Search

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. [ABSTRACT FROM AUTHOR]

This paper presents a gradient-based multi-hop clustering protocol combined with a mobile sink (MS) solution for efficient data gathering in wireless sensor networks. The main insight for the clustering algorithm is drawn from image processing field and namely from the watershed transform, widely used for image segmentation. The proposed algorithm creates multi-hop clusters whose cluster heads (CHs) as well as cluster members near the CHs have high energy reserves. Specifically, the energy of the sensors in a cluster increases progressively as getting closer to the CH. As the nodes closer to the CH are most burdened with relaying of data from other cluster members, the higher levels of available energy at these nodes prolong the network lifetime eventually. After cluster formation, a MS periodically visits each CH and collects the data from cluster members already gathered at the CH. Simulation results show the higher performance of the proposed scheme in comparison to other competent approaches in the literature. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), a lot of sensory traffic with redundancy is produced due to massive node density and their diverse placement. This causes the decline of scarce network resources such as bandwidth and energy, thus decreasing the lifetime of sensor network. Recently, the mobile agent (MA) paradigm has been proposed as a solution to overcome these problems. The MA approach accounts for performing data processing and making data aggregation decisions at nodes rather than bring data back to a central processor (sink). Using this approach, redundant sensory data is eliminated. In this article, we consider the problem of calculating near-optimal routes for MAs that incrementally fuse the data as they visit the nodes in a WSN. The order of visited nodes (the agent’s itinerary) affects not only the quality but also the overall cost of data fusion. Our proposed heuristic algorithm adapts methods usually applied in network design problems in the specific requirements of sensor networks. It computes an approximate solution to the problem by suggesting an appropriate number of MAs that minimizes the overall data fusion cost and constructs near-optimal itineraries for each of them. The performance gain of our algorithm over alternative approaches both in terms of cost and task completion latency is demonstrated by a quantitative evaluation and also in simulated environments through a Java-based tool. [ABSTRACT FROM AUTHOR]