3,609 research outputs found
Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications
We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches
QoS routing in ad-hoc networks using GA and multi-objective optimization
Much work has been done on routing in Ad-hoc networks, but the proposed routing solutions only deal with the best effort data traffic. Connections with Quality of Service (QoS) requirements, such as voice channels with delay and bandwidth constraints, are not supported. The QoS routing has been receiving increasingly intensive attention, but searching for the shortest path with many metrics is an NP-complete problem. For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. Also, the routing methods should be adaptive, flexible, and intelligent. In this paper, we use Genetic Algorithms (GAs) and multi-objective optimization for QoS routing in Ad-hoc Networks. In order to reduce the search space of GA, we implemented a search space reduction algorithm, which reduces the search space for GAMAN (GA-based routing algorithm for Mobile Ad-hoc Networks) to find a new route. We evaluate the performance of GAMAN by computer simulations and show that GAMAN has better behaviour than GLBR (Genetic Load Balancing Routing).Peer ReviewedPostprint (published version
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5G multi-layer routing strategies for TV white space secondary user access
As mobile applications and services have developed, the dramatic growth in user data traffic has led to the legacy channels becoming ever more congested with the commensurate requirement for more spectrum. This has motivated both regulatory bodies and industry to investigate innovative strategies to increase the existing spectral efficiency. Prominent examples include both Long Term Evolution (LTE) which employs orthogonal frequency-division modulation technology to improve bandwidth efficiency, and heterogeneous networks, which facilitate the offloading of data traffic between technologies such as from LTE to Wi-Fi and vice versa. Furthermore, as 5G mobile technology and related standards mature, there is an impetus to address the issue of secondary user (SU) spectrum access in which TV White Space (TVWS) is the prime contender. Two nascent viewpoints have emerged as to how this will evolve: i) greater coverage, ii) increased throughput allied with lower latency. This paper presents a novel TVWS framework that successfully fulfils both criteria to ensure 5G services can both exploit TVWS spectrum and protect the benefits of SU access and quality-of-service provision by using a routing strategy on the Access Network Discovery and Selection Function server to dynamically determine the most suitable heterogeneous technology for the new framework
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