Decentralised Control of Adaptive Sampling in Wireless Sensor Networks

The efficient allocation of the limited energy resources of a wireless sensor network in a way that maximises the information value of the data collected is a significant research challenge. Within this context, this paper concentrates on adaptive sampling as a means of focusing a sensor’s energy consumption on obtaining the most important data. Specifically, we develop a principled information metric based upon Fisher information and Gaussian process regression that allows the information content of a sensor’s observations to be expressed. We then use this metric to derive three novel decentralised control algorithms for information-based adaptive sampling which represent a trade-off in computational cost and optimality. These algorithms are evaluated in the context of a deployed sensor network in the domain of flood monitoring. The most computationally efficient of the three is shown to increase the value of information gathered by approximately 83%, 27%, and 8% per day compared to benchmarks that sample in a naive non-adaptive manner, in a uniform non-adaptive manner, and using a state-of-the-art adaptive sampling heuristic (USAC) correspondingly. Moreover, our algorithm collects information whose total value is approximately 75% of the optimal solution (which requires an exponential, and thus impractical, amount of time to compute)

Adaptive quantization in wireless sensor networks with encryption

We consider the estimation of a deterministic unknown parameter in an encrypted wireless sensor networks. Adaptive quantization is used on the sensor\u27s observation and the outputs of the sensors are then encrypted using a probabilistic cipher. In a conventional fixed quantization scheme, estimation error grows exponentially with the difference between the threshold and the unknown parameter to be estimated. Hence, to avoid this, we used and adaptive quantization scheme where each sensor adaptively adjusts its quantization threshold. We find the Cramer-Rao Lower Bound for the Ally Fusion Center (AFC) and then find the optimal estimate of the unknown parameter for the AFC. To find this, we first prove that the sequence of thresholds used for the quantization process forms a markov chain and that this chain is recurrent non-null and thus has a stationary distribution. This distribution is then obtained analytically in closed form as well as through numerical methods. The optimal estimate of the unknown parameter for the AFC is obtained asymptotically in the number of sensors. The performance of the Third Party Fusion Center (TPFC) is only computed through simulation and compared to that of AFC

Distributed estimation over a low-cost sensor network: a review of state-of-the-art

Proliferation of low-cost, lightweight, and power efficient sensors and advances in networked systems enable the employment of multiple sensors. Distributed estimation provides a scalable and fault-robust fusion framework with a peer-to-peer communication architecture. For this reason, there seems to be a real need for a critical review of existing and, more importantly, recent advances in the domain of distributed estimation over a low-cost sensor network. This paper presents a comprehensive review of the state-of-the-art solutions in this research area, exploring their characteristics, advantages, and challenging issues. Additionally, several open problems and future avenues of research are highlighted