Abstract: We investigate the problem of ecient wireless energy recharging in Wireless Rechargeable Sensor Networks (WRSNs). In
such networks a special mobile entity (called the Mobile Charger) traverses the network and wirelessly replenishes the energy
of sensor nodes. In contrast to most current approaches, we envision methods that are distributed, adaptive and use limited
network information. We propose three new, alternative protocols for ecient recharging, addressing key issues which we
identify, most notably (i) to what extent each sensor should be recharged (ii) what is the best split of the total energy between
the charger and the sensors and (iii) what are good trajectories the MC should follow. One of our protocols (
some distributed, limited sampling of the network status, while another one (
) reactively adapts to energy shortage alerts
judiciously spread in the network. As detailed simulations demonstrate, both protocols signicantly outperform known state
of the art methods, while their performance gets quite close to the performance of the global knowledge method (
also provide, especially in heterogeneous network deployments.
Abstract: This paper deals with early obstacles recognition in wireless sensor networks under various traffic
patterns. In the presence of obstacles, the efficiency of routing algorithms is increased by voluntarily avoiding some regions in the vicinity of obstacles, areas which we call dead-ends. In this paper, we first propose a fast convergent routing algorithm with proactive dead-end detection together with a formal definition and description of dead-ends. Secondly, we present a generalization of this algorithm which improves performances in all to many and all to all traffic patterns. In a third part we prove that this algorithm produces paths that are optimal up to a
constant factor of 2ð+1. In a fourth part we consider the reactive version of the algorithm which is an extension of a previously known early obstacle detection algorithm. Finally we give experimental results to illustrate the efficiency of our algorithms in different scenarios.