Optimal Service Distribution in WSN Service System Subject to Data Security Constraints

• Published in: Sensors (Basel, Switzerland) Vol. 14; no. 8; pp. 14180 - 14209
• Main Authors: Wu, Zhao, Xiong, Naixue, Huang, Yannong, Gu, Qiong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 04.08.2014; MDPI
• Subjects: Algorithms; Communication channels; Computer Communication Networks - instrumentation; Computer information security; Computer science; Computer Security - instrumentation; Construction; data security; Data transmission; genetic algorithm; Genetic algorithms; Optimization; Partitions; Random variables; reliability optimization; Remote Sensing Technology - instrumentation; Remote sensors; Reproducibility of Results; Sensors; services composition; Software; task partition; universal generating function; wireless sensor networks; Wireless Technology - instrumentation; United States > US; Colorado
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s140814180

Services composition technology provides a flexible approach to building Wireless Sensor Network (WSN) Service Applications (WSA) in a service oriented tasking system for WSN. Maintaining the data security of WSA is one of the most important goals in sensor network research. In this paper, we consider a WSN service oriented tasking system in which the WSN Services Broker (WSB), as the resource management center, can map the service request from user into a set of atom-services (AS) and send them to some independent sensor nodes (SN) for parallel execution. The distribution of ASs among these SNs affects the data security as well as the reliability and performance of WSA because these SNs can be of different and independent specifications. By the optimal service partition into the ASs and their distribution among SNs, the WSB can provide the maximum possible service reliability and/or expected performance subject to data security constraints. This paper proposes an algorithm of optimal service partition and distribution based on the universal generating function (UGF) and the genetic algorithm (GA) approach. The experimental analysis is presented to demonstrate the feasibility of the suggested algorithm.