Developing Energy-Aware Task Allocation Schemes in Cloud-Assisted Mobile Workflows

With the pervasiveness of mobile devices, satisfying spatial-temporal coverage requirements in interested regions while considering the quality of the sensing data and the budget constraint is a major research challenge in mobile crowdsensing (MCS). In this paper, we define a novel coverage metric, quality coverage, which considers both the fraction of subareas covered by sensor readings and the quality of sensing data in each covered subarea, for diversified location-based MCS tasks. In our system, as participants with high reliability level can contribute a lot to sensing tasks, we design an incentive model that provides higher bonus to more reliable participants. After knowing the reward for the per amount of sensing data, each worker can submit an interested task set that contains several unit tasks. The platform assigns MCS tasks to these mobile workers by solving the task allocation optimization problems, which select the optimal mobile worker set under the budget of data requesters to maximize diverse and spatial coverage level in the whole sensing area. As the task allocation problems are proven to be NP-hard, we propose a heuristic and a polynomialtime greedy approximation algorithm to solve optimization problems in two different application scenarios. Simulations using both real and synthetic data sets show that our algorithms outperform existing approaches.

Task allocation is a significant issue in crowd sensing, which trades off the data quality and sensing cost. Existing task allocation works are based on the assumption that there is plenty of users available in the candidate pool. However, for some specific applications, there may be only a few candidate users, resulting in the poor completion of tasks. To tackle this problem, in this paper, we investigate the task allocation problem with the assistance of social networks. We select a subset of users; if a user can not complete the task, he can propagate the task information to his friends. The object of this problem is to maximize the expected number of completed tasks. We prove that the task allocation problem is an NP-hard and submodular problem and then propose a native greedy selection (NGS) algorithm, which selects the user with maximum margin gain in each round. To improve the efficiency of the NGS algorithm, we further propose a fast greedy selection algorithm (FGS), which selects the user who can actually complete the maximum number of tasks. Experimental results show that although FGS gets slightly worse results in terms of the expected number of completed tasks, it can greatly reduce the running time of seed selection.

In the field of parallel computing, there is an essential problem which is called Task Allocation Problem(TAP). The task allocation problem (TAP) is a problem where many of tasks require to be allocated to a set of processors.The number of m tasks that is needed to be allocated with number of n processors where (m>n) so that the time needed to process all the tasks is minimized. This paper presents an efficient algorithm (TAP_ACO_GA) to solve the task allocation problem. The proposed algorithm is based on the idea of Ant Colony Optimization Algorithm(ACO) and the idea of Genetic Algorithm (GA) .The proposed algorithm is tested in different dataset and its results are compared with the results in[2] and the results show that TAP_ACO_GA is better than the other algorithm.

Continuous timely repair and replacement of infrastructures, equipment and utilities play an important role in maintaining the smooth-running of a city or local community. Thereby, to help individuals and businesses go about their daily activities with ease, it is vital to develop a proper method for automatically identifying and assigning capable workers for tasks. This paper defines the community management service task allocation problem as CMS-TAP and hence an end-to-end “recommendation + allocation” network, i.e. a task recommender and allocation optimization network (denoted as TROpt-NET), is then developed for handling such problem. TROpt-NET consists of two layers, namely one for predicting worker ability and the other for allocating tasks which are TR Layer and TA Layer, corresponding to “recommendation” and “allocation” of tasks. Different from operations research approaches where workers are assigned to jobs based on their pre-labelled skills and fixed locations, we propose a task recommender and allocation optimization network. The TR layer is a task recommender system designed to learn implicit worker abilities for different tasks using Neural Collaborative Filtering (NCF) by mining a historical dataset of worker task completion. Whereas in the TA layer a differential optimization approach for allocation is used because of its differentiable property and ability to allow for backpropagation to the prediction layer. In this study, we first formulate the CMS-TAP problem as a recommendation +optimization problem and then propose and end-to-end network architecture that tackles the problem in a real-world setting. TROpt-NET curbs uncertainty and assumptions in optimization by learning to more accurately approximate worker ability across different tasks. Additionally, the network can learn implicit worker abilities enabling optimal utilization of workers across a wide range of tasks, which is often ignored in task allocation problems. We find that normalizing worker ability across all tasks improves the implicit learning capability of the network and that good approximations don’t always lead to optimal allocation but learning allocations by backpropagating through recommendations improves the allocation objective. Offline experiments on a real-world large-scale dataset demonstrate the effectiveness of our proposed TROpt-NET.

Multirobot task allocation is a hot issue in the field of robot research. A new emotional model is used with the self-interested robot, which gives a new way to measure self-interested robots' individual cooperative willingness in the problem of multirobot task allocation. Emotional cooperation factor is introduced into self-interested robot; it is updated based on emotional attenuation and external stimuli. Then a multirobot pursuit task allocation algorithm is proposed, which is based on emotional cooperation factor. Combined with the two-step auction algorithm recruiting team leaders and team collaborators, set up pursuit teams, and finally use certain strategies to complete the pursuit task. In order to verify the effectiveness of this algorithm, some comparing experiments have been done with the instantaneous greedy optimal auction algorithm; the results of experiments show that the total pursuit time and total team revenue can be optimized by using this algorithm.

In order to obtain the optimal scheme of task allocation problem for multiple mobile robots. Considering task allocation problem is a multi-objective optimization problem in essence, a general multi-objective task allocation model is established. A new method named multi-objective optimization genetic algorithm based on decision preference information (DPIMOGA) is proposed to solve the optimal allocation scheme. Numerical simulations show that DPIMOGA can solve the problem with light decision burden and Real-time interaction, which denotes that the cooperative task allocation problem solved by DPIMOGA is feasible and effective for multiple mobile robots.

For dual three-phase permanent magnet synchronous machines (DPMSMs), this article presents three dynamic overmodulation methods for selecting optimal duty cycles with maximum dc-link utilization. Concretely, the voltage references computed by the current controller exceed the modulating constraints when suffering a large reference or sudden load change, resulting in a reduced performance under transient. To address this issue, this paper uses geometric projection to synthesize the voltage reference by selecting one or two active vectors, which are produced by two separate three-phase frames. Moreover, as vector space decomposition (VSD) models dual three-phase machines into two-dimensional orthogonal αβ and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">xy</i> subspaces, the other two proposed methods solve the optimal duty cycles by a quadratic program (QP) solver for the four active voltage vectors. The difference between these two methods lies in the different selection of active voltage vectors in αβ and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">xy</i> frames. Specifically, these two strategies based on the QP solver allow the cost function formulated as a single linear and bound constraint QP, which is calculated by an active set method. Finally, the experimental investigations for a DPMSM are presented to prove the viability and flexibility of the proposed overmodulation methodologies.

In this paper, we consider the task allocation problem for autonomous agents where the distances between any two agents should always be less than a predetermined value. To easily tackle the problem, we introduce a novel concept referred to as a virtual task that helps to decouple the task allocation from the path planning in the joint task allocation and path planning problem. An overall task allocation procedure with two steps is proposed. After the virtual tasks are generated with preliminary clustering in the first step, tasks are clustered into groups and allocated to agents in the second step. To reduce the computational complexity of optimal clustering and task allocation, we propose a suboptimal method called the cluster-level traveling salesman problem (TSP). Numerical results show that the cluster-level TSP suffers only little performance degradation compared to the optimal method, even though its computational costs are dramatically reduced.

This paper considers a novel scenario where the service provider encounters an insufficiency of computation and energy resources simultaneously. To complete the task on time, the service provider has to perform partial computation offloading and energy fetching, exploiting the idle resources of nearby edge nodes. However, computation offloading and energy fetching incur costs which are interdependent. On the other hand, the edge nodes are selfish and need an incentive, but the information between the service provider and the edge nodes is asymmetric. Therefore, how to offload the task and fetch energy while stimulating the edge nodes is a challenge. To minimize the expenditure of service provider, we investigate the task allocation problem where the incentive problem is evolved and formulated with contract theory. Noticing that the task allocation problem and the incentive problem are interdependent and cannot be solved through traditional optimization approaches, we decouple the problems and first theoretically derive the expression of feasible contracts for the given allocation scheme. Then we propose a low complexity sparrow algorithm to find the sub-optimal solution to the task allocation problem, with which the desired contracts are obtained. Simulations show that the proposed scheme achieves superior performance in terms of total expenditure, potential computing resource and grid power reduction.

Based on the task allocation problem in customer collaborative design ,which is a new product innovation mode, the task allocation was realized by using a task allocation mechanism based on the negotiation and a protocol was proposed for task allocation. Considering the effecting factors of the collaborative design, a decision-making algorithm based on the comprehensive evaluation was proposed in order to realize the optimal allocation of the task.

Task allocation is one of the cor e steps to effectively exploit the capabilities of cooperative control of multiple Uninhabited Aerospace Vehicle (UAV) teams. Task allocation is an NP -complete problem. In this paper, we present a new task allocation algorithm that is based on the principl es of genetic algorithm (GA). GA is a class of adaptive search algorithms derived from biological population genetics. We discuss the adaptation and implementation of the GA search strategy to the task allocation problem in the cooperative control of multi ple UAVs. Simulation results indicate that the GA strategy is a feasible approach for the task allocation problem .

Recent years have witnessed a revolution in Spatial Crowdsourcing (SC), in which people with mobile connectivity can perform spatio-temporal tasks that involve travel to specified locations. In this paper, we identify and study in depth a new multi-center-based task allocation problem in the context of SC, where multiple allocation centers exist. In particular, we aim to maximize the total number of the allocated tasks while minimizing the average allocated task number difference. To solve the problem, we propose a two-phase framework, called Task Allocation with Geographic Partition, consisting of a geographic partition phase and a task allocation phase. The first phase is to divide the whole study area based on the allocation centers by using both a basic Voronoi diagram-based algorithm and an adaptive weighted Voronoi diagram-based algorithm. In the allocation phase, we utilize a Reinforcement Learning method to achieve the task allocation, where a graph neural network with the attention mechanism is used to learn the embeddings of allocation centers, delivery points and workers. Extensive experiments give insight into the effectiveness and efficiency of the proposed solutions.

We investigate the emergence of swarm intelligence using task allocation in large robot swarms. First, we compare task decomposition graphs of different levels of richness and measure the emergent intelligence arising from self-organized task allocation by deriving STOCH-N1, a stochastic allocation algorithm which contextualizes per-robot task allocation decisions based on a previous task's neighborhood within the graph. The results are compared to other state of the art algorithms. Second, we derive MAT-OPT: a greedy algorithm that optimally solves the swarm task allocation problem by representing the swarm's task allocation space as a matroid under some restrictive assumptions. We compare the MAT-OPT allocation method, which disregards task dependencies, with STOCH-N1, which emphasizes collective learning of graph structure (including dependencies). Results from an object gathering task show that swarm emergent intelligence (1) is sensitive to the richness of task decomposition graphs (2) is positively correlated with performance, (3) arises out of learning and exploitation of graph connectivity and structure, rather than graph content.

BDBM: A distributed network simplification method for solving task allocation problems

Mobile crowdsensing (MCS) is a new and promising paradigm of data collection in large-scale sensing and computing. A large group of users with mobile devices are recruited in a specific area to accomplish sensing tasks. An essential aspect of an MCS application is task allocation, which aims to efficiently assign sensing tasks to the recruited workers. Due to various resource and quality constraints, the MCS task allocation problem is often an NP-hard optimization problem. Traditional greedy or heuristic approaches are usually subject to performance loss in a certain degree so as to maintain tractability or accommodate special requirements such as incentive constraints. In this paper, we attempt to employ a deep reinforcement learning method to search for a more efficient task allocation solution. Specifically, we use a double deep Q-network (DDQN) to solve the task allocation problem as a path-planning problem with time windows. Our formulated problem takes into account location-dependency and time-sensitivity of sensing tasks, as well as the resource limits of workers in terms of maximum travelling distances. Simulations are conducted to compare the DDQN-based solution with two standard baseline solutions. The results show that our proposed solution outperforms the baseline solutions in terms of the platform’s profit and the coverage of tasks.