Task Priority Research Articles

Service Satisfaction-Oriented Task Offloading and UAV Scheduling in UAV-Enabled MEC Networks

With the development of computation-intensive applications, unmanned aerial vehicle (UAV)-enabled multi-access edge computing (MEC) provides task offloading service for the users with or without terrestrial infrastructure support. Meanwhile, the next generation of UAVs communication systems are expected to be user-centric. Therefore, more attention should be paid to users’ satisfaction with the offered service. In this paper, we study the service satisfaction-oriented task offloading and UAV scheduling problem for UAV-enabled MEC networks, where the task priorities are considered based on the delay requirements of users’ tasks and the remaining energy status of users. Specifically, we firstly divide the users into different groups via the K-means-based grouping algorithm. Then, we develop a novel user satisfaction model by jointly considering the task processing delay and energy saving, based on which a total user satisfaction maximization problem is formulated to jointly optimize the task offloading decisions and UAV scheduling strategy. To solve the formulated problem, we decompose it into two sub-problems, i.e., the UAV scheduling sub-problem and the task offloading sub-problem. To solve the first sub-problem, we develop a genetic algorithm (GA)-based UAV scheduling algorithm through dealing with multiple balanced assignment problems. To address the second sub-problem, a GA-based task offloading algorithm is developed. Then, we propose a joint task offloading and UAV scheduling optimization algorithm to solve the original optimization problem. Finally, simulation results demonstrate that the proposed optimization algorithm not only converges fast, but also improves the total users’ satisfaction greatly. The total users’ satisfaction improved by the proposed scheme is up to 16.9% in the case of 170 users.

Collaborative Scheduling Algorithm for Dependent Tasks based on Multi-Agent RL in VEC Environment

To address the problem of limited computing power of vehicles, Vehicle Edge Computing (VEC) allows vehicles to schedule tasks to edge nodes with sufficient resources. In this paper, we propose a multi-agent reinforcement learning (MARL) approach to solve the multi-task scheduling problem in a dynamic VEC environment. First, we model the cooperative scheduling problem of dependent tasks in the VEC environment, considering the task priority and edge node load balancing in the task scheduling process. We define the optimization objective as minimizing the task processing delay and find it is an NP-hard problem. Then, we design a distributed algorithm SCMA based on MARL. The algorithm enables vehicles to find the optimal scheduling strategy by cooperating and sharing resources with each other. Finally, we use SUMO to simulate the road network topology and generate vehicle traffic trajectories. We construct heterogeneous vehicular applications for simulation experiments using the DAG generator. Compared with existing algorithms, the simulation results validate the superiority of the SCMA algorithm.

Multi‐layer task scheduling and resource allocation schemes considering idle resource and task priority in IoT networks

AbstractWith more and more interconnected smart devices (ISDs) accessing the Internet of Things (IoT), massive and diverse tasks need to be transformed and computed. Mobile edge computing enables the offloading of tasks to nearby servers to enhance processing efficiency, which makes ISDs idle, causing resource waste and failing to satisfy the high real‐time requirements of tasks. Besides, when tasks with different priorities are processed in the order they are generated, it will be difficult for IoT to guarantee a timely response to high‐priority tasks. To address the aforementioned issues, we establish an edge‐terminal‐local architecture by software‐defined networking to centrally manage idle ISD resource (2ISDR). Then the proposed two‐step scheduling mechanism with preemptive priority queue ensures the real‐time responses to high‐priority tasks, and the minimum resource allocation coefficients make offloading effective. Finally, we also propose a modified NSGA‐III algorithm named MNSGA‐III, which is designed to make decisions about offloading and solve resource allocation for tasks, and we correct infeasible solutions by a two‐step correction function to ensure the feasibility of MNSGA‐III. Experimental results show that the method can ensure a timely response to high‐priority tasks and optimize processing time, energy consumption, and economic cost through the utilization of 2ISDR.

Multi-objective secure aware workflow scheduling algorithm in cloud computing based on hybrid optimization algorithm

Cloud computing provides the on-demand service of the user with the use of distributed physical machines, in which security has become a challenging factor while performing various tasks. Several methods were developed for the cloud computing workflow scheduling based on optimal resource allocation; still, the security consideration and efficient allocation of the workflow are challenging. Hence, this research introduces a hybrid optimization algorithm based on multi-objective workflow scheduling in the cloud computing environment. The Regressive Whale Water Tasmanian Devil Optimization (RWWTDO) is proposed for the optimal workflow scheduling based on the multi-objective fitness function with nine various factors, like Predicted energy, Quality of service (QoS), Resource utilization, Actual task running time, Bandwidth utilization, Memory capacity, Make span equivalent of the total cost, Task priority, and Trust. Besides, secure data transmission is employed using the triple data encryption standard (3DES) to acquire enhanced security for workflow scheduling. The method’s performance is evaluated using the resource utilization, predicted energy, task scheduling cost, and task scheduling time and acquired the values of 1.00000, 0.16587, 0.00041, and 0.00314, respectively.

“We must fight!”. The resistance of the Jews to the genocidal policy in Kremenchuk (1941–1943)

The article examines an almost unexplored topic about the resistance of the Jews to the Nazi genocidal policy in Kremenchuk. The author reviewed historiographical developments on this issue, outlined terminology, and characterized the source base (a part of the sources is introduced into scientific circulation for the first time). The article analyzes the factors that influenced Holocaust flow in the city: the evolution of the “Final Solution to the Jewish Question” during the occupation of the town; events at the front that determined the priority of current tasks for rear structures; demands or the lack of demands for Jewish labor, etc. In accordance with this, the author researched the condition of local Jews and Jews-prisoners of war who got to the Kremenchuk due to the encirclement of parts of the South-Western Front and analyzed the dynamics of the implementation of the Holocaust in the city.The article considers the aspects that prevented the development of a purely Jewish resistance movement in Kremenchuk. Therefore the author explores the opposition of Jews at the individual level and their activities as a member of underground organizations that actively helped Jews (those who were prisoners of war, local people, and those who arrived in the city during the occupation). Consequently, it was investigated that thanks to the cooperation of leaders from among the Soviet prisoners of war and local activists, it was possible to create an effective infrastructure for providing help and rescue to persecuted groups, including Jews. For example, in the «Patriot of the Motherland» underground organization, this infrastructure consisted of a prisoner of war camp, the first city hospital, a Red Cross station, and underground apartments. This enabled those persecuted within the framework of underground organizations to effectively resist the Nazi genocidal policy. The article also analyzes, on an individual level, how both local and Jewish prisoners of war, as well as those who arrived in Kremenchuk, resisted the genocide.

Priority-aware path planning and user scheduling for UAV-mounted MEC networks: A deep reinforcement learning approach

Owing to the flexibility and controllability, unmanned aerial vehicle (UAV) is frequently integrated into mobile edge computing (MEC) network to improve the system performance. This paper investigates a novel multi-user multi-hotspot MEC network supported by a UAV, where the UAV can help compute the tasks offloaded from end users (EUs) in multiple hotspots. In this network, we consider the task priority and task size are dynamic, due to the EUs’ demands. We then propose a task priority-based system utility model to evaluate the network performance, which considers the priorities of tasks based on the urgent or non-urgent level. We further formulate a utility maximization problem that jointly optimizes the UAV’s access path and the EUs’ offloading strategy, while satisfying the constraints related to the UAV’s battery capacity and UAV’s duration of flight. Since the formulated problem is a NP-hard problem, we present a deep reinforcement learning (DRL) based scheme as a solution. The DRL scheme utilizes principles from reinforcement learning to address the optimization problem effectively, resulting in a dynamic solution. Simulation results demonstrate that the proposed DRL scheme outperforms alternative benchmark schemes in terms of system utility.

A Multiple Agile Satellite Staring Observation Mission Planning Method for Dense Regions

To fully harness the burgeoning array of in-orbit satellite resources and augment the efficacy of dynamic surveillance of densely clustered terrestrial targets, this paper delineates the following methodologies. Initially, we leverage the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm to aggregate the concentrated terrestrial targets, taking into account the field-of-view peculiarities of agile staring satellites. Subsequently, we architect a model for a synergistic multiple angle earth observation satellites (AEOSs) mission planning with the optimization objectives of observational revenue, minimal energy expenditure, and load balancing, factoring in constraints such as target visibility time window, AEOSs maneuverability, and satellite storage. To tackle this predicament, we propose an improved heuristic ant colony optimization (ACO) algorithm, utilizing the task interval, task priority, and the length of time a task can start observation as heuristic information. Furthermore, we incorporate the notion of the max–min ant system to regulate the magnitude of pheromone concentration, and we amalgamate global and local pheromone update strategies to expedite the convergence rate of the algorithm. We also introduce the Lévy flight improved pheromone evaporation coefficient to bolster the algorithm’s capacity to evade local optima. Ultimately, through a series of simulation experiments, we substantiate the significant performance improvements achieved by the improved heuristic ant colony algorithm compared to the standard ant colony algorithm. We furnish proof of its efficacy in resolving the planning of multiple AEOS staring observation missions.

Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment

Cloud Computing model provides on demand delivery of seamless services to customers around the world yet single point of failures occurs in cloud model due to improper assignment of tasks to precise virtual machines which leads to increase in rate of failures which effects SLA based trust parameters (Availability, success rate, turnaround efficiency) upon which impacts trust on cloud provider. In this paper, we proposed a task scheduling algorithm which captures priorities of all tasks, virtual resources from task manager which comes onto cloud application console are fed to task scheduler which takes scheduling decisions based on hybridization of both Harris hawk optimization and ML based reinforcement algorithms to enhance the scheduling process. Task scheduling in this research performed in two phases i.e. Task selection and task mapping phases. In task selection phase, all incoming priorities of tasks, VMs are captured and generates schedules using Harris hawks optimization. In task mapping phase, generated schedules are optimized using a DQN model which is based on deep reinforcement learning. In this research, we used multi cloud environment to tackle availability of VMs if there is an increase in upcoming tasks dynamically and migrate tasks to one cloud to another to mitigate migration time. Extensive simulations are conducted in Cloudsim and workload generated by fabricated datasets and realtime synthetic workloads from NASA, HPC2N are used to check efficacy of our proposed scheduler (FTTHDRL). It compared against existing task schedulers i.e. MOABCQ, RATS-HM, AINN-BPSO approaches and our proposed FTTHDRL outperforms existing mechanisms by minimizing rate of failures, resource cost, improved SLA based trust parameters.

A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing

Task scheduling poses a wide variety of challenges in the cloud computing paradigm, as heterogeneous tasks from a variety of resources come onto cloud platforms. The most important challenge in this paradigm is to avoid single points of failure, as tasks of various users are running at the cloud provider, and it is very important to improve fault tolerance and maintain negligible downtime in order to render services to a wide range of customers around the world. In this paper, to tackle this challenge, we precisely calculated priorities of tasks for virtual machines (VMs) based on unit electricity cost and these priorities are fed to the scheduler. This scheduler is modeled using a deep reinforcement learning technique which is known as the DQN model to make decisions and generate schedules optimally for VMs based on priorities fed to the scheduler. This research is extensively conducted on Cloudsim. In this research, a real-time dataset known as Google Cloud Jobs is used and is given as input to the algorithm. This research is carried out in two phases by categorizing the dataset as a regular or large dataset with real-time tasks with fixed and varied VMs in both datasets. Our proposed DRFTSA is compared to existing state-of-the-art approaches, i.e., PSO, ACO, and GA algorithms, and results reveal that the proposed DRFTSA minimizes makespan compared to PSO, GA, and ACO by 30.97%, 35.1%, and 37.12%, rates of failure by 39.4%, 44.13%, and 46.19%, and energy consumption by 18.81%, 23.07%, and 28.8%, respectively, for both regular and large datasets for both fixed and varied VMs.

SD-AETO: Service-Deployment-Enabled Adaptive Edge Task Offloading Scheme in MEC

In recent years, edge computing, as an important pillar for future networks, has been developing rapidly. Task offloading is a key part of edge computing that can provide computing resources for resource-constrained devices to run computing-intensive applications, accelerate computing speed and save energy. An efficient and feasible task offloading scheme can not only greatly improve the quality of experience (QoE) but also provide strong support and assistance for 5G/B5G networks, the industrial Internet of Things (IIoT), computing networks, etc. To achieve these goals, this paper proposes an adaptive edge task offloading scheme assisted by service deployment (SD-AETO) focusing on optimizing the energy utilization ratio (EUR) and the processing latency. In the pre-implementation stage of the SD-AETO scheme, a service deployment scheme is invoked to assist with task offloading considering each service’s popularity. The optimal service deployment scheme is obtained by using the approximate deployment graph (AD-graph). Furthermore, a task scheduling and queue offloading design procedure is proposed to complete the SD-AETO scheme based on task priority. The task priority is generated by corresponding service popularity and task offloading. Finally, we analyze our SD-AETO scheme and compare it with related approaches, and the results show that our scheme has a higher edge offloading rate and lower resource consumption for massive task scenarios in the edge network.

Impact of priority assignment on schedule-based attacks in real-time embedded systems

In this paper, we investigate the impact of priority assignment on schedule-based attacks in fixed-priority real-time systems. Through the schedule-based attacks, attackers may manipulate the input and output buffers of victim tasks, cause instability, and/or steal hidden information. The success of those attacks highly depends on the proper positioning of the malicious task with respect to the victim tasks in the actual schedule. Firstly, we develop an optimal priority assignment algorithm that incorporates the user specified priority preferences that reflect task types (anterior task, posterior task, or victim task), while still meeting all the deadlines. Secondly, we propose a dynamic delaying algorithm to further reduce posterior schedule-based attacks at run-time. Finally, we derive and compare the performance of six attack-aware priority-assignment policies as well as their dynamic extensions through comprehensive simulations. Our results suggest that the well-known RMS algorithm can be easily outperformed in terms of reducing schedule-based attacks, by properly specifying the relative priorities of malicious, anterior, and posterior tasks through our optimal algorithm. In particular, the AMP assignment, which assigns the anterior, malicious, and posterior tasks execution priorities in decreasing order, is shown to offer rather robust and consistent performance even at high system utilizations. Our framework explicitly incorporates the logical execution time and attack effective window constraints to model schedule-based attacks in realistic manner.

Collision avoidance control for redundant manipulators in construction operations: a null-space-based task-priority adjustment approach

PurposeThis paper aims to use redundant manipulators to solve the challenge of collision avoidance in construction operations such as welding and painting.Design/methodology/approachIn this paper, a null-space-based task-priority adjustment approach is developed to avoid collisions. The method establishes the relative position of the obstacle and the robot arm by defining the “link space,” and then the priority of the collision avoidance task and the end-effector task is adjusted according to the relative position by introducing the null space task conversion factors.FindingsNumerical simulations demonstrate that the proposed method can realize collision-free maneuvers for redundant manipulators and guarantee the tracking precision of the end-effector task. The experimental results show that the method can avoid dynamic obstacles in redundant manipulator welding tasks.Originality/valueA new formula for task priority adjustment for collision avoidance of redundant manipulators is proposed, and the original task tracking accuracy is guaranteed under the premise of safety.

An efficient task scheduling in fog computing using improved artificial hummingbird algorithm

IoT edge devices have become more popular due to the rapid growth in IoT applications in recent years. Task scheduling reduces latency and application computation times, while improving quality of service. In this paper, we introduce a new version of Artificial Hummingbird Algorithm (AHA) with Opposition-Based Learning (OBL), chaos mechanism, and Differential Evolution (DE) algorithm, called CODA. AHA is improved by using DE algorithms to determine the optimal configuration of chaotic maps and OBL for determining the optimal initial population. As a result of CODA's high ability, local optima can be avoided and exploration of a region of interest can be improved. CODA is then utilized to schedule tasks in fog computing systems. Analytic Hierarchy Process (AHP) is used to determine the priority of tasks. Task scheduling is primarily intended to reduce energy consumption, duration, and costs. To compare CODA's performance with that of other well-known meta-heuristics, 50 basic functions were used as benchmarks. Additionally, the proposed scheduling scheme is evaluated through different simulations. Energy consumption, makespan, and cost are better as a result of the implemented algorithm. When compared to the existing algorithms that include Artificial Hummingbird Algorithm (AHA), Gravitational Search Algorithm (GSA), Moth-Flame Optimization (MFO), Seagull Optimization Algorithm (SOA), Salp Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Sine Cosine Algorithm (SCA), Particle swarm optimization (PSO), Multi-Verse Optimizer (MVO), and Differential evolution (DE), the proposed CODA shows better output in satisfying the task scheduling process. On average, the CODA-based task scheduling model outperforms other research studies in terms of makespan by 46%, cost by 8%, and energy consumption by 41%.

Fault-Tolerant Trust-Based Task Scheduling Algorithm Using Harris Hawks Optimization in Cloud Computing.

Cloud computing is a distributed computing model which renders services for cloud users around the world. These services need to be rendered to customers with high availability and fault tolerance, but there are still chances of having single-point failures in the cloud paradigm, and one challenge to cloud providers is effectively scheduling tasks to avoid failures and acquire the trust of their cloud services by users. This research proposes a fault-tolerant trust-based task scheduling algorithm in which we carefully schedule tasks within precise virtual machines by calculating priorities for tasks and VMs. Harris hawks optimization was used as a methodology to design our scheduler. We used Cloudsim as a simulating tool for our entire experiment. For the entire simulation, we used synthetic fabricated data with different distributions and real-time supercomputer worklogs. Finally, we evaluated the proposed approach (FTTATS) with state-of-the-art approaches, i.e., ACO, PSO, and GA. From the simulation results, our proposed FTTATS greatly minimizes the makespan for ACO, PSO and GA algorithms by 24.3%, 33.31%, and 29.03%, respectively. The rate of failures for ACO, PSO, and GA were minimized by 65.31%, 65.4%, and 60.44%, respectively. Trust-based SLA parameters improved, i.e., availability improved for ACO, PSO, and GA by 33.38%, 35.71%, and 28.24%, respectively. The success rate improved for ACO, PSO, and GA by 52.69%, 39.41%, and 38.45%, respectively. Turnaround efficiency was minimized for ACO, PSO, and GA by 51.8%, 47.2%, and 33.6%, respectively.

The Role of Women's Leadership Characteristics in Shaping Job Autonomy and Its Effects on Employee Innovation Performance

Research aims: This study investigates the impact of women's leadership traits on employee innovation performance and the role of job autonomy as an intermediary.Design/Methodology/Approach: This study used a quantitative approach with a cross-sectional design, focusing on micro-level analysis, with 323 employees from a Pekanbaru Public Health Center (Puskesmas) as the target population. One hundred seventy-five samples were obtained using Cluster Random Sampling, and the SEM statistical test and Smart PLS 4.0 software were utilized for quantitative analysis.Research findings: Women's characteristic leadership supports the creation and development of employee innovation performance. It will be better if the provision of work autonomy accompanies it.Theoretical Contribution/Originality: Developing a job autonomy theory covers procedural autonomy, soft skills, and decision-making. Scheduling autonomy includes time flexibility and technical tasks. Autonomy within job criteria is assessed through task priority, management, and performance.Practitioners/Policy Implications: The research highlights the importance of work autonomy in Pekanbaru Health Centers, highlighting the role of women's leadership characteristics and proving that women possess the same abilities as men, thereby supporting employee innovation performance.Research Limitations/Implications: The study suggests that future research could benefit from a longitudinal design to assess theory constructs over time, considering the limitations of cross-sectional designs and the limited sample size of Pekanbaru City Health Center employees, allowing for a more comprehensive analysis.

An Energy-Efficient Dynamic Scheduling Method of Deadline-Constrained Workflows in a Cloud Environment

With the rapid development of cloud applications, the computing requests of cloud data centers have increased significantly, consuming a lot of energy, making cloud data centers unsustainable, which is very unfavorable from both the cloud provider’s point of view and the environmental point of view. Therefore, it is crucial to minimize energy consumption and improve resource utilization while ensuring user service quality constraints. In this paper, we propose a hybrid workflow scheduling algorithm (Online Hybrid Dynamic Scheduling, OHDS), which aims to minimize the energy consumption of tasks and maximize service resource utilization while satisfying the sub-deadline and data dependency constraints of workflow tasks. Firstly, the data dependencies between workflow tasks are considered for multi-task merging, and sub-deadline constraints are assigned to workflow tasks based on task priority. Secondly, based on the independent nature of the tasks of different workflows, a hybrid scheduling of multiple workflows is performed to reduce service idle time. Then, the workflow task scheduling priority and its sub-deadlines are dynamically adjusted, and the service status is sensed by the CPU utilization of the service, and the workload on the overloaded/underloaded service is balanced by dynamic migration of virtual machines. Finally, the OHDS method is compared with three existing scheduling methods to verify its better performance in terms of scheduling energy consumption, scheduling success rate and service resource utilization.

Young and older adults show similar anticipatory postural adjustments when stepping with different task priorities

Young and older adults show similar anticipatory postural adjustments when stepping with different task priorities

Improving the Performance of Delay-sensitive Multimedia Computation in Automotive Networks with DP-ERACOM using CloudSim Simulator

Abstract: We propose a Dynamic Priority-based Efficient Resource Allocation and Computing (DP-ERACOM) scheme for processing delay-sensitive multimedia computation in automotive networks. The proposed scheme breaks down each multimedia task into four smaller tasks and dynamically distributes resources based on the priority of the multimedia tasks. The three main processing and computing units in the proposed scheme are the load manager, compute cluster unit, and transmission unit. We evaluate the performance of the proposed scheme using CloudSim simulator. The results show that the proposed scheme can significantly improve the performance of delay-sensitive multimedia computation in automotive networks.

A Human-like Inverse Kinematics Algorithm of an Upper Limb Rehabilitation Exoskeleton

Powered exoskeleton rehabilitation is an effective way to help stroke patients recover their motor abilities. Bionic structures and human-like control strategies can be used to enhance both the safety and efficacy of exoskeletons. However, the motion characteristics of the shoulder complex are not sufficiently considered. In this paper, we designed a 7-degrees-of-freedom (DOF) upper limb rehabilitation exoskeleton, FREE (functional rehabilitation exoskeleton). The mechanical structures of the shoulder and forearm of FREE are in accordance with human anatomy, and can be used to perform a wide range of synergistic motion of multiple joints while keeping a safe distance from the patient’s head. A multiple-input-multiple-output (MIMO) shoulder girdle motion prediction model was developed to satisfy the synergy between humans and exoskeletons. Moreover, a constrained task priority and projected gradient-based inverse kinematics algorithm (CTPPG-IK) was proposed to achieve assistance with scapulohumeral rhythm. A motion capture system was used to collect different activities of daily life (ADL) motion data to validate the proposed algorithm. The experimental results show that the accuracy of the prediction model is higher than that of existing models, and the inverse kinematics algorithm can handle the end-effector task and joint space with a maximum angle error of 3.04×10−3 rad.

Scoping review of dual-task interference in individuals with intellectual disability.

Dual-task paradigms can provide insights on the structures and mechanisms underlying information processing and hold diagnostic, prognostic, and rehabilitative value for populations with cognitive deficits such as in individuals with intellectual disability (ID). In this paradigm, two tasks are performed separately (single-task context) and concurrently (dual-task context). The change in performance from single- to dual-task context represents dual-task interference. Findings from dual-task studies have been largely inconsistent on whether individuals with ID present with dual-task-specific deficits. The current review aimed to map the published literature on dual-task methods and pattern of dual-task interference in individuals with ID. A scoping review based on Arksey and O'Malley's five-stage methodological framework was performed. Seventeen electronic databases and registries were searched to identify relevant studies, including gray literature. Charted data from included studies were analyzed quantitatively and qualitatively. PRISMA guidelines informed the reporting of this review. Twenty-two studies involving 1,102 participants (656 with ID and 446 without ID) met the review's inclusion criteria. Participants in the included studies were heterogeneous in sex, age (range 3-59 years), etiology and ID severity. Included studies characterized their ID-sample in different ways, most commonly using intelligence quotient (IQ) scores. Other measures of intellectual function (e.g., mental age, ID severity, verbal and/or visuospatial ability scores) were also used, either solely or in combination with IQ. Methods of dual-task testing varied across studies, particularly in relation to dual-task combinations, equation of single-task performance between groups, measurement and reporting of dual-task performance for each single-task, and task priority instructions. Thematic content of the included studies were: (1) structural interference to dual-tasking; (2) etiology-based differences in dual-tasking; (3) gait and balance dual-task performance; (4) testing executive function using dual-task paradigms; and (5) training effect on dual-task performance. Although the evidence consistently supported the intact dual-tasking ability of individuals with ID, the pattern of dual-task interference was inconsistent. Likewise, the evidence was inconclusive regarding dual-task deficit specific to individuals with ID because of heterogeneity in dual-task study designs among included studies.