The significant advancement in Internet of Things (IoT) adoption has enabled Multi-access Edge Computing (MEC) to mitigate IoT sensors' limited computational, transmission power constraints, and data distribution overhead. However, integrating MEC with the IoT ecosystem poses several challenges, resulting in integrity issues with the MECs, impacting their capacity to effectively serve users seeking data generated by IoT sensors. To address this, we propose SmartSLA, a blockchainbased solution to ensure Quality of Service (QoS) from thirdparty IoT devices. SmartSLA leverages the decentralized and immutable nature of blockchain to combat the shortcomings of MECs. Using smart contracts, we develop a blockchain solution to address a multi-objective optimization problem centered around QoS, user satisfaction, and maximizing the lifespan of IoT devices. Leveraging blockchain, we developed an edge-enhanced IoT network and assessed the efficacy of proposed novel dynamic reassignment strategies to enhance user satisfaction with data delivery. Our architecture demonstrate the high performance over time in regards to user satisfaction and SLA adherence, as well as the scalability compared to existing work.

Post-quantum cryptography (PQC) is essential to secure vehicular ad-hoc networks (VANETs) against emerging quantum computing threats. However, selecting an appropriate Post-Quantum Key Encapsulation Mechanism (PQ-KEM) is challenging due to varying performance metrics such as key generation time, encapsulation/decapsulation latency, and ciphertext overhead. This issue becomes particularly critical in VANETs, where vehicles and roadside units (RSUs) must rapidly and securely exchange data under dynamic network conditions. Current methods typically overlook the initial key distribution phase, leaving communications vulnerable at the earliest interaction. To address these challenges, we created an extensive, open-source benchmark dataset that rigorously evaluates several candidate PQ-KEM algorithms based on performance factors relevant to vehicular environments. Leveraging this benchmark, we developed a lightweight deep learning model that dynamically selects the most suitable PQ-KEM algorithm by predicting optimal performance considering security requirements and real-time conditions such as message size and network congestion. Each recommended PQ-KEM algorithm is authenticated using Dilithium-2 post-quantum signatures, ensuring secure and quantum-resilient initial key distribution between vehicles and RSUs. Our comprehensive simulations demonstrate that our adaptive PQ-KEM selector significantly reduces end-to-end latency and ciphertext overhead without compromising security, thus enhancing secure, efficient communication in VANET scenarios.

The convergence of urban informatics and vehicle intelligence has given rise to smart connected vehicles, which have immense potential as edge computing platforms for various applications. However, harnessing the full efficiency of these platforms presents challenges due to the diverse resource requirements, capabilities, and vehicle types, as well as unpredictable vehicle movements. To address these obstacles, a novel task offloading framework based on digital twin (DT) technology has been proposed for the Internet of Vehicles (IoV). This DT-based framework capitalizes on historical data and workload predictions to optimize the utilization of edge devices. It streamlines the offloading process by enabling tasks to be accepted and processed by the source vehicle without relying on external devices. The proposed system is designed to learn and forecast vehicle mobility patterns and computation waiting times, facilitating efficient allocation of computing resources at edge locations. Consequently, this approach enhances the quality of service by ensuring swift and effective task processing, irrespective of the vehicles’ unpredictable movements. The proposed approach is compared with a deep sequential model based on reinforcement learning, collaborative multiaccess edge computing (MEC), and energy-efficient MEC via reinforcement learning model. Our method demonstrates an improvement in task execution and overall offloading performance compared to these techniques during peak vehicle arrival rates. Likewise, substantial enhancements are observed in other benchmark parameters.

The vehicular edge computing model provides computational support to nearby vehicles requiring low-latency computation. However, existing algorithms typically only consider immediate neighbor nodes as potential vehicles for computation offloading. In this study, we address this limitation by modeling the task offloading problem using a Graph Neural Network approach. This approach incorporates dynamic properties of vehicles and edges, facilitating a more effective selection of vehicles for task offloading. By leveraging message passing and aggregate feature techniques, our proposed approach expands the search space for source vehicles, thereby distributing the workload across a wider spectrum, particularly benefiting vehicles that frequently generate computational tasks. Evaluation of our proposed system demonstrates significant improvements in the utilization factor of available vehicles, a reduction in queuing states, and an overall system efficiency of 92%.

Vehicle-to-vehicle (V2V) infrastructure facilitates wireless communication among vehicles within close proximity. This allows sharing of contextual information such as speed, location, direction, traffic, route closures, human behavior mental conditions to improve traffic flow, reduce collisions, and enhance safety on the road. However, the assumption of honest peers along with the over-reliability on the information shared in the network can pose a serious threat to human safety. A digital twin is a concept that enables a system to develop a virtual environment that mimics the real-life scenario for any situation. The availability of powerful computing equipment inside vehicles can be leveraged to construct a digital twin of any scenario that in conjunction with a dynamic trust model can increase the reliability of information obtained in a network. Along this direction, in this vision paper, we have highlighted some of the challenges to developing a collaborative trusted data-sharing framework to host digital twin-assisted services over connected vehicles to improve the reliability of the decision-making information.

The industrial environment augments resource-constrained devices to bring services closer to autonomous devices. However, over time, these devices get overburdened due to computational workload, which results in degraded network performance. Therefore, the devices are programmed to share resources with nearby devices. However, owing to real-time collaboration, there is the possibility that the device moves to an undefined state and starts behaving maliciously. This can impact the entire collaborative environment laid to meet the industrial product deadline. In this article, we propose an industrial simulation framework that enables the resource-sharing environment and identifies the undefined device behavior. Furthermore, our detection scheme is based on an intelligent model trained on device behavior through the machine-in-a-loop mechanism and deployed at network intersections, i.e., edge nodes. The proposed technique improves the efficiency of the collaborative network by 30%.

Smart vehicles are equipped with onboard computing units designed to run in-vehicle applications. However, due to limited computing power, the onboard units are unable to execute compute-intensive tasks and those that require near real-time processing. Therefore tasks are offloaded to nearby fog/edge devices that have more powerful processors. However, the fog devices are static, placed at fixed locations such as intersections, and have a limited communication range. Therefore, they can only facilitate vehicles in their immediate vicinity and only limited areas of the city can be covered to provide services on demand. In this article, we propose an unmanned aerial vehicle (UAV)-based computing framework design termed Skywalker to provide computing in regions where there are no static fog units thereby extending coverage. Skywalker’s contributions are threefold: 1) it allows for load-aware UAV placement and provisions a swarm of UAVs to fly to areas experiencing a gap in service where the size of the swarm is proportional to the demand; 2) it implements multiple scheduling algorithms that the UAVs swarm employs to divide up the task processing responsibility for individual UAVs within the swarm; and 3) a zone-based delivery mechanism is being proposed to facilitate the return of completed tasks, either through direct delivery or relay-based methods. The choice between these options depends on the distance covered by the requesting vehicle from the UAV swarm. The efficiency of the framework is compared with existing techniques and it is found that it can greatly extend coverage during peak traffic hours while providing low communication delay and consuming minimum energy.

Connected vehicle technology is rapidly evolving. In the U.S., the government targets that 50% of all vehicles be electric by 2030 in order to reduce climate change. This initiative comes in the wake of an increased spate of ransomware attacks targeting transportation companies. Unlike traditional ransomware targeting computer networks, the compromise of vehicles can have disastrous consequences on businesses and ultimately the national economy as was evident in the recent Colonial pipeline attack. This research proposes a novel framework to study the spread of ransomware and its impact on connected vehicles. Our contribution is fourfold: 1) three different ransomware infection vectors are considered for connected vehicles, namely, a hotspot attack, OBD dongle attack, and malicious over-the-air updates; 2) business impact of the ransomware is analyzed on a ride-hailing service; 3) a fog computing architecture is used to reduce latency in vehicle-to-vehicle communication and vehicle to base station communication; and 4) the effectiveness of safeguard controls are studied in mitigating the ransomware spread. Our results show that ransomware can have a debilitating impact on connected vehicle businesses due to their high mobility and connectivity with attacks on average impacting earnings by 45% per hour.

With an ever-increasing number of connected devices on roads, it becomes unsustainable to provide nearby specialized execution resources (compute and storage) for servicing innovative applications. Moreover, the vehicular environment being inherently ad hoc and opportunistic, not to mention highly mobile, makes it unsuitable to use traditional cloud computing due to delayed and interrupted services. Thus, there is a possibility to introduce potential collaboration among nearby connected vehicles. However, the underlying decision model for the selection of the most suitable vehicle for task offloading is challenging in such a dynamic environment. In this study, we propose a collaborative vehicular computing framework that adopts online learning for efficient task assignment between local and neighboring computing resources. The underlying workload adaptive task offloading intends to balance out the workload across neighboring vehicles. The framework is compared against three techniques including two adaptive learning techniques in terms of service delay, efficiency, task delivery rate, task failures, and learning regret. The results demonstrate the effectiveness of the proposed resource-sharing network, improving service quality and throughput for servicing innovative intelligent transportation applications.

With recent advancements in software-defined vehicles, over-the-air (OTA) software updates are crucial to roll out new software and patches for connected vehicles. Traditionally, outdated vehicles are recalled by the manufacturers, however, owners are notoriously difficult to reach with recall notices. Also, organizational and procedural challenges result in many outdated vehicles with insecure and unstable software. In this paper, we propose a dissemination framework for OTA updates using a federated fog environment. Pushing the update to all vehicles via the fog nodes may congest the network, leading up to a single-point failure for update dissemination, as well as, disruption to other installed services at the fog nodes. We propose a software-defined mechanism to select a pivot, which gets the update from the fog node and streams it to other vehicles. Moreover, a timer-barrier mechanism is proposed to identify any malicious vehicles that are barred from participating in pivot selection. The experimental evaluation demonstrates that the proposed scheme improves network convergence time by 40% with 95% node trustworthiness.

The industrial Internet of Things (IIoT) has emerged as an essential paradigm to enhance industrial operations and productivity through efficient utilization of available resources. The paradigm allows industrial devices to share computing resources based on locality constraints to support innovative services. In this article, we propose a trusted multihop collaborative computing model for the efficient utilization of nearby devices in an IIoT environment. To establish trust, we explore a social-aware incentive scheme managed by network edge, using distributed ledgers. The extensive simulation results demonstrate the effectiveness of the proposed model in the presence of malicious nodes in the industrial environment.

Collaborative robots are an emerging area where robots share resources among each other for mutual benefit. They are particularly becoming popular in a modern industrial environment, primarily to improve production efficiency. This often involves some sort of decision making at the robots. Traditionally, the robots are connected to the cloud infrastructure and edge locations to offload compute-intensive tasks. But due to the dynamic workload generated and additional network delay, cloud, and edge locations are deemed unsuitable computing paradigms for use in industrial robots. In this article, we propose a symbiotic robotics framework where robots share their onboard computing capabilities for effective task offloading and its execution. Furthermore, the underlying symbiotic paradigm incorporates the concept of repute based on every successful task offloading and execution. The experimental results demonstrate a significant performance gain in terms of offloading time, task completion time, and overall efficiency when compared to two classical task offloading schemes.

Streaming large amounts of data to cloud data centers cause network congestion resulting in high network and energy consumption. The concept of fog computing is introduced to reduce workload from backbone networks and support delay-sensitive Internet of Things (IoT) applications. The concept places compute, storage, and network services closer to the source of the requests. In general fog-based simulators are used for better understanding and optimum fog resource allocation. Unfortunately, most of the simulators lack core features like network delay, latency, packet error rate, energy consumption, and distributed fog node management. In this paper, we propose a fog simulation framework termed as xFogSim to support latency-sensitive applications at the fog layer with multi-objective optimization to trade-off cost, availability, and performance among the fog federation. Moreover, during peak load, the framework provides locality-aware distributed broker node management that enables borrowing resources from nearby fog locations to meet service and energy requirements. The results show that the framework is lightweight, configurable, and scalable, capable of handling a large number of user requests using dynamic resource provisioning across the fog federation.

Abstract Cloud adoption has significantly increased using the infrastructure-as-a-service (IaaS) paradigm, in order to meet the growing demands of computing, storage and networking, in small as well as large enterprises. Different vendors provide their customized solutions for OpenStack deployment on bare metal or virtual infrastructure. Among these many available IaaS solutions, OpenStack stands out as being an agile and open-source platform. However, its deployment procedure is a time-taking and complex process with a learning curve. This paper addresses the lack of basic infrastructure automation in almost all of the OpenStack deployment projects. We propose a flexible framework to automate the process of infrastructure bring up for deployment of several OpenStack distributions, as well as resolving dependencies for a successful deployment. Our experimental results demonstrate the effectiveness of the proposed framework in terms of automation status and deployment time, that is, reducing the time spent in preparing a basic virtual infrastructure by four times, on average.

Smart cities are based on connected devices generating large quantities of data every instant. These data can be stored at a nearby edge location for initial processing but later sending the data to the backend data centers for storage and further analysis consumes considerable network bandwidth. In this article, we propose a large-scale data migration framework using vehicles. The framework uses a neural network to identify suitable vehicles as data mules, ones moving toward the data destination, potentially reducing the load from backend networks in terms of bandwidth usage and overall energy consumption. We compare the framework with data transfers using the traditional Internet and an approach without machine intelligence. The proposed framework performs well in terms of data loss, transfer time, energy, and CO2 emissions. From experiments, we demonstrate that the approach achieves a 67% success rate with data transfers $193\times $ faster than the average Internet bandwidth of 21.28 Mb/s. Moreover, the resulting CO2 emissions for 30-TB data transfers stood at 6.403 kg, which is significantly lower compared to 1172.8 kg for the Internet.

The concept of smart farming has led to the use of technology to enhance agricultural productivity. With access to low-cost sensors and management systems, more farmers are adopting this technology to achieve sustainable growth. However, in literature, there are no simulation platforms to help researchers and users understand sensor deployment, and data collection and processing. In this article, we propose a framework designed to provide a complete farming ecosystem. The toolkit facilitates users to simulate custom farming scenarios, specifically to identify sensor placement, coverage area, line-of-sight deployment, and data gathering through the relay mechanism or airborne systems, mobility models for mobile nodes, energy models for on-ground sensors and airborne vehicles, and backend computing support using the fog computing paradigm. Furthermore, in most of the existing works, network parameters are ignored, which can impact the overall performance of any deployed system. Therefore, the proposed framework also provides a benchmark in terms of transmission delay, packet delivery ratio, energy consumption, and system resources usage.

Advancement of technology has enabled access to innovative applications for connected devices. To handle growing computation requirements, the backend cloud data centers become an inefficient solution due to the caused network overhead. This is generally alleviated using edge locations deployed to meet the increasing computing demands. This article proposes a micro-level fog unit deployment to facilitate delay-sensitive applications. To manage imbalanced workloads due to the traffic density, the framework established a fog federation acting as a consortium where underutilized resources are shared to provide service quality. Moreover, we implement a price-based workload balancing algorithm to limit offloading among fog units relative to other consortium members. The experimental results show a balanced offload rate compared to traditional algorithms. Moreover, other measures, such as queue length, end-to-end delay, and workload balancing, demonstrate performance gain under the federation. Overall, 72% energy reduction is achieved through the proposed technique in comparison with the traditional nonfederated model.

Summary Existing simulators are designed to simulate a few thousand nodes due to the tight integration of modules. Thus, with limited simulator scalability, researchers/developers are unable to simulate protocols and algorithms in detail, although cloud simulators provide geographically distributed data centers environment but lack the support for execution on distributed systems. In this paper, we propose a distributed simulation framework referred to as CloudSimScale. The framework is designed on top of highly adapted CloudSim with communication among different modules managed using IEEE Std 1516 (high‐level architecture). The underlying modules can now run on the same or different physical systems and still manage to discover and communicate with one another. Thus, the proposed framework provides scalability across distributed systems and interoperability across modules and simulators.

Parallel discrete event simulation frameworks have been widely used to analyze the performance of traditional applications under different scenarios. The existing frameworks are designed to work on a cluster and cloud-based computing environments. With the current advances in the Internet of Things, there is a strong need to revamp such traditional frameworks and make use of the smart connected-devices as an underlying infrastructure to perform simulations. In this article, we propose a new simulation framework, which has been specifically designed to work with diverse heterogeneous devices. The framework allows these heterogeneous mobile devices to participate in a distributed simulation while managing network latency, using device profiles that are maintained by the simulation framework. Moreover, in the proposed framework, random and context-aware simulation task distributions have been explored to manage the devices’ sporadic connectivity. Evaluation results using the well-known PHOLD benchmark demonstrate a gain in the overall efficiency of the proposed simulation system.

On battery-operated devices, energy and power consumption are main concerns. With the recent advancement of technology, mobile devices can be integrated with traditional systems for running complex computations. In fact, mobile devices can easily become part of computational networks and share their computational and memory resources. Despite this, traditional simulation frameworks are not designed to perform well on heterogeneous networks. This is mainly due to the limited computational resources that are available on mobile devices. In this paper, we propose SEECSSim (SEECSSim is derived from School of Electrical Engineering and Computer Science (SEECS)) that is a simulation framework specifically designed for mobile devices. SEECSSim includes state-of-the-art distributed synchronization algorithms that are implemented to run on mobile or embedded devices. To benchmark the proposed framework, the well-known PHOLD model is used and performance results are reported in terms of execution time, CPU usage, memory and energy consumption.

Age-related Macular Degeneration (AMD) is an eye disease which affects elderly people. Cholesterol deposits in central part of retina, known as macula, damages the photoreceptors present in a particular area of eye. AMD usually effects only central vision of patient. In medical field various imaging techniques are used for diagnosis of eye diseases. Optical Coherence Tomography (OCT) is a relatively newer technique that is found to be very useful in analyzing eyes. In this research, we used OCT images to automatically detect and classify AMD. First we extract the retinal layer known as Retinal Pigment Epithelium by utilizing Graph Theory Dynamic Programming technique, after successfully enhancing the quality of OCT image by using Wiener filter. We used a unique feature set consisting of features extracted from difference signal of RPE and Inner Segment Outer Segment layer of RPE. Feature set includes approximation coefficient, entropy and spectrum energy of the resulting difference signal. Support Vector Machine classifier was used to classify AMD affected and normal image. The developed system gives an accuracy of 95% for AMD detection.

State-of-the-art cloud simulators in use today are limited in the number of features they provide, lack real network communication models, and do not provide extensive Graphical User Interface (GUI) to support developers and researchers to extend the behavior of the cloud environment. We propose CloudNetSim++, a comprehensive packet level simulator that enables simulation of cloud environments. CloudNetSim++ can be used to evaluate a wide spectrum of cloud components, such as processing elements, storage, networking, Service Level Agreement (SLA), scheduling algorithms, fine grained energy consumption, and VM consolidation algorithms. CloudNetSim++ offers extendibility, which means that the developers and researchers can easily incorporate own algorithms for scheduling, workload consolidation, VM migration, and SLA agreement. The simulation environment of CloudNetSim++ offers a rich GUI that provides a high level view of distributed data centers connected with various network topologies. The package also includes an energy computation module that provides a fine grained analysis of energy consumed by each component. This paper shows the flexibility and effectiveness of CloudNetSim++ through experimental results demonstrated using real-world data center workloads. Moreover, to demonstrate the correctness of CloudNetSim++, we performed formal modeling, analysis, and verification using High-level Petri Nets, Satisfiability Modulo Theories (SMT), and Z3 solver.

Cloud computing offers the potential to make parallel discrete event simulation capabilities more widely accessible to users who are not experts in this technology and do not have ready access to high performance computing equipment. Services hosted within the ldquocloudrdquo can potentially incur processing delays due to load sharing among other active services, and can cause optimistic simulation protocols to perform poorly. This paper proposes a mechanism termed the Time Warp Straggler Message Identification Protocol (TW-SMIP) to address optimistic synchronization and performance issues associated with executing parallel discrete event simulation in cloud computing environments.