Best-Harmonically-Fit Periodic Task Assignment Algorithm on Multiple Periodic Resources

This paper investigates the duration-aware task assignment problem for heterogeneous mobility users in MCS (DTAH problem). There are two types of users in MCS, vehicle users and pedestrian users. The vehicle user gets to the task fast, but the task duration is short. Pedestrian user meets the task duration limit, but it takes a long time to reach the task. Given a set of tasks and two types of users, each user, and each task has a duration limit. The DTAH problem is how to assign tasks to maximize the utility of the system. We prove that the DTAH problem is NP-hard by reducing the weighted maximum set coverage problem to the DTAH problem. Then, we solve the problem from a pedestrian perspective and a vehicle perspective. From the pedestrian user perspective, we propose a pedestrian user task assignment (PTA) algorithm based on the Kuhn-Munkres algorithm. From the vehicle user perspective, we propose a greedy vehicle user task assignment (VTA) algorithm. We prove that the VTA algorithm can obtain the approximate ratio of 1-1/e to the optimal value. Finally, we design the heterogeneous user task assignment (HTA) algorithm based on these PTA and VTA algorithms. Extensive experiments have proved that our proposed HTA algorithm achieves more efficient performance than other comparison algorithms.

This works develops a novel hierarchical algorithm for task assignment (TA), coordination and communication of multiple UAVs engaging multiple targets and conceives an ad-hoc routing algorithm for synchronization of target lists utilizing a distributed computing topology. Assuming limited communication bandwidth and range, coordination of UAV motion is achieved by implementing a simple behavioral flocking algorithm utilizing a tree topology for target list routing. The TA algorithm is based on a graph-theoretic approach, in which a node locates all the detectable targets, identifies them and computes its distance to each target. The node then produces an attack plan that minimizes the sum of distances of the UAVs in the subtree of a given node to the targets. Simulation experiments show that the combination of flocking and TA algorithms gives the best performance. Clear-cut advantages of the TA algorithm are shown to exist in cases where the hit probability tends to one. An improvement of efficiency, representing the ratio between killed targets and the number of missile launches, is obtained for larger numbers of UAVs only if the engagement times are long enough, utilizing the improved coverage achieved by more UAVs.

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.

The existing task assignment algorithms usually solve only a point-based model. This paper proposes a novel algorithm for task assignment in detection search tasks. Firstly, the optimal reconnaissance path is generated by considering the drone’s position and attitude information, as well as the type of heterogeneous targets present in the actual scene. Subsequently, an adaptive crowding distance calculation (ACD-NSGA-II) is proposed based on the relative position of solutions in space, taking into account the spatial distribution of parent solutions and constraints imposed by uncertain targets and terrain. Finally, comparative experiments using digital simulation are conducted under two different target probability scenarios. Moreover, the improved algorithm is further evaluated across 100 cases, and a comparison of the Pareto solution set with other algorithms is conducted to demonstrate the algorithm’s overall adaptability.

SRAMs are aimed at multiprocessing

On the computational complexity of periodic scheduling

This work develops distributed hierarchical task assignments and communication algorithms for flocks of unmanned aerial vehicles dispersed in an unknown theater while engaging multiple moving targets. The dynamical changes require distributed algorithms, without relying on a central agent, which may constitute a single point-of-failure and is exposed to communication breaks. Our methodology overcomes the typical deficiencies of a centralized solution by organizing the agents in spanning trees. Whenever possible, the spanning trees merge into a single tree that clusters the maximum number of agents. Using relaxation methods and inputs from its parent and children, every agent attempts to optimize its own solution for task assignment while communicating using an ad-hoc protocol. Simulation experiments show clear-cut advantages of using the proposed task assignment and communication algorithms.

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.

The task assignment and path planning of a multi-AUVs system has now attracted considerable attention and become a hotspot in the research. In this paper, a novel algorithm of multi-AUVs task assignment and path planning based on Biologically Inspired Neural Network Map (BINN) is proposed. Firstly, the grid map is built by discretizing the three-dimensional underwater environment into many equal grids. Secondly, the activity values of all AUVs in the BINN maps of each target are calculated. Then, the AUV with the highest activity value in the BINN map of the target is selected as the winning AUV for the target. Finally, the winning AUV performs path planning according to the BINN strategy. Through the simulation experiment, it is proved that the novel BINN algorithm proposed in this paper can effectively and reasonably distribute multi-AUVs and reduce the overall sailing distance.

Multiple mobile robot system under decentralized control has become more popular because of advantage of fault tolerance and extensibility. This paper focuses on a planning method for an interactive transportation task by multiple mobile robot system. In this task, task assignment is important to solve interaction among mobile robots. In this paper, we propose the self-generating task assignment algorithm in order to realize efficient sorting task. First, we considered parallelism in transportation task. Second, we defined variable of “Progress” in order to estimate value of tasks which can execute independently and in parallel. Using Progress, robots can select executable parallel task and generate task assignment based on efficiency. We developed multi-robot simulator and multi robot system in order to verify the effectiveness.

Pipelined execution of streaming applications enable processing of high-throughput data under performance constraint. We present an integrated approach to synthesizing pipelined software for dual-core architectures. We target streaming applications modeled as task graphs that are amenable to static analysis. We develop a versatile task assignment algorithm that considers the combined effect of workload imbalance between processors and inter-processor communication. Our technique, which runs in pseudo-linear time, provably maximizes application throughput. Furthermore, we develop an approximation algorithm for task assignment whose complexity is strictly polynomial. It provides the designer with an adjustable knob to controllably trade solution quality with algorithm runtime and memory requirement. Empirical throughput measurements using an FPGA-based dual-core system validate our theoretical results. Our exact algorithm consistently outperforms a recent competitor. Compared to exact task assignment, the approximate method runs about 3 times faster, requires about 20 times less memory, and results in only 1% to 5% throughput loss.

The ubiquity of mobile internet has led to the success of Spatial Crowdsourcing platforms like real-time taxi-hailing services, online food ordering services, etc. A critical component of such services is the task assignment algorithm employed for assigning the tasks to the workers of the platform. Our study of the literature in this domain shows that most of the task assignment algorithms developed for spatial crowdsourcing platforms address the problem from a utilitarian perspective, i.e., they optimise for only kind of entity. In contrast, we address the task assignment problem in spatial crowdsourcing platforms from an egalitarian perspective. An egalitarian approach aims to optimise the expectation of all entities involved. Specifically, we aim to minimise the waiting time for the customers and workers, while maximising the profit earned by the platform. To the best of our knowledge, ours is the only study that achieves this objective in a fully-online setting, with deadlines for both customers and workers. We propose two heuristic algorithms to solve the problem, and evaluate our algorithms on a real taxi-trips records dataset. Our algorithms exhibit a superior performance than the state-of-the-art algorithm for the fully-online bottleneck matching problem with deadlines, in terms of solution quality, running time and response time.

An online approach for joint task assignment and worker evaluation in crowd-sourcing

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.

Crowdsourced ranking and top-k queries have attracted significant attention recently. Their goal is to combine human cognitive abilities and machine intelligence to rank computer hostile but human friendly items. Many task assignment algorithms and inference approaches have been proposed to publish suitable micro-tasks to the crowd, obtain informative answers, and aggregate the rank from noisy human answers. However, they are all focused on single query processing. To the best of our knowledge, no prior work helps users manage multiple crowdsourced top-k queries. We propose a toolkit, which seamlessly works with most existing inference and task assignment methods, for crowdsourced top-k query management. Our toolkit attempts to optimize human resource allocation and continuously monitors query quality at any stage of the crowdsourcing process. A user can terminate a query early, if the estimated quality already fulfills her requirements. Besides, the toolkit provides user-friendly interfaces for users to initialize queries, monitor execution status, and do more operations by hand.