Data aggregator

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

Secure Data Aggregation with MAC Authentication in Wireless Sensor Networks

Abstract: Recently, several data aggregation schemes based on privacy homomorphism encryption have been proposed and investigated on wireless sensor networks. These data aggregation schemes provide better security compared with traditional aggregation since cluster heads (aggregator) can directly aggregate the ciphertexts without decryption; consequently, transmission overhead is reduced. Based on our survey of existing research efforts for ensuring secure data aggregation, a novel approach that uses homomorphic encryption and Message Authentication Codes (MAC) to achieve confidentiality, authentication and integrity for secure data aggregation in wireless sensor networks is proposed. Our experiments show that our proposed secure aggregation method significantly reduces computation and communication overhead and can be practically implemented in on-the-shelf sensor platforms.

A Two-Tier Adaptive Data Aggregation Approach for M2M Group-Communication

Abstract: Network lifetime is the time interval in which the nodes are operational. Considering that machine-to-machine (M2M) devices have limited energy resources, an important challenge in M2M communications is to prolong the network lifetime. The constrained application protocol (CoAP) supports multi-target monitoring applications in M2M communications, allowing the creation and maintenance of groups, as well as their periodic communication. It is essential to aggregate the CoAP group-communication over the paths to increase the network lifetime of low-power M2M devices, since data aggregation reduces the use of energy-consuming hardware (e.g., central processing unit and wireless interface). However, the current data aggregation solutions do not specify how to support data aggregation with multiple CoAP-based groups in multi-target monitoring applications. In this paper, the proposed approach, called two-tier aggregation for multi-target applications (TTAMAs), aggregates the data originated from nodes belonging to either the same or different CoAP groups. Furthermore, TTAMA is an adaptive solution because it performs the data aggregation in accordance with the CoAP configurations, such as communication periodicity and data aggregation functions. We compare TTAMA with current data aggregation approaches that use minimum spanning tree and shortest path tree. The results show that TTAMA outperforms the related works in terms of network lifetime and energy consumption.

Reducing Energy Consumption by Data Aggregation in M2M Networks

Abstract: Machine-to-machine (M2M) communications have emerged as a new technology for next-generation communications. As the number of M2M devices and the amount of transmitted data increase, applying data aggregation is an efficient way to improve energy efficiency of M2M networks. In this paper, we devise an analytical model to compute the energy consumption and delivery delay in packet delivery by using data aggregation. Then we develop an extensive simulation to validate our proposed analytical model. Numerical results show that it is essential to smartly configure the parameters for data aggregation in M2M networks. Our study provides guidelines to determine the parameters in terms of the buffering time and the maximum number of buffered packets for data aggregation.

A Comprehensive Review and Performance Evaluation of Recent Trends for Data Aggregation and Routing Techniques in IoT Networks

Abstract: Internet of things (IoT) is a ubiquitous network which supports and offers a system that observes and manages the physical world through the aggregation, filtering, and investigation of generated data using IoT devices. Aggregation of data and routing of nodes in IoT devices are always challenging tasks. A well-organized data aggregation and routing of nodes is necessary factor for successful placement and use of IoT devices. IoT devices usually share large amount of data that can be converted into information. The information is aggregated to enhance the overall efficiency of the IoT network. Data aggregation is the process in which information is collected and expressed for the purpose of statistical analysis. Routing in the IoT network plays a vital role. IoT devices act as routers for sending information to the gateways. The routing of data affects the power consumption of progressing IoT devices. For these reasons, aggregation of data and routing of nodes are important for IoT devices. This paper conveys and evaluates comparison on current data aggregation and routing techniques of IoT devices. Ad hoc On-demand Distance Vector (AODV) routing protocol is simulated for ten different mobility conditions, and its performance is observed in respect of throughput, delay, and packet delivery ratio.

A Survey of Distributed Data Aggregation Algorithms

Abstract: Distributed data aggregation is an important task, allowing the decentralized determination of meaningful global properties, that can then be used to direct the execution of other applications. The resulting values result from the distributed computation of functions like COUNT, SUM and AVERAGE. Some application examples can found to determine the network size, total storage capacity, average load, majorities and many others. In the last decade, many different approaches have been proposed, with different trade-offs in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of aggregation algorithms, it can be difficult and time consuming to determine which techniques will be more appropriate to use in specific settings, justifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally defines the concept of aggregation, characterizing the different types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.