Kuljeet Kaur

An intelligent RFID-enabled authentication scheme for healthcare applications in vehicular mobile cloud

Recently, vehicular cloud computing (VCC) has emerged as the one of the fast growing technologies with an aim to provide uninterrupted services to the moving clients even on-the-fly. One of the services provided by VCC is the mobile healthcare in which patient can be provided diagonosis from anywhere during their mobility. This paper proposes an intelligent RFID-enabled authentication scheme for healthcare applications in VCC environment. In the proposed scheme, a Petri Nets-based authentication model is used for authentication of tags, and readers. Both server, and tag authentications are protected by strong elliptical curve cryptography (ECC)-based key generation mechanism. The proposed scheme is found to be secure with respect to mutual authentication of servers and tags, replay attack, tracking attack, users anonymity, eavesdropping, and cloning with forward secrecy. To evaluate the effectiveness of the proposed scheme, it is evaluated with respect to the overhead generated, computation complexity, and % service delivery, where its performance is found better than the case where it is not applied.

Decision Tree and SVM-Based Data Analytics for Theft Detection in Smart Grid

Nontechnical losses, particularly due to electrical theft, have been a major concern in power system industries for a long time. Large-scale consumption of electricity in a fraudulent manner may imbalance the demand-supply gap to a great extent. Thus, there arises the need to develop a scheme that can detect these thefts precisely in the complex power networks. So, keeping focus on these points, this paper proposes a comprehensive top-down scheme based on decision tree (DT) and support vector machine (SVM). Unlike existing schemes, the proposed scheme is capable enough to precisely detect and locate real-time electricity theft at every level in power transmission and distribution (T&D). The proposed scheme is based on the combination of DT and SVM classifiers for rigorous analysis of gathered electricity consumption data. In other words, the proposed scheme can be viewed as a two-level data processing and analysis approach, since the data processed by DT are fed as an input to the SVM classifier. Furthermore, the obtained results indicate that the proposed scheme reduces false positives to a great extent and is practical enough to be implemented in real-time scenarios.

A Colored Petri Net Based Frequency Support Scheme Using Fleet of Electric Vehicles in Smart Grid Environment

The ever-growing dependency of modern life on electricity may impose huge burden on smart grids (SGs). This dependency affects the demand-supply gap and may lead to undesirable frequency fluctuations. In the worst case, these fluctuations may result in blackouts. In this direction, fleet of electric vehicles (EVs) may play a crucial role in reducing these fluctuations to a great extent. So, this paper proposes a novel scheme for efficient frequency support in SG environment by utilizing fleet of EVs. These EVs act as controllable loads and work in close coordination with aggregators and charging stations. Aggregators play a crucial role in regulating charging and discharging rates of EVs while meeting their energy requirements with the help of the proposed colored petri net based controller. The proposed scheme has been evaluated with respect to publicly available frequency regulation data acquired from PJM and ERCOT. In addition to this, the scheme has also been compared with an existing approach and results clearly depict that the proposed scheme is more scalable in comparison to the existing schemes in V2G environment.

Container-as-a-Service at the Edge: Trade-off between Energy Efficiency and Service Availability at Fog Nano Data Centers

In the last few years, we have witnessed the huge popularity of one of the most promising technologies of the modern era: the Internet of Things. In IoT, various smart objects (smart sensors, embedded devices, PDAs, and smartphones) share their data with one another irrespective of their geographical locations using the Internet. The amount of data generated by these connected smart objects will be on the order of zettabytes in the coming years. This huge amount of data creates challenges with respect to storage and analytics given the resource constraints of these smart devices. Additionally, to process the large volume of information generated, the traditional cloud-based infrastructure may lead to long response time and higher bandwidth consumption. To cope up with these challenges, a new powerful technology, edge computing, promises to support data processing and service availability to end users at the edge of the network. However, the integration of IoT and edge computing is still in its infancy. Task scheduling will play a pivotal role in this integrated architecture. To handle all the above mentioned issues, we present a novel architecture for task selection and scheduling at the edge of the network using container-as-a-service (CoaaS). We solve the problem of task selection and scheduling by using cooperative game theory. For this purpose, we developed a multi-objective function in order to reduce the energy consumption and makespan by considering different constraints such as memory, CPU, and the users budget. We also present a real-time internal and external container migration technique for minimizing the energy consumption. For task selection and scheduling, we have used lightweight containers instead of the conventional virtual machines to reduce the overhead and response time as well as the overall energy consumption of fog devices, that is, nano data centers (nDCs). Our empirical results demonstrate that the proposed scheme reduces the energy consumption and the average number of SLA violations by 21.75 and 11.82 percent, respectively.

Lightweight Authentication Protocol for RFID-Enabled Systems Based on ECC

Radio Frequency Identification(RFID) is a leading wireless technology with respect to Automatic Identification and Data Capture(AIDC). With its increasing popularity amongst the researchers and industries, it has been successful in paving its way to various domains including supply chain management, healthcare, agriculture, aviation, etc. Potential applications of RFID range from tracking of assets to real-time human monitoring. However, with its wide-scale deployment, RFID systems have become more vulnerable to different kinds of active and passive attacks leading to various issues such as information leakage, identity revelation, spoofing, tracking, etc. Thus, privacy needs to be embedded in such systems so as to maintain highest levels of privacy and authenticity at all times. In order to address these issues, this paper proposes an efficient and lightweight authentication protocol using Elliptical Curve Cryptography(ECC). It is found to be safe as it establishes mutual authentication between the server and tags; while protecting against replay, tracking, eavesdropping, and cloning risks. In addition to this, AVISPA has been used to formally verify the security features of the protocol. The obtained results indicate that it is more preferable for RFID- enabled devices and provides better security than its previous counterparts.

Renewable Energy-based Multi-Indexed Job Classification and Container Management Scheme for Sustainability of Cloud Data Centers

Cloud computing has emerged as one of the most popular technologies of the modern era for providing on-demand services to the end users. Most of the computing tasks in cloud data centers are performed by geodistributed data centers which may consume a hefty amount of energy for their operations. However, the usage of renewable energy resources with appropriate server selection and consolidation can mitigate the energy related issues in cloud environment. Hence, in this paper, we propose a renewable energy-aware multi-indexed job classification and scheduling scheme using container as-a-service for data centers sustainability. In the proposed scheme, incoming workloads from different devices are transferred to the data center which has sufficient amount of renewable energy available with it. For this purpose, a renewable energy-based host selection and container consolidation scheme is also designed. The proposed scheme has been evaluated using Google workload traces. The results obtained prove 15%, 28%, and 10.55% higher energy savings in comparison to the existing schemes of its category.

A Game of Incentives: An Efficient Demand Response Mechanism using Fleet of Electric Vehicles

With the explosive penetration of Electric Vehicles (EVs) in the last decade, the load on the existing grids have exaggerated manifold. This has led to severe demand-supply imbalances causing grid instability and reliability issues. Towards this end, Demand Reponse (DR) management has been identified as an important means to tackle this problem. Thus, in this paper, a robust Stackelberg Game has been proposed wherein the Utility Provider (UP) and fleet of EVs are assumed to be playing the roles of a competitive leader and followers, respectively. In the considered game, EVs charging problem has been formulated as a non-cooperative game; in which EVs decide their charging slot in accordance with the real-time electricity prices announced by the UP. The existence and uniqueness of the formulated Stackelberg Game has been theoretically proved in the paper. Further, the efficacy of the formulated game has been validated on real-time data traces obtained from Haryana State Electricity Board, India.

Multiobjective Optimization for Frequency Support Using Electric Vehicles: An Aggregator-Based Hierarchical Control Mechanism

In the last few years, there has been an exponential increase in the penetration of electric vehicles (EVs) due to their eco-friendly nature, and ability to support bidirectional energy exchanges with the smart grid. Besides serving transportation needs and reducing the carbon footprints in the environment, EVs are widely used for instantaneous grid frequency support. However, the existing research proposals have concentrated majorly on unidirectional vehicle-to-grid (V2G) support using fleet of EVs, which in turn leads to reduced frequency regulation and reserve capacity of participating EVs. Motivated from these facts, in this paper, an “aggregator-based hierarchical control mechanism” for secondary frequency regulation (SFR) using a fleet of EVs has been presented. In the proposed solution, EVs’ scheduling problem has been formulated to provide optimal SFR, while satisfying EVs’ energy demands under battery degradation constraints. This multiobjective primal problem (Mo-PP) under multiple constraints is solved using an approximation approach. This task is achieved by decomposing the complex Mo-PP into four different subproblems (SPs), corresponding to controllers deployed at different layers. The designed SPs are then iteratively solved using interior point method. In summary, the tradeoff between SFR and EVs bidirectional energy demands has been investigated in this paper. Moreover, battery degradation issues induced due to frequent charging and discharging cycles of EVs are also explored. Optimal dispatch of regulation signals among the aggregators and charging stations also takes into account the advantages of conventional droop mechanism. Lastly, widely accepted Pennsylvania–New Jersey–Maryland and ERCOT regulation data have been used to perform extensive simulations. The results obtained demonstrate that the proposed scheme achieves 22.6% and 6.8% better performance in comparison with the existing schemes based on colored petri net and proportional integral derivative controller, respectively.

Fleet of electric vehicles for frequency support in Smart Grid using 2-layer hierarchical control mechanism

Penetration of electric vehicles (EVs) in the market, have gained significant momentum in the last couple of years. EVs not only deliver efficient transportation facilities, but also reduce the carbon footprints in the environment. In addition to this, EVs have the intrinsic ability to support the grid during peak and off-peak hours, which can be efficiently utilized to provide instantaneous frequency support. Motivated from these facts, this paper primarily aims to exploit the distributed energy from EVs efficiently, for providing coordinated grid support during frequency deviations. This has been achieved by using a 2-layer hierarchical control mechanism, comprising of physical and control layers. The physical layer constitutes of different entities such as-charging stations, aggregators and a substation, while the control layer is composed of two controllers, i.e., central and local. These controllers employ droop characteristics for load redistribution at different levels. Furthermore, the local controllers regulate the charging and discharging rates of participating EVs in order to support simultaneous and bi-directional energy exchanges. The publically available PJM data has been used to validate the results obtained, which clearly indicate the efficacy of the proposed scheme in the real-time scenario.

Smart grid with cloud computing: Architecture, security issues and defense mechanism

Smart Grid Network (SGN) is one of the innovative trends towards efficient and intelligent use of the conventional and non conventional resources of energy with respect to electric power generation, transmission and distribution. However, with the ever growing dependence and demand of modern life and industry on electricity, there arises a need of an integrated platform to manage these services in more efficient, reliable and transparent manner than ever before. In order to control the entire grid operations in a decentralized manner and analyze huge chunks of user specific data in an optimized manner, SGNs require dedicated resources. Thus, cloud computing platform is considered to be novel approach to fulfill these requirements and provide a more resilient and smarter approach to manage them. To primarily focus on these requirements, we present the basic architectural model to integrate SGNs with cloud computing technology. We also present a comprehensive survey on different security issues pertaining to this solution and the different ways to tackle them.