Today is the era of the Internet of Things (IoT). The recent advances in hardware and information technology have accelerated the deployment of billions of interconnected, smart and adaptive devices in critical infrastructures like health, transportation, environmental control, and home automation. Transferring data over a network without requiring any kind of human-to-computer or human-to-human interaction, brings reliability and convenience to consumers, but also opens a new world of opportunity for intruders, and introduces a whole set of unique and complicated questions to the field of Digital Forensics. Although IoT data could be a rich source of evidence, forensics professionals cope with diverse problems, starting from the huge variety of IoT devices and non-standard formats, to the multi-tenant cloud infrastructure and the resulting multi-jurisdictional litigations. A further challenge is the end-to-end encryption which represents a trade-off between users’ right to privacy and the success of the forensics investigation. Due to its volatile nature, digital evidence has to be acquired and analyzed using validated tools and techniques that ensure the maintenance of the Chain of Custody. Therefore, the purpose of this paper is to identify and discuss the main issues involved in the complex process of IoT-based investigations, particularly all legal, privacy and cloud security challenges. Furthermore, this work provides an overview of the past and current theoretical models in the digital forensics science. Special attention is paid to frameworks that aim to extract data in a privacy-preserving manner or secure the evidence integrity using decentralized blockchain-based solutions. In addition, the present paper addresses the ongoing Forensics-as-a-Service (FaaS) paradigm, as well as some promising cross-cutting data reduction and forensics intelligence techniques. Finally, several other research trends and open issues are presented, with emphasis on the need for proactive Forensics Readiness strategies and generally agreed-upon standards.

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A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues

Security has become the primary concern in many telecommunications industries today as risks can have high consequences. Especially, as the core and enable technologies will be associated with 5G network, the confidential information will move at all layers in future wireless systems. Several incidents revealed that the hazard encountered by an infected wireless network, not only affects the security and privacy concerns, but also impedes the complex dynamics of the communications ecosystem. Consequently, the complexity and strength of security attacks have increased in the recent past making the detection or prevention of sabotage a global challenge. From the security and privacy perspectives, this paper presents a comprehensive detail on the core and enabling technologies, which are used to build the 5G security model; network softwarization security, PHY (Physical) layer security and 5G privacy concerns, among others. Additionally, the paper includes discussion on security monitoring and management of 5G networks. This paper also evaluates the related security measures and standards of core 5G technologies by resorting to different standardization bodies and provide a brief overview of 5G standardization security forces. Furthermore, the key projects of international significance, in line with the security concerns of 5G and beyond are also presented. Finally, a future directions and open challenges section has included to encourage future research.

/pdf/a-survey-on-security-and-privacy-of-5g-technologies-1hq5wqw2pb.pdf

A Survey on Security and Privacy of 5G Technologies: Potential Solutions, Recent Advancements, and Future Directions

This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

Weather forecasting and prediction using hybrid C5.0 machine learning algorithm

With an extensive growth in user demand for high throughput, large capacity, and low latency, the ongoing deployment of Fifth-Generation (5G) systems is continuously exposing the inherent limitations of the system, as compared with its original premises. Such limitations are encouraging researchers worldwide to focus on next-generation 6G wireless systems, which are expected to address the constraints. To meet the above demands, future radio network architecture should be effectively designed to utilize its maximum radio spectrum capacity. It must simultaneously utilize various new techniques and technologies, such as Carrier Aggregation (CA), Cognitive Radio (CR), and small cell-based Heterogeneous Networks (HetNet), high-spectrum access (mmWave), and Massive Multiple-Input-Multiple-Output (M-MIMO), to achieve the desired results. However, the concurrent operations of these techniques in current 5G cellular networks create several spectrum management issues; thus, a comprehensive overview of these emerging technologies is presented in detail in this study. Then, the problems involved in the concurrent operations of various technologies for the spectrum management of the current 5G network are highlighted. The study aims to provide a detailed review of cooperative communication among all the techniques and potential problems associated with the spectrum management that has been addressed with the possible solutions proposed by the latest researches. Future research challenges are also discussed to highlight the necessary steps that can help achieve the desired objectives for designing 6G wireless networks.

/pdf/issues-challenges-and-research-trends-in-spectrum-management-2dm57ywj43.pdf

Issues, challenges, and research trends in spectrum management: A comprehensive overview and new vision for designing 6g networks

This paper explores three machine learning models for weather prediction namely Support Vector Machine (SVM), Artificial Neural Network(ANN) and a Time Series based Recurrent Neural Network (RNN). It also discussed the steps followed to achieve results. RNN using time series along with a linear SVC and a five-layered neural network is used to predict the weather. The results of these models are analyzed and compared on the basis of Root Mean Squared Error between the predicted and actual values. For weather Forecasting, this paper uses Pandas, NumPy, Keras, Git, Matplotlib, TensorFlow, Anaconda and Google Cloud Services. It is found that Time Series based RNN does the best job of predicting the weather

Weather Forecasting Using Machine Learning Techniques

5G technology targets to address the limitations of previous cellular standards and a potential key enabler for future IoT. This paper presents a comprehensive survey of the enabling technologies for 5G IoT. While there has been a consensus in the overall requirements of 5G wireless systems (eg data rate, network capacity, delay etc.) various enabling technologies have been considered and studied to achieve these performance targets. Some of these wireless technologies like massive input massive output (MIMO), heterogeneous networks (HetNets), device-to-device (D2D) communication has already become the focus of global industry. These technologies enable the 5G system to achieve improved data rate, increased capacity, decreased latency and enhanced spectral efficiency. This paper aimed at describing them and discussing their potentials.

Comprehensive Survey of Key Technologies Enabling 5G-IoT

The software maintenance work is presently much more expensive than what it should be and takes more time to implement. It consumes a significant amount of resources in software development. Object-oriented technique has become a defacto standard in the software development community. Therefore, it is necessary to design object based software system keeping maintainability in mind. In this work data clustering technique's K-means clustering is used to identify maintainable classes at design level. We also show the significant level of goodness of clusters using chi-square test.

Maintenance activities in object oriented software systems using K-means clustering technique: A review

Software reuse has become very popular in software development. There are several beneficial aspects of object oriented systems including code reuse, reusability and reusability of testing efforts. Reusable software components are the building blocks that can make a system able to respond quickly to change. This paper presents the hypothesis that the testing effort in object-oriented software can be inherited and reused similar to that of the coding effort. Therefore the testability and maintainability of the object-oriented software can be improved by adapting a reusability approach. In this paper some new metrics namely Reusability of a Class in a System (RCS), Average Degree of Reusability (AR), Specialize class to Base class Reusable Metric (SBRM) have been proposed.

Reusability metrics and effect of reusability on testing of object oriented systems

The need for a better classification and prediction system in educational data mining field has highlighted the problems related to the student performance. The aim of this paper is to provide an improved model in comparison to the performance of the existing model by modifying the method used in the previous model. The objectives for achieving the aim includes applying the clustering techniques on the student placement dataset and comparing performance of the clustering techniques such as canopy, EM, FarthestFirst, HierarchicalClusterer and SimpleKMeans. The EM clustering technique is identified as better techniques with 92.5% accuracy. The output information of the clustering in the form of clusters is appended to the student dataset which is used as input to the Naive Bayes technique for predicting the class of student instances. This combined approach is compared to previous approach of using only Naive Bayes for prediction purpose. It resulted in increased performance of Naive Bayes from 95.99% to 98.75%. Hence, the earlier model is modified by adding stage of EM Clustering technique as well as two more required stages of “Institute conducts Mock test and Mock Interviews” and “Campus Placement”. This modified classification and prediction model has improved efficiency and may help the institute management in a better way of handling the problem of student’s placement.

Modified Classification and Prediction Model for Improving Accuracy of Student Placement Prediction

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