Data aggregator

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

A new data aggregation scheme via adaptive compression for wireless sensor networks

Abstract: Data aggregation is necessary for extending the network lifetime of wireless sensor nodes with limited processing and power capabilities, since energy expended in transmitting a single data bit would be at least several orders of magnitude higher when compared to that needed for a 32-bit computation. Therefore, in this article, a novel nonlinear adaptive pulse coded modulation-based compression (NADPCMC) scheme is proposed for data aggregation in a wireless sensor network (WSN). The NADPCMC comprises of two estimators—one at the source or transmitter and the second one at the destination node. The estimator at the source node approximates the data value for each sample. The difference between the data sample and its estimate is quantized and transmitted to the next hop node instead of the actual data sample, thus reducing the amount of data transmission and rending energy savings. A similar estimator at the next hop node or base station reconstructs the original data. It is demonstrated that repeated application of the NADPCMC scheme along the route in a WSN results in data aggregation. Satisfactory performance of the proposed scheme in terms of distortion, compression ratio, and energy efficiency and in the presence of estimation and quantization errors for data aggregation is demonstrated using the Lyapunov approach. Then the performance of the proposed scheme is contrasted with the available compression schemes in an NS-2 environment through several benchmarking datasets. Simulation and hardware results demonstrate that almost 50p energy savings with low distortion levels below 5p and low overhead are observed when compared to no compression. Iteratively applying the proposed compression scheme at the cluster head nodes along the routes over the network yields an additional improvement of 20p in energy savings per aggregation with an overall distortion below 8p.

EPPDA: An Efficient and Privacy-Preserving Data Aggregation Scheme with Authentication and Authorization for IoT-Based Healthcare Applications

Abstract: Nowadays, IoT technology is used in various application domains, including the healthcare, where sensors and IoT enabled medical devices exchange data without human interaction to securely transmit collected sensitive healthcare data towards healthcare professionals to be reviewed and take proper actions if needed. The IoT devices are usually resource-constrained in terms of energy consumption, storage capacity, computational capability, and communication range. In healthcare applications, many miniaturized devices are exploited for healthcare data collection and transmission. Thus, there is a need for secure data aggregation while preserving the data integrity and privacy of the patient. For that, the security, privacy, and aggregation of health data are very important aspects to be considered. This paper proposes a novel secure data aggregation scheme called “An Efficient and Privacy-Preserving Data Aggregation Scheme with authentication for IoT-Based Healthcare applications” (EPPDA). EPPDA is based to verification and authorization phase to verify the legitimacy of the nodes that need to join the process of aggregation. EPPDA, also, uses additive homomorphic encryption to protect data privacy and combines it with homomorphic MAC to check the data integrity. The major advantage of homomorphic encryption is allowing complex mathematical operations to be performed on encrypted data without knowing the contents of the original plain data. The proposed system is developed using MySignals HW V2 platform. Security analysis and experimental results show that our proposed scheme guarantees data privacy, messages authenticity, and integrity, with lightweight communication overhead and computation.

A Privacy-Preserving Homomorphic Scheme With Multiple Dimensions and Fault Tolerance for Metering Data Aggregation in Smart Grid

Abstract: Advanced Metering Infrastructure (AMI) facilitates the communication between smart meters and network operators in smart grid. For better demand-response management, smart meters are supposed to send live or sometimes periodic consumption reports. If such reports are intercepted or eavesdropped by a malicious entity, customers’ privacy is compromised, since vital information can be inferred from power consumption data. In this article, we propose a novel homomorphic privacy-preserving protocol (called NHP3) for data aggregation that has a low computational cost compared to its rivals. It is fault-tolerant, supports multi-category aggregation, and can do batch verification at the intermediate aggregator as well as the central system. The proposed protocol is secure even when the gateway or aggregator turns malicious. It does not allow any compromised meter to find other users’ consumption information either. Moreover, in this scheme, the central system cannot infer any usage data even if it is curious and gains access to the data packets sent from meters to the intermediate aggregator. A comprehensive and comparative analysis is carried out at the end of this article which shows the advantages of the proposed scheme in terms of security features and cost.

SEEDA: Secure end-to-end data aggregation in Wireless Sensor Networks

Abstract: Wireless Sensor Network (WSN) consists of a large number of nodes with limited sensing, computation and communication capabilities. In such network consisting of resource constrained nodes, data transmission is a energy-consuming operation. Hence to increase the lifetime of the network it is essential to reduce the number of bits transmitted. One widely used method for reducing the data transmission is data aggregation. The security issues such as data integrity, confidentiality and freshness in data aggregation become crucial when the WSN is deployed in a remote or hostile environment where sensors are prone to node failures and compromises. Secure data aggregation schemes are suitable to achieve security in data aggregation. In this paper we propose a Secure Data Aggregation scheme which provides end-to-end data privacy. The average number of bits transmitted per node is reduced by 30%–50% compared to the scheme in [16].

Energy efficient intra-cluster data aggregation technique for wireless sensor network

Abstract: Cluster based routing strategies are popular categories of routing protocols among wireless sensor networks (WSNs). In a typical cluster based routing protocols, few nodes are elected as cluster Head (CH) nodes and they form clusters with other nodes of the networks known as cluster members. The data sensed by cluster members is sent to CH node for data aggregation and further processing. Data Aggregation is considered to be one of the important methods used to prolong the network lifetime of WSN. Data aggregation is quite popular among cluster based routing protocols for WSN, where the data packets are aggregated by intermediate nodes till it reaches to BS. Data aggregation ultimately helps in reducing the number of data messages thereby assisting in network life prolongation. In this paper, an intra-cluster data aggregation technique (ICA) for WSNs is proposed. ICA constructs the intra cluster data aggregation path from a source node to its CH node in an energy efficient way. The data packets are aggregated along the aggregation path by intermediate relay nodes till the message reaches to designated CH node. Comparative analysis of ICA, LEACH and LEACH-C protocols are carried by considering the parameters such as number of alive nodes, energy consumption, number of data packets received by BS. Results obtained show that ICA performs better compared to LEACH and LEACH-C protocols.