Energy-efficient target coverage in wireless sensor networks

Abstract

Abstract-A critical aspect of applications with wireless sensor networks is network lifetime. Power-constrained wireless sensor networks are usable as long as they can communicate sensed data to a processing node. Sensing and communications consume energy, therefore judicious power management and sensor scheduling can effectively extend network lifetime. To cover a set of targets with known locations when ground access in the remote area is prohibited, one solution is to deploy the sensors remotely, from an aircraft. The lack of precise sensor placement is compensated by a large sensor population deployed in the drop zone, that would improve the probability of target coverage. The data collected from the sensors is sent to a central node (e.g. cluster head) for processing.In this paper we propose an efficient method to extend the sensor network life time by organizing the sensors into a maximal number of set covers that are activated successively. Only the sensors from the current active set are responsible for monitoring all targets and for transmitting the collected data, while all other nodes are in a low-energy sleep mode. By allowing sensors to participate in multiple sets, our problem formulation increases the network lifetime compared with related work [2], that has the additional requirements of sensor sets being disjoint and operating equal time intervals. In this paper we model the solution as the maximum set covers problem and design two heuristics that efficiently compute the sets, using linear programming and a greedy approach. Simulation results are presented to verify our approaches.

Maximizing Lifetime of Target Coverage in Wireless Sensor Networks Using Learning Automata

“…Cardei and Wu [8] survey recent sensor coverage problems proposed in literature and categorize them according to the following design criteria: objective of the problem: maximize network lifetime or minimize the number of sensors deployed sensor deployment method: deterministic versus random…”

“…Since the RE is aware of the underlying penalty probability distribution of the system, depending Fig. 2 The Automaton-environment feedback loop [8] on the penalty probability c i corresponding to α i , it 'prompts' the LA with a reward (typically denoted by the value '0'), or a penalty (typically denoted by the value '1'). The reward/penalty information (corresponding to the action) provided to the LA helps it to choose the subsequent action.…”

Towards Optimal Event Detection and Localization in Acyclic Flow Networks

“…Sensing coverage, in general, has been studied under different assumptions. [23] uses both a greedy approach, and linear programming to approximate the set covering problem. These problems consider only minimizing the number of vertices to cover edges in a graph.…”

Power allocation for orthogonally-observed multi-target sensor networks

“…In multi-target scenarios the main topics for investigations address the tracking and coverage problem. For example, the focus in [2] is to maximize the lifetime s.t. power constraints and coverage regions.…”