An Adaptive Data Gathering Scheduler Based on Data Variance for Energy Efficiency in Mobile Social Networks

Abstract

Mobile Social Networks (MSNs) consist of numerous mobile nodes that exhibit social characteristics such as gender, age, and more. Nowadays, with the rise in popularity of smartphones, these devices serve as nodes in mobile social networks, inheriting their users’ characteristics. These networks utilize a “store-carry-and-forward” mechanism for transmitting and delivering packets. New applications, like smart city monitoring, necessitate the deployment of sensors in urban areas to gather relevant information. The sensed data must be collected through mobile nodes (in this case, smartphones) and transmitted to a base station or other interested nodes. In these applications, if the data collection period is brief, smartphones will experience high energy consumption, and a significant amount of redundant data will be produced. Conversely, if the collection period is extended, some data may be lost. Several schemes have been proposed for data collection in wireless sensor networks. Unfortunately, in these schemes, nodes are either constantly in the data collection phase or gather data at fixed time intervals through simple scheduling. It appears that adaptive data collection based on the differences in the collected data could be more effective. This paper proposes a new Data Gathering Scheduler based on the Differences in collected data, DGSD. In this method, if the difference between the last two data points is low, the next time slot is set to be longer; as the difference between the last two data points increases, the time slot is shortened. Simulation results indicate that energy consumption with DGSD improves when compared to related works in discovering the same number of events.