Optimized Random Deployment of Energy Harvesting Sensors for Field Reconstruction in Analog and Digital Forwarding Systems

Abstract

This work examines the large-scale deployment of energy harvesting sensors for the purpose of sensing and reconstruction of a spatially correlated Gaussian random field. The sensors are powered solely by energy harvested from the environment and are deployed randomly according to a spatially nonhomogeneous Poisson point process whose density depends on the energy arrival statistics at different locations. Random deployment is suitable for applications that require deployment over a wide and/or hostile area. During an observation period, each sensor takes a local sample of the random field and reports the data to the closest data-gathering node if sufficient energy is available for transmission.The realization of the random field is then reconstructed at the fusion center based on the reported sensor measurements. For the purpose of field reconstruction, the sensors should, on the one hand, be more spread out over the field to gather more informative samples, but should, on the other hand, be more concentrated at locations with high energy arrival rates or large channel gains toward the closest data-gathering node. This tradeoff is exploited in the optimization of the random sensor deployment in both analog and digital forwarding systems. More specifically, given the statistics of the energy arrival at different locations and a constraint on the average number of sensors, the spatially-dependent sensor density and the energy-aware transmission policy at the sensors are determined for both cases by minimizing an upper bound on the average mean-square reconstruction error. The efficacy of the proposed schemes are demonstrated through numerical simulations.

Big Data Collection in Large-Scale Wireless Sensor Networks

“…This is economical in terms of energy consumption compared to direct communication, that is to say, that each sensor node directly routes these data to the sink without a relay. In contrast, clustering results in higher latency than in flat structuring [120,121]. In addition, in this type of architecture, the most common problem is called the access point problem [122], that is to say, that sensor nodes that are in proximity to the sink are rapidly depleting their energy compared to other sensor nodes.…”

A Bio-Inspired Deployment Method for Data Collection Networks in Wide White Areas

“…Most of the presented schemes aim at covering completely and reliably a given area [11]. Different node probability distributions are studied, they are non-homogeneous point processes in most cases [4], [12]. In [12] a random deployment scheme is proposed for covering an area while taking into account the dynamic of the sensed physical parameter.…”

“…Different node probability distributions are studied, they are non-homogeneous point processes in most cases [4], [12]. In [12] a random deployment scheme is proposed for covering an area while taking into account the dynamic of the sensed physical parameter. Nevertheless, these works focus on the coverage of the full area instead of selected points of interest in a wide area.…”

Wireless power transfer for distributed estimation in wireless passive sensor networks

“…The inequality in (9) follows from the fact that (a + b) 2 ≤ 2(a 2 + b 2 ). By taking the derivative of the objective function in (9) and set it as zero, the optimal k can be obtained as…”

“…. , N. Details of the derivations are omitted due to space limitation, but are similar to that in [9]. Notice that the approximation in (11) is made by assuming that the quantization error i is uniformly distributed and is uncorrelated with its input x i .…”