A novel authentication protocol to ensure confidentiality among the Internet of Medical Things in covid-19 and future pandemic scenario.

Diagnosing the patients remotely, controlling the medical equipment, and monitoring the quarantined patients are some of the necessary and frequent activities in COVID-19. Internet of Medical Things (IoMT) makes this works easy and feasible. Sharing information from patients and sensors associated with the patients to doctors is always an integral part of IoMT. Unauthorized access to such information may invite adversaries to disturb patients financially and mentally; furthermore, leaks in its confidentiality will lead to dangerous health concerns for patients. While ensuring authentication and confidentiality, We must focus on the constraints of IoMT, such as low energy consumption, deficient memory, and the dynamic nature of devices. Numerous protocols have been proposed for authentication in healthcare systems such as IoMT and telemedicine. However, many of these protocols were neither computationally efficient nor provided confidentiality, anonymity, and resistance against several attacks. In the proposed protocol, we have considered the most common scenario of IoMT and tried to overcome the limitations of existing works. Describing the system module and security analysis proves it is a panacea for COVID-19 and future pandemics.

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks.

With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes. It can also exploit the underutilized computation resources of nearby vehicles that can act as vehicular fog nodes (VFNs) and provide delay- and energy-aware computing services. However, the capacity planning and dimensioning of VFC, which come under a class of facility location problems (FLPs), is a challenging issue. The complexity arises from the spatio-temporal dynamics of vehicular traffic, varying resource demand from PD applications, and the mobility of VFNs. This paper proposes a multi-objective optimization model to investigate the facility location in VFC networks. The solutions to this model generate optimal VFC topologies pertaining to an optimized trade-off (Pareto front) between the service delay and energy consumption. Thus, to solve this model, we propose a hybrid Evolutionary Multi-Objective (EMO) algorithm called Swarm Optimized Non-dominated sorting Genetic algorithm (SONG). It combines the convergence and search efficiency of two popular EMO algorithms: the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO). First, we solve an example problem using the SONG algorithm to illustrate the delay-energy solution frontiers and plotted the corresponding layout topology. Subsequently, we evaluate the evolutionary performance of the SONG algorithm on real-world vehicular traces against three quality indicators: Hyper-Volume (HV), Inverted Generational Distance (IGD) and CPU delay gap. The empirical results show that SONG exhibits improved solution quality over the NSGA-II and SMPSO algorithms and hence can be utilized as a potential tool by the service providers for the planning and design of VFC networks.