Abstract:Abstract:One important problem which may arise in designing a deployment strategy for a wireless sensor network is how to deploy a specific number of sensor nodes throughout an unknown network area so that the covered section of the area is maximized. In a mobile sensor network, this problem can be addressed by first deploying sensor nodes randomly in some initial positions within the area of the network, and then letting sensor nodes to move around and find their best positions according to the positions of t… Show more
“…The interested reader could refer to the survey in [Savvides et al 2001] which provides detailed discussion on location discovery algorithms in MSN and the GPS-free localization model in [Wang & Xu 2010]. In our case, we assume that nodes (i) either know their position directly through GPS or (ii) they could adopt other localization techniques [Alcan et al 2010, Esnaashari andMeybodi 2011] or (iii) a hybrid scheme, as, in our model, location information from neighbouring nodes (either absolute or relative w.r.t. the position of the vanguard) is exploited for the relocation directives.…”
We present a robust, dynamic scheme for the automatic self-deployment and relocation of mobile sensor nodes (e.g., unmanned ground vehicles, robots) around areas where phenomena take place. Our scheme aims (i) to sense environmental contextual parameters and accurately capture the spatio-temporal evolution of a certain phenomenon (e.g., fire, air contamination) and (ii) to fully automate the deployment process by letting nodes relocate, self-organize (and self-reorganize) and optimally cover the focus area. Our intention is to 'opportunistically' modify the previous placement of nodes to attain high quality phenomena monitoring. The required intelligence is fully distributed within the mobile sensor network so that the deployment algorithm is executed incrementally by different nodes. The presented algorithm adopts the Particle Swarm Optimization technique, which yields very promising results as reported in the paper (performance assessment). Our findings show that the proposed algorithm captures a certain phenomenon with very high accuracy while maintaining the network-wide energy expenditure at low levels. Random occurrences of similar phenomena put stress upon the algorithm which manages to react promptly and efficiently manage the available sensing resources in the broader setting.
“…The interested reader could refer to the survey in [Savvides et al 2001] which provides detailed discussion on location discovery algorithms in MSN and the GPS-free localization model in [Wang & Xu 2010]. In our case, we assume that nodes (i) either know their position directly through GPS or (ii) they could adopt other localization techniques [Alcan et al 2010, Esnaashari andMeybodi 2011] or (iii) a hybrid scheme, as, in our model, location information from neighbouring nodes (either absolute or relative w.r.t. the position of the vanguard) is exploited for the relocation directives.…”
We present a robust, dynamic scheme for the automatic self-deployment and relocation of mobile sensor nodes (e.g., unmanned ground vehicles, robots) around areas where phenomena take place. Our scheme aims (i) to sense environmental contextual parameters and accurately capture the spatio-temporal evolution of a certain phenomenon (e.g., fire, air contamination) and (ii) to fully automate the deployment process by letting nodes relocate, self-organize (and self-reorganize) and optimally cover the focus area. Our intention is to 'opportunistically' modify the previous placement of nodes to attain high quality phenomena monitoring. The required intelligence is fully distributed within the mobile sensor network so that the deployment algorithm is executed incrementally by different nodes. The presented algorithm adopts the Particle Swarm Optimization technique, which yields very promising results as reported in the paper (performance assessment). Our findings show that the proposed algorithm captures a certain phenomenon with very high accuracy while maintaining the network-wide energy expenditure at low levels. Random occurrences of similar phenomena put stress upon the algorithm which manages to react promptly and efficiently manage the available sensing resources in the broader setting.
“…This dynamicity is necessary in many applications such as mobile ad hoc and sensor networks [20]. And this kind of ICLA is called as the dynamic irregular cellular learning automata (DICLA).…”
“…To attain high coverage in wireless sensor networks, an automatic node deployment is proposed in [47]. For solving vertex coloring problem, Torkestani and Meybodi proposed an algorithm in [48].…”
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