Efficient on-demand multi-node charging techniques for wireless sensor networks

This paper deals with wireless charging in sensor networks and explores efficient policies to perform simultaneous multi-node power transfer through a mobile charger (MC).The proposed solution, called On-demand Multi-node Charging (OMC), features an original threshold-based tour launching (TTL) strategy, using request grouping, and a path planning algorithm based on minimizing the number of stopping points in the charging tour. Contrary to existing solutions, which focus on shortening the charging delays, OMC groups incoming charging requests and optimizes the charging tour and the mobile charger energy consumption. Although slightly increasing the waiting time before nodes are charged, this allows taking advantage of multiple simultaneous charges and also reduces node failures. At the tour planning level, a new modeling approach is used. It leverages simultaneous energy transfer to multiple nodes by maximizing the number of sensors that are charged at each stop. Given its NP-hardness, tour planning is approximated through a clique partitioning problem, which is solved using a lightweight heuristic approach. The proposed schemes are evaluated in offline and on-demand scenarios and compared against relevant state-of-the-art protocols. The results in the offline scenario show that the path planning strategy reduces the number of stops and the energy consumed by the mobile charger, compared to existing offline solutions. This is with further reduction in time complexity, due to the simple heuristics that are used. The results in the on-demand scenario confirm the effectiveness of the path planning strategy. More importantly, they show the impact of path planning, TTL and multi-node charging on the efficiency of handling the requests, in a way that reduces node failures and the mobile charger energy expenditure

Semi-invertible Convolutional Neural Network for Overall Survival Prediction in Head and Neck Cancer

International audienceThe paper addresses the issue of overall survival prediction in head and neck cancer as an effective mean of improving clinical diagnosis and treatment planning. A new solution is proposed using semi-invertible convolutional networks. Our model exploits the 3D features of computed tomography (CT) scans to enrich the dataset used in the learning phase and thereby improve the prediction accuracy. This is achieved by designing a first architecture featuring a combination of a CNN classifier with a fully convolutional network pre-processor. The latter has been replaced in the second solution by an invertible network to deal with the memory constraints noticed in the first architecture. Obtained results showed that both architectures have led to considerable improvements in terms of prediction accuracy (0.75) compared to state-of-the-art solutions

Information Security in Wireless Sensor Networks

In this chapter, we provide a comprehensive survey of security issues in wireless sensor networks. We show that the main features of WSNs, namely their limited resources, wireless communications, and close physical coupling with environment, are the main causes of the their security vulnerabilities. We discuss the main attacks stemming from these vulnerabilities, along with the solutions proposed in the literature to cope with them. The security solutions are analyzed with respect to the different layers of the network protocol stack and cover the following issues: Key management, secure data dissemination, secure data aggregation, secure channel access and secure node compromise

A study of wireless sensor network architectures and projects for traffic light monitoring

Vehicular traffic is increasing around the world, especially in urban areas. This increase results in a huge traffic congestion, which has dramatic consequences on economy, human health, and environment. Traditional methods used for traffic management, surveillance and control become inefficient in terms of performance, cost, maintenance, and support, with the increased traffic. Wireless Sensor Networks (WSN) is an emergent technology with an effective potential to overcome these difficulties, and will have a great added value to intelligent transportation systems (ITS). In this survey, we review traffic light projects and solutions. We discuss their architectural and engineering challenges, and shed some light on the future trends as well