Data gathering and aggregation with selective transmission technique to optimize the lifetime of Internet of Things networks

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%.