696 research outputs found

    Reliable many-to-many routing in wireless sensor networks using ant colony optimisation

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    A wireless Sensor Network (WSN) consists of many simple sensor nodes gathering information, such as air temperature or pollution. Nodes have limited energy resources and computational power. Generally, a WSN consists of source nodes that sense data and sink nodes that require data to be delivered to them; nodes communicate wirelessly to deliver data between them. Reliability is a concern as, due to energy constraints and adverse environments, it is expected that nodes will become faulty. Thus, it is essential to create fault-tolerant routing protocols that can recover from faults and deliver sensed data efficiently. Often studied are networks with a single sink. However, as applications become increasingly sophisticated, WSNs with multiple sources and multiple sinks become increasingly prevalent but the problem is much less studied. Unfortunately, current solutions for such networks are heuristics based on specific network properties, such as number of sources and sinks. It is beneficial to develop efficient (fault-tolerant) routing protocols, independent of network architecture. As such, the use of meta heuristics are advocated. Presented is a solution for efficient many-to-many routing using the meta heuristic Ant Colony Optimisation (ACO). The contributions are: (i) a distributed ACObased many-many routing protocol, (ii) using the novel concept of beacon ants, a fault-tolerant ACO-based routing protocol for many-many WSNs and (iii) demonstrations of how the same framework can be used to generate a routing protocol based on minimum Steiner tree. Results show that, generally, few message packets are sent, so nodes deplete energy slower, leading to longer network lifetimes. The protocol is scalable, becoming more efficient with increasing nodes as routes are proportionally shorter compared to network size. The fault-tolerant variant is shown to recover from failures while remaining efficient, and successful at continuously delivering data. The ACO-based framework is used to create Steiner Trees in WSNs, an NP-hard problem with many potential applications. The ACO concept provides the basis for a framework that enables the generation of efficient routing protocols that can solve numerous problems without changing the ACO concept. Results show the protocols are scalable, efficient, and can successfully deliver data in numerous different topologies

    Implementation of Bio Inspired Algorithm in Identification of Best Route via Ant Colony Optimization, Energy Level & Throughput with Encryption

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    WSN has terribly minimum life time for information Transmission. Packets drop is sometimes expected. Emmet Colony optimisation is most popular idle supported secretion worth within the network or SRTLD is employed once secretion Substance isn\'t gift supported Power, Location, and Routing & Security. we tend to additionally contemplate Node\'s turnout, price excluding energy state. We tend to write the Packets throughout Transmission for Secured Communication. DOI: 10.17762/ijritcc2321-8169.150317

    A Survey on Urban Traffic Optimisation for Sustainable and Resilient Transportation Network

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    Nowadays, sustainability and resilience have become a major consideration that cannot be neglected in urban development. People are starting to consider utilizing the urban infrastructure environment to maintain and improve the functionality and availability of the urban system when unexpected events take place. Traffic congestion is always a major issue in urban planning, especially when the vehicles in the roadway keep growing and the local authorities are lack of solutions to manage or distribute the traffics in the city. It has huge impact on urban sustainability and resilience such as overload of the city’s infrastructure, and air pollution, etc. This paper presents a survey on the challenges of developing sustainable and resilient transportation networks and the current urban traffic optimisation methods, as a possible solution to address such challenges. It aims to describe and define the state of the art on the research on sustainable and resilient transportation networks in urban development and a taxonomy of different traffic optimisation methods used for avoiding traffic congestion and improve urban traffic management

    Energy-efficient routing protocols in heterogeneous wireless sensor networks

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    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

    On-demand fuzzy clustering and ant-colony optimisation based mobile data collection in wireless sensor network

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    In a wireless sensor network (WSN), sensor nodes collect data from the environment and transfer this data to an end user through multi-hop communication. This results in high energy dissipation of the devices. Thus, balancing of energy consumption is a major concern in such kind of network. Appropriate cluster head (CH) selection may provide to be an efficient way to reduce the energy dissipation and prolonging the network lifetime in WSN. This paper has adopted the concept of fuzzy if-then rules to choose the cluster head based on certain fuzzy descriptors. To optimise the fuzzy membership functions, Particle Swarm Optimisation (PSO) has been used to improve their ranges. Moreover, recent study has confirmed that the introduction of a mobile collector in a network which collects data through short-range communications also aids in high energy conservation. In this work, the network is divided into clusters and a mobile collector starts from the static sink or base station and moves through each of these clusters and collect data from the chosen cluster heads in a single-hop fashion. Mobility based on Ant-Colony Optimisation (ACO) has already proven to be an efficient method which is utilised in this work. Additionally, instead of performing clustering in every round, CH is selected on demand. The performance of the proposed algorithm has been compared with some existing clustering algorithms. Simulation results show that the proposed protocol is more energy-efficient and provides better packet delivery ratio as compared to the existing protocols for data collection obtained through Matlab Simulations

    A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications

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    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

    Optimisation of Mobile Communication Networks - OMCO NET

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    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

    Survey on Various Aspects of Clustering in Wireless Sensor Networks Employing Classical, Optimization, and Machine Learning Techniques

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    A wide range of academic scholars, engineers, scientific and technology communities are interested in energy utilization of Wireless Sensor Networks (WSNs). Their extensive research is going on in areas like scalability, coverage, energy efficiency, data communication, connection, load balancing, security, reliability and network lifespan. Individual researchers are searching for affordable methods to enhance the solutions to existing problems that show unique techniques, protocols, concepts, and algorithms in the wanted domain. Review studies typically offer complete, simple access or a solution to these problems. Taking into account this motivating factor and the effect of clustering on the decline of energy, this article focuses on clustering techniques using various wireless sensor networks aspects. The important contribution of this paper is to give a succinct overview of clustering
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