Optimal Sensor Collaboration for Parameter Tracking Using Energy Harvesting Sensors

In this paper, we design an optimal sensor collaboration strategy among neighboring nodes while tracking a time-varying parameter using wireless sensor networks in the presence of imperfect communication channels. The sensor network is assumed to be self-powered, where sensors are equipped with energy harvesters that replenish energy from the environment. In order to minimize the mean square estimation error of parameter tracking, we propose an online sensor collaboration policy subject to real-time energy harvesting constraints. The proposed energy allocation strategy is computationally light and only relies on the second-order statistics of the system parameters. For this, we first consider an offline non-convex optimization problem, which is solved exactly using semidefinite programming. Based on the offline solution, we design an online power allocation policy that requires minimal online computation and satisfies the dynamics of energy flow at each sensor. We prove that the proposed online policy is asymptotically equivalent to the optimal offline solution and show its convergence rate and robustness. We empirically show that the estimation performance of the proposed online scheme is better than that of the online scheme when channel state information about the dynamical system is available in the low SNR regime. Numerical results are conducted to demonstrate the effectiveness of our approach

Wireless Power Transfer and Data Collection in Wireless Sensor Networks

In a rechargeable wireless sensor network, the data packets are generated by sensor nodes at a specific data rate, and transmitted to a base station. Moreover, the base station transfers power to the nodes by using Wireless Power Transfer (WPT) to extend their battery life. However, inadequately scheduling WPT and data collection causes some of the nodes to drain their battery and have their data buffer overflow, while the other nodes waste their harvested energy, which is more than they need to transmit their packets. In this paper, we investigate a novel optimal scheduling strategy, called EHMDP, aiming to minimize data packet loss from a network of sensor nodes in terms of the nodes' energy consumption and data queue state information. The scheduling problem is first formulated by a centralized MDP model, assuming that the complete states of each node are well known by the base station. This presents the upper bound of the data that can be collected in a rechargeable wireless sensor network. Next, we relax the assumption of the availability of full state information so that the data transmission and WPT can be semi-decentralized. The simulation results show that, in terms of network throughput and packet loss rate, the proposed algorithm significantly improves the network performance.Comment: 30 pages, 8 figures, accepted to IEEE Transactions on Vehicular Technolog

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

Energy sustainable paradigms and methods for future mobile networks: A survey

In this survey, we discuss the role of energy in the design of future mobile networks and, in particular, we advocate and elaborate on the use of energy harvesting (EH) hardware as a means to decrease the environmental footprint of 5G technology. To take full advantage of the harvested (renewable) energy, while still meeting the quality of service required by dense 5G deployments, suitable management techniques are here reviewed, highlighting the open issues that are still to be solved to provide eco-friendly and cost-effective mobile architectures. Several solutions have recently been proposed to tackle capacity, coverage and efficiency problems, including: C-RAN, Software Defined Networking (SDN) and fog computing, among others. However, these are not explicitly tailored to increase the energy efficiency of networks featuring renewable energy sources, and have the following limitations: (i) their energy savings are in many cases still insufficient and (ii) they do not consider network elements possessing energy harvesting capabilities. In this paper, we systematically review existing energy sustainable paradigms and methods to address points (i) and (ii), discussing how these can be exploited to obtain highly efficient, energy self-sufficient and high capacity networks. Several open issues have emerged from our review, ranging from the need for accurate energy, transmission and consumption models, to the lack of accurate data traffic profiles, to the use of power transfer, energy cooperation and energy trading techniques. These challenges are here discussed along with some research directions to follow for achieving sustainable 5G systems.Comment: Accepted by Elsevier Computer Communications, 21 pages, 9 figure

Data Collection and Information Freshness in Energy Harvesting Networks

An Internet of Things (IoT) network consists of multiple devices with sensor(s), and one or more access points or gateways. These devices monitor and sample targets, such as valuable assets, before transmitting their samples to an access point or the cloud for storage or/and analysis. A critical issue is that devices have limited energy, which constrains their operational lifetime. To this end, researchers have proposed various solutions to extend the lifetime of devices. A popular solution involves optimizing the duty cycle of devices; equivalently, the ratio of their active and inactive/sleep time. Another solution is to employ energy harvesting technologies. Specifically, devices rely on one or more energy sources such as wind, solar or Radio Frequency (RF) signals to power their operations. Apart from energy, another fundamental problem is the limited spectrum shared by devices. This means they must take turns to transmit to a gateway. Equivalently, they need a transmission schedule that determines when they transmit their samples to a gateway. To this end, this thesis addresses three novel device/sensor selection problems. It first aims to determine the best devices to transmit in each time slot in an RF Energy-Harvesting Wireless Sensor Network (EH-WSN) in order to maximize throughput or sum-rate. Briefly, a Hybrid Access Point (HAP) is responsible for charging devices via downlink RF energy transfer. After that, the HAP selects a subset of devices to transmit their data. A key challenge is that the HAP has neither channel state information nor energy level information of device. In this respect, this thesis outlines two centralized algorithms that are based on cross-entropy optimization and Gibbs sampling. Next, this thesis considers information freshness when selecting devices, where the HAP aims to minimize the average Age of Information (AoI) of samples from devices. Specifically, the HAP must select devices to sample and transmit frequently. Further, it must select devices without channel state information. To this end, this thesis outlines a decentralized Q-learning algorithm that allows the HAP to select devices according to their AoI. Lastly, this thesis considers targets with time-varying states. As before, the aim is to determine the best set of devices to be active in each frame in order to monitor targets. However, the aim is to optimize a novel metric called the age of incorrect information. Further, devices cooperate with one another to monitor target(s). To choose the best set of devices and minimize the said metric, this thesis proposes two decentralized algorithms, i.e., a decentralized Q-learning algorithm and a novel state space free learning algorithm. Different from the decentralized Q-learning algorithm, the state space free learning algorithm does not require devices to store Q-tables, which record the expected reward of actions taken by devices

Efficient calculation of sensor utility and sensor removal in wireless sensor networks for adaptive signal estimation and beamforming

Wireless sensor networks are often deployed over a large area of interest and therefore the quality of the sensor signals may vary significantly across the different sensors. In this case, it is useful to have a measure for the importance or the so-called "utility" of each sensor, e.g., for sensor subset selection, resource allocation or topology selection. In this paper, we consider the efficient calculation of sensor utility measures for four different signal estimation or beamforming algorithms in an adaptive context. We use the definition of sensor utility as the increase in cost (e.g., mean-squared error) when the sensor is removed from the estimation procedure. Since each possible sensor removal corresponds to a new estimation problem (involving less sensors), calculating the sensor utilities would require a continuous updating of different signal estimators (where is the number of sensors), increasing computational complexity and memory usage by a factor. However, we derive formulas to efficiently calculate all sensor utilities with hardly any increase in memory usage and computational complexity compared to the signal estimation algorithm already in place. When applied in adaptive signal estimation algorithms, this allows for on-line tracking of all the sensor utilities at almost no additional cost. Furthermore, we derive efficient formulas for sensor removal, i.e., for updating the signal estimator coefficients when a sensor is removed, e.g., due to a failure in the wireless link or when its utility is too low. We provide a complexity evaluation of the derived formulas, and demonstrate the significant reduction in computational complexity compared to straightforward implementations

A survey of network lifetime maximization techniques in wireless sensor networks

Emerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, selfconfiguring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteri

Q-learning Channel Access Methods for Wireless Powered Internet of Things Networks

The Internet of Things (IoT) is becoming critical in our daily life. A key technology of interest in this thesis is Radio Frequency (RF) charging. The ability to charge devices wirelessly creates so called RF-energy harvesting IoT networks. In particular, there is a hybrid access point (HAP) that provides energy in an on-demand manner to RF-energy harvesting devices. These devices then collect data and transmit it to the HAP. In this respect, a key issue is ensuring devices have a high number of successful transmissions. There are a number of issues to consider when scheduling the transmissions of devices in the said network. First, the channel gain to/from devices varies over time. This means the efficiency to deliver energy to devices and to transmit the same amount of data is different over time. Second, during channel access, devices are not aware of the energy level of other devices nor whether they will transmit data. Third, devices have non-causal knowledge of their energy arrivals and channel gain information. Consequently, they do not know whether they should delay their transmissions in hope of better channel conditions or less contention in future time slots or doing so would result in energy overflow