Secure and robust multi-constrained QoS aware routing algorithm for VANETs

Secure QoS routing algorithms are a fundamental part of wireless networks that aim to provide services with QoS and security guarantees. In Vehicular Ad hoc Networks (VANETs), vehicles perform routing functions, and at the same time act as end-systems thus routing control messages are transmitted unprotected over wireless channels. The QoS of the entire network could be degraded by an attack on the routing process, and manipulation of the routing control messages. In this paper, we propose a novel secure and reliable multi-constrained QoS aware routing algorithm for VANETs. We employ the Ant Colony Optimisation (ACO) technique to compute feasible routes in VANETs subject to multiple QoS constraints determined by the data traffic type. Moreover, we extend the VANET-oriented Evolving Graph (VoEG) model to perform plausibility checks on the exchanged routing control messages among vehicles. Simulation results show that the QoS can be guaranteed while applying security mechanisms to ensure a reliable and robust routing service

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

A Novel Approach for Enhancing Routing in Wireless Sensor Networks using ACO Algorithm

Wireless Sensors Network (WSN) is an emergent technology that aims to offer innovative capacities. In the last decade, the use of these networks increased in various fields like military, science, and health due to their fast and inexpressive deployment and installation. However, the limited sensor battery lifetime poses many technical challenges and affects essential services like routing. This issue is a hot topic of search, many researchers have proposed various routing protocols aimed at reducing the energy consumption in WSNs. The focus of this work is to investigate the effectiveness of integrating ACO algorithm with routing protocols in WSNs. Moreover, it presents a novel approach inspired by ant colony optimization (ACO) to be deployed as a new routing protocol that addresses key challenges in wireless sensor networks. The proposed protocol can significantly minimize nodes energy consumption, enhance the network lifetime, reduce latency, and expect performance in various scenarios

BSRS: Best Stable Route Selection Algorithm for Wireless Sensor Network Applications

Topological changes in sensor networks frequently render routing paths unusable. Such recurrent path failures have detrimental effects on the network ability to support QoS-driven services. Because of connectivity richness in sensor networks, there often exist multiple paths between a source and a destination. Since many applications require uninterrupted connectivity of a session, the ability to find long-living paths can be very useful. In this paper, we propose Best Stable Route Selection (BSRS) approach based on Artificial Bee Colony based search algorithm, ensures that contributes stable quality performance of network and to calculate the best stable path services randomly based on QoS parameter requirements and existing circulation load; so that efficient route selection can easily capture by designing of proposed BSRS approach. The implementation of the proposed BSRS technique is implemented using NS2 simulation environment and the AODV routing protocol is used to incorporate the proposed algorithm. The experimental results are measured in terms of end to end delay, throughput, packet delivery ratio, and energy consumption and routing overhead. The results show the proposed BSRS algorithm improves the flexibility of network node and performance of network when multiple inefficient paths exist

HBMFTEFR: Design of a Hybrid Bioinspired Model for Fault-Tolerant Energy Harvesting Networks via Fuzzy Rule Checks

Designing energy harvesting networks requires modelling of energy distribution under different real-time network conditions. These networks showcase better energy efficiency, but are affected by internal & external faults, which increase energy consumption of affected nodes. Due to this probability of node failure, and network failure increases, which reduces QoS (Quality of Service) for the network deployment. To overcome this issue, various fault tolerance & mitigation models are proposed by researchers, but these models require large training datasets & real-time samples for efficient operation. This increases computational complexity, storage cost & end-to-end processing delay of the network, which reduces its QoS performance under real-time use cases. To mitigate these issues, this text proposes design of a hybrid bioinspired model for fault-tolerant energy harvesting networks via fuzzy rule checks. The proposed model initially uses a Genetic Algorithm (GA) to cluster nodes depending upon their residual energy & distance metrics. Clustered nodes are processed via Particle Swarm Optimization (PSO) that assists in deploying a fault-tolerant & energy-harvesting process. The PSO model is further augmented via use of a hybrid Ant Colony Optimization (ACO) Model with Teacher Learner Based Optimization (TLBO), which assists in value-based fault prediction & mitigation operations. All bioinspired models are trained-once during initial network deployment, and then evaluated subsequently for each communication request. After a pre-set number of communications are done, the model re-evaluates average QoS performance, and incrementally reconfigures selected solutions. Due to this incremental tuning, the model is observed to consume lower energy, and showcases lower complexity when compared with other state-of-the-art models. Upon evaluation it was observed that the proposed model showcases 15.4% lower energy consumption, 8.5% faster communication response, 9.2% better throughput, and 1.5% better packet delivery ratio (PDR), when compared with recently proposed energy harvesting models. The proposed model also showcased better fault prediction & mitigation performance when compared with its counterparts, thereby making it useful for a wide variety of real-time network deployments

QoS multicast tree construction in IP/DWDM optical internet by bio-inspired algorithms

Copyright @ Elsevier Ltd. All rights reserved.In this paper, two bio-inspired Quality of Service (QoS) multicast algorithms are proposed in IP over dense wavelength division multiplexing (DWDM) optical Internet. Given a QoS multicast request and the delay interval required by the application, both algorithms are able to find a flexible QoS-based cost suboptimal routing tree. They first construct the multicast trees based on ant colony optimization and artificial immune algorithm, respectively. Then a dedicated wavelength assignment algorithm is proposed to assign wavelengths to the trees aiming to minimize the delay of the wavelength conversion. In both algorithms, multicast routing and wavelength assignment are integrated into a single process. Therefore, they can find the multicast trees on which the least wavelength conversion delay is achieved. Load balance is also considered in both algorithms. Simulation results show that these two bio-inspired algorithms can construct high performance QoS routing trees for multicast applications in IP/DWDM optical Internet.This work was supported in part ny the Program for New Century Excellent Talents in University, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1, the National Natural Science Foundation of China under Grant no. 60673159 and 70671020, the National High-Tech Reasearch and Development Plan of China under Grant no. 2007AA041201, and the Specialized Research Fund for the Doctoral Program of Higher Education under Grant no. 20070145017

An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks

Maximizing the lifetime of wireless sensor networks (WSNs) is a challenging problem. Although some methods exist to address the problem in homogeneous WSNs, research on this problem in heterogeneous WSNs have progressed at a slow pace. Inspired by the promising performance of ant colony optimization (ACO) to solve combinatorial problems, this paper proposes an ACO-based approach that can maximize the lifetime of heterogeneous WSNs. The methodology is based on finding the maximum number of disjoint connected covers that satisfy both sensing coverage and network connectivity. A construction graph is designed with each vertex denoting the assignment of a device in a subset. Based on pheromone and heuristic information, the ants seek an optimal path on the construction graph to maximize the number of connected covers. The pheromone serves as a metaphor for the search experiences in building connected covers. The heuristic information is used to reflect the desirability of device assignments. A local search procedure is designed to further improve the search efficiency. The proposed approach has been applied to a variety of heterogeneous WSNs. The results show that the approach is effective and efficient in finding high-quality solutions for maximizing the lifetime of heterogeneous WSNs