Task Assignment Algorithm Research Articles

Efficient Routing for Precedence-Constrained Package Delivery for Heterogeneous Vehicles

This paper studies the precedence-constrained task assignment problem for a team of heterogeneous vehicles to deliver packages to a set of dispersed customers subject to precedence constraints that specify which customers need to be visited before which other customers. A truck and a micro drone with complementary capabilities are employed where the truck is restricted to travel in a street network and the micro drone, restricted by its loading capacity and operation range, can fly from the truck to perform the last-mile package deliveries. The objective is to minimize the time to serve all the customers respecting every precedence constraint. The problem is shown to be NP-hard, and a lower bound on the optimal time to serve all the customers is constructed by using tools from graph theory. Then, integrating with a topological sorting technique, several heuristic task assignment algorithms are proposed to solve the task assignment problem. Numerical simulations show the superior performances of the proposed algorithms compared with popular genetic algorithms. Note to Practitioners —This paper presents several task assignment algorithms for the precedence-constrained package delivery for the team of a truck and a micro drone. The truck can carry the drone moving in a street network, while the drone completes the last-mile package deliveries. The practical contributions of this paper are fourfold. First, the precedence constraints on the ordering of the customers to be served are considered, which enables complex logistic scheduling for customers prioritized according to their urgency or importance. Second, the package delivery optimization problem is shown to be NP-hard, which clearly shows the need for creative approximation algorithms to solve the problem. Third, the constructed lower bound on the optimal time to serve all the customers helps to clarify for practitioners the limiting performance of a feasible solution. Fourth, the proposed task assignment algorithms are efficient and can be adapted for real scenarios.

Investigations on System Modeling Simulations for Solving Heterogeneous WSN Task Assignment Problem using Multilayer Feed Forward Neural Networks

In the modern smart era, various real-time applications are achieved through Heterogeneous Wireless Sensor Networks (HeWSN) comprising a set of multi-functional wireless sensor nodes. In comparison to legacy nodes, the contemporary nodes are smaller and faster with multi-sensing capabilities. The real-time application missions are achieved by effectively assigning tasks to the ideal nodes in the HeWSN by employing a task assignment algorithm. Though various WSN tasks assignment algorithms have been proposed from time to time, it requires maximal human intervention to achieve the application missions. To mitigate human intervention, the task assignment in WSN could be powered by machine learning techniques. Despite the technological advancements, WSN is still assumed to be resource-constrained and employing a powerful machine learning technique may not be ideal. Investigations of the existing machine learning techniques catering to the needs of the WSN have been carried out and have led to the selection of Multilayer Feed Forward Network (MLFFN). Further, a HeWSN application is modelled and simulated to train the system. A total of 39,000 individual experimentations was performed by varying the hidden layers and training sets. Performance analysis was performed on the successful task assignment ratio. Investigations performed are (i) finding the highest successful task assignment ratio, (ii) configuration of the hidden layer and training set ranges in which the maximum successful task assignment ratio is obtained. In a nutshell, in-depth discussions to adapt MLFFN in a HeWSN application has been presented along with the future scope of the work

Preference-aware Task Assignment in Spatial Crowdsourcing: from Individuals to Groups

With the ubiquity of smart devices, Spatial Crowdsourcing (SC) has emerged as a new transformative platform engaging mobile users to perform tasks by physically traveling to specified locations. In this paper, we propose a novel preference-aware task assignment system based on workers' temporal preferences, which includes two components: History-based Context-aware Tensor Decomposition (HCTD) for workers' temporal preferences modeling and preference-aware task assignment. We model workers' preferences with a three-dimension tensor. Supplementing missing entries of the tensor through HCTD with the assistant of historical data and other two context matrices, we recover workers' preferences for different categories of tasks in different time slots. Several preference-aware individual task assignment algorithms are then devised, aiming to maximize the total number of task assignments at every time instance, where we give higher priorities to workers who are more interested in the tasks. To make our proposed framework applicable to more scenarios, we further optimize the original framework by proposing strategies to allow each task to be assigned to a group of workers such that the task can be completed by these workers simultaneously, where workers' tolerable waiting time and consensus are considered. We conduct extensive experiments using a real dataset, verifying the practicability of the methods.

Computational Offloading for Energy Constrained Devices in Multi-Hop Cooperative Networks

As the number of mobile devices that natively support ad hoc communication protocols increase, large ad hoc networks can be created not only to facilitate communication among the mobile devices, but also to assist devices that are executing computationally intensive applications. Prior work has developed computation offloading systems for mobile devices, but this work has focused exclusively on offloading to single hop neighbors, due in part to the practical challenges of setting up multi-hop networks using existing ad hoc communication protocols. However, limiting the offloading of computation to one-hop neighbors inherently restricts the number of devices that can participate in the distributed computation. By presenting a heuristic, aimed at avoiding partitioning the network, as well as an iterative task assignment algorithm that can optimize the assignment of computational tasks to devices in a multi-hop cooperative network, we are able to evaluate the effect of computational offloading in multi-hop networks. Experimental results, obtained from an implementation on Android devices, are integrated with an analytical model that enables the evaluation of system performance under a variety of conditions. These experimental and analytic results demonstrate the benefit of enabling computation offloading to all devices in a multi-hop cooperative network.

A Novel Hybrid Auction Algorithm for Multi-UAVs Dynamic Task Assignment

The auction algorithm is a widely used method for task assignments. However, most existing auction algorithms yield poor performance when applied to multi-UAVs dynamic task assignment. To end this, we propose a novel hybrid “Two-Stage” auction algorithm based on the hierarchical decision mechanism and an improved objective function, which simultaneously realizes heterogeneous multi-UAVs dynamic task assignment with limited resources of each UAV and avoidance obstacle path planning. In the first stage, according to the novel proposed hierarchical decision mechanism, we select a task that is urgently needed to be performed in the task group by using the decision function and three attribute values of tasks. After the first stage, it will result in a reasonable auction sequence, instead of random auction sequence as in previous algorithms. In the second stage, by considering the coverage factor and adaptive-limitation penalty term, a novel objective function is proposed and directs related UAVs for auction. In addition, we combine the structural advantages of the centralized and distributed auction algorithm, which greatly promotes its performance in dynamic task assignment. The experimental results demonstrate that the proposed method outperforms many state-of-the-art models in efficiency and robustness.

Delay-Constrained Teleoperation Task Scheduling and Assignment for Human+Machine Hybrid Activities Over FiWi Enhanced Networks

With the advent of semi-autonomous robotic assistance systems, their integration into human teams is starting to gain steam as part of the vision of human+machine hybrid activities. Unlike their fully autonomous counterparts, semi-autonomous robotic systems mainly rely on human assistance from time to time via teleoperation when human expertise is needed to accomplish a given task. As these robots will need to request human assistance via teleoperation, mapping these requests to human teleoperators stands as a difficult optimization problem. In this paper, after shedding some light on our envisioned FiWi enhanced network infrastructure and its role in realizing the emerging Tactile Internet, we formulate the problem of joint prioritized scheduling and assignment of delay-constrained teleoperation tasks to human operators with the objective to minimize the average weighted task completion time, maximum tardiness, and average operational expenditure (OPEX) per task. We then propose our context-aware prioritized scheduling and task assignment (CAPSTA) algorithm to achieve suitable trade-offs between the contradicting objectives of the problem. Further, to estimate the end-to-end packet delay of local and non-local teleoperation over FiWi enhanced networks, we develop our analytical framework, which flexibly allows for the coexistence of conventional human-to-human (H2H) and haptic human-to-machine (H2M) traffic.

A Workload Balanced Algorithm for Task Assignment and Path Planning of Inhomogeneous Autonomous Underwater Vehicle System

Task assignment is an important research topic in multiple autonomous underwater vehicle (AUV) cooperative working system. However, many studies concentrate on minimizing total distance of AUVs serving targets at different locations, and mostly do not pay attention to workload balance among inhomogeneous AUVs. What is more, most of them do not think of the effect of ocean current while distributing tasks. To solve these problems, a novel dual competition strategy based on self-organizing map (SOM) neural network is put forward. An AUV makes use of surplus sailing distance to a target when it competes with others for engaging the target. In order to fulfill a balanced task assignment among AUVs, a task balance coefficient is also proposed. Meanwhile, a hybrid path planning approach is applied to guide AUVs to reach their targets safely. The good performance of the proposed algorithm for distributing tasks among AUVs is demonstrated through simulation studies. From the comparison study of SOM algorithm, Hungarian algorithm, $k$ -means algorithm, and the proposed dual competition strategy, it can be found that the task assignment with the proposed strategy is more rational and fair.

When Crowdsourcing Meets Social IoT: An Efficient Privacy-Preserving Incentive Mechanism

Crowdsourcing is an effective paradigm in human centric computing for addressing problems by utilizing human computation power, especially in booming social Internet of Things (IoT). By leveraging mutual friendship between computing entities (i.e., workers), collaborative tasks can thus be routed and finally fulfilled by multihop friends with high expertise. However, crowdsourcing in social IoT may reveal the privacy of task requesters which results in a large dilemma. In this paper, we focus on designing a multihop routing incentive mechanism which can also preserve task requester’s privacy. Specifically, a utility maximization problem under privacy and budget feasibility constraints is formulated. Defining the conditions for privacy insurance, we give guidelines on how many subtasks should an entire task be divided into, and analyze the tradeoff between privacy and task accuracy. To enable efficient crowdsourcing task routing in social IoT, we first consider 1-hop myopic routing case and propose a near-optimal task assignment algorithm with 1/2 approximation ratio for an arbitrary prior knowledge. We further design multihop payment policy to establish an equilibrium where workers are motivated to forward subtasks to their friends with the best expertise. The extensive simulations validate that our mechanism achieves a high level of average information gain with modest privacy guarantee.

A Two-Layer Task Assignment Algorithm for UAV Swarm Based on Feature Weight Clustering

For the large-scale operations of unmanned aerial vehicle (UAV) swarm and the large number of UAVs, this paper proposes a two-layer task and resource assignment algorithm based on feature weight clustering. According to the numbers and types of task resources of each UAV and the distances between different UAVs, the UAV swarm is divided into multiple UAV clusters, and the large-scale allocation problem is transformed into several related small-scale problems. A two-layer task assignment algorithm based on the consensus-based bundle algorithm (CBBA) is proposed, and this algorithm uses different consensus rules between clusters and within clusters, which ensures that the UAV swarm gets a conflict-free task assignment solution in real time. The simulation results show that the algorithm can assign tasks effectively and efficiently when the number of UAVs and targets is large.

Path planning and guidance algorithms for SAR formation reconfiguration: Comparison between centralized and decentralized approaches

This paper presents innovative approaches for guidance of spacecraft formations during reconfiguration, developed under the paradigms of cluster autonomy and safe maneuverability. A task-assignment algorithm identifies those satellites that need to be rearranged in order to minimize the total propellant consumption. The decision process relies on convex optimization procedures which estimate control laws and maneuvering trajectories, accounting for thruster technology constraints and collision avoidance. The path-planning has a twofold implementation. In the centralized approach, the reconfiguration strategy is elaborated by a single unit: the maneuvering trajectories are generated considering solely collision avoidance constraints at expenses of computational costs. The alternative method is conceived such that each satellite autonomously determines its tasks. This de-centralized architecture allows the algorithm to run faster, but entails a higher traffic on the intersatellite communication link. In addition, collision avoidance is approximated to make each optimization independent from the other. For preliminary validation of the developed routines, a specific mission scenario is considered, with a distributed Synthetic Aperture Radar in a low Earth orbit that has to be reconfigured for tomographic applications. Simulation results show that, once remote-sensing requirements are specified within the limits set by standard products, the developed algorithms can manage autonomously the maneuver towards the corresponding operative pattern. In details, the centralized approach allows the reconfiguration to be handled with mean delta-v savings around 30%. On the contrary, strategies are elaborated by the decentralized planners about 7 times faster.

Social-Aware UAV-Assisted Mobile Crowd Sensing in Stochastic and Dynamic Environments for Disaster Relief Networks

In this paper, we consider a social-aware unmanned aerial vehicle (UAV) assisted mobile crowd sensing (MCS) system for disaster relief networks, and investigate how to recruit suitable UAVs to perform sensing tasks in stochastic and dynamic environments (both UAVs and tasks arrive stochastically). We formulate the task assignment problem into a dynamic matching problem, and propose a multiple-waitlist based task assignment (MWTA) algorithm to find the stable matching in time-varying environment. We prove that MWTA can achieve the dynamic stability containing the strategy-proofness, efficiency, and envy-freeness. Simulation results demonstrate the performance improvement of our proposed scheme compared with the traditional matching algorithm applied to deterministic matching model in stochastic setting.

Target-bundled genetic algorithm for multi-unmanned aerial vehicle cooperative task assignment considering precedence constraints

This paper presents a modified genetic algorithm using target-bundle-based encoding and tailored genetic operators to effectively tackle cooperative multiple task assignment problems of heterogeneous unmanned aerial vehicles. In the cooperative multiple task assignment problem, multiple tasks including reconnaissance, attack, and verification have to be sequentially performed on each target (e.g. ground control stations, tanks, etc.) by one or multiple unmanned aerial vehicles. Due to the precedence constraints of different tasks, a singular task-execution order may cause deadlock situations, i.e. one or multiple unmanned aerial vehicles being trapped in infinite waiting loops. To address this problem, a target-bundled genetic algorithm is proposed. As a key element of target-bundled genetic algorithm, target-bundle-based encoding is derived to fix multiple tasks on each target as a target-bundle. And individuals are generated by fixing the task-execution order on each target-bundle subject to task precedence constraints. During the evolution process, bundle-exchange crossover and multi-type mutation operators are customized to generate deadlock-free offspring. Besides, the time coordination method is developed to ensure that task-execution time satisfies task precedence constraints. The comparison results on numerical simulations demonstrate that target-bundled genetic algorithm outperforms particle swarm optimization and random search methods in terms of optimality and efficiency.

Research on Task Assignment Model and Algorithm of Online Picking System Based on Multi-objective

In view of the task assignment problem of the picking station in the online Parts-to-Picker Picking System, a mathematical model was established that targeting the minimum number of total rounds of the bins and the equalization of the picking of each station, constrained by the uniqueness of the order attribution and the maximum amount of orders processed by the workstation. Then, the solution flow and method of multi-objective function problem based on Genetic Algorithm are designed. The algorithm constrains the secondary objective function and defines the rules of the objective function constraint. Finally, an empirical study on the actual Parts-to-Picker System of an e-commerce enterprise is carried out. The results show that the algorithm can effectively reduce the two objective function values and verify the effectiveness of the algorithm.

Task Assignment Algorithm Based on Trust in Volunteer Computing Platforms

In volunteer computing (VC), the expected availability time and the actual availability time provided by volunteer nodes (VNs) are usually inconsistent. Scheduling tasks with precedence constraints in VC under this situation is a new challenge. In this paper, we propose two novel task assignment algorithms to minimize completion time (makespan) by a flexible task assignment. Firstly, this paper proposes a reliability model, which uses a simple fuzzy model to predict the time interval provided by a VN. This reliability model can reduce inconsistencies between the expected availability time and actual availability time. Secondly, based on the reliability model, this paper proposes an algorithm called EFTT (Earliest Finish Task based on Trust, EFTT), which can minimize makespan. However, EFTT may induce resource waste in task assignment. To make full use of computing resources and reduce task segmentation rate, an algorithm IEFTT (improved earliest finish task based on trust, IEFTT) is further proposed. Finally, experimental results verify the efficiency of the proposed algorithms.

Preference-Aware Task Assignment in Spatial Crowdsourcing

With the ubiquity of smart devices, Spatial Crowdsourcing (SC) has emerged as a new transformative platform that engages mobile users to perform spatio-temporal tasks by physically traveling to specified locations. Thus, various SC techniques have been studied for performance optimization, among which one of the major challenges is how to assign workers the tasks that they are really interested in and willing to perform. In this paper, we propose a novel preference-aware spatial task assignment system based on workers’ temporal preferences, which consists of two components: History-based Context-aware Tensor Decomposition (HCTD) for workers’ temporal preferences modeling and preference-aware task assignment. We model worker preferences with a three-dimension tensor (worker-task-time). Supplementing the missing entries of the tensor through HCTD with the assistant of historical data and other two context matrices, we recover worker preferences for different categories of tasks in different time slots. Several preference-aware task assignment algorithms are then devised, aiming to maximize the total number of task assignments at every time instance, in which we give higher priorities to the workers who are more interested in the tasks. We conduct extensive experiments using a real dataset, verifying the practicability of our proposed methods.

Transient fault aware application partitioning computational offloading algorithm in microservices based mobile cloudlet networks

Mobile Cloudlet Computing paradigm (MCC) allows execution of resource-intensive mobile applications using computation cloud resources by exploiting computational offloading method for resource-constrained mobile devices. Whereas, computational offloading needs the mobile application to be partitioned during the execution in the MCC so that total execution cost is minimized. In the MCC, at the run-time network contexts (i.e., network bandwidth, signal strength, latency, etc.) are intermittently changed, and transient failures (due to temporary network connection failure, services busy, database disk out of storage) often occur for a short period of time. Therefore, transient failure aware partitioning of the mobile application at run-time is a challenging task. Since, existing MCC offers computational monolithic services by exploiting heavyweight virtual machines, which incurs with long VM startup time and high overhead, and these cannot meet the requirements of fine-grained microservices applications (e.g., E-healthcare, E-business, 3D-Game, and Augmented Reality). To cope up with prior issues, we propose microservices based mobile cloud platform by exploiting containerization which replaces heavyweight virtual machines, and we propose the application partitioning task assignment (APTA) algorithm which determines application partitioning at run-time and adopts the fault aware (FA) policy to execute microservices applications robustly without interruption in the MCC. Simulation results validate that the proposed microservices mobile cloud platform not only shrinks the setup time of run-time platform but also reduce the energy consumption of nodes and improve the application response time by exploiting APTA and FA to the existing VM based MCC and application partitioning strategies.

PriRadar: A Privacy-Preserving Framework for Spatial Crowdsourcing

Privacy leakage is a serious issue in spatial crowdsourcing in various scenarios. In this paper, we study privacy protection in spatial crowdsourcing. The main challenge is to efficiently assign tasks to nearby workers without needing to know the exact locations of tasks and workers. To address this problem, we propose a privacy-preserving framework without online trusted third parties. We devise a grid-based location protection method, which can protect the locations of workers and tasks while keeping the distance-aware information on the protected locations such that we can quantify the distance between tasks and workers. We propose an efficient task assignment algorithm, which can instantly assign tasks to nearby workers on encrypted data. To protect the task content, we leverage both attribute-based encryption and symmetric-key encryption to establish secure channels through servers, which ensures that the task is delivered securely and accurately by any untrusted server. Moreover, we analyze the security properties of our method. We have conducted real experiments on real-world datasets. Experimental results show that our method outperforms existing approaches.

Video transcoding scheme of multimedia data-hiding for multiform resources based on intra-cloud

Recently, intra-cloud research has been actively conducted to reduce the waste of idle resources in distributed desktops and to increase resource utilization. Intra-cloud integrates the idle resources of distributed desktops to provide computing and storage services to users. Existing intra-cloud have only studied storage of large files and simple computing services. Research is needed for computing services of multimedia field such as video and audio in the intra-cloud. This paper proposes a diversify scheme for multiform video resources (DSMVR), which is a video transcoding scheme of multimedia data-hiding based on the parallel computing framework and the intra-cloud environment, in order to transcode for multiform resource types within the intra-cloud, which composed to computing infrastructure using legacy desktops. Its target users are community user groups within a certain size. By using a small-scale server group, parallel processing framework and improved task assignment algorithm, high-speed video transcoding can be realized by using ffmpeg, which is a vast software suite of libraries and programs designed for handling video, audio, and other multimedia files and streams, and different-definition videos are generated step by step at high speed. By using the DSMVR scheme, the size of a task can be dynamically analyzed in order to select the number of task processing servers required, thus ensuring the high scalability of the DSMVR. Thanks to these operations, the user can smoothly play videos at resolutions that are suitable for different smart devices.

Location-Based Online Task Assignment and Path Planning for Mobile Crowdsensing

Mobile crowdsensing has been a promising and cost-effective sensing paradigm for the Internet of Things by exploiting the proliferation and prevalence of sensor-rich mobile devices held/worn by mobile users. In this paper, we study the task assignment and path planning problem in mobile crowdsensing, which aims to maximize total task quality under constraints of user travel distance budgets. We first formulate the problem mathematically when all task and user arrival information is known a priori and prove it to be NP-hard. Then, we focus on studying the scenarios where users and tasks arrive dynamically and accordingly design four online task assignment algorithms, including quality/progress-based algorithm (QPA), task-density-based algorithm (TDA), travel-distance-balance-based algorithm (DBA), and bio-inspired travel-distance-balance-based algorithm (B-DBA). All the four algorithms work online for task assignment upon arrival of a new user. The former three algorithms work in a greedy manner for assigning tasks, one task each time, where the QPA prefers the task leading to largest ratio of task quality increment to travel cost, the TDA tends to guide user to high-task-density areas, and the DBA further considers travel distance balance information. The last algorithm B-DBA integrates the travel-distance-balance-aware metric in the DBA and bio-inspired search for further improved task assignment performance. Complexities of the proposed algorithms are deduced. Simulation results validate the effectiveness of our algorithms; B-DBA has the best performance among the four algorithms in terms of task quality, and furthermore, it outperforms existing work in this area.

Swarm UAVs Task and Resource Dynamic Assignment Algorithm Based on Task Sequence Mechanism

This paper proposed an unmanned air vehicle (UAV) swarm task and resource dynamic assignment algorithm based on task sequence mechanism. By establishing a task sequence, each UAV strictly separates the necessary task time and synchronization waiting time. For the found new targets, each UAV quickly determines its available time period. According to the available time and task resources, an auction algorithm and consensus algorithm are used to decompose the task assignment into initial distributed assignment phase and swarm consensus phase to develop real-time conflict-free task solutions for UAV swarm. The simulation experiment verified that the algorithm could assign different tasks to UAVs in a real-time distributed manner with the limited resources of each UAV.