EMS: Efficient Monitoring System to Detect Non-Cooperative Nodes in IoT-Based Vehicular Delay Tolerant Networks (VDTNs)

Rehman, Ghani Ur; Zubair, Muhammad; Qasim, Iqbal; Badshah, Afzal; Mahmood, Zafar; Aslam, Muhammad; Jilani, Syeda Fizzah

doi:10.3390/s23010099

Cited by 10 publications

(6 citation statements)

References 41 publications

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“…In Ref. [8], the authors proposed an efficient monitoring system called EMS to detect non-cooperative nodes in IoT-based vehicular delay-tolerant networks (VDTNs). EMS utilizes a combination of machine learning [9] and statistical analysis techniques and achieves high accuracy in detecting non-cooperative nodes in VDTNs.…”

Section: Related Work 21 Collaborative Optimization For Trams and CVmentioning

confidence: 99%

Enhancing Intersection Performance for Tram and Connected Vehicles through a Collaborative Optimization

Louati

Kariri

2023

Sustainability

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This article tackles a pervasive problem in connected transportation networks: the issue of conflicting right-of-way between trams and Connected Vehicles (CV) at intersections. Trams are typically granted a semi-exclusive right-of-way, leading to a clash with CV. To resolve this challenge, the study introduces a Transit Signal Priority (TSP) system and a guidance framework that seeks to minimize unintended delays for trams while minimizing the negative impact on CV, passenger comfort, energy consumption, and overall travel time. The proposed framework employs a collaborative optimization system and an improved genetic algorithm to adjust both the signal phase duration and the operating path. The study is based on data collected from a simulated intersection that includes the signal phase sequence and duration. The findings demonstrate that the proposed framework was able to reduce the transit time for trams by 45.8% and the overall transit time for trams 481 and CVs by 17.1% compared to the conventional method. Additionally, the system was able to reduce energy consumption by 34.7% and the non-comfort index by 25.8%. Overall, this research contributes to the development of a more efficient and sustainable transportation system for the future.

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Section: Related Work 21 Collaborative Optimization For Trams and CVmentioning

confidence: 99%

Enhancing Intersection Performance for Tram and Connected Vehicles through a Collaborative Optimization

Louati

Kariri

2023

Sustainability

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“…The Internet of Things (IoT) devices have similar resource constraints (e.g., energy, computation, and storage capacity) to the automotive ECUs [7,8]. The Trusted Computing Group (TCG) has released a reference report [9] describing how secure software and firmware update for embedded systems can be performed using trusted computing technologies.…”

Section: Related Workmentioning

confidence: 99%

MT-SOTA: A Merkle-Tree-Based Approach for Secure Software Updates over the Air in Automotive Systems

Bazzi,

Shaout,

2023

Applied Sciences

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The automotive industry has seen a dynamic transformation from traditional hardware-defined to software-defined architecture enabling higher levels of autonomy and connectivity, better safety and security features, as well as new in-vehicle experiences and richer functions through software and ongoing updates of both functional and safety-critical features. Service-oriented architecture plays a pivotal role in realizing software-defined vehicles and fostering new business models for OEMs. Such architecture evolution demands new development paradigms to address the increasing complexity of software. This is crucial to guarantee seamless software development, integration, and deployment—all the way from cloud or backend repositories to the vehicle. Additionally, it calls for enhanced collaboration between car manufacturers and suppliers. Simultaneously, it introduces challenges associated with the necessity for ongoing updates and support ensuring vehicles remain safe and up to date. Current approaches to software updates have primarily been implemented for traditional vehicle architectures, which mostly comprise specialized electronic control units (ECUs) designed for specific functions. These ECUs are programmed with a single comprehensive executable that is then flashed onto the ECU all at once. Different approaches should be considered for new software-based vehicle architectures and specifically for ECUs with multiple independent software packages. These packages should be updated independently and selectively for each ECU. Thus, we propose a new scheme for software updates based on a Merkle tree approach to cope with the complexity of the new software architecture while addressing safety and security requirements of real-time and resource-constrained embedded systems in the vehicle. The Merkle-tree-based software updates over the air (MT-SOTA) proposal enables secure updates for individual software clusters. These clusters are developed and integrated by diverse entities with varying release timelines. Our study demonstrates that the MT-SOTA scheme can enhance the speed of software update execution without significantly increasing the process overhead. Additionally, it offers necessary defense against potential cyberthreats. The results of the performed technical analysis and experiments of the MT-SOTA implementation are presented in this paper.

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“…Conversely, the efficiency of utilizing the abundant spectrum remains suboptimal. Cognitive radio (CR) emerges as an innovative approach to address the scarcity of spectrum resources [3][4][5][6][7][8]. In CR networks, cognitive users (CUs) are authorized to use frequency bands designated for primary users (PUs) for communication, ensuring minimal interference to PUs.…”

Section: Introductionmentioning

confidence: 99%

Jointly Active/Passive Beamforming Optimization for Intelligent-Reflecting Surface-Assisted Cognitive-IoT Networks

Zhou,

Deng,

2024

Electronics

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To overcome challenges such as limited energy availability for terminal devices, constrained network coverage, and suboptimal spectrum resource utilization, with the overarching objective of establishing a sustainable and efficient interconnection infrastructure, we introduce an innovative Intelligent Reflective Surface (IRS) technology. This cutting-edge IRS technology is employed to architect a wireless and energy-efficient cognitive secure communication network assisted by IRS. To further optimize the overall energy harvesting of this network, we present a cognitive secure resource allocation scheme, aiming to maximize the system’s total collected energy. This scheme carefully considers various constraints, including transmission power constraints for cognitive base stations, power constraints for jammer devices, interference limitations for all primary users, minimum security rate constraints for all cognitive Internet of Things (IoT) devices, and phase shift constraints for IRS. We establish a comprehensive hybrid cognitive secure resource allocation model, encompassing joint cognitive transmission beam design, jammer device transmission beam design, and phase shift design. Given the non-convex nature of the formulated problem and the intricate coupling relationships among variables, we devise an effective block coordinate descent (BCD) iterative algorithm. The realization of joint cognitive/jammer base station transmission beam design and phase shift design employs sophisticated techniques such as continuous convex approximation methods and semi-definite programming. Simulation results underscore the superior performance of the proposed scheme compared to existing resource allocation approaches, particularly in terms of total harvested energy and other critical metrics.

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EMS: Efficient Monitoring System to Detect Non-Cooperative Nodes in IoT-Based Vehicular Delay Tolerant Networks (VDTNs)

Cited by 10 publications

References 41 publications

Enhancing Intersection Performance for Tram and Connected Vehicles through a Collaborative Optimization

Enhancing Intersection Performance for Tram and Connected Vehicles through a Collaborative Optimization

MT-SOTA: A Merkle-Tree-Based Approach for Secure Software Updates over the Air in Automotive Systems

Jointly Active/Passive Beamforming Optimization for Intelligent-Reflecting Surface-Assisted Cognitive-IoT Networks

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