Task Allocation Algorithm Research Articles

Threshold Bundle-based Task Allocation for Multiple Aerial Robots

This paper focuses on the large-scale task allocation problem for multiple Unmanned Aerial Vehicles (UAVs). One of the great challenges with task allocation is the NP-hardness for both computation and communication. This paper proposes an efficient decentralised task allocation algorithm for multiple UAVs to handle the NP-hardness while providing an optimality bound of solution quality. The proposed algorithm can reduce computational and communicating complexity by introducing a decreasing threshold and building task bundles based on the sequential greedy algorithm. The performance of the proposed algorithm is examined through Monte-Carlo simulations of a multi-target surveillance mission. Simulation results demonstrate that the proposed algorithm achieves similar solution quality compared with benchmark task allocation algorithms but consumes much less running time and consensus steps.

Bee-inspired task allocation algorithm for multi-UAV search and rescue missions

Task allocation plays a pivotal role in the optimisation of multi-unmanned aerial vehicle (multi-UAV) search and rescue (SAR) missions in which the search time is critical and communication infrastructure is unavailable. These two issues are addressed by the proposed BMUTA algorithm, a bee-inspired algorithm for autonomous task allocation in multi-UAV SAR missions. In BMUTA, UAVs dynamically change their roles to adapt to changing SAR mission parameters and situations by mimicking the behaviour of honeybees foraging for nectar. Four task allocation heuristics (auction-based, max-sum, ant colony optimisation, and opportunistic task allocation) were thoroughly tested in simulated SAR mission scenarios to comparatively assess their performances relative to that of BMUTA. The experimental results demonstrate the ability of BMUTA to achieve a superior number of rescued victims with much shorter rescue times and runtime intervals. The proposed approach demonstrates a high level of flexibility based on its situational awareness, high autonomy, and economic communication scheme.

Bee-inspired task allocation algorithm for multi-UAV search and rescue missions

Task allocation plays a pivotal role in the optimisation of multi-unmanned aerial vehicle (multi-UAV) search and rescue (SAR) missions in which the search time is critical and communication infrastructure is unavailable. These two issues are addressed by the proposed BMUTA algorithm, a bee-inspired algorithm for autonomous task allocation in multi-UAV SAR missions. In BMUTA, UAVs dynamically change their roles to adapt to changing SAR mission parameters and situations by mimicking the behaviour of honeybees foraging for nectar. Four task allocation heuristics (auction-based, max-sum, ant colony optimisation, and opportunistic task allocation) were thoroughly tested in simulated SAR mission scenarios to comparatively assess their performances relative to that of BMUTA. The experimental results demonstrate the ability of BMUTA to achieve a superior number of rescued victims with much shorter rescue times and runtime intervals. The proposed approach demonstrates a high level of flexibility based on its situational awareness, high autonomy, and economic communication scheme.

Dynamic Placement Optimization for Bio-Inspired Self-Repairing Hardware

Bio-inspired self-repairing hardware is a distributed self-adaptive system, characterized by powerful fault-tolerant ability and environment adaptivity. However, it suffers from some difficulties such as large resource consumption and degraded circuit performances. From the viewpoint of cybernetics and computer science, the cellular differentiation and substitution process of bio-inspired self-repairing hardware can be converted into dynamic placement problems on a reconfigurable system. Current systems can only generate some predefined fault-free placements from a finite number of initial placements. The aim of this paper was to achieve high-quality placements from arbitrary initial placements. Based on P systems, an analysis has been made on the limitations of current systems and an improved computing system has been developed to achieve the ergodic property. Its computational power has been verified by a constructive proof. Moreover, centering on the problem how to improve the placement quality on a distributed platform, the optimization model, task allocation, optimization strategy, and membrane optimization algorithm have been designed and developed. The optimization performances were verified and the calculation amount was exhibited by experiments. Finally, it indicated by comparison that the proposed approach would reduce the resource consumption and maintain good circuit performances.

Energy Efficient Task Cooperation for Multi-UAV Networks: A Coalition Formation Game Approach

In this paper, we study the multi-task cooperation problem for unmanned aerial vehicle (UAV) swarms, where the UAV energy consumption is taken into consideration during location scheduling and task implementation. One task may need the cooperation of several UAVs with specific capabilities. To avoid unreasonable task allocation, we quantify the mission properties of UAVs and task areas. We comprehensively consider the overlapping and complementary relationship of the UAV's task types, so that UAVs can form corresponding collective execution tasks according to the task attributes. Based on the coalition game theory, we model the distributed task assignment problem of UAVs as a coalition formation game (CFG). We propose a task allocation algorithm, and then prove that it can achieve the joint optimization of energy and task completion by decision-making of UAVs in finite iterations. With the equilibrium properties of coalition formation in UAV networks, we further optimize the position of UAVs to minimize the network energy consumption. Simulation results verify that the proposed method can reduce the flight loss with high task completion degree.

LCBPA: two-stage task allocation algorithm for high-dimension data collecting in mobile crowd sensing network

Mobile crowd sensing (MCS) is a novel emerging paradigm that leverages sensor-equipped smart mobile terminals (e.g., smartphones, tablets, and intelligent wearable devices) to collect information. Compared with traditional data collection methods, such as construct wireless sensor network infrastructures, MCS has advantages of lower data collection costs, easier system maintenance, and better scalability. However, the limited capabilities make a mobile crowd terminal only support limited data types, which may result in a failure of supporting high-dimension data collection tasks. This paper proposed a task allocation algorithm to solve the problem of high-dimensional data collection in mobile crowd sensing network. The low-cost and balance-participating algorithm (LCBPA) aims to reduce the data collection cost and improve the equality of node participation by trading-off between them. The LCBPA performs in two stages: in the first stage, it divides the high-dimensional data into fine-grained and smaller dimensional data, that is, dividing an m-dimension data collection task into k sub-task by K-means, where (k < m). In the second stage, it assigns different nodes with different sensing capability to perform sub-tasks. Simulation results show that the proposed method can improve the task completion ratio, minimizing the cost of data collection.

ISAR Imaging Task Allocation for Multi-Target in Radar Network Based on Potential Game

In this paper, a task allocation strategy for multi-target inverse synthetic aperture radar (ISAR) imaging in radar network is proposed by using the potential game theory. The multi-target ISAR imaging task allocation problem in this paper refers to that the targets are in different radar beams and the purpose is to accomplish all the imaging tasks with high resolution within the limited time. First of all, the multi-target imaging task allocation problem is formulated. According to the imaging task allocation problem, a resolution optimization game is proposed to search for a satisfied strategy profile for all the targets, and the proposed game is proven to be an exact potential game which has at least one Nash equilibrium (NE). The optimal and suboptimal solution of the imaging task allocation problem can converge to a pure-strategy NE. Then, a best response-based imaging task allocation algorithm is proposed to solve the proposed game model. The simulation experiments demonstrate that the proposed game approach which can achieve a satisfied strategy profile for the targets with a lower computation complexity is an effective method for the multi-target imaging task allocation problem.

Maximizing MapReduce job speed and reliability in the mobile cloud by optimizing task allocation

Mobile cloud computing has become a widespread phenomenon owing to the rapid development and proliferation of mobile devices all over the globe. Furthermore, revolutionary mobile hardware technologies, such as 5G and IoT, have led to increased competition for mobile intelligence among tech-giants, like Google, Apple, and Facebook, further leading to developments in the field of mobile cloud computing. However, several challenges still remain; above all, resolving the task allocation problem that determines the nodes on which tasks will be executed is of paramount importance, and therefore, is the focus of many previous studies on mobile cloud. To this end, we propose a novel Mobile MapReduce Task Allocation(MTA) strategy that simultaneously maximizes both job speed and reliability by modeling communication delay and task reliability. Based on extensive evaluations using various task allocation strategies, representative workloads, and real mobility traces on a mobile MapReduce simulator validated against a platform running on an actual smartphone cluster, we show that MTA significantly outperformed the state-of-the-art task allocation algorithms by up to 3.7 times and 41%, respectively, in terms of job speed and reliability, confirming its resource efficiency and scalability as well.

An Efficient Scheduling Algorithm for Multi-Robot Task Allocation in Assembling Aircraft Structures

Efficient utilization of cooperating robots in the assembly of aircraft structures relies on balancing the workload of the robots and ensuring collision-free scheduling. We cast this problem as that of allocating a large number of repetitive assembly tasks, such as drilling holes and installing fasteners, among multiple robots. Such task allocation is often formulated as a traveling salesman problem, which is NP-hard, implying that computing an exactly optimal solution is computationally prohibitive for real-world applications. The problem complexity is further exacerbated by intermittent robot failures necessitating real-time task reallocation. In this letter, we present an efficient method that exploits workpart geometry and problem structure to initially generate balanced and conflict-free robot schedules under nominal conditions. Subsequently, we deal with the failures by allowing the robots to first complete their nominal schedules and then employing a market-based optimizer to allocate the leftover tasks. Results show an improvement of 11.5% in schedule efficiency as compared to an optimized greedy multi-agent scheduler on a four robot system, which is especially promising for aircraft assembly processes that take many hours to complete. Moreover, the computation times are similar and small, typically hundreds of milliseconds.

A location-constrained crowdsensing task allocation method for improving user satisfaction

Mobile crowdsensing is a special data collection manner which collects data by smart phones taken by people every day. It is essential to pick suitable workers for different outdoor tasks. Constrained by participants’ locations and their daily travel rules, they are likely to accomplish light outdoor tasks by their way without extra detours. Previous researchers found that people prefer to accomplish a certain number of tasks at a time; thus, we focus on assigning light outdoor tasks to workers by considering two optimization objectives, including (1) maximizing the ratio of light tasks for different workers and (2) maximizing the worker’s satisfaction on assigned tasks. This task allocation problem is a non-deterministic polynomial-time-hard due to two reasons, that is, tasks and workers are many-to-many relationships and workers move from different places to different places. Considering both optimization objectives, we design the global-optimizing task allocation algorithm, which greedily selects the most appropriate participant until either no participant can be chosen or no tasks can be assigned. For the purpose of emulating real scenarios, different scales of maps, tasks, and workers are simulated to evaluate algorithms. Experimental results verify that the proposed global-optimizing method outperforms baselines on both maximization objectives.

ACD3GPSO: automatic clustering-based algorithm for multi-robot task allocation using dynamic distributed double-guided particle swarm optimization

PurposeThe multi-robot task allocation (MRTA) problem is a challenging issue in the robotics area with plentiful practical applications. Expanding the number of tasks and robots increases the size of the state space significantly and influences the performance of the MRTA. As this process requires high computational time, this paper aims to describe a technique that minimizes the size of the explored state space, by partitioning the tasks into clusters. In this paper, the authors address the problem of MRTA by putting forward a new automatic clustering algorithm of the robots' tasks based on a dynamic-distributed double-guided particle swarm optimization, namely, ACD3GPSO.Design/methodology/approachThis approach is made out of two phases: phase I groups the tasks into clusters using the ACD3GPSO algorithm and phase II allocates the robots to the clusters. Four factors are introduced in ACD3GPSO for better results. First, ACD3GPSO uses the k-means algorithm as a means to improve the initial generation of particles. The second factor is the distribution using the multi-agent approach to reduce the run time. The third one is the diversification introduced by two local optimum detectors LODpBest and LODgBest. The last one is based on the concept of templates and guidance probability Pguid.FindingsComputational experiments were carried out to prove the effectiveness of this approach. It is compared against two state-of-the-art solutions of the MRTA and against two evolutionary methods under five different numerical simulations. The simulation results confirm that the proposed method is highly competitive in terms of the clustering time, clustering cost and MRTA time.Practical implicationsThe proposed algorithm is quite useful for real-world applications, especially the scenarios involving a high number of robots and tasks.Originality/valueIn this methodology, owing to the ACD3GPSO algorithm, task allocation's run time has diminished. Therefore, the proposed method can be considered as a vital alternative in the field of MRTA with growing numbers of both robots and tasks. In PSO, stagnation and local optima issues are avoided by adding assorted variety to the population, without losing its fast convergence.

Distributed task allocation method based on self-awareness of autonomous robots

Multi-robot task allocation problem is an important research field in multi-robot systems. The multi-robot task allocation problem combined with affective computing is an interesting frontier problem. This paper proposes a distributed affective robot pursuit task allocation algorithm based on self-awareness, which combines cognitive intelligence with emotional intelligence to establish self-awareness for affective robots. This paper defines the cognitive mechanism, environmental detection mechanism and self-decision mechanism of affective robots, so that the self-aware affective robots can make decisions autonomously by grasping the information of itself and the environment. The experimental results show better efficiency of this task allocation algorithm at each task scale, and the allocation efficiency is gradually improved as the number of robot increases.

Practical task allocation for software fault-tolerance and its implementation in embedded automotive systems

Due to the advent of active safety features and automated driving capabilities, the complexity of embedded computing systems within automobiles continues to increase. Such advanced driver assistance systems (ADAS) are inherently safety-critical and must tolerate failures in any subsystem. However, fault-tolerance in safety-critical systems has been traditionally supported by hardware replication, which is prohibitively expensive in terms of cost, weight, and size for the automotive market. Recent work has studied the use of software-based fault-tolerance techniques that utilize task-level hot and cold standbys to tolerate fail-stop processor and task failures. The benefit of using standbys is maximal when a task and any of its standbys obey the placement constraint of not being co-located on the same processor. We propose a new heuristic based on a “tiered” placement constraint, and show that our heuristic produces a better task assignment that saves at least one processor up to 40% of the time relative to the best known heuristic to date. We then introduce a task allocation algorithm that, for the first time to our knowledge, leverages the run-time attributes of cold standbys. Our empirical study finds that our heuristic uses no more than one additional processor in most cases relative to an optimal allocation that we construct for evaluation purposes using a creative technique. We also extend our heuristic to support mixed-criticality systems which allow for overload operation. We have designed and implemented our software fault-tolerance framework in AUTOSAR, an automotive industry standard. We use this implementation to provide an experimental evaluation of our task-level fault-tolerance features. Finally, we present an analysis of the worst-case behavior of our task recovery features.

Optimal Task Allocation Algorithms for Energy Constrained Multihop Wireless Networks

In recent years, multihop wireless networks have been playing a key role in many Internet of Things applications. Due to the limited resources of wireless nodes, extending the network lifetime is one of the most crucial issues, which needs to be concerned. This paper aims to maximize the network lifetime by appropriately distributing the tasks of the applications for each node in the network. First, a centralized optimal task allocation algorithm for multihop wireless networks (COTAM) is proposed by modeling the problem of maximizing the network lifetime as a linear programming (LP) problem. As the centralized algorithm requires knowing all the network parameters in advance, COTAM is mostly restricted to the off-line optimization in known environments. To extend the usability of the approach, this paper further proposes a distributed optimal task allocation algorithm (DOTAM) based on Dantzig–Wolf decomposition. DOTAM divides the centralized large-sized LP problem into small-sized subproblems, which are independently executed by each node. The proposed COTAM and DOTAM are tested by applying both the artificially generated applications and a realistic application. The extensive results demonstrate that DOTAM achieves the same performance as COTAM. Comparing with existing methods, they provide significant improvements on extending the network lifetime.

Energy Harvesting Network With Wireless Distributed Computing

Bulky processing tasks are expected to burden the limited resources of energy harvesters by draining the stored energy, and thereby, reaching rapidly to energy causality constraint. In such scenario, energy harvesters flip into sleep mode, and thereby, the execution time of the next task will be delayed until the energy harvesters revert back into active mode. To tackle this problem, this paper proposes a novel energy harvesting network (EHN) that deploys wireless distributed computing (WDC) network within the decision making process (DMP). The DMP is formulated as constrained partially observable Markov decision process in order to enable the energy harvesters to act under uncertainty. Furthermore, various challenges of WDC networks, e.g., nominating the collaborating nodes and task allocation, have been addressed herein. Unlike conventional research works on WDC networks, a system model is proposed for WDC network based on divisible load theory instead of graph theory. In addition, an adaptive task allocation algorithm is proposed to distribute the task efficiently among the collaborating nodes. Finally, the novel EHN system is analyzed and compared against the conventional research works on WDC, offloading computing, and local computing-EHN, where the proposed system is found to outperform in terms of energy and delay.

A Bid-Based Grouping Method for Communication-Efficient Decentralized Multi-UAV Task Allocation

This paper presents an extension to a decentralized multi-UAV task allocation algorithm, consensus-based bundle algorithm (CBBA), to improve the communication efficiency in the plan consensus process. The presented algorithm termed grouped consensus-based bundle algorithm (G-CBBA) provides a systematic way of grouping the UAVs based on their task preference represented by the initial guess created by the UAVs. G-CBBA features a nested iteration between two layers of plan consensus: local and global. The local stage aims at plan consensus over the list of tasks shared within a group, while the global stage ensures agreement across the entire network over the whole task set. The proposed two-layer scheme can improve the communication efficiency by reducing the need for propagating irrelevant bids while preserving robust convergence property of CBBA. Numerical experiments demonstrate reduction in the number of messages to be communicated without incurring substantial performance degradation compared to the baseline CBBA.

DESIGNING A TASK ALLOCATOR FRAMEWORK FOR DISTRIBUTED COMPUTING

Software Frameworks attempt to capture and implement a software system architecture that is reusable. A frameworks, thus, is a semicode that needs to be customized for a particular reuse. The problem of finding an optimal task allocation in distributed computing system (DCS) is an NP-hard. There are various task allocation algorithms and hence a Task Allocator may implement any of them. Any Task Allocator, hence will have many portions that can be reused to define and implement a Task Allocator. In distributed system a Task Allocation mechanism may be replaced by a new one if a standardized definition of a reusable system architecture for this purpose is available. This work attempts at formalizing a system architecture of a Task Allocator by proposing a framework for the purpose. Here we start the design methodology for OO software and identify the various parts of the software system architecture for task allocation. This effort finally results into a semicode framework. The interesting conclusions include “Identification of that code portion of the semicode framework that does not change when reused”, “Characteristics of the code portion that need customization” and the nature of framework definitions that need to be coded at the time of reuse. In this work OO design of various activities of task allocation process has been carried out as per the OO design methodology. To be objects have been identified the dynamic and functional modeling along with identification use cases, corresponding scenarios and data flow diagrams.

A New Task Allocation Protocol for Extending Stability and Operational Periods in Internet of Things

Internet of Things (IoT) is a groundbreaking technology which recently enjoyed a large increase in interest as it connects both digital and physical worlds together and allowing different devices with various capabilities to share their resources to perform a demanded task. Most of IoT objects are heterogeneous by their nature and are supplied with limited battery life. Therefore, distributing tasks among these objects is a big challenge. One of the primary objectives of task allocation algorithms is minimizing the energy consumption while maximizing the task allocation reliability. In this paper, we address the problem of task allocation in IoT as an optimization problem. We develop a nature-inspired meta-heuristic algorithm with heterogeneity aware heuristics to tackle the formulated problem. The proposed algorithm aims to ensure reliable task allocation while boosting the energy efficiency and the stability period of the network. Extensive experimental analysis of the proposed algorithm is realized using MATLAB and OMNET++ simulation scenarios. The results are compared with the most relevant algorithm in the literature and the superiority of the proposed algorithm in terms of energy efficiency has been proved.

Task Allocation in Eco-friendly Mobile Crowdsensing: Problems and Algorithms

Mobile crowdsensing has emerged as a new sensing paradigm which has many advantages over traditional sensing paradigms. In this paper, we focus on the task allocation problem for eco-friendly mobile crowdsensing which aims to minimize carbon emissions under various constraints such as task deadline and road traffic constraints. We first describe the system model of eco-friendly mobile crowdsensing and formulate the task allocation problem in offline scenario and online scenario, respectively. Then we propose Eco-Friendly Task Allocation algorithm (EFTA) to address the allocation problem in offline scenario. This algorithm consists of two processes including transportation selection and worker-task matching. After this, we propose Online Eco-Friendly Task Allocation algorithm (OEFTA) to tackle the allocation problem in online scenario. The algorithm adopts greedy online task assignment/reassignment upon arrival of a new task or a new worker. Extensive simulation results show our proposed algorithms have much better performance than baseline algorithms.

A trust-aware task allocation method using deep q-learning for uncertain mobile crowdsourcing

Mobile crowdsourcing has emerged as a promising collaboration paradigm in which each spatial task requires a set of mobile workers in near vicinity to the target location. Considering the desired privacy of the participating mobile devices, trust is considered to be an important factor to enable effective collaboration in mobile crowdsourcing. The main impediment to the success of mobile crowdsourcing is the allocation of trustworthy mobile workers to nearby spatial tasks for collaboration. This process becomes substantially more challenging for large-scale online spatial task allocations in uncertain mobile crowdsourcing systems. The uncertainty can mislead the task allocation, resulting in performance degradation. Moreover, the large-scale nature of real-world crowdsourcing poses a considerable challenge to spatial task allocation in uncertain environments. To address the aforementioned challenges, first, an optimization problem of mobile crowdsourcing task allocation is formulated to maximize the trustworthiness of workers and minimize movement distance costs. Second, for the uncertain crowdsourcing scenario, a Markov decision process-based mobile crowdsourcing model (MCMDP) is formulated to illustrate the dynamic trust-aware task allocation problem. Third, to solve large-scale MCMDP problems in a stable manner, this study proposes an improved deep Q-learning-based trust-aware task allocation (ImprovedDQL-TTA) algorithm that combines trust-aware task allocation and deep Q-learning as an improvement over the uncertain mobile crowdsourcing systems. Finally, experimental results illustrate that the ImprovedDQL-TTA algorithm can stably converge in a number of training iterations. Compared with the reference algorithm, our proposed algorithm achieves effective solutions on the experimental data sets.