Energy efficient source localization on a Manhattan grid wireless sensor network

Abstract

For wireless sensor networks, many decentralized algorithms have been developed to address the problem of locating a source that emits acoustic or electromagnetic waves based on received signal strength. Among the motivations for decentralized algorithms is that they reduce the number of transmissions between sensors, thereby increasing sensor battery life. Whereas most such algorithms are designed for arbitrary sensor placements, such as random placements, this paper focuses on applications that permit a choice of sensor placement. In particular, to make communications costs small, it is proposed to place sensors uniformly along evenly spaced rows and columns, i.e., a Manhattan grid. For such a placement, the Midpoint Algorithm is proposed, which is a simple noniterative decentralized algorithm. The results of this paper show that Manhattan grid networks offer improved accuracy vs. energy tradeoff over randomly distributed networks. Results also show the proposed Midpoint Algorithm offers further energy savings over the recent POCS algorithm.Index Terms-Wireless sensor networks.

Performance-energy tradeoffs in cutset wireless sensor networks

“…Previously, it was found that Manhattan grid sensor deployments allow for a significant reduction in communication energy over random or lattice deployments with only a modest loss in error performance for the problem of decentralized RSS localization [8]. This new work provides analytical and experimental evidence for an identical performance-energy tradeoff, but in the context of a centralized RSS localization problem.…”

“…In particular, our results suggest that there may be a fundamental advantage of honeycomb networks over other networks in terms of energy efficiency, which should be further investigated in future research. More efficient, decentralized algorithms for solving the source localization problem in cutset networks, such as the algorithm in [8], should also be investigated. Finally, future work should consider communication issues in cutset networks, such as scheduling, routing, and bounds on network throughput.…”

“…For fixed hardware constraints, network energy consumption can be reduced by using efficient communication protocols [1][2][3], distributed algorithms [4,5], and sensor-placement strategies [6,7]. Recently, variants on all three of the above strategies were proposed to reduce the energy required in solving a single source, receivedsignal-strength (RSS)-based localization problem [8]. The authors proposed using a Manhattan grid sensor layout where sensors are placed along evenly-spaced rows and columns, as shown in Figure 1(e), and showed that in the context of specific decentralized estimation and communication strategies, this permitted a tradeoff in which communication energy could be substantially reduced with only modest decreases in performance.…”

Image interpolation from Manhattan cutset samples via orthogonal gradient method

“…It has also been shown that there are energy-saving advantages to placing wireless sensor networks in a Manhattan geometry; specifically, Manhattan grid geometries allow sensors to be placed closer together than random or lattice networks at the same density, allowing for lower-energy communication between sensors. The authors in [1] proposed the Midpoint Algorithm for solving a source localization problem from data collected from Manhattan wireless sensor networks. Both this algorithm and the Manhattan grid geometry offered significant energy savings over other distributed localization algorithms on random/lattice networks.…”

Sampling 2-D signals on a union of lattices that intersect on a lattice

“…If sensors are deployed on a Manhattan grid, as opposed to random placement, then the energy costs of data transmission tends tend to be much smaller [6]. Such "Manhattan networks" can be used to efficiently solve the problem of RSS-based source localization [7].…”