Nowadays, wireless networks have been widely deployed in our daily lives, providing people with convenient Internet of Things (IoT) services in healthcare, smart cities, transportation, etc. However, the open nature of communication mediums leaves IoT devices susceptible to unauthorized access by rogue devices, leading to significant privacy breaches and property damage. Among various security measures, radio frequency (RF) fingerprinting stands out as a promising device identification technique, owing to RF fingerprints’ uniqueness and forgery-resistant nature. Existing methods, however, overlook the structural relationship of a transmitter’s internal hardware paths, affecting the performance and efficiency of RF fingerprint identification. Inspired by the internal hardware paths, this article introduces a novel deep-learning-based RF fingerprinting approach, multidimensional RF fingerprinting enhancement (MRFE). MRFE enhances RF fingerprinting by dissecting raw IQ signals into multiple dimensions and proposing a novel fingerprint strengthen layer (FSL) to extract multidimensional fingerprints from the separate hardware paths, then leveraging attention mechanisms to fuse them into an enhanced RF fingerprint. The enhanced fingerprint captures more detailed physical hardware characteristics, effectively enhancing device identification accuracy. Our MRFE’s open-source implementation has been validated on the public ORACLE RF fingerprinting data set, achieving an impressive 99.33% accuracy in identifying 16 high-end bit-similar transmitters with identical configurations.

Featured by low cost and high mobility, unmanned aerial vehicles (UAVs)-ground assisted communication has been considered as a promising solution to provide fast service recovery for rescue and emergency response with IoT devices across disaster regions. As the terrestrial infrastructures can be annihilated or partially damaged after disaster, the UAV assisted communication system has to be self-organized in highly dynamic and partially observable environment. In this article, we will explore an emergency communication system in post disaster areas with edge nodes and UAV assistance. The system is designed to provide cost effective communication with temporary infrastructures for end users in sophisticated environments. To this end, we first formulate the problem and propose optimal solutions according to observable workloads and communication channel connectivity. Moreover, considering the unknown environment scenario, we further present a cooperative learning-based solution, including hybrid design of edge agents and UAV agents, in which the environmental statistics learned by geo-distributed edge agents can also be utilized by UAV agents for UAV-based node selection and UAV hovering control. Through extensive experiments, we demonstrate the superiority of our proposed solutions in terms of service quality and energy conservation in diverse environments.

Wi-Fi plays an essential role in various emerging Internet of Things (IoT) services and applications in smart cities and communities, such as IoT access, data transmission, and intelligent control. However, the openness of such wireless communication medium makes IoT extremely vulnerable to conventional Wi-Fi attacks, of which one is rouge access point (RAP) attacks. This attack brings about serious privacy leakage and property damage to IoT users, motivating in-depth research on RAP attack detection in both academic and industrial communities. Recently, the phase error extracted from channel state information (CSI) has been extensively explored as a physical-layer hardware fingerprint to realize RAP detection. However, in this article, we discover that the phase error suffers from an fingerprint fracture phenomenon (FFP), leading to the complete failure of environment noise filters applied in state-of-the-art approaches and resulting in unsatisfactory detection accuracy. Inspired by our significant discovery, we propose a deep-learning RAP detection method named DL-PEDR. It innovatively offers an Auto-NRK network to effectively remove the environment interference on phase error drift range and inputs it into a Self-ACC network as a reconstructed device fingerprint to accurately authenticate the access point (AP) identity. Through comprehensive evaluation experiments with 30 commonly used Wi-Fi routers, we demonstrate that DL-PEDR achieves a 100% device distinction rate and a 96.6% RAP detection rate under dynamic environments. Moreover, we collect and share more than 1.5 million pieces of CSI data to alleviate the need for large-scale public CSI data sets.

Phrase search encryption, as an important technique in cloud-based IoT system, allows users to retrieve encrypted IoT data that contains a set of consecutive keywords. It plays an important role in cloud-based e-healthcare diagnosis system, machine learning applications for cloud-based IoT system, etc. However, to the best of our knowledge, the existing phrase search encryption schemes cannot achieve the complete verification for search results. They either cannot verify whether the returned files correctly containing the query phrase or cannot verify whether all files containing this query phrase are returned. Result verification is very important for some cloud-based IoT applications. If the search result is incorrect in the cloud-based e-healthcare diagnosis system, it will lead to misdiagnosis even endanger the patient's life. In order to deal with this problem, this article explores how to achieve verifiable phrase search over encrypted cloud-based IoT data. Specifically, we design novel look-up tables which can be utilized to determine and verify the position relationship among keywords. Meanwhile, we adopt a two-phase query strategy. In the first query phase, the data user can know the identifiers of files containing the keywords in the query phrase, and generate the search trapdoor based on these identifiers for the next phase. In the second query phase, the data user can obtain the verification information to check whether all files containing the query phrase are correctly returned. We present the security analysis of our scheme and conduct extensive experiments. The results prove the high security and efficiency of our proposed scheme.