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Energy-Efficient Power Allocation for Secure SWIPT in IoT-DAS Using Fractional Optimization 1st

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Energy-Efficient Power Allocation for Secure SWIPT in IoT-DA...

1st International Conference of IoT and its Applications, ICIA2020

作者： Bulla, Aaqib Mehraj, Shahid Communication Control and Learning Lab National Institute of Technology Srinagar India

ISBN: (纸本)9789811676369

In this paper, we study secrecy and energy-efficiency optimization in a distributed antenna system (DAS)-based IoT network with energy harvesting nodes. Considering simultaneous wireless information and power transfer (SWIPT), we define secure energy efficiency (SEE) as ratio of the achievable secrecy rate to the total consumed power. Our goal is to maximize SEE subject to maximum transmit power constraint of distributed antenna (DA) ports and minimum energy-harvesting requirement of the IoT devices. For a single IoT device and single eavesdropper, maximizing SEE is formulated as a constrained fractional optimization problem, and the optimal solution is derived by solving Karush–Kuhn–Tucker (KKT) conditions. We also consider the case of an energy-harvesting eavesdropper and attempt to restrain it from harvesting the energy from the radio signal. This is achieved by introducing one more constraint into the optimization problem corresponding to the energy harvesting requirement of the eavesdropper. Further, we also consider a general case of multiple IoT devices and multiple eavesdroppers in an N-port DAS. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy harvesting

Modelling Energy Consumption of Domestic Households via Supervised and Unsupervised learning: A Case Study 1

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2nd Symposium on Machine learning and Metaheuristics Algorithms, and Applications, SoMMA 2020

作者： Shah, Shahid Mehraj Communication Control and Learning Lab Department of Electronics and Communication Engineering National Institute of Technology Srinagar Jammu and Kashmir India

ISBN: (数字)9789811604195

ISBN: (纸本)9789811604188

Electricity energy billing system is prevalent in most of the places in the world. Also digitization of these electricity bills has also been successfully implemented in various underdeveloped countries as well. The vast amount of data is available regarding the energy consumption of consumers. In this paper we consider a case study of one city, about which we have electricity energy data for several years. We first classify consumers based on their average energy usage via clustering algorithms. We also have survey data of several houses. In that survey, we have building information, family information and also appliance information. We use various regression techniques to disaggregate the energy usage corresponding to various appliances. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Energy utilization

Supervised Machine learning Approaches for Attack Detection in the IoT Network 1st

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Supervised Machine Learning Approaches for Attack Detection ...

1st International Conference of IoT and its Applications, ICIA2020

作者： Ahmad, Mir Shahnawaz Shah, Shahid Mehraj Communication Control and Learning Lab Department of Electronics and Communication Engineering National Institute of Technology Srinagar J&K Srinagar India

ISBN: (纸本)9789811676369

In recent years, the Internet of Things (IoT) has emerged to be one of the vital fields with a vast application domain. IoT network brings heterogeneous devices closer to each other, which eventually helps to solve various real-world problems. Apart from all the applications, the devices in the IoT network are more prone to malicious attacks because of their limited computational power and other resources thereby shrinking the application domain of IoT. Due to the massive size of the IoT network, it becomes very challenging to monitor each and every device for the purpose of attack detection. However, because of the intelligent, self-learning and adaptive nature of various machine learning approaches, they can be used for detecting the malicious traffic in the IoT network. In this regard, we have investigated the applicability of various supervised machine learning classifiers on network security laboratory knowledge discovery in databases (NSL-KDD) dataset and compared their performances. This study shows that random forest classifier and stochastic gradient descent classifier (with log loss function) outperform the other classifiers, studied in this paper, for classifying the IoT network traffic into malicious and non-malicious. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

Activity Modeling of Individuals in Domestic Households Using Fuzzy Logic 1

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2nd Symposium on Machine learning and Metaheuristics Algorithms, and Applications, SoMMA 2020

作者： Dyuthi, Sristi Ram Shah, Shahid Mehraj Communication Control and Learning Lab Department of Electronics and Communication Engineering National Institute of Technology Srinagar Srinagar Jammu and Kashmir India Department of ECE University of California San Diego San Diego United States

ISBN: (数字)9789811604195

ISBN: (纸本)9789811604188

A model which predicts the activities of each individual in a household is developed. This model is used to simulate the activities of 25,000 households in a town in Kerala (a state in southern region of India). A fuzzy-logic based approach is used to estimate the probabilities of an individual to be in a particular state/activity. Then an optimization problem is formulated to compute the activity transitions. Further, the activity transitions of the individuals within a house are tied in an appropriate way. These activity transitions are then used to simulate a Markov chain of the activities for a sample set of households in a town in Kerala. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Fuzzy logic

MITIGATING MALICIOUS INSIDER ATTACKS IN THE INTERNET OF THINGS USING SUPERVISED MACHINE learning TECHNIQUES

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Scalable Computing 2021年第1期22卷 13-28页

作者： Ahmad, MIR Shahnawaz Shah, Shahid Mehraj Communication Control & Learning Lab Department of Electronics & Communication Engineering National Institute of Technology SrinagarJ&K India

The interconnection of large number of smart devices and sensors for critical information gathering and analysis over the internet has given rise to the Internet of Things (IoT) network. In recent times, IoT has emerged as a prime field for solving diverse real-life problems by providing a smart and affordable solutions. The IoT network has various constraints like: limited computational capacity of sensors, heterogeneity of devices, limited energy resource and bandwidth etc. These constraints restrict the use of high-end security mechanisms, thus making these type of networks more vulnerable to various security attacks including malicious insider attacks. Also, it is very difficult to detect such malicious insiders in the network due to their unpredictable behaviour and the ubiquitous nature of IoT network makes the task more difficult. To solve such problems machine learning techniques can be used as they have the ability to learn the behaviour of the system and predict the particular anomaly in the system. So, in this paper we have discussed various security requirements and challenges in the IoT network. We have also applied various supervised machine learning techniques on available IoT dataset to deduce which among them is best suited to detect the malicious insider attacks in the IoT network. © 2021 SCPE. All Rights Reserved.

关键词： Internet of things

Unsupervised Ensemble Based Deep learning Approach for Attack Detection in IoT Network

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arXiv 2022年

作者： Ahmed, Mir Shahnawaz Shah, Shahid Mehraj Communication Control Learning Lab Department of Electronics & Communication Engineering National Institute of Technology Srinagar India

The Internet of Things (IoT) has altered living by controlling devices/things over the Internet. IoT has specified many smart solutions for daily problems, transforming cyber-physical systems (CPS) and other classical fields into smart regions. Most of the edge devices that make up the Internet of Things have very minimal processing power. To bring down the IoT network, attackers can utilise these devices to conduct a variety of network attacks. In addition, as more and more IoT devices are added, the potential for new and unknown threats grows exponentially. For this reason, an intelligent security framework for IoT networks must be developed that can identify such threats. In this paper, we have developed an unsupervised ensemble learning model that is able to detect new or unknown attacks in an IoT network from an unlabelled dataset. The system-generated labelled dataset is used to train a deep learning model to detect IoT network attacks. Additionally, the research presents a feature selection mechanism for identifying the most relevant aspects in the dataset for detecting attacks. The study shows that the suggested model is able to identify the unlabelled IoT network datasets and DBN (Deep Belief Network) outperform the other models with a detection accuracy of 97.5% and a false alarm rate of 2.3% when trained using labelled dataset supplied by the proposed approach. © 2022, CC BY.

关键词： Internet of things

Secure energy efficiency: Power allocation and outage analysis for SWIPT-in-DAS based IoT

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arXiv 2021年

作者： Bulla, Aaqib Shah, Shahid M. Communication Control & Learning Lab Department of Electronics & Communication Engineering National Institute of Technology Srinagar India

In this paper we study secure energy efficiency (SEE) for simultaneous wireless information and power transfer (SWIPT) in a distributed antenna system (DAS) based IoT network. We consider a system in which both legitimate users (Bobs) and eavesdroppers (Eves) have power splitting (PS) receivers to simultaneously decode information and harvest energy from the received signal. When the channel state information (CSI) is known at the transmitter, we analyze the effect of an energy harvesting eavesdropper (EHE) over the maximization of SEE of the system. Next, considering the fact that perfect CSI is hard to achieve in practice, we characterize the system performance in terms of the outage probability of SEE. For the given SWIPT-in-DAS setup, we derive the closed form expression for the outage probability of SEE and with the help of numerical results, we study the effect of transmit power levels, number of distributed antenna (DA) ports and the PS ratio of devices. To the best of our knowledge, this is the first attempt to define the outage probability of SEE for SWIPT-in-DAS. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy efficiency

Achievable Sum-rate of variants of QAM over Gaussian Multiple Access Channel with and without security

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arXiv 2023年

作者： Showkat, Shifa Dar, Zahid Bashir Shah, Shahid Mehraj Communication Control & Learning Lab Dept. of Electronics and Communication Engineering National Institute of Technology Srinagar India

The performance of next generation wireless systems (5G/6G and beyond) at the physical layer is primarily driven by the choice of digital modulation techniques that are bandwidth and power efficient, while maintaining high data rates. Achievable rates for Gaussian input and some finite constellations (BPSK/QPSK/QAM) are well studied in the literature. However, new variants of Quadrature Amplitude Modulation (QAM) such as Cross-QAM (XQAM), Star-QAM (S-QAM), Amplitude and phase shift keying (APSK), and Hexagonal Quadrature Amplitude Modulation (H-QAM) are not studied in the context of achievable rates for meeting the demand of high data rates. In this paper, we study achievable rate region for different variants of M-QAM like Cross-QAM, H-QAM, Star-QAM and APSK. We also compute mutual information corresponding to the sum rate of Gaussian Multiple Access Channel (G-MAC), for hybrid constellation scheme, e.g., user 1 transmits using Star-QAM and user 2 by H-QAM. From the results, it is observed that S-QAM gives the maximum sum-rate when users transmit same constellations. Also, it has been found that when hybrid constellation is used, the combination of Star-QAM & H-QAM gives the maximum rate. In the next part of the paper, we consider a scenario wherein an adversary is also present at the receiver side and is trying to decode the information. We model this scenario as Gaussian Multiple Access Wiretap Channel (G-MAW-WT). We then compute the achievable secrecy sum rate of two user G-MAC-WT with discrete inputs from different variants of QAM (viz, X-QAM, H-QAM and S-QAM).It has been found that at higher values of SNR, S-QAM gives better values of SSR than the other variants. For hybrid inputs of QAM, at lower values of SNR, combination of APSK and S-QAM gives better results and at higher values of SNR, combination of HQAM and APSK gives greater value of SSR. © 2023, CC BY.

关键词： Quadrature amplitude modulation

Outage Analysis of Energy Efficiency in a Finite-Element-IRS Aided communication System

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arXiv 2022年

作者： Bulla, Aaqib Shah, Shahid M. Communication Control & Learning Lab. Department of Electronics & Communication Engineering National Institute of Technology Srinagar India

In this paper, we study the performance of an energy efficient wireless communication system, assisted by a finite-element-intelligent reflecting surface (IRS). With no instantaneous channel state information (CSI) at the transmitter, we characterize the system performance in terms of the outage probability (OP) of energy efficiency (EE). Depending upon the availability of line-of-sight (LOS) paths, we analyze the system for two different channel models, viz. Rician and Rayleigh. For an arbitrary number of IRS elements (N), we derive the approximate closed-form solutions for the OP of EE, using Laguerre series and moment matching methods. The analytical results are validated using the Monte-Carlo simulations. Moreover, we also quantify the rate of convergence of the derived expressions to the central limit theorem (CLT) approximations using the Berry-Esseen inequality. Further, we prove that the OP of EE is a strict pseudo-convex function of the transmit power and hence, has a unique global minimum. To obtain the optimal transmit power, we solve the OP of EE as a constrained optimization problem. To the best of our knowledge, the OP of EE as a performance metric, has never been previously studied in IRS-assisted wireless communication systems. © 2022, CC BY.

关键词： Energy efficiency