Alessandro Biason

Joint Transmission and Energy Transfer Policies for Energy Harvesting Devices With Finite Batteries

One of the main concerns in traditional wireless sensor networks (WSNs) is energy efficiency. In this work, we analyze two techniques that can extend network lifetime. The first is ambient energy harvesting (EH), i.e., the capability of the devices to gather energy from the environment, whereas the second is wireless energy transfer (ET), that can be used to exchange energy among devices. We study the combination of these techniques, showing that they can be used jointly to improve the system performance. We consider a transmitter-receiver pair, showing how the ET improvement depends upon the statistics of the energy arrivals and the energy consumption of the devices. With the aim of maximizing a reward function, e.g., the average transmission rate, we find performance upper bounds with and without ET, define both online and offline optimization problems, and present results based on realistic energy arrivals in indoor and outdoor environments. We show that ET can significantly improve the system performance even when a sizable fraction of the transmitted energy is wasted and that, in some scenarios, the online approach can obtain close to optimal performance.

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an Internet of Things (IoT) device is often where most of the energy is consumed. For this reason, most research so far has focused on low-power circuit and energy-efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing, and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness, and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; and 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application.

Battery-Powered Devices in WPCNs

Wireless powered communication networks are becoming an effective solution for improved self-sustainability of mobile devices. In this context, a hybrid access point transfers energy to a group of nodes, which use the harvested energy to perform computation or transmission tasks. While the availability of the wireless energy transfer mechanism opens up new frontiers, an appropriate choice of the network parameters (e.g., transmission powers, transmission duration, and amount of transferred energy) is required in order to achieve high performance. In this paper, we study the throughput optimization problem in a system composed of an access point, which recharges the batteries of two devices at different distances. In the literature, the main focus so far has been on slot-oriented optimization, in which all the harvested energy is used in the same slot in which it is harvested. However, this approach is strongly suboptimal, because it does not exploit the possibility to store the energy and use it at a later time. Thus, instead of considering the slot-oriented case, we address the long-term maximization. This assumption greatly increases the optimization complexity, as it requires to consider, e.g., the channel state statistics and the batteries evolution. Our objective is to find the best scheduling scheme, both for the energy transferred by the access point and for the data sent by the two nodes. We discuss how to perform the maximization with optimal as well as approximate techniques and show that the slot-oriented policies proposed so far are strongly suboptimal in the long run.

On the effects of battery imperfections in an energy harvesting device

Energy Harvesting allows the devices in a Wireless Sensor Network to recharge their batteries through environmental energy sources. While in the literature the main focus is on devices with ideal batteries, in reality several inefficiencies have to be considered to correctly design the operating regimes of an Energy Harvesting Device (EHD). In this work we describe how the throughput optimization problem changes under real battery constraints in an EHD. In particular, we consider imperfect knowledge of the state of charge of the battery and storage inefficiencies, i.e., part of the harvested energy is wasted in the battery recharging process. We formulate the problem as a Markov Decision Process, basing our model on some realistic observations about transmission, consumption and harvesting power. We find the performance upper bound with a real battery and numerically discuss the novelty introduced by the real battery effects. We show that using the old policies obtained without considering the real battery effects is strongly suboptimal and may even result in zero throughput.

Transmission policies for an energy harvesting device with a data queue

Since wireless sensors may be deployed in hard-to-reach or remote areas, managing their energy availability has become an important task. In order to prolong the network lifetime, several techniques have been adopted, and Wireless Sensor Networks (WSNs) with Energy Harvesting (EH) capabilities have been recently studied and deployed. We consider the case of an Energy Harvesting Device (EHD) with a packet data queue, with the main goal of maximizing the long-term average transmission rate. At each time instant, the device has different energy and data queue levels and can gather energy from the environment and receive or generate packets that are stored in the queue. We assume that the energy expenditure is mainly due to data transmission. We initially suppose to have a small battery and we study a particular subset of all policies, called almost geometric. Then, we analyze a system where the data buffer is finite and large with respect to the battery, computing the Optimal Policy (OP) and introducing a simple Low-Complexity almost geometric Policy (LCP). Finally, we numerically show that LCP can be considered as a good lower bound for OP.

Low-complexity policies for Wireless Sensor Networks with two energy harvesting devices

Recently, Wireless Sensor Networks (WSNs) with energy harvesting capabilities are experiencing increasing interest with respect to traditional ones, due to their promise of extended lifetime. We consider the case of a pair of Energy Harvesting Devices (EHDs), with the main goal of maximizing the long-term aggregate average importance associated with the transmitted data. The devices, at each time instant, have data of different importance levels to be transmitted, as well as different battery energy levels. In order to avoid collisions, a Central Controller (CC) allows at most the transmission of a single EHD per time slot. Assuming a negligible processing cost in terms of energy, our objective is to identify low-complexity transmission policies, that achieve good performance with respect to the optimal one. We numerically show that two policies, namely the Balanced Policy (BP) and the Heuristic Constrained Energy Independent Policy (HCEIP), despite being independent of the battery energy levels, achieve near optimal performance in most cases of interest, and can be easily found with an adaptation to the ambient energy supply. Moreover, we derive analytically an approximation of the BP and show that this policy can be considered a good lower bound for the performance of the Optimal Policy.

Joint Optimization of Energy Efficiency and Data Compression in TDMA-Based Medium Access Control for the IoT

Energy efficiency is a key requirement for the Internet of Things, as many sensors are expected to be completely stand-alone and able to run for years without battery replacement. Data compression aims at saving some energy by reducing the volume of data sent over the network, but also affects the quality of the received information. In this work, we formulate an optimization problem to jointly design the source coding and transmission strategies for time-varying channels and sources, with the twofold goal of extending the network lifetime and granting low distortion levels. We propose a scalable offline optimal policy that allocates both energy and transmission parameters (i.e., times and powers) in a network with a dynamic Time Division Multiple Access (TDMA)-based access scheme.

Energy Harvesting communication system with SOC-dependent energy storage losses

The popularity of Energy Harvesting Devices (EHDs) has grown in the past few years, thanks to their capability of prolonging the network lifetime. In reality, EHDs are affected by several inefficiencies, e.g., energy leakage, battery degradation or storage losses. In this work we consider an energy harvesting transmitter with storage inefficiencies. In particular, we assume that when new energy has to be stored in the battery, part of this is wasted and the losses depend upon the current state of charge of the device. This is a practical realistic assumption, e.g., for a capacitor, that changes the structure of the optimal transmission policy. We analyze the throughput maximization problem with a dynamic programming approach and prove that, given the battery status and the channel gain, the optimal transmission policy is deterministic. We derive numerical results for the energy losses in a capacitor and show the presence of a loop effect that degrades the system performance if the optimal policy is not considered.

Long-term throughput optimization in WPCN with battery-powered devices

Wireless powered communication networks are becoming an effective solution for improving self sustainability of mobile devices. In this context, a hybrid access point transfers energy to a group of nodes, which use the harvested energy to perform computation or transmission tasks. While the availability of the wireless energy transfer mechanism opens up new frontiers, it also requires an appropriate choice of the network parameters (e.g., transmission powers, transmission duration, amount of transferred energy, etc.) in order to achieve high performance. In this work, we study the throughput optimization problem in a system composed of an access point which recharges the batteries of two devices at different distances. In the literature, the main focus so far has been on slot-oriented optimization, in which all the harvested energy is used in the same slot in which it is harvested. However, this approach may be strongly suboptimal because it does not exploit the possibility to store the energy and use it at a later time. Thus, instead of considering the slot-oriented case, we address the long-term maximization. This assumption greatly increases the optimization complexity, as it requires to consider, e.g., the channel state realizations, its statistics and the batteries time evolution. Our objective is to find the best scheduling scheme, both for the energy transferred by the access point and for the data sent by the two nodes. We discuss how to perform the optimization and show that the slot-oriented policies proposed so far are strongly sub-optimal in the long-term case. Our scenario can be considered as a first step toward the study of more complex and distributed schemes in wireless energy-transfer scenarios in the presence of battery-powered nodes.

On optimal policies in full-duplex wireless powered communication networks

The optimal resource allocation scheme in a full-duplex Wireless Powered Communication Network (WPCN) composed of one Access Point (AP) and two wireless devices is analyzed and derived. AP operates in a full-duplex mode and is able to broadcast wireless energy signals in downlink and receive information data in uplink simultaneously. On the other hand, each wireless device is assumed to be equipped with Radio-Frequency (RF) energy harvesting circuitry which gathers the energy sent by AP and stores it in a finite capacity battery. The harvested energy is then used for performing uplink data transmission tasks. In the literature, the main focus so far has been on slot-oriented optimization. In this context, all the harvested RF energy in a given slot is also consumed in the same slot. However, this approach leads to sub-optimal solutions because it does not take into account the Channel State Information (CSI) variations over future slots. Differently from most of the prior works, in this paper we focus on the long-term weighted throughput maximization problem. This approach significantly increases the complexity of the optimization problem since it requires to consider both CSI variations over future slots and the evolution of the batteries when deciding the optimal resource allocation. We formulate the problem using the Markov Decision Process (MDP) theory and show how to solve it. Our numerical results emphasize the superiority of our proposed full-duplex WPCN compared to the half-duplex WPCN and reveal interesting insights about the effects of perfect as well as imperfect self-interference cancellation techniques on the network performance.