Dual Sink-Based Optimized Sensing for Intelligent Transportation Systems

Wireless sensor networks (WSNs) as one of the non-negligible components of the Internet of Things (IoT) have proven to be a pillar of the Intelligent Transportation Systems (ITS). The tasks of collecting, processing and fusing the information related to traffic, accidents, congestion and also the detection of pavement distress on roads, are efficiently handled and monitored by WSN-based IoT. However, the energy constraints of the sensor nodes deployed along the roadside, create a perturbing concern for their realization in architecture. Therefore, to address this concern, in this paper, we have proposed an optimized sensing technique that employs two sinks. We term it as Dual sink-based Optimized Clustering Architecture employing Tunicate Swarm Algorithm (TSA), i.e., DOCAT in short. The fitness function of DOCAT integrates the novel fitness parameters for Cluster Head (CH) selection. The parameters are: 1) Residual and initial Energy, 2) Distance of the node from sink, 3) Intra-Cluster Average Distance (ICAD), 4) Network's average energy, and 5) Energy threshold. DOCAT is anticipated to be employed for accident prone roads, from where the critical accidental information is transmitted to healthcare venues through the IoT platform. The simulation results reveal that DOCAT acquires the proliferated performance compared to several similar algorithms in terms of the network reliability, network lifetime, throughput, and energy efficiency.

Automated summary

This paper proposes an optimized sensing technique utilizing two sinks to address energy constraints of sensor nodes deployed along roadsides, known as DOCAT. The technology is expected to be used for accident-prone roads to transmit critical accident information, and has shown improved network performance compared to similar algorithms.