Ata Ullah

Bio: Ata Ullah is an academic researcher from National University of Modern Languages. The author has contributed to research in topics: Computer science & Server. The author has an hindex of 14, co-authored 47 publications receiving 465 citations. Previous affiliations of Ata Ullah include University of Science and Technology Beijing.

Secure Healthcare Data Aggregation and Transmission in IoT—A Survey

Abstract: Internet of medical things (IoMT) is getting researchers’ attention due to its wide applicability in healthcare Smart healthcare sensors and IoT enabled medical devices exchange data and collaborate with other smart devices without human interaction to securely transmit collected sensitive healthcare data towards the server nodes Alongside data communications, security and privacy is also quite challenging to securely aggregate and transmit healthcare data towards Fog and cloud servers We explored the existing surveys to identify a gap in literature that a survey of fog-assisted secure healthcare data collection schemes is yet contributed in literature This paper presents a survey of different data collection and secure transmission schemes where Fog computing based architectures are considered A taxonomy is presented to categorize the schemes Fog assisted smart city, smart vehicle and smart grids are also considered that achieve secure, efficient and reliable data collection with low computational cost and compression ratio We present a summary of these scheme along with analytical discussion Finally, a number of open research challenges are identified Moreover, the schemes are explored to identify the challenges that are addressed in each scheme

Advances in Position Based Routing Towards ITS Enabled FoG-Oriented VANET–A Survey

Abstract: With the rapid growth in connected vehicles and related innovative applications, it is getting keen interest among the researchers. Vehicular ad-hoc networks (VANETs) comprise of interconnected vehicles with sensing capabilities to exchange traffic, weather and emergency information. Intelligent transportation system (ITS) supports better coordination among vehicles and in providing reliable services. In VANET, topology is dynamic due to the high mobility of vehicles, therefore, the existing topology-based schemes are not suitable. In this paper, we have explored position based routing (PBR) protocols for VANETs by presenting a taxonomy. The existing survey papers have focused on PBR schemes but we have further focused on considering PBR for the city environment along with connectivity aware routing schemes. Moreover, linear programming, genetic algorithms, and regression-based schemes are also included. To further evaluate the strengths and weaknesses of PBR schemes, a categorical evaluation of different architectures, path strategies, and carry-forward strategies are presented. Currently, no architecture is presented for FoG-oriented VANET using parked vehicles as guards for anchor points or junctions. To fill this aspiring demand, we have presented a FoG-oriented VANET architecture that can support the PBR by utilizing road junctions for path selection. It also involves the vehicles in the parking area for packet transmission. Further, we have proposed to use parked vehicles near junction as an option for selecting guarding vehicle along with ITS as well. It reduces the probability of extensive carry forward-based communication due to the absence of guarding nodes. The records at parked vehicles can be upgraded at neighboring parked vehicles as well during the beaconing or exchange of data messages. The architecture supports better packet delivery ratios, end-to-end delay, transmission time, and communication cost. Moreover, opportunities and challenges of the proposed architecture are also explored to attract researchers toward this area of research.

Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

Abstract: With the ever-growing demand of electric power, it is quite challenging to detect and prevent Non-Technical Loss (NTL) in power industries. NTL is committed by meter bypassing, hooking from the main lines, reversing and tampering the meters. Manual on-site checking and reporting of NTL remains an unattractive strategy due to the required manpower and associated cost. The use of machine learning classifiers has been an attractive option for NTL detection. It enhances data-oriented analysis and high hit ratio along with less cost and manpower requirements. However, there is still a need to explore the results across multiple types of classifiers on a real-world dataset. This paper considers a real dataset from a power supply company in Pakistan to identify NTL. We have evaluated 15 existing machine learning classifiers across 9 types which also include the recently developed CatBoost, LGBoost and XGBoost classifiers. Our work is validated using extensive simulations. Results elucidate that ensemble methods and Artificial Neural Network (ANN) outperform the other types of classifiers for NTL detection in our real dataset. Moreover, we have also derived a procedure to identify the top-14 features out of a total of 71 features, which are contributing 77% in predicting NTL. We conclude that including more features beyond this threshold does not improve performance and thus limiting to the selected feature set reduces the computation time required by the classifiers. Last but not least, the paper also analyzes the results of the classifiers with respect to their types, which has opened a new area of research in NTL detection.

Fog-assisted secure healthcare data aggregation scheme in IoT-enabled WSN

Abstract: With the rapid increase in number of sensing devices for healthcare, researchers are getting growing interest due to wide application support and new challenging scenarios. Internet of Things (IoT) comprises of huge number of connected devices with a variety of sensing support especially in medical health parameters sensing. In these scenarios, secure data collection and transmission to centralized servers is quite challenging to protect against several attacks for illegal data access. Existing solutions suffer from storage, communication and energy overheads. To resolve these issues, this paper presents a FoG assisted scheme for healthcare data aggregation in a secure and efficient manner.. We have involved the peer-to-peer communication between healthcare sensing devices and wearables to share secret data with an aggregating node that can share data with FoG server. In this scenario, an aggregator may be away from FoG server and cannot transmit data directly. However, it can share the encrypted data with the neighboring aggregator to transmit data to FoG server by appending in its current aggregated data. FoG server can extract the required values from the data and can save in the local repository that can be further updated later in cloud repositories. For these functionalities, we have presented two algorithms for message receiving at aggregator and message extraction at FoG server. Moreover, compression mechanism is also presented to further reduce the communication costs. We have performed simulations using TCL and C files in NS2.35 to generate trace files and then executed AWK scripts to extract results. Results prove the supremacy of proposed scheme over existing schemes in terms of storage, communication, transmission ratio, energy consumption and resilience.

Systematic Literature Review on Energy Efficient Routing Schemes in WSN – A Survey

Abstract: In wireless sensor networks (WSN), routing is quite challenging area of research where packets are forwarded through multiple nodes to the base station. The packet being sent over the network should be shared in an energy efficient manner. It also considers the residual power of battery to enhance the network life time. Existing energy efficient routing solutions and surveys are presented but still there is a need for Systematic Literature Review (SLR) to identify the valid problems. This paper performs SLR for energy efficiency routing with 172 papers at initial stage. Next, 50 papers are shortlisted after filtration based on quality valuation and selection criteria by ensuring relevance with energy efficiency. Initially, we present literature that includes schemes for threshold sensitive, adaptive periodic threshold sensitive, power efficient, hybrid energy efficient distribution and low energy adaptive mechanisms. Result of systematic review reveals that consumption of energy is the most fundamental issue in WSN however, is not noticed by the researchers and practitioners where as it can contribute for the improvement of the energy efficiency. It also elaborates the weaknesses of the existing approaches which make them inappropriate for energy efficient routing in WSN.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Decision-making in structure solution using Bayesian estimates of map quality: the PHENIX autosol wizard

Abstract: Ten measures of experimental electron-density-map quality are examined and the skewness of electron density is found to be the best indicator of actual map quality. A Bayesian approach to estimating map quality is developed and used in the PHENIX AutoSol wizard to make decisions during automated structure solution.

The Future of Healthcare Internet of Things: A Survey of Emerging Technologies

Abstract: The impact of the Internet of Things (IoT) on the advancement of the healthcare industry is immense. The ushering of the Medicine 4.0 has resulted in an increased effort to develop platforms, both at the hardware level as well as the underlying software level. This vision has led to the development of Healthcare IoT (H-IoT) systems. The basic enabling technologies include the communication systems between the sensing nodes and the processors; and the processing algorithms for generating an output from the data collected by the sensors. However, at present, these enabling technologies are also supported by several new technologies. The use of Artificial Intelligence (AI) has transformed the H-IoT systems at almost every level. The fog/edge paradigm is bringing the computing power close to the deployed network and hence mitigating many challenges in the process. While the big data allows handling an enormous amount of data. Additionally, the Software Defined Networks (SDNs) bring flexibility to the system while the blockchains are finding the most novel use cases in H-IoT systems. The Internet of Nano Things (IoNT) and Tactile Internet (TI) are driving the innovation in the H-IoT applications. This paper delves into the ways these technologies are transforming the H-IoT systems and also identifies the future course for improving the Quality of Service (QoS) using these new technologies.

A Survey of Internet of Things (IoT) Authentication Schemes

Abstract: The Internet of Things (IoT) is the ability to provide everyday devices with a way of identification and another way for communication with each other. The spectrum of IoT application domains is very large including smart homes, smart cities, wearables, e-health, etc. Consequently, tens and even hundreds of billions of devices will be connected. Such devices will have smart capabilities to collect, analyze and even make decisions without any human interaction. Security is a supreme requirement in such circumstances, and in particular authentication is of high interest given the damage that could happen from a malicious unauthenticated device in an IoT system. This paper gives a near complete and up-to-date view of the IoT authentication field. It provides a summary of a large range of authentication protocols proposed in the literature. Using a multi-criteria classification previously introduced in our work, it compares and evaluates the proposed authentication protocols, showing their strengths and weaknesses, which constitutes a fundamental first step for researchers and developers addressing this domain.