Underwater sensors boost energy balance with smart mobile edge computing
Mobility-Aware Mobile Edge Computing for Energy-Balanced Data Collection in IoT-Enabled Underwater Sensor Networks
Neural and Evolutionary ComputingNetworking and Internet Architecture
Summary
Underwater sensor networks help collect important data but often run out of energy quickly and lose connection. The authors developed a new method that uses smart, moving underwater vehicles with computing power to gather data more efficiently and balance energy use. Their system carefully plans underwater vehicle routes considering water pressure and buoyancy, improving how long the network lasts and how well data is collected. Simulations show it works better than older methods for long-term underwater sensor tasks.
What this means in practice
- •For underwater network operators: Manage underwater sensor networks to improve energy distribution and extend network lifetime through adaptive mobile edge data collection.
- •For autonomous underwater vehicle teams: Plan efficient AUV routes that handle underwater forces for better performance in data gathering missions.
Authors
Nguyen Thi My Binh, Do Dinh Hoan, Tong Tien Manh, Vu Minh Yen, Dang Trong Hop, Ho Viet Duc Luong
Abstract
Underwater wireless sensor networks (UWSNs) face critical challenges in energy imbalance, connectivity, and data collection due to harsh environments. To address these issues, this paper focuses on the problem is how to achieve energy-balanced data collection in IoT-enabled UWSNs with a mobility-aware mobile edge computing (MEC), named MECEBDC. Unlike existing approaches, the proposed scheme jointly optimizes energy-aware clustering, cluster head selection, and AUV-assisted data collection. Furthermore, we design a novel metaheuristic-based AUV routing algorithm that incorporates realistic underwater mobility dynamics, explicitly considering gravitational pressure and buoyancy forces acting on the AUV. By leveraging the computing, storage, and mobility capabilities of MEC-enabled AUVs, the proposed approach enables efficient trajectory planning and adaptive data gathering in a stable 3D underwater environment. Extensive simulations and theoretical analysis demonstrate that the proposed method significantly reduces node energy consumption, improves data collection efficiency, and prolongs network lifetime compared with existing schemes, making it well suited for long-term underwater IoT applications.