Distributed Detection in Energy Harvesting Wireless Sensor Networks

A conventional wireless sensor networks (WSN), consisting of sensors powered by nonrechargeable batteries, has a strictly limited lifetime. Energy harvesting (EH) from the environment is a promising solution to address the energy constraint problem in conventional WSNs, and to render these networks to self-sustainable networks with perpetual lifetimes. In EH-powered WSNs, where sensors are capable of harvesting and storing energy, power control is necessary to balance the rates of energy harvesting and energy consumption for data transmission. In addition, wireless communication channels change randomly in time due to fading. These together prompt the need for developing new power control strategies for an EH-enabled transmitter that can best exploit and adapt to the random energy arrivals and time-varying fading channels. We consider parallel structure EH-powered WSNs tasked with solving a binary distributed detection problem. Sensors process locally their observations, adapt their transmission according to the battery and fading channel states, and transmit their data symbols to the fusion center (FC) over orthogonal fading channels. We study adaptive transmission schemes that optimize detection performance metrics at the FC, subject to certain battery and transmit power constraints. In the first part, modeling the random energy arrival as a Poisson process, we propose a novel transmit power control strategy that is parameterized in terms of the channel gain quantization thresholds and the scale factors corresponding to the quantization intervals and we find the jointly optimal quantization thresholds and the scale factors such that detection metric at the FC is maximized. We have assumed that the battery operates at the steady-state and the energy arrival and channel models are independent and identically distributed across transmission blocks. In the second part, we assume the battery is not at the steady-state and both the channel and the energy arrival are modeled as homogeneous finite-state Markov chains. Therefore, the power control optimization problem at hand becomes a multistage stochastic optimization problem and can be solved via the Markov decision process (MDP) framework. This is the first work that develops MDP-based channel-dependent power control policy for distributed detection in EH-powered WSNs

Resource-Aware Design Of Wireless Control Systems

This work is motivated by modern monitoring and control infrastructures appearing in smart homes, urban environments, and industrial plants. These systems are characterized by multiple sensor and actuator devices at different physical locations, communicating wirelessly with each other. Desired monitoring and control performance requires efficient wireless communication, as the more information the sensors convey the more precise actuation becomes. However wireless communication is constrained by the inherent uncertainty of the wireless medium as well as resource limitations at the devices, e.g., limited power resources. The increased number of wireless devices in such environments further necessitates the management of the shared wireless spectrum with direct account of control performance. To address these challenges, the goal of this work is to provide control-aware and resource-aware communication policies. This is first examined in the fundamental problem of allocating transmit power resources for wireless closed loop control. Opportunistic online adaptation of power to plant and wireless channel conditions is shown to be essential in achieving the optimal tradeoff between control performance and power utilization. Optimal structural properties of channel access mechanisms are also considered for the problem of guaranteeing multiple control performance requirements over a shared wireless medium. This includes scheduling mechanisms implemented by central authorities, as well as decentralized mechanisms implemented independently by the wireless devices with emerging wireless interferences. Again the mechanisms exhibit an opportunistic adaptation to varying wireless channel conditions, especially designed to explore the tradeoffs between different communication links and meet control performance requirements. The structural characterization is augmented with tractable optimization algorithms to compute these channel access mechanisms. Finally, as control is naturally a dynamic task that requires a long term planning, appropriate dynamic algorithms adapting to the varying control system states are examined. Besides adapting dynamically, the proposed algorithms provide guarantees about long term control performance and resource utilization by construction

Bayesian Learning Strategies in Wireless Networks

This thesis collects the research works I performed as a Ph.D. candidate, where the common thread running through all the works is Bayesian reasoning with applications in wireless networks. The pivotal role in Bayesian reasoning is inference: reasoning about what we don’t know, given what we know. When we make inference about the nature of the world, then we learn new features about the environment within which the agent gains experience, as this is what allows us to benefit from the gathered information, thus adapting to new conditions. As we leverage the gathered information, our belief about the environment should change to reflect our improved knowledge. This thesis focuses on the probabilistic aspects of information processing with applications to the following topics: Machine learning based network analysis using millimeter-wave narrow-band energy traces; Bayesian forecasting and anomaly detection in vehicular monitoring networks; Online power management strategies for energy harvesting mobile networks; Beam training and data transmission optimization in millimeter-wave vehicular networks. In these research works, we deal with pattern recognition aspects in real-world data via supervised/unsupervised learning methods (classification, forecasting and anomaly detection, multi-step ahead prediction via kernel methods). Finally, the mathematical framework of Markov Decision Processes (MDPs), which also serves as the basis for reinforcement learning, is introduced, where Partially Observable MDPs use the notion of belief to make decisions about the state of the world in millimeter-wave vehicular networks. The goal of this thesis is to investigate the considerable potential of inference from insightful perspectives, detailing the mathematical framework and how Bayesian reasoning conveniently adapts to various research domains in wireless networks

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