Node Embedding for Security-Aware Clustering of Mobile Information-Centric Sensor Networks

In cluster-based information-centric wireless sensor networks (ICWSNs), mobile sensor nodes are grouped into clusters in rounds. In each cluster, a cluster head (CH) is selected, which collects, aggregates, and forwards locally sensed data to a sink node. CHs further store a copy of data for the round period to act as cache nodes and deliver data to mobile users upon requests. Nevertheless, clustering and securing mobile ICWSNs are challenging. This is because, in addition to sensor nodes’ and users’ mobility, sensor nodes are often resource constrained. Therefore, clustering and security resource allocation in mobile ICWSNs should be carefully redesigned to ensure efficient ICWSN operation, data security, and timely data access to mobile users. This article proposes a node embedding with security resource allocation (NESRA) clustering algorithm for mobile ICWSNs in rounds. NESRA allocates security resources to sensor nodes based on the location, mobility, and energy resources available in the first step. An optimization problem is formulated to select CHs that maximize network coverage and minimize data delivery delay to mobile users in the second step. In the third step, NESRA utilizes network representation learning that embeds sensor nodes’ location, mobility, and expected energy expenditure features into a 2-D space to form well-separated clusters of sensing nodes. Compared to existing works, NESRA achieves lower energy consumption, nodes’ death rate, and latency and allows higher throughput and cache nodes’ utilization with stable data security. Still, NESRA has some challenges to overcome in high-mobility networks.