A bayesian multilevel modeling approach for data query in wireless sensor networks

In power-limited Wireless Sensor Network (WSN), it is important to reduce the communication load in order to achieve energy savings. This paper applies a novel statistic method to estimate the parameters based on the realtime data measured by local sensors. Instead of transmitting large real-time data, we proposed to transmit the small amount of dynamic parameters by exploiting both temporal and spatial correlation within and between sensor clusters. The temporal correlation is built on the level-1 Bayesian model at each sensor to predict local readings. Each local sensor transmits their local parameters learned from historical measurement data to their cluster heads which account for the spatial correlation and summarize the regional parameters based on level-2 Bayesian model. Finally, the cluster heads transmit the regional parameters to the sink node. By utilizing this statistical method, the sink node can predict the sensor measurements within a specified period without directly communicating with local sensors. We show that this approach can dramatically reduce the amount of communication load in data query applications and achieve significant energy savings

O-SPIN: an Opportunistic Data Dissemination Protocol for Folk-enabled Information System in Least Developed Countries

International audienceWithout universal access to the Internet, Least Developed Countries are left by the wayside of the digital revolution. Research is underway to overstep the barrier to the development of information technology services in these areas. In this context, the Folk-IS (Folk-enabled Information System) is a new fully decentralized and participatory approach, in which, each individual can transparently perform data management and networking tasks through highly secure, portable, and low-cost storage and computing personal devices, as physically moving, so that global services can finally be delivered by crowd. In this paper we propose Opportunistic SPIN (O-SPIN), an information dissemination protocol that augments the well-known data-centric energy-aware SPIN protocol to enable networking facilities for Folk-nodes, by exploiting opportunistic contacts among users. Performance of the proposed solution has been evaluated through simulations carried out in the OMNeT++ framework under different settings. Achieved results demonstrate its effectiveness and efficiency in the information dissemination process

Weighted Energy Efficient Cluster Based Routing for Wireless Sensor Networks

Part 6: NetworkingInternational audienceWireless sensor network comprises of numerous tiny sensor nodes to form an ad hoc distributed sensing and data propagation network to collect the context information on the physical environment. Wireless sensor networks are used for remotely monitoring tasks and effective for data gathering in a variety of environments. Minimizing energy dissipation and maximizing network lifetime are one of the central concerns to design applications and protocols for wireless sensor networks. Cluster based architectures are one of the most practical solutions in order to cope with the requirements of large scale of sensor networks. Clustering results in a reduced number of messages that propagate through the network in order to accomplish a sensing task. In this paper, we have presented a brief review of the state of the art scenario of various routing protocols and propose a weighted energy efficient cluster based routing for wireless sensor networks. Conducted simulation experiments on different scenarios shown its ability to obtain effective & efficient communications among different clusters and achieves scalability in large scale of wireless sensor networks

A Security Framework for Wireless Sensor Networks

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Genetic centralized dynamic clustering in wireless sensor networks

In order to overcome the energy loss involved by communications in wireless sensor networks (WSN), the use of clustering has proven to be effective. In this paper, we proposed a dynamic centralized genetic algorithm (GA)-based clustering approach to optimize the clustering configuration (cluster heads and cluster members) to limit node energy consumption. The obtained simulation results show that the proposed technique overcomes the LEACH clustering algorithm