Kai Han

TL;DR: This article surveys research problems in duty-cycled wireless sensor networks, aiming at revealing insights into the following three key questions: what are the meaningful (algorithm design) problems for DC-WSNs, which problems have been studied and which have not, andWhat are the essential techniques behind the existing solutions?

TL;DR: In this paper, the authors study incentive mechanisms for a novel Mobile Crowdsensing Scheduling (MCS) problem, where a mobile crowdsensing application owner announces a set of sensing tasks, then human users (carrying mobile devices) compete for the tasks based on their respective sensing costs and available time periods, and finally the owner schedules as well as pays the users to maximize its own sensing revenue under a certain budget.

TL;DR: A novel framework, Budget LImited robuSt crowdSensing (BLISS), is presented, to handle the problem of robustness of crowdsensing toward uncontrollable sensing quality through an online learning approach and achieves logarithmic regret bounds and Hannan-consistency.

TL;DR: This paper studies a quality-aware Bayesian pricing problem for mobile crowdsensing where the users' sensing costs and qualities for participating in crowdsensing are drawn from known distributions learned from historical information, and proposes a novel "ironing method" that transforms the problem from a non-submodular optimization problem into a submodular one by leveraging the newly discovered properties of PBD.

TL;DR: A non- AdaptGreedy IM algorithm called EPIC is proposed, which not only has the same worst-case performance bounds with that of the state-of-the-art non-adaptive IM algorithms, but also has a reduced expected approximation error.