Collaborative Computing System Research Articles

Modified genetic algorithm and fine-tuned long short-term memory network for intrusion detection in the internet of things networks with edge capabilities

The emergence of smart cities is an example of how new technologies, such as the Internet of Things (IoT), have facilitated the creation of extensive interconnected and intelligent ecosystems. The widespread deployment of IoT devices has enabled the provision of constant environmental feedback, thereby facilitating the automated adaptation of associated systems. This has brought about a fundamental transformation in the way contemporary society functions. The security of emerging technologies such as IoT has become a significant challenge due to the added complexities, misconfigurations, and conflicts between modern and legacy systems. This challenge has a notable impact on the reliability and accessibility of existing infrastructure. Edge computing (EC) is a collaborative computing system that brings data processing and analysis closer to the edge of the network, where the data is generated, rather than in a centralized cloud environment. The utilization of the IoT has become more prevalent in both everyday life and the manufacturing sector, with a particular emphasis on critical infrastructure. The IoT is presently being utilized across diverse domains, including but not limited to industrial, agricultural, healthcare, and logistical sectors. The security of IoT networks has implications for the safety of individuals, the security of the nation, and economic development. Notwithstanding, conventional intrusion detection techniques that rely on centralized cloud-based systems that have been suggested in previous studies for IoT network security are insufficient to meet the requirements for data confidentiality, network capacity, and prompt responsiveness. In addition, the integration of IoT applications into smart devices has been shown to augment their functionalities. However, it is important to note that this integration also brings about potential security vulnerabilities. Furthermore, a significant number of contemporary IoT devices exhibit restricted security capabilities, rendering them vulnerable to intricate attacks and impeding the extensive integration of IoT technologies. Also, a lot of IoT network devices have been put in place that don't have hardware security measures. This means that traditional intrusion detection systems (IDS) aren't enough to protect the IoT network ecosystem. To address these issues, this research suggests the IoT-Defender framework, which combines a Modified Genetic Algorithm (MGA) model with a deep Long Short-Term Memory (LSTM) network to find cyberattacks in IoT networks. This research represents a pioneering attempt to employ the MGA for feature selection and the GA for fine-tuning the LSTM parameters within an EC framework. The parameters of the LSTM model were fine-tuned through the manipulation of the number of hidden layers, utilizing the GA fitness function. The customization of the MGA aimed to enhance its performance in selecting relevant features, optimizing the use of limited resources on IoT devices and edge nodes. The fine-tuning process involved optimizing hyperparameters, architecture, and training strategies to maximize the LSTM network's effectiveness in learning and detecting patterns in IoT network traffic. The synergy between the MGA and LSTM aimed at creating a comprehensive and efficient IDS. The feature selection by the MGA contributes to improving the LSTM’s performance by providing it with more relevant and discriminating features. In order to solve the issue of class imbalance, we utilize the focal loss function, which provides greater weights to minority classes, hence improving the model’s capacity to learn from those particular classes. The performance of the IoT-Defender model was assessed on the BoT-IoT, UNSW-NB15, and N-BaIoT datasets utilizing a Raspberry Pi IoT device. The results of our study show that the IoT-Defender model works better than other methods. This is shown by its accuracy score of 99.41%, detection rate of 99.78%, precision score of 98.50%, false alarm rate of 2.56%, mean intersection over union (mIoU) of 0.68, and training time of 81.3 seconds on BoT-IoT. The proposed IoT model is designed to be lightweight and can be installed on edge servers to detect cyber-attacks in real-time, specifically in the context of IoT security.

Joint Task Offloading and Resource Allocation for Fog-Based Intelligent Transportation Systems: A UAV-Enabled Multi-Hop Collaboration Paradigm

Unmanned aerial vehicles (UAVs) have been widely used in Intelligent Transportation Systems (ITS) due to their rapid deployment and high mobility, which are considered as a promising solution to expand the scope of communication, especially in inaccessible areas. However, there is a lack of a universal and extensible multi-hop collaboration model in the existing research on UAV-involved ITS. In this paper, we innovatively introduce a novel UAV-enabled multi-hop collaborative fog computing (FC) system model, in which several moving UAVs with unpredictable locations provide effective and efficient communication and computation services for ground user equipments (UEs). With this model, we mathematically formulate a joint user association, UAV association, task offloading, transmission power, computation resource allocation, and UAV location optimization problem, which is a mixed integer nonlinear programming (MINLP) problem and challenging to deal with. To solve the non-convex problem, we propose a novel multi-hop collaborative algorithm to derive the optimal task offloading and resource allocation decisions for each UAV. Simulation results demonstrate the superiority of the UAV-enabled multi-hop collaborative FC system and validate the effectiveness of the proposed scheme.

An UAV and EV based mobile edge computing system for total delay minimization

In this paper, we studied a collaborative mobile edge computing(MEC) system based on unmanned aerial vehicle(UAV) and electric vehicle(EV), in which the EV moves to the target area carrying the UAV to provide computing and offloading services to the user equipment(UE). We aimed at minimizing all the UE tasks delay by optimizing the task offloading ratio, the UAV hovering position, and the computing resource allocation of EV and UAV, respectively. The problem was formulated as a Nonlinear Programming(NLP) problem, and we decomposed it into EV related subproblem and UAV related subproblem by the Block-Coordinate Descent(BCD) method. For EV related subproblem, we obtained the optimal offloading ratio by making the computing time on the UAV equal to its offloading time, and proved the method is feasible. For the UAV related subproblem, we introduced the non-orthogonal multiple access(NOMA) and successive interference cancellation(SIC) techniques to improve the communication efficiency, and we obtained the optimal hovering position by using the successive convex approximation(SCA) technique twice to transfer this subproblem into a convex problem. Finally, an overall iterative algorithm was proposed. To verify the effectiveness of our algorithm, we compared our scheme with ‘PSO’, ‘GT’, ‘MC’ and benchmark algorithm.

Research on Cloud-Edge-End Collaborative Computing Offloading Strategy in the Internet of Vehicles Based on the M-TSA Algorithm

In the Internet of Vehicles scenario, the in-vehicle terminal cannot meet the requirements of computing tasks in terms of delay and energy consumption; the introduction of cloud computing and MEC is an effective way to solve the above problem. The in-vehicle terminal requires a high task processing delay, and due to the high delay of cloud computing to upload computing tasks to the cloud, the MEC server has limited computing resources, which will increase the task processing delay when there are more tasks. To solve the above problems, a vehicle computing network based on cloud-edge-end collaborative computing is proposed, in which cloud servers, edge servers, service vehicles, and task vehicles themselves can provide computing services. A model of the cloud-edge-end collaborative computing system for the Internet of Vehicles is constructed, and a computational offloading strategy problem is given. Then, a computational offloading strategy based on the M-TSA algorithm and combined with task prioritization and computational offloading node prediction is proposed. Finally, comparative experiments are conducted under task instances simulating real road vehicle conditions to demonstrate the superiority of our network, where our offloading strategy significantly improves the utility of task offloading and reduces offloading delay and energy consumption.

Maximization of Value of Service for Mobile Collaborative Computing Through Situation-Aware Task Offloading

Mobile collaborative computing (MCC) is an emerging platform for effectively improving the quality of mobile service by exploiting the idling computational resources in distributed mobile devices (MDs) through peer-to-peer task offloading. Recently, diverse MCC applications have been developed to provide multiple functional benefits and individualized value to users. In this paper, we propose to use a new concept of value of service (VoS) to represent the total value of all tasks and devices with respect to their performance including latency and energy consumption. To improve service provisioning under fast-varying conditions, a situation-aware offloading scheme is proposed to maximize VoS by opportunistically leveraging the changing resource availability conditions. Specifically, we consider a collaborative computing system where a user can offload input data of computation to other available MDs. VoS maximization for two popular offloading scenarios, i.e., binary and partial offloading, are formulated separately. Decision making of binary offloading is an NP-hard problem and solved by a novel heuristic algorithm which achieves suboptimal solution in polynomial time. Partial offloading is formulated as a non-convex problem involving task partition decision. By exploiting the unique characteristics of the problem, we propose an adapted barrier method (ABM) which achieves significant improvements in convergence efficiency.

Multi-resource fair allocation with bandwidth requirement compression in the cloud–edge system

For the multi-resource fair allocation problem in cloud–edge collaborative computing systems, we propose a mechanism called dominant resource fairness in the cloud–edge collaborative computing system (DRF-CE). DRF-CE enables users to deploy tasks to cloud and edge servers and considers the particularity of cloud server bandwidth resources. By introducing bandwidth demand compression, DRF-CE allows users to have two different bandwidth resource demands. DRF-CE satisfies envy-freeness, Pareto efficiency, strategyproofness and the weak sharing incentive property. To implement DRF-CE, we design an algorithm and evaluate its performance via simulations driven by a concrete example and Google cluster traces. Simulation results prove that DRF-CE is superior to traditional approaches in terms of users’ dominant share, resource utilization and the number of tasks performed by each user. Furthermore, the simulation results show that considering bandwidth demand compression can improve user efficiency by approximately 40% and the central processing unit (CPU) utilization by more than 25%.

Joint Offloading and Resource Allocation for Multi-User Multi-Edge Collaborative Computing System

In this paper, we consider a computation offloading problem in a three-tier network consisting of multiple mobile users (MUs), multiple edge clouds, and a central cloud. On this basis, we formulate a system Energy Time Cost ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ETC$</tex-math></inline-formula> ) minimization problem by jointly optimizing the resource allocation, mobile-edge matching decision, and offloading decision. Due to the difficulty of directly solving the formulated problem, we decompose it and propose two offloading algorithms, namely Gale-Shapley based Minimum/Sequential Offloading Algorithm (GS-MOA/GS-SOA), to optimize offloading decisions by minimizing the system <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ETC$</tex-math></inline-formula> , in which the mobile-edge matching strategies based on the proposed Gale-Shapley based Mobile-Edge mAtching aLgorithm (GS-MEAL) and resource allocations are optimized iteratively. The simulations show the effectiveness of our proposed algorithm.

Assessing Open-Ended Human-Computer Collaboration Systems: Applying a Hallmarks Approach.

There is a growing desire to create computer systems that can collaborate with humans on complex, open-ended activities. These activities typically have no set completion criteria and frequently involve multimodal communication, extensive world knowledge, creativity, and building structures or compositions through multiple steps. Because these systems differ from question and answer (Q&A) systems, chatbots, and simple task-oriented assistants, new methods for evaluating such collaborative computer systems are needed. Here, we present a set of criteria for evaluating these systems, called Hallmarks of Human-Machine Collaboration. The Hallmarks build on the success of heuristic evaluation used by the user interface community and past evaluation techniques used in the spoken language and chatbot communities. They consist of observable characteristics indicative of successful collaborative communication, grouped into eight high-level properties: robustness; habitability; mutual contribution of meaningful content; context-awareness; consistent human engagement; provision of rationale; use of elementary concepts to teach and learn new concepts; and successful collaboration. We present examples of how we used these Hallmarks in the DARPA Communicating with Computers (CwC) program to evaluate diverse activities, including story and music generation, interactive building with blocks, and exploration of molecular mechanisms in cancer. We used the Hallmarks as guides for developers and as diagnostics, assessing systems with the Hallmarks to identify strengths and opportunities for improvement using logs from user studies, surveying the human partner, third-party review of creative products, and direct tests. Informal feedback from CwC technology developers indicates that the use of the Hallmarks for program evaluation helped guide development. The Hallmarks also made it possible to identify areas of progress and major gaps in developing systems where the machine is an equal, creative partner.

Collaborative Edge Computing With FPGA-Based CNN Accelerators for Energy-Efficient and Time-Aware Face Tracking System

Convolutional neural networks (CNNs) have become the critical technology to realize face detection and face recognition in the face tracking (FT) system. However, traditional CNNs usually have nontrivial computational time and high energy consumption, making them inappropriate to be deployed in the large-scale time-sensitive FT system. To address this challenge, we design an artificial intelligence and Internet of Things (AIoT) empowered edge-cloud collaborative computing (ECCC) system based on the energy-efficient field-programmable gate array (FPGA)-based CNN accelerators for the purpose of realizing a low-latency and low-power FT. First, we present the AIoT-empowered ECCC system architecture, which consists of an intelligent computing subsystem, an Internet-of-Things (IoT) subsystem, an edge-cloud collaborative subsystem, and an application subsystem. In what follows, we investigate the enabling technologies for these subsystems. Thereafter, we develop an FPGA-based hardware accelerator dedicated to the compact MobileNet CNN by using the hardware design techniques, such as systolic array, matrix tiling, fixed-point precision, and parallelism. Furthermore, we integrate the FPGA accelerators with CPUs and GPUs to build a context-aware CPU/GPU/FPGA heterogeneous computing system. Finally, we implement a delay-aware energy-efficient scheduling algorithm dedicated to this heterogeneous system. With the above hardware and software codesign mechanism, the energy cost and execution time of CNNs can be decreased significantly. The real-world experiments on the CPU/GPU/FPGA-based ECCC system proved the effectiveness of the proposed schemes in reducing the latency and improving the power efficiency of the FT system.

Data Collection Method of Large Scale WSNS Mobile Node Based on Compressed Sensing and Intelligent Optimization

Aiming at the defects of large-scale large scale wireless sensor network data processing network traffic and high task latency, a data collection scheme of mobile node based on discrete elastic collision optimization algorithm and adaptive block compression sensing is proposed. Firstly, by analyzing the relationship between the network partitioning and the node deployment, an adaptive block compressed sensing data collection strategy is proposed to realize sensor node based on adaptive network block compressed sensing data collection. Designing mobile node data acquisition path planning strategy and multiple mobile nodes The collaborative computer system adopts the fitness value constraint transformation processing technology and the parallel discrete elastic collision optimization algorithm to achieve the purpose of balancing network node energy consumption and reducing data processing task delay. Finally, the simulation results show that the data collection scheme can effectively realize high-efficiency processing of large-scale sensor network data, and reduce network traffic and network task delay, and better balance network node energy consumption.

Enabling Collaborative Computing Sustainably Through Computational Latency-Based Pricing

Utilizing the idle computing resources from the distributed Internet of Things devices can sustainably increase the computational capacity and thereby effectively alleviate the pressure on resource-constrained devices, which is referred to as collaborative computing. However, extra computing consumption potentially impacts the local computation tasks of collaborative computing devices. Hence, it is essential to design an efficient incentive mechanism for computational resources sharing. Specifically, we consider the collaborative computing system where a user offloads the computation-intensive and latency-sensitive tasks to multiple idle computing devices (ICDs) by a centralized computing sharing platform (CSP). We first propose a computational latency-based pricing mechanism from the perspective of the quality-of-experience performance; then, a game-theoretic computing task allocation approach is developed among the CSP and multiple ICDs to maximize all participants’ profit. The CSP first determines the optimal task partition dynamically upon the tasks’ arrival; then, the ICDs derive the optimal central processing unit-cycle frequency correspondingly. Simulation results demonstrate that the overall computational latency of our proposed mechanism is significantly decreased, and achieves by at least 13.5 percent improvement compared with the existing schemes. Meanwhile, the profit of all participants is maximum in collaborative computing, which is improved by 41.5 and 27.9 percent for the CSP and the ICDs, respectively.

Q-Learning-Based Task Offloading and Resources Optimization for a Collaborative Computing System

Mobile edge computing (MEC) can effectively overcome the shortcomings of high-latency in mobile cloud computing (MCC) by deploying the cloud resources, e.g., storage and computational capability, to the edge. However, the limited computation capability of the MEC restricts the scalability of offloading. Therefore, the basic requirements of the MEC system are to explore effective offloading decisions and resource allocation methods. To address it, we develop a collaborative computing system composed of local computing (mobile device), MEC (edge cloud) and MCC (central cloud). Based on the proposed collaborative computing system, we design a novel Q-learning based computation offloading (QLCOF) policy to achieve the optimal resource allocation and offloading scheme by prescheduling the computation side for each task from a global perspective. Specifically, we first model the offloading decision process as a Markov decision process (MDP) and design a state loss function (STLF) to measure the quality of experience (QoE). After that, we define the cumulation of STLFs as the system loss function (SYLF) and formulate an SYLF minimization problem. Due to the difficulty to directly solve the formulated problem, we decompose it into multiple subproblems and preferentially optimize the transmission power and computation frequency of the edge cloud by the quasi-convex bisection and polynomial analysis method, respectively. Based on the precalculated offline transmission power and edge cloud computation frequency, we develop a Q-learning based offloading (QLOF) scheme to minimize the SYLF by optimizing offloading decisions. Finally, the numeral results show that the proposed QLOF scheme effectively reduces the SYLF under different parameters.

Sensor-cloud data acquisition based on fog computation and adaptive block compressed sensing

The emergence of sensor-cloud system has completely changed the one-to-one service mode of traditional wireless sensor networks, and it greatly expands the application field of wireless sensor networks. As the high delay of large-scale data processing tasks in sensor-cloud, a sensor-cloud data acquisition scheme based on fog computing and adaptive block compressive sensing is proposed. First, the sensor-cloud framework based on fog computing is constructed, and the fog computing layer includes many wireless mobile nodes, which helps to realize the implementation of information transfer management between lower wireless sensor networks layer and upper cloud computing layer. Second, in order to further reduce network traffic and improve data processing efficiency, an adaptive block compressed sensing data acquisition strategy is proposed in the lower wireless sensor networks layer. By dynamically adjusting the size of the network block and building block measurement matrix, the implementation of sensor compressed sensing data acquisition is achieved; in order to further balance the lower wireless sensor networks’ node energy consumption, reduce the time delay of data processing task in fog computing layer, the mobile node data acquisition path planning strategy and multi-mobile nodes collaborative computing system are proposed. Through the introduction of the fitness value constraint transformation processing technique and parallel discrete elastic collision optimization algorithm, the efficient processing of the fog computing layer data is realized. Finally, the simulation results show that the sensor-cloud data acquisition scheme can effectively achieve large-scale sensor data efficient processing. Moreover, compared with cloud computing, the network traffic is reduced by 20% and network task delay is reduced by 12.8%–20.1%.

2013 Lush Science Prize

The annual Lush Science Prize is designed to reward outstanding contributions to 21st Century Toxicology Research. A Background Paper is prepared each year prior to the judging process, in order to provide the judging panel with a brief overview of current developments in the field of Replacement alternatives, particularly those relevant to the concept of toxicity pathways. The Background Paper includes information on some key institutional developments in the area--such as the OECD's Adverse Outcome Pathway Project, the Hamner Institute's work, and the Human Toxome Project, and on the phenomenon of collaborative computer systems relevant to the field. From the literature review that was also performed as part of the background research, the two papers receiving the highest score were recommended for consideration by the judges for the 2013 Science Prize.

Preface

The HCOMP conference was created as a venue for exchanging ideas and developments on principles, experiments, and implementations of systems that rely on programmatic access to human intellect to perform some aspect of computation, or where human perception, knowledge, reasoning, or coordinated physical activities contributes to the operation of larger systems and applications. Topics covered in the evolving discipline of human computation and crowdsourcing include efforts in human-computer interaction (HCI), computer-supported collaborative work (CSCW), cognitive psychology, organizational behavior, economics, information retrieval, databases, computer systems, optimization, and multiple areas of artificial intelligence (AI), such as vision, speech, robotics, machine learning, mechanism and market design, and planning. The links to AI are as strong as those to HCI, CSCW, and economics; human computation promises to play an important role in research on principles of artificial intelligence as well as in the engineering of systems that can take advantage of the (changing) complementarities of human and machine intellect.

Integration technology of collaborative computing system over multiple virtual machines oriented for finite element analysis

Finite element analysis (FEA) refers to simulating real system by using limited number of unknowns to appraisal an approximate real system with infinite number. To overcome the shortcomings of FEA in stand-alone Ansys, a collaborative computing system over multiple virtual machines (CCSMVM) is proposed to convert stand-alone Ansys into multi-machines. We present the architecture and workflow of CCSMVM, and then focus on key technologies of task construction and scheduling. A FEA application related to FPBS is developed to verify the prototype of CCSMVM integrated Ansys. The experiments show that: (1) the feasibility of key technologies has been verified since load balancing among virtual machines is achieved. (2) CCSMVM can complete the tasks in short completion time with high resource unitization. (3) Our task scheduling algorithm can complete the tasks in shorter time than other algorithms. It is concluded that CCSMVM can accelerate the execution of tasks by exchanging the running speed with resources.

The Case for Mobile Two-Factor Authentication

User authentication is a core building block of any secure collaborative computing system. And, because of the enhanced interaction between mobile applications and Web ser vices, mobile device user authentication is even more frequent.

GroupSim: A Collaborative Environment for Discrete Event Simulation Software Development for the World Wide Web

The simulation process involves the collaboration of different participants, such as simulation analysts, programmers, statisticians, and users of the simulation software. Many simulation tasks such as modeling, verification, validation, and design for experimentation require the participants to meet. It is understood that these meetings are time-consuming and expensive. This paper proposes a collaborative environment to help with the tasks of discrete event simulation software development using the World Wide Web platform. The environment, named GroupSim, is based on a collaborative computer system and uses the concepts of distributed modeling with automatic program generation and distributed control of experimentation. The authors show some examples to illustrate the use of the environment and discuss some issues related to collaborative environments such as concurrency control, access control, awareness, and performance.

Embodiment and copresence in collaborative interfaces

As collaborative computer systems are evolving, the use of spatial, three-dimensional interfaces for multiplayer games, groupware systems, and multi-user chat systems, for example, is increasing rapidly. This paper provides a theoretical underpinning for understanding the relevance of user embodiments and copresence within such three-dimensional collaborative computer interfaces. Firstly, the issue of embodiment is traced back through its origins in philosophy and psychology literature, and theories are identified, potentially helpful in understanding key issues concerning user embodiments in collaborative virtual environments. A hybrid avatar/agent model to achieve permanent user embodiments in such environments is discussed. Since copresence of other users within such environments has been shown to be an important factor for the experience of presence, a prototype embodied conversational agent has been designed to simulate copresence. A series of controlled experiments involving the prototype agent is discussed, highlighting the effects of simulated copresence on users’ experience of presence. Results suggest that, despite its shortcomings, the prototype agent does seem to have increased participants’ experience of presence. Evidence was found that even limited copresence as provided by the current prototype agent is sufficient to help users feel presence in the environment. The results seem to confirm that copresence simulated by agents can complement avatar technology and therefore that a hybrid avatar/agent model can potentially achieve permanent virtual presence of all participants.

Scenario-based design of cooperative systems

Over the past few years, scenario-based design has attained a growing interest as a way to incorporate a focus on the future use of an application into the construction of software. Scenarios have, however, mostly been used in the design of user-interfaces and hence focused on single-user situations. Based on experiences from applying scenarios in the re-design of an Hospital Information System in the Danish healthcare sector, this paper describes how collaborative scenarios can be used in the design of cooperative computer systems and what such col-laborative scenarios should contain. The paper concludes that such scenarios were useful in bridging the gab between understanding collaborative work practices and designing collaborative computer systems.