Data aggregator

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

Autoregressive integrated moving average model–based secure data aggregation for wireless sensor networks:

Abstract: Nodes in a wireless sensor network are normally constrained by hardware and environmental conditions and face challenges of reduced computing capabilities and system security vulnerabilities. This ...

Achieving Energy Conservation by Cluster Based Data Aggregation in Wireless Sensor Networks

Abstract: In Wireless Sensor Networks (WSN), one of the major issues is to maximize the network lifetime. Since all sensor nodes directly send the data to the Base station, the energy requirement is very high. This reduces the lifetime of the network. One of the solutions is to partition the network into various clusters which avoids direct communication. In this paper we propose an Energy efficient Cluster Based Data Aggregation (ECBDA) scheme for sensor networks. In this algorithm, Cluster members send the data only to its corresponding local cluster head, there by communication overhead is reduced. Data generated from neighboring sensors are often redundant and highly correlated. So the cluster head performs data aggregation to reduce the redundant packet transmission. In our approach, clusters are formed in a non-periodic manner to avoid unnecessary setup message transmissions. Re-clustering is performed only when CH needs to balance the load among the nodes. The simulation results show that our approach effectively reduces the energy consumption and hence the network lifetime is also increased.

An Energy-Efficient Data Gathering Protocol for Wireless Sensor Networks

Abstract: An energy-efficient data gathering protocol called E2DGP that takes advantage of spatial and temporal correlation of sampling data for WSNs is proposed in this paper. E2DGP includes a clustering method of balancing energy consumption, a data prediction transmission strategy and an energy-aware multihop routing algorithm. In clustering process phase, the initial probability of node for cluster head election is derived from mathematical relation between application’s seamless coverage fraction and numbers of required cluster heads. In data aggregation phase, the spatial correlation of data within a cluster is utilized by cluster head to aggregate sampling data. According to temporal correlation of sampling data, cluster heads send data to sink node using prediction transmission strategy while satisfying the transmission precision in the data transmission phase, and the lifetime of network is greatly prolonged by this strategy. In order to mitigate the hot spot problem among cluster heads, a greedy geographic and energy-aware multihop routing algorithm is presented for inter-cluster communication. Simulation results show that E2DGP outperforms EECS and LEACH in terms of network lifetime by balancing energy consumption and decrease of transmission while meeting desired application-specific requirements.

PADP: Efficient Privacy-Preserving Data Aggregation and Dynamic Pricing for Vehicle-to-Grid Networks

Abstract: With the fast development of Internet of Things (IoT) especially for smart grid and electric vehicle (EV) networking, vehicle-to-grid (V2G) communications have been increasingly studied and recognized as one of the most convincing tools for general road transportation, to effectively reduce the oil demands and gas emissions. Unfortunately, a series of security and privacy issues have significantly impeded its wide adoption. The existing work mainly focused on the static environment, which cannot be directly applied to the mobile setting where EVs travel across regions. The dynamic pricing metric in V2G networks depends on the real-time electricity usage aggregation in one region. To address this issue, in this article, an efficient privacy-preserving data aggregation and dynamic pricing service PADP in V2G IoT is proposed, by designing an identity-based sequential aggregate signed data (SASD) based on factoring and a threshold homomorphic encryption. In the proposed threshold homomorphic encryption, a legal ciphertext can be generated if and only if no less than threshold $k$ individual illegal ciphertexts are aggregated. Therefore, the aggregated power consumption data can be successfully decrypted while the individual power consumption privacy of honest EV users can be well protected against even the collusion between a malicious power charging station and compromised EVs. Furthermore, the technique of SASD guarantees entity authentication with a minimized amount of transmitted data. Finally, formal security proof and extensive performance evaluation demonstrate the effectiveness and practicability of our proposed PADP.

Query Privacy Preserving for Data Aggregation in Wireless Sensor Networks

Abstract: Wireless Sensor Networks (WSNs) are increasingly involved in many applications. However, communication overhead and energy efficiency of sensor nodes are the major concerns in WSNs. In addition, the broadcast communication mode of WSNs makes the network vulnerable to privacy disclosure when the sensor nodes are subject to malicious behaviours. Based on the abovementioned issues, we present a Queries Privacy Preserving mechanism for Data Aggregation (QPPDA) which may reduce energy consumption by allowing multiple queries to be aggregated into a single packet and preserve data privacy effectively by employing a privacy homomorphic encryption scheme. The performance evaluations obtained from the theoretical analysis and the experimental simulation show that our mechanism can reduce the communication overhead of the network and protect the private data from being compromised.