Compression vs Transmission Tradeoffs for Energy Harvesting Sensor Networks

The operation of Energy Harvesting Wireless Sensor Networks (EHWSNs) is a very lively area of research. This is due to the increasing inclination toward green systems, in order to reduce the energy consumption of human activities at large and to the desire of designing networks that can last unattended indefinitely (see, e.g., the nodes employed in Wireless Sensor Networks, WSNs). Notably, despite recent technological advances, batteries are expected to last for less than ten years for many applications and their replacement is often prohibitively expensive. This problem is particularly severe for urban sensing applications, think of, e.g., sensors placed below the street level to sense the presence of cars in parking lots, where the installation of new power cables is impractical. Other examples include body sensor networks or WSNs deployed in remote geographic areas. In contrast, EHWNs powered by energy scavenging devices (renewable power) provide potentially maintenance-free perpetual network operation, which is particularly appealing, especially for highly pervasive Internet of Things. Lossy temporal compression has been widely recognized as key for Energy Constrained Wireless Sensor Networks (WSN), where the imperfect reconstruction of the signal is often acceptable at the data collector, subject to some maximum error tolerance. The first part of this thesis deals with the evaluation of a number of lossy compression methods from the literature, and the analysis of their performance in terms of compression efficiency, computational complexity and energy consumption. Specifically, as a first step, a performance evaluation of existing and new compression schemes, considering linear, autoregressive, FFT-/DCT- and Wavelet-based models is carried out, by looking at their performance as a function of relevant signal statistics. After that, closed form expressions for their overall energy consumption and signal representation accuracy are obtained through numerical fittings. Lastly, the benefits that lossy compression methods bring about in interference-limited multi-hop networks are evaluated. In this scenario the channel access is a source of inefficiency due to collisions and transmission scheduling. The results reveal that the DCT-based schemes are the best option in terms of compression efficiency but are inefficient in terms of energy consumption. Instead, linear methods lead to substantial savings in terms of energy expenditure by, at the same time, leading to satisfactory compression ratios, reduced network delay and increased reliability performance. The subsequent part of the thesis copes with the problem of energy management for EHWSNs where sensor batteries are recharged via the energy harvested through a solar panel and sensors can choose to compress data before transmission. A scenario where a single node communicates with a single receiver is considered. The task of the node is to periodically sense some physical signal and report the measurements to the receiver (sink). We assume that this task is delay tolerant, i.e., the sensor can store a certain number of measurements in the memory buffer and send one or more packets of data after some time. Since most physical signals exhibit strong temporal correlation, the data in the buffer can often be compressed by means of a lossy compression method in order to reduce the amount of data to be sent. Lossy compression schemes allow us to select the compression ratio and trade some accuracy in the data reconstruction at the receiver for more energy savings at the transmitter. Specifically, our objective is to obtain the policy, i.e., the set of decision rules that describe the node behavior, that jointly maximizes throughput and reconstruction fidelity at the sink while meeting some predefined energy constraints, e.g., the battery charge level should never go below a guard threshold. To obtain this policy, the system is modeled as a Constrained Markov Decision Process (CMDP), and solved through Lagrangian Relaxation and Value Iteration Algorithm. The optimal policies are then compared with heuristic policies in different energy budget scenarios. Moreover the impact of the delay on the knowledge of the Channel State Information is investigated. Two more parts of this thesis deal with the development of models for the generation of space-time correlated signals and for the description of the energy harvested by outdoor photovoltaic panels. The former are very useful to prove the effectiveness of the proposed data gathering solutions as they can be used in the design of accurate simulation tools for WSNs. In addition, they can also be considered as reference models to prove theoretical results for data gathering or compression algorithms. The latter are especially useful in the investigation and in the optimization of EHWSNs. These models will be presented at the beginning and then intensively used for the analysis and the performance evaluation of the schemes that are treated in the remainder of the thesis.