Energy Management in RFID-Sensor Networks: Taxonomy and Challenges

Ubiquitous Computing is foreseen to play an important role for data production and network connectivity in the coming decades. The Internet of Things (IoT) research which has the capability to encapsulate identification potential and sensing capabilities, strives towards the objective of developing seamless, interoperable and securely integrated systems which can be achieved by connecting the Internet with computing devices. This gives way for the evolution of wireless energy harvesting and power transmission using computing devices. Radio Frequency (RF) based Energy Management (EM) has become the backbone for providing energy to wireless integrated systems. The two main techniques for EM in RFID Sensor Networks (RSN) are Energy Harvesting (EH) and Energy Transfer (ET). These techniques enable the dynamic energy level maintenance and optimisation as well as ensuring reliable communication which adheres to the goal of increased network performance and lifetime. In this paper, we present an overview of RSN, its types of integration and relative applications. We then provide the state-of-the-art EM techniques and strategies for RSN from August 2009 till date, thereby reviewing the existing EH and ET mechanisms designed for RSN. The taxonomy on various challenges for EM in RSN has also been articulated for open research directives

UAV-enabled wireless-powered Iot wireless sensor networks

Future massive internet of thing (IoT) networks will enable the vision of smart cities, where it is anticipated that a massive number of sensor devices, in the order of tens of millions devices, ubiquitously deployed to monitor the environment. Main challenges in such a network are how to improve the network lifetime and design an e cient data aggregation process. To improve the lifetime, using low-power passive sensor devices have recently shown great potential. Ambient backscattering is a novel technology which provides low-power long-range wireless communication expanding the network lifetime signi cantly. On the other hand, in order to collect the sensed data from sensor devices deployed over a wide area, unmanned aerial vehicles (UAVs) has been considered as a promising technology, by leveraging the UAV's high mobility and line-of-sight (LOS) dominated air-ground channels. The UAV can act as data aggregator collecting sensed data from all sensors. In this thesis, we consider medium-access control (MAC) policies for two sensor data collection scenarios. First, the objective is to collect individual sensor data from the eld. The challenge in this case is to determine how a large number of sensors should access the medium so that data aggregation process performed in a fast and reliable fashion. Utilizing conventional orthogonal medium access schemes (e.g., time-division vi multiple access (TDMA) and frequency-division multiple access (FDMA)), is highly energy consuming and spectrally ine cient. Hence, we employ non-orthogonal multiple access (NOMA) which is envisaged as an essential enabling technology for 5G wireless networks especially for uncoordinated transmissions. In Chapter 2, we develop a framework where the UAV is used as a replacement to conventional terrestrial data collectors in order to increase the e ciency of collecting data from a eld of passive backscatter sensors, and simultaneously it acts as a mobile RF carrier emitter to activate backscatter sensors. In the MAC layer, we employ uplink power-domain NOMA scheme to e ectively serve a large number of passive backscatter sensors. Our objective is to optimize the path, altitude, and beamwidth of the UAV such that the network throughput is maximized. In Chapter 3, we consider the scenario where there are a separate data collector and RF carrier emitter such that the former is a gateway on the ground and the latter is a single UAV hovering over the eld of backscatter sensors. Secondly, we consider a case where only a function of sensed data is of interest rather than individual sensor values. A new challenge arises where the problem is to design a communication policy to improve the accuracy of the estimated function. Recently, over-the-air computation (AirComp) has emerged to be a promising solution to enable merging computation and communication by utilizing the superposition property of wireless channels, when a function of measurements are desired rather than individual in massive IoT sensor networks. One of the key challenges in AirComp is to compensate the e ects of channel. Motivated by this, in Chapter 4, we propose a UAV assisted communication framework to tackle this problem by a simple to implement sampling-then-mapping mechanism

Design, analysis and optimization of visible light communications based indoor access systems for mobile and internet of things applications

Demands for indoor broadband wireless access services are expected to outstrip the spectrum capacity in the near-term spectrum crunch . Deploying additional femtocells to address spectrum crunch is cost-inefficient due to the backhaul challenge and the exorbitant system maintenance. According to an Alcatel-Lucent report, most mobile Internet access traffic happens indoors. To alleviate the spectrum crunch and the backhaul challenge problems, visible light communication (VLC) emerges as an attractive candidate for indoor wireless access in the 5G architecture. In particular, VLC utilizes LED or fluorescent lamps to send out imperceptible flickering light that can be captured by a smart phone camera or photodetector. Leveraging power line communication and the available indoor infrastructure, VLC can be utilized with a small one-time cost. VLC also facilitates the great advantage of being able to jointly perform illumination and communications. Integration of VLC into the existing indoor wireless access networks embraces many challenges, such as lack of uplink infrastructure, excessive delay caused by blockage in heterogeneous networks, and overhead of power consumption. In addition, applying VLC to Internet-of-Things (IoT) applications, such as communication and localization, faces the challenges including ultra-low power requirement, limited modulation bandwidth, and heavy computation and sensing at the device end. In this dissertation, to overcome the challenges of VLC, a VLC enhanced WiFi system is designed by incorporating VLC downlink and WiFi uplink to connect mobile devices to the Internet. To further enhance robustness and throughput, WiFi and VLC are aggregated in parallel by leveraging the bonding technique in Linux operating system. Based on dynamic resource allocation, the delay performance of heterogeneous RF-VLC network is analyzed and evaluated for two different configurations - aggregation and non-aggregation. To mitigate the power consumption overhead of VLC, a problem of minimizing the total power consumption of a general multi-user VLC indoor network while satisfying users traffic demands and maintaining an acceptable level of illumination is formulated. The optimization problem is solved by the efficient column generation algorithm. With ultra-low power consumption, VLC backscatter harvests energy from indoor light sources and transmits optical signals by modulating the reflected light from a reflector. A novel pixelated VLC backscatter is proposed and prototyped to address the limited modulation bandwidth by enabling more advanced modulation scheme than the state-of-the-art on-off keying (OOK) scheme and allowing for the first time orthogonal multiple access. VLC-based indoor access system is also suitable for indoor localization due to its unique properties, such as utilization of existing ubiquitous lighting infrastructure, high location and orientation accuracy, and no interruption to RF-based devices. A novel retroreflector-based visible light localization system is proposed and prototyped to establish an almost zero-delay backward channel using a retroreflector to reflect light back to its source. This system can localize passive IoT devices without requiring computation and heavy sensing (e.g., camera) at the device end

A Case for Time Slotted Channel Hopping for ICN in the IoT

Recent proposals to simplify the operation of the IoT include the use of Information Centric Networking (ICN) paradigms. While this is promising, several challenges remain. In this paper, our core contributions (a) leverage ICN communication patterns to dynamically optimize the use of TSCH (Time Slotted Channel Hopping), a wireless link layer technology increasingly popular in the IoT, and (b) make IoT-style routing adaptive to names, resources, and traffic patterns throughout the network--both without cross-layering. Through a series of experiments on the FIT IoT-LAB interconnecting typical IoT hardware, we find that our approach is fully robust against wireless interference, and almost halves the energy consumed for transmission when compared to CSMA. Most importantly, our adaptive scheduling prevents the time-slotted MAC layer from sacrificing throughput and delay

Data Collection in Two-Tier IoT Networks with Radio Frequency (RF) Energy Harvesting Devices and Tags

The Internet of things (IoT) is expected to connect physical objects and end-users using technologies such as wireless sensor networks and radio frequency identification (RFID). In addition, it will employ a wireless multi-hop backhaul to transfer data collected by a myriad of devices to users or applications such as digital twins operating in a Metaverse. A critical issue is that the number of packets collected and transferred to the Internet is bounded by limited network resources such as bandwidth and energy. In this respect, IoT networks have adopted technologies such as time division multiple access (TDMA), signal interference cancellation (SIC) and multiple-input multiple-output (MIMO) in order to increase network capacity. Another fundamental issue is energy. To this end, researchers have exploited radio frequency (RF) energy-harvesting technologies to prolong the lifetime of energy constrained sensors and smart devices. Specifically, devices with RF energy harvesting capabilities can rely on ambient RF sources such as access points, television towers, and base stations. Further, an operator may deploy dedicated power beacons that serve as RF-energy sources. Apart from that, in order to reduce energy consumption, devices can adopt ambient backscattering communication technologies. Advantageously, backscattering allows devices to communicate using negligible amount of energy by modulating ambient RF signals. To address the aforementioned issues, this thesis first considers data collection in a two-tier MIMO ambient RF energy-harvesting network. The first tier consists of routers with MIMO capability and a set of source-destination pairs/flows. The second tier consists of energy harvesting devices that rely on RF transmissions from routers for energy supply. The problem is to determine a minimum-length TDMA link schedule that satisfies the traffic demand of source-destination pairs and energy demand of energy harvesting devices. It formulates the problem as a linear program (LP), and outlines a heuristic to construct transmission sets that are then used by the said LP. In addition, it outlines a new routing metric that considers the energy demand of energy harvesting devices to cope with routing requirements of IoT networks. The simulation results show that the proposed algorithm on average achieves 31.25% shorter schedules as compared to competing schemes. In addition, the said routing metric results in link schedules that are at most 24.75% longer than those computed by the LP

Gaussian functional shapes-based type-II fuzzy membership-based cluster protocol for energy harvesting IoT networks

With the advancements in Internet of Things (IoT) technologies, energy harvesting IoT devices are becoming significantly important. These tiny IoT devices can harvest bounded energy, thus need an efficient protocol to conserve the energy in more efficient manner. From the review, it is found that the development of an efficient energy efficient protocol for energy harvesting IoT is still an open area of research. It is found that fuzzy based energy harvesting IoTs has shown significant improvement over the existing protocols. However, the fuzzy logic suffers from the data uncertainty issue. Therefore, in this paper, Gaussian functional shapes-based type-II fuzzy membership function is used to elect the cluster heads among the IoT devices to reduce the energy consumption of energy harvest IoTs. Thereafter, inter-cluster data aggregation is used. Finally, the communication between the elected cluster heads and the cloud servers or sink. Extensive experiments are drawn by considering the existing and the proposed protocols for energy harvesting IoTs. Comparative analysis reveals that the proposed type-II fuzzy membership function-based protocol outperforms the existing protocols in terms of bandwidth analysis, throughput, conserve energy, network lifetime, and average consumed energy