TMPAD: Time-Slot-Based Medium Access Control Protocol to Meet Adaptive Data Requirements for Trusted Nodes in Fog-Enabled Smart Cities

Alvi, Ahmad Naseem; Ali, Mumtaz; Saleh, Mohamed Saad; Alkhathami, Mohammed; Alsadie, Deafallah; Alghamdi, Bushra; Alenzi, Badriya

doi:10.3390/app14031319

Cited by 3 publications

(1 citation statement)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, an Efficient Execution of Offloaded IIoT Trusted Tasks (EEOIT) mechanism for secure and reliable computing is proposed. EEOIT assists fog nodes in executing most of the tasks transmitted by valid and trustable nodes in its processing cycle by applying the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) [23].…”

Section: Introductionmentioning

confidence: 99%

Secure Computing for Fog-Enabled Industrial IoT

Alvi,

Ali,

Saleh

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

Smart cities are powered by several new technologies to enhance connectivity between devices and develop a network of connected objects which can lead to many smart industrial applications. This network known as the Industrial Internet of Things (IIoT) consists of sensor nodes that have limited computing capacity and are sometimes not able to execute intricate industrial tasks within their stipulated time frame. For faster execution, these tasks are offloaded to nearby fog nodes. Internet access and the diverse nature of network types make IIoT nodes vulnerable and are under serious malicious attacks. Malicious attacks can cause anomalies in the IIoT network by overloading complex tasks, which can compromise the fog processing capabilities. This results in an increased delay of task computation for trustworthy nodes. To improve the task execution capability of the fog computing node, it is important to avoid complex offloaded tasks due to malicious attacks. However, even after avoiding the malicious tasks, if the offloaded tasks are too complex for the fog node to execute, then the fog nodes may struggle to process all legitimate tasks within their stipulated time frame. To address these challenges, the Trust-based Efficient Execution of Offloaded IIoT Trusted tasks (EEOIT) is proposed for fog nodes. EEOIT proposes a mechanism to detect malicious nodes as well as manage the allocation of computing resources so that IIoT tasks can be completed in the specified time frame. Simulation results demonstrate that EEOIT outperforms other techniques in the literature in an IIoT setting with different task densities. Another significant feature of the proposed EEOIT technique is that it enhances the computation of trustable tasks in the network. The results show that EEOIT entertains more legitimate nodes in executing their offloaded tasks with more executed data, with reduced time and with increased mean trust values as compared to other schemes.

show abstract

Section: Introductionmentioning

confidence: 99%

Secure Computing for Fog-Enabled Industrial IoT

Alvi,

Ali,

Saleh

et al. 2024

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

TIHCS: Trust-Based Improved QoS for Health Care Systems in Smart Cities

Ali,

Naseem Alvi,

Alkhathami

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Securing Web Data and Privacy in AIoT Systems

Mihailescu,

Nita

2024

Semantic Web Technologies and Applications in Artificial Intelligence of Things

View full text Add to dashboard Cite

The exponential growth of Artificial Intelligence of Things (AIoT) has resulted in an unparalleled fusion of AI with IoT technologies, giving rise to intricate systems that present vast opportunities for automation, productivity, and data-centric decision-making. Nevertheless, this amalgamation also poses substantial obstacles regarding safeguarding online information and upholding confidentiality. The chapter extensively examines the difficulties associated with these issues and the tactics employed to surmount them. The chapter commences by delineating the distinctive susceptibilities inherent in AIoT systems, with a particular emphasis on how the interconnection of AI and IoT technologies gives rise to novel avenues for data breaches and privacy infringements. It then explores the most recent approaches and technologies used to protect data sent over AIoT networks. These include improved encryption methods, secure data transfer protocols, and solutions based on blockchain technology. A substantial chunk of the chapter focuses on privacy-preserving strategies in AIoT. The text examines the equilibrium between data usefulness and privacy protection. It delves into techniques like anonymization, differential privacy, and federated learning as means to safeguard user data while ensuring the effectiveness of AIoT systems. The chapter also examines regulatory and ethical factors, thoroughly examining current and developing legislation and regulations that oversee data security and privacy in AIoT. The content incorporates case studies and real-world examples to demonstrate the pragmatic implementation of theoretical principles. Ultimately, the chapter predicts forthcoming patterns and difficulties in this swiftly progressing domain, providing valuable perspectives on possible AIoT security and privacy protocol advancements. This resource is vital for professionals, researchers, and students engaged in AIoT, cybersecurity, and data privacy. It provides them with the necessary information and tools to protect against the ever-changing threats in this dynamic field.

show abstract

TMPAD: Time-Slot-Based Medium Access Control Protocol to Meet Adaptive Data Requirements for Trusted Nodes in Fog-Enabled Smart Cities

Cited by 3 publications

References 31 publications

Secure Computing for Fog-Enabled Industrial IoT

Secure Computing for Fog-Enabled Industrial IoT

TIHCS: Trust-Based Improved QoS for Health Care Systems in Smart Cities

Securing Web Data and Privacy in AIoT Systems

Contact Info

Product

Resources

About