A Cluster–based Approach for Minimizing Energy Consumption by Reducing Travel Time of Mobile Element in WSN

Envoy Node Identification (ENI) and Halting Location Identifier (HLI) algorithms have been developed to reduce the travel time of Mobile Element (ME) by determining Optimal Path(OP) in Wireless Sensor Networks. Data generated by cluster members will be aggregated at the Cluster Head (CH) identified by ENI for onward transmission to the ME and it likewise decides an ideal path for ME by interfacing all CH/Envoy Nodes (EN). In order to reduce the tour length (TL) further HLI determines finest number of Halting Locations that cover all ENs by taking transmission range of CH/ENs into consideration. Impact of ENI and HLI on energy consumption and travel time of ME have been examined through simulations

Diagnóstico em nível de sistema para redes de sensores sem fio : uma heurística

Orientadora : Profª. Drª. Andréa WeberDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 27/08/2015Inclui referências : f. 75-81Resumo: Diagnóstico em nível de sistema e uma sub-área de tolerância a falhas. O objetivo de um algoritmo de diagnostico em nível de sistema e reportar, para todas as unidades sem-falha de um sistema distribuído, o estado das demais unidades do sistema. A di- agnosticabilidade do sistema depende de algumas propriedades topologicas do grafo de diagnostico. Nesse contexto, um assinalamento de testes e um conjunto de testes mótuos entre as n unidades de um sistema. Um sistema com n unidades e dito t-diagnosticavel se o numero de unidades falhas nao ultrapassar t e satisfizer as seguintes condicões: (i) n > 2t + 1; e (ii) cada unidade for testada por, no mínimo, t outras unidades. Um sistema t-diagnosticóvel e definido como ótimo se n = 2t + 1. Considera-se o problema da definicao de um assinalamento de testes para a identificaçao de nós com falha em uma Rede de Sensores Sem Fio - RSSF. Dado um conjunto de 2t +1 sensores, a abordagem Optimal Design Testing Assignment - ODTA [36] gera um assinalamento de testes ótimo do ponto de vista da diagnosticabilidade do sistema. Entretanto, o problema da escolha em termos da distancia dos 2t +1 sensores que farao parte do assinalamento de testes tem características de um problema computacionalmente intratavel. Devido a ausencia de tal prova, apresenta-se o aprimoramento da heurística do ODTA para a escolha deste conjunto de sensores. Por meio da heurística Set of Sensors Chosen by Centroid and Radius - SSCCR apresentada, e possível selecionar em tempo polinomial tal conjunto nao somente otimo em termos de número de sensores, mas com uma considerável melhora dos resultados em termos de distancia geográfica entre os sensores. Por fim, apresenta-se a comparacõo das duas heurísticas abordadas com a solucõo ítima obtida pela formulacao do problema em programacao linear inteira, na qual pode-se confirmar que a heurística SSCCR apresenta melhor desempenho em relacao a heurística ODTA na escolha do conjunto de sensores com relacao a distancia entre eles e ate mesmo, em algumas situacoes, pode proporcionar o alcance de valores íotimos e consequentemente obter a reduçcõao do consumo de energia na execucao do diagnostico de falhas.Abstract: System-level diagnosis is a subset of fault tolerance. The goal of a system-level diagnosis algorithm is to report the state of the units of a distributed system to all fault-free units of the system. The diagnosability of the system depends on some topological properties of the diagnostic graph. In this context, a test assignment is a set of mutual tests between n units of a system. A system with n units is called t-diagnosable if the number of faulty units does not exceed t and it satisfies the following conditions: (i) n > 2t +1; and (ii) each unit is tested at least by t other units. A t-diagnosable system is said to be optimal if n = 2t +1. Consider the problem of defining a test assignment to identify faults in a wireless sensor network (WSN). Given a set of 2t +1 sensors, the approach Optimal Design Testing Assignment - ODTA [36] generates an optimal test assignment for the diagnosability of the system. However, the problem of the choice of 2t + 1 sensors that will take part of the testing assignment has characteristics of a computationally intractable problem. Due to the absence of such proof, the improvement of ODTA heuristics is presented. According to the heuristic Set of Sensors Chosen by Centroid and Radius - SSCCR it is possible to select that set in polynomial time, optimal not only in terms of number of sensors, but with a considerable improvement of results in terms of geographical distance between the sensors. Finally, a comparison of the two heuristics with the optimal solution obtained by the problem formulated in integer linear programming is presented, which confirms that the heuristic SSCCR has better performance compared with ODTA heuristic, and in many cases achieves optimal values, and consequently achieve the reduction of energy consumption in the implementation of fault diagnosis

Assinalamentos de testes para um algoritmo de diagnóstico em nível de sistema para redes de sensores sem fio

Resumo: Este trabalho se propõe a comparar três abordagens de construção de assinalamentos de testes para um algoritmo de diagnóstico em nível de sistema. As abordagens apresentadas visam o problema da detecção de alarmes falsos (falsos positivos) em uma rede de sensores sem ó onde os sensores monitoram o ambiente com o objetivo de gerar alarmes sobre a ocorrência de determinados eventos. Considere uma rede de sensores onde um conjunto de t sensores próximos geograficamente enviam sinais de alarme a uma unidade central da rede, com maior capacidade de processamento, chamada sink, informando a detecção de determinado fenômeno. Para garantir que os alarmes gerados não são falsos, o sink solicita a execução de testes mútuos entre os sensores presentes na região que contém os nodos que reportaram os alarmes. O resultado dos testes é enviado ao sink que, então, utiliza um algoritmo de diagnóstico em nível de sistema para identificar os sensores falhos. O algoritmo de diagnóstico é bem sucedido na execução desta tarefa se os testes executados pelos sensores são suficientes para alcançar determinada diagnosticabilidade do sistema, a qual depende de propriedades topológicas da rede de sensores e de certas condições presentes na literatura para formar assinalamentos de teste t-diagnosticáveis. Este trabalho apresenta três estratégias de testes que asseguram que a iagnosticabilidade desejada para o sistema seja alcançada com um consumo minimizado de energia. Resultados experimentais avaliam o comportamento das estratégias e comparam o consumo de energia apresentado entre elas em redes com diferentes topologias e densidades, com diferentes valores de t e com variações na distância entre os sensores que geram alarmes

Self-organization and management of wireless sensor networks

Wireless sensor networks (WSNs) are a newly deployed networking technology consisting of multifunctional sensor nodes that are small in size and communicate over short distances. These sensor nodes are mainly in large numbers and are densely deployed either inside the phenomenon or very close to it. They can be used for various application areas (e.g. health, military, home). WSNs provide several advantages over traditional networks, such as large-scale deployment, highresolution sensed data, and application adaptive mechanisms. However, due to their unique characteristics (having dynamic topology, ad-hoc and unattended deployment, huge amount of data generation and traffic flow, limited bandwidth and energy), WSNs pose considerable challenges for network management and make application development nontrivial. Management of wireless sensor networks is extremely important in order to keep the whole network and application work properly and continuously. Despite the importance of sensor network management, there is no generalize solution available for managing and controlling these resource constrained WSNs. In network management of WSNs, energy-efficient network selforganization is one of the main challenging issues. Self-organization is the property which the sensor nodes must have to organize themselves to form the network. Selforganization of WSNs is challenging because of the tight constraints on the bandwidth and energy resources available in these networks. A self organized sensor network can be clustered or grouped into an easily manageable network. However, existing clustering schemes offer various limitations. For example, existing clustering schemes consume too much energy in cluster formation and re-formation. This thesis presents a novel cellular self-organizing hierarchical architecture for wireless sensor networks. The cellular architecture extends the network life time by efficiently utilizing nodes energy and support the scalability of the system. We have analyzed the performance of the architecture analytically and by simulations. The results obtained from simulation have shown that our cellular architecture is more energy efficient and achieves better energy consumption distribution. The cellular architecture is then mapped into a management framework to support the network management system for resource constraints WSNs. The management framework is self-managing and robust to changes in the network. It is application-co-operative and optimizes itself to support the unique requirements of each application. The management framework consists of three core functional areas i.e., configuration management, fault management, and mobility management. For configuration management, we have developed a re-configuration algorithm to support sensor networks to energy-efficiently re-form the network topology due to network dynamics i.e. node dying, node power on and off, new node joining the network and cells merging. In the area of fault management we have developed a new fault management mechanism to detect failing nodes and recover the connectivity in WSNs. For mobility management, we have developed a two phase sensor relocation solution: redundant mobile sensors are first identified and then relocated to the target location to deal with coverage holes. All the three functional areas have been evaluated and compared against existing solutions. Evaluation results show a significant improvement in terms of re-configuration, failure detection and recovery, and sensors relocation