Mudassar Ahmad

IoT-based students interaction framework using attention-scoring assessment in eLearning

Students’ interaction and collaboration using Internet of Things (IoT) based interoperable infrastructure is a convenient way. Measuring student attention is an essential part of educational assessment. As new learning styles develop, new tools and assessment methods are also needed. The focus of this paper is to develop IoT-based interaction framework and analysis of the student experience of electronic learning (eLearning). The learning behaviors of students attending remote video lectures are assessed by logging their behavior and analyzing the resulting multimedia data using machine learning algorithms. An attention-scoring algorithm, its workflow, and the mathematical formulation for the smart assessment of the student learning experience are established. This setup has a data collection module, which can be reproduced by implementing the algorithm in any modern programming language. Some faces, eyes, and status of eyes are extracted from video stream taken from a webcam using this module. The extracted information is saved in a dataset for further analysis. The analysis of the dataset produces interesting results for student learning assessments. Modern learning management systems can integrate the developed tool to take student learning behaviors into account when assessing electronic learning strategies.

A Study on Elliptic Curve Digital Signature Algorithm (ECDSA) for Reliable E-Commerce Applications

Digital Signatures are becoming a core technology in today’s e-commerce world. The new technology of Elliptic Curve Digital Signature Algorithm (ECDSA) promises a better alternative for RSA in terms of smaller key size, higher performance and stronger security. ECC offers the highest strength per bit of any public-key cryptography system known today. Therefore, this paper intends to analyze the above factors in order to determine if it is best suited to be used in e-commerce applications.

A collision avoidance scheme for autonomous vehicles inspired by human social norms

Abstract This paper introduces the novel idea of using human social norms and human emotions to improve the collision avoidance of Autonomous Vehicles (AVs). Until now, the literature has been concerned with theoretical debates regarding ethical issues connected to AVs, while no practical steps have yet been undertaken. This paper introduces the concept of an artificial society of AVs with different personalities and with social norms coded into their autopilot so that they act like well-behaved drivers. For proof of concept, the standard agent modelling tool Netlogo is utilized to simulate the artificial society of AVs. Furthermore, comparisons are made with random walk-based non-social-based collision avoidance techniques. Extensive testing has been carried out using the behaviour space tool to determine the performance of the proposed approach regarding the number of collisions. A comparative study undertaken with a random walk method indicates that the proposed approach provides a better option for tailoring the autopilots of future AVs, while also promising to be more socially acceptable and trustworthy regarding safe road travel.

CDCSS: cluster-based distributed cooperative spectrum sensing model against primary user emulation (PUE) cyber attacks

In cognitive radio network, the secondary users (SUs) use the spectrum of primary users for communication which arises the security issues. The status of SUs as legitimate users is compulsory for the stability of the system. This paper addresses the issue of delay caused by a band-selection decision process that directly affects the security and performance. The model cluster-based distributed cooperative spectrum sensing is proposed. In this model, cluster heads (CHs) exchange control information with other CHs and ordinary nodes. This model significantly reduced the delay, sensing, convergence, routing, in band-selection process. This also reduces the energy consumption while sensing the spectrum which seriously leads to performance upgradation. The simulated results show the improved performance of cognitive radio networks in terms of delay, packet loss ratio and bandwidth usage as compared to cluster-based cooperative spectrum sensing model. The opportunity for primary user emulation attacker is minimized as the overall delay is reduced.

Designing an Energy-Aware Mechanism for Lifetime Improvement of Wireless Sensor Networks: a Comprehensive Study

In this paper, we have presented a comprehensive study on designing an aware energy architecture of Wireless Sensor Networks. A summary and modelling of various major techniques in designing the constituents of clustered wireless sensor network architecture are given in detail. In continuation of it, we have also analysed our proposed scheme, Extended-Multilayer Cluster Designing Algorithm (E-MCDA) in a large network. Among its components, algorithms for time slot allocation, minimising the CH competition candidates, and cluster member selection to CH play underpinning roles to achieve the target. These incorporations in MCDA result in minimising transmissions, suppressing the unneeded response of transmissions and near equal size and equal load clusters. We have done extensive simulations in NS2 and evaluate the performance of E-MCDA in energy consumption at various aspects of energy, packets transmission, the number of designed clusters, the number of nodes per cluster and un-clustered nodes. It is found that the proposed mechanism optimistically outperforms the competition with MCDA and EADUC concerning parameters above.

A generic methodology for geo-related data semantic annotation

Geo‐related data, also known as spatial data, is represented using a vector used for representing longitude, elevation, and latitude. Specially built systems, well known as Geographical Information Systems (GIS), made use of such data for querying, manipulating, navigation, and analyzing. In the current era of data, science needs to involve smart interactive investigation involving Internet of Data (IoD) on predicting upcoming changes and spatial updates on the map is growing rapidly. To resolve issues concerning real‐time spatial data, transformation using semantic annotation can provide a better way to translate spatial relationships. These spatial relationships will support spatial analysis by linking different cause and effect with the help of reasoning mechanism. This researchs major focus is on a data transformation methodology for geo‐related semantic annotation. Spatial dataset gets stored in a database and then transformed into Extensible Markup Language (XML) and Resource Description Framework (RDF). Even for bi‐directional transformation to work properly, we need to map different schema level transformations. A deep research is conducted to consider available mappings, implementations, and updates to further improving data fusion for having better compatibility. Then, transformed data as results get analyzed and discussed based on the data mapping rules formulated. It is aimed to show the importance of reducing the response time of investigation and offer compatibility between the web and semantically enriched spatial data.

Real-time RDF adaptation model for smart human-care querying in IoT based mobile applications

Majorly, nowadays, the collected raw data through mobiles is huge based on sensors embedded in devices and IoT based applications. These applications use Internet of Things (IoT) and Big Data analytics services and daily activities as routines for recording and analyzing real-time data for human-care. Nowadays, mobile is having services built on sensors to reduce human involvement in data collection. Many issues concerning security and privacy can be resolved if we use data analytics in services to represent data as Resource Description Framework (RDF). The automated transformation mechanism in relational database taken from mobile sensors and applications into semantically annotated RDF stores. This study is comprised of a methodology for refining compatibility between different data models by introducing real-time RDF context model for adopting data to smart querying in mobile applications. Smart querying capabilities come from transformation between sensors with activity services data and RDF data store for mobile applications. Whereas, case study built-up out of applications data is used to show data adaptation process for smart querying for human-care in mobile devices. Multiple queries are used to extract mobile video information smartly and efficiently. According to results shows if standard deviation gets greater than mean that tend of values is spreading over a wider range of values.

Analysis of Factors Affecting Energy Aware Routing in Wireless Sensor Network

Among constituents of communication architecture, routing is the most energy squeezing process. In this survey article, we are targeting an innovative aspect of analysis on routing in wireless sensor network (WSN) that has never been seen in the available literature before. This article can be a guiding light for new researchers to comprehend the WSN technology, energy aware routing, and the factors that affect the energy aware routing in WSN. This insight comprehension then makes the ways easy for them in designing such types of algorithms as well as evaluating the authenticity and extending the existing algorithms of this category, since algebraic and graphical modelling of these factors is also demonstrated. Various available techniques used by existing routing algorithms to handle these factors in making themselves energy aware are also given. Further, they are analyzed along with the suggested improvements for the researchers. At the end, we presented our previously published research work as an example and case study of discussed factors. A rich list of references is also cited for interested readers to explore the related given points.

Loss Based Congestion Control Module for Health Centers Deployed by Using Advanced IoT Based SDN Communication Networks

Many healthcare centers are deploying advanced Internet of Things (IoT) based on Software-Defined Networks (SDNs). Transmission Control Protocol (TCP) was developed to control the data transmission in wide range of networks and provides reliable communication by using many caching and congestion control schemes. TCP is predestined to always increase and decrease its congestion window size to make changes in traffic. Nowadays, about 50% IoT based SDN traffic is controlled by TCP CUBIC, which is the default congestion control scheme in Linux operating system. The aim of this research is to develop a new content-caching based congestion control scheme for advanced IoT enabled SDN networks to achieve better performance in healthcare infrastructure network environments. In this research, Congestion Control Module for Loss Event (CCM-LE) is proposed to enhance the performance of TCP CUBIC in advanced IoT based on SDN. Network Simulator 2 (NS-2) is used to simulate the experiments of CCM-LE and state-of-the-art schemes. Results show that the performance of CCM-LE outperforms by 19% as compared to state-of-the-art schemes.

ARFC: Advance response function of TCP CUBIC for IoT-based applications using big data: Real-time Behavioral Monitoring in IoT Applications using Big Data Analytics

The throughput of a TCP flow depends on the average size of Congestion Window (cwnd) of a congestion control mechanism being used during the communication. The size of cwnd depends upon the usage of available link bandwidth during communication. The response function is a measure of average throughput of a single TCP flow of congestion control mechanism as the level of random packet loss is varied. Nowadays, many organizations are deploying IoT based applications for the analytics of their Big Data. TCP CUBIC and TCP Compound are the default congestion control mechanisms in Linux and Microsoft Windows Operating Systems respectively. Whereas, TCP Reno which is also known as Standard TCP congestion control mechanism is act as a trademark congestion control mechanism. During communication among different IoT applications, TCP CUBIC flows use more available link bandwidth as compared to TCP Reno flows, thus, the average cwnd size of TCP CUBIC flows are greater than the TCP Reno flows. It implies that these both kinds of flows are not sharing available link fairly, which refers as low TCP friendliness of TCP CUBIC. It means that the throughput of TCP CUBIC flows is higher than the trademark congestion control mechanism, i.e., TCP Reno flows. As a result, friendliness behavior or fair share of available link bandwidth among TCP CUBIC flows and TCP Reno flows is decreased. In other words, TCP friendliness of TCP CUBIC flows is reduced. Now, it is important to decrease the average cwnd size of TCP CUBIC flows, such that available link bandwidth can be shared fairly among flows of TCP CUBIC and TCP Reno. The aim of this research is to enhance the TCP friendliness behavior of TCP CUBIC congestion control mechanism for IoT based applications using Big Data. In this paper, an Advance Response Function of TCP CUBIC (ARFC) is designed to share fairly available link bandwidth among flows of TCP CUBIC and TCP Reno. Results show that TCP friendliness behavior of TCP CUBIC is increased by using ARFC. Overall, 18.6% performance is increased by using ARFC.