One of the main research challenges faced in Wireless Sensor Networks (WSNs) is to preserve continuously and effectively the coverage of an area (or region) of interest to be monitored, while simultaneously preventing as much as possible a network failure due to battery-depleted nodes. In this paper we propose a protocol, called Distributed Lifetime Coverage Optimization protocol (DiLCO), which maintains the coverage and improves the lifetime of a wireless sensor network. First, we partition the area of interest into subregions using a classical divide-and-conquer method. Our DiLCO protocol is then distributed on the sensor nodes in each subregion in a second step. To fulfill our objective, the proposed protocol combines two effective techniques: a leader election in each subregion, followed by an optimization-based node activity scheduling performed by each elected leader. This two-step process takes place periodically, in order to choose a small set of nodes remaining active for sensing during a time slot. Each set is built to ensure coverage at a low energy cost, allowing to optimize the network lifetime. Simulations are conducted using the discrete event simulator OMNET++. We refer to the characterictics of a Medusa II sensor for the energy consumption and the computation time. In comparison with two other existing methods, our approach is able to increase the WSN lifetime and provides improved coverage performances.
Summary The periodic sensor networks (PSNs) represent the bigger provider of data to the Internet of Things (IoT) due to their use in a wide range of IoT applications. Examples of IoT applications using PSNs are disaster recovery, connected vehicles, smart healthcare, smart cities, smart grids, and networks of robots. In PSNs, the large volume of data gathering and aggregation represent significant challenges that must be handled in the IoT applications. Therefore, it is necessary to find a dynamic way to gather data and get rid of the redundancy in the gathered data prior to transferring it to the sink (base station) for the sake of extending the PSN lifetime and preserving its energy. This article proposes data gathering and aggregation with selective transmission (DGAST) technique for optimizing lifetime in PSNs of IoT applications. DGAST gathers periodically the sensor data to extend the sensor's battery lifetime. DGAST protocol divides the lifetime of PSN into rounds. There are four phases in each round: data gathering, data aggregation, selective transmission, and adjusting the frequency of samples taken for each node in the context of dynamic climate change of the sensed environment. OMNeT++ simulator and real sensory data gathered at Intel Lab are used in the simulation experiments. The results of the simulation demonstrate DGAST efficiency in comparison with prefix frequency filtering (PFF) and Harb protocols, that is, overhead reduction up to 67% in gathered data, 73% in transmitted data, and 78% in consumed energy while maintaining the accuracy of sent data as high as 94.6%.
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