Abstract
This paper proposes a novel event-triggered formulation as an extension of the recently develo- ped generalized gossip algorithm for decision/awareness propagation in mobile sensor networks modeled as proximity networks. The key idea is to expend energy for communication (message transmission and reception) only when there is any event of interest in the region of surveillance. The idea is implemented by using an agent’s belief about presence of a hotspot as feedback to change its probability of (communication) activity. In the original formulation, the evolution of network topology and the dynamics of decision propagation were completely decoupled which is no longer the case as a consequence of this feedback policy. Analytical results and numeri- cal experiments are presented to show a significant gain in energy savings with no change in the first moment characteristics of decision propagation. However, numerical experiments show that the second moment characteristics may change and theoretical results are provided for upper and lower bounds for second moment characteristics. Effects of false alarms on network formation and communication activity are also investigated.
Highlights
Application of mobile sensor networks for monitoring environment is increasingly ubiquitous for both military applications such as undersea mine hunting, anti-submarine warfare, and nonmilitary applications such as weather monitoring and prediction (Lehning et al, 2009; Choi and How, 2011; Mukherjee et al, 2011)
OF GENERALIZED GOSSIP POLICY This section briefly describes the formulation and key results of the generalized gossip policy proposed in Sarkar et al (2013)
Threats are modeled as local hotspots within the surveillance region and only a few agents possibly have a non-zero probability of detecting the threat
Summary
Application of mobile sensor networks for monitoring environment is increasingly ubiquitous for both military applications such as undersea mine hunting, anti-submarine warfare, and nonmilitary applications such as weather monitoring and prediction (Lehning et al, 2009; Choi and How, 2011; Mukherjee et al, 2011). Results show that the (user-defined) generalizing parameter in the agent interaction policy can control the trade-off between Propagation Radius (i.e., how far a decision spreads from its source) and Localization Gradient (i.e., the extent to which the spatial variations may affect localization of the source), as well as the temporal convergence properties. This policy efficiently facilitates distributed decision propagation in a mobile-agent network, this is not efficient in terms of energy consumption by the agents.
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