Navarun Gupta

Cloud computing is reserving its position in the market as the next disruptive utility paradigm. It is found on the pay-as-you-use model. Cloud computing is changing the way information technology (IT) operates for individuals as well as for companies. Cloud computing comes with different offerings to accommodate diverse applications. It comes with many successful adoption stories and a few unfortunate ones that are related to security breaches. Security concerns are what is making many companies reluctant to fully embrace the cloud realm. To enhance trust and entice adoption between cloud clients (CC) and cloud service providers (CSP), a new paradigm of depending on involving a third-party auditor (TPA) has been introduced. Hence, implementing a solution with a TPA comes with its toll in terms of trust and processing overhead. A lightweight security protocol to give the CC extra control with tools to audit the TPA and the CSP is paramount to the solution. In this paper, we are introducing a novel protocol: the lightweight accountable privacy-preserving (LAPP) protocol. Our proposed protocol is lightweight in terms of processing and communication costs. It is based on a newly introduced mathematical model along with two algorithms. We have conducted simulation experiments to measure the impact of our method. We have compared LAPP to the most eminent privacy-preserving methods in the cloud research field, using the open source cloud computing simulator GreenCloud. Our simulation results showed superiority in performance for LAPP in regard to time complexity, accuracy, and computation time on auditing. The aim of the time complexity and computation time on auditing simulations is to measure the lightweight aspect of our proposed protocol as well as to improve the quality of service.

Due to rapid development in mobile communication technology in recent years, the demand for high quality and high capacity networks with thorough coverage has become a major necessity. Several models have been developed for predicting wireless signal coverage in urban areas, but these models suffer from inadequately calculating certain conditions, such as weather and building materials, especially window size. In this paper, we propose a new path loss prediction model based on the measurement of new indicators, such as window size, temperature, and humidity conditions, after which an extensive statistical analysis using a linear regression technique was implemented in order to validate the new indicators. As the new indicators were incorporated into the Okumura model to derive a new path loss model, the results showed that the proposed model provides an accurate prediction of the received signal strength in a given propagation environment. Our model enhanced the prediction of path loss by 10% when compared to the Okumura and by 15% when compared to the COST-Hata.