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 12 publications

(3 citation statements)

References 16 publications

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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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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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“…High workload, latency, and response time are considered limitations of cloud computing. Furthermore, data congestion is also one serious concern of cloud computing, the central nature of cloud computing may lead to congestion and data loss [4], [15]. Fog computing is an emerging domain that may enhance cloud features and aims to resolve issues such as latency, mobility, and location tracking and provide services to users [9], [12], [16].…”

Section: Introductionmentioning

confidence: 99%

“…Fog nodes act as local servers where it computes given tasks. Fog servers are also capable to process data at local servers and help to take timely actions [15], [18]. Furthermore, Fog computing reduces computation processes and user-related services and reduces the burden of traditional cloud computing data centers [19].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Anomaly Detection for Fog-Assisted IoVs Network

et al. 2023

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Internet of vehicles (IoVs) allows millions of vehicles to be connected and share information for various purposes. The main applications of IoVs are traffic management, emergency messages delivery, E-health, traffic, and temperature monitoring. On the other hand, IoVs lack in location awareness and geographic distribution, which is critical for some IoVs applications such as smart traffic lights and information sharing in vehicles. To support these topographies, fog computing was proposed as an appealing and novel term, which was integrated with IoVs to extend storage, computation, and networking. Unfortunately, it is also challenged with various security and privacy hazards, which is a serious concern of smart cities. Therefore, we can formulate that Fog-assisted IoVs (Fa-IoVs), are challenged by security threats during information dissemination among mobile nodes. These security threats of Fa-IoVs are considered as anomalies which is a serious concern that needs to be addressed for smooth Fa-IoVs network communication.Here, smooth communication refers to less risk of important data loss, delay, communication overhead, etc. This research work aims to identify research gaps in the Fa-IoVs network and present a deep learningbased dynamic scheme named CAaDet (Convolutional autoencoder Aided anomaly detection) to detect anomalies. CAaDet exploits convolutional layers with a customized autoencoder for useful feature extraction and anomaly detection. Performance evaluation of the proposed scheme is done by using the F1-score metric where experiments are carried out by exploiting a benchmark dataset named NSL-KDD. CAaDet also observes the behavior of fog nodes and hidden neurons and selects the best match to reduce false alarms and improve F1-score. The proposed scheme achieved significant improvement over existing schemes for anomaly detection. Identified research gaps in Fa-IoVs can give future directions to researchers and attract more attention to this new era.

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