On-Site and External Energy Harvesting in Underground Wireless

Energy efficiency is vital for uninterrupted long-term operation of wireless underground communication nodes in the field of decision agriculture. In this paper, energy harvesting and wireless power transfer techniques are discussed with applications in underground wireless communications (UWC). Various external wireless power transfer techniques are explored. Moreover, key energy harvesting technologies are presented that utilize available energy sources in the field such as vibration, solar, and wind. In this regard, the Electromagnetic(EM)- and Magnetic Induction(MI)-based approaches are explained. Furthermore, the vibration-based energy harvesting models are reviewed as well. These energy harvesting approaches lead to design of an efficient wireless underground communication system to power underground nodes for prolonged field operation in decision agriculture

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

Reinforcement Learning Based Resource Allocation for Energy-Harvesting-Aided D2D Communications in IoT Networks

It is anticipated that mobile data traffic and the demand for higher data rates will increase dramatically as a result of the explosion of wireless devices, such as the Internet of Things (IoT) and machine-to-machine communication. There are numerous location-based peer-to-peer services available today that allow mobile users to communicate directly with one another, which can help offload traffic from congested cellular networks. In cellular networks, Device-to-Device (D2D) communication has been introduced to exploit direct links between devices instead of transmitting through a the Base Station (BS). However, it is critical to note that D2D and IoT communications are hindered heavily by the high energy consumption of mobile devices and IoT devices. This is because their battery capacity is restricted. There may be a way for energy-constrained wireless devices to extend their lifespan by drawing upon reusable external sources of energy such as solar, wind, vibration, thermoelectric, and radio frequency (RF) energy in order to overcome the limited battery problem. Such approaches are commonly referred to as Energy Harvesting (EH) There is a promising approach to energy harvesting that is called Simultaneous Wireless Information and Power Transfer (SWIPT). Due to the fact that wireless users are on the rise, it is imperative that resource allocation techniques be implemented in modern wireless networks. This will facilitate cooperation among users for limited resources, such as time and frequency bands. As well as ensuring that there is an adequate supply of energy for reliable and efficient communication, resource allocation also provides a roadmap for each individual user to follow in order to consume the right amount of energy. In D2D networks with time, frequency, and power constraints, significant computing power is generally required to achieve a joint resource management design. Thus the purpose of this study is to develop a resource allocation scheme that is based on spectrum sharing and enables low-cost computations for EH-assisted D2D and IoT communication. Until now, there has been no study examining resource allocation design for EH-enabled IoT networks with SWIPT-enabled D2D schemes that utilize learning techniques and convex optimization. In most of the works, optimization and iterative approaches with a high level of computational complexity have been used which is not feasible in many IoT applications. In order to overcome these obstacles, a learning-based resource allocation mechanism based on the SWIPT scheme in IoT networks is proposed, where users are able to harvest energy from different sources. The system model consists of multiple IoT users, one BS, and multiple D2D pairs in EH-based IoT networks. As a means of developing an energy-efficient system, we consider the SWIPT scheme with D2D pairs employing the time switching method (TS) to capture energy from the environment, whereas IoT users employ the power splitting method (PS) to harvest energy from the BS. A mixed-integer nonlinear programming (MINLP) approach is presented for the solution of the Energy Efficiency (EE) problem by jointly optimizing subchannel allocation, power-splitting factor, power, and time together. As part of the optimization approach, the original EE optimization problem is decomposed into three subproblems, namely: (a) subchannel assignment and power splitting factor, (b) power allocation, and (c) time allocation. In order to solve the subproblem assignment problem, which involves discrete variables, the Q-learning approach is employed. Due to the large size of the overall problem and the continuous nature of certain variables, it is impractical to optimize all variables by using the learning technique. Instead dealing for the continuous variable problems, namely power and time allocation, the original non-convex problem is first transformed into a convex one, then the Majorization-Minimization (MM) approach is applied as well as the Dinkelbach. The performance of the proposed joint Q-learning and optimization algorithm has been evaluated in detail. In particular, the solution was compared with a linear EH model, as well as two heuristic algorithms, namely the constrained allocation algorithm and the random allocation algorithm, in order to determine its performance. The results indicate that the technique is superior to conventional approaches. For example, it can be seen that for the distance of $d = 10$ m, our proposed algorithm leads to EE improvement when compared to the method such as prematching algorithm, constrained allocation, and random allocation methods by about 5.26\%, 110.52\%, and 143.90\%, respectively. Considering the simulation results, the proposed algorithm is superior to other methods in the literature. Using spectrum sharing and harvesting energy from D2D and IoT devices achieves impressive EE gains. This superior performance can be seen both in terms of the average and sum EEs, as well as when compared to other baseline schemes

Energy and Delay Efficient Computation Offloading Solutions for Edge Computing

This thesis collects a selective set of outcomes of a PhD course in Electronics, Telecommunications, and Information Technologies Engineering and it is focused on designing techniques to optimize computational resources in different wireless communication environments. Mobile Edge Computing (MEC) is a novel and distributed computational paradigm that has emerged to address the high users demand in 5G. In MEC, edge devices can share their resources to collaborate in terms of storage and computation. One of the computational sharing techniques is computation offloading, which brings a lot of advantages to the network edge, from lower communication, to lower energy consumption for computation. However, the communication among the devices should be managed such that the resources are exploited efficiently. To this aim, in this dissertation, computation offloading in different wireless environments with different number of users, network traffic, resource availability and devices' location are analyzed in order to optimize the resource allocation at the network edge. To better organize the dissertation, the studies are classified in four main sections. In the first section, an introduction on computational sharing technologies is given. Later, the problem of computation offloading is defined, and the challenges are introduced. In the second section, two partial offloading techniques are proposed. While in the first one, centralized and distributed architectures are proposed, in the second work, an Evolutionary Algorithm for task offloading is proposed. In the third section, the offloading problem is seen from a different perspective where the end users can harvest energy from either renewable sources of energy or through Wireless Power Transfer. In the fourth section, the MEC in vehicular environments is studied. In one work a heuristic is introduced in order to perform the computation offloading in Internet of Vehicles and in the other a learning-based approach based on bandit theory is proposed

Relaying in the Internet of Things (IoT): A Survey

The deployment of relays between Internet of Things (IoT) end devices and gateways can improve link quality. In cellular-based IoT, relays have the potential to reduce base station overload. The energy expended in single-hop long-range communication can be reduced if relays listen to transmissions of end devices and forward these observations to gateways. However, incorporating relays into IoT networks faces some challenges. IoT end devices are designed primarily for uplink communication of small-sized observations toward the network; hence, opportunistically using end devices as relays needs a redesign of both the medium access control (MAC) layer protocol of such end devices and possible addition of new communication interfaces. Additionally, the wake-up time of IoT end devices needs to be synchronized with that of the relays. For cellular-based IoT, the possibility of using infrastructure relays exists, and noncellular IoT networks can leverage the presence of mobile devices for relaying, for example, in remote healthcare. However, the latter presents problems of incentivizing relay participation and managing the mobility of relays. Furthermore, although relays can increase the lifetime of IoT networks, deploying relays implies the need for additional batteries to power them. This can erode the energy efficiency gain that relays offer. Therefore, designing relay-assisted IoT networks that provide acceptable trade-offs is key, and this goes beyond adding an extra transmit RF chain to a relay-enabled IoT end device. There has been increasing research interest in IoT relaying, as demonstrated in the available literature. Works that consider these issues are surveyed in this paper to provide insight into the state of the art, provide design insights for network designers and motivate future research directions

Reliability performance analysis of half-duplex and full-duplex schemes with self-energy recycling

Abstract. Radio frequency energy harvesting (EH) has emerged as a promising option for improving the energy efficiency of current and future networks. Self-energy recycling (sER), as a variant of EH, has also appeared as a suitable alternative that allows to reuse part of the transmitted energy via an energy loop. In this work we study the benefits of using sER in terms of reliability improvements and compare the performance of full-duplex (FD) and half-duplex (HD) schemes when using multi-antenna techniques at the base station side. We also assume a model for the hardware energy consumption, making the analysis more realistic since most works only consider the energy spent on transmission. In addition to spectral efficiency enhancements, results show that FD performs better than HD in terms of reliability. We maximize the outage probability of the worst link in the network using a dynamic FD scheme where a small base station (SBS) determines the optimal number of antennas for transmission and reception. This scheme proves to be more efficient than classical HD and FD modes. Results show that the use of sER at the SBS introduces changes on the distribution of antennas for maximum fairness when compared to the setup without sER. Moreover, we determine the minimum number of active radio frequency chains required at the SBS in order to achieve a given reliability target

Resource allocation in future green wireless networks : applications and challenges

Over the past few years, green radio communication has been an emerging topic since the footprint from the Information and Communication Technologies (ICT) is predicted to increase 7.3% annually and then exceed 14% of the global footprint by 2040. Moreover, the explosive progress of ICT, e.g., the fifth generation (5G) networks, has resulted in expectations of achieving 10-fold longer device battery lifetime, and 1000-fold higher global mobile data traffic over the fourth generation (4G) networks. Therefore, the demands for increasing the data rate and the lifetime while reducing the footprint in the next-generation wireless networks call for more efficient utilization of energy and other resources. To overcome this challenge, the concepts of small-cell, energy harvesting, and wireless information and power transfer networks can be evaluated as promising solutions for re-greening the world. In this dissertation, the technical contributions in terms of saving economical cost, protecting the environment, and guaranteeing human health are provided. More specifically, novel communication scenarios are proposed to minimize energy consumption and hence save economic costs. Further, energy harvesting (EH) techniques are applied to exploit available green resources in order to reduce carbon footprint and then protect the environment. In locations where implemented user devices might not harvest energy directly from natural resources, base stations could harvest-and-store green energy and then use such energy to power the devices wirelessly. However, wireless power transfer (WPT) techniques should be used in a wise manner to avoid electromagnetic pollution and then guarantee human health. To achieve all these aspects simultaneously, this thesis proposes promising schemes to optimally manage and allocate resources in future networks. Given this direction, in the first part, Chapter 2 mainly studies a transmission power minimization scheme for a two-tier heterogeneous network (HetNet) over frequency selective fading channels. In addition, the HetNet backhaul connection is unable to support a sufficient throughput for signaling an information exchange between two tiers. A novel idea is introduced in which the time reversal (TR) beamforming technique is used at a femtocell while zero-forcing-based beamforming is deployed at a macrocell. Thus, a downlink power minimizationscheme is proposed, and optimal closed-form solutions are provided. In the second part, Chapters 3, 4, and 5 concentrate on EH and wireless information and power transfer (WIPT) using RF signals. More specifically, Chapter 3 presents an overview of the recent progress in green radio communications and discusses potential technologies for some emerging topics on the platforms of EH and WPT. Chapter 4 develops a new integrated information and energy receiver architecture based on the direct use of alternating current (AC) for computation. It is shown that the proposed approach enhances not only the computational ability but also the energy efficiency over the conventional one. Furthermore, Chapter 5 proposes a novel resource allocation scheme in simultaneous wireless information and power transfer (SWIPT) networks where three crucial issues: power-efficient improvement, user-fairness guarantee, and non-ideal channel reciprocity effect mitigation, are jointly addressed. Hence, novel methods to derive optimal and suboptimal solutions are provided. In the third part, Chapters 6, 7, and 8 focus on simultaneous lightwave information and power transfer (SLIPT) for indoor applications, as a complementary technology to RF SWIPT. In this research, Chapter 6 investigates a hybrid RF/visible light communication (VLC) ultrasmall cell network where optical transmitters deliver information and power using the visible light, whereas an RF access point works as a complementary power transfer system. Thus, a novel resource allocation scheme exploiting RF and visible light for power transfer is devised. Chapter 7 proposes the use of lightwave power transfer to enable future sustainable Federated Learning (FL)-based wireless networks. FL is a new data privacy protection technique for training shared machine learning models in a distributed approach. However, the involvement of energy-constrained mobile devices in the construction of the shared learning models may significantly reduce their lifetime. The proposed approach can support the FL-based wireless network to overcome the issue of limited energy at mobile devices. Chapter 8 introduces a novel framework for collaborative RF and lightwave power transfer for wireless communication networks. The constraints on the transmission power set by safety regulations result in significant challenges to enhance the power transfer performance. Thus, the study of technologies complementary to conventional RF SWIPT is essential. To cope with this isue, this chapter proposes a novel collaborative RF and lightwave power transfer technology for next-generation wireless networks

Power Beacon’s deployment optimization for wirelessly powering massive Internet of Things networks

Abstract. The fifth-generation (5G) and beyond wireless cellular networks promise the native support to, among other use cases, the so-called Internet of Things (IoT). Different from human-based cellular services, IoT networks implement a novel vision where ordinary machines possess the ability to autonomously sense, actuate, compute, and communicate throughout the Internet. However, as the number of connected devices grows larger, an urgent demand for energy-efficient communication technologies arises. A key challenge related to IoT devices is that their very small form factor allows them to carry just a tiny battery that might not be even possible to replace due to installation conditions, or too costly in terms of maintenance because of the massiveness of the network. This issue limits the lifetime of the network and compromises its reliability. Wireless energy transfer (WET) has emerged as a potential candidate to replenish sensors’ batteries or to sustain the operation of battery-free devices, as it provides a controllable source of energy over-the-air. Therefore, WET eliminates the need for regular maintenance, allows sensors’ form factor reduction, and reduces the battery disposal that contributes to the environment pollution. In this thesis, we review some WET-enabled scenarios and state-of-the-art techniques for implementing WET in IoT networks. In particular, we focus our attention on the deployment optimization of the so-called power beacons (PBs), which are the energy transmitters for charging a massive IoT deployment subject to a network-wide probabilistic energy outage constraint. We assume that IoT sensors’ positions are unknown at the PBs, and hence we maximize the average incident power on the worst network location. We propose a linear-time complexity algorithm for optimizing the PBs’ positions that outperforms benchmark methods in terms of minimum average incident power and computation time. Then, we also present some insights on the maximum coverage area under certain propagation conditions