The increasing amount of air pollution on a global scale is a major source of worry. The elevated levels of pollution are affecting the health of urban dwellers and motorists to a great extent. Since the situation has become so dire, it has become an absolute necessity to find effective ways to reduce air pollution and mitigate the negative effects of air pollution on human health. In this paper, the task of determining the least polluted route is addressed and how this can be used to curb pollution is discussed. The proposed work uses Dijkstra's algorithm, suggests a weight selection metric for air pollution parameters and analyzes the efficacy of the proposed algorithm in determining the least polluted route. The algorithm is tested on real-time data extracted from the Central Pollution Control Board, a statutory organization under the Ministry of Environment, Forest and Climate Change, Government of India. From the results obtained, it is found that the least polluted routes between a given source location and other different locations of a city can be determined using Dijkstra's algorithm accurately and the algorithm has a time complexity of O(V-2). Further, using a least polluted route map will help motorists take less polluted routes, diverting air pollution off congested streets and enabling national governments to create policies like constructing flyovers or widening roads in increasingly polluted areas.

Cloud computing is one of the fast-growing technologies in recent times. People are adopting cloud services often and they do not possess any other substitute for its services. At the same time, users have to be aware of privacy and security issues in the cloud environment. Due to the distributed nature of cloud computing, multi-domain support, and multi-user platform, the cloud-based system is more vulnerable to security threats. Security threats can be distributed denial of service attacks and intrusion prospects. Thus, organizations need to have techniques like intrusion detection as well as prevention, firewalls, encryption, authentication, etc. for securing the stored information on the cloud. Intruders attempt to identify loopholes to break security. For that, organizations are adopting the system for intrusion detection and prevention to provide privacy and security in the cloud environment. Attacks whether internal or external must be prevented and thus it is significant to adopt the technique of preventing and detection system for identifying intrusion. Therefore, this research intends to study the prevention and detection of intrusion in the cloud environment.

Globally, the rising level of air pollution is becoming a cause of serious concern. The situation has reached such an alarming situation that knowing the extent of air pollution well in advance has become an absolute necessity before we step out of our home. An advance prediction can help the urban travellers to know the possibility of enhanced pollution ahead of time at strategic locations of a city and thereby be useful in planning a less polluted route. Different cities of the world have pollutants levels with varied dispersion pattern. As a result, a generic prediction model is needed that can cater to all types of pollutants and which can offer better forecasts of pollution data irrespective of their dispersion levels. In this paper, the authors modelled recurrent neural network (RNN)-based bidirectional long short-term memory (Bi-LSTM) that forecasts pollutants concentration with least RMSE. The model is trained and tested on the pollution data of Delhi, the most polluted capital city in the world for the second consecutive year in 2019. Air pollutants like PM 10 , PM 2.5 , NO 2 , O 3, and CO are considered that have a varied dispersion pattern. To test the efficacy of our predictions, the model is tested on real data obtained from the Central Pollution Control Board (CPCB) of India. The performance metrics are also generated to evaluate the performance of the proposed model.

Rapid urbanization combined with an almost insatiable need for energy has spawned various forms of pollution. Researchers have found air pollution to be at the top of list of factors that cause the most fatalities among urban dwellers today. Scoping urban areas that harbor the most air pollutants and contaminants can help an urban dweller identify comparatively less polluted routes. However, processing of information related to air pollutants is time intensive. As such, temporal forecasting takes preeminence in designing a system that can provide information well in advance of concentration levels of air pollutants at any given time in day or night. In this paper, the authors approach problems related timely forecasts for predicting and tracing air pollution levels across major thoroughfares in urban environments, using fog computing and Internet of Things (IoT). The objective of the research and proposed method is to offer a time-sensitive forecasting to enable citizens to adopt a more agile route-planning approach at any given point of time. In the wake of rising deaths owing to air-borne pollutants and chemicals, results of the research conducted indicate an object-oriented approach toward building a smarter city.

Arguably, the rise in global population has been matched by the rapid rate of expansion of internet. Today, some of the prominent companies in information and media technology rely on content and advertising to fuel their growth. On the other hand, the insatiable thirst for information coupled with an incessant urge for instant consumption of digital media have made it impossible to overlook content delivery networks. As a cost-effective and robust means, cloud -based content delivery network (CCDN) service has considerable advantages. At the core of a CCDN service, lies a cloud -based data center infrastructure. This makes it possible to distribute content load over a large number of storage servers situated at various geographical locations. However, close on the heels of the advantages offered by CCDN service, comes an interminable challenge to stay ahead of high latency when meeting content delivery requests. We propose a collaborative filtering algorithm using weighted slope one method as a model to arrest network latency and optimize storage requirements. Our model demonstrates that it is able to stay ahead of over fitting, and increase accuracy of the results yielded. Using our approach, CCDN service providers will be able to boost content delivery speeds through determination of storage requirements in a flexible and cost-effective manner.

Cloud computing has become one of the most promising IT solutions and business trends in recent years. As the need for cloud is increasing and the requirements are getting more diverse and complex, the need to find an effective allocation strategy is increasing. In such a virtualized shared environment the resource requirements of applications keep changing from time to time. And in such a scenario, static allocation fails to solve the purpose. It leads to the inefficient utilization of resources and might affect the quality of the application. In this paper the allocation of resources on a university cloud is discussed. Cloud computing proves to be a powerful platform for higher education. Although the adoption of cloud on the university level has various advantages, a lot of other factors like dynamically changing resource requirements, etc. need to be taken into account. Here, a dynamic allocation mechanism is presented by modeling the resource requirements using a normal distribution based on which an algorithm to solve the resource allocation problem is proposed. The algorithm ensures that a particular virtual machine is never overloaded. It also tries to minimize the difference between the resources demanded and allocated so as to maintain the quality of an application running on a virtual machine.