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E-healthcare as a significant facet of the Internet of Things (IoT) relies on wearable devices to continuously monitor users’ vital signals for health-related purposes. The sensitive and vast nature of the collected data necessitate secure encryption and storage on the cloud. Simultaneously, there is a need to share these data for healthcare purposes. How to balance the privacy and usability of these data is a challenging problem in the e-healthcare applications. A central problem arose from the continuous data collection is how to effectively share the data collected in one specific time period when users are unwell. We formalize it as the sequential data sharing problem. This problem appears in various IoT applications apart from e-healthcare, such as video surveillance. In this paper, we explain why all existing solutions cannot address the above problem. Then, we propose a novel sequential data sharing system (SDSS), where the data encrypted at any specific time period can be efficiently shared. We first present a practical construction of SDSS that supports securely sharing data within one specific time period in a symmetric manner. The decryption key are retrieved sequentially by utilizing the shared key and hash function. All involved calculations in the system are lightweight. Data pertaining to other non-selected time periods remain unknown. We then extend the SDSS to a multiple-range version, enabling simultaneous sharing data for multiple specific time periods, and show an example. We formalize the definition of security models and analyze the security of our systems. Finally, we evaluate the performance of our systems using SHA-256 and AES-256. Experimental results demonstrate that our systems are highly efficient. It takes less than 1 millisecond to encrypt 100KB data and less than 0.4 milliseconds to decrypt the corresponding cipher data. Our proposed systems provide a solution to balance the privacy and usability in the context of sequentially collected data. We believe that this work will enhance the adoption and practicality of IoT applications. [ABSTRACT FROM AUTHOR]

Receivers and intermediary establishments typical to urban areas have unique freight behaviour and are understudied, particularly in developing countries. This paper uses establishment-based freight survey data to examine their freight generation patterns in the Chennai metropolitan area, India. Three sets of Simple Linear Regression (SLR) models, three single predictor proportional odds logit models, one multiple linear regression model, and one multiple predictor proportional odds logit model with business size (employment, area, and operational age) and indicators of establishment category as regressors are developed each for both freight production and freight attraction. Partial proportional odds logit models are developed in a few cases to overcome the limitations of proportional odds logit models. The best SLR model varied with the establishment category for freight production and attraction. The establishment area model is the best among single predictor proportional odds logit models. The multiple predictor proportional odds logit models marginally improved the fit over single predictor models. The proportional odds logit model results show that the establishment category has a greater impact on freight generation levels than business size variables. Since earlier studies rarely focused on receivers and intermediary establishments, policymakers may benefit from the developed models and study insights while estimating freight demand and developing freight policies. • Receivers and intermediary establishments' freight generation is studied. • Partial Proportional Odds (PO) logit models are developed for freight attraction. • Area model is the best single predictor PO logit model for freight generation. • Establishment type affects freight generation more than business size variables. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS)-aided near-field communications are investigated. First, the necessity of investigating RIS-aided near-field communications and the advantages brought about by the unique spherical wave-based near-field propagation are discussed. Then, the family of patch array-based RISs and metasurface-based RISs is introduced along with respective near-field channel models. A pair of fundamental performance limits of RIS-aided near-field communications, namely, their power scaling law and effective degrees of freedom (EDOF), are analyzed for both patch array-based and metasurface-based RISs, which reveals the potential performance gains that can be achieved. Furthermore, associated near-field beam training and beamforming design issues are studied, where a two-stage hierarchical beam training approach and a low-complexity element-wise beamforming design are proposed for RIS-aided near-field communications. Finally, a suite of open research problems is highlighted for motivating future research. [ABSTRACT FROM AUTHOR]