With the incorporation of Internet of Things (IoT) in healthcare systems immense new possibilities have emerged in the modern healthcare services. In recent times where people around the globe are suffering from the Covid-19 pandemic, providing remote healthcare services maintaining necessary social distancing through e-Healthcare has become an urgent priority. e-Healthcare services like patient's real-time health monitoring, exchange of electronic health reports, tele-consultation, remote surgery etc. require reliable and timely delivery of data and responses. To provide for such critical requirements it is essential to prioritize the forwarding of different types of data varies based on their throughput requirements and the delay sensitivities. In this paper, we study the network Quality of Service (QoS) control mechanism for e-Health services using the Software-Defined Networking (SDN) approach in a Fog based Healthcare Environment. We distinguish the data generated from various types of devices into priority classes looking at their priorities, throughput and privacy requirements in the application. Hence, we propose, OpenHealthQ, an OpenFlow based traffic shaping model using OpenFlow Queues to handle the healthcare data. OpenHealthQ provides a secure, on-demand and low-cost access to Healthcare 4.0's most demanding computing infrastructure in a distributed cloud architecture with SDN based fog nodes at the network's edge. Experimental studies show that OpenHealthQ is capable of reducing response time of the end host and significant increase in network throughput compared to the Best-Effort (BE) approach.

In this paper we present a new approach to IP packet traceback called SMITE which allows efficient traceback of the origin of a packet with incorrect or spoofed source addresses through an integration of software-defined networks (SDN) [15] and Multiprotocol Label Switching (MPLS) [13] in OpenFlow [19]. SMITE leverages the flexibility of SDN and the strength of MPLS network to achieve low false positive rate, ability to perform post-mortem traceback, reduction in storage pressure/ hardware investment and most importantly the ability to perform traceback for a single spoofed packet. SMITE also aims to overcome the difficulties and limitations of legacy traceback mechanisms.

In view of the exponential growth in the volume of wireless data communication among heterogeneous devices ranging from smart phones to tiny sensors across a wide range of applications, 3GPP LTE-A has standardized Machine Type Communication (MTC) which allows communication between entities without any human intervention. The future 5G cellular networks also envisage massive deployment of MTC Devices (MTCDs) which will increase the total number of connected devices hundredfold. This poses a huge challenge to the traditional cellular system processes, especially the traditional Mutual Authentication and Key Agreement (AKA) mechanism currently used in LTE systems, as the signaling load caused by the increasingly large number of devices may have an adverse effect on the regular Human to Human (H2H) traffic. A solution in the literature has been the use of group based architecture which, while addressing the authentication traffic, has their share of issues. This paper introduces Hierarchical Group based Mutual Authentication and Key Agreement (HGMAKA) protocol to address those issues and also enables the small cell heterogeneous architecture in line with 5G networks to support MTC services. The aggregate Message Authentication Code based approach has been shown to be lightweight and significantly efficient in terms of resource usage compared to the existing protocols, while being robust to authentication message failures, and scalable to heterogeneous network architectures.