Data aggregator

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

An Efficient Privacy-Preserving Data Aggregation Scheme in Smart Grid

Abstract: Today with development of smart grids (SG), security and efficiency topics are more important than the past. In smart grids, there are smart meter (SM) devices in residential area that send their measured data to control center (CC) for future analysis. This way, user data may pass through a few internal nodes to reach the CC. Hence, privacy preserving of user data is one of the biggest challenges in smart grid researches because by disclosing the user-related data, internal or external adversary can understand habits and behaviors of users. A solution to address this challenge is the data aggregation mechanism in which CC obtain the aggregated data of all of the users in a residential area (RA). In this paper, we present an efficient approach for data aggregation in smart grids using the AV-net mask and Paillier encryption system to preserve the user data privacy. The proposed protocol does not need any secure channel. Besides, the conducted security and performance analysis shows that not only the proposed approach is secure against eavesdropping attack and collusion up to n-2 level, but also its computational overhead is acceptable comparing the previous works.

Data Aggregation and Packet Bundling of Uplink Small Packets for Monitoring Applications in LTE

Abstract: In cellular massive machine-type communications, a device can transmit directly to the BS or through an aggregator (intermediate node). While direct device-BS communication has recently been the focus of 5G/3GPP research and standardization efforts, the use of aggregators remains a less explored topic. In this article we analyze the deployment scenarios in which aggregators can perform cellular access on behalf of multiple MTC devices. We study the effect of packet bundling at the aggregator, which alleviates overhead and resource waste when sending small packets. The aggregators give rise to a trade-off between access congestion and resource starvation, and we show that packet bundling can minimize resource starvation, especially for smaller numbers of aggregators. Under the limitations of the considered model, we investigate the optimal settings of the network parameters in terms of number of aggregators and packet bundle size. Our results show that, in general, data aggregation can benefit the uplink massive MTC in LTE by reducing the signaling overhead.

Reliable Adaptive Data Aggregation Route Strategy for a Trade-off between Energy and Lifetime in WSNs

Abstract: Mobile security is one of the most fundamental problems in Wireless Sensor Networks (WSNs). The data transmission path will be compromised for some disabled nodes. To construct a secure and reliable network, designing an adaptive route strategy which optimizes energy consumption and network lifetime of the aggregation cost is of great importance. In this paper, we address the reliable data aggregation route problem for WSNs. Firstly, to ensure nodes work properly, we propose a data aggregation route algorithm which improves the energy efficiency in the WSN. The construction process achieved through discrete particle swarm optimization (DPSO) saves node energy costs. Then, to balance the network load and establish a reliable network, an adaptive route algorithm with the minimal energy and the maximum lifetime is proposed. Since it is a non-linear constrained multi-objective optimization problem, in this paper we propose a DPSO with the multi-objective fitness function combined with the phenotype sharing function and penalty function to find available routes. Experimental results show that compared with other tree routing algorithms our algorithm can effectively reduce energy consumption and trade off energy consumption and network lifetime.

Analysis of data aggregation in wireless sensor network

Abstract: Public Key Cryptography was measured too expensive for WSN but it all changed due to the developments in software and hardware prototypes. This paper present and analyze the secure hierarchical data aggregation algorithm which uses an effective Public key cryptography to attain end to end security. Many data aggregation systems which are followed before uses either hop by hop decryption or uses symmetric key cryptography for end to end security but it is not energy efficient. This secure hierarchical data aggregation algorithm does not necessitate additional phase for data integrity verification and also it eludes additional transmissions and computational overhead on the sensor nodes to reduce the amount of energy used up by the network. This Paper measures the execution time and energy consumption of various cryptographic functions of the secure data aggregation algorithm on TelosB sensor network platform, programmed in nesC language and also analyses the performance of the algorithm in the Contiki Os simulator Cooja.

SAGE: Geo-Distributed Streaming Data Analysis in Clouds

Abstract: The continuous growth of sensor networks, stock exchanges, climate monitoring or scientific applications produces new streaming data at increasing rates. Managing and processing such data, sometimes generated from multiple geographical locations, raises important challenges as it requires real-time processing or data aggregation. Conventional solutions like DBMS, MapReduce or dedicated solutions adopting single-located environments fail to meet the demands required for processing the Geo-distributed streaming data. Public clouds like Azure, with data centers spread around the globe, offer the infrastructure which can handle such a processing. Our approach, proposes a service-oriented cloud architecture for performing the stream analysis, by composing services which are distributed among multiple cloud data centers. Hence, the computation is moved towards the multiple data sources exploiting the geographical data locality. The initial results showed good scalability of the approach, reaching 1000 cores in the Azure cloud, and performance improvements compared to single location processing of a factor of 3.3.