MINDS: Mobile Agent Itinerary Planning Using Named Data Networking in Wireless Sensor Networks

Mobile agents have the potential to offer benefits, as they are able either independently or cooperatively move throughout networks and collect/aggregate sensory data samples. They programmed autonomously visit stations through optimal paths, which established according application requirements. However, mobile agent routing protocols still suffer heavy computation/communication overheads, lack of route planning accuracy long-delay migrations. For this, aim find best-fitted paths for completing missions (e.g., collection) with minimised delay, maximised performance transmitted traffic. This article proposes a protocol collection called MINDS. The key goal this MINDS is reduce network traffic, maximise robustness minimise delay at same time. utilises Hamming distance technique partition sensor into number data-centric clusters. In turn, named networking approach used form cluster-heads data-centric, tree-based communication infrastructure. utilise modified version Depth-First Search algorithm tree infrastructure hop-count-aware fashion. As simulation results show, reduces path length, traffic increases compared two conventional benchmarks (ZMA TBID) in dense large wireless networks.