;
        RotateAngle = 2[pi] - [??][T.sub.k][s.sub.i]C;
END
ROTATE([s.sub.i], RotateDirection, RotateAngle); //[s.sub.i] rotated
  RotateAngle to RotateDirection
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
RETURN [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]


4.3. Single-Node Multitarget Self-Directed Optimal Coverage. The problem of single-node multitarget direction is how to cover maximal interested targets in node self-direction in the circumstance of multiple interested targets randomly located in FoV disk of some node (see Algorithm 3). The central idea of the algorithm is how to select the interested target subset, which is the maximal subset of the difference between any two targets deflection angles less than or equal to FoV vertex angle, as the target of node self-directed coverage optimization when measured by deflection angle of interested target to node current sensing direction.

4.4. Multinode Multitarget Self-Directed Coverage Optimization. The problem of multinode multitarget self-directed coverage optimization in WMSN is how to find the minimal node set S' covering a definite interested target set T after self-direction. According to coverage corresponding relationship after optimization, the self-direction is proceeded to cover the target node. For expediently showing the self-direction (direction and angle of rotation) of every node, the variance is introduced.

Node [s.sub.i] rotates to direction j in angle of Q, 1 as clockwise, 0 as anticlockwise, or 0

The multinode multitarget self-directed coverage optimization can be described as follows:

Minimize [absolute value of S'] (3)

Subject to t < S', [for all]t [member of] T, S' [subset or equal to] S (4)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (5)

where t < S' is t falling in [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] FoV After [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], which means that interested target t is covered after node set S' self-direction. Objective function (3) is minimal subset of multimedia sensor node which can cover all targets after self-direction. Objective function (4) is reciprocal value of coverage expectation for minimal node subset to all targets. Restrictive condition (5) promises that every interested target can be covered by node set S' after self-direction. Restrictive condition (5) promises that every sensor node e S' can only rotate to one direction at a given angle at any time.

ALGORITHM 3: Algorithm of single-node multi-interested target
self-direction optimal coverage.

Input: multimedia sensor node [s.sub.i], interested target set T,
  sensing radius [R.sub.s], FoV vertex angel [theta], sensing
  direction [s.sub.i]C
Output: [CT.sub.SET]([S.sub.i]), TSET as covered goal set after node
  self-direction opitimization

Define RotateDirection; // rotated direction of node, value as
  CLOCKWISE or ANTICLOCKWISE
Define RotateAngle; // rotated angel of node
Const N; //number of interested target
Let TSC = [empty set] // for angel between node sensing direction to
  all targets in target set T
Let CHA [N] [N] = {0}; // two-dimensional array, for difference among
  elements in some limit or not.
Let TSET = [empty set];
FOR each [T.sub.k] [member of] T
    Calculate [??][T.sub.k][s.sub.i]C; // angel between sensing
  direction [s.sub.i] to interested target [T.sub.k]
    TSC = TSC U {[??][T.sub.k][s.sub.i]C}
END
[??];
Sorting TSC from minimal to maximal;
Calculate the difference absolute value between every two elements in
  TSC. If the difference
  absolute value less than or equal to [theta], then set CHA
  corresponding element as 1;
All elements, which numerical of every line is 1 in top triangle of
  Matrix CHA, constitute the set
  with absolute value less than or equal to [theta];
Chose the line with maximal numerical equal to 1 in top triangle of
  Matrix CHA and put
  corresponding nodes into set TSET;
Form subset TSETTSC corresponding to set TSC based on the covered
  target set TSET;
Calculate the mean of maximum and minimum in TSETTSC and recorded
  as [??]
IF 0 < [??] [less than or equal to] [pi] THEN
    RotateDirection = anticlockwise;
    RotateAngle = [??];
END
IF [pi] < [??] [less than or equal to] 2[pi] THEN
      RotateDirection = clockwise;
      RotateAngle = 2[pi] - [??];
END
ROTATE([s.sub.i], RotateDirection, RotateAngle);
[C.sub.TSET]([s.sub.i]) = 1;
RETURN [C.sub.TSET]([s.sub.i]) TSET


To be described more vividly, a dilatation figure of multinode multitarget self-directed coverage optimization based on simple example in Figure 3 is shown in Figure 4. In literate [11, 12], the potential sensing direction of every node is fixed. But, in this algorithm, every node can define its potential sensing direction according to node covered by its FoV disk, which is named as self-direction. The top of figure includes multimedia sensor node, self-direction action, and interested target. The left side column represents node set S. The middle column is self-direction action when every node is covering interested target in its own FoV disk. The right column is interested target set T. Every multimedia sensor node [S.sub.i] is connected with its own self-direction action. Every node can only choose one side because every multimedia sensor node can only do one self-direction action at one time. Every interested target connects with corresponding self-direction action, which means some node can cover this target after self-direction.

Genetic algorithm is used. According to the methods used in [11, 13], the random algorithm in Algorithm 4 is used at first to generate initiate population. It means to generate all possible paths of node set covering all targets through self-direction action based on dilatation figure.

ALGORITHM 4: Random algorithm to generate all possible paths in
dilatation G.

Input: dilatation G
Output: subset of self-direction action(ROTATE)

randomly chose a self-direction action for every node and denote it
  as W;
Let W' = [empty set];
WHILE(every target [T.sub.i]} [member of]T is covered by
  self-direction action in W)
    Randomly chose a self-direction action Rotate e W
    W' = W;
    W = W - {Rotate};
END
RETURN W'


Random algorithm showed in Algorithm 4 may generate empty set. But through executing this algorithm repeatedly, a bigger set of feasible paths in dilatation can be generated, which can be used as initiate group for genetic algorithm. Discriminate algorithm based on genetic algorithm is shown in Algorithm 5.

5. Simulation Analysis

Simulation analysis is performed to multinode multi-interested target self-direction optimal coverage discriminate algorithm based on genetic algorithm [14]. The MATLAB 7.6.0 and genetic algorithm toolbox function are used. N multimedia sensor nodes and m interested targets are randomly sown into 400 m x 400 m area. Sensing direction of node is randomly distributed in [0, 2[pi]] and every interested target should be covered by FoV disk of at least one node. While simulation, the number of sensor nodes is set as 100, sensing angle of sensing node is set as [pi]/3, and sensing radius is set as 100 m. Ability of algorithm with 10, 20, and 30 interested targets is investigated individually. Algorithm runs 100 times under every circumstance. Figure 5 shows the stability of multitarget self-direction coverage optimization by this algorithm under three circumstances. Floating error range of optimal results is less than 3 nodes. The floating range of algorithm running results increased slightly with augmented interested target.

Figure 6 shows optimal solution of target function and ability tracking of algorithm in different number of interested targets. It can be discerned that our algorithm can generate optimal solution with fewer iteration frequencies, which would increase with augmented interested target. Figure 7 shows the relationship between number of interested targets and optimal solution of target function. The size of minimal node set which can totally monitor interested targets increases linearly with increasing in number of interested targets. Every data in figure means average value of algorithm ran 100 times.

6. Conclusion

In this paper, the factor of rotation angle, rotation direction, and distance between node and interested target when multimedia sensor node self-directed cover interested target in self-directed MSN is discussed. Also we investigate single-node single-target, multinode multitarget, and single-node multitarget self-directed coverage optimization, so node can self-direct in maximal coverage expectation value, which means covering more closer targets with lesser rotation angle. For multinode multitarget self-directed coverage optimization, the hypothesis that all nodes have fixed sensing direction in literature [11] is abandoned. The potential self-directed sensing direction in node is defined by number of interested targets in its FoV disk, which would more fit the feature of randomly deployed WMSN. Then the problem is abstracted into optimization problem with restrictive conditions. Genetic algorithm is used to discriminate. Also simulation analysis is performed. Further study may focus on how to realize multinode multitarget self-directed coverage optimization with induction of coverage expectation based on minimal node covering interested target.

ALGORITHM 5: Multinode multi-interested target self-direction optimal
coverage discriminate algorithm based on genetic algorithm.

Input: dilatation G
Output: minimal node set contented to target optimization

MAXGEN = 100; //maximal genetic algebra;
GGAP = 0.9; //generation gap;
trace = zeros(MAXGEN, 2); //initiate value of genetic algorithm
 ability tracking;
Repeatedly run algorithm in Algorithm 4 to generate a enough big
  feasible path set W;
Chrom = W; //initiate group
gen = 0;
ObjV = Target(Chrom); //calculate target function value (number of
  nodes) of initiate group;
WHILE gen < MAXGEN
   FitnV = ranking(ObjV); //allocate accommodation value
   SelCh = select("sus", TestChrom, FitnV, GGAP); //chose
   SelCh = recombin("xovsp", SelCh, 0.7); //recombinant
   SelCh = mutbga(SelCh, f); SelCh = fix(SelCh); //variation
   ObjVSel = Target(SelCh); //calculate target function value in
     descendant
   [Chrom ObjV] = reins(Chrom, SelCh, 1,1, ObjV, ObjVSel); //re-insert
   gen = gen + 1;
   remove individual in descendant which can not totally cover target
   set; descendant after removal of un-contented individual named as
     Chrom2;
   ObjV2 = Target(Chrom2);
   trace(gen, 1) = min(ObjV2);
   trace(gen, 2) = mean(ObjV2);
END
RETURN minimal node set which can cover T through self-direction


Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

http://dx.doi.org/10.1155/2014/416218

Acknowledgments

Foundation item: This paper is sponsored by Qing Lan Project and the National Natural Science Foundation of China (nos. 61170065, 61373017, 61171053, 61103195, and 61203217).

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[14] T He, C. Huang, B. M. Blum, J. A. Stankovic, and T Abdelzaher, "Range-free localization schemes for large scale sensor networks," in Proceedings of the 9th Annual International Conference on Mobile Computing and Networking (MobiCom '03), pp. 81-95, San Diego, Calif, USA, September 2003.

Yang Yang, (1,2) YufeiWang, (3) DechangPi, (1) Ruchuan Wang (3)

(1) College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China

(2) Department of Information Technology, Nanjing Radio and TV University, Nanjing City Vocational College, Nanjing, Jiangsu 210002, China

(3) College of Computer, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210003, China

Correspondence should be addressed to Yang Yang; nj.yangyang@163.com

Received 2 May 2014; Accepted 23 May 2014; Published 30 June 2014

Academic Editor: Shukui Zhang
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Title Annotation:Research Article
Author:Yang, Yang; YufeiWang; DechangPi; Wang, Ruchuan
Publication:The Scientific World Journal
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Date:Jan 1, 2014
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