Panda: Neighbor Discovery on a Power Harvesting Budget

Object tracking applications are gaining popularity and will soon utilize Energy Harvesting (EH) low-power nodes that will consume power mostly for Neighbor Discovery (ND) (i.e., identifying nodes within communication range). Although ND protocols were developed for sensor networks, the challenges posed by emerging EH low-power transceivers were not addressed. Therefore, we design an ND protocol tailored for the characteristics of a representative EH prototype: the TI eZ430-RF2500-SEH. We present a generalized model of ND accounting for unique prototype characteristics (i.e., energy costs for transmission/reception, and transceiver state switching times/costs). Then, we present the Power Aware Neighbor Discovery Asynchronously (Panda) protocol in which nodes transition between the sleep, receive, and transmit states. We analyze \name and select its parameters to maximize the ND rate subject to a homogeneous power budget. We also present Panda-D, designed for non-homogeneous EH nodes. We perform extensive testbed evaluations using the prototypes and study various design tradeoffs. We demonstrate a small difference (less then 2%) between experimental and analytical results, thereby confirming the modeling assumptions. Moreover, we show that Panda improves the ND rate by up to 3x compared to related protocols. Finally, we show that Panda-D operates well under non-homogeneous power harvesting

Towards Optimal Power Splitting in Simultaneous Power and Information Transmission

This is the author accepted manuscript. the final version is available from IEEE via the DOI in this recordData availability: All code is available under requestSimultaneous wireless information and power transfer (SWIPT) offers novel designs that could enhance the sustainability and resilience of communication systems. Due to the very limited receiving power from radio frequency (RF) signals, optimal splitting strategies play an essential role for many SWIPT systems. This paper investigates optimal power splitting from the outage perspective by formulating the power, information and joint outage performance using a Markov chain, and studying the boundary conditions for achieving an energy-neutral state. Our results show the intrinsic trade-off between power and information outage and propose a novel polynomial method to obtain optimal power splitting. A number of experiments confirm the performance of this method.Royal SocietyRoyal Society of Edinburgh-NSFCHuawei ProjectEuropean Union FP

Scaling Configuration of Energy Harvesting Sensors with Reinforcement Learning

With the advent of the Internet of Things (IoT), an increasing number of energy harvesting methods are being used to supplement or supplant battery based sensors. Energy harvesting sensors need to be configured according to the application, hardware, and environmental conditions to maximize their usefulness. As of today, the configuration of sensors is either manual or heuristics based, requiring valuable domain expertise. Reinforcement learning (RL) is a promising approach to automate configuration and efficiently scale IoT deployments, but it is not yet adopted in practice. We propose solutions to bridge this gap: reduce the training phase of RL so that nodes are operational within a short time after deployment and reduce the computational requirements to scale to large deployments. We focus on configuration of the sampling rate of indoor solar panel based energy harvesting sensors. We created a simulator based on 3 months of data collected from 5 sensor nodes subject to different lighting conditions. Our simulation results show that RL can effectively learn energy availability patterns and configure the sampling rate of the sensor nodes to maximize the sensing data while ensuring that energy storage is not depleted. The nodes can be operational within the first day by using our methods. We show that it is possible to reduce the number of RL policies by using a single policy for nodes that share similar lighting conditions.Comment: 7 pages, 5 figure

Variable Data Collection Rate System for a Wildlife Behavior Monitor

Behavior monitors typically collect data, and consequently spend energy, at fixed intervals. For devices that utilize energy harvesting, a fixed data collection interval may result in inefficient battery usage due to variability in available solar radiation. Work was performed for a system capable of adjusting a data collection rate, proportional to changes in battery charge, such that data obtained was maximized without sacrificing battery energy sustainability. Energy consumption, of an actual behavior monitor, was modeled to aid in design and evaluation of a changeable data collection rate system. Model validation was performed by comparing simulated to empirical data for battery charge over time. Proportional Integral Derivative (PID) control was used that changed the rate at which data was collected such that error was minimized between battery State Of Charge (SOC) and a reference point. Gain scheduling was incorporated as a mechanism to resist change in data collection rate caused by fluctuation in available SOC. Gain parameters for a discrete, time domain, PID controller were tuned using a manual, trial and error method. Results of tuning showed improved performance with the absence of Integral control. The system was evaluated by performing simulations for change in available solar energy. Results showed that data collection adjusted to changes in available energy and as a consequence, SOC remained within +/-5% of a reference point

Enhancing the Performance of Energy Harvesting Sensor Networks for Environmental Monitoring Applications

Fast development in hardware miniaturization and massive production of sensors make them cost efficient and vastly available to be used in various applications in our daily life more specially in environment monitoring applications. However, energy consumption is still one of the barriers slowing down the development of several applications. Slow development in battery technology, makes energy harvesting (EH) as a prime candidate to eliminate the sensor’s energy barrier. EH sensors can be the solution to enabling future applications that would be extremely costly using conventional battery-powered sensors. In this paper, we analyze the performance improvement and evaluation of EH sensors in various situations. A network model is developed to allow us to examine different scenarios. We borrow a clustering concept, as a proven method to improve energy efficiency in conventional sensor network and brought it to EH sensor networks to study its effect on the performance of the network in different scenarios. Moreover, a dynamic and distributed transmission power management for sensors is proposed and evaluated in both networks, with and without clustering, to study the effect of power balancing on the network end-to-end performance. The simulation results indicate that, by using clustering and transmission power adjustment, the power consumption can be distributed in the network more efficiently, which result in improving the network performance in terms of a packet delivery ratio by 20%, 10% higher network lifetime by having more alive nodes and also achieving lower delay by reducing the hop-count