Cellular-connected unmanned aerial vehicles (UAVs) are increasingly being deployed in emerging Internet of Things (IoT) applications, where reliable handover management is critical to ensure uninterrupted communication. Unlike terrestrial users, UAVs face frequent handovers due to antenna side-lobe coverage and fragmented aerial cell overlaps. Existing optimization strategies, however, are often limited to coarse-grained “whether-to-handover” decisions or unidirectional cell individual offset (CIO) tuning, resulting in degraded handover performance. To address these problems, this paper proposes an Event-triggered Bidirectional CIO Optimization with Hybrid prioritized experience replay based Deep Reinforcement Learning (EBCO-HDRL) algorithm. Under the A3 event-triggered mechanism, EBCO-HDRL jointly configures serving-to-neighbor and neighbor-to-serving CIOs, improving handover accuracy and reducing ping-pong events. An event-triggered optimization strategy adaptively reconfigures CIOs only when needed, mitigating computational overhead and enhancing training stability. To further improve sample efficiency, a Hybrid Prioritized Experience Replay (HPER) scheme is introduced, combining temporal-difference error and high-reward sampling, supported by a heterogeneous neural network design. Simulation results show that EBCO-HDRL significantly outperforms baseline algorithms in terms of precision, stability, and robustness, offering an effective solution for UAV handover management in cellular IoT networks.

The rapid growth of the Internet of Things (IoT) has led to the widespread use of companion apps for device management. However, these apps expose a critical vulnerability in the IoT ecosystem: insufficient verification procedures during device firmware updates (DFU), often resulting in firmware leakage. Once leaked, the firmware reveals sensitive design details, creating a straightforward path for attackers to reverse-engineer devices. To address this issue, we designed an automated analysis tool called FirmProj. It systematically evaluates firmware leakage risks by examining IoT companion apps. FirmProj combines advanced static analysis techniques with large language models to identify DFU modules, extract firmware files, and detect security vulnerabilities. In a large-scale study involving 10,047 IoT companion apps, FirmProj successfully retrieved 3,434 firmware files, uncovering severe flaws in DFU implementations that can lead to firmware leakage. These findings resulted in the assignment of 35 CVE IDs. Our results highlight the urgent need to strengthen firmware protection mechanisms throughout the IoT ecosystem.

Private browsing is a common feature of web browsers on desktop platforms. This feature protects the privacy of users browsing the Internet and, therefore, is widely welcomed by users. In recent years, with the popularity of smartphones, the private browsing mode has been introduced into mobile browsers. However, its deployment on mobile platforms has not been well evaluated. To bridge the gap, in this work, we systemically studied the private browsing modes of Android browser apps. Specifically, we proposed six private rules for mobile browsers to follow by combining the mobile browsing features with the previous research on private browsing. Furthermore, we designed an automated analysis framework, BroDroid, to detect whether mobile browsers violate these rules. Also, with BroDroid, we evaluated 49 popular browser apps crawled from Google Play. Finally, BroDroid successfully identified 58 violations, some of which come from the promised capabilities of the browser. We reported our discovered issues to the corresponding developers, and four of them (Yandex Browser, Mint Browser, Web Explorer, and Net Fast Web Browser) have acknowledged our findings. Our observation may be the tip of the iceberg, and more efforts should be put into improving the privacy protections of mobile browsers.

Location estimation is pivotal for numerous applications in industries and academia. The advent of fifth generation wireless communications and the Internet of Things has led to the deployment of a multitude of devices, necessitating efficient location estimations. Traditional methods, such as Time of Arrival and antenna arrays, have their limitations, including the need for time stamping, clock synchronization, and high hardware complexity. Here, we propose a low-cost 3-D location estimation method using a double-metasurface-antenna system. This method projects multiple targets onto two virtual subplanes, uses geometric constraints to find target correspondences, and calculates the 3-D coordinates by solving multiple triangles. This method is computationally efficient and requires only intensity measurements, significantly reducing hardware complexity. The proposed method promises to estimate multitarget, 3-D locations, particularly in commercial and low-cost scenarios for next generation wireless networks.

Abstract Summary: We present a software tool called GenomeLaser that determines the haplotypes of each person from unphased high-throughput genotypes in family pedigrees. This method features high accuracy, chromosome-range phasing distance, linear computing, flexible pedigree types and flexible genetic marker types. Availability and implementation: http://www.4dgenome.com/software/genomelaser.html. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.