Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Optimizing performance and energy efficiency of group communication and internet of things in cognitive radio networks

Data traffic in the wireless networks has grown at an unprecedented rate. While traditional wireless networks follow fixed spectrum assignment, spectrum scarcity problem becomes a major challenge in the next generations of wireless networks. Cognitive radio is a promising candidate technology that can mitigate this critical challenge by allowing dynamic spectrum access and increasing the spectrum utilization. As users and data traffic demands increases, more efficient communication methods to support communication in general, and group communication in particular, are needed. On the other hand, limited battery for the wireless network device in general makes it a bottleneck for enhancing the performance of wireless networks. In this thesis, the problem of optimizing the performance of group communication in CRNs is studied. Moreover, energy efficient and wireless-powered group communication in CRNs are considered. Additionally, a cognitive mobile base station and a cognitive UAV are proposed for the purpose of optimizing energy transfer and data dissemination, respectively. First, a multi-objective optimization for many-to-many communication in CRNs is considered. Given a many-to-many communication request, the goal is to support message routing from each user in the many-to-many group to each other. The objectives are minimizing the delay and the number of used links and maximizing data rate. The network is modeled using a multi-layer hyper graph, and the secondary users\u27 transmission is scheduled after establishing the conflict graph. Due to the difficulty of solving the problem optimally, a modified version of an Ant Colony meta-heuristic algorithm is employed to solve the problem. Additionally, energy efficient multicast communication in CRNs is introduced while considering directional and omnidirectional antennas. The multicast service is supported such that the total energy consumption of data transmission and channel switching is minimized. The optimization problem is formulated as a Mixed Integer Linear Program (MILP), and a heuristic algorithm is proposed to solve the problem in polynomial time. Second, wireless-powered machine-to-machine multicast communication in cellular networks is studied. To incentivize Internet of Things (IoT) devices to participate in forwarding the multicast messages, each IoT device participates in messages forwarding receives Radio Frequency (RF) energy form Energy Transmitters (ET) not less than the amount of energy used for messages forwarding. The objective is to minimize total transferred energy by the ETs. The problem is formulated mathematically as a Mixed Integer Nonlinear Program (MINLP), and a Generalized Bender Decomposition with Successive Convex Programming (GBD-SCP) algorithm is introduced to get an approximate solution since there is no efficient way in general to solve the problem optimally. Moreover, another algorithm, Constraints Decomposition with SCP and Binary Variable Relaxation (CDR), is proposed to get an approximate solution in a more efficient way. On the other hand, a cognitive mobile station base is proposed to transfer data and energy to a group of IoT devices underlying a primary network. Total energy consumed by the cognitive base station in its mobility, data transmission and energy transfer is minimized. Moreover, the cognitive base station adjusts its location and transmission power and transmission schedule such that data and energy demands are supported within a certain tolerable time and the primary users are protected from harmful interference. Finally, we consider a cognitive Unmanned Aerial Vehicle (UAV) to disseminate data to IoT devices. The UAV senses the spectrum and finds an idle channel, then it predicts when the corresponding primary user of the selected channel becomes active based on the elapsed time of the off period. Accordingly, it starts its transmission at the beginning of the next frame right after finding the channel is idle. Moreover, it decides the number of the consecutive transmission slots that it will use such that the number of interfering slots to the corresponding primary user does not exceed a certain threshold. A mathematical problem is formulated to maximize the minimum number of bits received by the IoT devices. A successive convex programming-based algorithm is used to get a solution for the problem in an efficiency way. It is shown that the used algorithm converges to a Kuhn Tucker point

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

Wireless Power Transfer Technologies, Applications, and Future Trends: A Review

Wireless Power Transfer (WPT) is a disruptive technology that allows wireless energy provisioning for energy- limited IoT devices, thus decreasing the over-reliance on batteries and wires. WPT could replace conventional energy provisioning (e.g., energy harvesting) and expand for deployment in many of our daily-life applications, including but not limited to healthcare, transportation, automation, and smart cities. As a new rising technology, WPT has attracted many researchers from academia and industry in terms of technologies and charging scheduling within a plethora of services and applications. Thus, in this paper, we review the most recent studies related to WPT, including the classifications, advantages, disadvantages, and main application domains. Furthermore, we review the recently designed wireless charging scheduling algorithms and schemes for wireless sensor networks. Our study provides a detailed survey of wireless charging scheduling schemes covering the main scheme classifications, evaluation metrics, application domains, advantages, and disadvantages of each charging scheme. We further summarize trends and opportunities for applying WPT at some intersections

Online Coordinated Charging of Plug-In Electric Vehicles in Smart Grid to Minimize Cost of Generating Energy and Improve Voltage Profile

This Ph.D. research highlights the negative impacts of random vehicle charging on power grid and proposes four practical PEV coordinated charging strategies that reduce network and generation costs by integrating renewable energy resources and real-time pricing while considering utility constraints and consumer concerns

Resource Scheduling for UAVs-aided D2D Networks: A Multi-objective Optimization Approach

Unmanned aerial vehicles (UAVs)-aided device-todevice (D2D) networks have attracted great interests with the development of 5G/6G communications, while there are several challenges about resource scheduling in UAVs-aided D2D networks. In this work, we formulate a UAVs-aided D2D network resource scheduling optimization problem (NetResSOP) to comprehensively consider the number of deployed UAVs, UAV positions, UAV transmission powers, UAV flight velocities, communication channels, and UAV-device pair assignment so as to maximize the D2D network capacity, minimize the number of deployed UAVs, and minimize the average energy consumption over all UAVs simultaneously. The formulated NetResSOP is a mixed-integer programming problem (MIPP) and an NP-hard problem, which means that it is difficult to be solved in polynomial time. Moreover, there are trade-offs between the optimization objectives, and hence it is also difficult to find an optimal solution that can simultaneously make all objectives be optimal. Thus, we propose a non-dominated sorting genetic algorithm-III with a Flexible solution dimension mechanism, a Discrete part generation mechanism, and a UAV number adjustment mechanism (NSGA-III-FDU) for solving the problem comprehensively. Simulation results demonstrate the effectiveness and the stability of the proposed NSGA-III-FDU under different scales and settings of the D2D networks

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

Failure Analysis in Next-Generation Critical Cellular Communication Infrastructures

The advent of communication technologies marks a transformative phase in critical infrastructure construction, where the meticulous analysis of failures becomes paramount in achieving the fundamental objectives of continuity, security, and availability. This survey enriches the discourse on failures, failure analysis, and countermeasures in the context of the next-generation critical communication infrastructures. Through an exhaustive examination of existing literature, we discern and categorize prominent research orientations with focuses on, namely resource depletion, security vulnerabilities, and system availability concerns. We also analyze constructive countermeasures tailored to address identified failure scenarios and their prevention. Furthermore, the survey emphasizes the imperative for standardization in addressing failures related to Artificial Intelligence (AI) within the ambit of the sixth-generation (6G) networks, accounting for the forward-looking perspective for the envisioned intelligence of 6G network architecture. By identifying new challenges and delineating future research directions, this survey can help guide stakeholders toward unexplored territories, fostering innovation and resilience in critical communication infrastructure development and failure prevention

Exploiting Mobile Energy Storages for Overload Mitigation in Smart Grid

The advancement of battery and electronic technologies pushes forward transportation electrification, accelerating the commercialization and prevalence of plug-in electric vehicles (PEVs). The development of PEVs is closely related to the smart grid as PEVs are considered as high power rating electric appliances that require frequent charging. As PEVs become regular transportation options, charging stations (CSs) are also extensively deployed in the smart grid to meet the PEV charging demand. During peak traffic hours, the increasing PEV charging demand could exceed the loading capacities of CS-connected transformers, causing heavy charging overload in-station. Without proper overload mitigation, the energy imbalance issues will result in severe feeder degradation and power quality issue. Therefore, solutions for CS overload mitigation are in urgent demand. Considering the rechargeable nature of PEV batteries, PEVs can serve as potential mobile energy storages (MESs) to carry energy from power nodes with excess energy to overloaded CSs to compensate the overloads. Compared to infrastructure upgrade and installing stationary energy storages at CSs, the utilization of PEVs not only minimizes the additional upgrade/installation expenditure, but also maximizes the energy utilization in the smart grid with high flexibility. However, the PEV utilization for overload mitigation is confronted with a variety of challenges due to vehicular mobility and the fear of battery degradation. Because of vehicular mobility, the CS operation dynamics become stochastic processes, increasing the difficulty of the CS demand estimation. Without accurate demand estimation, the overload condition cannot be timely predicted and controlled. Moreover, the stochastic on-road traffic could impair the time-efficiency of the PEV overload mitigation service. Further, as the overload mitigation service demands frequent charging and discharging, the fear of battery degradation could impede PEV owners from providing the service, making the overload mitigation tasks harder to fulfill. In this thesis, we address the above challenges to effectively utilize PEVs for overload mitigation in the smart grid. In specific, different approaches are designed according to the PEV properties at different commercialization stages. First, at the early PEV commercialization stage, power utility company purchases large battery capacity PEVs as utility-owned MESs (UMESs) whose only responsibility is fulfilling the energy compensation task. The fleet of UMESs is rather small due to the company's limited budget, and therefore UMESs priorly serve the CSs with large energy imbalance (e.g., 500-1000kWh). Thus, the stochastic CS charging demand needs to be accurately estimated and then UMESs can be scheduled to these CSs for overload mitigation. To achieve this objective, we develop a two-dimensional Markov Chain model to characterize the stochastic process in-station so that the CS charging demand can be precisely estimated. Based on the estimated CS demand status, a two-tier energy compensation framework is designed to schedule UMESs to the heavily overloaded CSs in a timely and cost-efficient manner. Second, at the medium stage of PEV commercialization, vehicle-fleet based companies are motivated by legislation to purchase a large fleet of PEVs which can be served as potential MESs, referred to as legislation-motivated MESs (LMESs). To deliver energy to overloaded CSs using LMESs would introduce a large amount of additional traffics to the transportation network. When injecting these LMES traffics into an already busy transportation network, unexpected traffic delay could occur, delaying the overload mitigation service. To avoid the potential traffic delay incurred by LMES service, we develop an energy-capacitated transportation network model to measure the road capacity of accommodating additional LMES traffics. Based on the developed model, a loading-optimized navigation scheme is proposed to calculate the optimal navigation routes for LMES overload mitigation. To stimulate LMESs following the optimal navigation, we propose a dynamic pricing scheme that adjusts the service price to align the LMES service routes with the optimal routes to achieve a time-efficient service result. Third, when PEVs are prevalent in the automobile market and become regular transportation options for every household, on-road private-owned PEVs can be efficiently used as energy porters to deliver energy to overloaded CSs, named as private MESs (PMESs). As the primary objective of PMESs is to reach their planned destinations, the monetary incentive is demanded to stimulate them actively participating in the overload mitigation tasks. Therefore, a hierarchical decision-making process between the utility operator (UO) and PMESs is in demand. Moreover, considering PMESs have different service preferences (e.g., the fear of battery degradation, the unwillingness of long service time, etc.), individual PMES decision making process on the task should be carefully modelled. Thus, we propose to characterize the price-service interaction between the operator and PMESs as a Stackelberg game. The operator acts as the leader to post service price to PMESs while PMESs act as followers, responding to the posted price to maximize their utility functions. In summary, the analysis and schemes proposed in this thesis can be adopted by the local power utility company to utilize PEVs for overload mitigation at overloaded power nodes. The proposed schemes are applicable during different PEV commercialization stage and present PEVs as a flexible solution to the smart grid overload issue