Towards Self-Control of Service Rate for Battery Management in Energy Harvesting Devices

We consider the operation of an energy harvesting wireless device (sensor node) powered by a rechargeable battery, taking non-idealities into account. In particular, we consider sudden decrease and increase of the battery level (leakage and charge recovery consequently) due to the inner diffusion processes in the battery. These processes are affecting the stability of the device operation. In particular, leakage accelerates the depletion of the battery, which results in inactive periods of the device and, thus, potential data loss. In this paper, we propose a simplified self-control management of a battery expressed by restrictions, which could be used for an efficient operational strategy of the device. To achieve this, we rely on the double-queue model which includes the imperfections of the battery operation and bi-dimensional battery value. This includes both apparent, i.e., available at the electrodes and true energy levels of a battery. These levels can be significantly different because of deep discharge events and can equalize thanks to charge recovery effect. We performed some simulation and observed that we can diminish the models variable number to predict possible unwanted events such as apparent discharge events (when the areas near electrodes are depleted while other areas of the battery still contain some energy) and data losses. This observation helps to achieve sufficiently effective self-control management by knowing and managing just few parameters, and therefore offers valuable directions for the development of autonomic and self-sustainable operation

Optimal Transmission Policies for Two-User Energy Harvesting Device Networks With Limited State-of-Charge Knowledge

This paper considers a wireless network composed of a pair of sensors powered by energy harvesting devices (EHDs), which transmit data to a receiver over a shared wireless channel. At any given time, based on the energy levels of the two rechargeable batteries of the sensors, a central controller (CC) decides on the amount of energy to be drawn from the two batteries and used for transmission. The problem considered is the maximization of the long-term average reward associated with data transmission, by optimizing the transmission strategy of the two nodes, in the case of a collision channel model and both i.i.d. and correlated energy arrivals. In addition, contrary to the traditional assumption that the amount of energy available to the sensors can be easily estimated, we derive the optimal policy in the cases where the state of charge (SOC) may not be perfectly known by the central controller, analyzing the performance degradation caused by this imperfect knowledge of the SOC. For this second scenario, supposing that the CC is only aware that each SOC is \u201cLOW\u201d or \u201cHIGH,\u201d we show that the impact of imperfect knowledge decreases with the two battery capacities and is negligible in most cases of practical interest

Stochastic Optimization and Machine Learning Modeling for Wireless Networking

In the last years, the telecommunications industry has seen an increasing interest in the development of advanced solutions that enable communicating nodes to exchange large amounts of data. Indeed, well-known applications such as VoIP, audio streaming, video on demand, real-time surveillance systems, safety vehicular requirements, and remote computing have increased the demand for the efficient generation, utilization, management and communication of larger and larger data quantities. New transmission technologies have been developed to permit more efficient and faster data exchanges, including multiple input multiple output architectures or software defined networking: as an example, the next generation of mobile communication, known as 5G, is expected to provide data rates of tens of megabits per second for tens of thousands of users and only 1 ms latency. In order to achieve such demanding performance, these systems need to effectively model the considerable level of uncertainty related to fading transmission channels, interference, or the presence of noise in the data. In this thesis, we will present how different approaches can be adopted to model these kinds of scenarios, focusing on wireless networking applications. In particular, the first part of this work will show how stochastic optimization models can be exploited to design energy management policies for wireless sensor networks. Traditionally, transmission policies are designed to reduce the total amount of energy drawn from the batteries of the devices; here, we consider energy harvesting wireless sensor networks, in which each device is able to scavenge energy from the environment and charge its battery with it. In this case, the goal of the optimal transmission policies is to efficiently manage the energy harvested from the environment, avoiding both energy outage (i.e., no residual energy in a battery) and energy overflow (i.e., the impossibility to store scavenged energy when the battery is already full). In the second part of this work, we will explore the adoption of machine learning techniques to tackle a number of common wireless networking problems. These algorithms are able to learn from and make predictions on data, avoiding the need to follow limited static program instructions: models are built from sample inputs, thus allowing for data-driven predictions and decisions. In particular, we will first design an on-the-fly prediction algorithm for the expected time of arrival related to WiFi transmissions. This predictor only exploits those network parameters available at each receiving node and does not require additional knowledge from the transmitter, hence it can be deployed without modifying existing standard transmission protocols. Secondly, we will investigate the usage of particular neural network instances known as autoencoders for the compression of biosignals, such as electrocardiography and photo plethysmographic sequences. A lightweight lossy compressor will be designed, able to be deployed in wearable battery-equipped devices with limited computational power. Thirdly, we will propose a predictor for the long-term channel gain in a wireless network. Differently from other works in the literature, such predictor will only exploit past channel samples, without resorting to additional information such as GPS data. An accurate estimation of this gain would enable to, e.g., efficiently allocate resources and foretell future handover procedures. Finally, although not strictly related to wireless networking scenarios, we will show how deep learning techniques can be applied to the field of autonomous driving. This final section will deal with state-of-the-art machine learning solutions, proving how these techniques are able to considerably overcome the performance given by traditional approaches

Stochastic Optimization of Energy Harvesting Wireless Communication Networks

Energy harvesting from environmental energy sources (e.g., sunlight) or from man-made sources (e.g., RF energy) has been a game-changing paradigm, which enabled the possibility of making the devices in the Internet of Things or wireless sensor networks operate autonomously and with high performance for years or even decades without human intervention. However, an energy harvesting system must be correctly designed to achieve such a goal and therefore the energy management problem has arisen and become a critical aspect to consider in modern wireless networks. In particular, in addition to the hardware (e.g., in terms of circuitry design) and application point of views (e.g., sensor deployment), also the communication protocol perspective must be explicitly taken into account; indeed, the use of the wireless communication interface may play a dominant role in the energy consumption of the devices, and thus must be correctly designed and optimized. This analysis represents the focus of this thesis. Energy harvesting for wireless system has been a very active research topic in the past decade. However, there are still many aspects that have been neglected or not completely analyzed in the literature so far. Our goal is to address and solve some of these new problems using a common stochastic optimization setup based on dynamic programming. In particular, we formulate both the centralized and decentralized optimization problems in an energy harvesting network with multiple devices, and discuss the interrelations between these two schemes; we study the combination of environmental energy harvesting and wireless energy transfer to improve the transmission rate of the network and achieve a balanced situation; we investigate the long-term optimization problem in wireless powered communication networks, in which the receiver supplies wireless energy to the terminal nodes; we deal with the energy storage inefficiencies of the energy harvesting devices, and show that traditional policies may be strongly suboptimal in this context; finally, we investigate how it is possible to increase secrecy in a wireless link where a third malicious party eavesdrops the information transmitted by an energy harvesting node