Optimizing the Lifetime of Sensor Networks with Uncontrollable Mobile Sinks and QoS Constraints


In past literature, it has been demonstrated that the use of mobile sinks (MSs) increases dramatically the lifetime of wireless sensor networks (WSNs). In applications where the MSs are humans, animals, or transportation systems, the mobility of the MSs is often uncontrollable and could also be random and unpredictable. This implies the necessity of algorithms tailored to handle uncertainty on the MS mobility. In this article, we define the lifetime optimization of a WSN in the presence of uncontrollable sink mobility and Quality of Service (QoS) constraints. After defining an ideal scheme (called Oracle) which provably maximizes network lifetime, we present a novel Swarm-Intelligence-based Sensor Selection Algorithm (SISSA), which optimizes network lifetime and meets predefined QoS constraints. Then we mathematically analyze SISSA and derive analytical bounds on energy consumption, number of messages exchanged, and convergence time. The algorithm is experimentally evaluated on practical experimental setups, and its performances are compared to that by the optimal Oracle scheme, as well as with the IEEE 802.15.4 MAC and TDMA schemes. Results conclude that SISSA provides on the average the 56% of the lifetime provided by Oracle and outperforms IEEE 802.15.4 and TDMA in terms of yielded network lifetime.


Computer Science

Research Center/Lab(s)

Intelligent Systems Center


This work was supported by NSF Grants No. CNS-1545037, CCF-1533918, DGE-1433659, CNS-1355505, and IIS-1404673.

Keywords and Phrases

Algorithms; Artificial intelligence; Energy utilization; Optimization; Quality of service; Standards; Time division multiple access; 802.15.4; Ieee 802.15.4 macs; Implementation; Quality of Service constraints; Swarm Intelligence; Telosb; Transportation system; Wireless sensor network (WSNs); Wireless sensor networks; TDMA

International Standard Serial Number (ISSN)

1550-4859; 1550-4867

Document Type

Article - Journal

Document Version


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© 2016 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

01 Mar 2016