Data aggregation mechanisms in the Internet of things: A systematic review of the literature and recommendations for future research

Pourghebleh, Behrouz; Navimipour, Nima Jafari

doi:10.1016/j.jnca.2017.08.006

Cited by 188 publications

(82 citation statements)

References 57 publications

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“…The Boolean OR and Boolean AND are used to find the synonyms and alternative spellings for each definition and their relationships for determining the search string . The selection of suitable keywords in terms of providing the relevant articles about replication mechanism has specified a search string in the P2P networks.…”

Section: Methodsmentioning

confidence: 99%

Data replication mechanisms in the peer‐to‐peer networks

Mohammadi

Navimipour

2019

Int J Communication

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Peer-to-peer (P2P) network is a distributed network in which nodes with similar capabilities exchange information with each other. Due to the nature of the P2P network distribution, numerous network message transformation is required to exchange the data between nodes over the network, which may increase access latency. Because of the high amount of the stored data in the P2P networks, the replication of data is very important. A large amount of data is handled using data replication to increase data access, reduce access latency, and increase data availability. This issue has an important role in the P2P networks, but there is no complete and systematic research in this field. Therefore, this paper aims to provide a comprehensive study of data replication mechanisms in the P2P networks. The 1027 papers have been identified and have been reduced to 213 main studies using the paper selection process. Moreover, in this paper, the major advances are reviewed in four main groups (structured, unstructured, super-peer, and hybrid networks), and the new challenges are also highlighted. Moreover, the open issues and guidelines for future studies are presented.

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Section: Methodsmentioning

confidence: 99%

Data replication mechanisms in the peer‐to‐peer networks

Mohammadi

Navimipour

2019

Int J Communication

Self Cite

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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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“…There is a specific level of redundancy that is present in the data which is collected from the nodes and so this procedure will be needed for decreasing unwanted information. Further, it can decrease the traffic load and save node energy [6].…”

Section: Sathish K Srinivasanmentioning

confidence: 99%

Efficient Localized Data Aggregation of IoT Devices and its Application in Agriculture

2020

IJRTE

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Abstract: The Internet of Things (IoT) has a likely future for all technologies that are associated to the life of humans. Any communication between the social environments along with the user contexts will be made through the smart interfaces. The IoT will have to link to different diverse devices found in the Wireless Sensor Network (WSN) . Thus, the routing optimization which is energy efficient has become the primary factor in the performance of the network in the IoT. The widely popular routing used in WSN, Multi-hop Low Energy Adaptive Clustering Hierarchy (LEACH) protocol, is found to be energy inefficient. The work will deal with the choice of finding the optimal path in routing through meta heuristic techniques to improve the lifespan of the network and the energy efficiency of the network. There are different techniques of metaheuristics such as Teacher Learning Based Optimization (TLBO) andParticle Swarm Optimization (PSO) were used effectively for finding optimal solutions for problems. In this work, the PSO-based algorithms were used for locating an optimal sink position to their nodes that make the network efficient in terms of energy. The TLBO metaheuristic is population-based and is based on the concept of teaching and the learning procedure observed in a classroom, which is adapted to the routing problem. The results of the experiment prove that the proposed technique was achieved better levels of performance compared to the other methods..

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Data aggregation mechanisms in the Internet of things: A systematic review of the literature and recommendations for future research

Cited by 188 publications

References 57 publications

Data replication mechanisms in the peer‐to‐peer networks

Data replication mechanisms in the peer‐to‐peer networks

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

Efficient Localized Data Aggregation of IoT Devices and its Application in Agriculture

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