Routing (electronic design automation)

Network packet

Algorithm

Wireless sensor network

Hierarchical routing

Static routing

Scalability

Computer science

Distributed computing

Computer network

Routing protocol

Database

Artificial intelligence

Cluster analysis

Base station

Machine learning

Journal of Parallel and Distributed Computing

Edge computing for Internet of Things (IoT) is a promising framework that can help small devices such as low-powered sensor nodes to accomplish complex computational tasks. The limited power supply of sensor nodes is one of their major limitations. A successful approach to improve the network lifetime and the overall scalability of the IoT supported wireless sensor networks (WSNs) is clustering. However, in a clustered IoT supported WSNs, some of the Cluster Heads (CHs) bear more traffic load than the others and therefore die sooner leading to decrease the network lifetime. To overcome this problem and maximize the network lifetime, the load of the CHs must be balanced. This research work suggests a new clustering method to balance the traffic load imposed on the cluster heads in IoT supported WSNs. The proposed clustering method uses a 1.2-approximation algorithm. In addition, we introduce an energy-aware routing algorithm for transmitting data packets from the CHs to their destination. The proposed routing algorithm distributes the communication load of the data packets among more nodes near the destination by a proper segmentation of the area. The simulation results show that the proposed clustering and routing algorithms in addition to being practical for large-scale IoT supported WSNs, cause the network to have a better performance compared to other similar algorithms.

An optimal cluster-based routing algorithm for lifetime maximization of Internet of Things