Integration of improved clustering and routing protocol in wireless sensor networks with crowd management systems

Abstract

Billions of devices are connected through wireless networks with utility in many areas of applications such as health, logistics, banking, smart homes, and smart cities. These devices can work in groups to achieve common objectives. However, these devices have limitations of residual battery power. One of the solutions is clustering in which nodes are grouped in which one node acts as Cluster-Head (CH) and other nodes as Cluster-Members (CMs). The responsibility of CH is to collect data from CMs and forward it to the Base-Station (BS). However, the transmission range of devices is limited and for large areas, multi-hop communication is required to collect data at BS. The solutions proposed in the literature either consider clustering or routing only. The literature review indicates a lack of unified clustering and routing protocol for the transmission of data to central BS. In this research, fixed-area-based equal clustering and routing protocol is proposed where nodes inside a given area form a cluster. These nodes select one node as CH and other nodes act as CMs. CH is selected based on the accumulative weight of the residual battery, node degree, and node-centrality of nodes. In the CH selection process, the next-hop forward and backward nodes are identified for establishing the routing path. Equal clustering has the drawback where the nodes close to the BS drain their batteries earlier due to the dual responsibility of data collection and forwarding. To solve this, an unequal-area-based clustering and routing solution is proposed where the cluster size near BS is small with fewer nodes and it increases as moves away from the center of the circular area. BS is deployed at the center of the area. The results of unequal clustering are better than that of equal clustering. The case study of Hajj is considered an application of the proposed protocols. In the scenario of Tawaf, a large number of pilgrims circulate the Kaaba a fixed number of times. The number of clusters and nodes inside each cluster is computed using the pilgrim's data. Initially, an abnormal behavior detection model for a large number of pedestrians is developed. This model is applied to sensor data and extended to sensor-based solutions operating under the proposed unequal clustering and routing protocol. It is identified that the sensor-based solution provides better performance gain in terms of network lifetime, abnormal behavior detection time, and network throughput. The study further identified limitations in existing research and proposed future research areas.