Communicating Using an Energy Harvesting Transmitter: Optimum Policies Under Energy Storage Losses

In this paper, short-term throughput optimal power allocation policies are derived for an energy harvesting transmitter with energy storage losses. In particular, the energy harvesting transmitter is equipped with a battery that loses a fraction of its stored energy. Both single user, i.e. one transmitter-one receiver, and the broadcast channel, i.e., one transmitter-multiple receiver settings are considered, initially with an infinite capacity battery. It is shown that the optimal policies for these models are threshold policies. Specifically, storing energy when harvested power is above an upper threshold, retrieving energy when harvested power is below a lower threshold, and transmitting with the harvested energy in between is shown to maximize the weighted sum-rate. It is observed that the two thresholds are related through the storage efficiency of the battery, and are nondecreasing during the transmission. The results are then extended to the case with finite battery capacity, where it is shown that a similar double-threshold structure arises but the thresholds are no longer monotonic. A dynamic program that yields an optimal online power allocation is derived, and is shown to have a similar double-threshold structure. A simpler online policy is proposed and observed to perform close to the optimal policy.Comment: Submitted to IEEE Transactions on Wireless Communications, August 201

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Improving the Efficiency of Energy Harvesting Embedded System

In the past decade, mobile embedded systems, such as cell phones and tablets have infiltrated and dramatically transformed our life. The computation power, storage capacity and data communication speed of mobile devices have increases tremendously, and they have been used for more critical applications with intensive computation/communication. As a result, the battery lifetime becomes increasingly important and tends to be one of the key considerations for the consumers. Researches have been carried out to improve the efficiency of the lithium ion battery, which is a specific member in the more general Electrical Energy Storage (EES) family and is widely used in mobile systems, as well as the efficiency of other electrical energy storage systems such as supercapacitor, lead acid battery, and nickel–hydrogen battery etc. Previous studies show that hybrid electrical energy storage (HEES), which is a mixture of different EES technologies, gives the best performance. On the other hand, the Energy Harvesting (EH) technique has the potential to solve the problem once and for all by providing green and semi-permanent supply of energy to the embedded systems. However, the harvesting power must submit to the uncertainty of the environment and the variation of the weather. A stable and consistent power supply cannot always be guaranteed. The limited lifetime of the EES system and the unstableness of the EH system can be overcome by combining these two together to an energy harvesting embedded system and making them work cooperatively. In an energy harvesting embedded systems, if the harvested power is sufficient for the workload, extra power can be stored in the EES element; if the harvested power is short, the energy stored in the EES bank can be used to support the load demand. How much energy can be stored in the charging phase and how long the EES bank lifetime will be are affected by many factors including the efficiency of the energy harvesting module, the input/output voltage of the DC-DC converters, the status of the EES elements, and the characteristics of the workload. In this thesis, when the harvesting energy is abundant, our goal is to store as much surplus energy as possible in the EES bank under the variation of the harvesting power and the workload power. We investigate the impact of workload scheduling and Dynamic Voltage and Frequency Scaling (DVFS) of the embedded system on the energy efficiency of the EES bank in the charging phase. We propose a fast heuristic algorithm to minimize the energy overhead on the DC-DC converter while satisfying the timing constraints of the embedded workload and maximizing the energy stored in the HEES system. The proposed algorithm improves the efficiency of charging and discharging in an energy harvesting embedded system. On the other hand, when the harvesting rate is low, workload power consumption is supplied by the EES bank. In this case, we try to minimize the energy consumption on the embedded system to extend its EES bank life. In this thesis, we consider the scenario when workload has uncertainties and is running on a heterogeneous multi-core system. The workload variation is represented by the selection of conditional branches which activate or deactivate a set of instructions belonging to a task. We employ both task scheduling and DVFS techniques for energy optimization. Our scheduling algorithm considers the statistical information of the workload to minimize the mean power consumption of the application while satisfying a hard deadline constraint. The proposed DVFS algorithm has pseudo linear complexity and achieves comparable energy reduction as the solutions found by mathematical programming. Due to its capability of slack reclaiming, our DVFS technique is less sensitive to small change in hardware or workload and works more robustly than other techniques without slack reclaiming

Data Collection in Two-Tier IoT Networks with Radio Frequency (RF) Energy Harvesting Devices and Tags

The Internet of things (IoT) is expected to connect physical objects and end-users using technologies such as wireless sensor networks and radio frequency identification (RFID). In addition, it will employ a wireless multi-hop backhaul to transfer data collected by a myriad of devices to users or applications such as digital twins operating in a Metaverse. A critical issue is that the number of packets collected and transferred to the Internet is bounded by limited network resources such as bandwidth and energy. In this respect, IoT networks have adopted technologies such as time division multiple access (TDMA), signal interference cancellation (SIC) and multiple-input multiple-output (MIMO) in order to increase network capacity. Another fundamental issue is energy. To this end, researchers have exploited radio frequency (RF) energy-harvesting technologies to prolong the lifetime of energy constrained sensors and smart devices. Specifically, devices with RF energy harvesting capabilities can rely on ambient RF sources such as access points, television towers, and base stations. Further, an operator may deploy dedicated power beacons that serve as RF-energy sources. Apart from that, in order to reduce energy consumption, devices can adopt ambient backscattering communication technologies. Advantageously, backscattering allows devices to communicate using negligible amount of energy by modulating ambient RF signals. To address the aforementioned issues, this thesis first considers data collection in a two-tier MIMO ambient RF energy-harvesting network. The first tier consists of routers with MIMO capability and a set of source-destination pairs/flows. The second tier consists of energy harvesting devices that rely on RF transmissions from routers for energy supply. The problem is to determine a minimum-length TDMA link schedule that satisfies the traffic demand of source-destination pairs and energy demand of energy harvesting devices. It formulates the problem as a linear program (LP), and outlines a heuristic to construct transmission sets that are then used by the said LP. In addition, it outlines a new routing metric that considers the energy demand of energy harvesting devices to cope with routing requirements of IoT networks. The simulation results show that the proposed algorithm on average achieves 31.25% shorter schedules as compared to competing schemes. In addition, the said routing metric results in link schedules that are at most 24.75% longer than those computed by the LP