Fast Sensory Data Aggregation in IoT Networks: Collision-Resistant Dynamic Approach

Nguyen, Tien-Dung; Le, Duc-Tai; Vo, Van-Vi; Kim, Moonseong; Choo, Hyunseung

doi:10.1109/jiot.2020.3007329

Cited by 24 publications

(11 citation statements)

References 32 publications

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“…shown in [38]. However, determining the waiting time for a link is not deterministic in a dutycycled network where a node is active at several slots per working period.…”

Section: A Motivationmentioning

confidence: 99%

Link-Delay-Aware Reinforcement Scheduling for Data Aggregation in Massive IoT

Nguyen

et al. 2022

IEEE Trans. Commun.

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Over the past few years, the use of wireless sensor networks in a range of Internet of Things (IoT) scenarios has grown in popularity. Since IoT sensor devices have restricted battery power, a proper IoT data aggregation approach is crucial to prolong the network lifetime. To this end, current approaches typically form a virtual aggregation backbone based on a connected dominating set or maximal independent set to utilize independent transmissions of dominators. However, they usually have a fairly long aggregation delay because the dominators become bottlenecks for receiving data from all dominatees. The problem of time-efficient data aggregation in multichannel duty-cycled IoT sensor networks is analyzed in this paper. We propose a novel aggregation approach, named LInk-delay-aware REinforcement (LIRE), leveraging active slots of sensors to explore a routing structure with pipeline links, then scheduling all transmissions in a bottom-up manner. The reinforcement schedule accelerates the aggregation by exploiting unused channels and time slots left off at every scheduling round.LIRE is evaluated in a variety of simulation scenarios through theoretical analysis and performance comparisons with a state-of-the-art scheme. The simulation results show that LIRE reduces more than 80% aggregation delay compared to the existing scheme.

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“…shown in [38]. However, determining the waiting time for a link is not deterministic in a dutycycled network where a node is active at several slots per working period.…”

Section: A Motivationmentioning

confidence: 99%

Link-Delay-Aware Reinforcement Scheduling for Data Aggregation in Massive IoT

Nguyen

et al. 2022

IEEE Trans. Commun.

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“…As the number of devices on an Internet of Things (IoT) network increases, so do the transmission bandwidth and transmission power consumption. In IoT networks, compressed data aggregation (CDA) has been proposed as an effective approach to reduce the amount of data collected [1][2][3]. In the WSN system, with regard to the time of packet transfer from one node to another node, the possibility of security risks was generated.…”

Section: Literature Surveymentioning

confidence: 99%

Aggregation Technique Using Dynamic Cross-Propagation Clustering Algorithm in Wireless Body Sensor Networks

Arulprakash

Baalamurugan

Dhanaraj

et al. 2022

Wireless Communications and Mobile Computing

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Wireless body sensor networks (WBSNs) are characterized by a large number of battery-powered wireless sensor nodes, and the most challenging aspects of WBSNs are sensor node energy consumption, delay, and security (communication and data) while maintaining regular wireless sensor network (WSN) capabilities. Data aggregation, as a common procedure in data gathering applications, can waste a lot of energy since sensor nodes may stay in the listen to state even when they are not receiving data during the data collection process. In this research work, introducing the Self-Executing-Dynamic Cross-Propagation Clustering (SE-DCPC) algorithm helps to improve the node energy consumption positively by turning nodes to the accessible state when not in use and waking them up when necessary. The algorithm is energy-based and uses a self-executing-based dynamic cross-propagation clustering system to send/receive scheduling data in the WSN platform. The energy level of the nodes is the most essential component in constructing network communication, in contrast to earlier clustering algorithms. The purpose of this proposed algorithm is to enhance the traditional notion of the clustering algorithm (location-based clustering), leading to the primary goal of enhancing the permanence of the wireless sensor network, which is to conserve network coverage, using self-executing DCPC clustering technology for location and power. The result of performance analysis of the SE-DCPC is achieved by simulation using two different communication processes of clustering and intelligence decision-making methods. The numerical results shows that SE-DCPC can effectively handle and maintain a high rate of network node energy consumption. The simulation result shows that the proposed approaches securely obtain the high throughput and very minimal delay at the client side, compared to existing clustering algorithm approaches. This SE-DCPC algorithm increased by 21.89% the communication medium lifetime and by 37% the energy consumption and reduced by 23.27% the overhead compared with existing clustering algorithms.

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“…Therefore, in the quest to achieve better accuracy, the WSN application may lose out on its effectiveness in terms of its NLT. So, there is a trade-off that exists between data accuracy versus data freshness and its effectiveness [110].…”

Section: Requirements and Tradeoffs In Data Aggregation Approachesmentioning

confidence: 99%

A Survey of Data Aggregation Protocols for Energy Conservation in WSN and IoT

Begum

Nandury

2022

Wireless Communications and Mobile Computing

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Conservation of energy has been a major concern for Wireless Sensor Networks (WSNs) and IoT applications. Several strategies were devised, aimed at optimizing energy consumption in these applications, based on: (a) use of low-powered hardware devices, (b) deploying mobile/relay agents for data collection, (c) clustering, and (d) data aggregation. Amongst these, data aggregation is widely acknowledged as an important tool to conserve energy in WSN and IoTs. The paper provides a comprehensive survey of various data aggregation strategies, discusses the efficacy of these strategies in handling issues that are typical to WSN and IoT applications. These issues severely impact the performance metrics such as: energy efficiency, latency, fault-tolerance, network throughput, and network lifetime. Therefore, to optimize the data aggregation approach, an application developer needs to arrive at optimal tradeoffs between these parameters. A major contribution of the paper is to present a holistic review of data aggregation approaches emphasizing the effect of topology, security, mobility, interference, and fault-tolerance in WSN and IoTs. Based on gap areas in literature, we throw open few challenges and present them as “posers”, and put-forth suggestions for further research.

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Fast Sensory Data Aggregation in IoT Networks: Collision-Resistant Dynamic Approach

Cited by 24 publications

References 32 publications

Link-Delay-Aware Reinforcement Scheduling for Data Aggregation in Massive IoT

Link-Delay-Aware Reinforcement Scheduling for Data Aggregation in Massive IoT

Aggregation Technique Using Dynamic Cross-Propagation Clustering Algorithm in Wireless Body Sensor Networks

A Survey of Data Aggregation Protocols for Energy Conservation in WSN and IoT

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