Performance-energy tradeoffs in cutset wireless sensor networks

Abstract

This work explores performance vs. communication energy tradeoffs in wireless sensor networks that use the recently proposed cutset deployment strategy in which sensors are placed densely along a grid of intersecting lines. For a given number of sensors, intersensor spacing is less for a cutset network than for a conventional lattice deployment, so that cutset networks require less communication energy, albeit with some potential loss in network performance. Previous work analyzed the energy-performance tradeoffs for square-grid cutset networks in the context of specific decentralized algorithms for source localization based on received signal strength (RSS). The current work also considers the RSS based source localization problem. However, it takes a more fundamental approach to analyzing the tradeoff by considering a centralized task, minimum energy communication paths, Maximum Likelihood estimation algorithms and Cramér-Rao bounds. Moreover, it analyzes triangular and honeycomb cutset deployments, in addition to square-grid ones. The results indicate that cutset networks offer sizable decreases in energy with only modest losses of performance.Index Terms-Wireless sensor networks, source localization.

Multidimensional Manhattan Sampling and Reconstruction

“…While the above may initially appear complex 5 , in the discrete-space, finite-support case discussed shortly, it will lead to a simple procedure that avoids the summations in ( 6) and (8).…”

“…1(a), given sampling intervals λ 1 , λ 2 > 0 and integers k 1 , k 2 > 1, samples are taken at intervals of λ 1 along horizontal rows spaced k 2 λ 2 apart, and also at intervals of λ 2 along vertical columns spaced k 1 λ 1 apart. conventional lattice or random deployments at the same density [7], [8], and (c) there are physical scenarios for which M-sampling is far more natural than traditional lattice sampling, such as when sampling from a moving vehicle, e.g., a ship sampling oxygen levels in a body of water. Similarly motivated by sampling from vehicles, the recent related work of Unnikrishnan and Vetterli [9], [10] considers sampling continuously along a grid of lines, i.e., with asymptotically large sampling rate.…”

“…M-sampling has also been proposed [4], [5], [7], [8] as a new approach to sampling grayscale images and other two-dimensional fields, with the motivations that (a) dense sampling along lines might capture edge transitions more completely than conventional lattice sampling with the same density, (b) sensor networks with a Manhattan deployment geometry need less power or less wire to transmit data than conventional lattice or random deployments at the same density [7], [8], and (c) there are physical scenarios for which M-sampling is far more natural than traditional lattice sampling, such as when sampling from a moving vehicle, e.g., a ship sampling oxygen levels in a body of water. Similarly motivated by sampling from vehicles, the recent related work of Unnikrishnan and Vetterli [9], [10] considers sampling continuously along a grid of lines, i.e., with asymptotically large sampling rate.…”

“…Note also that the expression(8) for Y (u) contains more terms in the sum than the corresponding expression(6) for Y (u).October 5, 2018 DRAFT…”

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

“…Finally, Manhattan sampling is useful in wireless sensor network applications where the goal is to estimate a two-dimensional field. 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].…”