Computational Paradigm Research Articles

Multiobjective Optimization for Joint Task Offloading, Power Assignment, and Resource Allocation in Mobile Edge Computing

Mobile edge computing (MEC) is an emerging computational paradigm for providing storage and computing capabilities in network edge, to improve the experience of users, to shorten the delay, and to reduce the energy consumption of mobile devices. In this article, we consider a multiuser and multiserver scenario, where each user has an application composed of multiple independent tasks that need to be executed, and each MEC server is equipped on a base station (BS) for assisting mobile users to execute computation-intensive and time-sensitive tasks. Multiobjective optimization for joint task offloading, power assignment, and resource allocation is studied to maximize the offloading gains of users. A multivariable and multiobjective optimization problem with three objectives is constructed. An efficient multiobjective evolutionary algorithm is developed to solve the problems of minimizing the response time, minimizing the energy consumption, and minimizing the cost. Simulation results verify the effectiveness of our algorithm, and show the method significantly improves the user’s offloading benefits. According to the author’s knowledge, this is the first paper on the exploration of multiobjective optimization of multiuser with multiple tasks and multiserver MEC system, in which the worst user offloading revenue is regarded as the optimization objectives.

Fractional order Lorenz based physics informed SARFIMA-NARX model to monitor and mitigate megacities air pollution

Air Pollution is an emerging disaster and considered one of the biggest challenges of the world to effectively control, mitigate and forecast due to abrupt variability, stochastic, and chaotic pattern of particulate matter (PM) in terms of time and space of the pollutants. Composition of ambient PM not only causes serious damage to public health but also emerging as a global hazard particularly for urban environment with negative impact on human health including morbidity. Mortality and ultimately towards unstable economy. In this study, hourly short-term trends of PM2.5 and air quality index (AQI) of Lahore city of Pakistan is monitored and mitigated by the design of fractional order Lorenz based physics informed hybrid computing paradigm SARFIMA-NARX for forecasting hourly pattern of next two days. The complex dynamics of earth system and its weather forecast are characterized by combination of biological, physical, and chemical processes governed by the different laws of science that provides additional information for the climate variation in terms of physics inform intelligence. The performance index based on statistical indicator of RMSE confirmed the high accuracy and efficiency of designed model to predict the pattern. The early predictions based on computational intelligence paradigm may serve as a surveillance system to reduce the air pollution through cost-effectiveness planning by environmental monitoring agencies.

In-Sensor Computing: Materials, Devices, and Integration Technologies.

The number of sensor nodes in the Internet of Things is growing rapidly, leading to a large volume of data generated at sensory terminals. Frequent data transfer between the sensors and computing units causes severe limitations on the system performance in terms of energy efficiency, speed, and security. To efficiently process a substantial amount of sensory data, a novel computation paradigm that can integrate computing functions into sensor networks should be developed. The in-sensor computing paradigm reduces data transfer and also decreases the high computing complexity by processing data locally. Here, the hardware implementation of the in-sensor computing paradigm at the device and array levels is discussed. The physical mechanisms that lead to unique sensory response characteristics and their corresponding computing functions are illustrated. In particular, bioinspired device characteristics enable the implementation of the functionalities of neuromorphic computation. The integration technology is also discussed and the perspective on the future development of in-sensor computing is provided.

Multi-Constrained and Edge-Enabled Selection of UAV Participants in Federated Learning Process

Unmanned aerial vehicles (UAVs) have gained increasing attention in boosting the performance of conventional networks due to their small size, high efficiency, low cost, and autonomously nature. The amalgamation of UAVs with both distributed/collaborative Deep Learning (DL) algorithms, such as Federated Learning (FL), and Blockchain technology have ushered in a new paradigm of Secure Multi-Access Edge Computing (S-MEC). Indeed, FL enables UAV devices to leverage their sensed data to build local DL models. The latter are then sent to a central node, e.g., S-MEC node, for aggregation, in order to generate a global DL model. Therefore, FL enables UAV devices to collaborate during several FL rounds in generating a learning model, while avoiding to share their local data, and thus ensuring UAVs’ privacy. However, UAV devices are usually limited in terms of resources such as battery, memory, and CPU. Some of the UAV devices may not be able to build a local learning models due to their resources capacity. Hence, there is a great need to select the adequate UAVs at each FL round, that are able to build a local DL model based on their resource capacities. In this paper, we design a novel and S-MEC-enabled framework that optimizes the selection of UAV participants at each FL training round, named FedSel. FedSel considers the available UAVs along with their resource capacities, in terms of energy, CPU, and memory, to determine which UAV device is able to participant in the FL process. Thus, we formulate the UAV selection problem as an Integer Linear Program, which considers the aforementioned constraints. We also prove that this problem is NP-hard, and suggest a Tabu Search (TS) metaheuristic-based approach to resolve it. Moreover, FedSel is built on top of blockchain technology, in order to ensure a secure selection of UAV participants, and hence building reliable FL-based models. Simulation results validate the efficiency of our FedSel scheme in balancing computational load among available UAVs and optimizing the UAV selection process.

Genetic-based optimization in fog computing: Current trends and research opportunities

Fog computing is a new computational paradigm that emerged from the need to reduce network usage and latency in the Internet of Things (IoT). Fog can be considered as a continuum between the cloud layer and IoT users that allows the execution of applications or storage/processing of data in network infrastructure devices. The heterogeneity and wider distribution of fog devices are the key differences between cloud and fog infrastructure. Genetic-based optimization is commonly used in distributed systems; however, the differentiating features of fog computing require new designs, studies, and experimentation. The growing research in the field of genetic-based fog resource optimization and the lack of previous analysis in this field have encouraged us to present a comprehensive, exhaustive, and systematic review of the most recent research works. Resource optimization techniques in fog were examined and analyzed, with special emphasis on genetic-based solutions and their characteristics and design alternatives. We defined a taxonomy of the optimization scope in fog infrastructures and used this optimization taxonomy to classify the 70 papers in this survey. Subsequently, the papers were assessed in terms of genetic optimization design. Finally, the benefits and limitations of each surveyed work are outlined in this paper. Based on these previous analyses of the relevant literature, future research directions were identified. We concluded that more research efforts are needed to address the current challenges in data management, workflow scheduling, and service placement. Additionally, there is still room for improved designs and deployments of parallel and hybrid genetic algorithms that leverage, and adapt to, the heterogeneity and distributed features of fog domains.

Solving the Federated Edge Learning Participation Dilemma: A Truthful and Correlated Perspective

An emerging computational paradigm, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">federated edge learning</i> (FEL), enables intelligent computing at the network edge with the feature of preserving data privacy for edge devices. Given their constrained resources, it becomes a great challenge to achieve high execution performance for FEL. Most of the state-of-the-arts concentrate on enhancing FEL from the perspective of system operation procedures, taking few precautions during the composition step of the FEL system. Though a few recent studies recognize the importance of FEL formation and propose server-centric device selection schemes, the impact of data sizes is largely overlooked. In this paper, we take advantage of game theory to depict the decision dilemma among edge devices regarding <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">whether to participate in FEL or not</i> given their heterogeneous sizes of local datasets. For realizing both the individual and global optimization, the server is employed to solve the participation dilemma, which requires accurate information collection for devices’ local datasets. Hence, we utilize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mechanism design</i> to enable truthful information solicitation. With the help of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">correlated equilibrium</i> , we derive a decision making strategy for devices from the global perspective, which can achieve the long-term stability and efficacy of FEL. For scalability consideration, we optimize the computational complexity of the basic solution to the polynomial level. Lastly, extensive experiments based on both real and synthetic data are conducted to evaluate our proposed mechanisms, with experimental results demonstrating the performance advantages.

Editorial Special Issue on Circuits and Systems for Emerging Computing Paradigms

AS Dennard’s law is coming to an end, on-chip power consumption reduction and throughput improvement due to technology scaling pose serious challenges; workloads of today’s applications (such as AI, big data, and the IoT) have also reached extremely high levels of complex computation. Power dissipation has become the fundamental barrier to scale computing performance across all technology platforms. Computation at nanoscales requires innovative approaches.

Efficient emotion recognition using hyperdimensional computing with combinatorial channel encoding and cellular automata

In this paper, a hardware-optimized approach to emotion recognition based on the efficient brain-inspired hyperdimensional computing (HDC) paradigm is proposed. Emotion recognition provides valuable information for human–computer interactions; however, the large number of input channels (> 200) and modalities (> 3 ) involved in emotion recognition are significantly expensive from a memory perspective. To address this, methods for memory reduction and optimization are proposed, including a novel approach that takes advantage of the combinatorial nature of the encoding process, and an elementary cellular automaton. HDC with early sensor fusion is implemented alongside the proposed techniques achieving two-class multi-modal classification accuracies of > 76% for valence and > 73% for arousal on the multi-modal AMIGOS and DEAP data sets, almost always better than state of the art. The required vector storage is seamlessly reduced by 98% and the frequency of vector requests by at least 1/5. The results demonstrate the potential of efficient hyperdimensional computing for low-power, multi-channeled emotion recognition tasks.

A Systematic Survey of Simulation Tools for Cloud and Mobile Cloud Computing Paradigm

Cloud computing (CC) provides fast and on-demand access to virtually unlimited computing resources. Mobile cloud computing (MCC) is a new technology that brings CC and mobile devices together. MCC allows mobile devices access to cloud services. Describe the basic architectures for various MCC applications, as well as a brief comparison of cloud and MCC. As CC becomes more common, researchers in this field may need to conduct real-world experiments in their studies. Configuring and running these experiments in real-world cloud environments is costly. As a consequence, modeling and simulation approaches are suitable solutions that can be used to simulate cloud computing environments. Several simulation tools tailored to CC have been developed. The most powerful simulation methods in this field of study are described in this article. Among them are CloudSim, CloudSim Plus, CloudAnalyst, iFogSim, and CloudReports.

The Striatus bridge

The paper describes the physical realisation of a 3D-concrete-printed, mortar-free, unreinforced masonry arched footbridge, designed for disassembly and reuse. The paper also details the novel integrated design, engineering and fabrication framework and the manufacturing and assembly processes used for the project. The research, motivated by the rapid growth in large-scale 3D concrete printing (3DCP), addresses the current lack of both design tools and integrated design-to-production solutions. It is guided by the insight regarding the applicability of design and analysis methods used in unreinforced masonry to large-scale, layered 3D printing with compression dominant materials such as concrete. Thus, the underlying computational framework and integrated design environment further extends and adapts advances in the computational design and analysis of unreinforced masonry structures to 3DCP masonry blocks. Adopting an unreinforced masonry paradigm for the design of 3DCP structures can make it possible to (i) reduce the amount of concrete used by allowing precise placement of concrete only where needed along the compressive flow of forces, (ii) reduce the amount of steel needed by reducing tensile and flexural strength requirements through a compression-appropriate design of both the global, shape and the block discretisation, and (iii) reuse components, repair the structures and recycle materials more easily. This paper builds on the relevance of the computational masonry paradigm to both delivering the ecological promises of 3DCP and to the development of a 3DCP-specific, design-to-production toolkit.

Probabilistic Circuit Implementation Based on P-Bits Using the Intrinsic Random Property of RRAM and P-Bit Multiplexing Strategy.

Probabilistic computing is an emerging computational paradigm that uses probabilistic circuits to efficiently solve optimization problems such as invertible logic, where traditional digital computations are difficult to solve. This paper proposes a true random number generator (TRNG) based on resistive random-access memory (RRAM), which is combined with an activation function implemented by a piecewise linear function to form a standard p-bit cell, one of the most important parts of a p-circuit. A p-bit multiplexing strategy is also applied to reduce the number of p-bits and improve resource utilization. To verify the superiority of the proposed probabilistic circuit, we implement the invertible p-circuit on a field-programmable gate array (FPGA), including AND gates, full adders, multi-bit adders, and multipliers. The results of the FPGA implementation show that our approach can significantly save the consumption of hardware resources.

Aerial Computing: A New Computing Paradigm, Applications, and Challenges

In existing computing systems, such as edge computing and cloud computing, several emerging applications and practical scenarios are mostly unavailable or only partially implemented. To overcome the limitations that restrict such applications, the development of a comprehensive computing paradigm has garnered attention in both academia and industry. However, a gap exists in the literature owing to the scarce research, and a comprehensive computing paradigm is yet to be systematically designed and reviewed. This study introduces a novel concept, called aerial computing, via the amalgamation of aerial radio access networks and edge computing, which attempts to bridge the gap. Specifically, first, we propose a novel comprehensive computing architecture that is composed of low-altitude computing, high-altitude computing, and satellite computing platforms, along with conventional computing systems. We determine that aerial computing offers several desirable attributes: global computing service, better mobility, higher scalability and availability, and simultaneity. Second, we comprehensively discuss key technologies that facilitate aerial computing, including energy refilling, edge computing, network softwarization, frequency spectrum, multi-access techniques, artificial intelligence, and big data. In addition, we discuss vertical domain applications (e.g., smart cities, smart vehicles, smart factories, and smart grids) supported by aerial computing. Finally, we highlight several challenges that need to be addressed and their possible solutions.

Outsourced Data Integrity Checking with Practical Key Update in Edge-Cloud Resilient Networks

The edge-cloud continuum is an advanced paradigm for cloud computing, which brings data storage and compute power closer to users or devices, and as a result, eliminates lag time and saves bandwidth. However, this new paradigm is harsh for secure storage and computation due to the separation of data ownership and control. Data security, especially outsourced data integrity in the edge-cloud resilient network, becomes one of the most fundamental challenges. To check the outsourced data integrity and address the efficient key update issue in this scenario, in this article, we propose a framework of outsourced data integrity checking with a practical key update in the edge-cloud resilient network. We first review the existing outsourced data integrity checking algorithms and then put forward a potential solution to achieving outsourced data integrity checking with a practical key update, which is composed of three phases, namely key request and update, local data upload, and outsourced data integrity auditing. We implement a prototype system for our proposal as well, which demonstrates its practicality.

Information Processing Capacity of a Single-Node Reservoir Computer: An Experimental Evaluation.

Physical dynamical systems are able to process information in a nontrivial manner. The machine learning paradigm of reservoir computing (RC) provides a suitable framework for information processing in (analog) dynamical systems. The potential of dynamical systems for RC can be quantitatively characterized by the information processing capacity (IPC) measure. Here, we evaluate the IPC measure of a reservoir computer based on a single-analog nonlinear node coupled with delay. We link the extracted IPC measures to the dynamical regime of the reservoir, reporting an experimentally measured nonlinear memory of up to seventh order. In addition, we find a nonhomogeneous distribution of the linear and nonlinear contributions to the IPC as a function of the system operating conditions. Finally, we unveil the role of noise in the IPC of the analog implementation by performing ad hoc numerical simulations. In this manner, we identify the so-called edge of stability as being the most promising operating condition of the experimental implementation for RC purposes in terms of computational power and noise robustness. Similarly, a strong input drive is shown to have beneficial properties, albeit with a reduced memory depth.

DFT calculations and machine learning approach to predict catalytic properties of nanoscale electrocatalysts in solution for clean fuel generation

<h2>Abstract</h2> A nanocatalyst is at the central position as a promoter of various chemical. Innovative design of highly functional catalyst materials has been, however, delayed. In molecular level computational electrochemistry new research paradigm has been established, which substantially incorporates IT-based artificial intelligence (AI) technology into machine learning algorism. Using the new computational methodology high-throughput screening of promising nanoparticle candidates has been attempted for various desired applications. Whether the frontier approach is successful or not is significantly controlled by the reliability and accuracy of input database. It is true that substantial amounts of the data are come by previous literatures and often ab-initio calculations with idealized model systems. The conditions in which the data were generated may be so different from the operando circumstances of the target materials. To secure extreme-level integrity of the database the in-situ measurement of nanoparticle structures should be carried out, from which the reliable correlation of the structure-performance-design principle can be identified. Using first-principles calculations we studied nanoparticles with adsorbate ligands in liquid solution to establish three-dimensional (3D) structure and property database, which are, then, analyzed through AI-based neural-network approach with high speed and accuracy. The information includes sizes, lattice distortions, and defects with picometer resolution under non-vacuum conditions. The computational outcomes are rigorously validated from the 3D liquid-cell electron microscopy. The approach is indeed ‘knowledge-based' AI, which can be expected to make groundbreaking ways toward the quantum nanoarchitecture for hybrid interface materials.

Energy consumption in cluster communication using mcsbch approach in WSN

Internet of Things (IoT) proposed a new digital computing paradigm enabling interaction between devices and machines. It deliberately creates connectivity between the internet, electronics, and other forms of hardware. A novel modern cluster supervisor-based cluster head selection algorithm (MCSBCH) is proposed for the Wireless Sensor Network (WSN). The proposed cluster supervisor mechanism is responsible for controlling and monitoring the network effectively. In this approach, the cluster supervisor is the heart of the network, and the whole mechanism work under its supervision. The Cluster supervisor (CS) monitors the node’s energy level and allocates CH (cluster head) node. Each node’s energy level is considered for electing the CH. Obviously when the cluster head energy level gets drained, then it allocates the next higher energy node as cluster head. The assigned CH is the next node with the highest energy level known as the backup node. This cluster supervisor (CS) is supported by the cluster head (CH) and other backup nodes in the network. The proposed MCSBCH is boosted with an enhanced clustering routing protocol. An experimental result is based on the aspect of a lifetime, energy consumption, and throughput, to test the proposed mechanism performance.

Cloud, Fog and Edge Computing: Security and Privacy Concerns

Abstract: The use of different cloud computing technologies increased with the rise of 5G technology and IoT. Cloud computing also enabled intense data processing and warehousing. Data storage in the cloud comes with several issues and security concerns. Moreover, when the volume of data created by every device increases, the conventional cloud computing architecture fails to address concerns such as excessive latency, bandwidth limits, and resource constraints. As a result, new computational paradigms such as edge and fog computing have been proposed to address the former's issues. Both of these paradigms offer computation and memory storage alternatives. Regardless of their benefits, implementing these technologies introduces many new privacy and security issues. This work presents a list of security and privacy concerns, as well as dangers, that exist in all three computing paradigms: cloud, fog, and edge computing. Keywords: Fog Computing, Cloud Computing, Edge Computing Security and privacy issues, Attacks

Метод оценки ресурсов в процессе функционально-потокового высокоуровневого синтеза СБИС

The problems of high-level synthesis of very large integrated circuits (VLSI) are considered. The review of the subject area shows that the use of the imperative model and corresponding programming languages does not provide efficient parallelization of algorithms and the possibility of efficient parallelization of programs. This leads to the impossibility of providing the required technical characteristics. This is due to the specifics of VLSI, which is essentially a scheme of parallel processing of information flows. An original VLSI synthesis method is presented. The method based on the functional-streaming paradigm of parallel computing. This method allows ensuring architectural independence and maximum coverage of implementation options. The route map of VLSI functional-flow method is outlined. The problem of estimating the requested hardware resources and clock frequency, necessary for solving, is formulated. This problem must be solved at the early stages of design. A method for estimating resources in the process of functional-flow synthesis is proposed. The method is based on the use of an additional meta-layer (HDL-graph). Taking into account the polymorphism of the solution of the resource estimation problem, it is proposed to use machine learning technologies in the new method. It is shown that the application of the indicated method in the synthesis process makes it possible to provide the most accurate assessment of resources. This is possible, because the HDL graph is a data flow graph typed and structured in accordance with the functional-flow model of parallel computing. Machine learning allows to most effectively obtain a solution to the problem of optimal selection of the required resources. The classes of resources for which an assessment is required are highlighted. Selected parameters for building a resource assessment model. The software implementation and comparison of the proposed resource estimation method based on linear regression models, neural networks and gradient boosting with known approaches is performed. It is shown that when using the technology of functional-flow synthesis when applying the proposed method for estimating the required resources and performance, an increase in the accuracy of the estimate at the high-level stage.

Blockchain and K-Means Algorithm for Edge AI Computing.

The current development of blockchain, technically speaking, still faces many key problems such as efficiency and scalability issues, and any distributed system faces the problem of how to balance consistency, availability, and fault tolerance need to be solved urgently. The advantage of blockchain is decentralization, and the most important thing in a decentralized system is how to make nodes reach a consensus quickly. This research mainly discusses the blockchain and K-means algorithm for edge AI computing. The natural pan-central distributed trustworthiness of blockchain provides new ideas for designing the framework and paradigm of edge AI computing. In edge AI computing, multiple devices running AI algorithms are scattered across the edge network. When it comes to decentralized management, blockchain is the underlying technology of the Bitcoin system. Due to its characteristics of immutability, traceability, and consensus mechanism of transaction data storage, it has recently received extensive attention. Blockchain technology is essentially a public ledger. This is done by recording data related to trust management to this ledger. To collaboratively complete artificial intelligence computing tasks or jointly make intelligent group decisions, frequent communication is required between these devices. By integrating idle computing resources in an area, a distributed edge computing platform is formed. Users obtain benefits by sharing their computing resources, and nodes in need complete computing tasks through the shared platform. In view of the identity security problems faced in the sharing process, this article introduces blockchain technology to realize the trust between users. All participants must register a secure identity in the blockchain network and conduct transactions in this security system. A K-means algorithm suitable for edge environments is proposed to identify different degradation stages of equipment operation reflected by multiple types of data. Based on the prediction of the fault state for a single type of data, the algorithm uses the historical data of multiple types of data together with the prediction data to predict the fault stage. During the research process, the average optimization energy consumption of K-means algorithm is 14.6% lower than that of GA. On the basis of designing a resource allocation scheme based on blockchain, the problem of how the participants can realize reliable resource use according to the recorded data on the chain is studied. The article implements the verification of the legality of the use of blockchain resources. In addition, a control node is introduced to master the global real-time information of the network to provide data support for the user's choice.

Linking Exploits from the Dark Web to Known Vulnerabilities for Proactive Cyber Threat Intelligence: An Attention-Based Deep Structured Semantic Model

Black hat hackers use malicious exploits to circumvent security controls and take advantage of system vulnerabilities worldwide, costing the global economy over $450 billion annually. While many organizations are increasingly turning to cyber threat intelligence (CTI) to help prioritize their vulnerabilities, extant CTI processes are often criticized as being reactive to known exploits. One promising data source that can help develop proactive CTI is the vast and ever-evolving Dark Web. In this study, we adopted the computational design science paradigm to design a novel deep learning (DL)- based exploit-vulnerability attention deep structured semantic model (EVA-DSSM) that includes bidirectional processing and attention mechanisms to automatically link exploits from the Dark Web to vulnerabilities. We also devised a novel device vulnerability severity metric (DVSM) that incorporates the exploit post date and vulnerability severity to help cybersecurity professionals with their device prioritization and risk management efforts. We rigorously evaluated the EVA-DSSM against state-of-theart non-DL and DL-based methods for short text matching on 52,590 exploit-vulnerability linkages across four testbeds: web application, remote, local, and denial of service. Results of these evaluations indicate that the proposed EVA-DSSM achieves precision at 1 scores 20% - 41% higher than non-DL approaches and 4% - 10% higher than DL-based approaches. We demonstrated the EVA-DSSM’s and DVSM’s practical utility with two CTI case studies: openly accessible systems in the top eight U.S. hospitals and over 20,000 Supervisory Control and Data Acquisition (SCADA) systems worldwide. A complementary user evaluation of the case study results indicated that 45 cybersecurity professionals found the EVADSSM and DVSM results more useful for exploit-vulnerability linking and risk prioritization activities than those produced by prevailing approaches. Given the rising cost of cyberattacks, the EVA-DSSM and DVSM have important implications for analysts in security operations centers, incident response teams, and cybersecurity vendors.