Development of an Artificial Fish Swarm Algorithm Based on aWireless Sensor Networks in a Hydrodynamic Background

Abstract

The main objective of the present study is the development of a new algorithm that can adapt to complex and changeable environments. An artificial fish swarm algorithm is developed which relies on a wireless sensor network (WSN) in a hydrodynamic background. The nodes of this algorithm are viscous fluids and artificial fish, while related 'events' are directly connected to the food available in the related virtual environment. The results show that the total processing time of the data by the source node is 6.661 ms, of which the processing time of crosstalk data is 3.789 ms, accounting for 56.89%. The total processing time of the data by the relay node is 15.492 ms, of which the system scheduling and the Carrier Sense Multiple Access (CSMA) rollback time of the forwarding is 8.922 ms, accounting for 57.59%. The total time for the data processing of the receiving node is 11.835 ms, of which the processing time of crosstalk data is 3.791 ms, accounting for 32.02%; the serial data processing time is 4.542 ms, accounting for 38.36%. Crosstalk packets occupy a certain amount of system overhead in the internal communication of nodes, which is one of the causes of node-level congestion. We show that optimizing the crosstalk phenomenon can alleviate the internal congestion of nodes to some extent.

A Dynamic Path Optimization Model of IOT Delivery Vehicles for E-commerce Logistics Distribution

“…The algorithm uses viscous fluids and artificial fish as algorithm nodes, while relevant events are directly linked to 'food'. This algorithm can effectively handle crosstalk data and improve the immunity of the algorithm to interference [15]. In summary, many experts and scholars have designed a large number of improved algorithms for optimising logistics distribution paths.…”