880 research outputs found

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

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    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

    A Trapezoidal Fuzzy Membership Genetic Algorithm (TFMGA) for Energy and Network Lifetime Maximization under Coverage Constrained Problems in Heterogeneous Wireless Sensor Networks

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    Network lifetime maximization of Wireless Heterogeneous Wireless Sensor Networks (HWSNs) is a difficult problem. Though many methods have been introduced and developed in the recent works to solve network lifetime maximization. However, in HWSNs, the energy efficiency of sensor nodes becomes also a very difficult issue. On the other hand target coverage problem have been also becoming most important and difficult problem. In this paper, new Markov Chain Monte Carlo (MCMC) is introduced which solves the energy efficiency of sensor nodes in HWSN. At initially graph model is modeled to represent HWSNs with each vertex representing the assignment of a sensor nodes in a subset. At the same time, Trapezoidal Fuzzy Membership Genetic Algorithm (TFMGA) is proposed to maximize the number of Disjoint Connected Covers (DCC) and K-Coverage (KC) known as TFMGA-MDCCKC. Based on gene and chromosome information from the TFMGA, the gene seeks an optimal path on the construction graph model that maximizes the MDCCKC. In TFMGA gene thus focuses on finding one more connected covers and avoids creating subsets particularly. A local search procedure is designed to TFMGA thus increases the search efficiency. The proposed TFMGA-MDCCKC approach has been applied to a variety of HWSNs. The results show that the TFMGA-MDCCKC approach is efficient and successful in finding optimal results for maximizing the lifetime of HWSNs. Experimental results show that proposed TFMGA-MDCCKC approach performs better than Bacteria Foraging Optimization (BFO) based approach, Ant Colony Optimization (ACO) method and the performance of the TFMGA-MDCCKC approach is closer to the energy-conserving strategy

    Unified Role Assignment Framework For Wireless Sensor Networks

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    Wireless sensor networks are made possible by the continuing improvements in embedded sensor, VLSI, and wireless radio technologies. Currently, one of the important challenges in sensor networks is the design of a systematic network management framework that allows localized and collaborative resource control uniformly across all application services such as sensing, monitoring, tracking, data aggregation, and routing. The research in wireless sensor networks is currently oriented toward a cross-layer network abstraction that supports appropriate fine or course grained resource controls for energy efficiency. In that regard, we have designed a unified role-based service paradigm for wireless sensor networks. We pursue this by first developing a Role-based Hierarchical Self-Organization (RBSHO) protocol that organizes a connected dominating set (CDS) of nodes called dominators. This is done by hierarchically selecting nodes that possess cumulatively high energy, connectivity, and sensing capabilities in their local neighborhood. The RBHSO protocol then assigns specific tasks such as sensing, coordination, and routing to appropriate dominators that end up playing a certain role in the network. Roles, though abstract and implicit, expose role-specific resource controls by way of role assignment and scheduling. Based on this concept, we have designed a Unified Role-Assignment Framework (URAF) to model application services as roles played by local in-network sensor nodes with sensor capabilities used as rules for role identification. The URAF abstracts domain specific role attributes by three models: the role energy model, the role execution time model, and the role service utility model. The framework then generalizes resource management for services by providing abstractions for controlling the composition of a service in terms of roles, its assignment, reassignment, and scheduling. To the best of our knowledge, a generic role-based framework that provides a simple and unified network management solution for wireless sensor networks has not been proposed previously

    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

    Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

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    Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

    A Survey of Coverage Problems in Wireless Sensor Networks

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    Coverage problem is an important issue in wireless sensor networks, which has a great impact on the performance of wireless sensor networks. Given a sensor network, the coverage problem is to determine how well the sensing field is monitored or tracked by sensors. In this paper, we classify the coverage problem into three categories: area coverage, target coverage, and barrier coverage, give detailed description of different algorithms belong to these three categories. Moreover, we specify the advantages and disadvantages of the existing classic algorithms, which can give a useful direction in this area

    Reliable Communication using Path Recovering in Wireless Sensor Network

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    Sensor technology is one in every of the quick growing technologies within the current scenario. And it's big selection of application additionally. The power of sensors to figure while not being monitored by someone is its distinctive quality. Wireless device network comprise of little sensors that have minimum communicatory and procedure power. Several anomalies square measure gift in WSNs. One such drawback may be a hole. Space barren of any node will be brought up as a hole. This degrades the performance of the full network. It affects the routing capability of the network terribly badly. The formation of holes in an exceedingly WSN is unavoidable thanks to the inner nature of the network. This paper deals with detective work and healing such holes in associate on demand basis

    Intelligent Sensor Networks

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    In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts
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