Task Assignment Algorithm Research Articles

Multi-Mission Oriented Joint Optimization of Task Assignment and Flight Path Planning for Heterogeneous UAV Cluster

This paper puts forward a joint optimization algorithm of task assignment and flight path planning for a heterogeneous unmanned aerial vehicle (UAV) cluster in a multi-mission scenario (MMS). The basis of the proposed algorithm is to establish constraint and threat models of a heterogeneous UAV cluster to simultaneously minimize range and maximize value gain and survival probability in an MMS under the constraints of task payload, range, and task requirement. On one hand, the objective function for the heterogeneous UAV cluster within an MMS is derived and it is adopted as a metric for assessing the performance of the joint optimization in task assignment and flight path planning. On the other hand, since the formulated joint optimization problem is a multi-objective, non-linear, and non-convex optimization model due to its multiple decision variables and constraints, the roulette wheel selection (RWS) principle and the elite strategy (ES) are introduced in an ant colony optimization (ACO) to solve the complex optimization model. The simulation results indicate that the proposed algorithm is superior and more efficient compared to other approaches.

EN‐DADA: Node task assignment algorithm for energy harvesting wireless multimedia sensor networks

AbstractToday, a directional wireless multimedia sensor network is a popular environment for solving the task assignment problem. Achieving long‐term frontal monitoring of moving objects is a crucial challenge for scholars in this field. Utilizing directional sensors equipped with energy harvesting is an effective technique to enhance network performance. In this way, the energy of nodes is no longer limited to batteries and can result in better frontal monitoring. In this method, each sensor categorizes tasks based on its own energy, allowing the determination of task execution nodes through bidding. The present study proposes a distributed algorithm for directional task assignment, EN‐DADA, based on energy harvesting. The task was first classified to determine the candidate node set that could execute the task, and then the task assignment was determined according to the monitoring income of each node in the candidate node set. The comparative analysis confirmed that the proposed method had advantages in terms of task revenue and network lifetime when using the same energy harvesting model.

Preference-Aware Group Task Assignment in Spatial Crowdsourcing: Effectiveness and Efficiency

With the diffusion of online mobile devices with geo-location capabilities, the infrastructure necessary for real-world deployment of Spatial Crowdsourcing (SC), where so-called mobile workers are assigned location-sensitive tasks, is in place. Some SC tasks cannot be completed by a single worker due to their complexity, but rather must be assigned to and completed by a group of users. Achieving such group assignments that satisfy all group members evenly is an open challenge. To address this challenge, we propose a novel preference-aware group task assignment framework encompassing two components: Mutual Information-based Preference Modeling (MIPM) and Preference-aware Group Task Assignment (PGTA). The MIPM component learns the preferences of groups contrastively by maximizing the mutual information between workers and worker groups based on worker-task and group-task interaction data and by using an attention mechanism to weight group members adaptively. In addition, curriculum negative sampling is adopted to generate a small number of negative workers for each worker group, following the principles of curriculum learning. Next, the PGTA component offers an optimal task assignment algorithm that employs tree decomposition to assign tasks to appropriate worker groups, with the aim of maximizing the number of task assignments while prioritizing more interested groups when assigning tasks. The task assignment framework also features preference-constrained pruning of unpromising worker groups to speed up the assignment process. Finally, we report extensive experiments that offer evidence of the effectiveness and practicality of the paper's proposal.

A Two-Stage Distributed Task Assignment Algorithm Based on Contract Net Protocol for Multi-UAV Cooperative Reconnaissance Task Reassignment in Dynamic Environments.

Multi-UAV systems have been widely used in reconnaissance, disaster relief, communication, and other fields. However, many dynamic events can cause a partial failure of the original mission during the mission execution process, in which case task reassignment should be carried out. How to reassign resources and tasks in multi-dynamic, multi-target, and multi-constraint events becomes a core issue in the enhancement of combat efficiency. This paper establishes a model of multi-UAV cooperative reconnaissance task reassignment that comprehensively considers various dynamic factors such as UAV performance differences, size of target areas, and time window constraints. Then, a two-stage distributed task assignment algorithm (TS-DTA) is presented to achieve multi-task reassignment in dynamic environments. Finally, this paper verifies the effectiveness of the TS-DTA algorithm through simulation experiments and analyzes its performance through comparative experiments. The experimental results show that the TS-DTA algorithm can efficiently solve the task reassignment problem in dynamic environments while effectively reducing the communication burden of UAV formations.

Joint time and energy-optimal approach to allocate task to actors in wireless sensor actor networks

In most applications of wireless sensor actor networks (WSANs) in harsh environments, minimizing the make-span and maximizing the residual energy are of paramount importance. The majority of existing task allocation approaches is typically concerned with one of the energy savings or time constraints. These approaches do not consider the types and various features of tasks WSANs may need to perform. Moreover, the limitation on the capacity of task queues has been overlooked by these approaches, and thus may not be applicable to some types of real applications such as search and rescue missions. To this end, a novel queue length aware task allocation (QLATA) approach is proposed that considers the energy consumption as well as the make-span. QLATA is aware of task queues constraint, types of tasks, and the distribution necessities of WSANs with hybrid architecture. QLATA comprises of two protocols, namely a Make-span Calculation Protocol (MsCP) and an Energy Consumption Calculation Protocol (ECCP). Through considering both time and energy, QLATA makes a tradeoff between minimizing make-span and maximizing the residual energies of actors by solving the joint optimization for make-span and energy consumption, simultaneously. A series of extensive simulation results on typical scenarios show shorter make-span and higher remaining energy in comparison to when opportunistic load balancing (OLB), minimum make-span task allocation (MMTa), stochastic task allocation (STA), and task assignment algorithm based on quasi-newton interior point (TA-QNIP) approaches is used. It is also shown that the proposed approach performs significantly better than the four compared algorithms in terms of network lifetime.

Consensus-based clustering and data aggregation in decentralized network of multi-agent systems.

Multi-agent systems are promising for applications in various fields. However, they require optimization algorithms that can handle large number of agents and heterogeneously connected networks in clustered environments. Planning algorithms performed in the decentralized communication model and clustered environment require precise knowledge about cluster information by compensating noise from other clusters. This article proposes a decentralized data aggregation algorithm using consensus method to perform COUNT and SUM aggregation in a clustered environment. The proposed algorithm introduces a trust value to perform accurate aggregation on cluster level. The correction parameter is used to adjust the accuracy of the solution and the computation time. The proposed algorithm is evaluated in simulations with large and sparse networks and small bandwidth. The results show that the proposed algorithm can achieve convergence on the aggregated data with reasonable accuracy and convergence time. In the future, the proposed tools will be useful for developing a robust decentralized task assignment algorithm in a heterogeneous multi-agent multi-task environment.

Edge Trusted Sharing: Task-Driven Decentralized Resources Collaborate in IoT

6G is committed to providing a fully connected world. The deployment and application of 6G technology in the Internet of Things (IoT) can efficiently collaborate IoT resources and realize resource sharing, which mainly encourages IoT development. However, due to the lack of trust between IoT resources, security and privacy become the main challenges. As an emerging technology, blockchain can solve the trust-absence issues and provide more benefits, but the introduction of blockchain also brings problems for IoT resource collaboration and sharing. This article first proposes a blockchain-enabled edge resource sharing (BEERS) architecture by combining blockchain and edge computing technology. Then, based on a typical resource sharing and collaboration scenario, the joint optimization problem of resource scheduling and task assignment (JRSTA) is constructed. We decompose JRSTA into a primal problem and a master problem and design a greedy-based task assignment (GBTA) algorithm to solve the primal problem. Based on the GBTA algorithm, the resource scheduling and task assignment (RSTA) algorithm is developed. Next, we design a layered parallel edge resource scheduling and task assignment (LPRSTA) mechanism to improve practicability. Finally, we analyze the security and the performance of our proposed architecture and algorithms. The results show that the proposed architecture can support the secure collaboration of IoT resources, and the proposed algorithm can achieve effective resource scheduling and task assignment.

Energy Criticality Avoidance-Based Delay Minimization Ant Colony Algorithm for Task Assignment in Mobile-Server-Assisted Mobile Edge Computing.

Mobile edge computing has been an important computing paradigm for providing delay-sensitive and computation-intensive services to mobile users. In this paper, we study the problem of the joint optimization of task assignment and energy management in a mobile-server-assisted edge computing network, where mobile servers can provide assisted task offloading services on behalf of the fixed servers at the network edge. The design objective is to minimize the system delay. As far as we know, our paper presents the first work that improves the quality of service of the whole system from a long-term aspect by prolonging the operational time of assisted mobile servers. We formulate the system delay minimization problem as a mixed-integer programming (MIP) problem. Due to the NP-hardness of this problem, we propose a dynamic energy criticality avoidance-based delay minimization ant colony algorithm (EACO), which strives for a balance between delay minimization for offloaded tasks and operational time maximization for mobile servers. We present a detailed algorithm design and deduce its computational complexity. We conduct extensive simulations, and the results demonstrate the high performance of the proposed algorithm compared to the benchmark algorithms.

Planning-based mobile crowdsourcing bidirectional multi-stage online task assignment

With the development of mobile Internet networks, Mobile CrowdSourcing (MCS) is becoming increasingly popular in online scenarios. This poses a new challenge to the task assignment mechanism in the mobile crowdsourcing system. Existing task assignment schemes are mainly platform-centric and worker-centric. The previous focuses are on the total utility, while the latter focuses on the interests of workers. In actual online scenarios, current researches are difficult to take both platform and worker wishes into consideration. In this paper, a Bidirectional Multi-Stage Online task assignment algorithm (BMSO) is proposed. It divides the task assignment process into three stages. The first stage selects tasks not only satisfy the basic Spatio-temporal constraints but also have a high acceptance value for workers by using two rounds of screening. The second stage combines price forecasting with reverse auctions, considering both of the platform’s benefits and workers’ wish. In the third stage, the workers and tasks which have not completed their assignments are assigned twice to improve the total utility. Furthermore, we solve the problem that traditional task-planning methods are unsuitable for mobile crowdsourcing scenarios. This paper proposes a Dynamic Voronoi diagram-based Task Planning algorithm (DVTP) that generates a Voronoi diagram using the real-time positions of the workers to find the most efficient task execution sequence for the workers. Finally, we conduct comprehensive experiments on real datasets. The results demonstrate our proposed BMSO algorithm achieves superior performances on total utility, running time, number of assigned tasks, and task assignment rate compared to other baseline algorithms.

Priority-aware task assignment and path planning for efficient and load-balanced multi-UAV operation

Unmanned aerial vehicles (UAVs) have emerged as a viable option in numerous real-world applications. In large mission planning systems, demanding the effective completion of a wide variety of tasks, multi-UAV systems are significantly preferred. Collaborative task assignment and path planning have a significant impact on autonomous operation of multi-UAV systems. In this study, we propose a priority-aware task assignment and path planning (AMTP) algorithm based on actor-critic multi-agent reinforcement learning for multi-UAV operations. We utilize an actor-critic multi-agent framework based on centralized training and decentralized execution for task assignment and path planning of heterogeneous UAVs. A multi-agent deep deterministic policy gradient framework is used to train the system to deal with both the elements of the dynamic environment and the heterogeneity of UAVs and tasks. The critic network evaluates the policy by maintaining the global network state information and combines the actions of agents to simplify the training state. On the other hand, agents decide which actions to comply with their local observations to improve the policy. The performance evaluation results obtained through simulation show that the proposed AMTP outperforms existing methods in terms of task completion rate, load balancing, energy efficiency, and convergence time.

Research on task assignment algorithm of heterogeneous aircraft cooperative cluster in dynamic scene

With the aim of minimizing task completion time and reducing resource consumption, this paper investigates the dynamic task allocation problem of a heterogeneous aircraft cooperative cluster with multiple fire spots in the forest. Firstly, we establish the fire point propagation model and task assignment model to enhance task assignment accuracy. Based on this, we propose the Chaotic Cosine Fusing T-Distribution Variation and Quasi-Reflection Learning Grey Wolf Optimization algorithm (CTQGWO), which integrates distributed mutation and quasi-reflective learning. By incorporating chaos population initialization of the Tent map, adaptive control parameter adjustment strategy, distributed mutation, and quasi-reflective learning strategy, we can enhance the convergence speed and accuracy of the algorithm. Finally, taking into account the complexity of the forest and the instability of the aircraft, we develop a task reassignment algorithm that can quickly allocate and reduce the number of communications. Through experiments, our algorithm enables rapid decision-making for forest fire emergency scheduling, under the condition of limited forest fire resources, effectively reducing the total rescue time and minimizing the consumption of rescue resources.

Extensions of Receding Horizon Task Assignment for Area Coverage in Dynamic Environments

This study presents a task reassignment strategy that can flexibly handle changes in dynamic environments. Most existing studies have only considered static environments where the mission is predetermined and unexpected events do not occur. However, in practice, the environment in which unmanned aerial vehicles perform their mission is complex and includes various pop-up events. Therefore, task reassignment is essential through re-optimization to adjust the previous plan, which is no longer optimal whenever dynamic events occur. The importance of this study is that the modified receding horizon task assignment algorithm is applied for accurate and efficient task reassignment. In addition, this study aims to provide a complete system architecture from operator input to motor commands for autonomous area coverage missions. Reliable performance validation is also performed through various simulations and a challenging outdoor flight experiment with real hardware implementation.

Hierarchical multi-UAVs task assignment based on dominance rough sets

The task assignment of multi-UAVs is important to realize the effective cooperation and enhance the comprehensive performance of multi-UAVs. Heuristic algorithms have become popular methods to solve this problem. As random searching algorithms, the performance of existing heuristic algorithms degrades when solving large-scale or high-dimensional problems, and depends on additional parameter settings. Unlike the idea of heuristic algorithms approximating the optimal solution by searching for the solution space, this paper takes a new approach of gradually acquiring the assignment result based on human cognition, called a hierarchical multi-UAVs task assignment by the optimal ranking strategy based on dominance rough sets (HORD). HORD first establishes the optimal ranking strategy for task assignment of all UAVs based on the dominance rough set theory. Then, a hierarchical multi-UAV task assignment algorithm is built. Comparative experimental results verify that the HORD algorithm speeds up the assignment and increases the total utility without relying on additional parameters.

Group-Based Distributed Auction Algorithms for Multi-Robot Task Assignment

This paper studies the multi-robot task assignment problem in which a fleet of dispersed robots needs to efficiently transport a set of dynamically appearing packages from their initial locations to corresponding destinations within prescribed time-windows. Each robot can carry multiple packages simultaneously within its capacity. Given a sufficiently large robot fleet, the objective is to minimize the robots’ total travel time to transport the packages within their respective time-window constraints. The problem is shown to be NP-hard, and we design two group-based distributed auction algorithms to solve this task assignment problem. Guided by the auction algorithms, robots first distributively calculate feasible package groups that they can serve, and then communicate to find an assignment of package groups. We quantify the potential of the algorithms with respect to the number of employed robots and the capacity of the robots by considering the robots’ total travel time to transport all packages. Simulation results show that the designed algorithms are competitive compared with an exact centralized Integer Linear Program representation solved with the commercial solver Gurobi, and superior to popular greedy algorithms and a heuristic distributed task allocation method. Note to Practitioners—This work presents two group-based distributed auction algorithms for a sufficiently large fleet of robots to efficiently transport a set of dynamically appearing dispersed packages from their initial locations to corresponding destinations within prescribed time-windows. Each robot can carry multiple packages simultaneously within its capacity, and the objective is to minimize the robots’ total travel time to transport all the packages within the prescribed time-windows. The paper’s practical contributions are threefold: First, the multi-robot task assignment problem is formulated through a robot-group assignment strategy, which enables complex logistic scheduling for tasks grouped according to their distributions and time-windows. Second, we theoretically show that the multi-robot task assignment problem is an NP-hard problem, which implies the necessity for designing approximate task assignment algorithms. Third, the proposed group-based distributed auction algorithms are efficient and can be adapted for real scenarios.

PriTAEC: Privacy-Preserving Task Assignment Based on Oblivious Transfer and Edge Computing in VANET

Spatial crowdsourcing, e.g., Vehicular Ad-Hoc Network (VANET)-based spatial crowdsourcing, is a new distributed computing paradigm, in which task assignments highly rely on the wisdom of the crowdsourcing platform. However, with the increase in user data leakage incidents, the existing task assignment methods are no longer sufficient to meet the privacy requirements of users. In the existing VANET-based spatial crowdsourcing, task assignments are usually performed by a trusted third party based on the real locations of tasks and drivers (task performers), which may lead to the leakage of the users' locations. Furthermore, the drivers usually prefer to query the nearest tasks to them in a geometric range, at this point sending query requests to remote crowdsourcing servers increases unnecessary response delays. To assign tasks securely and efficiently, we propose a privacy-preserving task assignment scheme based on OT and edge computing (PriTAEC), which is the first to apply Oblivious Transfer (OT) and edge computing to preserve the location privacy of VANET-based spatial crowdsourcing. In the scheme, we first utilize Hilbert Curve and Bloom Filter to implement location range queries. Then, we use geohash location encoding and Oblivious Transfer to achieve fine-grained location matching. In particular, we design a task assignment algorithm with an offline-online phase to improve the efficiency of task assignments. Finally, we prove the security of the scheme and evaluate its performance, which shows our scheme is secure and efficient.

Approximated Assignment Algorithms for Unordered and Ordered Tasks in Data Shared MEC Systems

The appearance of Mobile Edge Computing (MEC) successfully solves the bottlenecks of traditional Cloud based networks. Since mobile edges, e.g., base stations, and mobile devices have certain data processing capabilities, it is not necessary to offload all the tasks to the cloud for handling. Therefore, it is quite important to decide the optimal task assignment in MEC systems, and a series of algorithms have been proposed. However, the existing algorithms ignored the data distribution during task assignment, so that their applied ranges are quite limit. Considering the data sharing is quite important in a MEC system, this paper studies task assignment algorithms in Data Shared Mobile Edge Computing Systems in detail. Specifically, three algorithms are proposed to deal with the unordered and ordered holistic tasks respectively. Meanwhile, the situation that the tasks are divisible is also considered, and two algorithms for rearranging the divisible tasks are proposed for different optimization goals. The hardness of the problem, the correctness, complexities, and ratio bounds of the proposed algorithms are analyzed theoretically. Finally, extensive experimental results are carried out. Both theoretical analysis and experiment results show that all the proposed algorithms have high performance in terms of latency, satisfied rate, and energy consumption.

Adjustable Fully Adaptive Cross-Entropy Algorithms for Task Assignment of Multi-UAVs

This paper investigates the multiple unmanned aerial vehicle (multi-UAV) cooperative task assignment problem. Specifically, we assign different types of UAVs to accomplish the classification, attack, and verification tasks of targets under resource, precedence, and timing constraints. Due to complex coupling among these tasks, we decompose the considered problem into two subproblems: one with continuous and independent tasks and another with continuous and correlative tasks. To solve them, we first present an adjustable, fully adaptive cross-entropy (AFACE) algorithm based on the cross-entropy (CE) method, which serves as a stepping stone for developing other algorithms. Secondly, to overcome task precedence in the first subproblem, we propose a mutually independent AFACE (MIAFACE) algorithm, which converges faster than the CE method when obtaining the optimal scheme vectors of these continuous and independent tasks. Thirdly, to deal with task coupling in the second subproblem, we present a mutually correlative AFACE (MCAFACE) algorithm to find the optimal scheme vectors of these continuous and correlative tasks, while its computational complexity is inferior to that of the MIAFACE algorithm. Finally, numerical simulations demonstrate that the proposed MIAFACE (MCAFACE, respectively) algorithm consumes less time than the existing algorithms for the continuous and independent (correlative, respectively) task assignment problem.

Multi-robot task assignment for serving people quarantined in multiple hotels during COVID-19 pandemic.

Efficiently combating with the coronavirus disease 2019 (COVID-19) has been challenging for medics, police and other service providers. To reduce human interaction, multi-robot systems are promising for performing various missions such as disinfection, monitoring, temperature measurement and delivering goods to people quarantined in prescribed homes and hotels. This paper studies the task assignment problem for multiple dispersed homogeneous robots to visit a set of prescribed hotels to perform tasks such as body temperature assessment or oropharyngeal swabs for people quarantined in the hotels while trying to minimize the robots' total operation time. Each robot can move to multiple hotels sequentially within its limited maximum operation time to provide the service. The task assignment problem generalizes the multiple traveling salesman problem, which is an NP-hard problem. The main contributions of the paper are twofold: (I) a lower bound on the robots' total operation time to serve all the people has been found based on graph theory, which can be used to approximately evaluate the optimality of an assignment solution; (II) several efficient marginal-cost-based task assignment algorithms are designed to assign the hotel-serving tasks to the robots. In the Monte Carlo simulations where different numbers of robots need to serve the people quarantined in 30 and 90 hotels, the designed task assignment algorithms can quickly (around 30 ms) calculate near-optimal assignment solutions (within 1.15 times of the optimal value). Numerical simulations show that the algorithms can lead to solutions that are close to the optimal compared with the competitive genetic algorithm and greedy algorithm.

Risk-Aware Contextual Learning for Edge-Assisted Crowdsourced Live Streaming

This paper proposes an edge-assisted crowdsourced live video transcoding approach where the transcoding capabilities of the edge transcoders are unknown and dynamic. The resilience and trustworthiness of highly unstable transcoders in decision making are characterized with mean-variance-based measures to avoid making highly risky decisions. The risk level of each device’s situation is assessed and two upper confidence bounds of the variance of transcoding performance are presented. Based on the derived bounds and by leveraging the contextual information of devices, two risk-aware contextual learning schemes are developed to efficiently estimate the transcoding capabilities of the edge devices. Combining context awareness and risk sensitivity, a novel transcoding task assignment and viewer association algorithm is proposed. Simulation results demonstrate that the proposed algorithm achieves robust task offloading with superior network utility performance as compared to the linear upper confidence bound and the risk-aware mean-variance upper confidence bound-based algorithms. In particular, an epoch-based task assignment strategy is designed to reduce the task switching costs incurred in assigning the same transcoding task to different transcoders over time. This strategy also reduces the computational time needed. Numerical results confirm that this strategy achieves up to 86.8% switching costs reduction and 92.3% computational time reduction.

Location-and-Preference Joint Prediction for Task Assignment in Spatial Crowdsourcing

With the rapid development of mobile networks and the ubiquity of mobile devices, spatial crowdsourcing (SC), which refers to assigning spatial–temporal tasks to moving workers, has drawn increasing attention. Thus, many researchers aim at various task assignment methods in SC. However, existing works generally consider workers’ location and preference categories separately. Ignorance of the correlation between them can often lead to poor assignment results. In this article, we propose a location-and-preference joint prediction model (JPM) to predict workers’ locations and preference categories jointly at each sample timestamp. Based on the predictive location probability distribution and preference probability distribution, we elaborately design a greedy multiattribute joint task assignment algorithm (MAJA) to maximize the average number of completed tasks under constraints. Then, an overall procedure incorporating the JPM and MAJA, called the location-and-preference joint prediction-based task assignment (LPJTA), is implemented to focus on assigning tasks to workers who are near the task location and willing to perform the task based on predicting locations and preference categories. We theoretically analyze the time complexity and approximation ratio of the proposed methods and construct extensive experiments on three real datasets to empirically verify their effectiveness, comparing with the state-of-the-art baselines.