4,730 research outputs found

    Secure Clustering in DSN with Key Predistribution and WCDS

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    This paper proposes an efficient approach of secure clustering in distributed sensor networks. The clusters or groups in the network are formed based on offline rank assignment and predistribution of secret keys. Our approach uses the concept of weakly connected dominating set (WCDS) to reduce the number of cluster-heads in the network. The formation of clusters in the network is secured as the secret keys are distributed and used in an efficient way to resist the inclusion of any hostile entity in the clusters. Along with the description of our approach, we present an analysis and comparison of our approach with other schemes. We also mention the limitations of our approach considering the practical implementation of the sensor networks.Comment: 6 page

    A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks

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    A comprehensive analysis on the energy-efficient strategy in static Wireless Sensor Networks (WSNs) that are not equipped with any energy harvesting modules is conducted in this article. First, a novel generic mathematical definition of Energy Efficiency (EE) is proposed, which takes the acquisition rate of valid data, the total energy consumption, and the network lifetime of WSNs into consideration simultaneously. To the best of our knowledge, this is the first time that the EE of WSNs is mathematically defined. The energy consumption characteristics of each individual sensor node and the whole network are expounded at length. Accordingly, the concepts concerning EE, namely the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective, are proposed. Subsequently, the relevant energy-efficient strategies proposed from 2002 to 2019 are tracked and reviewed. Specifically, they respectively are classified into five categories: the Energy-Efficient Media Access Control protocol, the Mobile Node Assistance Scheme, the Energy-Efficient Clustering Scheme, the Energy-Efficient Routing Scheme, and the Compressive Sensing--based Scheme. A detailed elaboration on both of the basic principle and the evolution of them is made. Finally, further analysis on the categories is made and the related conclusion is drawn. To be specific, the interdependence among them, the relationships between each of them, and the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective are analyzed in detail. In addition, the specific applicable scenarios for each of them and the relevant statistical analysis are detailed. The proportion and the number of citations for each category are illustrated by the statistical chart. In addition, the existing opportunities and challenges facing WSNs in the context of the new computing paradigm and the feasible direction concerning EE in the future are pointed out

    Distributed Clustering Based on Node Density and Distance in Wireless Sensor Networks

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    Wireless Sensor Networks (WSNs) are special type of network with sensing and monitoring the physical parameters with the property of autonomous in nature. To implement this autonomy and network management the common method used is hierarchical clustering. Hierarchical clustering helps for ease access to data collection and forwarding the same to the base station. The proposed Distributed Self-organizing Load Balancing Clustering Algorithm (DSLBCA) for WSNs designed considering the parameters of neighbor distance, residual energy, and node density.  The validity of the DSLBCA has been shown by comparing the network lifetime and energy dissipation with Low Energy Adaptive Clustering Hierarchy (LEACH), and Hybrid Energy Efficient Distributed Clustering (HEED). The proposed algorithm shows improved result in enhancing the life time of the network in both stationary and mobile environment

    Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

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    Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

    Connectivity sharing for wireless mesh networks

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    Tesi en modalitat de cotutela: Universitat Politècnica de Catalunya i Katholieke Universiteit LeuvenInternet access is still unavailable to one-third of the world population due to the lack of infrastructure, high cost, and the digital divide. Many access-limited communities opt for shared Internet access where they build common network infrastructures to mitigate the cost. Internet connectivity in such infrastructures is typically provided by several limited, sometimes non-dedicated, gateways. Client nodes, i.e., the end-user hosts, use one gateway and switch to another when the first fails. In this scheme, the gateway configuration is done manually on the end-user side. This form of Internet connectivity is widespread and has the advantage that no central control is required, but it is also unreliable and inefficient due to several factors, such as unbalanced traffic load across the gateways. There is no doubt that the network would benefit from a gateway selection mechanism that can provide good connectivity to the client node as well as balanced load distribution and a dynamic adaptation to the current network state. However, providing such a dynamic gateway selection is complicated: since the perceived performance of the gateways changes frequently and might depend on the location of the client node in the network, and optimal selection would require the continuous monitoring of the gateway performance by the client node. The cost of such network-wide performance monitoring is high in large-scale networks and can outweigh the benefits of the dynamic gateway selection. The thesis's goal is to design a low-cost, distributed mechanism that provides an efficient and dynamic gateway selection while considering the overall balanced gateway selection distribution. To this end, we have split the problem of gateway selection into different sub-problems. First, we focus on reducing the cost of gateway performance monitoring. We propose an approach to reduce the number of monitoring requests generated by each node and analyze its effect on the gateway selection. Then, we present a collaborative monitoring method that allows neighbor nodes to share the load of the gateway monitoring. We show that every node can carry out the necessary tasks: performance monitoring, collaboration with its neighbors, and fault tolerance measures, with little computation and communication overhead. Second, to improve the gateway selection, we focus on making a selection decision that fulfills the individual performance requirements of the client nodes as well as global load balancing requirements. The solutions developed by us for the different sub-problems are embedded into a general and extensible, layered framework for gateway selection that we have called the Sense-Share-Select framework. Experimental validation and comparison with existing methods show that our framework provides accurate collaborative performance monitoring, improves the QoE for the nodes, and distributes the client nodes over the gateways in a balanced manner. The simplicity and flexibility of the framework make it adaptable to other network domains such as IoT networks and other scenarios where resource monitoring and load balancing are required.El acceso a Internet aún no está disponible para un tercio de la población mundial debido a la falta de infraestructuras, el alto costo y la brecha digital. Muchas comunidades con acceso limitado optan por el acceso compartido a Internet donde construyen infraestructuras de red comunitaria para mitigar el costo. La conectividad a Internet en dichas infraestructuras suele estar a cargo de varias puerta de enlaces limitadas en recursos, y a veces no dedicadas. Los nodos de cliente, es decir, los hosts de usuario final, utilizan una puerta de enlace y cambian a otra cuando falla la primera. En este esquema, la configuración de la puerta de enlace se realiza manualmente en el lado del usuario final. Esta forma de conectividad a Internet está muy extendida y tiene la ventaja de que no se requiere un control central, pero tampoco es confiable y eficiente debido a varios factores, como una carga desequilibrada de tráfico a través de las puertas de enlace. No hay duda de que la red se beneficiaría de un mecanismo de selección de pasarela que pueda proporcionar una buena conectividad al nodo cliente, así como una distribución equilibrada de la carga y una adaptación dinámica al estado actual de la red. Sin embargo, proporcionar una selección de puerta de enlace tan dinámica es complicado: dado que el rendimiento percibido de las puertas de enlace cambia con frecuencia y podría depender de la ubicación del nodo cliente en la red, y la selección óptima requeriría la supervisión continua del rendimiento de la puerta de enlace por parte del nodo cliente. El costo de dicha supervisión del rendimiento en toda la red es muy alto en redes de gran escala y puede superar los beneficios de la selección de puerta de enlace dinámica. El objetivo de la tesis es diseñar un mecanismo distribuido de bajo costo que proporcione una selección de puerta de enlace dinámica y eficiente al tiempo que considera la distribución general de selección de puerta de enlace equilibrada. Con este fin, hemos dividido el problema de la selección de la puerta de enlace en diferentes subproblemas. Primero, nos enfocamos en reducir el coste del monitoreo del rendimiento de la puerta de enlace. Proponemos un enfoque para reducir la cantidad de solicitudes de monitoreo generadas por cada nodo y analizar su efecto en la selección de la puerta de enlace. Luego, presentamos un método de monitoreo colaborativo que permite a los nodos vecinos compartir la carga del monitoreo de la puerta de enlace. Demostramos que cada nodo puede realizar las tareas necesarias: monitoreo del rendimiento, colaboración con sus vecinos y medidas de tolerancia a fallas, con poca sobrecarga de cómputo y comunicación. En segundo lugar, para mejorar la selección de la puerta de enlace, nos centramos en tomar una decisión de selección que cumpla con los requisitos de rendimiento individuales de los nodos del cliente, así como con los requisitos de equilibrio de carga global. Las soluciones desarrolladas por nosotros para los diferentes subproblemas están integradas en un marco general y extensible en capas para la selección de puertas de enlace que hemos llamado el marco Sense-Share-Select. La validación experimental y la comparación con los métodos existentes muestran que nuestro marco proporciona un monitoreo de rendimiento colaborativo preciso, mejora la QoE para los nodos y distribuye los nodos del cliente a través de las puertas de enlace de manera equilibrada. La simplicidad y flexibilidad del marco lo hacen adaptable a otros dominios de red, como las redes de IoT y otros escenarios donde se requiere monitoreo de recursos y equilibrio de carga.Postprint (published version
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