Performance Evaluation of LoRaWAN for Green Internet of Things

LoRa is a long-range, low power and single-hop wireless technology that has been envisioned for Internet of Things (IoT) applications having battery driven nodes. Nevertheless, increase in number of end devices and varying throughput requirements impair the performance of pure Aloha in LoRaWAN. Considering these limitations, we evaluate the performance of slotted Aloha in LoRaWAN using extensive simulations. We employed packet error rate (PER), throughput, delay, and energy consumption of devices under different payload sizes and varying number of end devices as benchmarks. Moreover, an analytical analysis of backlogged and non-backlogged under slotted Aloha LoRaWAN environment is also performed. The simulation shows promising results in terms of PER and throughput compared to the pure Aloha. However, increase in delay has been observed during experimental evaluation.Finally, we endorse slotted aloha LoRaWAN for Green IoT Environment

Measuring Learnability through Virtual Reality Laboratory Application: A User Study

The cutting-edge technology of virtual reality has changed almost every aspect of life in e-commerce, engineering, medicine, and entertainment. This technology has also made its way to the field of education in the form of virtual laboratories. A lack of student engagement and interest towards STEM subjects is reported in the literature. Several studies have been conducted to evaluate virtual reality in education, but these studies are limited in terms of participants and subject coverage. This study aimed to assess the effectiveness of virtual laboratories to develop student’s practical learning skills for secondary school physics. For this purpose, a desktop-based virtual laboratory application was developed based on the guidelines extracted from the literature. A user study was adopted as the main research method, and it was conducted with 184 students of 4 different schools. In each school, students were divided into two groups: experimental (used the virtual laboratory application) and control (used a physical laboratory). The data were collected through an academic quiz conducted at the end of the study. The mean score of the experimental group was 7.16, compared with 5.87 for the control group. The results revealed that the students’ learning using the virtual laboratory application was better compared with the control group. Interestingly, there was no significant difference in the performance of boys and girls in both groups. The usability questionnaire was also completed by 92 students of the experimental group to assess the application interface. The mean score was 73.5 (above average) with an internal consistency of 0.76. The participants found the virtual laboratory application to be user-friendly, easy to use, and supportive in learning

A survey on test suite reduction frameworks and tools

Software testing is a widely accepted practice that ensures the quality of a System under Test (SUT). However, the gradual increase of the test suite size demands high portion of testing budget and time. Test Suite Reduction (TSR) is considered a potential approach to deal with the test suite size problem. Moreover, a complete automation support is highly recommended for software testing to adequately meet the challenges of a resource constrained testing environment. Several TSR frameworks and tools have been proposed to efficiently address the test-suite size problem. The main objective of the paper is to comprehensively review the state-of-the-art TSR frameworks to highlights their strengths and weaknesses. Furthermore, the paper focuses on devising a detailed thematic taxonomy to classify existing literature that helps in understanding the underlying issues and proof of concept. Moreover, the paper investigates critical aspects and related features of TSR frameworks and tools based on a set of defined parameters. We also rigorously elaborated various testing domains and approaches followed by the extant TSR frameworks. The results reveal that majority of TSR frameworks focused on randomized unit testing, and a considerable number of frameworks lacks in supporting multi-objective optimization problems. Moreover, there is no generalized framework, effective for testing applications developed in any programming domain. Conversely, Integer Linear Programming (ILP) based TSR frameworks provide an optimal solution for multi-objective optimization problems and improve execution time by running multiple ILP in parallel. The study concludes with new insights and provides an unbiased view of the state-of-the-art TSR frameworks. Finally, we present potential research issues for further investigation to anticipate efficient TSR frameworks

Enhanced Energy Savings with Adaptive Watchful Sleep Mode for Next Generation Passive Optical Network

A single watchful sleep mode (WSM) combines the features of both cyclic sleep mode (CSM) and cyclic doze mode (CDM) in a single process by periodically turning ON and OFF the optical receiver (RX) of the optical network terminal (ONT) in a symmetric manner. This results in almost the same energy savings for the ONTs as achieved by the CSM process while significantly reducing the upstream delays. However, in this study we argue that the periodic ON and OFF periods of the ONT RX is not an energy efficient approach, as it reduces the ONT Asleep (AS) state time. Instead, this study proposes an adaptive watchful sleep mode (AWSM) in which the RX ON time of ONT is minimized during ONT Watch state by choosing it according to the length of the traffic queue of the type 1 (T1) traffic class. The performance of AWSM is compared with standard WSM and CSM schemes. The investigation reveals that by minimizing the RX ON time, the AWSM scheme achieves up to 71% average energy saving per ONT at low traffic loads. The comparative study results show that the ONT energy savings achieved by AWSM are 9% higher than the symmetric WSM with almost the same delay and delay variance performance

Prevalence of Diabetic Retinopathy and Correlation with HbA1c in Patients Admitted in Khyber Teaching Hospital Peshawar

Objective: To determine the prevalence of diabetic retinopathy in patients admitted in Khyber Teaching Hospital Peshawar and to correlate different stages of diabetic retinopathy with HbA1C levels. Methodology: This cross sectional study was conducted at Department of Ophthalmology, Khyber Teaching Hospital, MTI, Peshawar from December 2019 to May 2020. All patients over the age of 15 years who were diagnosed with diabetes mellitus were included in the study while patients with cataract or retinopathy due to other pathologies were excluded. All diabetic patients were admitted through outpatient department. In the ward their blood pressures were recorded and HbA1c levels were also measured. Visual acuity (VA) was checked. Screening for diabetic retinopathy was done by a consultant ophthalmologist by Optos Ultrawide Field Imaging of retina and Optical Coherence Tomography (OCT) of macula to establish stages of diabetic retinopathy and presence of diabetic macular edema respectively. Results: A total of 103 diabetic patients were included. Their retina was photographed, viewed and analyzed. Diabetic retinopathy, irrespective of the type, was found in 69 patients with a prevalence of 66.9%. Patients with lower ranges of HbA1c (below 6%) showed no evidence of DR. The clustering of majority of patients with diabetic retinopathy with HbA1c levels of 8 to 12 %, showed a significant relationship between high blood sugar levels and severity. Conclusion: In our study the higher frequency of retinopathy is alarming by considering it one of the leading causes of blindness in working class. It is highly recommended that routine ophthalmologic examination may be carried out along with optimal diabetic control

MalDroid: Secure DL-enabled intelligent malware detection framework

Nowadays, smartphones are provided with an abundance of capabilities. During the last decade, the availability of smartphone users and online mobile payment services and applications have substantially grown. Besides, the Android infotainment market is exponentially growing and thus potentially becoming a primary target for cyber adversaries and attackers. Likewise, varied Android vulnerability exploitation and targeted pervasive malware sophisticated attacks are also becoming a hot spot for both industry and academia. The authors present a secure by design efficient and intelligent Android detection framework against prevalent, sophisticated and persistent malware threats and attacks. A novel and highly proficient Cuda-enabled multi-class malware threat detection and identification Deep Learning (DL)-driven mechanism that leverages ConvLSTM2D and CNN has been proposed. The devised approach has been extensively evaluated on publicly available state-of-the-art datasets of Android applications (i.e. Android Malware Dataset (AMD), Androzoo). Standard and extended assessment metrics have been employed to thoroughly evaluate the proposed technique. Moreover, the performance of the proposed algorithm has been verified both with the constructed hybrid DL-driven algorithms and current benchmarks. Additionally, the proposed scheme is cross validated to explicitly show unbiased results

Secure IIoT-enabled industry 4.0

The Industrial Internet of things (IIoT) is the main driving force behind smart manufacturing, industrial automation, and industry 4.0. Conversely, industrial IoT as the evolving technological paradigm is also becoming a compelling target for cyber adversaries. Particularly, advanced persistent threats (APT) and especially botnets are the foremost promising and potential attacks that may throw the complete industrial IoT network into chaos. IIoT-enabled botnets are highly scalable, technologically diverse, and highly resilient to classical and conventional detection mechanisms. Subsequently, we propose a deep learning (DL)-enabled novel hybrid architecture that can efficiently and timely tackle distributed, multivariant, lethal botnet attacks in industrial IoT. The proposed approach is thoroughly evaluated on a current state-of-the-art, publicly available dataset using standard performance evaluation metrics. Moreover, our proposed technique has been precisely verified with our constructed hybrid DL-enabled architectures and current benchmark DL algorithms. Our devised mechanism shows promising results in terms of high detection accuracy with a trivial trade-off in speed efficiency, assuring the proposed scheme as an optimal and legitimate cyber defense in prevalent IIoTs. Besides, we have cross-validated our results to show utterly unbiased performanc

FineCodeAnalyzer: Multi-perspective source code analysis support for software developer through fine-granular level interactive code visualization

Source code analysis is one of the important activities during the software maintenance phase that focuses on performing the tasks including bug localization, feature location, bug/feature assignment, and so on. However, handling the aforementioned tasks on a manual basis (i.e. finding the location of buggy code from a large application) is an expensive, time-consuming, tedious, and challenging task. Thus, the developers seek automated support in performing the software maintenance tasks through automated tools and techniques. However, the majority of the reported techniques are limited to textual analysis where the real developers’ concerns are not properly considered. Moreover, existing solutions seem less useful for the developers. This work proposes a tool (called as FineCodeAnalyzer) that supports an interactive source code analysis grounded on structural and historical relations at fine granular-level between the source code elements. To evaluate the performance of FineCodeAnalyzer, we consider 74 developers that assess three main facets: (i) usefulness, (ii) cognitive-load, and (iii) time efficiency. For usefulness concern, the results show that FineCodeAnalyzer outperforms the developers’ self-adopted strategies in locating the code elements in terms of Precision, Recall, and F1-Measure of accurately locating the code elements. Specifically, FineCodeAnalyzer outperforms the developers’ strategies up to 47%, 76%, and 61% in terms of Precision, Recall, and F1-measure, respectively. Additionally, FineCodeAnalyzer takes 5% less time than developers’ strategies in terms of minutes of time. For cognitive-load, the developers found FineCodeAnalyzer to be 72% less complicated than manual strategies, in terms of the NASA Tool Load Index metric. Finally, the results indicate that FineCodeAnalyzer allows effectively locating the code elements than the developer’s adopted strategies

Collaborative detection of black hole and gray hole attacks for secure data communication in VANETs

Vehicle ad hoc networks (VANETs) are vital towards the success and comfort of self-driving as well as semi-automobile vehicles. Such vehicles rely heavily on data management and the exchange of Cooperative Awareness Messages (CAMs) for external communication with the environment. VANETs are vulnerable to a variety of attacks, including Black Hole, Gray Hole, wormhole, and rush attacks. These attacks are aimed at disrupting traffic between cars and on the roadside. The discovery of Black Hole attack has become an increasingly critical problem due to widespread adoption of autonomous and connected vehicles (ACVs). Due to the critical nature of ACVs, delay or failure of even a single packet can have disastrous effects, leading to accidents. In this work, we present a neural network-based technique for detection and prevention of rushed Black and Gray Hole attacks in vehicular networks. The work also studies novel systematic reactions protecting the vehicle against dangerous behavior. Experimental results show a superior detection rate of the proposed system in comparison with state-of-the-art techniques

Model to cope with Requirements Engineering Issues for Software Development Outsourcing

The anticipated benefits of Software Development Outsourcing (SDO) are not achieved in case of several projects because of the issues that emanate from Requirements Engineering (RE) process. This research work presents a Requirements Engineering Practices (REP) model to cope with the customarily occurring issues of the RE process for SDO. To formulate the model, five workshops have been conducted and Root Cause Analysis has been performed by considering 43 commonly occurring SDO RE process issues, and 147 RE practices to tackle the issues. To discover the root causes for commonly transpiring issues, 5-Whys technique has been employed. The relevant RE practices that can be used to deal with the root causes, have been endorsed by applying Brainstorming technique. For the 43 frequently occurring issues, 89 root causes have been discovered. Afterwards, 124 relevant RE practices have been recommended to eradicate the root causes and hence to address the corresponding issues. Thus, REP model postulates the root causes for commonly occurring issues of the SDO RE process, maps the root causes to the best relevant RE practices to address the corresponding issues. The model has been evaluated by an expert panel and evaluation results have been analysed through Inter-Rater Reliability analysis and Analysis of Means. The REP model supports the RE process for SDO by i). evading the adoption of random and inappropriate RE practices for dealing with the common issues of the process, ii) helping to attain the expected benefits of SDO