Integrated Data and Energy Communication Network: A Comprehensive Survey

OAPA In order to satisfy the power thirsty of communication devices in the imminent 5G era, wireless charging techniques have attracted much attention both from the academic and industrial communities. Although the inductive coupling and magnetic resonance based charging techniques are indeed capable of supplying energy in a wireless manner, they tend to restrict the freedom of movement. By contrast, RF signals are capable of supplying energy over distances, which are gradually inclining closer to our ultimate goal – charging anytime and anywhere. Furthermore, transmitters capable of emitting RF signals have been widely deployed, such as TV towers, cellular base stations and Wi-Fi access points. This communication infrastructure may indeed be employed also for wireless energy transfer (WET). Therefore, no extra investment in dedicated WET infrastructure is required. However, allowing RF signal based WET may impair the wireless information transfer (WIT) operating in the same spectrum. Hence, it is crucial to coordinate and balance WET and WIT for simultaneous wireless information and power transfer (SWIPT), which evolves to Integrated Data and Energy communication Networks (IDENs). To this end, a ubiquitous IDEN architecture is introduced by summarising its natural heterogeneity and by synthesising a diverse range of integrated WET and WIT scenarios. Then the inherent relationship between WET and WIT is revealed from an information theoretical perspective, which is followed by the critical appraisal of the hardware enabling techniques extracting energy from RF signals. Furthermore, the transceiver design, resource allocation and user scheduling as well as networking aspects are elaborated on. In a nutshell, this treatise can be used as a handbook for researchers and engineers, who are interested in enriching their knowledge base of IDENs and in putting this vision into practice

Optimal Sensing and Transmission in Energy Harvesting Sensor Networks

Sensor networks equipped with energy harvesting (EH) devices have attracted great attentions recently. Compared with conventional sensor networks powered by batteries, the energy harvesting abilities of the sensor nodes make sustainable and environment-friendly sensor networks possible. However, the random, scarce and non-uniform energy supply features also necessitate a completely different approach to energy management. A typical EH wireless sensor node consists of an EH module that converts ambient energy to electrical energy, which is stored in a rechargeable battery, and will be used to power the sensing and transmission operations of the sensor. Therefore, both sensing and transmission are subject to the stochastic energy constraint imposed by the EH process. In this dissertation, we investigate optimal sensing and transmission policies for EH sensor networks under such constraints. For EH sensing, our objective is to understand how the temporal and spatial variabilities of the EH processes would affect the sensing performance of the network, and how sensor nodes should coordinate their data collection procedures with each other to cope with the random and non-uniform energy supply and provide reliable sensing performance with analytically provable guarantees. Specifically, we investigate optimal sensing policies for a single sensor node with infinite and finite battery sizes in Chapter 2, status updating/transmission strategy of an EH Source in Chapter 3, and a collaborative sensing policy for a multi-node EH sensor network in Chapter 4. For EH communication, our objective is to evaluate the impacts of stochastic variability of the EH process and practical battery usage constraint on the EH systems, and develop optimal transmission policies by taking such impacts into consideration. Specifically, we consider throughput optimization in an EH system under battery usage constraint in Chapter 5

Resource Allocation for Energy Harvesting Communications

With the rapid development of energy harvesting technologies, a new paradigm of wireless communications that employs energy harvesting transmitters has become a reality. The renewable energy source enables the flexible deployment of the transmitters and prolongs their lifetimes. To make the best use of the harvested energy, many challenging research issues arise from the new paradigm of communications. In particular, optimal resource (energy, bandwidth, etc.) allocation is key to the design of an efficient wireless system powered by renewable energy sources. In this thesis, we focus on several resource allocation problems for energy harvesting communications, including the energy allocation for a single energy harvesting transmitter, and the joint energy and spectral resource allocation for energy harvesting networks. More specifically, the resource allocation problems discussed in this thesis are summarized as follows. We solve the problem of designing an affordable optimal energy allocation strategy for the system of energy harvesting active networked tags (EnHANTs), that is adapted to the identification request and the energy harvesting dynamic. We formulate a Markov decision process (MDP) problem to optimize the overall system performance which takes into consideration of both the system activity-time and the communication reliability. To solve the problem, both a static exhaustive search method and a modified policy iteration algorithm are employed to obtain the optimal energy allocation policy. We develop an energy allocation algorithm to maximize the achievable rate for an access-controlled energy harvesting transmitter based on causal observations of the channel fading states. We formulate the stochastic optimization problem as a Markov decision process (MDP) with continuous states and define an approximate value function based on a piecewise linear fit in terms of the battery state. We show that with the approximate value function, the update in each iteration consists of a group of convex problems with a continuous parameter and we derive the optimal solution to these convex problems in closed-form. Specifically, the computational complexity of the proposed algorithm is significantly lower than that of the standard discrete MDP method. We propose an efficient iterative algorithm to obtain the optimal energy-bandwidth allocation for multiple flat-fading point-to-point channels, maximizing the weighted sum-rate given the predictions of the energy and channel state. For the special case that each transmitter only communicates with one receiver and the objective is to maximize the total throughput, we develop efficient algorithms for optimally solving the subproblems involved in the iterative algorithm. Moreover, a heuristic algorithm is also proposed for energy-bandwidth allocation based on the causal energy and channel observations. We consider the energy-bandwidth allocation problem in multiple orthogonal and non-orthogonal flat-fading broadcast channels to maximize the weighted sum-rate given the predictions of energy and channel states. To efficiently obtain the optimal allocation, we extend the iterative algorithm originally proposed for multiple flat-fading point-to-point channels and further develop the optimal algorithms to solve the corresponding subproblems. For the orthogonal broadcast channel, the proportionally-fair (PF) throughput maximization problem is formulated and we derive the equivalence conditions such that the optimal solution can be obtained by solving a weighted throughput maximization problem. The algorithm to obtain the proper weights is also proposed. We consider the energy-subchannel allocation problem for energy harvesting networks in frequency-selective fading channels. We first assume that the harvested energy and subchannel gains can be predicted and propose an algorithm to efficiently obtain the energy-subchannel allocations for all links over the scheduling period based on controlled water-filling. The proposed algorithm is shown to be asymptotically optimal when the bandwidth of the subchannel goes to zero. A causal algorithm is also proposed based on the Q-learning technique that makes use of the statistics of the energy harvesting and channel fading processes

A Sub-nW 2.4 GHz Transmitter for Low Data-Rate Sensing Applications

This paper presents the design of a narrowband transmitter and antenna system that achieves an average power consumption of 78 pW when operating at a duty-cycled data rate of 1 bps. Fabricated in a 0.18 μm CMOS process, the transmitter employs a direct-RF power oscillator topology where a loop antenna acts as a both a radiative and resonant element. The low-complexity single-stage architecture, in combination with aggressive power gating techniques and sizing optimizations, limited the standby power of the transmitter to only 39.7 pW at 0.8 V. Supporting both OOK and FSK modulations at 2.4 GHz, the transmitter consumed as low as 38 pJ/bit at an active-mode data rate of 5 Mbps. The loop antenna and integrated diodes were also used as part of a wireless power transfer receiver in order to kick-start the system power supply prior to energy harvesting operation.Semiconductor Research Corporation. Interconnect Focus CenterSemiconductor Research Corporation. C2S2 Focus CenterNational Institutes of Health (U.S.) (Grant K08 DC010419)National Institutes of Health (U.S.) (Grant T32 DC00038)Bertarelli Foundatio

Survey of Energy Harvesting Technologies for Wireless Sensor Networks

Energy harvesting (EH) technologies could lead to self-sustaining wireless sensor networks (WSNs) which are set to be a key technology in Industry 4.0. There are numerous methods for small-scale EH but these methods differ greatly in their environmental applicability, energy conversion characteristics, and physical form which makes choosing a suitable EH method for a particular WSN application challenging due to the specific application-dependency. Furthermore, the choice of EH technology is intrinsically linked to non-trivial decisions on energy storage technologies and combinatorial architectures for a given WSN application. In this paper we survey the current state of EH technology for small-scale WSNs in terms of EH methods, energy storage technologies, and EH system architectures for combining methods and storage including multi-source and multi-storage architectures, as well as highlighting a number of other optimisation considerations. This work is intended to provide an introduction to EH technologies in terms of their general working principle, application potential, and other implementation considerations with the aim of accelerating the development of sustainable WSN applications in industry

Dual-battery empowered green cellular networks

With awareness of the potential harmful effects to the environment and climate change, on-grid brown energy consumption of information and communications technology (ICT) has drawn much attention. Cellular base stations (BSs) are among the major energy guzzlers in ICT, and their contributions to the global carbon emissions increase sustainedly. It is essential to leverage green energy to power BSs to reduce their on-grid brown energy consumption. However, in order to furthest save on-grid brown energy and decrease the on-grid brown energy electricity expenses, most existing green energy related works only pursue to maximize the green energy utilization while compromising the services received by the mobile users. In reality, dissatisfaction of services may eventually lead to loss of market shares and profits of the network providers. In this research, a dual-battery enabled profit driven user association scheme is introduced to jointly consider the traffic delivery latency and green energy utilization to maximize the profits for the network providers in heterogeneous cellular networks. Since this profit driven user association optimization problem is NP-hard, some heuristics are presented to solve the problem with low computational complexity. Finally, the performance of the proposed algorithm is validated through extensive simulations. In addition, the Internet of Things (IoT) heralds a vision of future Internet where all physical things/devices are connected via a network to promote a heightened level of awareness about our world and dramatically improve our daily lives. Nonetheless, most wireless technologies utilizing unlicensed bands cannot provision ubiquitous and quality IoT services. In contrast, cellular networks support large-scale, quality of service guaranteed, and secured communications. However, tremendous proximal communications via local BSs will lead to severe traffic congestion and huge energy consumption in conventional cellular networks. Device-to-device (D2D) communications can potentially offload traffic from and reduce energy consumption of BSs. In order to realize the vision of a truly global IoT, a novel architecture, i.e., overlay-based green relay assisted D2D communications with dual batteries in heterogeneous cellular networks, is introduced. By optimally allocating the network resource, the introduced resource allocation method provisions the IoT services and minimizes the overall energy consumption of the pico relay BSs. By balancing the residual green energy among the pico relay BSs, the green energy utilization is maximized; this furthest saves the on-grid energy. Finally, the performance of the proposed architecture is validated through extensive simulations. Furthermore, the mobile devices serve the important roles in cellular networks and IoT. With the ongoing worldwide development of IoT, an unprecedented number of edge devices imperatively consume a substantial amount of energy. The overall IoT mobile edge devices have been predicted to be the leading energy guzzler in ICT by 2020. Therefore, a three-step green IoT architecture is proposed, i.e., ambient energy harvesting, green energy wireless transfer and green energy balancing, in this research. The latter step reinforces the former one to ensure the availability of green energy. The basic design principles for these three steps are laid out and discussed. In summary, based on the dual-battery architecture, this dissertation research proposes solutions for the three aspects, i.e., green cellular BSs, green D2D communications and green devices, to hopefully and eventually actualize green cellular access networks, as part of the ongoing efforts in greening our society and environment

Ambient RF energy harvesting and efficient DC-load inductive power transfer

This thesis analyses in detail the technology required for wireless power transfer via radio frequency (RF) ambient energy harvesting and an inductive power transfer system (IPT). Radio frequency harvesting circuits have been demonstrated for more than fifty years, but only a few have been able to harvest energy from freely available ambient (i.e. non-dedicated) RF sources. To explore the potential for ambient RF energy harvesting, a city-wide RF spectral survey was undertaken in London. Using the results from this survey, various harvesters were designed to cover four frequency bands from the largest RF contributors within the ultra-high frequency (0.3 to 3 GHz) part of the frequency spectrum. Prototypes were designed, fabricated and tested for each band and proved that approximately half of the London Underground stations were found to be suitable locations for harvesting ambient RF energy using the prototypes. Inductive Power Transfer systems for transmitting tens to hundreds of watts have been reported for almost a decade. Most of the work has concentrated on the optimization of the link efficiency and have not taken into account the efficiency of the driver and rectifier. Class-E amplifiers and rectifiers have been identified as ideal drivers for IPT applications, but their power handling capability at tens of MHz has been a crucial limiting factor, since the load and inductor characteristics are set by the requirements of the resonant inductive system. The frequency limitation of the driver restricts the unloaded Q-factor of the coils and thus the link efficiency. The system presented in this work alleviates the use of heavy and expensive field-shaping techniques by presenting an efficient IPT system capable of transmitting energy with high dc-to-load efficiencies at 6 MHz across a distance of 30 cm.Open Acces

Leveraging Backscatter for Ultra-low Power Wireless Sensing Systems

The past few years have seen a dramatic growth in wireless sensing systems, with millions of wirelessly connected sensors becoming first-class citizens of the Internet. The number of wireless sensing devices is expected to surpass 6.75 billion by 2017, more than the world\u27s population as well as the combined market of smartphones, tablets, and PCs. However, its growth faces two pressing challenges: battery energy density and wireless radio power consumption. Battery energy density looms as a fundamental limiting factor due to slow improvements over the past several decades (3x over 22 years). Wireless radio power consumption is another key challenge because high-speed wireless communication is often far more expensive energy-wise than computation, storage and sensing. To make matters worse, wireless sensing devices are generating an increasing amount of data. These challenges raise a fundamental question --- how should we power and communicate with wireless sensing devices. More specifically, instead of using batteries, can we leverage other energy sources to reduce, if not eliminate, the dependence on batteries? Similarly, instead of optimizing existing wireless radios, can we fundamentally change how radios transmit wireless signals to achieve lower power consumption? A promising technique to address these questions is backscatter --- a primitive that enables RF energy harvesting and ultra-low-power wireless communication. Backscatter has the potential to reduce dependence on batteries because it can obtain energy by rectifying the wireless signals transmitted by a backscatter reader. Backscatter can also work by reflecting existing wireless signals (WiFi, BLE) when these are available nearby. Because signal reflection only consumes uWs of power, backscatter can enable ultra-low-power wireless communication. However, the use of backscatter for communicating with wireless sensing devices presents several challenges. First, decreasing RF power across distance limits the operational range of micro-powered backscatter devices. This raises the question of how to maintain a communication link with a backscatter device despite tiny amount of harvested power. Second, even though the backscatter RF front-end is extremely power-efficient, the computational and sensing overhead on backscatter sensors limit its ability to operate with a few micro-Watts of power. Such overhead is a negligible factor of overall power consumption for platforms where radio power consumption is high (e.g. WiFi or Bluetooth based devices). However, it becomes the bottleneck for backscatter based platforms. Third, backscatter readers are not currently deployed in existing indoor environments to provide a continuous carrier for carrying backscattered information. As a result, backscatter deployment is not yet widespread. This thesis addresses these challenges by making the following contributions. First, we design a network stack that enables continuous operation despite decreasing harvested power across distance by employing an OS abstraction --- task fragmentation. We show that such a network stack enables packet transfer even when the whole system is powered by a 3cmx3cm solar panel under natural indoor light condition. Second, we design a hardware architecture that minimizes the computational overhead of backscatter to enable over 1Mbps backscatter transmission while consuming less than 100uWs of power, a two order of magnitude improvement over the state-of-the-art. Finally, we design a system that can leverage both ambient WiFi and BLE signals for backscatter. Our empirical evaluation shows that we can backscatter 500bps data on top of a WiFi stream and 50kbps data on top of a Bluetooth stream when the backscatter device is 3m away from the commercial WiFi and Bluetooth receivers