Expanding cellular coverage via cell-edge deployment in heterogeneous networks: spectral efficiency and backhaul power consumption perspectives

Heterogeneous small-cell networks (HetNets) are considered to be a standard part of future mobile networks where operator/consumer deployed small-cells, such as femtocells, relays, and distributed antennas (DAs), complement the existing macrocell infrastructure. This article proposes the need-oriented deployment of smallcells and device-to-device (D2D) communication around the edge of the macrocell such that the small-cell base stations (SBSs) and D2D communication serve the cell-edge mobile users, thereby expanding the network coverage and capacity. In this context, we present competitive network configurations, namely, femto-on-edge, DA-onedge, relay-on-edge, and D2D-communication on- edge, where femto base stations, DA elements, relay base stations, and D2D communication, respectively, are deployed around the edge of the macrocell. The proposed deployments ensure performance gains in the network in terms of spectral efficiency and power consumption by facilitating the cell-edge mobile users with small-cells and D2D communication. In order to calibrate the impact of power consumption on system performance and network topology, this article discusses the detailed breakdown of the end-to-end power consumption, which includes backhaul, access, and aggregation network power consumptions. Several comparative simulation results quantify the improvements in spectral efficiency and power consumption of the D2D-communication-onedge configuration to establish a greener network over the other competitive configurations

A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications

The advent of Wireless Sensor Networks (WSNs) has inspired various sciences and telecommunication with its applications, there is a growing demand for robust methodologies that can ensure extended lifetime. Sensor nodes are small equipment which may hold less electrical energy and preserve it until they reach the destination of the network. The main concern is supposed to carry out sensor routing process along with transferring information. Choosing the best route for transmission in a sensor node is necessary to reach the destination and conserve energy. Clustering in the network is considered to be an effective method for gathering of data and routing through the nodes in wireless sensor networks. The primary requirement is to extend network lifetime by minimizing the consumption of energy. Further integrating cognitive radio technique into sensor networks, that can make smart choices based on knowledge acquisition, reasoning, and information sharing may support the network's complete purposes amid the presence of several limitations and optimal targets. This examination focuses on routing and clustering using metaheuristic techniques and machine learning because these characteristics have a detrimental impact on cognitive radio wireless sensor node lifetime

Optimal Wideband LPDA Design for Efficient Multimedia Content Delivery over Emerging Mobile Computing Systems

An optimal synthesis of a wideband Log-Periodic Dipole Array (LPDA) is introduced in the present study. The LPDA optimization is performed under several requirements concerning the standing wave ratio, the forward gain, the gain flatness, the front-to-back ratio and the side lobe level, over a wide frequency range. The LPDA geometry that complies with the above requirements is suitable for efficient multimedia content delivery. The optimization process is accomplished by applying a recently introduced method called Invasive Weed Optimization (IWO). The method has already been compared to other evolutionary methods and has shown superiority in solving complex non-linear problems in telecommunications and electromagnetics. In the present study, the IWO method has been chosen to optimize an LPDA for operation in the frequency range 800-3300 MHz. Due to its excellent performance, the LPDA can effectively be used for multimedia content reception over future mobile computing systems

UAV Aided Data Collection for Wildlife Monitoring using Cache-enabled Mobile Ad-hoc Wireless Sensor Nodes

Unmanned aerial vehicle (UAV) assisted data collection is not a new concept and has been used in various mobile ad hoc networks. In this paper, we propose a caching assisted scheme alternative to routing in MANETs for the purpose of wildlife monitoring. Rather than deploying a routing protocol, data is collected and transported to and from a base station using a UAV. Although some literature exists on such an approach, we propose the use of intermediate caching between the mobile nodes and compare it to a baseline scenario where no caching is used. The paper puts forward our communication design where we have simulated the movement of multiple mobile sensor nodes in a field that move according to the Levy walk model imitating wildlife animal foraging and a UAV that makes regular trips across the field to collect data from them. The unmanned aerial vehicle can collect data not only from the current node it is communicating with but also data of other nodes that this node came into contact with. Simulations show that exchanging cached data is highly advantages as the drone can indirectly communicate with many more mobile nodes

Uav-assisted data collection in wireless sensor networks: A comprehensive survey

Wireless sensor networks (WSNs) are usually deployed to different areas of interest to sense phenomena, process sensed data, and take actions accordingly. The networks are integrated with many advanced technologies to be able to fulfill their tasks that is becoming more and more complicated. These networks tend to connect to multimedia networks and to process huge data over long distances. Due to the limited resources of static sensor nodes, WSNs need to cooperate with mobile robots such as unmanned ground vehicles (UGVs), or unmanned aerial vehicles (UAVs) in their developments. The mobile devices show their maneuverability, computational and energystorage abilities to support WSNs in multimedia networks. This paper addresses a comprehensive survey of almost scenarios utilizing UAVs and UGVs with strogly emphasising on UAVs for data collection in WSNs. Either UGVs or UAVs can collect data from static sensor nodes in the monitoring fields. UAVs can either work alone to collect data or can cooperate with other UAVs to increase their coverage in their working fields. Different techniques to support the UAVs are addressed in this survey. Communication links, control algorithms, network structures and different mechanisms are provided and compared. Energy consumption or transportation cost for such scenarios are considered. Opening issues and challenges are provided and suggested for the future developments

An intelligent call admission control algorithm for load balancing in 5G-satellite networks

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Cellular networks are projected to deal with an immense rise in data traffic, as well as an enormous and diverse device, plus advanced use cases, in the nearest future; hence, future 5G networks are being developed to consist of not only 5G but also different RATs integrated. In addition to 5G, the user’s device (UD) will be able to connect to the network via LTE, WiMAX, Wi-Fi, Satellite, and other technologies. On the other hand, Satellite has been suggested as a preferred network to support 5G use cases. Satellite networks are among the most sophisticated communication technologies which offer specific benefits in geographically dispersed and dynamic networks. Utilising their inherent advantages in broadcasting capabilities, global coverage, decreased dependency on terrestrial infrastructure, and high security, they offer highly efficient, effective, and rapid network deployments. Satellites are more suited for large-scale communications than terrestrial communication networks. Due to their extensive service coverage and strong multilink transmission capabilities, satellites offer global high-speed connectivity and adaptable access systems. The convergence of 5G technology and satellite networks therefore marks a significant milestone in the evolution of global connectivity. However, this integration introduces a complex problem related to resource management, particularly in Satellite – Terrestrial Integrated Networks (STINs). The key issue at hand is the efficient allocation of resources in STINs to enhance Quality of Service (QoS) for users. The root cause of this issue originates from a vast quantity of users sharing these resources, the dynamic nature of generated traffic, the scarcity of wireless spectrum resources, and the random allocation of wireless channels. Hence, resource allocation is critical to ensure user satisfaction, fair traffic distribution, maximised throughput, and minimised congestion. Achieving load balancing is essential to guarantee an equal amount of traffic distributed between different RATs in a heterogeneous wireless network; this would enable optimal utilisation of the radio resources and lower the likelihood of call blocking/dropping. This research endeavours to address this challenge through the development and evaluation of an intelligent call admission control (CAC) algorithm based on Enhanced Particle Swarm Optimization (EPSO). The primary aim of this research is to design an EPSO-based CAC algorithm tailored specifically for 5G-satellite heterogeneous wireless networks. The algorithm's objectives include maximising the number of admitted calls while maintaining Quality of Service (QoS) for existing users, improving network resource utilization, reducing congestion, ensuring fairness, and enhancing user satisfaction. To achieve these objectives, a detailed research methodology is outlined, encompassing algorithm development, numerical simulations, and comparative analysis. The proposed EPSO algorithm is benchmarked against alternative artificial intelligence and machine learning algorithms, including the Artificial Bee Colony algorithm, Simulated Annealing algorithm, and Q-Learning algorithm. Performance metrics such as throughput, call blocking rates, and fairness are employed to evaluate the algorithms' efficacy in achieving load-balancing objectives. The experimental findings yield insights into the performance of the EPSO-based CAC algorithm and its comparative advantages over alternative techniques. Through rigorous analysis, this research elucidates the EPSO algorithm's strengths in dynamically adapting to changing network conditions, optimising resource allocation, and ensuring equitable distribution of traffic among different RATs. The result shows the EPSO algorithm outperforms the other 3 algorithms in all the scenarios. The contributions of this thesis extend beyond academic research, with potential societal implications including enhanced connectivity, efficiency, and user experiences in 5G-Satellite heterogeneous wireless networks. By advancing intelligent resource management techniques, this research paves the way for improved network performance and reliability in the evolving landscape of wireless communication