A Novel Energy-Efficient and Privacy-Preserving Data Aggregation for WSNs

Abstract

Data aggregation is a fundamental and efficient algorithm to reduce the communication overhead and energy consumption in wireless sensor networks (WSNs). However, WSNs need both energy-efficient and privacy-preserving when being deployed in an unprotected area. In this paper, we propose an energy-efficient and privacy-preserving data aggregation algorithm CBDA (the chain-based data aggregation). In the CBDA, sensor nodes will be organized as a tree topology. The leaf nodes of the tree sequentially reconnect with each other to form many chain topologies. For assuring data privacy, after data gathering, (1) the tail nodes (the nodes which on the tail of chain) need to slice their sensing data into J fragments. One fragment is kept by themselves, and they distribute the J −1 data fragments to their neighbor nodes. (2) Each tail node will inject some fake fragments into its J −1 fragments to interfere with adversaries. The CBDA can achieve less energy consumption and higher aggregation accuracy during data aggregation. We perform a comprehensive simulation to make a comparison among the CBDA with existing algorithms. The experimental results demonstrate that the CBDA outperforms the existing algorithms. INDEX TERMS Wireless sensor network, data processing, data privacy; energy conservation, accuracy.

A classy energy efficient spider monkey optimization based clustering and data aggregation models for wireless sensor network

“…Hu et al 27 employed a chain based data aggregation (CBDA) methodology for enhancing the performance and efficiency of WSN with minimized overhead and energy consumption. This work mainly objects to ensure the privacy and reliability of data transmission by using the multiparty computation method.…”

Federated Compressed Learning Edge Computing Framework with Ensuring Data Privacy for PM2.5 Prediction in Smart City Sensing Applications

“…In a centralized system, a huge amount of raw data from end devices is collected by central servers [ 28 ] and protected by using trust-based service management protocol [ 29 ], e.g., IoT-HiTrust [ 30 ]. However, the system transfers data over the network, allowing the leakage of critical data [ 31 ] and the increasing risk of side-channel attack [ 32 ]. The edge computing framework utilizes federated learning technology, prevents direct access to the data, moves the compute resource to the edge, and prevents the raw data exchange to the central server [ 33 , 34 , 35 , 36 ].…”

Towards Time-Sensitive and Verifiable Data Aggregation for Mobile Crowdsensing

“…erefore, TA should be able to revoke the keys from malicious IDs. Actually, the assumption of this model is widely used in previous work [18,19,21,22].…”

“…First of all, data aggregation requires a new mechanism to realize the key distribution of large-scale devices. Some existing aggregation schemes have strong security [18,19], but they use traditional one-to-one encryption. ese aggregation structures are complicated because the number of secret keys increases with the number of devices.…”