Joint Transmission and Energy Transfer Policies for Energy Harvesting Devices with Finite Batteries

One of the main concerns in traditional Wireless Sensor Networks (WSNs) is energy efficiency. In this work, we analyze two techniques that can extend network lifetime. The first is Ambient \emph{Energy Harvesting} (EH), i.e., the capability of the devices to gather energy from the environment, whereas the second is Wireless \emph{Energy Transfer} (ET), that can be used to exchange energy among devices. We study the combination of these techniques, showing that they can be used jointly to improve the system performance. We consider a transmitter-receiver pair, showing how the ET improvement depends upon the statistics of the energy arrivals and the energy consumption of the devices. With the aim of maximizing a reward function, e.g., the average transmission rate, we find performance upper bounds with and without ET, define both online and offline optimization problems, and present results based on realistic energy arrivals in indoor and outdoor environments. We show that ET can significantly improve the system performance even when a sizable fraction of the transmitted energy is wasted and that, in some scenarios, the online approach can obtain close to optimal performance.Comment: 16 pages, 12 figure

UMaine Engineering

Promotional magazine for the University of Maine\u27s College of Engineering. This issue explores the energy generation and energy transmission-related engineering programs being conducted by faculty, staff, and students with the College of Engineering. Topics include the College of Engineering\u27s response to COVID-19, particle research in aquaculture, Evergreen AI, and harnessing off-shore wind power.https://digitalcommons.library.umaine.edu/umaine_today/1078/thumbnail.jp

Incentivizing Signal and Energy Cooperation in Wireless Networks

Abstract-We consider a two-hop wireless network where the source(s) in the network have the ability to wirelessly power the relay(s) who also have their own data to send to the destination. Considering the fact that each node in the network aims to maximize its own metric, we adopt a game theoretic approach that foresees offering relaying of the sources' data in exchange for energy provided to the relays, and simultaneously offering energy to the relays in exchange for their relaying services. We first study a Stackelberg competition with the single relay node as the leader, and investigate the impact of having multiple source nodes in the system. We next study the reciprocal Stackelberg game with the single source as the leader, and investigate the inter-relay competition with multiple relays. We find that in the Stackelberg games, the leader can improve its individual utility by influencing the follower's decision accordingly, even more so when there are multiple followers. We next formulate a noncooperative game between the source and the relay and show the existence of a unique Nash equilibrium by an appropriate pricing mechanism. The equilibrium maximizes the total utility of the network and allows the destination to choose how much data to receive from each node

Distributed Detection in Energy Harvesting Wireless Sensor Networks

A conventional wireless sensor networks (WSN), consisting of sensors powered by nonrechargeable batteries, has a strictly limited lifetime. Energy harvesting (EH) from the environment is a promising solution to address the energy constraint problem in conventional WSNs, and to render these networks to self-sustainable networks with perpetual lifetimes. In EH-powered WSNs, where sensors are capable of harvesting and storing energy, power control is necessary to balance the rates of energy harvesting and energy consumption for data transmission. In addition, wireless communication channels change randomly in time due to fading. These together prompt the need for developing new power control strategies for an EH-enabled transmitter that can best exploit and adapt to the random energy arrivals and time-varying fading channels. We consider parallel structure EH-powered WSNs tasked with solving a binary distributed detection problem. Sensors process locally their observations, adapt their transmission according to the battery and fading channel states, and transmit their data symbols to the fusion center (FC) over orthogonal fading channels. We study adaptive transmission schemes that optimize detection performance metrics at the FC, subject to certain battery and transmit power constraints. In the first part, modeling the random energy arrival as a Poisson process, we propose a novel transmit power control strategy that is parameterized in terms of the channel gain quantization thresholds and the scale factors corresponding to the quantization intervals and we find the jointly optimal quantization thresholds and the scale factors such that detection metric at the FC is maximized. We have assumed that the battery operates at the steady-state and the energy arrival and channel models are independent and identically distributed across transmission blocks. In the second part, we assume the battery is not at the steady-state and both the channel and the energy arrival are modeled as homogeneous finite-state Markov chains. Therefore, the power control optimization problem at hand becomes a multistage stochastic optimization problem and can be solved via the Markov decision process (MDP) framework. This is the first work that develops MDP-based channel-dependent power control policy for distributed detection in EH-powered WSNs

Rf Energy Harvesting For Wireless Communication Systems: Statistical Models For Battery Recharging Time

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2014Thesis (M.Sc.) -- İstanbul Technical University, Instıtute of Science and Technology, 2014Yeni bir enerji kaynağı olarak nitelenebilecek olan enerji hasatlama sistemleri, enerji kullanan her cihazın çevrede bulunan mevcut enerji kaynaklarını kullanarak, enerji bakımından kendi kendine yetmesi olarak açıklanabilir. Özellikle düşük güç harcayan cihazlarla kullanıldığında enerji hasatlama bütünleyici bir çözüm olarak ortaya çıkmaktadır. Elektromanyetik frekans spektrumunun bir bölümü olan radyofrekans (RF) işaretleri de, haberleşme sistemleri için enerji hasatlama yapılabilecek enerji kaynaklarından biridir. Bu tezde, RF işaretinden enerji hasatlama konusu ele alınmakta ve RF işaretinden enerji hasatlama sistemlerinde pil şarj zamanının istatistiki olarak nitelenmesi üzerine bir çalışma yapılmaktadır.Energy harvesting systems contribute to energy requirements of low-power devices as renewable energy sources. Radio frequency (RF) signal energy can be used as an energy source for energy harvesting systems. The RF signal energy available in the medium is received by the antenna of RF energy harvesting system, and converted to DC signal energy to power the electrical device. In this thesis, RF energy harvesting is emphasized for providing energy to wireless communication devices. Moreover, the distributions of battery recharging time are characterized for various wireless channel models.Yüksek LisansM.Sc

Stochastic Optimization of Energy Harvesting Wireless Communication Networks

Energy harvesting from environmental energy sources (e.g., sunlight) or from man-made sources (e.g., RF energy) has been a game-changing paradigm, which enabled the possibility of making the devices in the Internet of Things or wireless sensor networks operate autonomously and with high performance for years or even decades without human intervention. However, an energy harvesting system must be correctly designed to achieve such a goal and therefore the energy management problem has arisen and become a critical aspect to consider in modern wireless networks. In particular, in addition to the hardware (e.g., in terms of circuitry design) and application point of views (e.g., sensor deployment), also the communication protocol perspective must be explicitly taken into account; indeed, the use of the wireless communication interface may play a dominant role in the energy consumption of the devices, and thus must be correctly designed and optimized. This analysis represents the focus of this thesis. Energy harvesting for wireless system has been a very active research topic in the past decade. However, there are still many aspects that have been neglected or not completely analyzed in the literature so far. Our goal is to address and solve some of these new problems using a common stochastic optimization setup based on dynamic programming. In particular, we formulate both the centralized and decentralized optimization problems in an energy harvesting network with multiple devices, and discuss the interrelations between these two schemes; we study the combination of environmental energy harvesting and wireless energy transfer to improve the transmission rate of the network and achieve a balanced situation; we investigate the long-term optimization problem in wireless powered communication networks, in which the receiver supplies wireless energy to the terminal nodes; we deal with the energy storage inefficiencies of the energy harvesting devices, and show that traditional policies may be strongly suboptimal in this context; finally, we investigate how it is possible to increase secrecy in a wireless link where a third malicious party eavesdrops the information transmitted by an energy harvesting node

Energy Harvesting Networked Nodes: Measurements, Algorithms, and Prototyping

Recent advances in ultra-low-power wireless communications and in energy harvesting will soon enable energetically self-sustainable wireless devices. Networks of such devices will serve as building blocks for different Internet of Things (IoT) applications, such as searching for an object on a network of objects and continuous monitoring of object configurations. Yet, numerous challenges need to be addressed for the IoT vision to be fully realized. This thesis considers several challenges related to ultra-low-power energy harvesting networked nodes: energy source characterization, algorithm design, and node design and prototyping. Additionally, the thesis contributes to engineering education, specifically to project-based learning. We summarize our contributions to light and kinetic (motion) energy characterization for energy harvesting nodes. To characterize light energy, we conducted a first-of-its kind 16 month-long indoor light energy measurements campaign. To characterize energy of motion, we collected over 200 hours of human and object motion traces. We also analyzed traces previously collected in a study with over 40 participants. We summarize our insights, including light and motion energy budgets, variability, and influencing factors. These insights are useful for designing energy harvesting nodes and energy harvesting adaptive algorithms. We shared with the community our light energy traces, which can be used as energy inputs to system and algorithm simulators and emulators. We also discuss resource allocation problems we considered for energy harvesting nodes. Inspired by the needs of tracking and monitoring IoT applications, we formulated and studied resource allocation problems aimed at allocating the nodes' time-varying resources in a uniform way with respect to time. We mainly considered deterministic energy profile and stochastic environmental energy models, and focused on single node and link scenarios. We formulated optimization problems using utility maximization and lexicographic maximization frameworks, and introduced algorithms for solving the formulated problems. For several settings, we provided low-complexity solution algorithms. We also examined many simple policies. We demonstrated, analytically and via simulations, that in many settings simple policies perform well. We also summarize our design and prototyping efforts for a new class of ultra-low-power nodes - Energy Harvesting Active Networked Tags (EnHANTs). Future EnHANTs will be wireless nodes that can be attached to commonplace objects (books, furniture, clothing). We describe the EnHANTs prototypes and the EnHANTs testbed that we developed, in collaboration with other research groups, over the last 4 years in 6 integration phases. The prototypes harvest energy of the indoor light, communicate with each other via ultra-low-power transceivers, form small multihop networks, and adapt their communications and networking to their energy harvesting states. The EnHANTs testbed can expose the prototypes to light conditions based on real-world light energy traces. Using the testbed and our light energy traces, we evaluated some of our energy harvesting adaptive policies. Our insights into node design and performance evaluations may apply beyond EnHANTs to networks of various energy harvesting nodes. Finally, we present our contributions to engineering education. Over the last 4 years, we engaged high school, undergraduate, and M.S. students in more than 100 research projects within the EnHANTs project. We summarize our approaches to facilitating student learning, and discuss the results of evaluation surveys that demonstrate the effectiveness of our approaches

Urubu: energy scavenging in wireless sensor networks

For the past years wireless sensor networks (WSNs) have been coined as one of the most promising technologies for supporting a wide range of applications. However, outside the research community, few are the people who know what they are and what they can offer. Even fewer are the ones that have seen these networks used in real world applications. The main obstacle for the proliferation of these networks is energy, or the lack of it. Even though renewable energy sources are always present in the networks environment, designing devices that can efficiently scavenge that energy in order to sustain the operation of these networks is still an open challenge. Energy scavenging, along with energy efficiency and energy conservation, are the current available means to sustain the operation of these networks, and can all be framed within the broader concept of “Energetic Sustainability”. A comprehensive study of the several issues related to the energetic sustainability of WSNs is presented in this thesis, with a special focus in today’s applicable energy harvesting techniques and devices, and in the energy consumption of commercially available WSN hardware platforms. This work allows the understanding of the different energy concepts involving WSNs and the evaluation of the presented energy harvesting techniques for sustaining wireless sensor nodes. This survey is supported by a novel experimental analysis of the energy consumption of the most widespread commercially available WSN hardware platforms.Há já alguns anos que as redes de sensores sem fios (do Inglês Wireless Sensor Networks - WSNs) têm sido apontadas como uma das mais promissoras tecnologias de suporte a uma vasta gama de aplicações. No entanto, fora da comunidade científica, poucas são as pessoas que sabem o que elas são e o que têm para oferecer. Ainda menos são aquelas que já viram a sua utilização em aplicações do dia-a-dia. O principal obstáculo para a proliferação destas redes é a energia, ou a falta dela. Apesar da existência de fontes de energia renováveis no local de operação destas redes, continua a ser um desafio construir dispositivos capazes de aproveitar eficientemente essa energia para suportar a operação permanente das mesmas. A colheita de energia juntamente com a eficiência energética e a conservação de energia, são os meios disponíveis actualmente que permitem a operação permanente destas redes e podem ser todos englobados no conceito mais amplo de “Sustentabilidade Energética”. Esta tese apresenta um estudo extensivo das várias questões relacionadas com a sustentabilidade energética das redes de sensores sem fios, com especial foco nas tecnologias e dispositivos explorados actualmente na colheita de energia e no consumo energético de algumas plataformas comercias de redes de sensores sem fios. Este trabalho permite compreender os diferentes conceitos energéticos relacionados com as redes de sensores sem fios e avaliar a capacidade das tecnologias apresentadas em suportar a operação permanente das redes sem fios. Este estudo é suportado por uma inovadora análise experimental do consumo energético de algumas das mais difundidas plataformas comerciais de redes de sensores sem fios

Efficient Spectrum Sensing and Sharing Techniques for Dynamic Wideband Spectrum Access

Besides enabling an enhanced mobile broadband access, fifth-generation (5G) wireless mobile networks are envisioned to support the connectivity of massive, heterogeneous Internet of Things (IoT) devices. Connecting these devices through 5G systems and providing them with their needed data rates require huge amounts of spectrum and power resources, thus calling for the development and design of innovative, dynamic resource identification, access and sharing methods that make effective use of these limited resources. This thesis focuses specifically on wideband spectrum sensing, and presents innovative techniques that enable efficient identification and recovery of unused spectrum opportunities in wideband dynamic spectrum access. Recent research efforts have focused on leveraging compressive sampling (CS) theory to enable wideband spectrum sensing recovery at sub-Nyquist rates. However, these approaches suffer from the following shortcomings. First, they consider homogenous wideband spectrum, where all bands are assumed to have similar primary users (PU)s traffic characteristics whereas in practice, the wideband spectrum occupancy is heterogeneous. Second, the number of measurements that receiver hardware designs are able to perform is practically way smaller than the number of measurements required by the CS-based sensing approaches. Third, the number of measurements required by the CS-based sensing approaches depends on the number of occupied bands (i.e., sparsity level), which is often unknown in advance and changes over time. Forth, current wideband spectrum databases suffer from scalability issues in that they incur lots of sensing overhead. This thesis proposes a set of new, complementary techniques that overcome these aforementioned challenges. More specifically, in this thesis, 1. We design efficient spectrum occupancy information recovery techniques for heterogeneous wideband spectrum access. Our proposed techniques exploit the block-like structure of spectrum occupancy behavior observed in wideband spectrum access networks to enable the development of compressed spectrum sensing algorithms. Our proposed spectrum sensing algorithms achieve more stable spectrum information recovery than that achieved by existing approaches. 2. We develop distributed CS-based spectrum sensing techniques for cooperative wideband spectrum access that require lesser measurements while overcoming time-variability of spectrum occupancy and addressing hidden terminal challenges. Also, we propose non-uniform sensing matrices design that exploits the heterogeneity in the wideband spectrum access to further improve the spectrum sensing recovery accuracy. 3. We develop scalable spectrum occupancy information recovery techniques for database-driven wideband spectrum access networks. The novelty of our developed techniques lies in combining the merit of compressive sampling theory with that of low-rank matrix theory to enable scalable and accurate wideband spectrum occupancy recovery at low sensing overhead. 4. We propose joint data and energy transfer optimization frameworks for powering mobile cellular devices through RF energy harvesting. Our proposed framework accounts for both the consumed power at the base station and the battery power available at the end users to ensure that end users achieve their required data rates with as little battery power consumption as possible. We also analytically derive closed-form expressions of the optimal power allocations required for meeting the data rate requirements of the downlink and uplink communications between the base station and its mobile users