An efficient route selection mechanism based on network topology in battery-powered internet of things networks

Taami, Tania; Azizi, Sadoon; Yarinezhad, Ramin

doi:10.1007/s12083-022-01426-0

Cited by 11 publications

(9 citation statements)

References 62 publications

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“…The presence of heterogeneity poses a significant challenge in the implementation of intelligent Internet of Things (IoT) systems. The subject matter under consideration refers to wireless networks, with a special emphasis on the idea of Wireless Networks [2][3] [4]. An obstacle encountered within the realm of hardware for the Internet of Things (IoT) pertains to the incorporation of intelligent devices into intelligent systems.…”

Section: Iot Challenges and Prospectsmentioning

confidence: 99%

“…The distribution of the clustering process among numerous nodes enhances the network's resilience by eliminating the presence of a singular point of failure capable of causing a complete system shutdown. In addition, the algorithm can effectively classify significant nodes even when confronted with noisy data by including robust outlier identification approaches, hence mitigating the potential for system failures [4]. Moreover, the implementation of a decentralized method might lead to enhanced expediency and efficiency in communication and decision-making procedures.…”

Section: Background and Related Workmentioning

confidence: 99%

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Untitled

2024

IJCNC

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IoT networking uses real items as stationary or mobile nodes. Mobile nodes complicate networking. Internet of Things (IoT) networks have a lot of control overhead messages because devices are mobile. These signals are generated by the constant flow of control data as such device identity, geographical positioning, node mobility, device configuration, and others. Network clustering is a popular overhead communication management method. Many cluster-based routing methods have been developed to address system restrictions. Node clustering based on the Internet of Things (IoT) protocol, may be used to cluster all network nodes according to predefined criteria. Each cluster will have a Smart Designated Node. SDN cluster management is efficient. Many intelligent nodes remain in the network. The network design spreads these signals. This paper presents an intelligent and responsive routing approach for clustered nodes in IoT networks. An existing method builds a new sub-area clustered topology. The Nodes Clustering Based on the Internet of Things (NCIoT) method improves message transmission between any two nodes. This will facilitate the secure and reliable interchange of healthcare data between professionals and patients. NCIoT is a system that organizes nodes in the Internet of Things (IoT) by grouping them together based on their proximity. It also picks SDN routes for these nodes. This approach involves selecting one option from a range of choices and preparing for likely outcomes problem addressing limitations on activities is a primary focus during the review process. Predictive inquiry employs the process of analyzing data to forecast and anticipate future events. This document provides an explanation of compact units. The Predictive Inquiry Small Packets (PISP) improved its backup system and partnered with SDN to establish a routing information table for each intelligent node, resulting in higher routing performance. Both principal and secondary roads are available for use. The simulation findings indicate that NCIoT algorithms outperform CBR protocols. Enhancements lead to a substantial 78% boost in network performance. In addition, the end-to-end latency dropped by 12.5%. The PISP methodology produces 5.9% more inquiry packets compared to alternative approaches. The algorithms are constructed and evaluated against academic ones.

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Section: Iot Challenges and Prospectsmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Untitled

2024

IJCNC

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“… Interoperability: As IoT devices have a wide range of platforms, protocols, and systems, the challenge is interoperating these heterogeneous platforms, protocols, and systems. Interoperability refers to the ability of nodes to exchange information [28].…”

Section: A Iot Challengesmentioning

confidence: 99%

An Energy‐Aware Technique for Task Allocation in the Internet of Things using Krill Herd Algorithm

Miao¹,

Xu²,

Chen³

et al. 2023

IJACSA

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The Internet of Things (IoT) is an innovative technology that connects the digital and physical worlds as well as allows physical devices capable of different capacities to share resources to accomplish tasks. Most IoT objects have limited battery life and are heterogeneous. Assignment of these objects is, therefore, extremely challenging. Energy consumption and reliability are the primary objectives of task allocation algorithms. We present an optimization solution to the IoT task allocation problem based on the krill herd algorithm. The algorithm increases the energy efficiency and stability of the network while providing a reliable task allocation solution. An extensive test of the proposed algorithm has been conducted using the MATLAB simulator. Compared to the most relevant method in the literature, our algorithm provides a higher level of energy efficiency.

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“…The study showed that optimizing power consumption in IoT devices may be achieved successfully using a data-driven selflearning technique. In [18], an effective path selection method was proposed for battery-powered IoT networks based on their network topology. The method was developed to improve the power efficiency of IoT networks by choosing the most efficient paths for data transmission.…”

Section: Introductionmentioning

confidence: 99%

A Power-Aware Method for IoT Networks with Mobile Stations and Dynamic Power Management Strategy

Saleh

2023

Eng. Technol. Appl. Sci. Res.

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The Internet of Things (IoT) plays a critical role in the digitalization of numerous industries, enabling increased automation, connectivity, and data collection in areas such as manufacturing, healthcare, transportation, and smart cities. This paper introduces a power-aware method for IoT networks using mobile stations and a dynamic power management strategy. The proposed method aims to improve power consumption and total packets received compared to the static-station balanced data traffic method. The proposed method uses a mobile station to dynamically adapt its transmission power based on the network conditions and the strength of the received signal. Furthermore, a dynamic power management strategy is employed to further decrease the power usage of the network by adjusting the power state of each station and IoT node according to its level of activity, data traffic, and communication requirements. Simulation results showed that the proposed method reduced power consumption by up to 64%, increased total packets received by 72%, and, as a result, increased network coverage and lifetime compared to the balanced data traffic method with static stations. This method can be employed in various IoT applications to improve power efficiency and increase network reliability.

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An efficient route selection mechanism based on network topology in battery-powered internet of things networks

Cited by 11 publications

References 62 publications

Untitled

Untitled

An Energy‐Aware Technique for Task Allocation in the Internet of Things using Krill Herd Algorithm

A Power-Aware Method for IoT Networks with Mobile Stations and Dynamic Power Management Strategy

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