Data aggregator

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

Edge Blockchain Assisted Lightweight Privacy-Preserving Data Aggregation for Smart Grid

Abstract: Compared with traditional power systems, smart grid is designed to provide effective and secure energy services. Data aggregation is one of the key technologies in wireless sensor networks, which reduces the amount of data transmission between nodes by merging similar data and simplifying redundant data, thus significantly reducing the computation cost and communication overhead of the system. Many data aggregation schemes have been developed for the smart grid in the past years. However, most of the data aggregation schemes ignore the data security and privacy protection issues of the edge layer. To solve these problems, in this article, we propose an edge blockchain assisted lightweight privacy-preserving data aggregation for smart grid, named EBDA. In this work, we integrate edge computing and blockchain to design a three-layer architecture data aggregation scheme for smart grid. This new architecture supports a two-level data aggregation scheme, which is more efficient and secure. Through theoretical analysis and simulations, EBDA shows great superiority in terms of resisting network attacks, reducing system computation costs and communication overhead compared with existing schemes.

Decentralized Strategies for Monitoring Structures using Wireless Smart Sensor Networks

Abstract: Driven by the needs to address problems with our rapidly aging civil infrastructure, structural health monitoring (SHM) has arisen as an important tool to improve maintenance and operation. Introduced as a promising alternative to the traditional wired sensors, wireless smart sensors offer unique features (low cost, wireless communication, onboard computation, and small size) that enable deployment of dense array of sensors essential for assessing structural damage. The centralized data collection approach, which the wired sensor system commonly employs, is not suitable to wireless smart sensor networks (WSSNs) due to limitations in the wireless communication; decentralized data aggregation and processing is required in the WSSNs. Rather than collecting uncondensed raw sensor data at a centralized location, in-network data processing, made possible by the onboard computational capability of smart sensors, is utilized to condense the raw data and extract meaningful information. By transferring only the condensed data to the centralized location, data communication over the wireless links can be greatly reduced. Decentralized data aggregation approaches can be placed in two broad categories: (a) independent processing (each node processes sensor data independently), and (b) coordinated processing (sensor nodes collaborate to process sensor data by sharing information). This report outlines the implementation of both decentralized data aggregation approaches for the WSSNs employing MEMSIC’s Imote2 smart sensor platform (http://www.memsic.com). Design considerations for developing WSSN applications are described herein, including network-wide flow and timing, fault-tolerant feature, and network topology to account for the decentralized data aggregation. WSSN applications introduced in this report can be downloaded at the Illinois SHM Project website (http://shm.cs.uiuc.edu).

Evaluation of the Precision-Privacy Tradeoff of Data Perturbation for Smart Metering

Abstract: Smart grid users and standardization committees require that utilities and third parties collecting metering data employ techniques for limiting the level of precision of the gathered household measurements to a granularity no finer than what is required for providing the expected service. Data aggregation and data perturbation are two such techniques. This paper provides quantitative means to identify a tradeoff between the aggregation set size, the precision on the aggregated measurements, and the privacy level. This is achieved by formally defining an attack to the privacy of an individual user and calculating how much its success probability is reduced by applying data perturbation. Under the assumption of time-correlation of the measurements, colored noise can be used to even further reduce the success probability. The tightness of the analytical results is evaluated by comparing them to experimental data.

Genetically derived secure cluster-based data aggregation in wireless sensor networks

Abstract: In wireless sensor networks (WSNs), the current cluster-based data aggregation technique consumes more energy. Also the secured data transmission are vital for enhancing the data authentication and confidentiality. In order to overcome these issues, in this study, the authors propose a genetically derived secure cluster-based data aggregation in WSN. Initially the cluster heads are selected based on the node connectivity, which acts as a data aggregator. Then, the clustering process is executed using the genetic algorithm. When a cluster member wants to transmit the data to aggregator, a data encryption technique are utilised that offers authenticity, confidentiality and integrity. By simulation results, the authors show that the proposed technique minimises the energy consumption, ensures data security and reduces the transmission overhead.

PrivSet: Set-Valued Data Analyses with Locale Differential Privacy

Abstract: Set-valued data is useful for representing a rich family of information in numerous areas, such as market basket data of online shopping, apps on mobile phones and web browsing history. By analyzing set-valued data that are collected from users, service providers could learn the demographics of the users, the patterns of their usages, and finally, improve the quality of services for them. However, privacy has been an increasing concern in collecting and analyzing users' set-valued data, since these data may reveal sensitive information (e.g., identities, preferences and diseases) about individuals. In this work, we propose a privacy preserving aggregation mechanism for set-valued data: PrivSet. It provides rigorous data privacy protection locally (e.g., on mobile phones or wearable devices) and efficiently (its computational overhead is linear to the item domain size) for each user, and meanwhile allowing effective statistical analyses (e.g., distribution estimation of items, distribution estimation of set cardinality) on set-valued data for service providers. More specifically, in PrivSet, within the constraints of local e-differential privacy, each user independently responses with a subset of the set-valued data domain with calibrated probabilities, hence the true positive/false positive rate of each item is balanced and the performance of distribution estimation is optimized. Besides presenting theoretical error bounds of PrivSet and proving its optimality over existing approaches, we experimentally validate the mechanism, the experimental results illustrate that the estimation error in PrivSet has been reduced by half when compared to state-of-the-art approaches.