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Electroencephalogram (EEG) is one of the most powerful tools that offer valuable information related to different abnormalities in the human brain. One of these abnormalities is the epileptic seizure. A framework is proposed for detecting epileptic seizures from EEG signals recorded from normal and epileptic patients. The suggested approach is designed to classify the abnormal signal from the normal one automatically. This work aims to improve the accuracy of epileptic seizure detection and reduce computational costs. To address this, the proposed framework uses the 54-DWT mother wavelets analysis of EEG signals using the Genetic algorithm (GA) in combination with other four machine learning (ML) classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Naive Bayes (NB). The performance of 14 different combinations of two-class epilepsy detection is investigated using these four ML classifiers. The experimental results show that the four classifiers produce comparable results for the derived statistical features from the 54-DWT mother wavelets; however, the ANN classifier achieved the best accuracy in most datasets combinations, and it outperformed the other examined classifiers. INDEX TERMS Electroencephalogram (EEG), discrete wavelet transform (DWT), epilepsy, artificial neural network, k-nearest neighbor (k-NN), support vector machine (SVM), naïve bayes (NB).

Deployment of small cells was introduced to support high data rate services and expand macro cell coverage for the envisioned 5G networks. A small cell network, which has a smaller size, along with the user equipment (UE) mobility, frequently undergoes unbalanced load status. Consequently, the network performance is affected in terms of throughput, increasing handover failure rate, and possibly higher link failure rate. Hence, load balancing has become an important part of recent researches on small cell networks. Mobility Load Balancing (MLB) involves load transfer from an overloaded small cell to under-loaded neighbouring small cells for the more load-balanced network. This transfer is performed by adjusting the handover parameters of the UEs according to the load situations of the small cells in the vicinity. However, inaccurate adjustment of parameters may lead to inefficient usage of network resources or degrade the Quality of Service (QoS). In this paper, we introduce a Utility-based Mobility Load Balancing algorithm (UMLB) and a new term named load balancing efficiency factor (LBEF). The UMLB algorithm considers the operator utility and the user utility for the MLB-based handover process. While LBEF is proposed to order the overloaded cells properly for the MLB algorithm operation. The simulation results show that the UMLB minimizes standard deviation with a higher average-UE data rate when compared to existing load balancing algorithms. Therefore, a well-balanced network is achieved. INDEX TERMS Small-cell network, mobility load balancing, measurement reporting, handover, cell individual offset, throughput. KHALED M. ADDALI (M'18) received the B.S. degree in electrical engineering from Mergheb University, Khoms, Libya, in 2004, and the M.S. degree in electrical engineering from Concordia University, Montreal, Canada, in 2012. He is currently pursuing the Ph.D. degree in electrical engineering with the École de Technologie supérieure (ÉTS), Université du Québec. From 2005 to 2008, he was an Operating Engineer with General Electricity Company of Libya (GECOL), Khoms. His research interests include resource and mobility management and the development of user association and load balancing techniques for 5G small-cell networks.

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Mobile ad hoc networks (MANETs) are very promising wireless technology and they offer wide range of possibilities for the future in terms of applications and coverage. Due to the complex nature of MANETS, their development processes face several challenges such as routing. Many routing algorithms have been proposed for MANETs. Reactive routing protocols are favored and popular in MANETs because they are more scalable and generate fewer overhead on the network. But, these protocols suffer from the broadcast storm problem due to the flooding strategy that is used in the route discovery process which causes redundancy, contention and collision problems. In order to reduce the effects of the broadcast problem, a Mobility and Load aware Routing scheme (MLR) is proposed in this paper. MLR controls the flooding process by restricting the rebroadcast messages on the slow speed and low loaded nodes. Each node decides whether to forward or drop the received request message based on several factors (such as speed and routing load) using Markovian Decision Process tool. Simulation results show that MLR scheme outperforms the original AODV protocol in terms of normalized routing load and average end-to-end delay.

Internet of Things (IoT) has grown increasingly in the past decade. This growth brings up several challenging issues for a successful continuous operation of IoT applications. Some of these challenges that need to be taken care of are resource constraints, central server overload, and the risk of illegal use of private data. On the other side, Blockchain technology is increasingly popular and has gained huge success in cryptocurrencies. It offers numerous vital qualities such as a technique for consensus, peer communications, confidence-building without a trustworthy third party, and a transaction controlled by conditions and functions using the intelligent contract technique. Blockchain is an excellent candidate to establish a decentralized, autonomous IoT system addressing the above issues. This study proposes an IoT-Blockchain integration architecture using an Ethereum Blockchain infrastructure within a rich-thin client IoT approach to address the challenges created by the limited IoT resources while implementing the Blockchain mining technique in IoT systems. The architecture depends on load distribution between the resources. Limited resource devices are the thin-clients, while the higher resource devices are assigned as rich-clients. Both clients can access the blockchain and collect the data, but rich-client only can execute the mining process. In addition, we implement a healthcare system based on our architecture in which surgical process management is carried out. We also prove our solution's efficiency by testing and comparing the architecture with typical IoT-based blockchain architectures. We conclude that our blockchain-IoT architecture is suited for many IoT applications while avoiding difficulties created by IoT device limitations.

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