Predictive Resource Management for Opportunistic Public Sensing Networks Using Fuzzy Logic
Gina Younes, Amr El Mougy · 2019
As the number of deployed sensor networks globally increased to billions, data collection from these networks became quite a challenging task. The wide proliferation of smartphones provides an inexpensive solution for the data collection challenge through opportunistic connections with sensors. However, the varying and unreliable nature of opportunistic data collection may lead to significant waste in the limited resources of the sensors. Thus, efficient management of the resources of the sensors is critical to ensure balance between conserving the energy of the sensors and minimizing the redundancy in data transfer from the sensor to the smartphone to the cloud through the smartphones Internet connection. Accordingly, this paper proposes solutions for resource management in opportunistic sensor networks based on a novel sensor selection algorithm and fuzzy logic. The sensor selection algorithm is used to limit the redundancy in data collected from sensors within close proximity, thus saving their energy. On the other hand, fuzzy logic is used to predict the presence of opportunistic connections based on the history of the sensor. Performance evaluation through computer simulations show that these solutions combined lead to significant increase in network lifetime and minimization of redundancy in data collection.