Aggregation Techniques for the Internet of Things: An Overview

Guidi, Barbara; Ricci, Laura

doi:10.1007/978-3-319-96550-5_7

Cited by 9 publications

(4 citation statements)

References 32 publications

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“…An overview of a set of aggregation techniques ranging from Space Filling Curves, to Q-digest, Wavelets, Gossip aggregation, and Compressive Sensing presented in [38].…”

Section: Related Workmentioning

confidence: 99%

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

et al. 2022

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The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in numerous sectors such as manufacturing, smart cities, transportation and Internet of Things by providing pervasive real-time applications. In this survey, we analyze the existing research trends with respect to Artificial Intelligence (AI) methods in WSN and the possible use of these methods for WSN enhancement. The main goal of data collection, aggregation and dissemination algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. In this paper, we highlight data collection, aggregation and dissemination challenges in WSN and present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021. We compare and contrast different algorithms on the basis of optimization criteria, simulation/real deployment, centralized/distributed kind, mobility and performance parameters. We conclude with possible future research directions. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to data collection, aggregation and dissemination challenges in WSN. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for the research community in identifying the mostly adapted methods and the benefits of using various AI methods for solving the challenges related to WSNs. Finally, we conclude the paper stating the open research issues and new possibilities for future studies.

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“…An overview of a set of aggregation techniques ranging from Space Filling Curves, to Q-digest, Wavelets, Gossip aggregation, and Compressive Sensing presented in [38].…”

Section: Related Workmentioning

confidence: 99%

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

et al. 2022

View full text Add to dashboard Cite

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“…The conventional data aggregation techniques have been divided into three types, cluster head-based data aggregation, tree-based data aggregation, and centralized data aggregation [Pourghebleh, and Navimipour, (2017)] [Guidi, and Ricci,( 2019)]. Energy efficiency should be the main consideration for providing a suitable data aggregation technique to resource-constrained IoT networks [Fitzgerald, et al(2018)].…”

Section: Related Workmentioning

confidence: 99%

IMPROVING DATA AGGREGATION EFFICIENCY USING MULTI-LAYER APPROACH IN IoT

Vanathi¹,

Rao²

2021

INDJCSE

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Recently, tremendous growth and interest in the deployment of tiny sensors in the Internet of Things (IoT) for smart applications improves human lives. With the increasing need for energy-efficient mechanisms in IoT communication, the data aggregation technique for reducing data transmissions is considered a significant research problem. The basic idea in most of the aggregation mechanisms is to build the clustering or aggregation tree in an application layer over IoT, resulting in high complexity. To solve such a problem, the proposed MLDA designs an energy-aware aggregation layer that focuses on utilizing the network layer factors in data aggregation by providing transparency of accessing the topology structure from the network layer. Moreover, the proposed work also focuses on the design of the load-balanced topology structure in the network layer for efficient routing and also takes support from such network structure for energy-efficient data aggregation. The proposed Multi-Layer based Data Aggregation approach (MLDA) avoids the hotspot problem and inefficient data aggregation. The MLDA achieves such goals by improving the network layer protocol, RPL activities and designing the aggregation layer to eliminate redundant transmissions. By using an energy-efficient network structure, the impact of redundant data transmissions on network resources and data aggregation efficiency are eliminated. To support SUM, AVG, MAX, and MIN aggregation functions without redundant data transmissions, the Double Hash Bloom Filter (DHBF), observation scheme, and merge sort are used in the developed aggregation layer. Thus, the proposed MLDA improves the data aggregation efficiency in terms of both energy and accuracy.

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“…For each coalition, a leader is periodically selected to aggregate, process and compress the cluster data before transmitting it to the base station. This work was extended in Reference , where the authors adapted the Chinese Restoration Process for cluster formation that takes into consideration the same criteria of Reference (For more details on the different data aggregation mechanisms in the IoT, please refer to References ). To simplify the working context of this research paper, the authors assumed that there is no problem of network congestion or saturation.…”

Section: System Architecture Overviewmentioning

confidence: 99%

An efficient indexing for Internet of Things massive data based on cloud‐fog computing

Benrazek

Kouahla

Farou

et al. 2020

Trans Emerging Tel Tech

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In recent years, the number of sensor and actuator nodes in the Internet of Things (IoT) networks has increased, generating a large amount of data. Most research techniques are based on dividing target data into subsets. On a large scale, this volume increases exponentially, which will affect search algorithms. This problem is caused by the inherent deficiencies of space partitioning. This paper introduces a new and efficient indexing structure to index massive IoT data called BCCF‐tree (Binary tree based on containers at the cloud‐fog computing level). This structure is based on recursive partitioning of space using the k‐means clustering algorithm to effectively separate space into nonoverlapping subspace to improve the quality of search and discovery algorithm results. A good topology should avoid a biased allocation of objects for separable sets and should not influence the structure of the index. BCCF‐tree structure benefits to the emerging cloud‐fog computing system, which represents the most powerful real‐time processing capacity provided by fog computing due to its proximity to sensors and the largest storage capacity provided by cloud computing. The paper also discusses the effectiveness of construction and search algorithms, as well as the quality of the index compared to other recent indexing data structures. The experimental results showed good performance.

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Aggregation Techniques for the Internet of Things: An Overview

Cited by 9 publications

References 32 publications

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

IMPROVING DATA AGGREGATION EFFICIENCY USING MULTI-LAYER APPROACH IN IoT

An efficient indexing for Internet of Things massive data based on cloud‐fog computing

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