The expansion of the Internet of Medical Things (IoMT) has enhanced the accuracy, real-time functionality, connectivity, and intelligence of medical examination practices. However, increased interconnectivity of medical equipment has rendered IoMT networks susceptible to several cyberattacks, particularly the Distributed Denial of Service (DDoS) attacks. Current intrusion detection systems (IDS) are insufficiently focused to address and counteract these advanced threats. Furthermore, due to the dynamic nature of IoMT traffic, IDS has considerable difficulty preserving its current threat detection capabilities. This study presents a hybrid deep learning-based intrusion detection system for IoMT networks (HIDS-IoMT). The proposed model hybridizes the Convolutional Neural Network (CNN) for feature extraction and the Long Short Term Memory neural network (LSTM) for sequence data prediction. We implement the designed IDS on a Raspberry Pi device using a fog computing architecture, enabling decentralized processing closer to IoMT devices, thereby enhancing responsiveness and reducing latency. We evaluate the suggested approach for intrusion detection using the IoTID20 and the Edge-IIoTset datasets. These datasets comprise a substantial and varied assortment of traffic flows from actual DDoS attacks, including SYN floods, UDP floods, HTTP floods, and others. We evaluated our novel methodology against distinct DoS attacks such as DDoS, SQL injection, Vulnerability scanner, Scan Host Port, Mirai, etc. Our proposed model attains an accuracy of 99.92%, a precision of 99.91%, a recall rate of 99.99%, and an F1-score of 99.95%, outperforming the latest in advanced methodologies.

The proliferation of the Internet of Medical Things (IoMT) has improved medical examination behaviors in their accuracy, real-time capability, interconnection, and intelligence level. However, the increasing connectivity of medical devices has also made IoMT networks vulnerable to various cyberattacks, particularly Distributed Denial of Service (DDoS) attacks. Existing intrusion detection systems (IDS) are not adequately equipped to handle and mitigate these sophisticated cyberattacks. Moreover, given the dynamic nature of IoMT traffic, where the Routing Protocol for Low-Power and Lossy Networks (RPL) is often deployed to adapt changes in network topology, the intrusion detection system faces significant challenges in maintaining up-to-date threat detection capabilities. This paper proposes a hybrid Deep Learning-Based Intrusion Detection System for the RPL protocol in IoMT networks. The suggested model, designated HIDS-RPL, results from the hybridization of the Convolutional Neural Network (CNN) for feature extraction and the Long Short Term Memory neural network (LSTM), typically employed for sequence data prediction. We evaluate the proposed algorithm to detect intrusions using the benchmark CIC-DDoS2019 dataset. This dataset contains a large and diverse collection of traffic flows from real-world DDoS attacks, including SYN floods, UDP floods, HTTP floods, etc. We evaluated our novel methodology against five DDoS attacks: DNS, UDP, UDP-Lag, NTP, and SYN. In comparison to the most recent methods, our suggested model achieves an accuracy of 99.87%, a precision of 98.5%, a recall rate of 98.64%, and an F1-score of 98.54%.

Precision agriculture, also referred to as smart farming, is one of the main pillars of modern society to achieve the Sustainable Development Goals (SDGs). Precision agriculture aims to improve the quality and quantity of production while conserving scarce natural resources. Smart farming has grown in recent years thanks to the adoption of modern technologies, including artificial intelligence (AI) and the Internet of Things (IoT). In this work, we consider an irrigation system for olive orchards based on unmanned aerial vehicles (UAVs). Specifically, UAVs ensure remote sensing (RS), which offers the advantage of collecting vital information on a large temporal and spatial scale (which cannot be achieved with traditional technologies). However, UAV-based irrigation systems face tremendous challenges due to the various requirements of a powerful computing ability, battery capacity, energy efficiency, and spectral efficiency for different connected devices. This paper addresses the energy efficiency and spectral efficiency trade-off problem of UAV-based irrigation systems. We propose to adopt massive multiple input, multiple output (M-MIMO) technology to ensure wireless communication. In fact, this technology plays a significant role in future sixth-generation (6G) wireless mobile networks and has the potential to enhance the energy efficiency as well as the spectral efficiency. We design a network model with a three-layered architecture and analytically compute the achievable spectral efficiency and the energy efficiency of the studied system. Then, we numerically determine the optimal number of ground base station antennas as well as the optimal number of IoT devices that should be used to ensure the maximum energy efficiency while guaranteeing a high spectral efficiency. The numerical results prove that the proposed UAV-based irrigation system outperforms conventional systems and demonstrate that the best spectral and energy efficiency trade-off is obtained by using the M-MMSE combiner.

Smart irrigation system, also referred as precision irrigation system, is an attractive solution to save the limited water resources as well as to improve crop productivity and quality. In this work, by using Internet of things (IoT), we aim to design a smart irrigation system for olive groves. In such IoT system, a huge number of low-power and low-complexity devices (sensors, actuators) are interconnected. Thus, a great challenge is to satisfy the increasing demands in terms of spectral efficiency. Moreover, securing the IoT system is also a critical challenge, since several types of cybersecurity threats may pose. In this paper, we address these issues through the application of the massive multiple-input multiple-output (M-MIMO) technology. Indeed, M-MIMO is a key technology of the fifth generation (5G) networks and has the potential to improve spectral efficiency as well as the physical layer security. Specifically, by exploiting the available M-MIMO channel degrees of freedom, we propose a physical layer security scheme based on artificial noise (AN) to prevent eavesdropping. Numerical results demonstrate that our proposed scheme outperforms traditional ones in terms of spectral efficiency and secrecy rate.