Energy-delay Aware Data Collection in Mobile Sink-based Wireless Sensor Networks with Optimized Visiting Points and Two-level Data Aggregation

This study addresses data collection challenges in wireless sensor networks (WSNs) employing mobile sinks (MSs). Although MSs improve flexibility and scalability, achieving balanced optimization of data collection delay and energy consumption remains challenging, as minimizing one objective typically comes at the expense of the other. To overcome this limitation, we propose the optimized grid-based data collection with mobile sink (ODCMS) approach. In ODCMS, the MS follows a predefined trajectory and visits strategically selected visiting points (VPs) to collect aggregated data. The number of VPs is minimized to reduce tour length and thus latency, while a two-level data aggregation scheme shortens transmission distances and lowers forwarding overhead, thereby decreasing energy consumption. Energy balancing is ensured through periodic rotation of aggregation roles to prevent early node depletion. The MS movement among VPs and the base station (BS) is modeled as a connected graph, and its trajectory is constructed as a Hamiltonian cycle using a backtracking algorithm. ODCMS also supports rapid reporting of urgent events to the BS prior to MS arrival. NS-3 simulations compared with EDEDA and EFDC demonstrate that ODCMS achieves shorter tour length and data collection delay, lower energy consumption, and longer network lifetime than other approaches.