V. Saritha

Bio: V. Saritha is an academic researcher from VIT University. The author has contributed to research in topics: Channel allocation schemes & Learning automata. The author has an hindex of 14, co-authored 41 publications receiving 626 citations. Previous affiliations of V. Saritha include Sri Padmavati Mahila Visvavidyalayam.

Learning Automata-Based QoS Framework for Cloud IaaS

Abstract: This paper presents a Learning Automata (LA)-based QoS (LAQ) framework capable of addressing some of the challenges and demands of various cloud applications. The proposed LAQ framework ensures that the computing resources are used in an efficient manner and are not over- or under-utilized by the consumer applications. Service provisioning can only be guaranteed by continuously monitoring the resource and quantifying various QoS metrics, so that services can be delivered in an on-demand basis with certain levels of guarantee. The proposed framework helps in ensuring guarantees with these metrics in order to provide QoS-enabled cloud services. The performance of the proposed system is evaluated with and without LA, and it is shown that the LA-based solution improves the performance of the system in terms of response time and speed up.

Learning automata as a utility for power management in smart grids

Abstract: The rapid growth of smart grid systems demands efficient management of smart grid services. Smart grids are expected to enable the delivery and management of electricity in a more reliable, efficient, economical, and secured manner. Thus, the development of effective power management solutions for smart grids to meet these challenges is an important area of research in recent times. In this article, we propose using learning automata (LA), a computational learning utility, for efficient power management in smart grids (LAPM). The proposed system, LAPM, helps in identifying the electricity required for various distribution substations and controls the usage of power by various devices (i.e., preventing unauthorized use of power). The use of LA enables performing a dynamic analysis of power usage and providing decision making for its effective usage. The system is evaluated on a real-life-resembling environment, with respect to parameters such as power utilization and customer satisfaction.

Quality-of-service-enabled ant colony-based multipath routing for mobile ad hoc networks

Abstract: Mobile ad hoc networks (MANETs) are dynamically changing and self-configuring networks. Owing to their widespread use for many applications, multipath routing in MANETs has been widely discussed for providing fault-tolerance routing, quality-of-service (QoS) and various other purposes. The authors propose a quality of service enabled ant colony-based multipath routing (QAMR) algorithm based on the foraging behaviour of ant colony for selecting path and transmitting data. In this approach, the path is selected based on the stability of the nodes and the path preference probability. The authors have considered bandwidth, delay and hop count as the QoS parameters along with the stability of node, number of hops and path preference probability factors. Simulations performed with network simulator 2 shows that the proposed algorithm is scalable and performs better at higher traffic load compared to the existing algorithms.

LACAV: an energy-efficient channel assignment mechanism for vehicular ad hoc networks

Abstract: Designing an efficient channel assignment system for Vehicular Ad hoc Networks (VANETs), which conserves energy, is a challenging task, primarily because of the high degrees of mobility of nodes in these networks. As the high mobility of nodes in vehicular networks leads to frequent handoffs, channel assignment in VANETs becomes a tedious task. In this paper, we propose a channel assignment mechanism using the concepts of learning automata (LA) and reusability. LA is used to optimize the performance of the proposed system by selecting suitable number of reserved channels for the handoff calls and reusability allows the channel to be reused by the different base stations (BSs) based on the reuse distance. The proposed system is designed to reduce the dropping probability. The proposed system is suitable for network architectures in which it is possible to arrange the BSs with different groups of channels sequentially in a particular order that helps in conserving energy. Our experiments clearly indicate that the system reduces the dropping probability and allows a continuous communication throughout the duration of the call. The performance of proposed algorithm is compared with the Vehicular Fast Handover Scheme (VFHS), and the Cooperative scheme for service channel reservation (CRaSCH) scheme in terms of handoff latency, and it is shown that the proposed algorithm performs better than VFHS and CRaSCH.

Virtual Backoff Algorithm: An Enhancement to 802.11 Medium-Access Control to Improve the Performance of Wireless Networks

Abstract: This paper presents a scheme, called the virtual backoff algorithm (VBA), which is based on the sequencing technique for efficient medium-access control. The proposed method minimizes the number of collisions while reducing the delays that occur during the backoff periods. We present an analytical study on MAC-layer issues, which are very important when accessing a channel over wireless networks. The VBA scheme uses fair distributed mechanisms to access a channel. We introduce a counter at each node to maintain the discipline of the nodes. The performance of the proposed method is evaluated under various conditions, and the obtained results are very promising. The enhanced protocol improves the utilization of bandwidth by increasing the throughput up to 75%, and the amount of collisions is reduced to 65% when compared with legacy protocols. The proposed scheme shows that the energy requirements are minimum due to the limitation on the number of transmissions.

A Survey on Smart Grid CommunicationInfrastructures: Motivations, Requirements andChallenges

Abstract: A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.

A review of the development of Smart Grid technologies

Abstract: Energy sustainability and environmental preservation have become worldwide concerns with the many manifestations of climate change and the continually increasing demand for energy. As cities and nations become more technologically advanced, electricity consumption rises to levels that may no longer be manageable if left unattended. The Smart Grid offers an answer to the shift to more sustainable technologies such as distributed generation and microgrids. A general public awareness and adequate attention from potential researchers and policy makers is crucial. This paper presents an overview of the Smart Grid with its general features, functionalities and characteristics. It presents the Smart Grid fundamental and related technologies and have identified the research activities, challenges and issues. It demonstrates how these technologies have shaped the modern electricity grid and continued to evolve and strengthen its role in the better alignment of energy demand and supply. Smart Grid implementation and practices in various locations are also unveiled. Concrete energy policies facilitate Smart Grid initiatives across the nations. Interestingly, Smart Grid practices in different regions barely indicate competition but rather an unbordered community of similar aspirations and shared lessons.

Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches

Abstract: As a powerful tool, the vehicular network has been built to connect human communication and transportation around the world for many years to come. However, with the rapid growth of vehicles, the vehicular network becomes heterogeneous, dynamic, and large scaled, which makes it difficult to meet the strict requirements, such as ultralow latency, high reliability, high security, and massive connections of the next-generation (6G) network. Recently, machine learning (ML) has emerged as a powerful artificial intelligence (AI) technique to make both the vehicle and wireless communication highly efficient and adaptable. Naturally, employing ML into vehicular communication and network becomes a hot topic and is being widely studied in both academia and industry, paving the way for the future intelligentization in 6G vehicular networks. In this article, we provide a survey on various ML techniques applied to communication, networking, and security parts in vehicular networks and envision the ways of enabling AI toward a future 6G vehicular network, including the evolution of intelligent radio (IR), network intelligentization, and self-learning with proactive exploration.