Execution Delay Research Articles

Implicit Extended Kalman Filter for Optical Terrain Relative Navigation Using Delayed Measurements

The exploration of celestial bodies such as the Moon, Mars, or even smaller ones such as comets and asteroids, is the next frontier of space exploration. One of the most interesting and attractive purposes from the scientific point of view in this field, is the capability for a spacecraft to land on such bodies. Monocular cameras are widely adopted to perform this task due to their low cost and system complexity. Nevertheless, image-based algorithms for motion estimation range across different scales of complexities and computational loads. In this paper, a method to perform relative (or local) terrain navigation using frame-to-frame features correspondences and altimeter measurements is presented. The proposed image-based approach relies on the implementation of the implicit extended Kalman filter, which works using nonlinear dynamic models and corrections from measurements that are implicit functions of the state variables. In particular, here, the epipolar constraint, which is a geometric relationship between the feature point position vectors and the camera translation vector, is employed as the implicit measurement fused with altimeter updates. In realistic applications, the image processing routines require a certain amount of time to be executed. For this reason, the presented navigation system entails a fast cycle using altimeter measurements and a slow cycle with image-based updates. Moreover, the intrinsic delay of the feature matching execution is taken into account using a modified extrapolation method.

Multi-Agent Deep Reinforcement Learning for Task Offloading in UAV-Assisted Mobile Edge Computing

Mobile edge computing can effectively reduce service latency and improve service quality by offloading computation-intensive tasks to the edges of wireless networks. Due to the characteristic of flexible deployment, wide coverage and reliable wireless communication, unmanned aerial vehicles (UAVs) have been employed as assisted edge clouds (ECs) for large-scale sparely-distributed user equipment. Considering the limited computation and energy capacities of UAVs, a collaborative mobile edge computing system with multiple UAVs and multiple ECs is investigated in this paper. The task offloading issue is addressed to minimize the sum of execution delays and energy consumptions by jointly designing the trajectories, computation task allocation, and communication resource management of UAVs. Moreover, to solve the above non-convex optimization problem, a Markov decision process is formulated for the multi-UAV assisted mobile edge computing system. To obtain the joint strategy of trajectory design, task allocation, and power management, a cooperative multi-agent deep reinforcement learning framework is investigated. Considering the high-dimensional continuous action space, the twin delayed deep deterministic policy gradient algorithm is exploited. The evaluation results demonstrate that our multi-UAV multi-EC task offloading method can achieve better performance compared with the other optimization approaches.

Solving User Priority in Cloud Computing Using Enhanced Optimization Algorithm in Workflow Scheduling.

Cloud computing is a procedure of stockpiling as well as retrieval of data or computer services over the Internet that allows all its users to remotely access the data centers. Cloud computing provides all required services to the users, but every platform has its share of pros and cons, and another major problem in the cloud is task scheduling or workflow scheduling. Multiple factors are becoming a challenge for scheduling in cloud computing namely the heterogeneity of resources, tasks, and user priority. User priority has been encountered as the most challenging problem during the last decade as the number of users is increasing worldwide. This issue has been resolved by an advanced encryption standard (AES) algorithm, which decreases the response time and execution delay of the user-request. There are multifarious tasks, for instance, deploying the data on the cloud, that will be executed according to first come first serve (FCFS) and not on the payment basis, which provides an ease to the users. These investigated techniques are 30.21%, 25.20%, 25.30%, 30.25%, 24.26%, and 36.9 8% improved in comparison with the traditional FFOA, DE, ABC, PSO, GA, and ETC, respectively. Moreover, during iteration number 5, this approach is 15.20%, 20.22%, 30.56%, 26.30%, and 36.23% improved than that of the traditional techniques FFOA, DE, ABC, PSO, GA, and ETC, respectively. This investigated method is more efficient and applicable in certain arenas where user priority is the primary concern and can offer all the required services to the users without any interruption.

Users' experience matter: Delay sensitivity-aware computation offloading in mobile edge computing

As a promising computing paradigm, Mobile Edge Computing (MEC) provides communication and computing capability at the edge of the network to address the concerns of massive computation requirements, constrained battery capacity and limited bandwidth of the Internet of Things (IoT) systems. Most existing works on mobile edge task ignores the delay sensitivities, which may lead to the degraded utility of computation offloading and dissatisfied users. In this paper, we study the delay sensitivity-aware computation offloading by jointly considering both user's tolerance towards delay of task execution and the network status under computation and communication constraints. Specifically, we use a specific multi-user and multi-server MEC system to define the latency sensitivity of task offloading based on the analysis of delay distribution of task categories. Then, we propose a scoring mechanism to evaluate the sensitivity-dependent utility of task execution and devise a Centralized Iterative Redirection Offloading (CIRO) algorithm to collect all information in the MEC system. By starting with an initial offloading strategy, the CIRO algorithm enables IoT devices to cooperate and iteratively redirect task offloading decisions to optimize the offloading strategy until it converges. Extensive simulation results show that our method can significantly improve the utility of computation offloading in MEC systems and has lower time complexity than existing algorithms.

Balanced multi-access edge computing offloading strategy in the Internet of things scenario

Internet of things devices can offload part of the tasks to the edge servers through the wireless network, thus the computing pressure and energy consumption are reduced. However, this will increase the cost of communication. When designing the offloading strategy for the edge computing scenario of the Internet of things, it is necessary to maintain the balance between task execution energy and experiment. Therefore, this paper proposes an offloading strategy which can optimize the energy consumption and time delay of task execution at the same time. This strategy satisfies different preferences of users. First, the above task is modeled as a multi-objective optimization problem, and the Pareto solution set is found by improving the strength Pareto evolutionary algorithm (SPEA2). Based on the Pareto set, the offloading strategy satisfying the requires of users with different preferences by offloading cost estimation. Second, a simulation experiment is carried out to verify the robustness of the improved SPEA2 algorithm under the influence of different main parameters. Compared with other representative algorithms, the improved SPEA2 algorithm is proved to minimize the task execution delay and energy consumption jointly.

Collaborative clustering parallel reinforcement learning for edge-cloud digital twins manufacturing system

To realize high-accuracy physical-cyber digital twin (DT) mapping in a manufacturing system, a huge amount of data need to be collected and analyzed in real-time. Traditional DTs systems are deployed in cloud or edge servers independently, whilst it is hard to apply in real production systems due to the high interaction or execution delay. This results in a low consistency in the temporal dimension of the physical-cyber model. In this work, we propose a novel efficient edge-cloud DT manufacturing system, which is inspired by resource scheduling technology. Specifically, an edge-cloud collaborative DTs system deployment architecture is first constructed. Then, deterministic and uncertainty optimization adaptive strategies are presented to choose a more powerful server for running DT-based applications. We model the adaptive optimization problems as dynamic programming problems and propose a novel collaborative clustering parallel Q-learning (CCPQL) algorithm and prediction-based CCPQL to solve the problems. The proposed approach reduces the total delay with a higher convergence rate. Numerical simulation results are provided to validate the approach, which would have great potential in dynamic and complex industrial internet environments.

An energy-aware application module for the fog-based internet of military things

Smart devices in various application areas are becoming increasingly prevalent for efficient handling of multiple critical activities. One such area of interest is high-security militarized environments. Due to military zones’ harsh and unpredictable nature, monitoring devices deployed in such environments must operate without power interruption for extended time periods. Therefore, it is essential to choose an appropriate application design for operating these “things” in the internet of things (IoT) environment such that energy can be conserved throughout the operating span of an application. This paper presents two application modules and analyzes their performance in terms of energy conservation considering a military-based IoT-Fog architecture. The two modules are: A sequential application module, and a master-worker application module. Experimental results show that the master-worker module incurs lower energy consumption and communication overhead than the sequential application module. Significantly, the master-worker module exhibits a lower delay in tuple execution by almost four milliseconds while also accounting for lower simulation time and higher network utilization. The module achieves significant savings in energy consumption, making it more effective in handling smart devices.

“Fed-DRL”: A Timeliness Optimization Method for Dynamic Data Acquisition System Based on Mobile Edge Computing

Age of Information (AoI) is a metric to describe the timeliness of a system proposed in recent years. It measures the freshness of the latest received data from the perspective of the target node in the system. This work studies a kind of dynamic data acquisition system for urban security that can update and control the situation of urban environmental security by collecting environmental data. The collected data packets need to be uploaded to the cloud center in time for data update, which has high requirements on the timeliness of the system and freshness of data. However, due to the limited computing capacity of mobile terminals and the pressure of bandwidth for data transmission, problems such as high data execution delay and transmission interruption are caused. Emerging mobile edge computing (MEC), a new model of computing that extends cloud computing capabilities to the edge network, promises to solve these problems. This work focuses on the timeliness of the system, as measured by the average AoI across all mobile terminals. First, a timeliness optimization model is defined, and a multi-agent deep reinforcement learning (DRL) algorithm combined with an attention mechanism is proposed to carry out computing offloading and resource allocation through the continuous interaction between agent and environment; then, in order to improve algorithm performance and data security, the federated learning mode is proposed to train agents; finally, the proposed algorithm is compared with other main baseline algorithms based on deep reinforcement learning. The simulation results show that the proposed algorithm not only outperforms other algorithms in optimizing system timeliness, but also improves the stability of training.

Intelligent user-collaborative edge device APC-based MEC 5G IoT for computational offloading and resource allocation

Mobile edge computing (MEC) supports delay-sensitive and excellent processing capacity services on the 5G Internet of Things (IoT) network. This research proposes an intelligent resource allocation policy to minimize average service latency and average energy consumption for an IoT system while maximizing the available processing capacity (APC). We express the APC as a function of communication and computation resources at the user and task edge devices. We demonstrated that reducing execution delay and energy usage could improve overall system service performance. We evaluate the savings in average latency and average energy consumption when we reduce execution delay by allocating resources to maximize APC. The 5G IoT network uses natural actor-critic deep reinforcement learning to tackle complicated resource allocation decisions, and the simulation shows that reducing execution time improves overall system performance. Our results improved execution time and energy usage compared to random search, greedy search, and deep Q-learning. In addition, our single centralized agent DRL outperforms Multi-agent DRL for the number of rewards and completed task achievable under different episodes.

A Distributed Algorithm for Task Offloading in Vehicular Networks With Hybrid Fog/Cloud Computing

Fog computing has been an effective paradigm of real-time applications in the IoT area, which enables task offloading at network edge devices. Particularly, many emerging vehicular applications require real-time interaction between the terminal users and computation servers, which can be implemented in fog-based architecture. However, it is still challenging to apply fog computing in vehicular networks due to high mobility of vehicles and uneven distribution of vehicle density, which may result in performance degradation, such as unbalanced workload and unexpected task failure. In this article, we investigate a new service scenario of task offloading under a three-layer service architecture, where the resources of vehicular fog (VF), fog server (FS), and central cloud (CC) are utilized in a cooperative way. On this basis, we formulate the probabilistic task offloading (PTO) problem by synthesizing task transmission, computation, and result retrieval, as well as characterizing the heterogeneity of computation servers. The objective of the PTO is to minimize the weighted sum of execution delay, energy consumption, and payment cost. To resolve the PTO problem, we propose a comprehensive task offloading algorithm by combining the alternating direction method of multipliers (ADMMs) and particle swarm optimization (PSO), called ADMM-PSO. The basic idea of the ADMM-PSO is to divide the PTO problem into multiple unconstrained subproblems and achieve the optimal solution in the form of an iterative coordination process. For each iteration, the solution is achieved by solving each subproblem with the PSO and updated based on a designed rule, which is able to converge to the optimal solution when the stop criterion is satisfied. Finally, we build the simulation model and implement the proposed algorithm for performance evaluation. The simulation results demonstrate the superiority of the proposed algorithm under a wide range of service scenarios.

Sequential delay-free strategy for sliding mode position observer considering analog and digital implementations of PMSM drives

ABSTRACT This paper proposes a sequential delay-free strategy to avoid side effects of the delays arising from both analog and digital implementations of permanent magnet synchronous machine (PMSM) drives on the accuracy of sliding mode position observers used in the high-speed conditions. First, after establishing the sliding mode observer (SMO) used for position estimation, the principles of the analog delay, low-pass filter (LPF) delay and execution delay together with their impacts are presented at length, posing the necessity of investigating the delay-free techniques. Then, novel current-based, position-based and speed-prediction-based compensation strategies are developed to deal with the three kinds of delays, respectively. Because the proposed compensation strategies are achieved based on the direct signal compensation theories, they are simple to be understood and implemented in practice. Third, the implementation sequence of the sequential delay compensation-based SMO is illustrated clearly. Thanks to the proposed delay compensation approaches, not only the position estimation accuracy but also the current regulation process of the closed-loop control can be improved. Finally, experiment is conducted on a three-phase 8000 rpm PMSM to verify the effectiveness of the proposed delay compensation-based SMO algorithms.

Deep reinforcement learning-based joint task and energy offloading in UAV-aided 6G intelligent edge networks

Edge networks are expected to play an important role in 6G where machine learning-based methods are widely applied, which promote the concept of Edge Intelligence. Meanwhile, Unmanned Aerial Vehicle (UAV)-enabled aerial network is significant in 6G networks to achieve seamless coverage and super-connectivity. To this end, a joint task and energy offloading problem is studied under a UAV-aided and energy-constrained intelligent edge network, consisting of a high altitude platform (HAP), multiple UAVs, and on-ground fog computing nodes (FCNs). To guarantee the energy supply of UAVs and FCNs, both simultaneous wireless information and power transfer (SWIPT), as well as laser charging techniques are considered. Specifically, we investigate a scenario where each UAV needs to execute a computation-intensive task during each time slot and can be powered by the laser beam transmitted from the HAP. Due to the limited computation resources, each UAV can offload part of the task and energy to the FCNs for collaborative computing, to reduce local energy consumption and the overall task execution delay by adopting SWIPT. Considering the dynamics of the network, e.g., the time-varying locations of UAVs and available computation resources of FCNs, the problem is formulated as a cooperative multi-agent Markov game for UAVs, which aims to maximize the total system utility, by optimizing the task partitioning and power allocation strategies of each UAV, regarding task size, average delay and energy consumption of task execution. To tackle this problem, we propose a multi-agent soft actor–critic (MASAC)-based approach to resolve the problem. Numerical simulation results prove the superiority of our proposed approach as compared with benchmark methods.

An efficient quantum based D2D computation and communication approach for the Internet of Things

The Internet of Things is made up of a set of embedded devices such as light detection sensors, ranging (LiDAR) sensors, and millimetre-wave sensors. These sensors generate a massive amount of data in which limited communication capacity is available to share a massive amount of data to Fog-based Roadside Units (RSU) for data process and analysis service. Fog-based RSU has become an emerging paradigm in intelligent transportation but needs research attention to design intelligent decision-making methods for data communication and computation at Fog-based RSU. To address these issues, we design a two-level Quantum based D2D Computation, Communication ( $$QDC^{2}$$ ) approach. First, design a bandwidth allocation strategy based on spatial importance score factors to resolve embedded devices’ data transmission issues. Second, design an adaptive equilibrium service offloading strategy based on device-centric measurements to assess the computation capacity and performance rate for resolving Fog-node computation consistency issues. Additionally, Fog-based RSU is interconnected with LAN helps to optimize service latency. Simulation results show that our approach achieved a high service reliability rate (79.56%), low error rate (0.9%), and an execution delay of 22.5 s for 15 devices compared to state-of-the-art approaches.

Fog-supported Low-latency Monitoring of System Disruptions in Industry 4.0: A Federated Learning Approach

Industry 4.0 is based on machine learning and advanced digital technologies, such as Industrial-Internet-of-Things and Cyber-Physical-Production-Systems, to collect and process data coming from manufacturing systems. Thus, several industrial issues may be further investigated including, flows disruptions, machines’ breakdowns, quality crisis, and so on. In this context, traditional machine learning techniques require the data to be stored and processed in a central entity, e.g., a cloud server. However, these techniques are not suitable for all manufacturing use cases, due to the inaccessibility of private data such as resources’ localization in real time, which cannot be shared at the cloud level as they contain personal and sensitive information. Therefore, there is a critical need to go toward decentralized learning solutions to handle efficiently distributed private sub-datasets of manufacturing systems. In this article, we design a new monitoring tool for system disruption related to the localization of mobile resources. Our tool may identify mobile resources (human operators) that are in unexpected locations, and hence has a high probability to disturb production planning. To do so, we use federated deep learning, as distributed learning technique, to build a prediction model of resources locations in manufacturing systems. Our prediction model is generated based on resources locations defined in the initial tasks schedule. Thus, system disruptions are detected, in real time, when comparing predicted locations to the real ones, that is collected through the IoT network. In addition, our monitoring tool is deployed at Fog computing level that provides local data processing support with low latency. Furthermore, once a system disruption is detected, we develop a dynamic rescheduling module that assigns each task to the nearest available resource while improving the execution accuracy and reducing the execution delay. Therefore, we formulate an optimization problem of tasks rescheduling, before solving it using the meta-heuristic Tabu search. The numerical results show the efficiency of our schemes in terms of prediction accuracy when compared to other machine learning algorithms, in addition to their ability to detect and resolve system disruption in real time.

A distributed AI/ML framework for D2D Transmission Mode Selection in 5G and beyond

In this paper we build on the Distributed Artificial Intelligence (DAI) Framework, which makes use of Belief-Desire-Intention extended (BDIx) agents residing on Device-to-Device (D2D) Mobile Devices in order to establish D2D communication in an efficient, distributed, autonomous and flexible way. To demonstrate the potentials of this framework, in this work we focus on D2D Transmission Mode Selection in 5G. Specifically, we build on our previous work and develop an enhanced version of DAIS, a specific plan executed by the BDIx agents, for selecting the D2D Transmission mode that the D2D Devices will operate, aiming to offer improved Spectral Efficiency (SE) and Power Consumption (PC). To set a benchmark and allow for a fairer comparison we also enhance the Distributed Sum Rate (DSR), a distributed algorithmic approach that focuses on maximising the aggregated data rate of all the links established in the network, with similar functionality as DAIS. Furthermore, an extensive comparative evaluation of the enhanced DAIS and DSR with a number of unsupervised ML clustering approaches adapted for D2D Communication(i.e., GMEANS, Fuzzy ART, MEC, and DBSCAN) is provided. Specifically, for each approach the following have been collected and compared: QoE and QoS Fairness, SE and PC achieved, efficiency of clusters created, signalling overhead caused (i.e., volume of messages exchanged), time execution delay, D2D Effectiveness, D2D Stability and D2D Productivity. Based on the insight gained into the performance of the enhanced DAIS, DSR and the unsupervised ML techniques, we discuss performance gain trade-offs in terms of SE and PC versus signalling overhead and control delay in responding to changes.

Joint Parallel Offloading and Load Balancing for Cooperative-MEC Systems With Delay Constraints

Mobile-edge computing (MEC) has been recognized as a promising solution to provide efficient communication and computation capabilities for mobile users (MUs). However, the problem relating to parallel offloading and load balancing with multiple cooperative MEC servers and massive delay-sensitive execution workloads remains to be investigated. In this paper, we study a joint parallel offloading and load balancing policy for such an MEC system. We formulate a long-term system average-cost (i.e., weighted sum of energy consumption and execution delays) stochastic programming problem under MU battery level stability and delay constraints. Our aims include optimizing the data sizes for uplink offloading transmissions for optimized communication and computation resource allocation among multi-users, and maximizing the computation capability utilization of cooperative MEC servers by load balancing. To solve this problem, we first design a Lyapunov-based centralized cost management algorithm (LYP-CCMA) to obtain the optimal system average-cost under the battery level stability constraints. Further, we propose two algorithms based on alternating direction method of multipliers (ADMM) to implement distributed resources allocation. Simulation results verify our analysis and demonstrate the superior performance of our proposed schemes over several baseline schemes.

A Fast Heuristic for Improving the Energy Efficiency of Asymmetric VFI-Based Manycore Systems

Voltage/Frequency Islands (VFIs) are practically used in multicore systems. VFIs cluster cores that share the same Voltage/Frequency (V/F) level for the entire application runtime. Using VFIs, switching V/F levels has less overhead per-core while saving more energy in exchange for tolerable execution delay. This paper targets the well-known K-means algorithm for clustering cores, where each cluster contains cores with similar computational workloads across applications phases. K-means produces sub-optimal clusters when cores workloads do not have the same variation across all phases. Furthermore, the workload variations require running a VFI with a different V/F level per-phase. This paper presents a fast heuristic that facilitates clustering by sorting computational workloads per-phase before applying K-means. The VFIs V/F levels are dynamically adjusted to meet energy budget constraints. This framework provides a guideline for users to choose a number of clusters that satisfies system configurations and energy efficiency preferences. Three parameters are evaluated to represent the computational workloads: utilization, Instruction-Per-Cycle (IPC), and execution time. For an application with highly varying workload, execution time achieves 52 and 65% more energy efficiency compared to utilization and IPC, respectively. Results show that the degree of workload variations for different applications impacts changing or fixing VFIs V/F levels.

Distributed deep learning for cooperative computation offloading in low earth orbit satellite networks

Low earth orbit (LEO) satellite network is an important development trend for future mobile communication systems, which can truly realize the "ubiquitous connection" of the whole world. In this paper, we present a cooperative computation offloading in the LEO satellite network with a three-tier computation architecture by leveraging the vertical cooperation among ground users, LEO satellites, and the cloud server, and the horizontal cooperation between LEO satellites. To improve the quality of service for ground users, we optimize the computation offloading decisions to minimize the total execution delay for ground users subject to the limited battery capacity of ground users and the computation capability of each LEO satellite. However, the formulated problem is a large-scale nonlinear integer programming problem as the number of ground users and LEO satellites increases, which is difficult to solve with general optimization algorithms. To address this challenging problem, we propose a distributed deep learning-based cooperative computation offloading (DDLCCO) algorithm, where multiple parallel deep neural networks (DNNs) are adopted to learn the computation offloading strategy dynamically. Simulation results show that the proposed algorithm can achieve near-optimal performance with low computational complexity compared with other computation offloading strategies.

Implementation of Metro Rail Project in Pune City

Abstract: In India, around 26 metro rail projects are in various phases of planning, construction, and operation. A number of new metro rail phases are being planned and will be operational in the near future. Various studies have revealed delays in metro rail project planning and execution in India and around the world. This research will attempt to comprehend a wide range of subjects. There are numerous alternatives for dealing with problems or impediments that the metro rail corporation encounters when constructing metro rail projects. Variables that may impede project execution, such as government funding capability, financial arrangements, and so on. To name a few, competing technologies, a workable operating plan, and stakeholder issues. The factors that determine metro rail operations The Pune Metro Rail Project will be utilized to examine potential projects. The scope of the study will be limited to mobilization. The study approach is centered on formal contacts with participants from the planning phase to the commissioning phase. The study's conclusions are crucial in various ways for Pune Metro Rail Corporation decision makers and project execution group. Developing procedural routines to help metro rail projects be completed more quickly and effectively. Keywords: Implementation of Pune Metro, Delay in the project, Infrastructure of the project, cost

Thailand's COVID‐19: How public financial management facilitated effective and accountable health sector responses

AbstractEffective Public Financial Management (PFM) systems are crucial during COVID‐19 pandemic to ensure timely mobilisation of sufficient resources and distribution to frontline service providers. All aspects of PFM, from budget acquisition to execution and expenditure reporting and auditing are important aspects in pursuing effective pandemic responses with transparency and accountability. This commentary analyzes how PFM in Thailand adapted to support purchasing of COVID‐19 health services, including laboratories and treatment, vaccines and vaccination servicing, and no‐fault compensation from adverse effects following immunisation. It also discusses the limitations which delay implementation. Financing COVID‐19 services was decided by the Cabinet under a State of Emergency Decree, resulting in expedited budget approval process. Though delays in budget execution were caused by bureaucratic budget spending rules, regulations and approvals, PFM adaptation allowed for services to be provided through the use of hospital revenue with rapid budget execution rules and regulations while maintaining accountability, reporting and auditing. Lastly, while reporting is mandatory with internal audit by related government agencies, and external audit by Office of the Auditor General in place, as of September 2021, report of the COVID‐19 expenditure in 2020 has yet to be made publicly available for transparency and check and balance by the public. It is unclear the degree to which audit systems are fully enforced. Overall, Thailand's PFM systems have provided rapid fund mobilisation sourcing from central budget and loans and clarity in authorisation of spending; the use of hospital revenue provides more flexibility and rapid budget execution.