Data aggregator

About: Data aggregator is a research topic. Over the lifetime, 2615 publications have been published within this topic receiving 40265 citations.

Multiple k-hop clusters based routing scheme to preserve source-location privacy in WSNs

Abstract: Privacy is becoming one of the most notable challenges threatening wireless sensor networks (WSNs). Adversaries may use RF (radio frequency) localization techniques to perform hop-by-hop trace back to the source sensor’s location. A multiple k-hop clusters based routing strategy (MHCR) is proposed to preserve source-location privacy as well as enhance energy efficiency for WSNs. Owing to the inherent characteristics of intra-cluster data aggregation, each sensor of the interference clusters is able to act as a fake source to confuse the adversary. Moreover, dummy traffic could be filtered efficiently by the cluster heads during the data aggregation, ensuring no energy consumption be burdened in the hotspot of the network. Through careful analysis and calculation on the distribution and the number of interference clusters, energy efficiency is significantly enhanced without reducing the network lifetime. Finally, the security and delay performance of MHCR scheme are theoretically analyzed. Extensive analysis and simulation results demonstrate that MHCR scheme can improve both the location privacy security and energy efficiency markedly, especially in large-scale WSNs.

Low-Latency Service Data Aggregation Using Policy Obligations

Abstract: The Internet of Things, large scale sensor networks or even in social media, are now well established and their use is growing daily. Usage scenarios in these fields highlight the requirement to process, procure, and provide information with almost zero latency. This work is introducing new concepts for enabling fast communication by limiting information flow through filtering concepts combined with data processing techniques adopted from complex event processing. Specifically we introduce a novel mediation services architecture using filter policies to reduce latency. The filter policies define when and what data services need to provide to the mediator and thus save on bandwidth. The filter policies describe temporal conditions between two events removing the need to keep a complete history while still allowing temporal reasoning. Promising experimental results highlight the advantages to be gained from the approach.

Integration, aggregation and exchange of farming device data: A high level perspective

Abstract: Most of the farming devices produce massive amounts of data. In most of the cases, it is necessary to store this data at a central location for further processing. Due to the huge volumes of data, strategies for data aggregation procedures are very important, in order to avoid oversized data sets. There is also an increasing need to share or exchange data with other stack holders in the farming business based on farming standards. This paper presents an integrated, effective, easy-to-use and flexible solution, which enables the farmers to integrate data from several farming devices, aggregate data at different granularity levels and exchange data bi-directionally with other farming systems. The paper also describes the implementation strategy based on a case study using farming standards and open source technologies.

RAHIM: Robust Adaptive Approach Based on Hierarchical Monitoring Providing Trust Aggregation for Wireless Sensor Networks

Abstract: In-network data aggregation has a great impact on the energy consumption in large-scale wireless sensor networks. However, the resource constraints and vulnerable deployment environments challenge the application of this technique in terms of security and efficiency. A compromised node may forge arbitrary aggregation value and mislead the base station into trusting a false reading. In this paper, we present RAHIM, a reactive defense to secure data aggregation scheme in cluster-based wireless sensor networks. The proposed scheme is based on a novel application of adaptive hierarchical level of monitoring providing accuracy of data aggregation result in lightweight manner, even if all aggregator nodes and a part of sensors are compromised in the network.

Data Aggregation Using Dynamic Selection of Aggregation Points Based on RSSI for Wireless Sensor Networks

Abstract: In wireless sensor networks (WSNs), due to dense deployment, sensory data gathered by sensor nodes in close proximity tend to exhibit high correlation and therefore redundant. Transmitting such redundant data is not practical in the energy-constrained WSNs. Data aggregation offers a key solution to reduce such redundancy by allowing intermediate nodes to aggregate raw data streams before routing them toward a sink node. This in turn reduces transmission energy consumption. Prior work in data aggregation often rely on node's location for selecting an aggregator node, a fusion point. In this work, we propose two data aggregation mechanisms where aggregator nodes are determined opportunistically without dependency on global knowledge of data flow, network topology and nodes' geographical location. These mechanisms aggregate and route data packets based on Received Signal Strength Indicator (RSSI). An aggregation identification (Agg_ID) is associated with each data packet generated by a sensor node. The RSSI and Agg_ID are used in the RSSI-Based Fowarding for favoring nodes closer to sink to be an aggregator and also a relay node. We show via simulation the performance of the proposed mechanisms in terms of normalized number of transmissions, total number of packets transmissions and receptions, average energy consumed per data packet, network lifetime, end-to-end delay and packet loss probability.