The potential growth of internet of things (IoT) brings people and things together to handle daily tasks in a smart way. The major advancement the IoT offers is quality data sensing and faster data analytics through hurdle-free communication. The increasing number of devices and heterogeneous network natures unwrap more challenges in terms of quality of service. Currently, the deep learning algorithm explores different dimensions of service quality gradually in IoT scenarios. In order to effectively handle a dynamic IoT environment, it is essential that the design of IoT must be supplemented with an intelligent agent for providing effective QoS. The traditional methods are not capable of utilizing historical data to find insights into service quality improvement. In this chapter, a comprehensive analysis of deep learning techniques for improving QoS of the internet of things is carried out. Deep learning solutions for improving QoS and the challenges involved are compared. The deep reinforcement learning (DRL) for improving QoS in IoT and its evaluation technique are also explored.
A Fuzzy set techniques which perhaps eliminate the several ambiguities on the particular issue to take right decision. Users may prefer the fuzzy approaches for their internal computation conflicts or manipulation to access boundless of data from cloud. Even though we have many security approaches, users are facing massive difficulties to safeguard their owning data in cloud environment. Protecting the confidential data from various hackers is the tedious problem in the current trend. So, possibly we will achieve the efficient security outcome by applying the different set of security principles. This proposed model is well suitable for securing the user confidential information's from the attackers in any situation. It focuses on clustering the different sort of input factors of customer validation attributes and evaluates these attributes to ensure the authenticity of unique users in cloud environment by ranking the distinguished threshold levels to prove user authentication function.
Nowadays, in any application specific domain security plays a vital role in the service access environment. Because customers needs to make use of distinct services and resources in an attacks free surface. In cloud computing environment, the security services and portal systems have been highly progressed based on the user requirements. However, cloud offers plenty of resources through the service vendor globally; still the multi clouds problems are not resolved completely. So, it is very essential to protect the data and other resources from the intruders are a most primary factor. The proposed work will establish a security layer on the multiple cloud environments by introducing the concept of "Serverless Computing". The serverless computing is a kind of cloud computing execution model through which the cloud service provider (CSP) can manage the allocation of system resources in a dynamic manner. This mechanism will follow the property of utility computing. This approach can be used to hide the operations on entire cloud server management end and capacity planning decisions and also uses no provisioned services. Any type of vulnerabilities is taken care of by the cloud service provider. In this process, attacks rate are compared with the traditional security architectures and each component is an entry point to the serverless application. With this approach, customers can control and monitor the workloads by using IDS / IPS technique.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.