Energy-efficient routing in wireless sensor networks

Efficient data collection is the core concept of implementing Industry4.0 on IoT platforms. This requires energy aware communication protocols for Wireless Sensor Networks (WSNs) where different functions, like sensing and processing on the IoT nodes must be supported by local battery power. Thus, energy aware network protocols, such as routing, became one of fundamental challenges in IoT data collection schemes. In our research, we have developed novel routing algorithms which guarantee minimum energy consumption data transfer which is achieved subject to pre-defined reliability constraints. We assume that data is transmitted in the form of packets and the routing algorithm identifies the paths over which the packets can reach the Base Station (BS) with minimum transmission energy, while the probability of successful packet transmission still exceeds a pre-defined reliability parameter. In this way, the longevity and the information throughput of the network is maximized and the low energy transmissions will considerably extend the lifetime of the IoT nodes. In this paper we propose a solution that maximizes the lifetime of the nodes

Energy-efficient bandwidth reservation for bulk data transfers in dedicated wired networks

International audienceThe ever increasing number of Internet connected end-hosts call for high performance end-to-end networks leading to an increase in the energy consumed by the networks. Our work deals with the energy consumption issue in dedicated network with bandwidth provisionning and in-advance reservations of network equipments and bandwidth for Bulk Data transfers. First, we propose an end-to-end energy cost model of such networks which described the energy consumed by a transfer for all the crossed equipments. This model is then used to develop a new energy-aware framework adapted to Bulk Data Transfers over dedicated networks. This framework enables switching off unused network portions during certain periods of time to save energy. This framework is also endowed with prediction algorithms to avoid useless switching off and with adaptive scheduling management to optimize the energy used by the transfers. 1 Introductio