Distributed Deployment Strategies for Improved Coverage in a Network of Mobile Sensors With Prioritized Sensing Field

Abstract

Efficient deployment strategies are proposed for a mobile sensor network, where the coverage priority of different points in the field is specified by a priority function. The multiplicatively weighted Voronoi (MW-Voronoi) diagram is utilized to find the coverage holes of the network for the case where the sensing ranges of different sensors are not the same. Under the proposed strategies, each sensor detects coverage holes within its MW-Voronoi region, and then moves in a proper direction to reduce their size. Since the coverage priority of the field is not uniform, the target location of each sensor is determined based on the weights of the vertices or the points inside the corresponding MW-Voronoi region. Simulations validate the theoretical results.Index Terms-Coverage, distributed deployment algorithm, mobile sensors, prioritized sensing field, wireless sensor networks,

Research on the optimization method for equipment allocation of chemical reconnaissance system based on the weighted Voronoi diagram

“…Through the above analysis we can see, issues about the optimal allocation for equipment of chemical reconnaissance system can be summed up as detecting the cavity size in the protection region under the condition of allocating the first kind of nodes at random and allocating the second kind of nodes regularly, then putting forward the allocation strategy of the third kind of nodes covering the higher priority areas [4] .…”

An Energy-Balanced Heuristic for Mobile Sink Scheduling in Hybrid WSNs

“…The routes are planned almost the same in path length for ensuring the load balance of mobile sensors. Leveraging the same assumption that only mobile sensors, while no static sensors, exist in the network, the technique in [10] studies the region coverage challenge, especially when the coverage priority of different fields may be different. Generally, these techniques consider the energy consumption of mobile sink movement, while the energy comsumption of data gathering at certain subregions is assumed to be the equivalent.…”

“…The scheduling of mobile sinks is an important research topic in hybrid WSNs and techniques have been proposed to address this problem from different perspectives [6], [8]- [10]. An inspiring survey about the evolution of sink mobility issues in WSNs has been presented in [27].…”

“…The mobile sink is assumed to have limitless energy compared with static sensors. The technique in [10] mitigates the region coverage issue when a region is relatively large and mobile sensors move independently following decentralized control principles. Generally, current techniques have studied the scheduling of mobile sinks in hybrid WSNs.…”

Coverage Enhancement Algorithms for Distributed Mobile Sensors Deployment in Wireless Sensor Networks

“…For each Edge in polygon (10) Find midpoint of Edge (11) Append midpoint to new polygon (12) End For each Edge (13) Until vertices are very close to each other (14) Record the statistical average of new candidate location into newpositions (15) End For each polygon (16) Checkforbettercoverage (VD, positions, newpositions) // Function for if NCL > CL (17) Newcov = calculatecoverage (VD, newpositions) (18) Newcovpercent = (newcov/MFL 2 ) * 100 (19) IF coverage enhanced then (20) IF Round ≤ MR (21) calcEnergy (positions, newposition, permeterEnergy) (22) position = newpositions (23) totalcov = newtotalcov (24) totalcovpercent = newtotalcovpercent (25) else (26) exit Loop (27) End IF Round (28) Else (29) Exit Loop (30) End IF coverage (31) Next Round (32) calculatecoverage average for all rounds (33) calculateEnergy consumption average for all rounds (34) calculateconvergence average for all rounds Algorithm 3: Edge Based Centroid (EBC) of th Voronoi polygon ( ).…”