Context Discovery in Mobile Environments: A Particle Swarm Optimization Approach

Anagnostopoulos, Christos; Hadjiefthymiades, Stathes

doi:10.1007/978-3-642-11482-3_11

Cited by 5 publications

(3 citation statements)

References 12 publications

(3 reference statements)

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“…Our scheme can be adopted by the above-mentioned opportunistic systems in order to enhance the quality/confidence of the collected context, thus, improving the provided CAA. Specifically, our scheme can enhance the context discovery process in [36] and the time-optimized data delivery mechanism in [38]. In context discovery [36], mobile collectors collaboratively explore, locate, and track sources that generate context.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, our scheme can enhance the context discovery process in [36] and the time-optimized data delivery mechanism in [38]. In context discovery [36], mobile collectors collaboratively explore, locate, and track sources that generate context. All collectors cooperatively pursue the acquisition of context of high quality by locating sources in an IoT environment.…”

Section: Related Workmentioning

confidence: 99%

“…In our scheme, a source can improve the quality of the discovered context through the time-optimized decision framework. Collectors can then explore areas in a collaborative way as proposed in [36] and, in turn, provide the collected information for the exploration area to corresponding CAAs. Moreover, our scheme can enhance the contextual data harvesting mechanism introduced in [38].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Contextual Information Collection in Internet of Things

Anagnostopoulos

2016

Int J Wireless Inf Networks

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As we are moving towards the Internet of Things (IoT), a significant growth of stationary and mobile sensing & computing IoT devices continuously generate enormous amounts of contextual information, e.g., environmental data. Contextual information collection, reasoning, and inference plays critical role in IoT. In this paper, we consider the contextual information collection & harvesting problem in which stationary sensing and computing devices (sources), which are incapable to communicate with each other either due to their long distance, or for energy efficiency, or spatially dispersed network, rely on mobile IoT devices (collectors) to 'drain' their acquired contextual information. (e.g., generating from IoT applications: smart cities, smart metering, and smart agriculture). At the contact instances with the collectors, sources have to decide whether to deliver the contextual information obtained so far or postpone their delivery for later hitting epochs in an effort to sense fresher (or more critical) contextual information. We rest on the principles of Optimal Stopping Theory and propose an intelligent context collection scheme in IoT environments. We show through simulations with synthetic and real mobility data the effectiveness of our scheme compared to other approaches. Keywords Contextual information collection • Internet of Things • timeoptimized stochastic information delivery • optimal stopping theory. 1 Introduction An aspect in context-aware mobile computing is the collection of contextual information (context) from certain sources in an Internet of Things (IoT) environment. We study the collection of context from stationary sources through

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Section: Related Workmentioning

confidence: 99%