1. Efficient Task Allocation for Mobile Crowdsensing
Based on Evolutionary Computing
Xi Tao and Wei Song
Faculty of Computer Science, University of New Brunswick, Fredericton, Canada
ABSTRACT
Mobile crowdsensing (MCS) offers a promising paradigm for big data collection in a large scale. It leverages the power of mobile smart devices, and shows various advantages over traditional sensing networks, such as high energy efficiency, cost-effectiveness, and flexibility. A key problem in MCS is to efficiently allocate distributed tasks to mobile users (MUs) while addressing various constraints, e.g., in terms of the quality of sensed data and collection cost. We take into account the clustering effect of sensing tasks and propose an efficient approach to solve the NP-hard task allocation problem. In our solution, a variant genetic algorithm (GA) is utilized to maximize the task complete ratio and balance the sensed data among tasks while respecting the MUs’ constraints. The simulation results show that the proposed GA-based solution significantly outperforms the baseline solution in terms of task complete ratio and data balance.
SCENARIO
Fig. 1 shows an example spatial distribution of sensing tasks and MUs. The objective of organizer is to receive reliable and high-quality data. Thus, task complete ratio and data balance are important performance. Meanwhile, MUs are willing to accept a task assignment only if they can receive a reward by finishing it. Therefore, incentive mechanism is important. Considering all the constraints together, the task allocation problem is converted to the path design problem for MUs, shown in Fig. 2. .
Fig. 1. Example scenario of crowdsensing.
Fig. 2. Task allocation problem.
RESULT
ADVANTAGE OF GA
GA solution (black) has a global vision in finding the paths, which leads to some advantages over the baseline (yellow). First, GA can plan the paths by incorporating more information. Thus, GA is aware of the existence of clusters, shown in Fig. 5. Second, GA can accept a temporary loss in current step to achieve a better overall benefit, which is shown in Fig. 6.
Fig. 3. Task complete ratio.
Fig. 4. Data balance.
CONCLUSION
We investigated the task allocation problem in mobile crowdsensing. In particular, we addressed the clustering effect of sensing. A GA-based solution is proposed. The simulation results show that GA outperforms the baseline in terms of task complete ratio and data balance. The performance improvements can be attributed to the advantages of GA-based algorithm.
Fig. 5. Cluster-awareness.
Fig. 6. Long-term gain.