Efficient energy management for the internet of things in smart cities

The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy-efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities

Resource Optimization in UAV-assisted IoT Networks: The Role of Generative AI

We investigate how generative Artificial Intelligence (AI) can be used to optimize resources in Unmanned Aerial Vehicle (UAV)-assisted Internet of Things (IoT) networks. In particular, generative AI models for real-time decision-making have been used in public safety scenarios. This work describes how generative AI models can improve resource management within UAV-assisted networks. Furthermore, this work presents generative AI in UAV-assisted networks to demonstrate its practical applications and highlight its broader capabilities. We demonstrate a real-life case study for public safety, demonstrating how generative AI can enhance real-time decision-making and improve training datasets. By leveraging generative AI in UAV- assisted networks, we can design more intelligent, adaptive, and efficient ecosystems to meet the evolving demands of wireless networks and diverse applications. Finally, we discuss challenges and future research directions associated with generative AI for resource optimization in UAV-assisted networks.Comment: Accepted - IEEE Internet of Things Magazin

Joint workload scheduling and BBU allocation in cloud-RAN for 5G networks

Copyright 2017 ACM. Cloud-radio access network (C-RAN) emerges as a solution to satisfy the demand for a diverse range of applications, massive connectivity, and network heterogeneity. C-RAN uses central cloud network for processing user requests. Efficient management of cloud resources (e.g., computation and transmission resources) is one of the important challenges in C-RAN. In this paper, we investigate a joint workload scheduling and baseband unit (BBU) allocation in Cloud-RAN for 5G networks. First, we establish a queueing model in C-RAN. We then formulate an optimization problem for joint workload scheduling and BBU allocation with the aim to minimize mean response time and aggregate power. Queueing stability and workload conservation constraints are considered in the optimization problem. To solve this problem, we propose an energy efficient joint workload scheduling and BBU allocation (EE-JWSBA) algorithm using the concept of queueing theory. The EE-JWSBA algorithm is evaluated via simulations by considering three different scheduling weights (e.g., random, normalized, and upper limit). Simulation results demonstrate the effectiveness of proposed scheme using different scheduling weights

Emerging Edge Computing Technologies for Distributed Internet of Things (IoT) Systems

The ever-increasing growth in the number of connected smart devices and various Internet of Things (IoT) verticals is leading to a crucial challenge of handling massive amount of raw data generated from distributed IoT systems and providing real-time feedback to the end-users. Although existing cloud-computing paradigm has an enormous amount of virtual computing power and storage capacity, it is not suitable for latency-sensitive applications and distributed systems due to the involved latency and its centralized mode of operation. To this end, edge/fog computing has recently emerged as the next generation of computing systems for extending cloud-computing functions to the edges of the network. Despite several benefits of edge computing such as geo-distribution, mobility support and location awareness, various communication and computing related challenges need to be addressed in realizing edge computing technologies for future IoT systems. In this regard, this paper provides a holistic view on the current issues and effective solutions by classifying the emerging technologies in regard to the joint coordination of radio and computing resources, system optimization and intelligent resource management. Furthermore, an optimization framework for edge-IoT systems is proposed to enhance various performance metrics such as throughput, delay, resource utilization and energy consumption. Finally, a Machine Learning (ML) based case study is presented along with some numerical results to illustrate the significance of edge computing.Comment: 16 pages, 4 figures, 2 tables, submitted to IEEE Wireless Communications Magazin

Solving MAX-SAT Problem by Binary Biogeograph-based Optimization Algorithm

© 2019 IEEE. Several sensing problems in wireless sensor networks (WSNs) can be modeled to maximum satisfaction (MAX-SAT) or SAT problems. Also, MAX-SAT is an established framework for computationally expensive problems in other fields. There exist efficient algorithms to solve the MAX-SAT, which is an NP-hard problem. The reason for remodeling various sensing problems to MAX-SAT is to use these algorithms to solve challenging sensing problems. In this paper, we test a binary Biogeography-based (BBBO) algorithm for the MAX-SAT as an optimization problem with a binary search space. The original BBO is a swarm intelligence-based algorithm, which is well-tested for continuous (and nonbinary) integer space optimization problems, but its use for the binary space was limited. Since the exact algorithm to solve the MAX-SAT problem using moderate computing resources is not well-known; therefore, swarm intelligence based evolutionary algorithms (EAs) can be helpful to find better approximate solutions with limited computing resources. Our simulation results demonstrate the experimental exploration of the binary BBO algorithm against binary (enhanced fireworks algorithm) EFWA, discrete ABC (DisABC) and Genetic Algorithm (GA) for several classes of MAX-SAT problem instances

On provision of resilient connectivity in cognitive unmanned aerial vehicles

Mobile ad-hoc network (MANET) can be established in the areas/scenarios where the infrastructure networks are either out of service or no more available. MANETs have a lot of applications in sensor networks. Generally, a MANET deploys mobile ground nodes to set up a network. However, there can be some severe scenarios such as flood, battlefield, rescue operations, etc. where these ground nodes cannot be deployed. In such cases, a network of unmanned aerial vehicles (UAVs) can be a more viable option. Normally, UAVs operates on IEEE L-Band, IEEE S-Band or ISM band. These bands are already overcrowded, therefore, UAVs will face the problem of the spectrum scarcity. To resolve this issue cognitive radio (CR) is a most promising technology. Hence, in this work, we focus on CR based UAVs. As CR is based on opportunistic spectrum access, therefore, it is quite possible that all UAVs do not have one single channel available to communicate with each other. They need to form clusters for their communication depending on the availability of the channel. However, channel availability is intermittent because of opportunistic spectrum access. This may result in reforming of the cluster again and again. To avoid this frequent re-clustering and to maintain connectivity among the UAVs, in this paper, we present a resilient clustering technique with a concept of introducing a backup channel for each cluster. Simulation results show the significance of the proposed technique

A survey and taxonomy on nonorthogonal multiple-access schemes for 5G networks

Copyright © 2017 John Wiley & Sons, Ltd. The intensity in the requirements of Internet of Things and mobile internet makes the efficiency of fifth-generation (5G) wireless communications very challenging to achieve. Accomplishing the drastically increasing demand of massive connectivity and high spectral efficiency is a strenuous task. Because of the very large number of devices, 5G wireless communication systems are inevitable to satisfy the traffic requirements. Recently, nonorthogonal multiple-access (NOMA) schemes are immensely being explored to address the challenges in 5G, which include effective bandwidth utilization, support for a massive number of devices, and low latency. This paper provides the reader with a holistic view of multiple-access schemes, methods, and strategies for optimization in NOMA. First, we discuss the taxonomy of multiple-access schemes in the literature; then, we provide a detailed discussion of objectives, constraints, problem types, and solution approaches for NOMA. This paper also discusses the decoding methods and key performance indicators used in NOMA. Finally, we outline future research directions

Charging infrastructure placement for electric vehicles: An optimization prospective

© 2017 IEEE. Electric Vehicles (EVs) can be considered as a step forward towards the green environment and economical transportation. Moreover, EVs offer fuel economy, clean environment, and less cost of vehicle charging as compared to gasoline refilling. These are the main motivations towards the adaptation of EVs by the users. In order to increase the penetration of EVs into the transportation system, the EV charging stations become necessary to fulfill the charging needs. The charging stations can be placed considering different scenarios and objectives. Placement of charging stations in the service area requires a huge amount of budget and their locations are critical to select. In this paper, we formulate an optimization problem with an objective to minimize the overall cost of the charging infrastructure placement subject to the constraint on charging requirements in the service area. The proposed problem is solved using the branch and bound algorithm. Simulations results show the effectiveness of proposed placement strategy to minimize overall placement cost