Social Internet of Things (SIoT) Localization for Smart Cities Traffic Applications

Elnour, Razan Abdelrahim Mohamed; Ali, Elmustafa Sayed; Yousif, Ibtihal; Saeed, Rashid A.; Mokhtar, Rania A.; Hayder, Gasim; Khalifa, Othman O.

doi:10.1007/978-3-031-26580-8_24

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…However, the accuracy of RSS-based localization schemes is susceptible to various factors, leading to biased measurement models. These errors in RSS measurements primarily encompass random errors and systematic biases [26][27][28]. Numerous RSS-based localization techniques are documented in the literature in greater detail.…”

Section: Introductionmentioning

confidence: 99%

Robust ISAC Localization in Smart Cities: A Hybrid Network Approach for NLOS Challenges with Uncertain Parameters

Althobaiti,

Khalil,

Saeed

2023

JSAN

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Accurate localization holds paramount importance across many applications within the context of smart cities, particularly in vehicular communication systems, the Internet of Things, and Integrated Sensing and Communication (ISAC) technologies. Nonetheless, achieving precise localization remains a persistent challenge, primarily attributed to the prevalence of non-line-of-sight (NLOS) conditions and the presence of uncertainties surrounding key wireless transmission parameters. This paper presents a comprehensive framework tailored to address the intricate task of localizing multiple nodes within ISAC systems significantly impacted by pervasive NLOS conditions and the ambiguity of transmission parameters. The proposed methodology integrates received signal strength (RSS) and time-of-arrival (TOA) measurements as a strategic response to effectively overcome these substantial challenges, even in situations where the precise values of transmitting power and temporal information remain elusive. An approximation approach is judiciously employed to facilitate the inherent non-convex and NP-hard nature of the original estimation problem, resulting in a notable transformation, rendering the problem amenable to a convex optimization paradigm. The comprehensive array of simulations conducted within this study corroborates the efficacy of the proposed hybrid cooperative localization method by underscoring its superior performance relative to conventional approaches relying solely on RSS or TOA measurements. This enhancement in localization accuracy in ISAC systems holds promise in the intricate urban landscape of smart cities, offering substantial contributions to infrastructure optimization and service efficiency.

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Section: Introductionmentioning

confidence: 99%

Robust ISAC Localization in Smart Cities: A Hybrid Network Approach for NLOS Challenges with Uncertain Parameters

Althobaiti,

Khalil,

Saeed

2023

JSAN

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Performance Evaluation of D2D Communications in of LTE-A Network

Hassan

Saeed

Khalifa

et al. 2023

2023 IEEE 3rd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engi

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Monitoring of Wildlife Using Unmanned Aerial Vehicle (UAV) With Machine Learning

Ahmed,

Hashim,

Saeed

et al. 2024

Applications of Machine Learning in UAV Networks

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Wildlife monitoring is critical for ecological study, conservation, and wildlife management, but traditional approaches have drawbacks. The combination of unmanned aerial vehicles (UAVs) with machine learning (ML) offers a viable approach to overcoming the limits of traditional wildlife monitoring methods and improving wildlife management and conservation tactics. The combination of UAVs and ML provides efficient and effective solutions for wildlife monitoring. UAVs with high-resolution cameras record airborne footage, while machine learning algorithms automate animal detection, tracking, and behavior analysis. The chapter discusses challenges, limitations, and future directions in using UAVs and ML for wildlife monitoring, addressing regulatory, technical, and ethical considerations, and emphasizing the need for ongoing research and technological advancements. Overall, the integration of UAVs and ML provides a promising solution to overcome the limitations of traditional wildlife monitoring methods and enhance wildlife management and conservation strategies.

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Social Internet of Things (SIoT) Localization for Smart Cities Traffic Applications

Cited by 3 publications

References 32 publications

Robust ISAC Localization in Smart Cities: A Hybrid Network Approach for NLOS Challenges with Uncertain Parameters

Robust ISAC Localization in Smart Cities: A Hybrid Network Approach for NLOS Challenges with Uncertain Parameters

Performance Evaluation of D2D Communications in of LTE-A Network

Monitoring of Wildlife Using Unmanned Aerial Vehicle (UAV) With Machine Learning

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