Software-defined networking is an evolving network architecture beheading the traditional network architecture focusing its disadvantages in a limited perspective. A couple of decades before, programming and networking were viewed as different domains which today with the lights of SDN bridging themselves together. This is to overcome the existing challenges faced by the networking domain and an attempt to propose cost-efficient effective and feasible solutions. Changes to the existing network architecture are inevitable considering the volume of connected devices and the data being held together. SDN introduces a decoupled architecture and brings customization within the network making it easy to configure, manage, and troubleshoot. This paper focuses on the evolving network architecture, the software-defined networking. Unlike a generic view on the evolving network, which makes work as a review, this work addresses various perspectives of the architecture leaving it an intermediate work in between the review of the literature and implementation, contributing towards factors like the design, programmability, security, security behaviors, and security lapses. This paper also analyses various weak points of the architecture and evolves the attack vectors in each plane leaving a conclusion to further progress towards identifying the impacts of the attacks and proposing mitigation strategies.

The average of global life expectancy at birth was 72 years in 2016 [1], however, the global healthy life expectancy at birth was only 63.3 years in the same year, 2016 [2]. Living a long life is not any more as challenging as assuring active and associated life [25]. We propose in this paper an IoT based holistic remote health monitoring system for chronically ill and elderly patients. It supports smart clinical decision help and prediction. The patient heterogeneous vital signs and contexts gathered from wore and surrounding sensors are semantically simplified and modeled via a validated ontology composed by FOAF (Friend of a Friend), SSN (Semantic Sensors Network)/SOSA (Sensor, Observation, Sample and Actuator) and ICNP (International Classification Nursing Practices) ontologies. The reasoner engine is based on a scalable set of inference rules cohesively integrated with a ML (Machine Learning) algorithm to ensure predictive analytic and preventive personalized health services. Experimental results prove the efficiency of the proposed system. Keywords Active and assisted living Ontologies ML Health monitoring Preventive personalized health services

Healthcare has a significant impact on human capital anywhere. Countries usually allocate financial resources to manage healthcare, which might impose a substantial financial burden on the scope of healthcare coverage. Thus, the healthcare sector must provide the best possible services at the lowest cost. This significant challenge can only be achieved through applying appropriate policies and technologies, including those used by healthcare insurance policy providers. This paper proposes an innovative, customer-centric, sustainable healthcare insurance policy model. The main objective of this model is to sustain wellness by applying technologies to avoid illness and provide wellbeing for patients by empowering self-care remotely. The proposed solution uses an adaptive ontology-based knowledge management system to satisfy customers and market needs. The proposed system creates a customized policy that consists of various packages to match customers’ healthcare needs based on their health status. The system was tested and validated using a real dataset.