Abstract-Wireless Sensor Network, a group of specialized sensors with a communication infrastructure for monitoring and controlling conditions at diverse locations, is a recent technology which is getting popularity day by day. Besides, cloud computing is a type of high-performance computing that uses a network of remote servers which simultaneously provides the service to store, manage and process data rather than a local server or personal computer. An architecture called sensor-cloud is also providing good services by combining the capabilities from both ends. In order to provide such services, a large volume of sensor network data needs to be transported to cloud gateway with a high amount of bandwidth and time requirement. In this paper, we have proposed an efficient sensor-cloud communication approach that minimizes the enormous bandwidth and time requirement by using statistical classification based on machine learning as well as compression using deflate algorithm with a minimal loss of information. Experimental results describe the overall efficiency of the proposed method over the traditional and related research.

The year 2020 will always be in the history of mankind due to the deadly outbreak of COVID-19. Many people are already infected around the world due to the spreading of this novel coronavirus. The virus mainly replicates through close contacts, so there are no other alternatives than to keep social distance, use proper safety gear, and maintain self-quarantine. As a result, the growth of the virus has changed the lifestyle of every individual to a great extent. It is also compelling the Governments to dictate strict lock-downs of the highly affected areas, impose work-from-home approaches where applicable, enforce strict social distancing standards, and so on. Some of the countries are also using smartphone-based applications for contact tracing to track the possibly infected individuals. However, there is a lot of discussion around the world about these contact tracing applications and also about their architecture, attribute, data privacy, and so on. In this paper, we have provided a comprehensive review of these contact tracing approaches in terms of their system architecture, key attributes, and data privacy. We have also outlined a list of potential research directions that can improvise the tracing performance while maintaining the privacy of the user to a great extent.

Now a days wireless networks and smart devices are getting popularity day by day. With the growing availability of the internet, these facilities have made our life much easier by providing a lot of automated services. Hence to make the user experience much convenient, service providers allow accessing these services through a variety of devices in many ways. Although these technological inventions are blessings to us, client authentication is of the critical issues as intruders may acquire unauthorized access and use their advanced knowledge to impersonate the actual user and steal valuable information. Single factor authentication, e.g. password, being the first authentication approach is no longer considered as secure whereas two factor authentication provides a bit higher protection level by extending a factor. Apart from these, a new concept called multi factor authentication is the most secured approach that requires a combination of two or more different factors. In this paper, a multi factor authentication method is proposed that includes knowledge, possession, location and inherence which can add a new dimension in the security area of user authentication in a hassle free manner.