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

Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

The deployment of the sensor nodes (SNs) always plays a decisive role in the system performance of wireless sensor networks (WSNs). In this work, we propose an optimal deployment method for practical heterogeneous WSNs which gives a deep insight into the trade-off between the reliability and deployment cost. Specifically, this work aims to provide the optimal deployment of SNs to maximize the coverage degree and connection degree, and meanwhile minimize the overall deployment cost. In addition, this work fully considers the heterogeneity of SNs (i.e. differentiated sensing range and deployment cost) and three-dimensional (3-D) deployment scenarios. This is a multi-objective optimization problem, non-convex, multimodal and NP-hard. To solve it, we develop a novel swarm-based multi-objective optimization algorithm, known as the competitive multi-objective marine predators algorithm (CMOMPA) whose performance is verified by comprehensive comparative experiments with ten other stateof-the-art multi-objective optimization algorithms. The computational results demonstrate that CMOMPA is superior to others in terms of convergence and accuracy and shows excellent performance on multimodal multiobjective optimization problems. Sufficient simulations are also conducted to evaluate the effectiveness of the CMOMPA based optimal SNs deployment method. The results show that the optimized deployment can balance the trade-off among deployment cost, sensing reliability and network reliability. The source code is available on https://github.com/iNet-WZU/CMOMPA.Comment: 25 page

Novel Scheme for Minimal Iterative PSO Algorithm for Extending Network Lifetime of Wireless Sensor Network

Clustering is one of the operations in the wireless sensor network that offers both streamlined data routing services as well as energy efficiency. In this viewpoint, Particle Swarm Optimization (PSO) has already proved its effectiveness in enhancing clustering operation, energy efficiency, etc. However, PSO also suffers from a higher degree of iteration and computational complexity when it comes to solving complex problems, e.g., allocating transmittance energy to the cluster head in a dynamic network. Therefore, we present a novel, simple, and yet a cost-effective method that performs enhancement of the conventional PSO approach for minimizing the iterative steps and maximizing the probability of selecting a better clustered. A significant research contribution of the proposed system is its assurance towards minimizing the transmittance energy as well as receiving energy of a cluster head. The study outcome proved proposed a system to be better than conventional system in the form of energy efficiency

A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications

The Artificial Fish Swarm Algorithm (AFSA) is inspired by the ecological behaviors of fish schooling in nature, viz., the preying, swarming, following and random behaviors. Owing to a number of salient properties, which include flexibility, fast convergence, and insensitivity to the initial parameter settings, the family of AFSA has emerged as an effective Swarm Intelligence (SI) methodology that has been widely applied to solve real-world optimization problems. Since its introduction in 2002, many improved and hybrid AFSA models have been developed to tackle continuous, binary, and combinatorial optimization problems. This paper aims to present a concise review of the family of AFSA, encompassing the original ASFA and its improvements, continuous, binary, discrete, and hybrid models, as well as the associated applications. A comprehensive survey on the AFSA from its introduction to 2012 can be found in [1]. As such, we focus on a total of {\color{blue}123} articles published in high-quality journals since 2013. We also discuss possible AFSA enhancements and highlight future research directions for the family of AFSA-based models.Comment: 37 pages, 3 figure

3D Multi-Objective Deployment of an Industrial Wireless Sensor Network for Maritime Applications Utilizing a Distributed Parallel Algorithm

Effective monitoring marine environment has become a vital problem in the marine applications. Traditionally, marine application mostly utilizes oceanographic research vessel methods to monitor the environment and human parameters. But these methods are usually expensive and time-consuming, also limited resolution in time and space. Due to easy deployment and cost-effective, WSNs have recently been considered as a promising alternative for next generation IMGs. This paper focuses on solving the issue of 3D WSN deployment in a 3D engine room space of a very large crude-oil carrier (VLCC), in which many power devices are also considered. To address this 3D WSN deployment problem for maritime applications, a 3D uncertain coverage model is proposed with a new 3D sensing model and an uncertain fusion operator, is presented. The deployment problem is converted into a multi-objective problems (MOP) in which three objectives are simultaneously considered: Coverage, Lifetime and Reliability. Our aim is to achieve extensive Coverage, long Lifetime and high Reliability. We also propose a distributed parallel cooperative co-evolutionary multi-objective large-scale evolutionary algorithm (DPCCMOLSEA) for maritime applications. In the simulation experiments, the effectiveness of this algorithm is verified in comparing with five state-of-the-art algorithms. The numerical outputs demonstrate that the proposed method performs the best with respect to both optimization performance and computation time

Route Swarm: Wireless Network Optimization through Mobility

In this paper, we demonstrate a novel hybrid architecture for coordinating networked robots in sensing and information routing applications. The proposed INformation and Sensing driven PhysIcally REconfigurable robotic network (INSPIRE), consists of a Physical Control Plane (PCP) which commands agent position, and an Information Control Plane (ICP) which regulates information flow towards communication/sensing objectives. We describe an instantiation where a mobile robotic network is dynamically reconfigured to ensure high quality routes between static wireless nodes, which act as source/destination pairs for information flow. The ICP commands the robots towards evenly distributed inter-flow allocations, with intra-flow configurations that maximize route quality. The PCP then guides the robots via potential-based control to reconfigure according to ICP commands. This formulation, deemed Route Swarm, decouples information flow and physical control, generating a feedback between routing and sensing needs and robotic configuration. We demonstrate our propositions through simulation under a realistic wireless network regime.Comment: 9 pages, 4 figures, submitted to the IEEE International Conference on Intelligent Robots and Systems (IROS) 201

Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey

In recent years, Wireless sensor communication is growing expeditiously on the capability to gather information, communicate and transmit data effectively. Clustering is the main objective of improving the network lifespan in Wireless sensor network. It includes selecting the cluster head for each cluster in addition to grouping the nodes into clusters. The cluster head gathers data from the normal nodes in the cluster, and the gathered information is then transmitted to the base station. However, there are many reasons in effect opposing unsteady cluster head selection and dead nodes. The technique for selecting a cluster head takes into factors to consider including residual energy, neighbors’ nodes, and the distance between the base station to the regular nodes. In this study, we thoroughly investigated by number of methods of selecting a cluster head and constructing a cluster. Additionally, a quick performance assessment of the techniques' performance is given together with the methods' criteria, advantages, and future directions