Zahoor Ali Khan

Higher Colleges of Technology · Computer Information Science

Most smartphones and tablets have either been produced or are about to be released, and the Android operating system is swiftly gaining market share. These days, customers utilize Android applications often for a broad variety of tasks. As a result, attackers now frequently target the Android platform. Many harmful applications have been discovered...

A large number of sensors are deployed for performing various tasks in the smart cities. The sensors are connected with each other through the Internet that leads to the emergence of Internet of Things (IoT). As the time passes, the number of deployed sensors is exponentially increasing. Not only this, the enhancement of sensors has also laid the b...

Abstract—Skin cancer is one of the most common and dangerous diseases due to a lack of awareness of its signs and methods for prevention. Skin cancer can be counted as a fourth burden worldwide, with the rate of deaths dramatically growing globally. Therefore, early detection at an early stage is necessary to stop the spread of cancer. In this pape...

Distributed denial of service (DDoS) attacks pose an increasing threat to businesses and government agencies. They harm internet businesses, limit access to information and services, and damage corporate brands. Attackers use application layer DDoS attacks that are not easily detectable because of impersonating authentic users. In this study, we ad...

Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network d...

The enhancement of Robustness (R) has gained significant importance in Scale-Free Networks (SFNs) over the past few years. SFNs are resilient to Random Attacks (RAs). However, these networks are prone to Malicious Attacks (MAs). This study aims to construct a robust network against MAs. An Intelligent Rewiring (INTR) mechanism is proposed to optimi...

In energy sectors, power utilities face financial losses due to Electricity Theft (ET). It happens when electricity is consumed without billing. Several methods are developed to detect ET automatically. Most of these methods only assess Electricity Consumption (EC) records. However, it is challenging to detect fraudulent consumers by only observing...

The routing in underwater acoustic sensor networks (UASNs) has become a challenging issue due to several problems. First, in UASN, the distance between the nodes changes due to their mobility with the water current, thus increasing the network's energy consumption. Second problem in UASNs is the occurrence of the void hole, which affects the networ...

Localization of sensors in Underwater Internet of Things (UIoTs) is difficult due to the mobility. This changing makes the routing decisions difficult, which results in unreliable communication. This paper proposes Adaptive Transmission based Geographic and Opportunistic Routing (ATGOR) protocol for reliable communication between nodes. ATGOR opera...

Data mining is the process to predict future trends. Data mining involving searching for patterns whose general purpose is to extract information using intelligent methods from a set of data and information turn it into an understandable structure for later use. Data mining using IoT becomes one of the leading providers applicable in several areas....

Due to the increase in the number of electricity thieves, the electric utilities are facing problems in providing electricity to their consumers in an efficient way. An accurate Electricity Theft Detection (ETD) is quite challenging due to the inaccurate classification on the imbalance electricity consumption data, the overfitting issues and the Hi...

In this paper, we propose a blockchain-based data sharing mechanism for Vehicular Network. We introduce edge service providers placed near to ordinary vehicle nodes to fulfill their requests. Smart vehicles generate a huge amount of data which is stored in the Interplanetary File System (IPFS). IPFS is a distributed file storage system that overcom...

High price fluctuations have a direct impact on electricity market. Thus, accurate and plausible price forecasts have been implemented to mitigate the consequences of price dynamics. This paper proposes two techniques to deal with the Electricity Price Forecasting (EPF) problem. Firstly, Convolutional Neural Network (CNN) model is used to predict t...

Electricity price forecasting is significant component of smart grid. Electricity systems are managed by the electricity market. The market operators perform electricity price forecasting for an efficient energy management. This paper deals with the electricity price forecasting based on deep learning. The fluctuations in electricity prices are due...

Due to the development of health care industry and digitization of medical data, recent years have experienced major changes in storage of electronic health record on cloud environment, making data exchange feasible between patient and healthcare provider. However, this new shift comes with the risk of security and privacy concerns of patient and d...

Electricity price forecasting is significant component of smart grid. Electricity systems are managed by the electricity market. The market operators perform electricity price forecasting for an efficient energy management. This paper deals with the electricity price forecasting based on deep learning. The fluctuations in electricity prices are due...

In this paper, we propose a blockchain-based data sharing mechanism for Vehicular Network. We introduce edge service providers placed near to ordinary vehicle nodes to fulfill their requests. Smart vehicles generate a huge amount of data which is stored in the Interplanetary File System (IPFS). IPFS is a distributed file storage system that overcom...

Due to the development of health care industry and digitization of medical data, recent years have experienced major changes in storage of electronic health record on cloud environment, making data exchange feasible between patient and healthcare provider. However, this new shift comes with the risk of security and privacy concerns of patient and d...

High price fluctuations have a direct impact on electricity market. Thus, accurate and plausible price forecasts have been implemented to mitigate the consequences of price dynamics. This paper proposes two techniques to deal with the Electricity Price Forecasting (EPF) problem. Firstly, Convolutional Neural Network (CNN) model is used to predict t...

The emergence of the smart grid has empowered the consumers to manage the home energy in an efficient and effective manner. In this regard, home energy management (HEM) is a challenging task that requires efficient scheduling of smart appliances to optimize energy consumption. In this paper, we proposed a meta-heuristic based HEM system (HEMS) by i...

An increase in the world's population results in high energy demand, which is mostly fulfilled by consuming fossil fuels (FFs). By nature, FFs are scarce, depleted, and non-eco-friendly. Renewable energy sources (RESs) photovoltaics (PVs) and wind turbines (WTs) are emerging alternatives to the FFs. The integration of an energy storage system with...

Accurate load and price forecasting is one of thecrucial stage in Smart Grid (SG). An efficient load and price fore-casting is required to minimize the large difference among powergeneration and consumption. Accurate selection and extractionof meaningful features from data are challenging. In this paper,New York Independent System Operator (NYISO)...

In this paper, an artificial neural network (ANN)based methodology is proposed to forecast electricity load and price. The performance of an ANN forecast model depends on appropriate input parameters. Parameter tuning of ANN is very important to increase the accuracy of electricity price and load prediction. This is done using mutual information an...

Short term electricity load and price accurate fore-casting are the key areas that need to be addressed in Smart Grids (SG). In this paper, a new model is proposed for accurate short term forecasting of load and price. First, irrelevant features are removed using Recursive Feature Elimination (RFE) and Decision Tree Regressor (DTR). Then, further d...

The communication between the Internet of Things (IoT) devices is not secure and reliable. A large number of security risks are involved. The existing security mechanisms are not easy to manage because they require extra resources and thus, increases the overall cost of the system. The IoT devices are resource-limited devices and they do not perfor...

The feature of bidirectional communication in a smart grid involves the interaction between consumer and utility for optimizing the energy consumption of the users. For optimal management of the energy at the end user, several demand side management techniques are implemented. This work proposes a home energy management system, where consumption of...

Due to the detrimental nature of aquatic environment , the design of routing protocols for underwater wireless sensor networks (UWSNs) faces numerous challenges, such as an optimal route selection, energy efficiency, propagation delay, etc. However, the energy efficiency is considered a key parameter while designing a routing strategy for UWSNs. Th...

Smart Grid (SG) plays vital role in modern electricity grid. The data is increasing with the drastic increase in number of users. An efficient technology is required to handle this dramatic growth of data. Cloud computing is then used to store the data and to provide numerous services to the consumers. There are various cloud Data Centers (DC), whi...

The increasing load demand in residential area and irregular electricity load profile encouraged us to propose an efficient Home Energy Management System (HEMS) for optimal scheduling of home appliances. We propose a multi-objective optimization based solution that shifts the electricity load from On-peak to Off-peak hours according to the defined...

Nowadays, constrained battery life expectancy is an important issue for reliable data delivery in an Underwater Wireless Sensor Network (UWSN). Conventional transmission methodologies increase the transmission overhead, i.e., the collision of packets, which influence the data transmission. Replacement of the sensors' battery in brutal underwater en...

The imbalance energy consumption and high data traffic at intermediate nodes degrade the network performance. In this paper, we propose: energy grade and balance load distribution (EGBLOAD) corona, EG without corona and DA without corona based schemes to distribute data traffic across the network nodes for efficient energy consumption. The dynamic...

Demand side management (DSM) in smart grid authorizes consumers to make informed decisions regarding their energy consumption pattern and helps the utility in reducing the peak load demand during an energy stress time. This results in reduced carbon emission, consumer electricity cost, and increased grid sustainability. Most of the existing DSM tec...

Recently a massive increase in the demand of energy has been reported in residential, industrial and commercial sectors. Traditional Grid (TG) with the aging infrastructure is unable to address the increasing demand problem. Smart Grid (SG) enhanced the TG by adopting information and communication based technological solutions to address the increa...

For efficient use of smart grid, exact prediction about the in-future coming load is of great importance to the utility. In this proposed scheme initially we converted daily Australian energy market operator load data to weekly data time series. Furthermore, we used eXtreme Gradient Boosting (XGBoost) for extracting features from the data. After fe...

Energy management using demand side management (DSM) techniques plays a key role in smart grid (SG) domain. Smart meters and energy management controllers are the important components of the SG. A lot of research has been done on energy management system (EMS) for scheduling the appliances. The aim of current research is to organize the power of th...

Optimal power flow (OPF) problem has become more significant for operation and planning of electrical power systems because of the increasing energy demand. OPF is very important for system operators to fulfill the electricity demand of the consumers efficiently and for the reliable operation of the power system. The key objective in OPF is to redu...

Reducing delay and latency in the cloud computing environment is a challenge for the present research community. This study performed a rigorous, comparative analysis of the fog computing paradigm and the conventional cloud computing paradigm in the context of the Smart Grid (SG). To meet the consumers’ demand and optimize cloud services to achieve...

The day by day increase in population is producing a gap between the demand and supply of electricity. Installation of new electricity generation system is not a good solution to tackle the high demand of electricity. To get the most out of the existing system, several demand response schemes have been presented by researchers. These schemes try to...

With the rapid pace in the evolution and development of technology, the demand of electrical energy is also increasing. Beside the production of energy from traditional and renewable energy sources, the energy management is also required to control the consumption of energy in commercial, industrial and residential houses to fulfill the fluctuating...

Nowadays, limited battery lifespan in Underwater Wireless Sensor Networks (UWSNs) is one of the key concerns for reliable data delivery. Traditional transmission approaches increase the transmission overhead, i.e., packet collision and congestion, which affects the reliable data delivery. Additionally, replacement of the sensors battery in the hars...

Students have different levels of intellectual capabilities and learning styles which affect their understanding of specific academic concepts and gaining specific skills. Educational institutions dedicate much efforts to support at-risk students. However, this support usually comes as a reaction of students’ low performance, while learners need pr...

Smart Grid (SG) is a modern electricity grid that enhance the efficiency and reliability of electricity generation, distribution and consumption. It plays an important role in modern energy infrastructure. Energy consumption and generation have fluctuating behaviour in SG. Load and price forecasting can decrease the variation between energy generat...

Underwater Wireless Sensor Networks (UWSNs) are promising and emerging framework having a wide range of applications. The underwater sensor deployment is beneficial; however, some factors limit the performance of the network, i.e., less reliability, high end-to-end delay and maximum energy dissipation. The provisioning of aforementioned factors hav...

In this paper, we attempt to predict short term price forecasting in Smart Grid (SG) deep learning and data mining techniques. We proposed a model for price forecasting, which consists of three steps: feature engineering, tuning classifier and classification. A hybrid feature selector is propose by fusing XG-Boost (XGB) and Decision Tree (DT). To p...

In this paper, month-ahead electricity load and price forecasting is done to achieve accuracy. The data of electricity load is taken from the Smart Meter (SM) in London. Electricity load data of five months is taken from one block SM along with the weather data. Data Analytics (DA) techniques are used in the paper for month-ahead electricity load a...

In this paper, an enhanced model for electricity load and price forecasting is proposed. This model consists of feature engineering and classification. Feature engineering consists of feature selection and extraction. For feature selection a hybrid feature selector is used which consists of Decision Tree (DT) and Recursive Feature Elimination (RFE)...

The advancement in Global Positioning System (GPS), has led to a huge number of location-based applications. Such applications can also be very useful for indoor environment; however, GPS technology struggles with indoor location mapping. Currently, there are various techniques, which are used for indoor localization namely: wireless fidelity-based...

Nowadays, to enhance the lifetime of an Under Water Sensor Networks (UWSNs), energy efficient and reliable data delivery in resource constraints are one of the key concerns. Traditional transmission approaches, increase the communication overhead which results in congestion and affects the reliable data delivery. In the current years, many routing...

Wireless Sensor Networks (WSNs) are facing different challenges in the routing procedure. Cost efficiency, low energy consumption and reliable data communication between nodes are the major challenges in the field of WSNs. During the transmission process of nodes, energy is also lost due to void holes. In the WSNs, location error and battery consum...

In this paper, depth and reliability aware delay 1 sensitive (DRADS), interference aware DRADS (iDRADS) and 2 cooperative iDRADS (Co-iDRADS) routing protocols are pro-3 posed for maximizing network good-put while minimizing end-4 to-end delay. We have introduced a new metric called depth 5 threshold to minimize the number of hops between source and...

In this paper, an enhanced model for electricity load and price forecasting is proposed. This model consists of feature engineering and classification. Feature engineering consists of feature selection and extraction. For feature selection a hybrid feature selector is used which consists of Decision Tree (DT) and Recursive Feature Elimination (RFE)...

Smart Grid (SG) is a modern electricity grid that enhance the efficiency and reliability of electricity generation, distribution and consumption. It plays an important role in modern energy infrastructure. Energy consumption and generation have fluctuating behaviour in SG. Load and price forecasting can decrease the variation between energy generat...

In this paper, month-ahead electricity load and price forecasting is done to achieve accuracy. The data of electricity load is taken from the Smart Meter (SM) in London. Electricity load data of five months is taken from one block SM along with the weather data. Data Analytics (DA) techniques are used in the paper for month-ahead electricity load a...