Novel congestion avoidance scheme for Internet of Drones

Yaqoob, Shumayla; Ullah, Ata; Awais, Muhammad; Katib, Iyad; Albeshri, Aiiad; Mehmood, Rashid; Raza, Mohsin; Islam, Saif ul; Rodrigues, Joel J. P. C.

doi:10.1016/j.comcom.2021.01.008

Cited by 13 publications

(6 citation statements)

References 16 publications

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“…In [10], the rapid growth of geo-distributed and ubiquitous devices is evident, with billions of such devices expected to emerge soon. In [11], it is highlighted that the enormous amount of data generated by smart devices requires reliable network infrastructures, which is often a point of conflict. The data gathered by smart settings can be used to enhance their performance and evolution.…”

Section: Related Workmentioning

confidence: 99%

Reliable Resource Allocation and Management for IoT Transportation Using Fog Computing

et al. 2023

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Resource allocation in smart settings, more specifically in Internet of Things (IoT) transportation, is challenging due to the complexity and dynamic nature of fog computing. The demands of users may alter over time, necessitating more trustworthy resource allocation and administration. Effective resource allocation and management systems must be designed to accommodate changing user needs. Fog devices don’t just run fog-specific software. Resource and link failures could be brought on by the absence of centralised administration, device autonomy, and wireless communication in the fog environment. Resources must be allocated and managed effectively because the majority of fog devices are battery-powered. Latency-aware IoT applications, such as intelligent transportation, healthcare, and emergency response, are now pervasive as a result of the enormous growth of ubiquitous computing. These services generate a large amount of data, which requires edge processing. The flexibility and services on-demand for the cloud can successfully manage these applications. It’s not always advisable to manage IoT applications exclusively in the cloud, especially for latency-sensitive applications. Thus, fog computing has emerged as a bridge between the cloud and the devices it supports. This is typically how sensors and IoT devices are connected. These neighbouring Fog devices control storage and intermediary computation. In order to improve the Fog environment reliability in IoT-based systems, this paper suggests resource allocation and management strategy. When assigning resources, latency and energy efficiency are taken into account. Users may prioritise cost-effectiveness over speed in a fog. Simulation was performed in the iFogSim2 simulation tool, and performance was compared with one of the existing state-of-the-art strategy. A comparison of results shows that the proposed strategy reduced latency by 10.3% and energy consumption by 21.85% when compared with the existing strategy.

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Section: Related Workmentioning

confidence: 99%

Reliable Resource Allocation and Management for IoT Transportation Using Fog Computing

et al. 2023

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“…However, it fails to concentrate on other parameters such as efficiency, overhead, throughput, etc. The authors of [26] introduced a method to avoid the congestion called fog-assisted smooth packet distribution. This method improves the packet delivery ratio and reduces the overhead but fails to improve the security of the network.…”

Section: Related Workmentioning

confidence: 99%

Bio-Inspired Dynamic Trust and Congestion-Aware Zone-Based Secured Internet of Drone Things (SIoDT)

Jghef¹,

Jasim

Ghanimi

et al. 2022

Drones

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The Internet of Drone Things (IoDT) is a trending research area where drones are used to gather information from ground networks. In order to overcome the drawbacks of the Internet of Vehicles (IoV), such as congestion issues, security issues, and energy consumption, drones were introduced into the IoV, which is termed drone-assisted IoV. Due to the unique characteristics of the IoV, such as dynamic mobility and unsystematic traffic patterns, the performance of the network is reduced in terms of delay, energy consumption, and overhead. Additionally, there is the possibility of the existence of various attackers that disturb the traffic pattern. In order to overcome this drawback, the drone-assisted IoV was developed. In this paper, the bio-inspired dynamic trust and congestion-aware zone-based secured Internet of Drone Things (BDTC-SIoDT) is developed, and it is mainly divided into three sections. These sections are dynamic trust estimation, congestion-aware community construction, and hybrid optimization. Initially, through the dynamic trust estimation process, triple-layer trust establishment is performed, which helps to protect the network from all kinds of threats. Secondly, a congestion-aware community is created to predict congestion and to avoid it. Finally, hybrid optimization is performed with the combination of ant colony optimization (ACO) and gray wolf optimization (GWO). Through this hybrid optimization technique, overhead occurs during the initial stage of transmission, and the time taken by vehicles to leave and join the cluster is reduced. The experimentation is performed using various threats, such as flooding attack, insider attack, wormhole attack, and position falsification attack. To analyze the performance, the parameters that are considered are energy efficiency, packet delivery ratio, routing overhead, end-to-end delay, packet loss, and throughput. The outcome of the proposed BDTC-SIoDT is compared with earlier research works, such as LAKA-IOD, NCAS-IOD, and TPDA-IOV. The proposed BDTC-SIoDT achieves high performance when compared with earlier research works.

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“…Existing heuristics such as Baseline1 and Baseline2 were compared with proposed DAPWTS-based algorithm components. We evaluated the overall performance and validity based on the above components, and all RPD results proposed that the algorithm is better when compared to all benchmark heuristics bound by deadline constraints [28,30,31].…”

Section: Task-sequencing Rulesmentioning

confidence: 99%

Dynamic Application Partitioning and Task-Scheduling Secure Schemes for Biosensor Healthcare Workload in Mobile Edge Cloud

Lakhan

Grønli

et al. 2021

Electronics

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Currently, the use of biosensor-enabled mobile healthcare workflow applications in mobile edge-cloud-enabled systems is increasing progressively. These applications are heavyweight and divided between a thin client mobile device and a thick server edge cloud for execution. Application partitioning is a mechanism in which applications are divided based on resource and energy parameters. However, existing application-partitioning schemes widely ignore security aspects for healthcare applications. This study devises a dynamic application-partitioning workload task-scheduling-secure (DAPWTS) algorithm framework that consists of different schemes, such as min-cut algorithm, searching node, energy-enabled scheduling, failure scheduling, and security schemes. The goal is to minimize the energy consumption of nodes and divide the application between local nodes and edge nodes by applying the secure min-cut algorithm. Furthermore, the study devises the secure-min-cut algorithm, which aims to migrate data between nodes in a secure form during application partitioning in the system. After partitioning the applications, the node-search algorithm searches optimally to run applications under their deadlines. The energy and failure schemes maintain the energy consumption of the nodes and the failure of the system. Simulation results show that DAPWTS outperforms existing baseline approaches by 30% in terms of energy consumption, deadline, and failure of applications in the system.

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Novel congestion avoidance scheme for Internet of Drones

Cited by 13 publications

References 16 publications

Reliable Resource Allocation and Management for IoT Transportation Using Fog Computing

Reliable Resource Allocation and Management for IoT Transportation Using Fog Computing

Bio-Inspired Dynamic Trust and Congestion-Aware Zone-Based Secured Internet of Drone Things (SIoDT)

Dynamic Application Partitioning and Task-Scheduling Secure Schemes for Biosensor Healthcare Workload in Mobile Edge Cloud

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