Low-Latency and Energy-Balanced Data Transmission Over Cognitive Small World WSN

Energy balancing and faster data transfer over a wireless sensor network (WSN) is an important problem in applications like cyber-physical systems, Internet of things, and context-aware pervasive systems. Addressing this problem leads to increased network lifetime and improved network feasibility for real time applications. In WSNs, sensor nodes transfer the data using multihop data transmission model. The large number of hops required for data transmission leads to poor energy balancing and large data latency across the network. In this paper, we utilize a recent development in social networks called small world characteristics for proposing a novel method of low-latency and energy-balanced data transmission over WSN. Small world WSN (SW-WSN) exhibits low average path length and high average clustering coefficient. A cognitive SW-WSN is developed by adding new links between a selected fraction of nodes and the sink. A new data routing method is also proposed by optimizing energy cost of the links. This method yields uniform energy consumption and faster data transfer. Experiments are conducted using simulations and real node deployments over a WSN testbed. The performance of the proposed method is evaluated by conducting exhaustive analysis of network lifetime, residual energy, and data latency over the WSN. Experimental results obtained indicate that the proposed cognitive small world model achieves energy balancing, increases network lifetime, improves energy efficiency, and reduces data latency when compared to results obtained using various state-of-the-art approaches. The results are motivating enough for the proposed method to be used in large and medium scale network applications.