A joint optimized data collection algorithm based on dynamic cluster-head selection and value of information in UWSNs

Underwater wireless sensor networks (UWSNs) have become one of the enabling technologies for the development of future ocean monitoring systems, where autonomous underwater vehicles (AUVs) provide a very attractive way for the data collection of sensor nodes. The data obtained by nodes may have different importance and time sensitivity, which is recorded as the value of information (VoI). The VoI often decays with time. The obtained VoI based on AUV is closely related to the path length of AUV. A longer AUV path lead to more time to collect data, along which less VoI could be obtained finally. Moreover, as sensor nodes are battery-powered and therefore energy-constrained, selecting the nodes with larger residual energy as the AUV path nodes helps to prolong the network lifetime. Therefore, we focus on AUV path planing with the objective of maximizing the lifetime of network while satisfying the constraint on VoI. In this paper, considering the node energy and VoI, we provide an Integer Linear Programming (ILP) model to find an optimal path of AUV. Then, a joint optimized data collection algorithm (JODA) that taking in account the influence of residual energy of nodes and VoI is proposed, which reduces the computational complexity. In our experiments the effectiveness of JODA has been proved by comparing its performance with the theoretical value determined by the ILP model. We also compare the performance of JODA with that of other data collection algorithms, namely TSP, GAAP and EEDA. The simulation results demonstrate that the JODA always outperforms all other algorithms in terms of the total VoI and network lifetime.