Forward and Backward Private Searchable Encryption for Cloud-Assisted Industrial IoT.

In the cloud-assisted industrial Internet of Things (IIoT), since the cloud server is not always trusted, the leakage of data privacy becomes a critical problem. Dynamic symmetric searchable encryption (DSSE) allows for the secure retrieval of outsourced data stored on cloud servers while ensuring data privacy. Forward privacy and backward privacy are necessary security requirements for DSSE.

An Efficient Pairing-Free Ciphertext-Policy Attribute-Based Encryption Scheme for Internet of Things.

The Internet of Things (IoT) is a heterogeneous network composed of numerous dynamically connected devices. While it brings convenience, the IoT also faces serious challenges in data security. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising cryptography method that supports fine-grained access control, offering a solution to the IoT's security issues.

ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring.

Addressing the increasing demand for remote patient monitoring, especially among the elderly and mobility-impaired, this study proposes the "ScalableDigitalHealth" (SDH) framework. The framework integrates smart digital health solutions with latency-aware edge computing autoscaling, providing a novel approach to remote patient monitoring. By leveraging IoT technology and application autoscaling, the "SDH" enables the real-time tracking of critical health parameters, such as ECG, body temperature, blood pressure, and oxygen saturation.

Enhancing Localization Efficiency and Accuracy in Wireless Sensor Networks.

Accuracy is the vital indicator in location estimation used in many scenarios, such as warehousing, tracking, monitoring, security surveillance, etc., in a wireless sensor network (WSN). The conventional range-free DV-Hop algorithm uses hop distance to estimate sensor node positions but has limitations in terms of accuracy.

LACP-SG: Lightweight Authentication Protocol for Smart Grids.

Smart grid (SG) recently acquired considerable attention due to their utilization in sustaining demand response management in power systems. Smart meters (SMs) deployed in SG systems collect and transmit data to the server. Since all communications between SM and the server occur through a public communication channel, the transmitted data are exposed to adversary attacks.

A computational offloading optimization scheme based on deep reinforcement learning in perceptual network.

Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer.