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

Energy-efficient routing protocols in heterogeneous wireless sensor networks

Sensor networks feature low-cost sensor devices with wireless network capability, limited transmit power, resource constraints and limited battery energy. The usage of cheap and tiny wireless sensors will allow very large networks to be deployed at a feasible cost to provide a bridge between information systems and the physical world. Such large-scale deployments will require routing protocols that scale to large network sizes in an energy-efficient way. This thesis addresses the design of such network routing methods. A classification of existing routing protocols and the key factors in their design (i.e., hardware, topology, applications) provides the motivation for the new three-tier architecture for heterogeneous networks built upon a generic software framework (GSF). A range of new routing algorithms have hence been developed with the design goals of scalability and energy-efficient performance of network protocols. They are respectively TinyReg - a routing algorithm based on regular-graph theory, TSEP - topological stable election protocol, and GAAC - an evolutionary algorithm based on genetic algorithms and ant colony algorithms. The design principle of our routing algorithms is that shortening the distance between the cluster-heads and the sink in the network, will minimise energy consumption in order to extend the network lifetime, will achieve energy efficiency. Their performance has been evaluated by simulation in an extensive range of scenarios, and compared to existing algorithms. It is shown that the newly proposed algorithms allow long-term continuous data collection in large networks, offering greater network longevity than existing solutions. These results confirm the validity of the GSF as an architectural approach to the deployment of large wireless sensor networks

A Quality of Service Aware Source Routing Based Protocol for Underwater Wireless Sensor Networks

Underwater Wireless Sensor Networks (UWSNs) handle many underwater applications such as environment monitoring, surveillance and navigation. These applications generate varied types of traffic such as continuous bit rate, sporadic and different packet sizes, leading to additional QoS requirements that are traffic and application dependent. This paper presents the development of a Quality of Service Aware Source Routing (QASR) protocol. QASR discovers multiple paths from the sources to the sinks and selects the most QoS compatible route among them. QASR is distinctive because it incorporates multiple QoS parameters such as Signal to Noise Ratio (SNR), latency and residual energy. Depending on which of these parameters are chosen, QASR has three variants, namely, QASR-Latency (QASR-L), QASR-Residual Energy (QASR-RE) and QASR-Signal to Noise Ratio (QASR-SNR). The performance of QASR protocol is compared against traditional source routing protocols, with simulations showing a reduction of about 10% to 20% in latency and about 5% to 10% lesser energy consumption than source routing. QASR protocol exhibits comparable performance to classic source routing protocols while simultaneously adhering to the QoS requirements of the application. It is also worth noting that the performance profile of all the three variants of QASR do not have sudden and drastic variations, with the performance profiles showing consistent trend-lines

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

MPIGA – Multipath Selection Using Improved Genetic Algorithm

The Wireless Multimedia Networks (WMNs) have developed due to the extensive applications of wireless devices and increasing availability of lower cost hardware. The WMNs are used to transmit the multimedia content like audio and video streaming and they can be deployed within a lower budget. These networks can also be used in real-time data applications that demand energy-efficient management and awareness of Quality of Service (QoS). The energy resources are limited in the wireless devices that lead to the significant threats on the QoS for WMNs. An energy-efficient routing technique is needed to handle the dynamic topology of WMN that includes a vital resource as energy. The energy-efficient routing method was proposed in this work for the purpose of data communication based on a cluster head selection from each cluster in addition to the multipath route selection to reduce the network overhead and energy consumption. The cluster heads for each cluster are selected based on Node Coverage & average residual energy parameters.In this work, the proposed energy efficient routing algorithm uses improved genetic algorithm (IGA)based on a cost function for dynamic selection of the best path. The proposed cost function uses link lifetime &average link delay parameters to estimate the link cost. The proposed algorithm’s performance compared with other previous routing methods based on extensive simulation analysis. The results showed that the proposed method achieves better performance over three other routing techniques