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

An Intelligent Mobility Prediction Scheme for Location-Based Service over Cellular Communications Network

One of the trickiest challenges introduced by cellular communications networks is mobility prediction for Location Based-Services (LBSs). Hence, an accurate and efficient mobility prediction technique is particularly needed for these networks. The mobility prediction technique incurs overheads on the transmission process. These overheads affect properties of the cellular communications network such as delay, denial of services, manual filtering and bandwidth. The main goal of this research is to enhance a mobility prediction scheme in cellular communications networks through three phases. Firstly, current mobility prediction techniques will be investigated. Secondly, innovation and examination of new mobility prediction techniques will be based on three hypothesises that are suitable for cellular communications network and mobile user (MU) resources with low computation cost and high prediction success rate without using MU resources in the prediction process. Thirdly, a new mobility prediction scheme will be generated that is based on different levels of mobility prediction. In this thesis, a new mobility prediction scheme for LBSs is proposed. It could be considered as a combination of the cell and routing area (RA) prediction levels. For cell level prediction, most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the New Markov-Based Mobility Prediction (NMMP) and Prediction Location Model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression and insufficient accuracy. In this thesis, Location Prediction based on a Sector Snapshot (LPSS) is introduced, which is based on a Novel Cell Splitting Algorithm (NCPA). This algorithm is implemented in a micro cell in parallel with the new prediction technique. The LPSS technique, compared with two classic prediction techniques and the experimental results, shows the effectiveness and robustness of the new splitting algorithm and prediction technique. In the cell side, the proposed approach reduces the complexity cost and prevents the cell level prediction technique from performing in time slots that are too close. For these reasons, the RA avoids cell-side problems. This research discusses a New Routing Area Displacement Prediction for Location-Based Services (NRADP) which is based on developed Ant Colony Optimization (ACO). The NRADP, compared with Mobility Prediction based on an Ant System (MPAS) and the experimental results, shows the effectiveness, higher prediction rate, reduced search stagnation ratio, and reduced computation cost of the new prediction technique

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

ACODV : Ant Colony Optimisation Distance Vector routing in ad hoc networks

A mobile ad hoc network is a collection of wireless mobile devices which dynamically form a temporary network, without using any existing network infrastructure or centralised administration. Each node in the network effectively becomes a router, and forwards packets towards the packet’s destination node. Ad hoc networks are characterized by frequently changing network topology, multi-hop wireless connections and the need for dynamic, efficient routing protocols. The overarching requirement for low power consumption, as battery powered sensors may be required to operate for years without battery replacement; An emphasis on reliable communication as opposed to real-time communication, it is more important for packets to arrive reliably than to arrive quickly; and Very scarce processing and memory resources, as these sensors are often implemented on small low-power microprocessors. This work provides overviews of routing protocols in ad hoc networks, swarm intelligence, and swarm intelligence applied to ad hoc routing. Various mechanisms that are commonly encountered in ad hoc routing are experimentally evaluated under situations as close to real-life as possible. Where possible, enhancements to the mechanisms are suggested and evaluated. Finally, a routing protocol suitable for such low-power sensor networks is defined and benchmarked in various scenarios against the Ad hoc On-Demand Distance Vector (AODV) algorithm.Dissertation (MSc)--University of Pretoria, 2005.Computer ScienceUnrestricte

ANTMANET: a novel routing protocol for mobile ad-hoc networks based on ant colony optimisation

The core aim of this research is to present “ANTMANET” a novel routing protocol for Mobile Ad-Hoc networks. The proposed protocol aims to reduce the network overhead and delay introduced by node mobility in MANETs. There are two techniques embedded in this protocol, the “Local Zone” technique and the “North Neighbour” Table. They take an advantage of the fact that the nodes can obtain their location information by any means to reduce the network overhead during the route discovery phase and reduced the size of the routing table to guarantee faster convergence. ANTMANET is a hybrid Ant Colony Optimisation-based (ACO) routing protocol. ACO is a Swarm Intelligence (SI) routing algorithm that is well known for its high-quality performance compared to other distributed routing algorithms such as Link State and Distance Vector. ANTMANET has been benchmarked in various scenarios against the ACO routing protocol ANTHOCNET and several standard routing protocols including the Ad-Hoc On-Demand Distance Vector (AODV), Landmark Ad-Hoc Routing (LANMAR), and Dynamic MANET on Demand (DYMO). Performance metrics such as overhead, end-to-end delay, throughputs and jitter were used to evaluate ANTMANET performance. Experiments were performed using the QualNet simulator. A benchmark test was conducted to evaluate the performance of an ANTMANET network against an ANTHOCNET network, with both protocols benchmarked against AODV as an established MANET protocol. ANTMANET has demonstrated a notable performance edge when the core algorithm has been optimised using the novel adaptation method that is proposed in this thesis. Based on the simulation results, the proposed protocol has shown 5% less End-to-End delay than ANTHOCNET. In regard to network overhead, the proposed protocol has shown 20% less overhead than ANTHOCNET. In terms of comparative throughputs ANTMANET in its finest performance has delivered 25% more packets than ANTHOCNET. The overall validation results indicate that the proposed protocol was successful in reducing the network overhead and delay in high and low mobility speeds when compared with the AODV, DMO and LANMAR protocols. ANTMANET achieved at least a 45% less delay than AODV, 60% less delay than DYMO and 55% less delay than LANMAR. In terms of throughputs; ANTMANET in its best performance has delivered 35% more packets than AODV, 40% more than DYMO and 45% more than LANMAR. With respect to the network overhead results, ANTMANET has illustrated 65% less overhead than AODV, 70% less than DYMO and 60 % less than LANMAR. Regarding the Jitter, ANTMANET at its best has shown 60% less jitter than AODV, 55% jitter less than DYMO and 50% less jitter than LANMAR

Secure multi-constrained QoS reliable routing algorithm for vehicular ad hoc networks (VANETs)

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonVehicular Ad hoc Networks (VANETs) are a particular form of wireless network made by vehicles communicating among themselves and with roadside base stations. A wide range of services has been developed for VANETs ranging from safety to infotainment applications. A key requirement for such services is that they are offered with Quality of Service (QoS) guarantees in terms of service reliability and availability. Furthermore, due to the openness of VANET’s wireless channels to both internal and external attacks, the application of security mechanisms is mandatory to protect the offered QoS guarantees. QoS routing plays an essential role in identifying routes that meet the QoS requirements of the offered service over VANETs. However, searching for feasible routes subject to multiple QoS constraints is in general an NP-hard problem. Moreover, routing reliability needs to be given special attention as communication links frequently break in VANETs. To date, most existing QoS routing algorithms are designed for stable networks without considering the security of the routing process. Therefore, they are not suitable for applications in VANETs. In this thesis, the above issues are addressed firstly by developing a link reliability model based on the topological and mathematical properties of vehicular movements and velocities. Evolving graph theory is then utilised to model the VANET communication graph and integrate the developed link reliability model into it. Based on the resulting extended evolving graph model, the most reliable route in the network is picked. Secondly, the situational awareness model is applied to the developed reliable routing process because picking the most reliable route does not guarantee reliable transmission. Therefore, a situation-aware reliable multipath routing algorithm for VANETs is proposed. Thirdly, the Ant Colony Optimisation (ACO) technique is employed to propose an Ant-based multi-constrained QoS (AMCQ) routing algorithm for VANETs. AMCQ is designed to give significant advantages to the implementation of security mechanisms that are intended to protect the QoS routing process. Finally, a novel set of security procedures is proposed to defend the routing process against external and internal threats. Simulation results demonstrate that high levels of QoS can be still guaranteed by AMCQ even when the security procedures are applied

APPLICATION OF SOFT COMPUTING TECHNIQUES OVER HARD COMPUTING TECHNIQUES: A SURVEY

Soft computing is the fusion of different constituent elements. The main aim of this fusion to solve real-world problems, which are not solve by traditional approach that is hard computing. Actually, in our daily life maximum problem having uncertainty and vagueness information. So hard computing fail to solve this problems, because it give exact solution. To overcome this situation soft computing techniques plays a vital role, because it has capability to deal with uncertainty and vagueness and produce approximate result. This paper focuses on application of soft computing techniques over hard computing techniques

A Self-Organising Distributed Location Server for Ad Hoc Networks

Wireless networks allow communication between multiple devices (nodes) without the use of wires. Range in such networks is often limited restricting the use of networks to small offices and homes; however, it is possible to use nodes to forward packets for others thereby extending the communication range of individual nodes. Networks employing such forwarding are called Multi-Hop Ad Hoc Networks (MANETS) Discovering routes in MANETS is a challenging task given that the topology is flat and node addresses reveal nothing about their place in the network. In addition, nodes may move or leave changing the network topology quickly. Existing approaches to discovering locations involve either broadcast dissemination or broadcast route discovery throughout the entire network. The reliance on the use of techniques that use broadcast schemes restricts the size of network that the techniques are applicable to. Routing in large scale ad hoc networks is therefore achieved by the use of geographical forwarding. Each node is required to know its location and that of its neighbours so that it may use this information for forward packets. The next hop chosen is the neighbour that is closest to the destination and a number of techniques are used to handle scenarios here the network has areas void of nodes. Use of such geographical routing techniques requires knowledge of the destination's location. This is provided by location servers and the literature proposes a number of methods of providing them. Unfortunately many of the schemes are limited by using a proportion of the network that increases with size, thereby immediately limiting the scalability. Only one technique is surveyed that provides high scalability but it has a number of limitations in terms of handling node mobility and failure. Ad hoc networks have limited capacity and so the inspiration for a technique to address these shortcomings comes from observations of nature. Birds and ants are able to organise themselves without direct communication through the observation of their environment and their peers. They provide an emergent intelligence based on individual actions rather than group collaboration. This thesis attempts to discover whether software agents can mimic this by creating a group of agents to store location information in a specific location. Instead of requiring central co-ordination, the agents observe one another and make individual decisions to create an emergent intelligence that causes them to resist mobility and node failures. The new technique is called a Self Organising Location Server (SOLS) and is compared against existing approaches to location servers. Most existing techniques do not scale well whereas SOLS uses a new idea of a home location. The use of this idea and the self organising behaviour of the agents that store the information results in significant benefits in performance. SOLS significantly out performs Terminode home region, the only other scalable approach surveyed. SOLS is able to tolerate much higher node failure rates than expected in likely implementations of large scale ad hoc networks. In addition, SOLS successfully mitigates node mobility which is likely to be encountered in an ad hoc network

Intelligent MANET optimisation system

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In the literature, various Mobile Ad hoc NETwork (MANET) routing protocols proposed. Each performs the best under specific context conditions, for example under high mobility or less volatile topologies. In existing MANET, the degradation in the routing protocol performance is always associated with changes in the network context. To date, no MANET routing protocol is able to produce optimal performance under all possible conditions. The core aim of this thesis is to solve the routing problem in mobile Ad hoc networks by introducing an optimum system that is in charge of the selection of the running routing protocol at all times, the system proposed in this thesis aims to address the degradation mentioned above. This optimisation system is a novel approach that can cope with the network performance’s degradation problem by switching to other routing protocol. The optimisation system proposed for MANET in this thesis adaptively selects the best routing protocol using an Artificial Intelligence mechanism according to the network context. In this thesis, MANET modelling helps in understanding the network performance through different contexts, as well as the models’ support to the optimisation system. Therefore, one of the main contributions of this thesis is the utilisation and comparison of various modelling techniques to create representative MANET performance models. Moreover, the proposed system uses an optimisation method to select the optimal communication routing protocol for the network context. Therefore, to build the proposed system, different optimisation techniques were utilised and compared to identify the best optimisation technique for the MANET intelligent system, which is also an important contribution of this thesis. The parameters selected to describe the network context were the network size and average mobility. The proposed system then functions by varying the routing mechanism with the time to keep the network performance at the best level. The selected protocol has been shown to produce a combination of: higher throughput, lower delay, fewer retransmission attempts, less data drop, and lower load, and was thus chosen on this basis. Validation test results indicate that the identified protocol can achieve both a better network performance quality than other routing protocols and a minimum cost function of 4.4%. The Ad hoc On Demand Distance Vector (AODV) protocol comes in second with a cost minimisation function of 27.5%, and the Optimised Link State Routing (OLSR) algorithm comes in third with a cost minimisation function of 29.8%. Finally, The Dynamic Source Routing (DSR) algorithm comes in last with a cost minimisation function of 38.3%