No abstract

No abstract

Task allocation or participant selection is a key issue in Mobile Crowd Sensing (MCS). While previous participant selection approaches mainly focus on selecting a proper subset of users for a single MCS task, multi-task-oriented participant selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes TaskMe, a participant selection framework for multi-task MCS environments. In particular, two typical multi-task allocation situations with bi-objective optimization goals are studied: (1) For FPMT (few participants, more tasks), each participant is required to complete multiple tasks and the optimization goal is to maximize the total number of accomplished tasks while minimizing the total movement distance.(2) For MPFT (more participants, few tasks), each participant is selected to perform one task based on pre-registered working areas in view of privacy, and the optimization objective is to minimize total incentive payments while minimizing the total traveling distance. Two optimal algorithms based on the Minimum Cost Maximum Flow theory are proposed for FPMT, and two algorithms based on the multi-objective optimization theory are proposed for MPFT. Experiments verify that the proposed algorithms outperform baselines based on a large-scale real-word dataset under different experiment settings (the number of tasks, various task distributions, etc.).

A series of chitosan±gelatin complexes was prepared by varying the ratio  of constituents. Differential scanning calorimetry was used to determine the amount of the different states of water. The interaction between chitosan and gelatin was checked by IR and X-ray analysis and was related to mechanical strength. The results indicate that the water take-up of a chitosan±gelatin complex is depressed by strong interactions within networks. Chitosan can improve the tensile strength of complex ®lms, and even with high water content these can keep appropriate tensile strength and higher elongation.

Wearable body area network is a key component of modern-day e-healthcare system (e.g., telemedicine), particularly as the number and types of wearable medical monitoring systems increase. The importance of such systems is reinforced in the current COVID-19 pandemic. In addition to the need for secure collection of medical data, there is also a need to process data in real-time. In our paper, we design an improved symmetric homomorphic cryptosystem and a fog-based communication architecture to support delay-or time-sensitive monitoring and other related applications. Specifically, the medical data can be analyzed at the fog servers in a secure manner. This will facilitate decision making, for example allowing relevant stakeholders to detect and respond to emergency situations, based on real-time data analysis. We present two attack games to demonstrate that our approach is secure (i.e. chosen plaintext attack resilience under the computational Diffie-Hellman assumption), and evaluate the complexity of its computations. A comparative summary of its performance and three other related approaches suggests that our approach enables privacy-assured medical data aggregation, and the simulation experiments using Microsoft Azure further demonstrate the utility of our scheme.