Data aggregator

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

PPCS: An Intelligent Privacy-Preserving Mobile-Edge Crowdsensing Strategy for Industrial IoT

Abstract: Mobile-edge crowdsensing is capable of providing a large amount of data via pervasive mobile terminals for Industrial Internet of Things (IIoT). However, the generated data often contain users’ sensitive information, which suggests the significance of privacy preserving in data aggregation and analysis for IIoT. Privacy preserving in mobile-edge crowdsensing have conflicting objectives, i.e., the edge fusion center (FC) requires data of better quality for data fusion with higher accuracy whereas participatory users (PUs) desire better privacy preserving by larger noise injection. Therefore, how to select proper noises to achieve the tradeoff between accuracy and privacy is a challenging problem. In addition, FC is subject to data tempering due to the lack of data reliability validations and incentive mechanisms. To tackle these problems, we propose a novel privacy-preserving mobile-edge crowdsensing strategy (PPCS) for IIoT. Specifically, PPCS provides a Kullback–Leibler privacy-preserving data aggregation using a reputation-based incentive mechanism. On the other hand, PPCS offers hypothesis test-based data reliability validation and PU’s reputation update, which collaborate to ease the impact of tampered data. Meanwhile, a reinforcement learning algorithm, the expected Sarsa, is applied to obtain the optimal test threshold. Theoretical analysis and experimental results show that PPCS is an energy-efficient strategy and the data provided by PPCS has a better aggregation accuracy than certain baseline strategies.

Efficient aggregation using first hop selection in WSNs

Abstract: We study the approaches to extending the lifetime of Wireless Sensor Networks (WSNs) based on in-network data aggregation at the First Hop (FH) away from each sensor data source, followed by flow-based routing of the resulting traffic. We introduce the concept of flow loss multiplier to express the impact of data aggregation on the conveyed data. A Mixed Integer Linear Programming (MILP) model is formulated for the problem of determining the optimal FH aggregation nodes that maximise network lifetime. Heuristics are proposed to obtain significant aggregation and to prolong the system lifetime. To facilitate performance evaluation, we adopt a flow loss multiplier that depends on the spatial relationship among sensed areas. Simulation results show that FH aggregation provides significant increase in network lifetime for both flow-based and tree-based delivery schemes, and that flow-based mechanisms provide better network lifetime than tree-based mechanisms but at the cost of added complexity and overhead.

A rule-based tool for gradual granular data aggregation

Abstract: In order to keep more detailed data available for longer periods, old data has to be reduced gradually to save space and improve query performance, especially on resource-constrained systems with limited storage and query processing capabilities. In this regard, some hand-coded data aggregation solutions have been developed; however, their actual usage have been limited, for the reason that hand-coded data aggregation solutions have proven themselves too complex to maintain. Maintenance need to occur as requirements change frequently and the existing data aggregation techniques lack flexibility with regards to efficient requirements change management. This paper presents an effective rule-based tool for data reduction based on gradual granular data aggregation. With the proposed solution, data can be maintained at different levels of granularity. The solution is based on high-level data aggregation rules. Based on these rules, data aggregation code can be auto-generated. The solution is effective, easy-to-use and easy-to-maintain. In addition, the paper also demonstrates the use of the proposed tool based on a farming case study using standard database technologies. The results show productivity of the proposed tool-based solution in terms of initial development time, maintenance time and alteration time as compared to a hand-coded solution.

Performance analysis of data aggregation algorithms in wireless sensor networks

Abstract: Resource-constrained is a critical issue in Wireless Sensor Networks (WSN) applications. Data aggregation (or data fusion) is one of the key techniques to solve the problem. Data aggregation can effectively reduce the data traffic, thereby reduce energy consumption, and extend the network's survival time. The research of data aggregation relates to many aspects of technology, so that the design of efficient data aggregation algorithm is a challenging task. In this paper, we investigate the effect of essential operations, scheduling algorithm and aggregation tree construction, has on data aggregation performance. The main performance such as end-to-end delay and energy-consumption of different aggregation schemes has been studied and measured by simulation experiment.

Data Aggregation Techniques in Wireless Sensor Networks: Literature Survey

Abstract: The wireless detector network is wide employed in totally different areas of application like military applications etc. Wireless detector network is nothing however the cluster of detector nodes those area unit human action to every alternative mistreatment radio frequencies through the bottom station. Every detector node in network has the battery that is important and crucial to outline the lifetime of detector node. Hence it’s needed to possess economical energy utilization with aim of extending the lifetime of detector networks. One approach that greatly reduces this consumption of power is reduction of within detector networks information traffic that successively reduces the information sent to the bottom station. This method of collection information and causation it to the bottom station is termed as information aggregation. There area unit has several information aggregation techniques conferred by varied researchers with aim of rising the performance of wireless detector network in terms of energy consumption, network out-turn, packet delivery quantitative relation etc. primarily the information aggregation algorithms collection and aggregating data in economical utilization of power with aim of extend the period. Throughout this review paper, we tend to area unit planning to present the various existing strategies for in-network information aggregation in WSNs.