Recently, adopting mobile energy chargers to replenish the energy supply of sensor nodes in wireless sensor networks has gained increasing attention from the research community. The utilization of the mobile energy chargers provides a more reliable energy supply than the systems that harvested dynamic energy from the surrounding environment. While pioneering works on the mobile recharging problem mainly focus on the optimal offline path planning for the mobile chargers, in this work, we aim to lay the theoretical foundation for the ondemand mobile charging problem, where individual sensor nodes request charging from the mobile charger when their energy runs low. Specifically, in this work we analyze the on-demand mobile charging problem using a simple but efficient Nearest-JobNext with Preemption (NJNP) discipline for the mobile charger, and provide analytical results on the system throughput and charging latency from the perspectives of the mobile charger and individual sensor nodes, respectively. To demonstrate how the actual system design can benefit from our analytical results, we present an example on determining the optimal remaining energy level for individual sensor nodes to send out their recharging requests. Through extensive simulation with real-world system settings, we verify our analysis matches the simulation results well and the system designs based on our analysis are effective.

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Recently, adopting mobile energy chargers to replenish the energy supply of sensor nodes in wireless sensor networks has gained increasing attention from the research community. Different from energy harvesting systems, the utilization of mobile energy chargers is able to provide more reliable energy supply than the dynamic energy harvested from the surrounding environment. While pioneering works on the mobile recharging problem mainly focus on the optimal offline path planning for the mobile chargers, in this work, we aim to lay the theoretical foundation for the on-demand mobile charging problem, where individual sensor nodes request charging from the mobile charger when their energy runs low. Specifically, in this work we analyze the On-Demand Mobile Charging (DMC) problem using a simple but efficient Nearest-Job-Next with Preemption (NJNP) discipline for the mobile charger, and provide analytical results on the system throughput and charging latency from the perspectives of the mobile charger and individual sensor nodes, respectively. To demonstrate how the actual system design can benefit from our analytical results, we present two examples on determining the essential system parameters such as the optimal remaining energy level for individual sensor nodes to send out their recharging requests and the minimal energy capacity required for the mobile charger. Through extensive simulation with real-world system settings, we verify that our analytical results match the simulation results well and the system designs based on our analysis are effective.

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Location based services have experienced an explosive growth and evolved from utilizing a single location to the whole trajectory. Due to the hardware and energy constraints, there are usually many missing data within a trajectory. In order to accurately recover the complete trajectory, crowdsensing provides a promising method. This method resorts to the correlation among multiple users' trajectories and the advanced compressive sensing technique, which significantly outperforms conventional interpolation methods on accuracy. However, as trajectories exposes users' daily activities, the privacy issue is a major concern in crowdsensing. While existing solutions independently tackle the accurate trajectory recovery and privacy issues, yet no single design is able to address these two challenges simultaneously. Therefore in this paper, we propose a novel Privacy Preserving Compressive Sensing (PPCS) scheme, which encrypts a trajectory with several other trajectories while maintaining the homomorphic obfuscation property for compressive sensing. Under PPCS, adversaries can only capture the encrypted data, so the user privacy is preserved. Furthermore, the homomorphic obfuscation property guarantees that the recovery accuracy of PPCS is comparable to the state-of-the-art compressive sensing design. Based on two publicly available traces with numerous users and long durations, we conduct extensive simulations to evaluate PPCS. The results demonstrate that PPCS achieves a high accuracy of <53 m and a large distortion between the encrypted and the original trajectories (a commonly adopted metric of privacy strength) of >9,000 m even when up to 50% original data are missing.

Dialogue state tracker is responsible for inferring user intentions through dialogue history. Previous methods have difficulties in handling dialogues with long interaction context, due to the excessive information. We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce redundant information's interference and improve long dialogue context tracking. Specially, we first apply a Slot Attention to learn a set of slot-specific features from the original dialogue and then integrate them using a Slot Information Sharing. The sharing improve the models ability to deduce value from related slots. Our model yields a significantly improved performance compared to previous state-of-the-art models on the Multi-WOZ dataset.