Explainable Artificial Intelligence (XAI) for Internet of Things: A Survey

Artificial intelligence (AI) and Machine Learning (ML) are widely employed to make the solutions more accurate and autonomous in many smart and intelligent applications in the Internet of Things (IoT). In these IoT applications, the performance and accuracy of AI/ML models are the main concerns; however, the transparency, interpretability, and responsibility of the models’ decisions are often neglected. Moreover, in AI/ML-supported next-generation IoT applications, there is a need for more reliable, transparent, and explainable systems. In particular, regardless of whether the decisions are simple or complex, how the decision is made, which features affect the decision, and their adoption and interpretation by people or experts are crucial issues. Also, people typically perceive unpredictable or opaque AI outcomes with skepticism, which reduces the adoption and proliferation of IoT applications. To that end, Explainable Artificial Intelligence (XAI) has emerged as a promising research topic that allows ante-hoc and post-hoc functioning and stages of black-box models to be transparent, understandable, and interpretable. In this paper, we provide an in-depth and systematic review of recent studies that use XAI models in the scope of the IoT domain. We classify the studies according to their methodology and application areas. Additionally, we highlight the challenges and open issues and provide promising future directions to lead the researchers in future investigations. IEE

A P4-assisted task offloading scheme for Fog networks: An intelligent transportation system scenario

In Fog-based Internet of Things (IoT) networks, Fog computing offers great promises to achieve efficient task processing among IoT devices and the Cloud with lower processing delay and a lighter bandwidth burden. However, the sustainability of these networks is critical so that Fog servers efficiently allocate/offload increasing task types to appropriate resources. In this paper, a novel task offloading scheme called Task Offloading Scheme with P4 (TOS-P4) is proposed for Fog-based IoT networks using Programming Protocol-independent Packet Processors (P4) technology. The proposed scheme is evaluated through an Intelligent Transportation System (ITS) application scenario and compared to a conventional model called Task Offloading Scheme with Software-Defined Networking controller (TOS-SDN). In this scenario, the network traffic is regulated by P4, allowing task offload decisions to be more flexible and programmable according to server loads and predetermined forwarding rules. We present the applicability of the proposed task offloading scheme in the Fog-based IoT network and its benefits to network performance in terms of latency and computation overhead. According to the experimental results, when the servers’ load status is measured at 5 s intervals, TOS-P4 is 6.54 times more efficient than TOS-SDN in waiting times of tasks received at Resource Poor (RP) Fog servers. Also, in TOS-SDN case, the average waiting time of a task on RP Fog servers is 30 times more compared to Resource Rich (RR) Fog servers. © 2023 Elsevier B.V.PPK and PSV acknowledge financial support from University Grants Commission and Department of Science and Technology, India, in the form of CSIR-UGC Senior Research and DST-Inspire Junior Research fellowship, respectively. SK acknowledges Department of Science and Technology, India, and University Grants Commission, India, for providing financial support in the form of DST-SERB Grant [EEQ/2016/000350] and UGC-BSR Research Start-Up-Grant [No. F.30–372/2017 (BSR)], respectively.EEQ/2016/000350; F.30–372/2017 (BSR); Department of Science and Technology, Ministry of Science and Technology, India, डीएसटी; University Grants Commission, UG

QNSGA-II: A Quantum Computing-Inspired Approach to Multi-Objective Optimization

2022 International Symposium on Networks, Computers and Communications, ISNCC 2022 -- 19 July 2022 through 21 July 2022 -- 182021This paper proposes a novel quantum computing-inspired approach to multi-objective optimization, called Quantum Computing Inspired Non-dominated Sorting Genetic Algorithm II (QNSGA-II). Although Non-dominated Sorting Genetic Algorithm II (NSGA-II) has been effectively used in the literature to solve a variety of optimization issues, it may encounter some difficulties especially in handling heavily constrained problems due to its premature convergence. The proposed approach mitigates this difficulty by combining conventional NSGA-II with the concept and principles of quantum computing. QNSGA-II exploits quantum bits and superposition of states to reduce the convergence time and improve search space capability by evolving the probabilistic model. This paper aims to provide more detailed information about our proposed algorithm and its advantages. © 2022 IEEE

Road to efficiency: Mobility-driven joint task offloading and resource utilization protocol for connected vehicle networks

Connected Vehicle Networks (CVNs) is an emerging technology that enables vehicles to communicate with each other and with various Internet of Things (IoT) devices of the transportation infrastructure to enhance safety, efficiency, and convenience. In CVN, task offloading is a critical issue due to utilizing high resource computation and dynamic network changes. Specifically, the dynamically changing computation capacity of the vehicles in traffic, as well as the location changes due to their mobility, may cause the result of the task offloading not to return to the task origin vehicle. On the other hand, traditional fixed-position fog networks in inter-vehicle task offloading schemes are limited in terms of tracking vehicles’ status on dynamic traffic and have high utilization costs. Mobile fog computing mitigates these problems by offering efficient and responsive task-processing providing utilization of nearby connected vehicles. Besides, it extends coverage of connected vehicles to support real-time communication of these vehicles. In this paper, a mobility-driven joint task offloading and resource utilization protocol called MobTORU is proposed to optimize resource utilization and efficient task-processing in CVNs. Also, we propose a resource-efficient and task offloading algorithm called RELiOff which is employed in MobTORU protocol for CVN. The proposed protocol and algorithm are evaluated through an Intelligent Transportation System (ITS) application scenario and the experiments using a real-world dataset containing real vehicular mobility traces. Experimental results show that our proposed protocol and algorithm have 93.8% efficiency on the overall system and 99.9% efficiency on processed tasks in the resource utilization of offloaded tasks achieved, respectively. © 2024 Elsevier B.V