Finite Horizon Online Lazy Scheduling with Energy Harvesting Transmitters over Fading Channels

Lazy scheduling, i.e. setting transmit power and rate in response to data traffic as low as possible so as to satisfy delay constraints, is a known method for energy efficient transmission.This paper addresses an online lazy scheduling problem over finite time-slotted transmission window and introduces low-complexity heuristics which attain near-optimal performance.Particularly, this paper generalizes lazy scheduling problem for energy harvesting systems to deal with packet arrival, energy harvesting and time-varying channel processes simultaneously. The time-slotted formulation of the problem and depiction of its offline optimal solution provide explicit expressions allowing to derive good online policies and algorithms

Energy Sharing for Multiple Sensor Nodes with Finite Buffers

We consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. Sensor nodes periodically sense the random field and generate data, which is stored in the corresponding data queues. The EH source harnesses energy from ambient energy sources and the generated energy is stored in an energy buffer. Sensor nodes receive energy for data transmission from the EH source. The EH source has to efficiently share the stored energy among the nodes in order to minimize the long-run average delay in data transmission. We formulate the problem of energy sharing between the nodes in the framework of average cost infinite-horizon Markov decision processes (MDPs). We develop efficient energy sharing algorithms, namely Q-learning algorithm with exploration mechanisms based on the $\epsilon$-greedy method as well as upper confidence bound (UCB). We extend these algorithms by incorporating state and action space aggregation to tackle state-action space explosion in the MDP. We also develop a cross entropy based method that incorporates policy parameterization in order to find near optimal energy sharing policies. Through simulations, we show that our algorithms yield energy sharing policies that outperform the heuristic greedy method.Comment: 38 pages, 10 figure

Optimal offline broadcast scheduling with an energy harvesting transmitter

We consider an energy harvesting transmitter broadcasting data to two receivers. Energy and data arrivals are assumed to occur at arbitrary but known instants. The goal is to minimize the total transmission time of the packets arriving within a certain time window, using the energy that becomes available during this time. An achievable rate region with structural properties satisfied by the two-user AWGN BC capacity region is assumed. Structural properties of power and rate allocation in an optimal policy are established, as well as the uniqueness of the optimal policy under the condition that all the data of the “weaker ” user are available at the beginning. An iterative algorithm, DuOpt, based on block coordinate descent that achieves the same structural properties as the optimal is described. Investigating the ways to have the optimal schedule of two consecutive epochs in terms of energy efficiency and minimum transmission duration, it has been shown that DuOpt achieves best performance under the same special condition of uniqueness. Index Terms Packet scheduling, energy harvesting, AWGN broadcast channel, energy-efficient scheduling

Scheduling for Cooperative Energy Harvesting Sensor Networks

In cooperative communication networks, the source node transmits its data to the destination either directly or cooperatively with a cooperating node. When using energy harvesting technology, where nodes collect their energy from the environment, the energy availability at the nodes becomes unpredictable due to the stochastic nature of energy harvesting processes. As a result, when the source has a transmission, it cannot immediately transmit its data cooperatively with the cooperating node. It first needs to determine whether the cooperating node has sufficient energy to forward its transmission or not. Otherwise, its transmitted data may get lost. Therefore, when using energy harvesting, the challenge is for the source to schedule its transmissions whether directly or cooperatively, such that the fraction of its events (sensed data) that are successfully reported to the destination is maximized. Hence, in this dissertation, we address the problem of cooperating node scheduling in energy harvesting sensor networks. We consider the problem for the case of a single cooperating node and the case of multiple cooperating nodes, as well as the scenarios of one-way and two-way cooperative communications. We propose a simple scheduling scheme, called feedback scheme, which enables the source to optimally schedule its transmissions whether directly or cooperatively. We show that the feedback scheme maximizes the system performance, but does not require auxiliary parameter optimization as does the-state-of-the-art scheme, i.e., the threshold-based scheme. However, the feedback scheme has the problem of overhead caused by transmitting the energy status of the cooperating node to the source. To overcome this burden, we introduce a statistical model that enables the source to estimate the energy status of the cooperating node. Because cooperation may result in the cooperating node performing worse than the source, we address this problem through fairness in the performance between the nodes in the network. In addition, we address the problem of scheduling for throughput maximization in a wireless energy harvesting uplink. We propose centralized and distributed algorithms that find the optimal solution, and we address complexity issues. Our algorithms are shown to have a linear or quadratic complexity compared to the exponential complexity of the brute force approach. Compared with cooperative transmission, our approach maximizes the network throughput such that no node\u27s throughput is adversely affected