Enhancing Game Policy Optimization in Mobile Crowdsourcing: A Reinforcement Learning Approach

The performance of two new heuristics that combine tabu search with a genetic crossover technique was examined for their usefulness in solving spatially constrained harvest scheduling problems. One heuristic utilized tabu search with 1-opt moves and a genetic crossover technique (TS/GA), and the other utilized tabu search with both 1-opt and 2-opt moves and a genetic crossover technique (TS2/GA). The heuristics were tested on four problems. Three problems used the simple unit-restriction model (URM) to portray greenup constraints, allowing them to be solved with mathematical programming techniques and thus providing a benchmark to compare against the solutions produced by the heuristic techniques. These were considered hypothetical problems, since the datasets were grids, and the age classes were assigned with three different rules. The fourth problem uses an operational dataset and includes both a maximum opening size constraint, which limits the size of an individual opening, and a maximum average opening size constraint, which represents the greenup constraint contained in the American Forest and Paper Association (AF&PA) Sustainable Forestry Initiative (AF&PA 2000). The greenup constraints contained in these problems accurately portray the greenup constraints facing the forest industry in the United States. For the three hypothetical problems, the TS/GA technique found solutions with an objective function value between 96.6% and 99.1% of an estimated optimal value, and between 93.4% to 94.4% of a relaxed linear programming value. The TS2/GA found better solutions for all three hypothetical problems, with the objective function values between 98.2% and 99.7% of the estimated optimal value and the 96.3% to 97.2% of the relaxed linear programming value. Compared with a general tabu search technique that used 1-opt moves (TS), adding the genetic crossover technique resulted in a 2% increase in the objective function value, and adding the 2-opt intensification capability resulted in a further 1.5% improvement. A similar pattern was observed when the problem using the operational dataset was solved. The addition of the genetic crossover technique did not increase the time required to produce a solution to any particular problem, yet the addition of the 2-opt procedure did. TS2/GA required about twice as much time as did TS or TS/GA when applied to the three hypothetical datasets, and 67% more time when applied to the problem using the operational dataset. FOR. SCI. 48(1):35–46.

Mobile crowdsensing (MCS) has heated up and has become a new paradigm of data collection. In the process of the task allocation of MCS, users are often required to provide their own location information with the server to conveniently dispatch some suitable tasks to them. However, it is possible for malicious servers to infer some sensitive information based on the user’s location such as the user’s home address or the user’s trajectory and so on which will cause serious privacy issues. Recently, differential privacy (DP) has become a promising privacy protection scheme. However, the existing DP schemes in location privacy protection for MCS do not pay attention to the accuracy of task allocation as equally as the effects of privacy protection, which often results in the task allocation to be inaccurate and inefficient. In order to overcome the shortcoming, we propose a novel MCS task allocation scheme integrating the mapping accuracy of task-worker with the privacy-preserving effect on the user’s location. Besides edge devices, its implementation system consists of a task allocation server and a third party, both of which are semitrusted, that is, both of them only know about the user’s rough location information but cannot obtain the user’s exact location. At first, to improve the response speed of MCS task requests and the location privacy protection of MCS users, our scheme can exploit more than one edge-computing node nearby a user to cooperatively participate in MCS task allocation by aggregating him/her and the near users into groups. Then, our scheme can further enhance the location privacy protection effect based on the Johnson–Lindenstrauss (JL) transformation, which can achieve accurate task allocation and hold the merits of DP. Finally, we verify the feasibility by some experiments based on two data sets. The performance is compared with that of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> he typical DP. The results show that our scheme not only provides strict privacy guarantees but also has higher performance.

Mobile crowd sensing (MCS) is a novel example of urban sensing, which uses smart phones, smart wearable devices, on-board sensors and other mobile sensor devices to collect data, and is widely used in intelligent transportation, medical care, environmental monitoring and other fields. A growing number of studies show that a good task allocation mechanism is an indispensable key factor in determining whether MCS applications can obtain good service quality. In this paper, we introduce the realistic background of MCS and combine some specific applications to exhaustively classify task allocation problems in MCS. Then, the typical algorithm for MCS task allocation is classified and illustrated. Finally, the current research challenges and future research directions are presented.

The pervasiveness of mobile phones and the increasing sensing capabilities of their built-in sensors have made mobile crowdsensing a promising approach for large-scale data collection. In mobile crowdsensing, a specific situation is that the service provider (SP) needs to recruit contributors to fulfill sensing tasks requested by consumers. There are several challenges for contributor selection and task allocation in mobile crowdsensing. Firstly, since some of the contributors may need to travel a certain distance to complete the sensing task, they may need to be compensated for their contributions, in proportion to the distance they need to travel. Secondly, the diversity in sensing devices and contributor behavior means that the sensing data from different contributors will have different Quality of Information (QoI). Moreover, the SP usually only has a limited budget to compensate the contributors. Thus, how to handle the trade-offs between traveling distances of the contributors and QoI of the sensing data in order to maximize the sensing revenue based on limited budget is an important consideration in mobile crowdsensing applications. In practice, the QoI from different contributors are usually unknown to the SP in advance, which makes contributor selection and task allocation even more challenging. In this paper, we propose a QoI-aware task allocation scheme (QOATA) for contributor selection and task allocation in mobile crowdsensing applications. Using an online learning approach to learn the QoI from different contributors, the scheme aims to achieve the highest sensing revenue under a limited budget. The effectiveness of QOATA is evaluated through extensive simulations.

Mobile crowdsensing (MCS) refers to the process in which a collection of mobile devices share sensing data, which can then be aggregated to develop policies or applications that benefit a community. Traditionally, mobile crowdsensing has been deployed using a platform-centric task allocation approach. These platform-centric systems are primarily concerned with gaining profits for the platform by minimizing rewards given to users. Instead of this approach, this paper focuses on a more altruistic task allocation approach by proposing a user-centric task allocation approach. In this regard, we have developed two task allocation schemes: Nearest User Task Allocation (NUTA) and Nearest User Fair Task Allocation (NUFTA) in order to facilitate an optimized and equitable workload for the users.

With the increasing number of smart devices, se-lecting proper workers for tasks is a major research challenge in mobile crowd-sensing (MCS) due to the heterogeneity of tasks and workers. In this paper, we formulate the task allocation problem as an optimization problem by taking into account various tasks requirements (i.e., sensor, location accessibility, reliability), and workers specifications (i.e., available sensors set, velocity) as well as task completion order. The objective of this study is to minimize the total distance moved by all workers. Furthermore, a tabu search algorithm is proposed to select workers, which uses asymmetric traveling salesman problem heuristics to find the task completion order. Simulations using real data sets show the performance of our proposed method.

Mobile CrowdSensing (MCS) has emerged as a novel paradigm for performing large-scale sensing tasks. Many incentive mechanisms have been proposed to encourage user participation in MCS. However, most of them ignore the inevitable cold start stage of MCS, where the MCS system has just begun releasing tasks. Also, they all adopt the single-round incentive without considerations of the continuous cumulative effect. Given the severe shortage of participants in the cold start stage of MCS, this paper proposes a Multi-Round Incentive Mechanism (MRIM). MRIM is based on monetary incentives by adopting multi-round cooperation and alternating between task information diffusion and task allocation operations, both of which are NP-hard problems even without inter-round coupling imposed by system budget constraints. We explore a method to predict the probability of users participating in tasks accurately. Furthermore, we present an efficient task information diffusion algorithm to maximize the number of users participating in tasks by submitting bids. We propose a fast task allocation algorithm based on truthful auction, comprising an approximation algorithm for solving the one-round winner selection and payment calculation. With budget constraints, MRIM maximizes the number of completed tasks by iteratively performing task information diffusion and task allocation. We also prove that MRIM also possesses desired properties such as computational efficiency, user rationality, platform profitability, and price truthfulness, which can further guarantee the robustness of MRIM. The extensive simulations conducted on real-world datasets have proved the efficiency of MRIM.

Task allocation is a major challenge in Mobile Crowd Sensing (MCS). While previous task allocation approaches follow either the opportunistic or participatory mode, this paper proposes to integrate these two complementary modes in a two-phased hybrid framework called HyTasker. In the offline phase, a group of workers (called opportunistic workers) are selected, and they complete MCS tasks during their daily routines (i.e., opportunistic mode). In the online phase, we assign another set of workers (called participatory workers) and require them to move specifically to perform tasks that are not completed by the opportunistic workers (i.e., participatory mode). Instead of considering these two phases separately, HyTasker jointly optimizes them with a total incentive budget constraint. In particular, when selecting opportunistic workers in the offline phase of HyTasker, we propose a novel algorithm that simultaneously considers the predicted task assignment for the participatory workers, in which the density and mobility of participatory workers are taken into account. Experiments on two real-world mobility datasets demonstrate that HyTasker outperforms other methods with more completed tasks under the same budget constraint.

When the project is scheduled with a given set of resources, it is difficult to find the optimal solution. Resourceconstrained scheduling problems (RCPSP) are generally NP-hard. In this paper, a high level heuristic procedure “Tabu Search Algorithm (TSA)” is proposed to provide good solutions to resource-constrained, deterministic activity duration project scheduling problems. We present the application results of the computational tabu search and OPL-CPLEX algorithm and compare them with that of earlier applicable researches along with a discussion about further research. Our computational results are presented, which establish the superiority of tabu search over the existing heuristic algorithms. Two different solution strategies are also discussed, namely tabu search and OPLCPLEX exact algorithm approach which can be used with the proposed model. Due to the execution time, we have shown that OPL-CPLEX's algorithm is a valid method with medium scale RCPSPs. For the considered deterministic problems, a good agreement has been obtained between theoretical and experimental results.

Mobile crowdsensing (MCS) is a popular paradigm to collect sensed data for numerous sensing applications. With the increment of tasks and workers in MCS, it has become indispensable to design efficient task allocation schemes to achieve high performance for MCS applications. Many existing works on task allocation focus on single-task allocation, which is inefficient in many MCS scenarios where workers are able to undertake multiple tasks. On the other hand, many tasks are time-limited, while the available time of workers is also limited. Therefore, time validity is essential for both tasks and workers. To accommodate these challenges, this paper proposes a multi-task allocation problem with time constraints, which investigates the impact of time constraints to multi-task allocation and aims to maximize the utility of the MCS platform. We first prove that this problem is NP-complete. Then two evolutionary algorithms are designed to solve this problem. Finally, we conduct the experiments based on synthetic and real-world datasets under different experiment settings. The results verify that the proposed algorithms achieve more competitive and stable performance compared with baseline algorithms.

The idea of this extended foreword to the special issue on applied local search is threefold. Firstly, we provide a brief and fundamental description of what is nowadays called local search. Components which have meanwhile become an integral part of the classical aspects on simulated annealing, tabu search and genetic algorithms are reviewed. Furthermore, today tabu search can be considered as the major pillar of local search. Hence, attention is drawn to a couple of tabu search issues more recently developed such as the reverse elimination method and ejection chains. Secondly, local search based knowledge engineering is developed to constitute a substantial part of knowledge based information systems. Within this general setting local search will be considered as one particular paradigm of machine learning. Thirdly, we are going to introduce what is considered to be the main subject of this issue, local search applications. We briefly embed the contents of the subsequent papers and group them with respect to their particular methods and models within the above mentioned framework.

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.

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.

Mobile crowdsensing (MCS) is an emerging technology, which provides a promising paradigm for completing complex sensing tasks. While existing studies for MCS mainly focus on designing incentive mechanisms to attract more participants or optimizing task allocation to maximize profit, the freshness of information, known as Age of Information (AoI), has been largely overlooked. In MCS systems, some Point of Interests (PoIs) need to be monitored through sampling by participants. High-frequency sampling can effectively ensure AoI performance, which also imposes significant costs on participants. Therefore, it is necessary to allocate appropriate sampling tasks and design the corresponding sample cycles and prices for participants. In this article, we address the joint problem of incentive mechanism and task allocation. First, we adopt the contract theory to model the incentive mechanism, where the crowdsensing platform (CP) offers a set of cycle-price combinations to participants. We establish the necessary and sufficient conditions for the feasibility of the contract and subsequently derive the optimal contract structure. Second, subject to the derived contract structure, we determine the optimal task allocation under specific conditions. For more general situations, we propose an iterative algorithm, which is based on pair switching with a proven convergence guarantee. Finally, the simulation results demonstrate the efficiency of the proposed contract-based algorithm, which also outperforms other incentive mechanisms.

This paper studies the problem of multi-task allocation in hotspots. Due to the frequent task requests in hotspots, the traditional mobile crowdsensing (MCS) cannot meet its requirements, so we adopt a distributed MCS architecture to complete this process. This paper proposes an MCS multi-task allocation framework based on edge interaction assistance, which uses edge nodes to distribute the computing load of MCS platform. In the case of short time and heavy tasks, what this paper needs to do is to minimize delay and cost while ensuring data quality, so as to express the multi-task assignment problem as a multi-objective optimization problem. Through the analysis of the process, we propose a two-phase allocation scheme (TPAS) to determine task allocation. The first phase is the task selection based on cosine similarity. The number of tasks can be reduced by considering the similarity of tasks. The second phase is task allocation based on the improved NSGA-II algorithm. The simulation results show that under the premise of satisfying the task data quality, TPAS can obtain lower task cost and delay, and significantly improve the task completion rate.KeywordsMobile crowdsensingMulti-task allocationSimilarity of tasksNSGA-II algorithm