Epistasis Detection (ED) was widely used for identifying potential risk disease variants in the human genome. A statistically meaningful ED typically requires a more extensive dataset to detect complex disease-associated Single Nucleotide Polymorphisms (SNPs), but a single institution generally possesses limited genome data. Thus, it is necessary to collect multi-institutional genome data to carry out research together. However, concerns regarding privacy and trustworthiness impede the sharing of massive genome data. Therefore, this study proposes a novel federated ED framework with the sequence perturbation privacy-preserving method to address the limitation of distributed data sharing (FedED-SegNAS). Firstly, to address the lack of interpretability in deep learning models, integrate fuzzy logic into Convolutional Neural Networks (CNNs), promoting the capabilities of CNN to represent the ambiguities of genomic data with high interpretability and reasonable accuracy. Secondly, consider using the Neural Architecture Search (NAS) method to optimize the federated neural architecture. Specifically, selecting the Particle Swarm Optimization (PSO) algorithm to automatically search the optimal neural architecture at different stages in federated learning based on adaptive multi-objectives decreases the communication cost and improves communication efficiency. Furthermore, to ensure the security of the parameter transfer process, design the sequence perturbation privacy-preserving method, grouping the upload and download parameters of federated learning and randomly perturbing the group number so that the attacker cannot obtain the corresponding result between the group number and parameters. Its rationality and security have been proven. The experiments conducted on a range of datasets demonstrate the superiority of the framework over state-of-the-art epistasis detection methods. FedED-SegNAS can reduce network complexity while protecting genome data security.

Driven by emerging wireless applications, numerous smart objects will be deployed, forming things networks interconnected by the Internet of Things (IoT). Many of these networks follow the low-power multihop wireless network (LPMWN) paradigm due to the good scalability of the distributed schemes adopted by LPMWNs. Although distributed schemes may operate satisfactorily most of the time, they are often complex and costly to manage. To simplify network management, the LoRa (long range) wireless technology has been introduced into LPMWNs for centralized network control. Unfortunately, due to LoRa’s inherent limitation that a downlink packet must be in response to a precedent uplink packet, the existing protocols typically require every node to periodically send an uplink packet to the gateway for downlink network command dissemination. This causes intensive uplink collisions and quickly depletes LoRa bandwidth, especially when the network is large. In light of these pitfalls, we propose a (cross-interface downlink relay (CDR)) scheme, which leverages the ZigBee communications that are already available in LPMWNs to relay LoRa downlink data traffic for bandwidth-efficient and delay-guaranteed data dissemination. CDR determines the optimal network topology for bandwidth-minimized data relay while adjusting system parameters to ensure each node’s delay requirement under network dynamics. A prototype system is implemented by integrating LoRa and ZigBee into an IoT platform. Extensive outdoor experiments show that the bandwidth consumption of CDR is 39.3% lower than a state-of-the-art LoRa data dissemination protocol for ZigBee-based LPMWNs under a moderate data traffic and delay requirement. Besides, CDR improves energy efficiency and network lifetime significantly.

In the field of motion monitoring in smart home, the 5G technology can be applied to Internet of Things systems for facilitating our daily life. In this paper, a comprehensive study on the fall detection system based on 5G network is presented. Starting with analyzing the moving stability, a wearable foot pressure measurement system is devised. Furthermore, the center of pressure during moving is computed by using the pressure data. Besides, the signal transmitting security issue is also considered. The physical layer security authentication and the cross-layer encryption are employed and integrated within the security strategy. As a case study, we evaluate the proposed method on fall detection tasks in smart home and the experimental results establish strong evidence of a satisfying performance.

Biometric identification has verified its effectiveness in personal identity verification because of the uniqueness and noninvasion. In this research, we tend to apply the detection of biometric information to a remote sensing system for the purpose of security area monitoring. Our system is established by collecting signals from the coming individuals via the remote measurement in the specific condition where both kinds of data are detected to determine the identity. Specifically, the measuring of gait signals and facial images is integrated to provide a way of improving the detection accuracy and the robustness. In addition, the fuzzy association rule (FAR) is employed for data analysis in line with the outcomes of different methods. As such, the signals are integrated and transmitted for further processing and remote identification. Experiments are conducted to demonstrate the capability of the proposed system. With the training data increases, a high detection accuracy of 95.2% is obtained, which makes it a promising basis for the realization of remote identity verification.