User authentication is a critical component of IoT environments, serving as the security bridge between users and devices to safeguard data transmission and prevent unauthorized access. While WiFi-based authentication via motion recognition has shown potential in single-user scenarios, its effectiveness diminishes significantly in multi-user environments. Detecting subtle movements, such as breathing, from multiple users simultaneously poses a significant challenge, pushing the capabilities of current WiFi authentication systems to their limits. In this paper, we presentBreathEye, a multi-user authentication system that leverages only a pair of commercial WiFi devices to detect and authenticate the subtle respiratory patterns of multiple individuals simultaneously. The core insight of our approach lies in exploiting the inherent variability in individual breathing patterns, which manifest in short-term energy fluctuations and long-term dependencies within the breathing signals. To this end, we propose a dual-attention fusion mechanism that captures these subtle differences, enabling the effective disentanglement of individual breathing signals from overlapping multi-user data. To further enhance practicality,BreathEyeincorporates a few-shot learning framework to minimize user registration time and reduce system training overhead by analyzing independent breathing signals for authentication. Extensive experiments demonstrate thatBreathEyeachieves authentication accuracies of over 99%, 92%, and 87% in single-, two-, and three-user scenarios, respectively, highlighting the system's effectiveness.

Abstract Untargeted poisoning attacks pose a serious threat to federated learning. However, existing untargeted poisoning attacks have limitations. Most attacks assume that the adversary can control a large number of real clients, which is difficult to achieve in practice. Although the poisoning attack based on fake clients overcomes dependence on real clients. It causes the model to classify all data into default categories, which limits the effectiveness of the attack. Additionally, the fake local model updates are consistent, making them easily detectable by existing defenses. the attack is less stealth. To address these issues, we propose an Untargeted Poisoning Attack based on Fake Clients called UPA-FC. The attack manipulates key model layers based on their importance to enhance its effectiveness. We also introduce a random flipping strategy to reduce similarity between fake local updates, enhancing the stealth of the attack. To defend against UPA-FC, we propose a clustering-based defense scheme called D-UPA-FC. This scheme analyzes the distance matrix using a clustering algorithm. It determines the optimal clusters by calculating Euclidean distances to aggregate the global model. Experimental results show that UPA-FC outperforms the existing poisoning attack in terms of both effectiveness and stealth, while D-UPA-FC effectively defends against the UPA-FC.

A large number of Internet of Things (IoT) devices such as sensor nodes are deployed in various urban infrastructures to monitor surrounding information. However, it is still a challenging issue to collect data in a low‐cost, high‐quality, and reliable manner through IoT technique. Although the recruitment of mobile vehicles (MVs) to collect urban data has proved to be an effective method, most existing data collection systems lack a trust detection mechanism for malicious terminal nodes and malicious vehicles, which should lead to security vulnerabilities in practice. This paper proposes a novel data collection strategy based on a layered trust mechanism (DC‐LTM). The strategy recruits MVs as data collectors of the sensor nodes based on the data value in the city, evaluates the trustworthiness of the data reported by the nodes, and records the results to the cloud data center. Furthermore, in order to make the data collection system more efficient and trust mechanism more reliable, we introduce unmanned aerial vehicles (UAVs) dispatched by data centers to actively verify the core sensor node data and use the core sensor data as baseline data to evaluate the credibility of the vehicles and the trust value of the whole network sensor nodes. Different from the previous strategies, UAVs adopts the DC‐LTM method to obtain the node data while actively obtaining the trust value of MVs and nodes, which effectively improves the quality of data acquisition. Simulation results show that the mechanism effectively distinguishes malicious vehicles that provide false data in exchange for payment and reduces the total cost of system recruitment payments. At the same time, the proposed incentive mechanism encourages vehicle to complete the evaluation task and improves the accuracy of node trust evaluation. The recognition rates of false data attacks and flooding attacks as well as the recognition error rate of normal nodes are 100%, 98.9%, and 3.9%, respectively, which improves the quality of system data collection as a whole.

This paper shows how cognitive radio (CR) can help to optimize system power consumption of multiple input multiple output (MIMO) communication systems. Leveraging results from information theory and capabilities of a CR (e.g., the awareness of the component capabilities and characteristics), a theoretical framework is developed to minimize the system power consumption of MIMO systems while still considering radiated power. This paper mathematically formulates the system power consumption minimization problem under a sum rate constraint for MIMO systems. The impact of channel correlation and partial channel state information at the transmitter is considered. Numerical algorithms are developed to solve the constrained optimization problem. The simulation results show that significant power savings (e.g., up to 75% for a 4 x 4 MIMO system with Class A power amplifiers) can be achieved compared to conventional power allocation schemes. The results also show that the more computationally efficient suboptimal heuristic algorithms can achieve power savings comparable to the exhaustive search algorithm.

In this paper, we show how cognitive radio can help minimize energy consumption of a wireless mobile communication device. We propose an energy optimization framework using cognitive radio for a given quality of service requirement based on the channel and the radio capabilities. The cognitive radio not only adjusts modulation, coding, and radiated power, as with conventional adaptive modulation, but also adjusts component characteristics (e.g., power amplifier characteristics) so that the radio operates with the highest energy efficient possible way. Simulation results show that significant energy savings (up to 75%) can be achieved compared to conventional adaptive modulation. This framework also can be applied to optimize radio operations to achieve additional goals.

Abstract Summary: Low-coverage genomes (LCGs) are becoming an increasingly important source of data for phylogenetic studies. However, assembly of these genomes is time consuming, difficult and lags behind sequence generation. THOR is a fast, stringent application for targeted reconstruction of sequence orthologs in unassembled LCGs. Using a 4× coverage set of mouse whole-genome sequence reads, THOR could partially or completely reconstruct 416/1000 human promoter ortholog regions in ∼7.3 min/promoter. THOR's reconstruction rate improves markedly with both higher-coverage, and less divergent target species. Availability: THOR is implemented in java and is currently available as source code and as a web service (www.bcgsc.ca/services/thor) for reconstructing human sequences. Contact: [email protected]