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End-to-End Encryption (E2EE) plays an essential role in safeguarding user privacy and protecting sensitive data across various communication platforms, including messaging applications, email services, and Internet of Things (IoT) devices. This paper presents a Hybrid Cryptography-Based E2EE method implemented on a Software Defined Networking (SDN) infrastructure, to strengthen bidirectional data security between hosts and IoT devices via the non-secure Message Queuing Telemetry Transport (MQTT) port. By addressing the threat of Man-in-the-Middle (MitM) attacks, the proposed system ensures that only authorized users can decrypt transmitted messages. This paper thoroughly analyzes the implementation and advantages of our Hybrid Cryptography-Based E2EE method by comparing RSA and ECC encryption techniques. ECC-256 is favored for key generation, owing to its high efficiency and speed, measured at 0.4009 ms. Additionally, through a comparison of RSA, AES, and ChaCha20 algorithms, AES-256 emerges as the optimal encryption choice, demonstrating the fastest encryption and decryption times for publishing 0.2758 ms and 0.1781 ms, respectively and for subscribing, with encryption at 0.2542 ms and decryption at 0.1577 ms. Along with its minimal packet size and low resource consumption, our proposed Hybrid Cryptography-Based E2EE method, implemented on SDN infrastructure, validate it's effectiveness in securing digital communications within SDN environments compared to existing solutions. [ABSTRACT FROM AUTHOR]

The effective management of services based on Quality of Experience (QoE) is crucial for the successful deployment of multimedia services in advanced networks like 5G/6G. This necessitates the use of appropriate tools for monitoring, predicting, and managing quality, with Machine Learning (ML) playing a pivotal role. However, predicting the QoE of multimedia streams poses a challenge due to its dependence on numerous influencing factors, and it must adapt to dynamic environments with large-scale data. Machine learning approaches provide a method to quantify the complex relationships between these influencing factors and QoE. In this paper, we propose an innovative improved mixture of classification for assessing the QoE of video streaming. Our approach combines the predictive capabilities of ensemble learning models rather than single models, as experts. Also, we have employed a robust gating network trained by minimizing an improved error function that combines the Classification Loss and the gating loss. We assess our method through a comprehensive simulation model encompassing diverse wireless network environments and various video sequences. The experimental results reveal substantial enhancements in QoE, ensuring users have stable, high-quality video streaming sessions. [ABSTRACT FROM AUTHOR]

Energy is seen as a significant factor in wireless sensor networks (WSNs). It is a challenge to balance between battery lifetime of the different sensors and network lifetime. The main contribution of the proposed approach is to decrease the energy consumption of each sensor node, overcome unbalanced energy usage among sensor nodes, reduce the data gathering time and enhance the network lifetime. To achieve these goals, we combine the Hierarchical Agglomerative algorithm and an optimal path selection method. First, the suitable cluster heads (CHs) are elected based on the Euclidean distance and the residual energy of each sensor node. Then, the base station is situated at the center of the field, which will be partitioned into equal subareas, one for every mobile data collector (MDC). Second, the Kruskal algorithm is used to create an optimal data gathering path from each subset of elected cluster heads. Finally, each mobile data collector travels the optimal path to collect the data from the set of cluster heads of each subarea and returns periodically to the base station to upload gathered data. Computer simulation proves that the proposed approach outperforms existing ones in terms of data gathering time, residual energy and network lifetime. [ABSTRACT FROM AUTHOR]

With the extreme density of devices and fast change of their directions in massive MIMO networks, a fast adaptive beamforming algorithm is required to provide high directivity and an enhanced signal-to-interference and noise ratio (SINR). Blind adaptive beamforming is suitable but less efficient, while non-blind adaptive beamforming is more efficient hut requires significant training time. This study proposes a hybrid adaptive beamforming algorithm that addresses these issues. The algorithm integrates an improved direction-finding method to estimate the directions of arrival (DoAs) of incident signals at the antenna array, even in coherent signals cases, and a cascading combination of a blind and non-blind algorithms. The proposed algorithm generates an accurate main beam toward the desired direction and deep nulls in the direction of interfering signals, resulting in enhanced SINR. Compared to other algorithms, our approach achieves better performance without requiring additional antenna elements. [ABSTRACT FROM AUTHOR]

By the recent improvement of the internet of things (IoT), the need to implement wireless networks is increasing. It is a challenge to balance between battery lifetime of the different sensors and network lifetime. Many studies proved the importance of using clustering and Mobile Data Collectors (MDCs) to extend the operating time of sensor nodes. A mobile data collector is used to gather the data recorded by the nodes over a short transmission range. The proposed approach aims to decrease the energy consumption of each sensor node by using the Genetic Algorithm (GA) and mobile data collector. So, we suggest a clustering algorithm to find suitable Cluster Heads and form clusters. Then, we employ the genetic algorithm to construct an optimal data gathering path for MDC. Computer simulation proves that the proposed approach outperforms existing ones. [ABSTRACT FROM AUTHOR]

Fear of falling is becoming the most common fear among people aged 65 and over,with a prevalence that varies within the range of 12% to 92% [ 1 ]. Monitoring the elderly's physical activity is highly recommended in order to improve their health. Several reports have demonstrated that a fall does not always result in a fear of falling, which may be caused by factors other than the fall itself[2][3]. Falls, on the other hand, and fear of falling share one feature: they are both associated with walking disorders. Falls are considered a significant risk that primarily affects the elderly. Slowness of movement, loss of motion, and balance issues are common in the elderly. In this article, we have proposed an intelligent insole system to detect falls, monitor patients' physiological parameters, and reduce their fear of falling. Thus, the elderly will be surrounded in real time by professional clinicians working remotely to control them during their daily activities. [ABSTRACT FROM AUTHOR]

Since the pandemic, everything became online and everyone has to sit on their desk for a whole day without moving. Whether you're a student or a teleworker, you must have experienced back pain due to prolonged hours sitting in front of your laptop. If your posture is bad, several areas of stress may develop within your muscle tissue, spinal joints (lumbar facets), and discs. These stresses may be relieved once the offending posture is corrected or may continue to accumulate, slowly weakening the affected structure. One of the major causes of back pain nowadays is bad posture, so you must have insight into the importance of keeping a good one. In this paper, we propose a sitting posture monitoring system based on machine learning algorithms for posture prediction. The system consists of smart belt equipped with inertial sensors for posture detection. Seven machine learning algorithms are applied for posture prediction: KNN, SVM, Decision Tree, Random Forest, Naive Bayes, Boosting and K-means (unsupervised). The tests results demonstrate that the Random Forest algorithm with 30 trees has the best accuracy 99.19% and precision 98.70% compared to the other machine learning algorithms. However it has complexity computing that makes the prediction time higher 67.7ms. The SVM has the shortest prediction time 0.64ms. But the SVM accuracy 98.56% is lower than the Random Forest. The Decision Tree has more than 90% accuracy for each posture position with acceptable training time 8.96ms. [ABSTRACT FROM AUTHOR]

With the large use of wireless sensor devices, the interest in positioning and tracking by means of wireless sensor networks is expected to grow further. Particularly, accurate localization of a moving target is a fundamental requirement in several Machine to Machine monitoring applications. Tracking using Received Signal Strength Indicator (RSSI) has been frequently adopted thanks to the availability and the low cost of this parameter. In this paper, we propose an innovative target tracking algorithm which combines learning regression tree approach and filtering methods using RSSI metric. Regression Tree algorithm is investigated in order to estimate the position using the RSSI. This method is combined to filtering approaches yielding to more refined results. The suggested approach is evaluated through simulations and experiments. We also compare our method to existing algorithms available in the literature. The numerical and experimental results show the relevance and the efficiency of our method. [ABSTRACT FROM AUTHOR]

With the large adoption of small and smart objects, the interest of researchers to define new protocols to meet Internet of Things (IoT) requirements is growing. In particular, the Industrial Internet of Things (IIoT) aims to provide high reliability and upper bounded end-to-end latency while interconnecting a large collection of sensors and actuators. The TimeSlotted Channel Hopping (TSCH) mode of IEEE 802.15.4 exploits a slow channel hopping approach to combat efficiently external interference (e.g. Wi-Fi, Bluetooth). We envision in the future very dense deployments and we investigate here the mutual influence among different colocated TSCH networks. We first study analytically with a simple model the performance of several TSCH networks, able or not to be synchronized with each other. We emphasize the multiplication of collisions, exacerbated by clock drifts when no cooperation is achieved. We also highlight experimentally that a mutual synchronization improves very significantly the reliability. We propose here a mechanism to re-synchronize distributively the border routers of different TSCH networks. Our thorough experimental evaluation illustrates the relevance of our approach, able to guarantee high-reliability, even in very dense topologies. [ABSTRACT FROM AUTHOR]