We all know that this is a tough time of COVID-19 with which the whole world is fighting. It is a virus that has taken many lives and affected the lack of people across the globe. It is a virus that is transmitted with close contact and droplet and is not airborne. The common symptoms include fever, cough, and fatigue. This paper focus on proposing a solution that can help detect the virus and keep people away from the infected person. The solution uses a Thermal Camera, which has a heat sensor and can detect any difference in temperature, and the camera can be integrated with access control systems in many places like Hospitals, Police stations, Factories, Universities, etc. which has staff walking in daily. The camera will not allow access to the person having high body temperature as fever is a symptom for COVID-19 and that person can be further examined for the virus. Many doctors are getting this infection while treating people, if we integrate such a solution then it can be easy to save the lives of many others up to an extent.

In the present scenario, IoT is a platform where everyday gadgets are becoming smarter, each day processing will become shrewd, and every day verbal exchange is becoming informative. Even as the IoT is still searching for its basic form, its result has already started in making exquisite strides as a universal solution media for the associated situation. The study which focused on architecture always paves the conformation of associated discipline. The shortage of overall architectural abilities is at this time resisting the researchers to get a way of the scope of procedures based on energy efficient IoT. In this endeavor Service Oriented Architecture (SOA) based IoT architecture and algorithm have been proposed to address energy efficiency in IoT environment. Experimental result has been shown to lay bare the effectiveness of the proposed approach.Povzetek: V tem rokopisu je bila predlagana energetsko učinkovita arhitektura interneta stvari na osnovi storitveno usmerjene arhitekture (SOA) z algoritmom.

Cloud Computing, the efficiency of task scheduling is proportional to the effectiveness of users. The improved scheduling efficiency algorithm (also known as the improved Wild Horse Optimization, or IWHO) is proposed to address the problems of lengthy scheduling time, high-cost consumption, and high virtual machine load in cloud computing task scheduling. First, a cloud computing task scheduling and distribution model is built, with time, cost, and virtual machines as the primary factors. Second, a feasible plan for each whale individual corresponding to cloud computing task scheduling is to find the best whale individual, which is the best feasible plan; to better find the optimal individual, we use the inertial weight strategy for the Improved whale optimization algorithm to improve the local search ability and effectively prevent the algorithm from reaching premature convergence. To deliver services and access to shared resources, Cloud Computing (CC) employs a cloud service provider (CSP). In a CC context, task scheduling has a significant impact on resource utilization and overall system performance. It is a Nondeterministic Polynomial (NP)-hard problem that is solved using metaheuristic optimization techniques to improve the effectiveness of job scheduling in a CC environment. This incentive is used in this study to provide the Improved Wild Horse Optimization with Levy Flight Algorithm for Task Scheduling in cloud computing (IWHOLF-TSC) approach, which is an improved wild horse optimization with levy flight algorithm for cloud task scheduling. Task scheduling can be addressed in the cloud computing environment by utilizing some form of symmetry, which can achieve better resource optimization, such as load balancing and energy efficiency. The proposed IWHOLF-TSC technique constructs a multi-objective fitness function by reducing Makespan and maximizing resource utilization in the CC platform. The IWHOLF-TSC technique proposed combines the wild horse optimization (WHO) algorithm and the Levy flight theory (LF). The WHO algorithm is inspired by the social behaviours of wild horses. The IWHOLF-TSC approach's performance can be validated, and the results evaluated using a variety of methods. The simulation results revealed that the IWHOLF-TSC technique outperformed others in a variety of situations.

No abstract