The fog computing paradigm has drawn significant research interest as it focuses on bringing cloud-based services closer to Internet of Things (IoT) users in an efficient and timely manner. Most of the physical devices in the fog computing environment, commonly named fog nodes, are geographically distributed, resource constrained, and heterogeneous. To fully leverage the capabilities of the fog nodes, large-scale applications that are decomposed into interdependent Application Modules can be deployed in an orderly way over the nodes based on their latency sensitivity. In this article, we propose a latency-aware Application Module management policy for the fog environment that meets the diverse service delivery latency and amount of data signals to be processed in per unit of time for different applications. The policy aims to ensure applications’ Quality of Service (QoS) in satisfying service delivery deadlines and to optimize resource usage in the fog environment. We model and evaluate our proposed policy in an iFogSim-simulated fog environment. Results of the simulation studies demonstrate significant improvement in performance over alternative latency-aware strategies.

Latency-Aware Application Module Management for Fog Computing Environments

Evolution of microgrids with converter-interfaced generations: Challenges and opportunities

This study presents a novel micro-grid protection scheme based on Hilbert-Huang transform (HHT) and machine learning techniques. Initialisation of the proposed approach is done by extracting the three-phase current signals at the targeted buses of different feeders. The obtained non-stationary signals are passed through the empirical mode decomposition method to extract different intrinsic mode functions (IMFs). In the next step using HHT to the selected IMFs component, different needful differential features are computed. The extracted features are further used as an input vector to the machine learning models to classify the fault events. The proposed micro-grid protection scheme is tested for different protection scenarios, such as the type of fault (symmetrical, asymmetrical and high impedance fault), micro-grid structure (radial and mesh) and mode of operation (islanded and grid connected) and so on. Three different machine learning models are tested and compared in this framework: Naive Bayes classifier, support vector machine and extreme learning machine. The extensive simulated results from a standard IEC micro-grid model prove the effectiveness and reliability of the proposed micro-grid protection scheme.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-gtd.2017.0502

Detection and classification of micro-grid faults based on HHT and machine learning techniques

Before inception of Internet of Things (IoT), personal computers and laptop were used to handle daily tasks of individuals like mail surfing, access to bank portal, observing current temperature, among others. Nowadays, IoT-enabled smart devices like smart mobile phones, PDAs, and tablets are being used by them for such tasks due to rapid growth in IoT. Smart homes have been widely accepted by individuals and organizations world wide due to their many advantages. Home security systems can be defined as monitoring of complete home/some portion of home from a remotely located or centralized location. It allows the user to watch all activities inside the home from a remote location that ultimately gives satisfaction to the owner of the home. Many home security systems exist, but they have some challenging issues like: delay, non-web enabled and difficult to handle during transfer of alerts to user in situation where any unusual event occurred inside the home. If any unusual event encountered inside the home, where security systems deployed, then system must be capable enough to send alert to the user without any delay by phone, text, or email. Cameras and other latest network technologies have enabled us to remotely monitor the home more effectively and efficiently from our smart phone. Hence, considering the above mentioned facts, in this paper, we have proposed an advanced Internet of Thing based Security Alert System for Smart Home in order to detect an intruder or any unusual event at home, when nobody is available there. This low-cost home security system utilizes a small pyroelectric Infrared (PIR) module and raspberry pi for minimizing the delay during process of e-mail alert. This paper also confirms the advantage of Raspberry Pi flexibility and broad probability of its usage. Preliminary analyses have shown encouraging results.

An advanced Internet of Thing based Security Alert System for Smart Home

https://onlinelibrary.wiley.com/doi/pdf/10.1002/2050-7038.12008

Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review

In this paper, Hilbert Huang transform (HHT) and weighted bidirectional extreme learning machine (WBELM) are integrated to detect and classify power quality events (PQEs) in real time Empirical mode decomposition is used to decompose the nonstationary PQEs into the monocomponent mode of oscillation, known as intrinsic mode functions (IMFs) The efficacious features are extracted by applying the Hilbert transform (HT) on the IMFs An efficient WBELM computational intelligence technique is proposed to recognize the single, as well as multiple PQEs and its performances are compared with the recently developed classifiers such as support vector machine, least-square support vector machine, extreme learning machine, and bidirectional extreme learning machine The recognition architecture of HHT integrated with WBELM (HHT-WBELM) method is tested and compared with the empirical wavelet transform associated with HT and WBELM method, and tunable-Q wavelet transform along with HT and WBELM method The faster learning speed, lesser computational complexity, superior classification accuracy, and short event detection time prove that the proposed HHT-WBELM method can be implemented in the online power quality monitoring system Finally, a hardware prototype is developed based on the digital signal processor to verify the cogency of the proposed method in real time The feasibility of the proposed method is tested and validated by both the simulation and laboratory experiments

Automatic Power Quality Events Recognition Based on Hilbert Huang Transform and Weighted Bidirectional Extreme Learning Machine

An islanding detection algorithm for distributed generation based on Hilbert–Huang transform and extreme learning machine

This paper proposes a new signal segmentation method, reduced-sample empirical mode decomposition, to extract the highly correlated monocomponent mode of oscillations. The two efficacious power quality indices are extracted from the Hilbert transformed (HT) array of the first three intrinsic mode functions. A novel class-specific weighted random vector functional link network (CSWRVFLN) classifier is proposed to recognize the complex power quality disturbances (PQDs). The performance of reduced sample Hilbert–Huang transform (RSHHT) combined with CSWRVFLN (RSHHT-CSWRVFLN) method is tested and compared with tunable-Q Wavelet transform associated with HT and CSWRVFLN and empirical wavelet transform along with HT and CSWRVFLN methods. The short event detection, lesser computational complexity, superior classification accuracy, and robust antinoise performance are the major advantages of the proposed RSHHT-CSWRVFLN method. Furthermore, a field-programmable gate array embedded processor is used to test and validate the feasibility of the proposed method for online monitoring the PQDs.

FPGA-Based Online Power Quality Disturbances Monitoring Using Reduced-Sample HHT and Class-Specific Weighted RVFLN

Smart home security control system has became indispensable in daily life. The design and development of a home security system, based on human face recognition technology and remotely monitoring technology, to confirm visitor identity and to control door accessibility has been reported in this paper. This paper describes about the implementation and deployment of wireless control system and accessibility in to a home environment for authenticated people only. A wireless network technique ZigBee based and image processing technique PCA based, dedicatedly make the security system alive as per the request. ZigBee module and electromagnetic door lock module combinedly operate the door accessibility, has been designed and developed. Face detection and recognition algorithms, as well as a wireless interface are used to detect and identify visitors and send an email and/or an alert message about the current home environment status via GSM network automatically to the home owner's mobile phone or any communication devices. The concerned authority can control the system through his/her mobile phone or any communication devices by sending AT Commands to GSM MODEM or by taking necessary actions for authentication through email, which is again password protected. Users can monitor visitors and control the door lock on active Web pages enhanced with JavaScript and HTML. This system finds a wide application in areas where physical presence is not possible all the time. The entire control system is built using ARM1176JZF-S microcontroller and tested for actual use in home environment.

Mrutyunjaya Sahani

Papers

Automatic Power Quality Events Recognition Based on Hilbert Huang Transform and Weighted Bidirectional Extreme Learning Machine

An islanding detection algorithm for distributed generation based on Hilbert–Huang transform and extreme learning machine

FPGA-Based Online Power Quality Disturbances Monitoring Using Reduced-Sample HHT and Class-Specific Weighted RVFLN

Web-based online embedded door access control and home security system based on face recognition

A real-time power quality events recognition using variational mode decomposition and online-sequential extreme learning machine