Enabling Heterogeneous IoT Networks over 5G Networks with Ultra-Dense Deployment—Using MEC/SDN

The Internet of things (IoT) is the third evolution of the traditional Internet that enables interaction and communication among machines. Many IoT platforms and networks have been developed, and recently, market sectors have started to develop specific IoT applications and services. Integrating heterogeneous IoT networks with the existing ones, mainly with the cellular networks, is a great demand. IoT represents one of the main use cases of the fifth-generation (5G) cellular system as announced by the 3rd Generation Partnership Project (3GPP) and the International Telecommunication Union (ITU). Integrating IoT networks with 5G networks face many challenges related to dense deployment and a massive number of expected connected devices. Thus, IoT network availability and scalability are the main requirements that should be achieved. To this end, this work provides a framework for integrating heterogeneous IoT networks with the 5G networks. The proposed system considers dense deployment and system scalability and availability requirements as announced by ITU and 3GPP. Our proposed structure deploys three main communication paradigms; mobile edge computing (MEC), device-to-device communications (D2D), and software-defined networking (SDN). Our proposed system is evaluated over a reliable environment for various deployment scenarios, and the results validate the proposed structure. The proposed IoT/5G reduces the percentage of blocked tasks by an average of 30% than other traditional IoT networks. This increases the overall system availability and scalability since IoT networks can have more devices and tasks than existing IoT networks. Furthermore, our proposed structure reduces the overall consumed energy by an average of 20% than existing IoT networks, which is an effective metric for IoT networks

Novel Hybrid Fusion-Based Technique for Securing Medical Images

The security of images has gained great interest in modern communication systems. This is due to the massive critical applications that are based on images. Medical imaging is at the top of these applications. However, the rising number of heterogenous attacks push toward the development of securing algorithms and methods for imaging systems. To this end, this work considers developing a novel authentication, intellectual property protection, ownership, and security technique for imaging systems, mainly for medical imaging. The developed algorithm includes two security modules for safeguarding various picture kinds. The first unit is accomplished by applying watermarking authentication in the frequency domain. The singular value decomposition (SVD) is performed for the host image’s discrete cosine transform (DCT) coefficients. The singular values (S) are divided into 64 × 64 non-overlapping blocks, followed by embedding the watermark in each block to be robust to any attack. The second unit is made up of two encryption layers to provide double-layer security to the watermarked image. The double random phase encryption (DRPE) and chaotic encryption have been tested and examined in the encryption unit. The suggested approach is resistant to common image processing attacks, including rotation, cropping, and adding Gaussian noise, according to the findings of the experiments. The encryption of watermarked images in the spatial and DCT domains and fused watermarked images in the DCT domain are all discussed. The transparency and security of the method are assessed using various measurements. The proposed approach achieves high-quality reconstructed watermarks and high security by using encryption to images and achieves robustness against any obstructive attacks. The developed hybrid algorithm recovers the watermark even in the presence of an attack with a correlation near 0.8

Ultra-Reliable Low-Latency Communications: Unmanned Aerial Vehicles Assisted Systems

Ultra-reliable low-latency communication (uRLLC) is a group of fifth-generation and sixth-generation (5G/6G) cellular applications with special requirements regarding latency, reliability, and availability. Most of the announced 5G/6G applications are uRLLC that require an end-to-end latency of milliseconds and ultra-high reliability of communicated data. Such systems face many challenges since traditional networks cannot meet such requirements. Thus, novel network structures and technologies have been introduced to enable such systems. Since uRLLC is a promising paradigm that covers many applications, this work considers reviewing the current state of the art of the uRLLC. This includes the main applications, specifications, and main requirements of ultra-reliable low-latency (uRLL) applications. The design challenges of uRLLC systems are discussed, and promising solutions are introduced. The virtual and augmented realities (VR/AR) are considered the main use case of uRLLC, and the current proposals for VR and AR are discussed. Moreover, unmanned aerial vehicles (UAVs) are introduced as enablers of uRLLC. The current research directions and the existing proposals are discussed

Empowering the Internet of Things Using Light Communication and Distributed Edge Computing

With the rapid growth of connected devices, new issues emerge, which will be addressed by boosting capacity, improving energy efficiency, spectrum usage, and cost, besides offering improved scalability to handle the growing number of linked devices. This can be achieved by introducing new technologies to the traditional Internet of Things (IoT) networks. Visible light communication (VLC) is a promising technology that enables bidirectional transmission over the visible light spectrum achieving many benefits, including ultra-high data rate, ultra-low latency, high spectral efficiency, and ultra-high reliability. Light Fidelity (LiFi) is a form of VLC that represents an efficient solution for many IoT applications and use cases, including indoor and outdoor applications. Distributed edge computing is another technology that can assist communications in IoT networks and enable the dense deployment of IoT devices. To this end, this work considers designing a general framework for IoT networks using LiFi and a distributed edge computing scheme. It aims to enable dense deployment, increase reliability and availability, and reduce the communication latency of IoT networks. To meet the demands, the proposed architecture makes use of MEC and fog computing. For dense deployment situations, a proof-of-concept of the created model is presented. The LiFi-integrated fog-MEC model is tested in a variety of conditions, and the findings show that the model is efficient

Empowering the Internet of Things Using Light Communication and Distributed Edge Computing

With the rapid growth of connected devices, new issues emerge, which will be addressed by boosting capacity, improving energy efficiency, spectrum usage, and cost, besides offering improved scalability to handle the growing number of linked devices. This can be achieved by introducing new technologies to the traditional Internet of Things (IoT) networks. Visible light communication (VLC) is a promising technology that enables bidirectional transmission over the visible light spectrum achieving many benefits, including ultra-high data rate, ultra-low latency, high spectral efficiency, and ultra-high reliability. Light Fidelity (LiFi) is a form of VLC that represents an efficient solution for many IoT applications and use cases, including indoor and outdoor applications. Distributed edge computing is another technology that can assist communications in IoT networks and enable the dense deployment of IoT devices. To this end, this work considers designing a general framework for IoT networks using LiFi and a distributed edge computing scheme. It aims to enable dense deployment, increase reliability and availability, and reduce the communication latency of IoT networks. To meet the demands, the proposed architecture makes use of MEC and fog computing. For dense deployment situations, a proof-of-concept of the created model is presented. The LiFi-integrated fog-MEC model is tested in a variety of conditions, and the findings show that the model is efficient

Secure and Reliable IoT Networks Using Fog Computing with Software-Defined Networking and Blockchain

Designing Internet of Things (IoT) applications faces many challenges including security, massive traffic, high availability, high reliability and energy constraints. Recent distributed computing paradigms, such as Fog and multi-access edge computing (MEC), software-defined networking (SDN), network virtualization and blockchain can be exploited in IoT networks, either combined or individually, to overcome the aforementioned challenges while maintaining system performance. In this paper, we present a framework for IoT that employs an edge computing layer of Fog nodes controlled and managed by an SDN network to achieve high reliability and availability for latency-sensitive IoT applications. The SDN network is equipped with distributed controllers and distributed resource constrained OpenFlow switches. Blockchain is used to ensure decentralization in a trustful manner. Additionally, a data offloading algorithm is developed to allocate various processing and computing tasks to the OpenFlow switches based on their current workload. Moreover, a traffic model is proposed to model and analyze the traffic indifferent parts of the network. The proposed algorithm is evaluated in simulation and in a testbed. Experimental results show that the proposed framework achieves higher efficiency in terms of latency and resource utilization

Chaotic salp swarm algorithm for SDN multi-controller networks

Software-defined networking (SDN) is a novel network paradigm that enables flexible management for networks. However, with the increase in network capacity, a single controller of SDN has many limitations on both performance and scalability. Distributed multi-controller deployment is a promising method to satisfy fault tolerant and scalability. There are still open research issues related to controllers placement, and the optimal number of deployed controllers. In this paper, a dynamic optimization algorithm that is based on the Salp Swarm Optimization Algorithm (SSOA) is developed with the introduction of chaotic maps for enhancing the optimizer’s performance. The algorithm dynamically evaluates the optimum number of controllers and the optimal connections between switches and controllers in large scale SDN networks. In order to evaluate the proposed algorithm, several experiments were conducted and implemented in various scenarios. Moreover, the algorithm was compared to the linear and meta-heuristic algorithms. Simulation results show that the proposed algorithm outperforms meta-heuristic algorithms and a game theory based algorithm in terms of execution time and reliability.publishedVersionPeer reviewe

An Efficient Deep Learning Approach for Colon Cancer Detection

Colon cancer is the second most common cause of cancer death in women and the third most common cause of cancer death in men. Therefore, early detection of this cancer can lead to lower infection and death rates. In this research, we propose a new lightweight deep learning approach based on a Convolutional Neural Network (CNN) for efficient colon cancer detection. In our method, the input histopathological images are normalized before feeding them into our CNN model, and then colon cancer detection is performed. The efficiency of the proposed system is analyzed with publicly available histopathological images database and compared with the state-of-the-art existing methods for colon cancer detection. The result analysis demonstrates that the proposed deep model for colon cancer detection provides a higher accuracy of 99.50%, which is considered the best accuracy compared with the majority of other deep learning approaches. Because of this high result, the proposed approach is computationally efficient

Seamless Handover Scheme for MEC/SDN-Based Vehicular Networks

With the recent advances in the fifth-generation cellular system (5G), enabling vehicular communications has become a demand. The vehicular ad hoc network (VANET) is a promising paradigm that enables the communication and interaction between vehicles and other surrounding devices, e.g., vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communications. However, enabling such networks faces many challenges due to the mobility of vehicles. One of these challenges is the design of handover schemes that manage the communications at the intersection of coverage regions. To this end, this work considers developing a novel seamless and efficient handover scheme for V2X-based networks. The developed scheme manages the handover process while vehicles move between two neighboring roadside units (RSU). The developed mechanism is introduced for multilane bidirectional roads. The developed scheme is implemented by multiple-access edge computing (MEC) units connected to the RSUs to improve the implementation time and make the handover process faster. The considered MEC platform deploys an MEC controller that implements a control scheme of the software-defined networking (SDN) controller that manages the network. The SDN paradigm is introduced to make the handover process seamless; however, implementing such a controlling scheme by the introduction of an MEC controller achieves the process faster than going through the core network. The developed handover scheme was evaluated over the reliable platform of NS-3, and the results validated the developed scheme. The results obtained are presented and discussed