Investigation of maximum lifetime and minimum delay trade-off in underwater sensor networks

Yıldız H. U.

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, vol.32, no.7, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 32 Issue: 7
  • Publication Date: 2019
  • Doi Number: 10.1002/dac.3924
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: end-to-end delay, integer linear programming, multi-objective optimization, network lifetime, underwater acoustic sensor networks, OPTIMIZATION
  • TED University Affiliated: Yes


Underwater acoustic sensor networks (UASNs) are subjected to harsh characteristics of underwater acoustic channel such as severe path losses, noise, and high propagation delays. Among these constraints, propagation delay (more generally, end-to-end delay) is the most dominating limitation especially for time-critical UASN applications. Although the minimization of end-to-end delay can be achieved by using the minimum hop routing, this solution cannot lead prolonged lifetimes since nodes consume excessive energy for transmission over long links. On the other hand, the maximization of network lifetime is possible by using energy efficient paths, which consist of relatively short links but high number of hops. However, this solution results in long end-to-end delays. Hence, there is a trade-off between maximizing the network lifetime and minimizing the end-to-end delay in UASNs. In this work, we develop a novel multi-objective-optimization (MOO) model that jointly maximizes the network lifetime while minimizing the end-to-end delay. We systematically analyze the effects of limiting the end-to-end delay on UASN lifetime. Our results reveal that the minimum end-to-end delay routing solution results in at most 72.93% reduction in maximum network lifetimes obtained without any restrictions on the end-to-end delay. Nevertheless, relaxing the minimum end-to-end delay constraint at least by 30.91% yields negligible reductions in maximum network lifetimes.