Data aggregator

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

Privacy-Preserving Data Aggregation Protocol for Fog Computing-Assisted Vehicle-to-Infrastructure Scenario

Abstract: Vehicle-to-infrastructure (V2I) communication enables moving vehicles to upload real-time data about road surface situation to the Internet via fixed roadside units (RSU). Thanks to the resource restriction of mobile vehicles, fog computation-enhanced V2I communication scenario has received increasing attention recently. However, how to aggregate the sensed data from vehicles securely and efficiently still remains open to the V2I communication scenario. In this paper, a light-weight and anonymous aggregation protocol is proposed for the fog computing-based V2I communication scenario. With the proposed protocol, the data collected by the vehicles can be efficiently obtained by the RSU in a privacy-preserving manner. Particularly, we first suggest a certificateless aggregate signcryption (CL-A-SC) scheme and prove its security in the random oracle model. The suggested CL-A-SC scheme, which is of independent interest, can achieve the merits of certificateless cryptography and signcryption scheme simultaneously. Then we put forward the anonymous aggregation protocol for V2I communication scenario as one extension of the suggested CL-A-SC scheme. Security analysis demonstrates that the proposed aggregation protocol achieves desirable security properties. The performance comparison shows that the proposed protocol significantly reduces the computation and communication overhead compared with the up-to-date protocols in this field.

A new data aggregation approach for WSNs based on open pits mining

Abstract: Wireless sensor networks consist of a large number of sensor nodes with limited energy, which is widely used in various Internet of things scenarios in recent years. Regarding the vast use of smart objects and applications, one of the big challenges is to collect and analyse the data. Sensor energy limitations and data redundancy are the primary challenges in these networks and reduce network lifetime as well. Therefore, the nodes try to eliminate redundant data, before transferring it to the central station. Data aggregation in IoT such as wireless sensor network plays an important role because in IoT there are heterogeneous data collected from different sources which need more energy to send data. One of the solutions to reduce energy, in this case, is to process and aggregate data prior to sending it. Data aggregation is an effective technique in reducing the data redundancy as well as improving energy efficiency; It also increases the lifespan of Wireless Sensor Networks. Integrating and combining relevant and identical data prevents sending additional packets, and minimizes the redundancy, saves energy, and increases network lifetime. The main purpose of this paper is to provide a new data aggregation method based on the open-pit mining idea efficiently. In this approach, the wireless sensor network is divided into several clusters, and in each cluster, a central node is specified, around which some hypothetical pits are considered to aggregate and send data.

Processing geo-dispersed big data in an advanced mapreduce framework

Abstract: Big data has emerged as a new era of information generation and processing. Big data applications are expected to provide a lot of benefits and convenience to our lives. Cloud computing is a popular infrastructure that has the resources for big data processing. As the number of mobile devices is fast increasing, mobile cloud computing is becoming an important part of many big data applications. In this article, we propose a novel MapReduce-based framework to process geo-dispersed big data in mobile cloud architecture. The proposed framework supports simple as well as complex operations on geo-dispersed big data, and uses various data aggregation schemes to satisfy different application requirements.

A Privacy-Preserving Noise Addition Data Aggregation Scheme for Smart Grid

Abstract: Smart meters are applied to the smart grid to report instant electricity consumption to servers periodically; these data enable a fine-grained energy supply. However, these regularly reported data may cause some privacy problems. For example, they can reveal whether the house owner is at home, if the television is working, etc. As privacy is becoming a big issue, people are reluctant to disclose this kind of personal information. In this study, we analyzed past studies and found that the traditional method suffers from a meter failure problem and a meter replacement problem, thus we propose a smart meter aggregation scheme based on a noise addition method and the homomorphic encryption algorithm, which can avoid the aforementioned problems. After simulation, the experimental results show that the computation cost on both the aggregator and smart meter side is reduced. A formal security analysis shows that the proposed scheme has semantic security.