Long-range wide area network (LoRaWAN) has been newly designed to exploit the 2.4 GHz unlicensed band instead of the traditional sub-GHz bands. However, this makes LoRaWAN frequently suffer from wireless communication failures caused by the cross-technology interference (CTI) from coexisting Wi-Fi networks using the same 2.4 GHz band. As a physical-layer solution to this problem, this paper presents CoWiL to combat the CTI from Wi-Fi to LoRa (the physical layer of LoRaWAN) in the 2.4 GHz band for better coexistence between LoRaWAN and Wi-Fi networks. Existing approaches address this problem by sacrificing Wi-Fi transmission performance or assuming that Wi-Fi interferes with only a small portion of LoRa signals. Unlike them, CoWiL does not affect normal Wi-Fi communications and is workable regardless of the degree of the CTI. This is achieved by implementing CoWiL at a LoRa receiver to directly extract LoRa data out of the CTI from Wi-Fi. Specifically, CoWiL exploits the correlation of the signal demodulation results between the preamble and the payload of a LoRa signal. A novel frequency bin mask is generated based on the demodulated preamble and then applied to the following payload for data decoding. Experimental results in various real-world environments show that in comparison with the existing solutions, CoWiL can reduce the packet error rate of LoRa transmissions by up to 96% under the CTI from Wi-Fi.

This study develops a deep learning-based Intrusion Detection System (IDS) for the Open Fronthaul (OFH) interface in Open Radio Access Network (O-RAN) architecture, focusing on detecting and mitigating Distributed Denial of Service (DDoS) attacks. It aims to enhance O-RAN network security, particularly in the CUS-Plane of the OFH interface, by employing advanced deep learning techniques for threat prediction and prevention. The research contributes practical insights and solutions to improve the security resilience of O-RAN's open architecture.

In this paper, we introduce the Enhanced Smart Exponential-Threshold-Linear (Enhanced-SETL) algorithm, a new approach that uses the multi-variable Deep Reinforcement Learning (DRL) framework to simultaneously optimize multiple settings of the Contention Window (CW) in IEEE 802.11 wireless networks. Unlike traditional DRL methods that adjust only a single CW parameter, our innovative approach simultaneously optimizes both the CW minimum (CWmin) and CW Threshold (CWThreshold), significantly improving network traffic control. We utilize a Double Deep Q-learning Network (DDQN) for dynamic updates of these CW settings, broadcasted across the dense Wi-Fi networks. This dual adjustment method, coupled with dynamic, data-driven updates, not only enhances throughput, but also reduces collision rates, and ensures fairness access across both static and dynamic wireless environments. Enhanced-SETL achieves a throughput improvement ranging from 3.55% up to 43.73% and from 3.98% up to 30.15% in static and dynamic scenarios over standard protocols and state-of-the-art DRL models, while maintaining a fairness index near 99% across diverse stations, showcasing its effectiveness and adaptability in various network conditions.

The website security is an important issue that must be pursued to protect Internet users. Traditionally, blacklists of malicious websites are maintained, but they do not help in the detection of new malicious websites. This work proposes a machine learning architecture for intelligent detecting malicious URLs. Forty-one features of malicious URLs are extracted from the data processes of domain, Alexa and obfuscation. ANOVA (Analysis of Variance) test and XGBoost (eXtreme Gradient Boosting) algorithm are used to identify the 17 most important features. Finally, dataset is used to learn the XGBoost classifier, which has a detection accuracy of more than 99%.

This work presents a novel indoor positioning system that exploits a hybrid algorithm for future productivity 4.0. In the proposed system, LEDs are signal transmitters that are deployed on a ceiling. The target receiver is a smart sensor that receives the frequency, color temperature and luminance of the LEDs. First, the system determines the frequency of the LEDs to select a subspace to narrow down from a positioning area and to compute the position of the target. This work proposes a hybrid fingerprint that is based on RSSI values. RSSI signals are easily disturbed by refraction or diffraction. This research is based on line-of-sight light emission, which increases the accuracy of indoor positioning.

This study achieves Quality-of-Service (QoS) management in heterogeneous networking using a distributed multi-agent scheme (DMAS) based on the concept of cooperation and the awareness algorithm. The proposed scheme is developed for supporting QoS management in a user-accepted and cost-effective fashion, which consists of a collection of problem-solving agents with three modules: the knowledge source, the in-cloud blackboard system, and the control engine built into the scheme. A set of problem-solving agents autonomously process local tasks and cooperatively interoperate via an in-cloud blackboard system to guarantee QoS. An awareness algorithm, called the Q-learning algorithm, calculates the exceptive rewards of a handoff to all access networks. These rewards are then used by these problem-solving agents to determine what to do. Through operations and cooperation among the active agents, a policy is selected and a user-accepted schedule that meets the specified QoS is generated. Compared with traditional QoS management mechanisms, the proposed DMAS scheme has a 36% lower packet loss ratio in video streaming applications and a 34% lower average delay in VoIP applications with only a minor sacrifice in system computational complexity.