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

Trading Wireless Information and Power Transfer: Relay Selection to Minimize the Outage Probability

This paper studies the outage probability minimization problem for a multiple relay network with energy harvesting constraints. The relays are hybrid nodes used for simultaneous wireless information and power transfer from the source radio frequency (RF) signals. There is a trade-off associated with the amount of time a relay node is used for energy and information transfer. Large intervals of information transfer implies little time for energy harvesting from RF signals and thus, high probability of outage events. We propose relay selection schemes for a cooperative system with a fixed number of RF powered relays. We address both causal and non-causal channel state information cases at the relay--destination link and evaluate the trade-off associated with information/power transfer in the context of minimization of outage probability.Comment: IEEE GlobalSiP, 201

Collaborative beamforming schemes for wireless sensor networks with energy harvesting capabilities

In recent years, wireless sensor networks have attracted considerable attention in the research community. Their development, induced by technological advances in microelectronics, wireless networking and battery fabrication, is mainly motivated by a large number of possible applications such as environmental monitoring, industrial process control, goods tracking, healthcare applications, to name a few. Due to the unattended nature of wireless sensor networks, battery replacement can be either too costly or simply not feasible. In order to cope with this problem and prolong the network lifetime, energy efficient data transmission protocols have to be designed. Motivated by this ultimate goal, this PhD dissertation focuses on the design of collaborative beamforming schemes for wireless sensor networks with energy harvesting capabilities. On the one hand, by resorting to collaborative beamforming, sensors are able to convey a common message to a distant base station, in an energy efficient fashion. On the other, sensor nodes with energy harvesting capabilities promise virtually infinite network lifetime. Nevertheless, in order to realize collaborative beamforming, it is necessary that sensors align their transmitted signals so that they are coherently combined at the destination. Moreover, sensor nodes have to adapt their transmissions according to the amounts of harvested energy over time. First, this dissertation addresses the scenario where two sensor nodes (one of them capable of harvesting ambient energy) collaboratively transmit a common message to a distant base station. In this setting, we show that the optimal power allocation policy at the energy harvesting sensor can be computed independently (i.e., without the knowledge of the optimal policy at the battery operated one). Furthermore, we propose an iterative algorithm that allows us to compute the optimal policy at the battery operated sensor, as well. The insights gained by the aforementioned scenario allow us to generalize the analysis to a system with multiple energy harvesting sensors. In particular, we develop an iterative algorithm which sequentially optimizes the policies for all the sensors until some convergence criterion is satisfied. For the previous scenarios, this PhD dissertation evaluates the impact of total energy harvested, number of sensors and limited energy storage capacity on the system performance. Finally, we consider some practical schemes for carrier synchronization, required in order to implement collaborative beamforming in wireless sensor networks. To that end, we analyze two algorithms for decentralized phase synchronization: (i) the one bit of feedback algorithm previously proposed in the literature; and (ii) a decentralized phase synchronization algorithm that we propose. As for the former, we analyze the impact of additive noise on the beamforming gain and algorithm’s convergence properties, and, subsequently, we propose a variation that performs sidelobe control. As for the latter, the sensors are allowed to choose their respective training timeslots randomly, relieving the base station of the burden associated with centralized coordination. In this context, this PhD dissertation addresses the impact of number of timeslots and additive noise on the achieved received signal strength and throughputEn los últimos años, las redes de sensores inalámbricas han atraído considerable atención en la comunidad investigadora. Su desarrollo, impulsado por recientes avances tecnológicos en microelectrónica y radio comunicaciones, está motivado principalmente por un gran abanico de aplicaciones, tales como: Monitorización ambiental, control de procesos industriales, seguimiento de mercancías, telemedicina, entre otras. En las redes de sensores inalámbricas, es primordial el diseño de protocolos de transmisión energéticamente eficientes ya que no se contempla el reemplazo de baterías debido a su coste y/o complejidad. Motivados por esta problemática, esta tesis doctoral se centra en el diseño de esquemas de conformación de haz distribuidos para redes de sensores, en el que los nodos son capaces de almacenar energía del entorno, lo que en inglés se denomina energy harvesting. En primer lugar, esta tesis doctoral aborda el escenario en el que dos sensores (uno de ellos capaz de almacenar energía del ambiente) transmiten conjuntamente un mensaje a una estación base. En este contexto, se demuestra que la política de asignación de potencia óptima en el sensor con energy harvesting puede ser calculada de forma independiente (es decir, sin el conocimiento de la política óptima del otro sensor). A continuación, se propone un algoritmo iterativo que permite calcular la política óptima en el sensor que funciona con baterías. Este esquema es posteriormente generalizado para el caso de múltiples sensores. En particular, se desarrolla un algoritmo iterativo que optimiza las políticas de todos los sensores secuencialmente. Para los escenarios anteriormente mencionados, esta tesis evalúa el impacto de la energía total cosechada, número de sensores y la capacidad de la batería. Por último, se aborda el problema de sincronización de fase en los sensores con el fin de poder realizar la conformación de haz de forma distribuida. Para ello, se analizan dos algoritmos para la sincronización de fase descentralizados: (i) el algoritmo "one bit of feedback" previamente propuesto en la literatura, y (ii) un algoritmo de sincronización de fase descentralizado que se propone en esta tesis. En el primer caso, se analiza el impacto del ruido aditivo en la ganancia y la convergencia del algoritmo. Además, se propone una variación que realiza el control de lóbulos secundarios. En el segundo esquema, los sensores eligen intervalos de tiempo de forma aleatoria para transmitir y posteriormente reciben información de la estación base para ajustar sus osciladores. En este escenario, esta tesis doctoral aborda el impacto del número de intervalos de tiempo y el ruido aditivo sobre la ganancia de conformación

Joint Data Routing and Power Scheduling for Wireless Powered Communication Networks

In a wireless powered communication network (WPCN), an energy access point supplies the energy needs of the network nodes through radio frequency wave transmission, and the nodes store the received energy in their batteries for their future data transmission. In this paper, we propose an online stochastic policy that jointly controls energy transmission from the EAP to the nodes and data transfer among the nodes. For this purpose, we first introduce a novel perturbed Lyapunov function to address the limitations on the energy consumption of the nodes imposed by their batteries. Then, using Lyapunov optimization method, we propose a policy which is adaptive to any arbitrary channel statistics in the network. Finally, we provide theoretical analysis for the performance of the proposed policy and show that it stabilizes the network, and the average power consumption of the network under this policy is within a bounded gap of the minimum power level required for stabilizing the network