Survey of Requirement Management Techniques for Safety Critical Systems

Gillani, Maryam; Ullah, Ata; Niaz, Hafiz Adnan

doi:10.1109/macs.2018.8628389

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…Data trustworthiness becomes complicated when an infinite number of devices communicate concurrently to effect the desired data collection. Hardware hacks for safety-critical systems are even more critical to handle [148]. Lack of tamper resistance (intentional causing of malfunctions by users) in sensors and devices allows exploitation of physical access to system and vehicular products.…”

Section: Hardware Hacksmentioning

confidence: 99%

Data collection protocols for VANETs: a survey

Gillani

Niaz

Farooq

et al. 2022

Complex Intell. Syst.

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We live in the era of Intelligent Transport Systems (ITS), which is an extension of Vehicular AdHoc Networks (VANETs). In VANETs, vehicles act as nodes connected with each other and sometimes with a public station. Vehicles continuously exchange and collect information to provide innovative transportation services; for example, traffic management, navigation, autonomous driving, and the generation of alerts. However, VANETs are extremely challenging for data collection, due to their high mobility and dynamic network topologies that cause frequent link disruptions and make path discovery difficult. In this survey, various state-of-the-art data collection protocols for VANETs are discussed, based on three broad categories, i.e., delay-tolerant, best-effort, and real-time protocols. A taxonomy is designed for data collection protocols for VANETs that is essential to add precision and ease of understandability. A detailed comparative analysis among various data collection protocols is provided to highlight their functionalities and features. Protocols are evaluated based on three parametric phases. First, protocols investigation based on six necessary parameters, including delivery and drop ratio, efficiency, and recovery strategy. Second, a 4-D functional framework is designed to fit most data collection protocols for quick classification and mobility model identification, thus eradicating the need to read extensive literature. In the last, in-depth categorical mapping is performed to deep dive for better and targeted interpretation. In addition, some open research challenges for ITS and VANETs are discussed to highlight research gaps. Our work can thus be employed as a quick guide for researchers to identify the technical relevance of data collection protocols of VANETs.

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Section: Hardware Hacksmentioning

confidence: 99%

Data collection protocols for VANETs: a survey

Gillani

Niaz

Farooq

et al. 2022

Complex Intell. Syst.

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“…Requirements volatility prediction is a part of RCM, which is one of the three parts of requirements management. Gillani M. [24] analysed the activities that should be performed and the techniques and tools used in RM for safety critical systems. However, the review is for RM in a specific domain, it is not specifically for ML methods and with a short time interval.…”

Section: Related Workmentioning

confidence: 99%

A systematic mapping study on machine learning methodologies for requirements management

Wang

et al. 2022

IET Software

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Requirements management (RM) plays an important role in requirements engineering. The development of machine learning (ML) is in full swing, and many ML software management techniques had been used to improve the performance of RM methods. However, as no research study is known that exists systematically to summarise the ML methods used in RM. To fill this gap, this paper adopts the systematic mapping study to survey the state-ofthe-art ML methods for RM primary studies and were finally selected in this mapping, which was published on 36 conferences and journals. The 24 factors affecting the ML method of RM are determined, of which 9, 11 and 4 are the three parts of RM, namely requirements baseline maintenance, requirements traceability and requirements change management separately. The 18 objectives of the ML method for RM are summarised, of which 6, 7 and 5 are the three parts of RM. The eight ML methods used in RM and their time sequence are summarised. The 18 evaluation indexes for RM in the ML method are determined, and the performance of these methods on these parameters is analysed. The research direction of this paper is of great significance to the research of researchers in demand management. K E Y W O R D S machine learning, requirement management, systematic mapping study | INTRODUCTIONThe importance of requirements management (RM) in requirements engineering has now been recognised. This is an important means of ensuring that software development activities are based on the requirements benchmark, thereby ensuring the quality of the system. RM consists of requirements baseline maintenance (RBM), requirements traceability (RT) and requirements change management (RCM) [1].Machine learning (ML) is an interdisciplinary subject involving probability theory, statistics, algorithm complexity theory etc. It can be used to simulate human learning behaviour, acquire new knowledge or skills and reorganise existing knowledge structures to continuously improve their performance [2]. The ML research is in full swing and has been widely used in expert systems, natural language processing, pattern recognition, computer vision and other fields.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…In WSN, QoS aims to fulfil the requirements for users and applications. Its standards vary from different application nature and requirements [21]. Thus it is a difficult task to define the QoS standards according to different requirements.…”

Section: G Qosmentioning

confidence: 99%

Role of Machine Learning in WSN and VANETs

Gillani

Niaz

Tayyab

2021

IJECER

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Dynamic nature of Vehicular Ad-hoc Networks (VANETs) and Wireless Sensor Networks (WSN) makes them hard to deal accordingly. For such dynamicity, Machine learning (ML) approaches are considered favourable. ML can be described as the process or method of self-learning without human intervention that can assist through various tools to deal with heterogeneous data to attain maximum beneﬁts from the network. In this paper, a quick summary of primary ML concepts are discussed along with several algorithms based on ML for WSN and VANETs. Afterwards, ML based WSN and VANETs application, open issues, challenges of rapidly changing networks and various algorithms in relation to ML models and techniques are discussed. We have listed some of the ML techniques to take additional consideration of this emergent ﬁeld. A summary is given for ML techniques application with their complexities to cover on open issues to kick start further research investigation. This paper provides excellent coverage of state-of-the-art ML applications that are being used in WSN and VANETs with their comparative analysis.

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Survey of Requirement Management Techniques for Safety Critical Systems

Cited by 9 publications

References 9 publications

Data collection protocols for VANETs: a survey

Data collection protocols for VANETs: a survey

A systematic mapping study on machine learning methodologies for requirements management

Role of Machine Learning in WSN and VANETs

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