Computational Paradigm Research Articles

Mobility-aware Vehicular Cloud formation mechanism for Vehicular Edge Computing environments

Rapid advancements in vehicular technology and increased vehicle modernization have led to the emergence of intelligent and interconnected entities. As a result, the Vehicular Edge Computing (VEC) paradigm has gained prominence. This paradigm enables the provision of cloud computing services close to vehicular users by utilizing the idle computational resources of vehicles to execute tasks that require computing power beyond what is available locally. Aggregating these computational resources in the vehicular context is known as Vehicular Cloud (VCloud) formation. However, leveraging and aggregating these resources poses several challenges due to the dynamic nature of the vehicular environment. One of the main challenges is the efficient selection of vehicles to assume management roles in the distribution of computational power within the group, often referred to as leading vehicles. This research presents a mobility-aware mechanism called PREDATOR to enhance the VCloud formation process. In this mechanism, the Roadside Unit (RSU) provides vehicular mobility predictions, enabling the selection of the most stable vehicles within the RSU coverage area to assume leadership roles in the VCloud. In this context, vehicle stability is associated with a vehicle’s time within the RSU coverage area, known as dwell time. PREDATOR employs a microscopic perspective to select vehicles with the longest dwell time in the VCloud, allowing for efficient management of computational resource utilization. Simulation results have demonstrated that PREDATOR not only increases the VCloud lifetime but also minimizes leader changes, reduces network message exchange, mitigates packet collisions, and facilitates the effective utilization of aggregated vehicular resources compared to state-of-the-art approaches.

A coordinates-based hierarchical computing framework towards spatial data processing

The vehicle-based mobile base station (VMBS) has been developed by NTT a few years ago, which plays an important role in reconstructing an emergency communication network by integrating with mobile edge computing to support the cloud service for data pre-processing tasks. Spatial data processing refers to characteristics, such as fast-changing, massive, and potentially infinite. Considering the capacities of network bandwidth and storage of VMBS, new challenging issues have emerged such as how to quantify information loss and how to optimize data processing in the paradigm of mobile edge computing. To address these two challenges, we design a coordinates-based hierarchical computing framework for spatial data processing, named HierCom. Using HierCom, the optimal position of VMBS can be selected to decrease the transmission delay, thereby reducing information loss in the data processing in an edge computing environment. Furthermore, we also propose a near least neighbor-based algorithm to optimize the information loss. Finally, simulations on the real-world dataset show that the information loss of our method is reduced by 27% compared with the baseline method.

Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing

In recent years, the Edge Computing (EC) paradigm has emerged as an enabling factor for developing technologies like the Internet of Things (IoT) and 5G networks, bridging the gap between Cloud Computing services and end-users, supporting low latency, mobility, and location awareness to delay-sensitive applications. An increasing number of solutions in EC have employed machine learning (ML) methods to perform data classification and other information processing tasks on continuous and evolving data streams. Usually, such solutions have to cope with vast amounts of data that come as data streams while balancing energy consumption, latency, and the predictive performance of the algorithms. Ensemble methods achieve remarkable predictive performance when applied to evolving data streams due to several models and the possibility of selective resets. This work investigates a strategy that introduces short intervals to defer the processing of mini-batches. Well balanced, our strategy can improve the performance (i.e., delay, throughput) and reduce the energy consumption of bagging ensembles to classify data streams. The experimental evaluation involved six state-of-art ensemble algorithms (OzaBag, OzaBag Adaptive Size Hoeffding Tree, Online Bagging ADWIN, Leveraging Bagging, Adaptive RandomForest, and Streaming Random Patches) applying five widely used machine learning benchmark datasets with varied characteristics on three computer platforms. As a result, our strategy can significantly reduce energy consumption in 96% of the experimental scenarios evaluated. Despite the trade-offs, it is possible to balance them to avoid significant loss in predictive performance.

Physical Reservoir Computing Based on Nanoscale Materials and Devices

AbstractBioinspired computation systems can achieve artificial intelligence, bypassing fundamental bottlenecks and cost constraints. Computational frameworks suited for temporal/sequential data processing such as recurrent neural networks (RNNs) suffer from problems of high complexity and low efficiency. Physical systems assembled with nanoscale materials and devices represent as an alternative route to serve as the core component for physically implanted reservoir computing. In this review, an overview of the development of the paradigm of physical reservoir computing (PRC) is provided and the typical physical reservoirs constructed with nanomaterials and nanodevices are described. The physical reservoirs based on multiple nanomaterials overcome the problems of RNN, show strong robustness, and effectively deal with tasks with improved reliability and availability. Finally, the challenges and perspectives of nanomaterial and nanodevice‐based PRC as a component of next‐generation machine learning systems are discussed.

Fuzzy-based Misbehavior Detection for Internet of Things in Multi-access Edge Computing Environment

Today, an emerging paradigm of edge computing, called Multi-Access Edge Computing (MEC), is widely used in a broad range of applications. MEC has been recently used as a driving technology for 5G networks. However, MEC encompasses some security problems, such as communicating with untrusted third-party service providers commonly known as location-based service (LBS) providers. In this paper, we propose a novel fuzzy-based misbehavior detection system for Internet of Things (IoT) in a 5G-based MEC environment to solve the previous problems. Our proposed system is comprised of a multi-tier architecture that is linked by IoT gateways and mobile core network. In our architecture, we employ a novel mechanism that embeds a security entity called Regional Certificate Authority in a 5G macro base station. This security entity acts as a passport for the IoT gateway to communicate with other security entities present in our system that are located in the core infrastructure. Besides that, our system utilizes a distributed certificate revocation mechanism for malicious LBS providers via simultaneously updating the internal blocklists employed by more than one system entity. In addition, we adopt in our architecture a novel penalty system for malicious LBS providers, which is used for penalizing providers in case proved to be malicious as well as actively isolating them from the network. On the other hand, our system provides conditional anonymity for the incident’s reporter. Also, we present various security features maintained by the proposed system to thwart well-known attacks as well as we demonstrate efficiency and robustness of our system. Finally, we provide the overhead of our system and compare it with other state-of-the-art approaches where results show that the proposed system relatively incurs the least overhead.

Opportunities and Challenges of Quantum Computing for Engineering Optimization

Abstract Quantum computing as the emerging paradigm for scientific computing has attracted significant research attention in the past decade. Quantum algorithms to solve the problems of linear systems, eigenvalue, optimization, machine learning, and others have been developed. The main advantage of utilizing quantum computer to solve optimization problems is that quantum superposition allows for massive parallel searching of solutions. This article provides an overview of fundamental quantum algorithms that can be utilized in solving optimization problems, including Grover search, quantum phase estimation, quantum annealing, quantum approximate optimization algorithm, variational quantum eigensolver, and quantum walk. A review of recent applications of quantum optimization methods for engineering design, including materials design and topology optimization, is also given. The challenges to develop scalable and reliable quantum algorithms for engineering optimization are discussed.

Deep Reinforcement Learning for Workload Prediction in Federated Cloud Environments.

The Federated Cloud Computing (FCC) paradigm provides scalability advantages to Cloud Service Providers (CSP) in preserving their Service Level Agreement (SLA) as opposed to single Data Centers (DC). However, existing research has primarily focused on Virtual Machine (VM) placement, with less emphasis on energy efficiency and SLA adherence. In this paper, we propose a novel solution, Federated Cloud Workload Prediction with Deep Q-Learning (FEDQWP). Our solution addresses the complex VM placement problem, energy efficiency, and SLA preservation, making it comprehensive and beneficial for CSPs. By leveraging the capabilities of deep learning, our FEDQWP model extracts underlying patterns and optimizes resource allocation. Real-world workloads are extensively evaluated to demonstrate the efficacy of our approach compared to existing solutions. The results show that our DQL model outperforms other algorithms in terms of CPU utilization, migration time, finished tasks, energy consumption, and SLA violations. Specifically, our QLearning model achieves efficient CPU utilization with a median value of 29.02, completes migrations in an average of 0.31 units, finishes an average of 699 tasks, consumes the least energy with an average of 1.85 kWh, and exhibits the lowest number of SLA violations with an average of 0.03 violations proportionally. These quantitative results highlight the superiority of our proposed method in optimizing performance in FCC environments.

RASM: Resource-Aware Service Migration in Edge Computing based on Deep Reinforcement Learning

Multi-access Edge Computing (MEC) paradigm allows devices to offload their intensive service tasks that require high Quality of Experience (QoE). Devices mobility forces services to migrate between MECs to maintain QoE in terms of delay. The decision on when to migrate a service requires a cost and QoE tradeoff, and destination MEC selection needs to be done upon latency and resource availability constraints to minimize migrations. To this end, we propose a novel Resource-Aware Service Migration (RASM) mechanism using Deep Q-Network (DQN) to make migration decisions by achieving tradeoff between the QoE in terms of delay and migration cost. Moreover, DQN learns the best policy for maximizing QoE by selecting the migration destination based on the MECs proximity to the device and estimated resource availability at the servers using queuing model. Results show faster convergence to optimal policy, reduced average end-to-end service delay by 27%, and smaller service rejection rate by 24% comparing to the state-of-the-art.

Cloud Computing Paradigm in Academics

Cloud computing has gained significant popularity due to its convenience and accessibility, especially with the surge in remote work arrangements during the global lockdown. Integrating cloud computing with education offers a unique and high-quality experience for educators, administrative staff, parents, guardians, and students. This paper emphasizes the importance of embracing a cloud computing paradigm in educational institutions, backed by extensive research in ed-tech cloud-based concepts. The study examines the intricacies associated with adopting cloud computing, with a focus on the initial decision-making process. Furthermore, the paper conducts a comparative analysis of the three leading cloud computing platforms to identify the most suitable and optimal choice for implementing the suggested model. By leveraging cloud services, educational institutions can enhance their capabilities and provide an enriched learning environment for all stakeholders.

Enhancement of Cloud-native applications with Autonomic Features

The Autonomic Computing paradigm reduces complexity in installing, configuring, optimizing, and maintaining heterogeneous systems. Despite first discussing it a long ago, it is still a top research challenge, especially in the context of other technologies. It is necessary to provide autonomic features to the Cloud-native execution environment to meet the rapidly changing demands without human support and continuous improvement of their capabilities. The present work attempts to answer how to explore autonomic features in Cloud-native environments. As a solution, we propose using the AMoCNA framework. It is rooted in Autonomic Computing. The success factors for the AMoCNA implementation are its execution controllers. They drive the management actions proceeding in a Cloud-native execution environment. A similar concept already exists in Kubernetes, so we compare both execution mechanisms. This research presents guidelines for including autonomic features in Cloud-native environments. The integration of Cloud-native Applications with AMoCNA leads to facilitating autonomic management. To show the potential of our concept, we evaluated it. The developed executor performs cluster autoscaling and ensures autonomic management in the infrastructure layer. The experiment also proved the importance of observations. The knowledge gained in this process is a good authority of information about past and current state of Cloud-native Applications. Combining this knowledge with defined executors provides an effective means of achieving the autonomic nature of Cloud-native applications.

Design of a Novel Edge-Centric Cloud Architecture for m-Learning Performance Effectiveness by Leveraging Distributed Computing Paradigms’ Potentials

This article aims to design a novel edge-centric hierarchical cloud architecture to optimize mobile learning (m-learning) performance during learners executing computation-intensive learning applications. This research adopts the potential of distributed computing paradigms, that is, mobile edge, for improving the effectiveness of m-learning performance in higher education. Edge computing enables computing at the network’s edge and effectively avoids latencies while processing learners’ computational requests. The envisioned architecture was designed on the ETSI MEC ISG protocols and deployed on the university mobile cloud infrastructure. Additionally, a use case was designed, focusing the edge computing’s latency-avoiding ability, and executing it in a real-time environment involving sixteen students from an academic course. The execution results validated the architecture’s contribution, such as tasks executed in the local server, optimized learner privacy, reduced latencies, instant access, lowered bandwidth consumption, and continued tasks’ execution despite the failure of smart nodes. The result influences user acceptance and attracts designers to extend the architecture base focusing on machine learning algorithms (for learning analytics) and blockchain (to prevent malicious attacks) to improve the effectiveness of learning management system performance.

Efficient Data as a Service in Fog Computing: An Adaptive Multi-Agent Based Approach

Data as a Service (DaaS) offers an effective provisioning model able to exploit the advantages of cloud computing in terms of accessibility and scalability when data providers need to make their data available to different data consumers. Nevertheless, in settings where data are generated at the edge and they need to be propagated (e.g., Industry 4.0, Smart Cities), DaaS model suffers of some limitations: data transfer from the edge to the cloud – and viceversa – could require a significant time and privacy issues could hamper the possibility to move the data. Goal of this paper is to propose a DaaS model based on the Fog Computing paradigm, which combines the advantages of both cloud and edge computing. The proposed solution implements an adaptive multi-agent system where each agent autonomously manages the placement of data in the most convenient location considering the quality of service requirements of the user that it is serving. To guarantee the collaboration of the agents without imposing a centralized control, a reinforcement learning algorithm will be enacted to balance between the local optimum for the single data consumers and the satisfaction of the global requirements of all consumers.

Urban quantum leap: A comprehensive review and analysis of quantum technologies for smart cities

Contemporary smart city solutions rely on standardized von Neumann architecture, in which single data units are coded as “0” or “1.” Conversely, urban quantum technologies rely on the fundamental principles of quantum physics, transcending the conventions of the current computational paradigm. On the one hand, urban quantum technologies hold managerial relevance for future smart cities. On the other hand, they are often overlooked by smart city researchers. Accordingly, their value as a breakthrough technological paradigm is still largely unexplored. In this article, we look at how quantum technologies may contribute to existing smart city solutions, including the Internet of Things, cloud computing, big data, ICT, smart transportation, artificial intelligence, and blockchain. First, through a semi-systematic review of eighty articles on quantum computing within the social science domain, we identify two relevant classes of urban quantum technologies: quantum communication and quantum computing. Second, we establish a comprehensive taxonomy of conventional smart city solutions based on the automated content analysis of 567 abstracts of articles on the technological aspects of smart cities. Third, we investigate potential associations between two classes of technologies (conventional smart city solutions and urban quantum technologies) by analyzing the semantic relationships between eighty articles on quantum technologies according to the frequency of keywords denoting different types of conventional smart city solutions. Finally, we triangulate our findings through a thematic analysis of potential uses of quantum technologies within identified categories of smart city solutions.

MFLCES: Multi-Level Federated Edge Learning Algorithm Based on Client and Edge Server Selection

This research suggests a multi-level federated edge learning algorithm by leveraging the advantages of Edge Computing Paradigm. Model aggregation is partially moved from a cloud center server to edge servers in this framework, and edge servers are connected hierarchically depending on where they are located and how much computational power they have. At the same time, we considered an important issue: the heterogeneity of different client computing resources (such as device processor computing power) and server communication channels (which may be limited by geography or device). For this situation, a client and edge server selection algorithm (CESA) based on a greedy algorithm is proposed in this paper. Given resource constraints, CESA aims to select as many clients and edge servers as possible to participate in the model computation in order to improve the accuracy of the model. The simulation results show that, when the number of clients is high, the multi-level federated edge learning algorithm can shorten the model training time and improve efficiency compared to the traditional federated learning algorithm. Meanwhile, the CESA is able to aggregate more clients for training in the same amount of time compared to the baseline algorithm, improving model training accuracy.

Network-assisted processing of advanced IoT applications: challenges and proof-of-concept application

AbstractRecent advances in the area of the Internet of Things shows that devices are usually resource-constrained. To enable advanced applications on these devices, it is necessary to enhance their performance by leveraging external computing resources available in the network. This work presents a study of computational platforms to increase the performance of these devices based on the Mobile Cloud Computing (MCC) paradigm. The main contribution of this paper is to research the advantages and possibilities of architectures with multiple offloading options. To this end, a review of architectures that use a combination of the computing layers in the available infrastructure to perform this paradigm and outsource processing load is presented. In addition, a proof-of-concept application is introduced to demonstrate its realization along all the network layers. The results of the simulations confirm the high flexibility to offload numerous tasks using different layers and the ability to overcome unfavorable scenarios.

Research on the standardization strategy of granular computing

As intelligent systems continue to evolve, problems are becoming increasingly complex. The constant abundance of data puts a higher demand on the value of data utilization. Granular computing is a new computational paradigm for complex problem-solving. It takes structured thinking, structured problem-solving methods, and structured information processing patterns as its research objects and belongs to the scope of higher-level human cognitive mechanism research. The development and application of granular computing must be more standardized and unified. The granular computing standardization strategy is the most direct means to promote the regularization of granular computing. In this paper, we first sort out the main applications of granular computing in standards. According to the characteristics of granular computing, a framework of its standard system is proposed to provide a reference for the subsequent research of granular computing standards. The next direction of the granular computing standards strategy is discussed, and solutions are given.

Optimal evaluation of re-opening policies for COVID-19 through the use of metaheuristic schemes

A new contagious disease or unidentified COVID-19 variants could provoke a new collapse in the global economy. Under such conditions, companies, factories, and organizations must adopt reopening policies that allow their operations to reduce economic effects. Effective reopening policies should be designed using mathematical models that emulate infection chains through individual interactions. In contrast to other modeling approaches, agent-based schemes represent a computational paradigm used to characterize the person-to-person interactions of individuals inside a system, providing accurate simulation results. To evaluate the optimal conditions for a reopening policy, authorities and decision-makers need to conduct an extensive number of simulations manually, with a high possibility of losing information and important details. For this reason, the integration of optimization and simulation of reopening policies could automatically find the realistic scenario under which the lowest risk of infection was attained. In this paper, the metaheuristic technique of the Whale Optimization Algorithm is used to find the solution with the minimal transmission risk produced by an agent-based model that emulates a hypothetical re-opening context. Our scheme finds the optimal results of different generical activation scenarios. The experimental results indicate that our approach delivers practical knowledge and essential estimations for identifying optimal re-opening strategies with the lowest transmission risk.

Computational Intelligence and Soft Computing Paradigm for Cheating Detection in Online Examinations

Covid-19 has been a life-changer in the sphere of online education. With complete lockdown in various countries, there has been a tumultuous increase in the need for providing online education, and hence, it has become mandatory for examiners to ensure that a fair methodology is followed for evaluation, and academic integrity is met. A plethora of literature is available related to methods to mitigate cheating during online examinations. A systematic literature review (SLR) has been followed in our article which aims at introducing the research gap in terms of the usage of soft computing techniques to combat cheating during online examinations. We have also presented state-of-the-art methods followed, which are capable of mitigating online cheating, namely, face recognition, face expression recognition, head posture analysis, eye gaze tracking, network data traffic analysis, and detection of IP spoofing. A discussion on improvement of existing online cheating detection systems has also been presented.

BSEIFFS: Blockchain-secured edge-intelligent forest fire surveillance

This work aims to address two major issues in forest fire management: accurate real-time forecast and end-to-end secured data delivery between validated entities. To do so, we introduce BSEIFFS, a secured and intelligent forest fire prediction architecture realized through the convergence of Blockchain technology, mist computing, edge computing and machine learning paradigms. The proposed architecture renders Blockchain-backed security and Support Vector Regression based wildfire prediction at the edge by offloading the execution overheads to containerized cloud applications. Two algorithms namely Uncorrelated Data Transmission (UDT) algorithm and Blockchain based Audit Trail validation (BAT) algorithm are proposed to reduce number of repetitive packet transmissions and rendering a lightweight Blockchain based identity validation scheme at the edge respectively. The prediction model is trained using real-world meteorological data and tested on a steel manufacturing facility where controlled combustion and quenching processes closely resemble fire and rain scenarios. Apart from the Google Cloud Platform, tools such as IBM NodeRed, Siemens IBA PDA softwares are used to implement the BSEIFFS architecture. Extensive experimentation and comparative evaluation are performed with relevant baseline approaches. BSEIFFS achieves a prediction accuracy of 98.61%, ROC AUC score of 0.9668 with 0.1127 as false positive rate. The BSEIFFS SVR predictor with rbf kernel (MAE = 1.840, RMSE = 5.728) performs better than similar baseline approaches. The proposed UDT algorithm causes 82.02% reduction in packet transmissions through the network. BSEIFFS also achieves atleast 44.84% lesser energy consumption, 11.91% higher throughput and 71.91% lesser roundtrip time in comparison with baseline approaches. BSEIFFS can massively extend the benefits of the imminent B5G/6G technologies to perform fine-grained and faster computations.

Batch normalization-free weight-binarized SNN based on hardware-saving IF neuron

Neuromorphic computing realizes low-latency and low-power computing by emulating the neural structure and operation of the human brain, and is considered a key research area for third-generation artificial intelligence. However, current neuromorphic computing faces the problems of huge synaptic memory consumption and complex neuron calculations. This paper proposes a batch normalization (BN)-free weight-binarized SNN based on hardware-saving IF neurons to reduce storage requirements and improve the computational efficiency of neuromorphic computing. Hardware-friendly backpropagation through time (BPTT)-based algorithm and SG function are proposed to calculate the gradients of the “integrate” process and “fire” process of IF neuron, respectively. Weight binarization is carried out during training to reduce storage requirements, and spatio-temporal batch normalization (BN) operations are introduced to ensure high performance. During inference, a simple adaptive-threshold IF neuron model is proposed to achieve the effect equivalent to the computationally expensive spatio-temporal BN operation without any performance loss. The proposed BN-free binarized SNNs based on hardware-saving IF neuron achieves competitive accuracies of 99.36%, 94.79%, 90.39%, and 67.10% on the N-MNIST, DvsGesture, N-TIDIGITS18, and DVS-CIFAR10 datasets, respectively, which are comparable to full-precision SNNs, but the weight sizes are significantly reduced by ∼ 97%. Furthermore, robustness experiments show that the binary SNN is more robust to weight noise than the full-precision SNN. This paper presents an efficient algorithm-hardware co-design paradigm for hardware-friendly and high-performance neuromorphic computing.