A systematic review of IoT in healthcare: Applications, techniques, and trends

With the development of internet of things (IoT), capabilities of computing, networking infrastructure, storage of data and management have come very close to the edge of networks. This has accelerated the necessity of Fog computing paradigm. Due to availability of Internet, most of our business operations are integrated with IoT platform. Fog computing has enhanced the strategy of collecting and processing, huge amount of data. On the other hand, attacks and malicious activities has adverse consequences on the development of IoT, Fog, and cloud computing. This has led to development of many security models using fog computing to protect IoT network. Therefore, for dynamic and highly scalable IoT environment, a distributed architecture based intrusion detection system (IDS) is required that can distribute the existing centralized computing to local fog nodes and can efficiently detect modern IoT attacks. This paper proposes a novel distributed ensemble design based IDS using Fog computing, which combines k-nearest neighbors, XGBoost, and Gaussian naive Bayes as first-level individual learners. At second-level, the prediction results obtained from first level is used by Random Forest for final classification. Most of the existing proposals are tested using KDD99 or NSL-KDD dataset. However, these datasets are obsolete and lack modern IoT-based attacks. In this paper, UNSW-NB15 and actual IoT-based dataset namely, DS2OS are used for verifying the effectiveness of the proposed system. The experimental result revealed that the proposed distributed IDS with UNSW-NB15 can achieve higher detection rate upto 71.18% for Backdoor, 68.98% for Analysis, 92.25% for Reconnaissance and 85.42% for DoS attacks. Similarly, with DS2OS dataset, detection rate is upto 99.99% for most of the attack vectors.

A distributed ensemble design based intrusion detection system using fog computing to protect the internet of things networks

In recent years, the usage of smart mobile applications to facilitate day‐to‐day activities in various domains for enhancing the quality of human life has increased widely. With rapid developments of smart mobile applications, the edge computing paradigm has emerged as a distributed computing solution to support serving these applications closer to mobile devices. Since the submitted workloads to the smart mobile applications changes over the time, decision making about offloading and edge server provisioning to handle the dynamic workloads of mobile applications is one of the challenging issues into the resource management scope. In this work, we utilized learning automata as a decision‐maker to offload the incoming dynamic workloads into the edge or cloud servers. In addition, we propose an edge server provisioning approach using long short‐term memory model to estimate the future workload and reinforcement learning technique to make an appropriate scaling decision. The simulation results obtained under real and synthetic workloads demonstrate that the proposed solution increases the CPU utilization and reduces the execution time and energy consumption, compared with the other algorithms.

Joint computation offloading and resource provisioning for edge‐cloud computing environment: A machine learning‐based approach

The 5th Generation (5G) network design is still under exploration and will require an innovative approach to network monitoring and management with a relentless focus on customer needs. Among the recent technologies, software-defined networking (SDN) and network functions virtualization (NFV), which are already offering network programmability and automation, have been proposed to improve resource management in 5G systems. Furthermore, the enhanced telecom operations map (eTOM) can be used to manage both End-to-End (E2E) service and end-customer experience. In this paper, we illustrate a novel SDN-based architecture for 5G networks that will be able to enhance quality-of-experience (QoE) monitoring and management by integrating SDN and eTOM. This architecture is deployed and evaluated by simulations of a service-level agreement (SLA) verification scenario. As a result, the implemented platform is capable of identifying SLA violation and can successfully enforce suitable mechanisms to dynamically adjust network parameters, thereby satisfying the SLA constraints.

Novel QoE monitoring and management architecture with eTOM for SDN-based 5G networks

With the widespread adoption of mobile technologies, mobile-assisted learning is gaining lots of momentum. This new learning paradigm promotes education across different contexts, which is a key factor that contributes to enhancing learning irrespective of the conditions and location of the learner. Therefore, it creates an authentic learning setting whereby students can make meaningful connections to the real world while learning takes place. Previous research works in the field of mobile learning showed that improper design of learning elements is still present in mobile systems and consequently results in poor dynamic content adaptation. Some attempts to adapt learning contents with appropriate instructional design principles are conducted, but with moderate exploitation of smart technological assets in mobile learning systems and limited pedagogical reflections and cognitive factors. In this paper, a learning efficiency model chart is derived using important learning factors that can be considered to enhance mobile learning experiences. Some popular learning theories are analysed and compared with the proposed learning efficiency model chart. This investigation is considered to significantly reduce complexities that exist in mobile learning platforms and promote an enhanced mobile learning experience.

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Cognitive load management in mobile learning systems: principles and theories

The outstanding advances in computing as well as the explosion of devices and services requirements are making communication mobile networking more and more complex to manage. In addition, multimedia services such as provided by the IP Multimedia Subsystem (IMS) require a dynamic Quality of Service (QoS) management. However, traditional Long Term Evolution (LTE) networking approaches have become too closed and limited in terms of providing Quality of Service (QoS). For this purpose, it has been necessary to look for new approaches. Among these approaches is the integration of the Software Defined Network (SDN) in the LTE network. With this approach the LTE mobile network will benefit from the SDN features to separate the control plane from the data plane and to enable a dynamic configuration of network resources and then a flexible QoS management. This paper presents a new architecture SDN-based for LTE packet core to enhance the QoS management. The architecture is implemented and evaluated for a Video on Demand (VoD) by testing the Quality of Experience (QoE) in terms of the Video Mean Opinion Score (VMOS).

QoS management SDN-based for LTE/EPC with QoE evaluation: IMS use case

All real-time services provided by the IP Multimedia Subsystem (IMS) require QoS mechanisms to avoid traffic congestion. Recently, the Software Defined Networking (SDN) paradigm has emerged to enable dynamic network resources, and then a way to perform QoS management. The paper presents an approach to integrate the IMS with the SDN paradigm. The work is motivated by the lack of QoS management of real-time flows in the IMS transport layer, which can be performed through implementation over an SDN controller. The architecture proposed implements new modules in the SDN controller in order to enhance QoS management in the IMS transport layer. The feasibility and the performance of the approach proposed are assessed by experiments.

Towards a dynamic QoS management in IMS transport layer via SDN

New generation networks (NGN) are based on revolutionary aspects in IT and telecom fields. Huge throughput as well as a simplified infrastructure are based on the data center result of virtualization and cloud computing. Nevertheless, a very important question about the management part is always relevant, regardless of QoS management, of business, or of security. The chapter aims to propose a management architecture, which invokes the notion of broker and also of controller to centralize the management of networks and services.

Broker-Controller for NGN Management: A New NGOSS-Based Management Map for New Generation Network

The success of a telecom operator depends on several criteria such as the ability to offer an improved architecture management. The IP Multimedia Subsystem (IMS) has emerged to allow to telecom operator to unify all access technologies namely wireless, wire line, and extended to agnostic access. However, the services provided by the IMS require Quality of Service (QoS) management with Service Level Agreement (SLA) negotiation which can be achieved by several solutions. Among these solutions, we propose the use of the New Generation Operations Systems and Software (NGOSS) Framework which is a functional framework used to model and analyze networks and services activities. To better understand the relationship and the projection of NGOSS Framework and IMS platform, the article proposes a new architecture of monitoring IMS services with correction of the Quality of Service (QoS) degradation. The performance of the proposed architecture is evaluated by experiments.

Sara Khairi

Papers

Novel QoE monitoring and management architecture with eTOM for SDN-based 5G networks

QoS management SDN-based for LTE/EPC with QoE evaluation: IMS use case

Towards a dynamic QoS management in IMS transport layer via SDN

Broker-Controller for NGN Management: A New NGOSS-Based Management Map for New Generation Network

NGN Management with NGOSS Framework-Based IMS Use Case