End-to-End Delay Minimization in Multi-Channel, TDMA Wireless Sensor Networks by Particle Swarm Optimization

Many Smart City, Internet of Things (IoT) applications would require wireless multi-hop, data collecting networks with low latency and high throughput. Optimal use of multi-channels and timeslots has the potential to achieve such network performance. Finding scheduling allocation of multiple channels and time slots for optimal end-to-end delay in multihop networks is an NP-complete problem. Particle Swarm Optimization (PSO) is a fast, scalable technique to find near-optimal scheduling solution for this problem. This paper addresses some deficiencies of the existing PSO based end-to-end delay minimization problem in wireless sensor networks. An upgraded PSO modeling is proposed and the performance improvements are presented through simulations. Two other capacity enhancement solutions (Non-Orthogonal Multiple Access, Multiple Interfaces) are also simulated using proposed approach and their corresponding performance results are investigated.