Equilibrium is priceless: selfish task allocation for mobile crowdsourcing network

Recent years have witnessed major innovations in mobile crowdsourcing networks. For selfish participants, conventional methods resort to incentive mechanism design for resource utilization, which might overlook the inherent equilibrium property among mobile users. In contrast to these proposals, we investigate the problem that whether or not the selfish users could be enabled to endorse stable task sharing with balanced allocations without incentive mechanism designs. Before making a positive answer to this problem, we need to address the following challenge, i.e., users have to make their balancing decisions with only very limited and dynamic local load information, which could possibly incur longer convergence time and imbalanced task allocations. In tackling this difficulty, we propose two distributed selfish load balancing schemes, the max-weight best response policy for strong information scenario, where load information could be sufficiently collected; and the proportional allocation policy for weak information scenario. We make experimental studies to validate proposed schemes. In our simulation study with real trace data, the proposed schemes converge fast in many typical settings with fairly good balancing performance. As for data traces from RollerNet (Tournoux et al., The accordion phenomenon 2009), the performance of load balancing and convergence property are further validated.