Parallel Genetic Algorithm Decoder Scheme Based on DP-LDPC codes for industrial IoT scenarios

The new concept of Industry 4.0 has been developed: it includes both Internet of Things (IoT) structure and the local networks that are still needed to carry out real-time tasks. Genetic algorithms are successfully used for decoding some classes of error correcting codes, and offer very good performances when solving large optimization problems. This article proposes a decoder based on parallel Genetic Algorithms (PGAD) for Decoding Low Density Parity Check (LDPC) codes. The proposed algorithm gives large gains over the Sum-Product decoder, which proves its efficiency, the best performances are obtained for Ring Crossover (RC) as a type of crossover and the tournament as a type of selection. Furthermore, the performances of the new decoder are improved using Multi-criteria method. For the LDPC code, simulation results showed that our Proposed PGAD exceeds the sum-product by a gain of 1.5 dB at BER = 10-4, and the PGAWS exceeds the sum-product by 2.5 dB

Spectrum sensing for smart embedded devices in cognitive networks using machine learning algorithms

Spectrum sensing is an essential step in cognitive radio-based dynamic spectrum management. Spectrum sensing to detect the presence of the licensed signals in a particular frequency band is one of the most important research topics in cognitive radio. To identify primary user (PU) presence, we propose a low cost and low power consumption implementation of spectrum sensing operation based on real signals. These signals are generated by smart embedded devices at 433 MHz wireless transmitter using ASK (Amplitude-Shift Keying) and FSK (Frequency-Shift Keying) modulation type. The reception interface is constructed using an RTL-SDR dongle connected to MATLAB software. The signal detection is done by using four techniques: the artificial neural network (ANN), support vector machine (SVM), Decision Trees (TREE), and k-nearest neighbors (KNN). This article comparatively analyzed the performance of the classifiers to identify the best method for spectrum sensing between the three techniques. The performance evaluation of our proposed model is the probability of detection (Pd) and the false alarm probability (Pfa). Results show also that the sensing is susceptible to signal to noise ratio value. This comparative study has been demonstrated that the spectrum sensing operation by ANN and SVM can be more accurate than KNN, TREE, and some other classical detectors

Human-Wildlife Conflict Early Warning System Using the Internet of Things and Short Message Service

This research article was published by Engineering, Technology and Applied Science Research, Vol. 12 No. 2Human-wildlife conflict (HWC) is an important challenge to communities living in areas bordering wildlife game parks and reserves. It is more evident in the United Republic of Tanzania, whose economy depends on wildlife tourism. This paper proposes a low-cost and low-power early warning system using the Internet of Things (IoT) and Short Message Service (SMS) to support HWC respond teams in mitigating these challenges. The system comprises three primary units: sensing, processing, and alerting. The sensing unit consists of a Passive Infrared (PIR) sensor, a Global Positioning System (GPS), and a Raspberry Pi camera. The PIR sensor detects the proximity of the animal using the heat signature, GPS senses and records the current location, while the Raspberry Pi camera has the primary purpose of taking a picture after the PIR sensor detects the proximity of the animal. The processing unit with a Raspberry microcomputer performs data processing and image inferencing using the You Only Look Once (YOLO) algorithm. Last is the alerting unit, which includes a Global System for Mobile (GSM) communications module for sending SMS messages to the human-wildlife conflict response team and the nearer community response team leader whenever wild animals are spotted near the park’s border. The system detects, identifies, and reports the detected wild animals. The GPRS provides internet connectivity to support data collection, storage, and monitoring in the cloud

Characteristics and coastal effects of a destructive marine storm in the Gulf of Naples (southern Italy)

Destructive marine storms bring large waves and unusually high surges of water to coastal areas, resulting in significant damages and economic loss. This study analyses the characteristics of a destructive marine storm on the strongly inhabited coastal area of Gulf of Naples, along the Italian coasts of the Tyrrhenian Sea. This is highly vulnerable to marine storms due to the accelerated relative sea level rise trend and the increased anthropogenic impact on the coastal area. The marine storm, which occurred on 28 December 2020, was analyzed through an unstructured wind-wave coupled model that takes into account the main marine weather components of the coastal setup. The model, validated with in situ data, allowed the establishment of threshold values for the most significant marine and atmospheric parameters (i.e., wind intensity and duration) beyond which an event can produce destructive effects. Finally, a first assessment of the return period of this event was evaluated using local press reports on damage to urban furniture and port infrastructures

Characteristics and coastal effects of a destructive marine storm in the Gulf of Naples (southern Italy)

Destructive marine storms bring large waves and unusually high surges of water to coastal areas, resulting in significant damages and economic loss. This study analyses the characteristics of a destructive marine storm on the strongly inhabited coastal area of Gulf of Naples, along the Italian coasts of the Tyrrhenian Sea. This is highly vulnerable to marine storms due to the accelerated relative sea level rise trend and the increased anthropogenic impact on the coastal area. The marine storm, which occurred on 28 December 2020, was analyzed through an unstructured wind-wave coupled model that takes into account the main marine weather components of the coastal setup. The model, validated with in situ data, allowed the establishment of threshold values for the most significant marine and atmospheric parameters (i.e., wind intensity and duration) beyond which an event can produce destructive effects. Finally, a first assessment of the return period of this event was evaluated using local press reports on damage to urban furniture and port infrastructures

Design of Folded Dipole with Double U Shaped Slot UHF RFID Tag Using Genetic Algorithm Optimization for Healthcare Sensing Applications

A novel folded dipole with double U slots RFID Tag antenna for wearable RFID sensing applications is designed and studied. The compact tag structure consists of folded dipole and two U slots shapes for miniaturization of antenna radiating part as well as to enhance its radiation performance in UHF band. A Genetic Algorithm optimization technique has been utilized with HFSS software for optimization of the proposed tag dimensions, in order to achieve better return loss and good realized-gain. The proposed epidermal tag was placed at very close proximity of human skin, which represents the big challenge due to the high losses of human tissues that could strongly degrade the radiation efficiency of the tag. By means of detailed simulations and numerical study our novel RFID tag placed on human torso presents good gain and well matching impedance across the operational bandwidth

On the Ultra-Reliable and Low-Latency Communications for Tactile Internet in 5G Era

New generations of mobile telephony succeed every decade, each bringing an evolution or even a revolution. Nowadays, the Internet of Things and the tactile Internet are starting to grow, and 5G technology is there to enable these services. 5G technology has introduced three types of services, namely eMBB (for services requiring very high bit rates), mMTC (for massive connection of user equipment), and uRLLC (for critical services requiring very high reliability and extremely reduced latency). In this paper, we have dealt with some issues encountered by uRLLC services for tactile Internet services. In this article, we have studied the transmission of very small packets as required by the 5G uRLLC services. We also examined the probability of transmission error and its variation concerning the transmission delay and the length of the packet transmitted. This study was conducted considering its application in the Tactile Internet

A framework of optimizing the deployment of IoT for precision agriculture industry

The massive growth of wireless communications in recent years is mostly due to new connectivity demands and advances in technology development of low power) transceivers. An example of the unique demands is the increasing exchange of data in Internet services, which has led to wireless network deployment for data transmissions. The coordination of the IoT devices, smart systems, and agriculture can contribute directly to the development of the farmer’s practices by building their farm more intelligent and digital. However, enhancing farming practices requires inspecting farm equipment and farmer’s experiences, which can be analyzed through the interconnectedness of IoT objects to collect farm data over the Internet to launch smart digital agriculture. It is challenging to control all farming processes (especially in real-time), this remaining as the main limitation of traditional farming. In this work, we focus on how wireless sensors can play a vital role in smart farm systems and allow processing the large amount of data generated in batches or real-time to analyze it, retrieve insights from it, and create a Smart Digital Farm. This paper proposes hierarchical-logic mapping and deployment algorithms to tackle the problem of poor network connectivity and sensing coverage in random IoT deployment

Securing mobile edge computing using hybrid deep learning method

In recent years, Mobile Edge Computing (MEC) has revolutionized the landscape of the telecommunication industry by offering low-latency, high-bandwidth, and real-time processing. With this advancement comes a broad range of security challenges, the most prominent of which is Distributed Denial of Service (DDoS) attacks, which threaten the availability and performance of MEC’s services. In most cases, Intrusion Detection Systems (IDSs), a security tool that monitors networks and systems for suspicious activity and notify administrators in real time of potential cyber threats, have relied on shallow Machine Learning (ML) models that are limited in their abilities to identify and mitigate DDoS attacks. This article highlights the drawbacks of current IDS solutions, primarily their reliance on shallow ML techniques, and proposes a novel hybrid Autoencoder–Multi-Layer Perceptron (AE–MLP) model for intrusion detection as a solution against DDoS attacks in the MEC environment. The proposed hybrid AE–MLP model leverages autoencoders’ feature extraction capabilities to capture intricate patterns and anomalies within network traffic data. This extracted knowledge is then fed into a Multi-Layer Perceptron (MLP) network, enabling deep learning techniques to further analyze and classify potential threats. By integrating both AE and MLP, the hybrid model achieves higher accuracy and robustness in identifying DDoS attacks while minimizing false positives. As a result of extensive experiments using the recently released NF-UQ-NIDS-V2 dataset, which contains a wide range of DDoS attacks, our results demonstrate that the proposed hybrid AE–MLP model achieves a high accuracy of 99.98%. Based on the results, the hybrid approach performs better than several similar techniques