Data aggregator

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

Modelling data-aggregation in multi-replication data centric storage systems for wireless sensor and actor networks

Abstract: This paper studies data-centric storage (DCS) as a suitable system to perform data aggregation on wireless sensor and actor networks (WSANs), in which sensor and actor nodes collaborate together in a fully distributed way without any central base station that manages the network or provides connectivity to the outside world. The authors compare different multi-replication DCS proposals and choose the best one to be applied when studying data aggregation. In addition, the authors provide mathematical models for the production, consumption and overall network traffic for different application profiles. Those application profiles are based on the ability of a particular application to perform data aggregation and on what type of traffic is dominant, either the consumption or the production one. Furthermore, the authors provide closed formulas for each application profile that defines the optimal number of replicas that minimise the overall network traffic. Finally, the authors validate the proposed models via simulation.

Secure data aggregation in WSN using iterative filtering algorithm

Abstract: In WSN Aggregation is considered as the most susceptible to node compromising attacks, as wireless sensor networks are not more secured so they are more vulnerable to this type of attacks. For WSN it is essential to check the trustworthiness of the data & reputation of sensor nodes. To handle this issue Iterative Filtering acts as a best option. In Iterative filtering algorithm (IF) the data is aggregated concurrently from multiple sources & IF also render trust assessment of these sources. The trust assessment is in the form of similar weight factors assigned to data supplied by each source. Considering the security as major issue in WSN, In this paper we proposed an advancement for IF method by providing approximation which will make them collusion robust and is converging fast. Advancement in the Iterative Filtering algorithm will enhance the performance of the system with good potential for implementation in WSN, IF algorithm is stretched with novel method for collusion detection & revocation based on an initial approximation of the aggregate values as well as distribution of differences of each sensor readings. The proposed system performance is checked through extensive simulation in C#. The simulation demonstrates the improved results. The RMS value in the graphs for series 2(0.1, 0.18, 0.22, 0.22, 0.22, 0.3, 0.36, 0.38) with standard deviation ranging from 0.5 to 4 shows that the proposed system performance is better as compare to existing system.

LPM2DA: a lattice-based privacy-preserving multi-functional and multi-dimensional data aggregation scheme for smart grid

Abstract: The smart grid provides efficient transmission of energy and data. However, the frequent gathering of users’ consumption data discloses users’ privacy. Plenty of data aggregation schemes have been introduced to preserve the privacy of users’ private information. Unfortunately, with the advent of quantum machines, most of these schemes will be rendered vulnerable and insecure. Hence, to preserve privacy and provide other security services like integrity and authentication in smart grid, we attempt to introduce a secure scheme based on lattice-based cryptography named LPM2DA: a lattice-based privacy-preserving multi-functional and multi-dimensional data aggregation scheme. The proposed scheme enables the control center to acquire temporal and spatial aggregation of multi-dimensional data in a privacy-preserving manner. Also, it empowers the control center to calculate different statistical functions such as mean, variance, and skewness on users’ multi-dimensional data. Eventually, through analytical evaluation, we illustrate the efficiency of the proposed scheme.

A smart city development kit for designing Web and mobile Apps

Abstract: Smart City services' effectiveness is enabled by the integration and availability of data coming from city operators on different domains: mobility, energy, health, water, telecom, tourism, culture, etc. They may be open and private data, static and real time. The most cases, smart city developers still have to develop their applications by studying several data sets and API sources, recovering the data models, reconciliating and aggregating data manually, creating applications exploiting low level Web Service and/or REST Call without the support of development tools, and neither of a semantic data aggregator. In this paper, an innovative tool for smart city web and mobile Apps development is presented. It exploits the Km4City data aggregator and semantic model, and includes: (i) tools for assisting developers for generating calls to Smart City API via visual queries on a graphical user interface; and (ii) open source Apps Development Kit for shortening the development. Finally, the paper reports about experimental results performed in the usage of the tool in the context of a large national project called Sii-Mobility which involved several developers. The same approach is presently adopted by other large projects as well.