Heuristic Surface Path Planning Method for AMV-Assisted Internet of Underwater Things

Ocean exploration is one of the fundamental issues for sustainable development human society, which also basis realizing concept Internet Underwater Things (IoUT) applications, such as smart ocean city. The collaboration heterogeneous autonomous marine vehicles (AMVs) based on underwater wireless communication known a practical approach to exploration, typically with surface vehicle (ASV) and glider (AUG). However, difference in their specifications movements makes following problems collaborative work. First, when an AUG floats certain depth, ASV interacts via communication, interaction has time limit position have be synchronized; secondly, case where multiple AUGs are exploring underwater, needs plan sequence interactions ensure timely efficient data collection. Accordingly, this paper proposes heuristic path planning method collection AMVs (HSPP-HA). HSPP-HA optimizes schedule between through modified shuffled frog-leaping algorithm (SFLA). It applies spatial-temporal k-means clustering initializing memeplex group SFLA adapt time-sensitive by weighting spatial temporal proximities adopts adaptive convergence factor varies iterations balance local global searches minimize potential optimum problem each search. Through simulations, proposed shows advantages terms access rate, length rate compared recent classic methods.