A hybrid data collection scheme to achieve load balancing for underwater sensor networks

Abstract

Underwater wireless sensor networks possess considerable potential to monitor large and hostile underwater environments by reliably sensing, collecting, and forwarding data toward the surface sinks. Although the research community has made promising efforts, barriers such as continuous node mobility, longer delays, unavailability of location information, and energy limitations must be addressed. Taking this into account, this research aims to develop a hybrid and intelligent data collection scheme that considers node position and network characteristics during data forwarding. To accomplish the objective, the network is divided into two layers. The top layer, considered more dynamic, follows a hop-by-hop data forwarding scheme. The lower layer, experiencing stable water currents, follows a clustering-based data collection method. The proposed scheme, called Multilayer Dynamic Data Forwarding (MD2F), is suitable for large and deep underwater areas. MD2F is scalable as it uses a multi-sink architecture, while single or multiple autonomous underwater vehicles (AUVs) can be utilized depending on the area being monitored. Implementing hop-by-hop transmission and clustering-based data collection at different layers balances the network load, thereby increasing the network life. Results show that MD2F exhibits better performance when compared with Multilayer Cluster-based Energy Efficient (MLCEE) and Energy efficient and link reliable routing (E2LR) schemes, both are very close in working behavior. The results are encouraging in terms of delivery ratio, network throughput, and end-to-end delays. Alongside achieving these targets, the network also exhibits less energy consumption through load balancing.

Keywords

• Underwater sensor networks
• Hybrid data collection
• Dynamic clustering
• Load balancing
• Energy efficiency
• Underwater surveillance and monitoring