Data aggregator

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

Distributed data aggregation in machine-to-machine communication networks based on coalitional game

Abstract: Machine-to-machine (M2M) communications have emerged as a flourishing technology for next-generation communications, and are undergoing rapid development while inspiring numerous applications. However, unique features of M2M communications, such as the massive number of machine type devices (MTD) and delay sensitive applications require specific considerations. To enhance the communication efficiency with delay sensitive short messages, a key strategy is to utilize data aggregation. To facilitate an efficient distributed data aggregation among MTDs with different urgency levels, we propose a game theoretic mechanism based on the coalitional game. Through the proposed algorithm, MTDs autonomously collaborate and self-organize into disjoint independent and stable coalitions, and send their data through a coalition head known as the aggregator. Within each coalition, the utility of the users is defined in such a way that maximum cooperation is compelled. Finally, we discuss the stability of the resulting network structure, and analyse the performance of the proposed scheme.

Data Aggregation in Distributed Sensor Networks: Towards An Adaptive Timing Control

Abstract: Data aggregation in Wireless Sensor Networks (WSNs) has attracted wide attention since it could reduce the amount of wireless data communication and thus save power consumption, which is one of the top concerns in low-power WSN systems. Given a Data Aggregation Tree (DAT) for data query purpose in a WSN, and the expected query accuracy, we show the feasibility to use a low-cost timing control scheme to adaptively adjust the waiting period in each DAT level so that the end-to-end query delay could be optimized, i.e. without unnecessary pause in an aggregation node. For such an adaptive timing control scheme, we propose to use a Finite State Machine (FSM)- based auto-feedback control algorithm to adjust the waiting period of each DAT node to "timely" respond to the queries. Our sensor network simulation results and hardware experiments verify the validity of our proposed timing control scheme.

Smart power grid aggregation method and system for differential privacy security and fault tolerance

Abstract: The invention discloses a smart power grid aggregation method and system for differential privacy security and fault tolerance The method comprises the steps that a smart electric meter records and reports the electricity consumption of users in real time; a control center is responsible for collecting, processing and analyzing real-time electricity consumption data; a gateway is responsible for instruction delivery, data aggregation and security transmission between the control center and the users; a credible center is responsible for managing the whole system, and main function modules include a system initialization function module, a data aggregation request function module, a data aggregation request relay function module, a user data report function module, a security data aggregation function module and an aggregated data recovery module According to the smart power grid aggregation method and system, the common fault tolerant function is supported, a difference privacy attack can be resisted, and the phenomenon that a hostile attacker exposes and obtains privacy information of the users by eavesdropping user communication links can be resisted; when some user data are not reported successfully, the electricity consumption of all the users with normal data reporting can still be aggregated; the smart power grid aggregation method and system have the high elastic expandability and can support efficient million-scale smart power grid electricity consumption data aggregation

Tree-Based Data Aggregation Approach in Periodic Sensor Networks Using Correlation Matrix and Polynomial Regression

Abstract: Sensor networks are a collection of sensor nodes that co-operatively transmit sensed data to a base station. One of the well-known characteristics of Wireless Sensor Networks (WSNs) is its limited resources. Energy consumption of the network's nodes is considered one of the major challenges faced by researchers nowadays. On the other hand, data aggregation helps in reducing the redundant data transferred through the WSNs. This fact implies that aggregation of data is considered a very crucial technique for reducing the energy consumption across the WSN. Local aggregation and Prefix filtering are two methods used in which they utilize a tree based bi-level periodic data aggregation approach implemented on the source node and on the aggregator levels. In this paper an efficient model for multivariate data reduction is proposed based on periodic data aggregation on two sensor levels, in addition to polynomial regression functions. The performance of the model was evaluated using SensorScope network which is deployed at the Grand-St-Bernard located between Switzerland and Italy. The results show the advantages of the proposed model as it allows 84% reduction rate and 93% approximation accuracy after reduction. The simulations were done using the R software.

A Novel Secure Data Aggregation in IoT using Particle Swarm Optimization Algorithm

Abstract: Internet of Things (IoT) is a network paradigm in which data aggregation and data security plays a vital role. Data aggregation in IoT describes collection of information from different users and data security means encryption of collected data using cryptography method. The proposed work comprises of devices and gateway to perform data aggregation and data encryption. Data aggregation is performed using clustering in which data are clustered and secured by Particle Swarm Optimization (PSO) algorithm which finds the cluster head. After finding cluster head, nodes requests to join as cluster member. PSO computes fitness function using metrics i.e. energy, end-to-end delay, scoring factor, packet drops and successful packet transformation. After completion of clustering process, data encryption process is held in which, cluster head collects data from the cluster members and encrypts it using Elliptic Curve Cryptography (ECC) method. Finally, encrypted data are dispatched to gateway device. Experimental result shows, the proposed work on Secure Particle Swarm Optimization (SPSO)prompts better performance in following metrics i.e. delay, throughput and energy consumption.