Mobile agents-based data aggregation in WSNs: benchmarking itinerary planning approaches
| Autor: | Ioannis E. Venetis, Damianos Gavalas, Grammati Pantziou, Charalampos Konstantopoulos |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Computer Networks and Communications
Computer science 020206 networking & telecommunications 02 engineering and technology Benchmarking computer.software_genre Set (abstract data type) Data aggregator 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Data mining Electrical and Electronic Engineering Routing (electronic design automation) Heuristics computer Wireless sensor network Energy (signal processing) Information Systems |
| Zdroj: | Wireless Networks. 24:2111-2132 |
| ISSN: | 1572-8196 1022-0038 |
| DOI: | 10.1007/s11276-017-1460-y |
| Popis: | Data aggregation represents one of the most challenging and well-studied subjects in the Wireless Sensor Networks (WSN) literature. The energy constraints of sensor nodes call for energy-efficient data aggregation methods so as to prolong network lifetime. Among other approaches, Mobile Agents (MAs) have been proposed to improve the performance of data aggregation in WSNs. In such approaches, the itineraries followed by travelling agents largely determine the overall performance of the data aggregation tasks. Along this line, several heuristics have been proposed to perform efficient itinerary planning for MAs. However, a direct comparison of the proposed algorithms is not straightforward, as they are typically performed on the ground of different parameter instances and assumptions about the underlying network and nodes capabilities. This article provides a critical review and qualitative evaluation of the most prominent itinerary planning algorithms. More importantly, having implemented and simulated a set of eleven (11) itinerary planning algorithms, we compare their performance upon a common parameter space, making realistic network-level assumptions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink