Path planning for mobile DCs in future cities

In future smart-cities, public transportation vehicles are planned to serve as data couriers (DCs) in order to exchange massive amounts of data chunks. In this research, we study the path planning problem for these DCs while optimizing their counts and their total traveled distances. As the total collected load on a given DC route cannot exceed its storage capacity, it is important to decide on the size of the exchanged data-packets (images, videos, etc.) and the sequence of the targeted data sources to be visited. We propose a hybrid heuristic approach for public data delivery in smart-city settings. In this approach, public vehicles are utilized as DCs that read/collect data from numerously distributed Access Points (APs) and relay it back to a central processing base-station in the city. We also introduce a cost-based fitness function for DCs election in the smart-city paradigm. Our cost-based function considers resource limitations in terms of DCs count, storage capacity, and energy consumption. Extensive simulations are performed, and the results confirm the effectiveness of the proposed approach in comparison to other heuristic approaches with respect to total traveled distances and overall time complexity.