Evolving IoT honeypots
- Authors: Genov, Todor Stanislavov
- Date: 2022-10-14
- Subjects: Internet of things , Malware (Computer software) , QEMU , Honeypot , Cowrie
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/362819 , vital:65365
- Description: The Internet of Things (IoT) is the emerging world where arbitrary objects from our everyday lives gain basic computational and networking capabilities to become part of the Internet. Researchers are estimating between 25 and 35 billion devices will be part of Internet by 2022. Unlike conventional computers where one hardware platform (Intel x86) and three operating systems (Windows, Linux and OS X) dominate the market, the IoT landscape is far more heterogeneous. To meet the growth demand the number of The System-on-Chip (SoC) manufacturers has seen a corresponding exponential growth making embedded platforms based on ARM, MIPS or SH4 processors abundant. The pursuit for market share is further leading to a price war and cost-cutting ultimately resulting in cheap systems with limited hardware resources and capabilities. The frugality of IoT hardware has a domino effect. Due to resource constraints vendors are packaging devices with custom, stripped-down Linux-based firmwares optimized for performing the device’s primary function. Device management, monitoring and security features are by and far absent from IoT devices. This created an asymmetry favouring attackers and disadvantaging defenders. This research sets out to reduce the opacity and identify a viable strategy, tactics and tooling for gaining insight into the IoT threat landscape by leveraging honeypots to build and deploy an evolving world-wide Observatory, based on cloud platforms, to help with studying attacker behaviour and collecting IoT malware samples. The research produces useful tools and techniques for identifying behavioural differences between Medium-Interaction honeypots and real devices by replaying interactive attacker sessions collected from the Honeypot Network. The behavioural delta is used to evolve the Honeypot Network and improve its collection capabilities. Positive results are obtained with respect to effectiveness of the above technique. Findings by other researchers in the field are also replicated. The complete dataset and source code used for this research is made publicly available on the Open Science Framework website at https://osf.io/vkcrn/. , Thesis (MSc) -- Faculty of Science, Computer Science, 2022
- Full Text:
- Date Issued: 2022-10-14
- Authors: Genov, Todor Stanislavov
- Date: 2022-10-14
- Subjects: Internet of things , Malware (Computer software) , QEMU , Honeypot , Cowrie
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/362819 , vital:65365
- Description: The Internet of Things (IoT) is the emerging world where arbitrary objects from our everyday lives gain basic computational and networking capabilities to become part of the Internet. Researchers are estimating between 25 and 35 billion devices will be part of Internet by 2022. Unlike conventional computers where one hardware platform (Intel x86) and three operating systems (Windows, Linux and OS X) dominate the market, the IoT landscape is far more heterogeneous. To meet the growth demand the number of The System-on-Chip (SoC) manufacturers has seen a corresponding exponential growth making embedded platforms based on ARM, MIPS or SH4 processors abundant. The pursuit for market share is further leading to a price war and cost-cutting ultimately resulting in cheap systems with limited hardware resources and capabilities. The frugality of IoT hardware has a domino effect. Due to resource constraints vendors are packaging devices with custom, stripped-down Linux-based firmwares optimized for performing the device’s primary function. Device management, monitoring and security features are by and far absent from IoT devices. This created an asymmetry favouring attackers and disadvantaging defenders. This research sets out to reduce the opacity and identify a viable strategy, tactics and tooling for gaining insight into the IoT threat landscape by leveraging honeypots to build and deploy an evolving world-wide Observatory, based on cloud platforms, to help with studying attacker behaviour and collecting IoT malware samples. The research produces useful tools and techniques for identifying behavioural differences between Medium-Interaction honeypots and real devices by replaying interactive attacker sessions collected from the Honeypot Network. The behavioural delta is used to evolve the Honeypot Network and improve its collection capabilities. Positive results are obtained with respect to effectiveness of the above technique. Findings by other researchers in the field are also replicated. The complete dataset and source code used for this research is made publicly available on the Open Science Framework website at https://osf.io/vkcrn/. , Thesis (MSc) -- Faculty of Science, Computer Science, 2022
- Full Text:
- Date Issued: 2022-10-14
Peer-to-peer energy trading system using IoT and a low-computation blockchain network
- Authors: Ncube, Tyron
- Date: 2021-10-29
- Subjects: Blockchains (Databases) , Internet of things , Renewable energy sources , Smart power grids , Peer-to-peer architecture (Computer networks) , Energy trading system
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10962/192119 , vital:45197
- Description: The use of renewable energy is increasing every year as it is seen as a viable and sustain- able long-term alternative to fossil-based sources of power. Emerging technologies are being merged with existing renewable energy systems to address some of the challenges associated with renewable energy, such as reliability and limited storage facilities for the generated energy. The Internet of Things (IoT) has made it possible for consumers to make money by selling off excess energy back to the utility company through smart grids that allow bi-directional communication between the consumer and the utility company. The major drawback of this is that the utility company still plays a central role in this setup as they are the only buyer of this excess energy generated from renewable energy sources. This research intends to use blockchain technology by leveraging its decentralized architecture to enable other individuals to be able to purchase this excess energy. Blockchain technology is first explained in detail, and its main features, such as consensus mechanisms, are examined. This evaluation of blockchain technology gives rise to some design questions that are taken into consideration to create a low-energy, low-computation Ethereum-based blockchain network that is the foundation for a peer-to-peer energy trading system. The peer-to-peer energy trading system makes use of smart meters to collect data about energy usage and gives users a web-based interface where they can transact with each other. A smart contract is also designed to facilitate payments for transactions. Lastly, the system is tested by carrying out transactions and transferring energy from one node in the system to another. , Thesis (MSc) -- Faculty of Science, Computer Science, 2021
- Full Text:
- Date Issued: 2021-10-29
- Authors: Ncube, Tyron
- Date: 2021-10-29
- Subjects: Blockchains (Databases) , Internet of things , Renewable energy sources , Smart power grids , Peer-to-peer architecture (Computer networks) , Energy trading system
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10962/192119 , vital:45197
- Description: The use of renewable energy is increasing every year as it is seen as a viable and sustain- able long-term alternative to fossil-based sources of power. Emerging technologies are being merged with existing renewable energy systems to address some of the challenges associated with renewable energy, such as reliability and limited storage facilities for the generated energy. The Internet of Things (IoT) has made it possible for consumers to make money by selling off excess energy back to the utility company through smart grids that allow bi-directional communication between the consumer and the utility company. The major drawback of this is that the utility company still plays a central role in this setup as they are the only buyer of this excess energy generated from renewable energy sources. This research intends to use blockchain technology by leveraging its decentralized architecture to enable other individuals to be able to purchase this excess energy. Blockchain technology is first explained in detail, and its main features, such as consensus mechanisms, are examined. This evaluation of blockchain technology gives rise to some design questions that are taken into consideration to create a low-energy, low-computation Ethereum-based blockchain network that is the foundation for a peer-to-peer energy trading system. The peer-to-peer energy trading system makes use of smart meters to collect data about energy usage and gives users a web-based interface where they can transact with each other. A smart contract is also designed to facilitate payments for transactions. Lastly, the system is tested by carrying out transactions and transferring energy from one node in the system to another. , Thesis (MSc) -- Faculty of Science, Computer Science, 2021
- Full Text:
- Date Issued: 2021-10-29
Building the field component of a smart irrigation system: A detailed experience of a computer science graduate
- Authors: Pipile, Yamnkelani Yonela
- Date: 2021-10
- Subjects: Irrigation efficiency Computer-aided design South Africa , Irrigation projects Computer-aided design South Africa , Internet of things , Machine-to-machine communications , Smart water grids South Africa , Raspberry Pi (Computer) , Arduino (Programmable controller) , ZigBee , MQTT (MQ Telemetry Transport) , MQTT-SN , XBee
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10962/191814 , vital:45167
- Description: South Africa is a semi-arid area with an average annual rainfall of approximately 450mm, 60 per cent of which goes towards irrigation. Current irrigation systems generally apply water in a uniform manner across a field, which is both inefficient and can kill the plants. The Internet of Things (IoT), an emerging technology involving the utilization of sensors and actuators to build complex feedback systems, present an opportunity to build a smart irrigation solution. This research project illustrates the development of the field components of a water monitoring system using off the shelf and inexpensive components, exploring at the same time how easy or difficult it would be for a general Computer Science graduate to use hardware components and associated tools within the IoT area. The problem was initially broken down through a classical top-down process, in order to identify the components such as micro-computers, micro- controllers, sensors and network connections, that would be needed to build the solution. I then selected the Raspberry Pi 3, the Arduino Arduino Uno, the MH-Sensor-Series hygrometer, the MQTT messaging protocol, and the ZigBee communication protocol as implemented in the XBee S2C. Once the components were identified, the work followed a bottom-up approach: I studied the components in isolation and relative to each other, through a structured series of experiments, with each experiment addressing a specific component and examining how easy was to use the component. While each experiment allowed the author to acquire and deepen her understanding of each component, and progressively built a more sophisticated prototype, towards the complete solution. I found the vast majority of the identified components and tools to be easy to use, well documented, and most importantly, mature for consumption by our target user, until I encountered the MQTT-SN (MQTT-Sensor Network) implementation, not as mature as the rest. This resulted in us designing and implementing a light-weight, general ZigBee/MQTT gateway, named “yoGa” (Yonella's Gateway) from the author. At the end of the research, I was able to build the field components of a smart irrigation system using the selected tools, including the yoGa gateway, proving practically that a Computer Science graduate from a South African University can become productive in the emerging IoT area. , Thesis (MSc) -- Faculty of Science, Computer Science, 2021
- Full Text:
- Date Issued: 2021-10
- Authors: Pipile, Yamnkelani Yonela
- Date: 2021-10
- Subjects: Irrigation efficiency Computer-aided design South Africa , Irrigation projects Computer-aided design South Africa , Internet of things , Machine-to-machine communications , Smart water grids South Africa , Raspberry Pi (Computer) , Arduino (Programmable controller) , ZigBee , MQTT (MQ Telemetry Transport) , MQTT-SN , XBee
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10962/191814 , vital:45167
- Description: South Africa is a semi-arid area with an average annual rainfall of approximately 450mm, 60 per cent of which goes towards irrigation. Current irrigation systems generally apply water in a uniform manner across a field, which is both inefficient and can kill the plants. The Internet of Things (IoT), an emerging technology involving the utilization of sensors and actuators to build complex feedback systems, present an opportunity to build a smart irrigation solution. This research project illustrates the development of the field components of a water monitoring system using off the shelf and inexpensive components, exploring at the same time how easy or difficult it would be for a general Computer Science graduate to use hardware components and associated tools within the IoT area. The problem was initially broken down through a classical top-down process, in order to identify the components such as micro-computers, micro- controllers, sensors and network connections, that would be needed to build the solution. I then selected the Raspberry Pi 3, the Arduino Arduino Uno, the MH-Sensor-Series hygrometer, the MQTT messaging protocol, and the ZigBee communication protocol as implemented in the XBee S2C. Once the components were identified, the work followed a bottom-up approach: I studied the components in isolation and relative to each other, through a structured series of experiments, with each experiment addressing a specific component and examining how easy was to use the component. While each experiment allowed the author to acquire and deepen her understanding of each component, and progressively built a more sophisticated prototype, towards the complete solution. I found the vast majority of the identified components and tools to be easy to use, well documented, and most importantly, mature for consumption by our target user, until I encountered the MQTT-SN (MQTT-Sensor Network) implementation, not as mature as the rest. This resulted in us designing and implementing a light-weight, general ZigBee/MQTT gateway, named “yoGa” (Yonella's Gateway) from the author. At the end of the research, I was able to build the field components of a smart irrigation system using the selected tools, including the yoGa gateway, proving practically that a Computer Science graduate from a South African University can become productive in the emerging IoT area. , Thesis (MSc) -- Faculty of Science, Computer Science, 2021
- Full Text:
- Date Issued: 2021-10
A Model for Intrusion Detection in IoT using Machine Learning
- Authors: Nkala, Junior Ruddy
- Date: 2019
- Subjects: Internet of things
- Language: English
- Type: Thesis , Masters , MSc (Computer Science )
- Identifier: http://hdl.handle.net/10353/17180 , vital:40863
- Description: The Internet of Things is an open and comprehensive global network of intelligent objects that have the capacity to auto-organize, share information, data and resources. There are currently over a billion devices connected to the Internet, and this number increases by the day. While these devices make our life easier, safer and healthier, they are expanding the number of attack targets vulnerable to cyber-attacks from potential hackers and malicious software. Therefore, protecting these devices from adversaries and unauthorized access and modification is very important. The purpose of this study is to develop a secure lightweight intrusion and anomaly detection model for IoT to help detect threats in the environment. We propose the use of data mining and machine learning algorithms as a classification technique for detecting abnormal or malicious traffic transmitted between devices due to potential attacks such as DoS, Man-In-Middle and Flooding attacks at the application level. This study makes use of two robust machine learning algorithms, namely the C4.5 Decision Trees and K-means clustering to develop an anomaly detection model. MATLAB Math Simulator was used for implementation. The study conducts a series of experiments in detecting abnormal data and normal data in a dataset that contains gas concentration readings from a number of sensors deployed in an Italian city over a year. Thereafter we examined the classification performance in terms of accuracy of our proposed anomaly detection model. Results drawn from the experiments conducted indicate that the size of the training sample improves classification ability of the proposed model. Our findings noted that the choice of discretization algorithm does matter in the quest for optimal classification performance. The proposed model proved accurate in detecting anomalies in IoT, and classifying between normal and abnormal data. The proposed model has a classification accuracy of 96.51% which proved to be higher compared to other algorithms such as the Naïve Bayes. The model proved to be lightweight and efficient in-terms of being faster at training and testing as compared to Artificial Neural Networks. The conclusions drawn from this research are a perspective from a novice machine learning researcher with valuable recommendations that ensure optimal classification of normal and abnormal IoT data.
- Full Text:
- Date Issued: 2019
- Authors: Nkala, Junior Ruddy
- Date: 2019
- Subjects: Internet of things
- Language: English
- Type: Thesis , Masters , MSc (Computer Science )
- Identifier: http://hdl.handle.net/10353/17180 , vital:40863
- Description: The Internet of Things is an open and comprehensive global network of intelligent objects that have the capacity to auto-organize, share information, data and resources. There are currently over a billion devices connected to the Internet, and this number increases by the day. While these devices make our life easier, safer and healthier, they are expanding the number of attack targets vulnerable to cyber-attacks from potential hackers and malicious software. Therefore, protecting these devices from adversaries and unauthorized access and modification is very important. The purpose of this study is to develop a secure lightweight intrusion and anomaly detection model for IoT to help detect threats in the environment. We propose the use of data mining and machine learning algorithms as a classification technique for detecting abnormal or malicious traffic transmitted between devices due to potential attacks such as DoS, Man-In-Middle and Flooding attacks at the application level. This study makes use of two robust machine learning algorithms, namely the C4.5 Decision Trees and K-means clustering to develop an anomaly detection model. MATLAB Math Simulator was used for implementation. The study conducts a series of experiments in detecting abnormal data and normal data in a dataset that contains gas concentration readings from a number of sensors deployed in an Italian city over a year. Thereafter we examined the classification performance in terms of accuracy of our proposed anomaly detection model. Results drawn from the experiments conducted indicate that the size of the training sample improves classification ability of the proposed model. Our findings noted that the choice of discretization algorithm does matter in the quest for optimal classification performance. The proposed model proved accurate in detecting anomalies in IoT, and classifying between normal and abnormal data. The proposed model has a classification accuracy of 96.51% which proved to be higher compared to other algorithms such as the Naïve Bayes. The model proved to be lightweight and efficient in-terms of being faster at training and testing as compared to Artificial Neural Networks. The conclusions drawn from this research are a perspective from a novice machine learning researcher with valuable recommendations that ensure optimal classification of normal and abnormal IoT data.
- Full Text:
- Date Issued: 2019
A model for smart factories in the pharmaceutical manufacturing sector
- Authors: Mugwagwa, Basil
- Date: 2019
- Subjects: Internet of things , Manufacturing processes -- Automation Drug factories Pharmaceutical technology
- Language: English
- Type: Thesis , Masters , MBA
- Identifier: http://hdl.handle.net/10948/41897 , vital:36607
- Description: Since the turn of the century, the manufacturing industry has metamorphosed from manually driven systems to digitalisation. Product life cycles have shortened and customer demands have become more intense. Globalisation has brought about challenges that drive the need for smart manufacturing. Industry 4.0 has emerged as a response to these demands. The integration of various processes, facilities and systems throughout the value chain and digitalisation of physical systems is promoted in Industry 4.0. Due to increased competitive pressures, organisations are strategically looking at automation to deliver competitive advantage in delivering products at the right cost, quality, time and volumes to the customers. Organisations are therefore looking for manufacturing solutions that are technology driven, such as cyber-physical systems, big data, collaborative robots and the Internet of Things. This allows autonomous communication throughout the value chain between machine-to-machine and human-to-machine. The smart factory, a component of Industry 4.0, is a self-organised, modular, highly flexible and reconfigurable factory that enables the production of customised products at low cost, therefore maximising profitability. Smart manufacturing can bring about competitive advantages for an organisation. Labour concerns have been raised against automation and smart manufacturing, citing potential job losses, workforce redundancy and potential employee lay-offs. This unease, in turn, influences the employees’ attitude towards technology, which could lead either to its acceptance or refusal. The purpose of this research is to enhance the understanding of smart factories in the pharmaceutical industry by conducting a systematic analysis of the factors which influence the attitude of those involved towards a smart factory implementation. This study focuses on the perceptions among employees and management. The research is a quantitative study consisting of a literature review of the key concepts related to Industry 4.0, smart factories and technology-acceptance theories. The empirical study consisted of surveys completed by management and employees of one of the pharmaceutical manufacturers in South Africa. The questionnaire used in this research consists of questions regarding demographic data and questions regarding the perception of change and factors influencing attitudes towards the acceptance of technology, within the pharmaceutical manufacturing company. Descriptive statistics were used to summarise the data into a more condensed form, which could simplify the identification of patterns in the data. Inferential statistics were used to validate if the conclusions made from the sample data could be inferred to a larger population. Various factors influence perceptions about ease of use and usefulness, which then, in turn, influence attitudes and the intention to use technology. These factors have been examined by numerous authors in the technology acceptance literature. Recommended factors based on the statistical analysis of the questionnaire results were identified. A model, supported by Exploratory Factor Analysis, Correlations and ANOVA Testing identified the following factors as having an influence on the Attitude towards the Positive Impact of Smart Factories, within the pharmaceutical manufacturing company: Training and Development, Individual Characteristics, Trust, Organisational Culture, Resources and Costs and Job Security. The importance of each factor was identified to understand its function how to improve the implementation of smart factories. The research results indicated that the perception of management and employees is different on factors like such as Training, Individual Characteristics, Trust, Resources and Costs, Automation and Support and Parent Company in relation to technology acceptance. There was however no difference in perception between managers and employees on Security, Government Laws and Regulations, Organisational Culture, Peer Support and Organisational Support in relation to technology acceptance. The research study contributed to the identification and understanding of the factors influencing the implementation of smart factories in the pharmaceutical industry.
- Full Text:
- Date Issued: 2019
- Authors: Mugwagwa, Basil
- Date: 2019
- Subjects: Internet of things , Manufacturing processes -- Automation Drug factories Pharmaceutical technology
- Language: English
- Type: Thesis , Masters , MBA
- Identifier: http://hdl.handle.net/10948/41897 , vital:36607
- Description: Since the turn of the century, the manufacturing industry has metamorphosed from manually driven systems to digitalisation. Product life cycles have shortened and customer demands have become more intense. Globalisation has brought about challenges that drive the need for smart manufacturing. Industry 4.0 has emerged as a response to these demands. The integration of various processes, facilities and systems throughout the value chain and digitalisation of physical systems is promoted in Industry 4.0. Due to increased competitive pressures, organisations are strategically looking at automation to deliver competitive advantage in delivering products at the right cost, quality, time and volumes to the customers. Organisations are therefore looking for manufacturing solutions that are technology driven, such as cyber-physical systems, big data, collaborative robots and the Internet of Things. This allows autonomous communication throughout the value chain between machine-to-machine and human-to-machine. The smart factory, a component of Industry 4.0, is a self-organised, modular, highly flexible and reconfigurable factory that enables the production of customised products at low cost, therefore maximising profitability. Smart manufacturing can bring about competitive advantages for an organisation. Labour concerns have been raised against automation and smart manufacturing, citing potential job losses, workforce redundancy and potential employee lay-offs. This unease, in turn, influences the employees’ attitude towards technology, which could lead either to its acceptance or refusal. The purpose of this research is to enhance the understanding of smart factories in the pharmaceutical industry by conducting a systematic analysis of the factors which influence the attitude of those involved towards a smart factory implementation. This study focuses on the perceptions among employees and management. The research is a quantitative study consisting of a literature review of the key concepts related to Industry 4.0, smart factories and technology-acceptance theories. The empirical study consisted of surveys completed by management and employees of one of the pharmaceutical manufacturers in South Africa. The questionnaire used in this research consists of questions regarding demographic data and questions regarding the perception of change and factors influencing attitudes towards the acceptance of technology, within the pharmaceutical manufacturing company. Descriptive statistics were used to summarise the data into a more condensed form, which could simplify the identification of patterns in the data. Inferential statistics were used to validate if the conclusions made from the sample data could be inferred to a larger population. Various factors influence perceptions about ease of use and usefulness, which then, in turn, influence attitudes and the intention to use technology. These factors have been examined by numerous authors in the technology acceptance literature. Recommended factors based on the statistical analysis of the questionnaire results were identified. A model, supported by Exploratory Factor Analysis, Correlations and ANOVA Testing identified the following factors as having an influence on the Attitude towards the Positive Impact of Smart Factories, within the pharmaceutical manufacturing company: Training and Development, Individual Characteristics, Trust, Organisational Culture, Resources and Costs and Job Security. The importance of each factor was identified to understand its function how to improve the implementation of smart factories. The research results indicated that the perception of management and employees is different on factors like such as Training, Individual Characteristics, Trust, Resources and Costs, Automation and Support and Parent Company in relation to technology acceptance. There was however no difference in perception between managers and employees on Security, Government Laws and Regulations, Organisational Culture, Peer Support and Organisational Support in relation to technology acceptance. The research study contributed to the identification and understanding of the factors influencing the implementation of smart factories in the pharmaceutical industry.
- Full Text:
- Date Issued: 2019
South Africa’s readiness of the smart built environment towards 2035
- Authors: Holmes, Clinton Keith
- Date: 2019
- Subjects: Internet of things , City planning Technology -- Social aspects
- Language: English
- Type: Thesis , Masters , MBA
- Identifier: http://hdl.handle.net/10948/40128 , vital:35758
- Description: It is imperative that society works together with government and industry to find solutions in solving the problem of the high utilisation of natural resources in the built environment. Natural resources are not infinite, and the increasing population are compounding the problem. The high level of unemployment in South Africa could be increased dramatically if the old skills in the industry become redundant due to new technology and there are not enough skills to apply to these technologies. This study set out to investigate the extent of the readiness of South Africa for the Smart built environment towards 2035 with an aim to provide valuable information for decision making to the government, policy makers, academic and training institutions and business leaders. South Africa boasts about the achievements of the four major municipalities namely, Cape Town, Johannesburg, Tshwane and eThekwini in terms of their commitment towards the aim for net zero carbon emissions of newly built buildings by 2050. The commitment for sustainable solutions in all sectors is echoed by the Minister of Environmental affairs, as part of the Paris agreement. South Africa does not lack the ability to plan for eventualities. This is evident by the myriad of strategies and policies that can be found all over the government information sharing outlets. The South African government is failing in implementing these policies and strategies that have been around for more than a decade. A lack of execution, lack of transparency as well as a lack of accountability is a hindrance to South Africa’s general growth path. The realisation of the preferred future rest on the acceptance, by all South Africans, that technological advancement is inevitable, and that a joint and inclusive effort should be made to prepare for such a future. South Africa has the ability and appetite to change the future for the better. Two fundamental areas of improvement are to create a united South Africa where people are held accountable for their actions. The unisons should transcend across the various South African government departments but must also include industry, entrepreneurs and the public to create a future where technology is embraced, and innovation encouraged, instead of waiting for technology to dictate a specific future.
- Full Text:
- Date Issued: 2019
- Authors: Holmes, Clinton Keith
- Date: 2019
- Subjects: Internet of things , City planning Technology -- Social aspects
- Language: English
- Type: Thesis , Masters , MBA
- Identifier: http://hdl.handle.net/10948/40128 , vital:35758
- Description: It is imperative that society works together with government and industry to find solutions in solving the problem of the high utilisation of natural resources in the built environment. Natural resources are not infinite, and the increasing population are compounding the problem. The high level of unemployment in South Africa could be increased dramatically if the old skills in the industry become redundant due to new technology and there are not enough skills to apply to these technologies. This study set out to investigate the extent of the readiness of South Africa for the Smart built environment towards 2035 with an aim to provide valuable information for decision making to the government, policy makers, academic and training institutions and business leaders. South Africa boasts about the achievements of the four major municipalities namely, Cape Town, Johannesburg, Tshwane and eThekwini in terms of their commitment towards the aim for net zero carbon emissions of newly built buildings by 2050. The commitment for sustainable solutions in all sectors is echoed by the Minister of Environmental affairs, as part of the Paris agreement. South Africa does not lack the ability to plan for eventualities. This is evident by the myriad of strategies and policies that can be found all over the government information sharing outlets. The South African government is failing in implementing these policies and strategies that have been around for more than a decade. A lack of execution, lack of transparency as well as a lack of accountability is a hindrance to South Africa’s general growth path. The realisation of the preferred future rest on the acceptance, by all South Africans, that technological advancement is inevitable, and that a joint and inclusive effort should be made to prepare for such a future. South Africa has the ability and appetite to change the future for the better. Two fundamental areas of improvement are to create a united South Africa where people are held accountable for their actions. The unisons should transcend across the various South African government departments but must also include industry, entrepreneurs and the public to create a future where technology is embraced, and innovation encouraged, instead of waiting for technology to dictate a specific future.
- Full Text:
- Date Issued: 2019
A model for smart factories in the automotive sector
- Authors: Leo, Jo-Anne Ronell
- Date: 2018
- Subjects: Internet of things , Manufacturing processes -- Automation Labor supply -- Effect of technological innovations on Cloud computing
- Language: English
- Type: Thesis , Masters , MBA
- Identifier: http://hdl.handle.net/10948/22531 , vital:29997
- Description: The manufacturing industry is on the brink of transformation, with smart factories and digitalisation at the forefront. New challenges such as globalisation, cost pressure and consumer demands are imminent in the current environment. In order to overcome these challenges, the Fourth Industrial Revolution, also known as Industry 4.0 has emerged. Industry 4.0 promotes the computerisation of manufacturing facilities and emphasises an end-to-end digitalisation of physical resources and integration of processes through the entire value chain. The smart factory, a component of Industry 4.0, is a self-organised, modular, highly flexible and reconfigurable factory that enables production of customised products at low cost, therefore maximising profitability. Manufacturing processes are driven by technologies such as cyber-physical systems, big data, collaborative robots and the Internet of Things. This allows autonomous communication throughout the value chain between machine-to-machine and human-to-machine. Organisations consider automation and technology as strategic business tools which are used to increase short and long term profits and realise operating objectives. In contrast, the implementation of automation and technology in the workplace raises labour concerns, fear of layoffs and redundancies among the workforce. This unease, in turn, influences the employees’ attitude towards technology which could lead either to its acceptance or refusal thereof. The purpose of this research is to enhance the understanding of smart factories in the automotive industry by conducting a systematic analysis of the factors which influence the attitude of those involved towards a smart factory implementation. This study focuses on the perceptions among employees and management. The research is an quantitative study consisting of a literature review of the key concepts related to Industry 4.0, smart factories and technology-acceptance theories. The empirical study consisted of surveys completed by management and employees of one of the automotive Original Equipment Manufacturers (OEM), in South Africa. The questionnaire used in this research consists of questions regarding demographic data and questions regarding the perception of change and factors influencing the attitudes towards the acceptance of technology within the OEM. To summarise the data into a more condensed form which could simplify the identification of patterns in the data, descriptive statistics were used. Inferential statistics were used to validate if the conclusions made from the sample data could be inferred to a larger population. Various factors influence the perception about ease of use and usefulness, which then in turn influences the attitude and the intention to use technology. These factors have been examined by numerous authors in the technology acceptance literature. Recommended factors based on the statistical analysis of the questionnaire results were identified. A model identified the following factors as having an influence on the Attitude towards the Positive Impact of Smart Factories within the OEM: Skills and Training, Individual Characteristics, Trust, Organisational Culture, Resources and Costs and Job Security. The importance of each factor was identified to understand its function in how to improve the implementation of smart factories. This research suggested improvements for the automotive OEM based on the statistical analysis of the survey results. Inconclusive results were indicated on three variables and these should be improved, namely, Organisational Culture, Job Security and Security and International / National Standards. Two sub-groups were defined by different job levels and different perceptions were found concerning the factors that were measured. People at different job levels in the OEM perceived medium to large significant differences in all the factors comprising the proposed model of the Attitude towards a Smart Factory. The smart factory model developed in this study specified the factors which influence the Attitude towards a Smart Factory within the automotive sector and the effect these factors have on technology acceptance.
- Full Text:
- Date Issued: 2018
- Authors: Leo, Jo-Anne Ronell
- Date: 2018
- Subjects: Internet of things , Manufacturing processes -- Automation Labor supply -- Effect of technological innovations on Cloud computing
- Language: English
- Type: Thesis , Masters , MBA
- Identifier: http://hdl.handle.net/10948/22531 , vital:29997
- Description: The manufacturing industry is on the brink of transformation, with smart factories and digitalisation at the forefront. New challenges such as globalisation, cost pressure and consumer demands are imminent in the current environment. In order to overcome these challenges, the Fourth Industrial Revolution, also known as Industry 4.0 has emerged. Industry 4.0 promotes the computerisation of manufacturing facilities and emphasises an end-to-end digitalisation of physical resources and integration of processes through the entire value chain. The smart factory, a component of Industry 4.0, is a self-organised, modular, highly flexible and reconfigurable factory that enables production of customised products at low cost, therefore maximising profitability. Manufacturing processes are driven by technologies such as cyber-physical systems, big data, collaborative robots and the Internet of Things. This allows autonomous communication throughout the value chain between machine-to-machine and human-to-machine. Organisations consider automation and technology as strategic business tools which are used to increase short and long term profits and realise operating objectives. In contrast, the implementation of automation and technology in the workplace raises labour concerns, fear of layoffs and redundancies among the workforce. This unease, in turn, influences the employees’ attitude towards technology which could lead either to its acceptance or refusal thereof. The purpose of this research is to enhance the understanding of smart factories in the automotive industry by conducting a systematic analysis of the factors which influence the attitude of those involved towards a smart factory implementation. This study focuses on the perceptions among employees and management. The research is an quantitative study consisting of a literature review of the key concepts related to Industry 4.0, smart factories and technology-acceptance theories. The empirical study consisted of surveys completed by management and employees of one of the automotive Original Equipment Manufacturers (OEM), in South Africa. The questionnaire used in this research consists of questions regarding demographic data and questions regarding the perception of change and factors influencing the attitudes towards the acceptance of technology within the OEM. To summarise the data into a more condensed form which could simplify the identification of patterns in the data, descriptive statistics were used. Inferential statistics were used to validate if the conclusions made from the sample data could be inferred to a larger population. Various factors influence the perception about ease of use and usefulness, which then in turn influences the attitude and the intention to use technology. These factors have been examined by numerous authors in the technology acceptance literature. Recommended factors based on the statistical analysis of the questionnaire results were identified. A model identified the following factors as having an influence on the Attitude towards the Positive Impact of Smart Factories within the OEM: Skills and Training, Individual Characteristics, Trust, Organisational Culture, Resources and Costs and Job Security. The importance of each factor was identified to understand its function in how to improve the implementation of smart factories. This research suggested improvements for the automotive OEM based on the statistical analysis of the survey results. Inconclusive results were indicated on three variables and these should be improved, namely, Organisational Culture, Job Security and Security and International / National Standards. Two sub-groups were defined by different job levels and different perceptions were found concerning the factors that were measured. People at different job levels in the OEM perceived medium to large significant differences in all the factors comprising the proposed model of the Attitude towards a Smart Factory. The smart factory model developed in this study specified the factors which influence the Attitude towards a Smart Factory within the automotive sector and the effect these factors have on technology acceptance.
- Full Text:
- Date Issued: 2018
Dynamic service orchestration in heterogeneous internet of things environments
- Authors: Chindenga, Edmore
- Date: 2016
- Subjects: Internet of things
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10353/8001 , vital:31457
- Description: Internet of Things (IoT) presents a dynamic global revolution in the Internet where physical and virtual “things” will communicate and share information. As the number of devices increases, there is a need for a plug-and–interoperate approach of deploying “things” to the existing network with less or no human need for configuration. The plug-and-interoperate approach allows heterogeneous “things” to seamlessly interoperate, interact and exchange information and subsequently share services. Services are represented as functionalities that are offered by the “things”. Service orchestration provides an approach to integration and interoperability that decouples applications from each other, enhancing capabilities to centrally manage and monitor components. This work investigated requirements for semantic interoperability and exposed current challenges in IoT interoperability as a means of facilitating services orchestration in IoT. The research proposes a platform that allows heterogeneous devices to collaborate thereby enabling dynamic service orchestration. The platform provides a common framework for representing semantics allowing for a consistent information exchange format. The information is stored and presented in an ontology thereby preserving semantics and making the information comprehensible to machines allowing for automated addressing, tracking and discovery as well as information representation, storage, and exchange. Process mining techniques were used to discover service orchestrations. Process mining techniques enabled the analysis of runtime behavior of service orchestrations and the semantic breakdown of the service request and creation in real time. This enabled the research to draw observations that led to conclusions presented in this work. The research noted that the use of semantic technologies facilitates interoperability in heterogeneous devices and can be implemented as a means to bypass challenges presented by differences in IoT “things”.
- Full Text:
- Date Issued: 2016
- Authors: Chindenga, Edmore
- Date: 2016
- Subjects: Internet of things
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: http://hdl.handle.net/10353/8001 , vital:31457
- Description: Internet of Things (IoT) presents a dynamic global revolution in the Internet where physical and virtual “things” will communicate and share information. As the number of devices increases, there is a need for a plug-and–interoperate approach of deploying “things” to the existing network with less or no human need for configuration. The plug-and-interoperate approach allows heterogeneous “things” to seamlessly interoperate, interact and exchange information and subsequently share services. Services are represented as functionalities that are offered by the “things”. Service orchestration provides an approach to integration and interoperability that decouples applications from each other, enhancing capabilities to centrally manage and monitor components. This work investigated requirements for semantic interoperability and exposed current challenges in IoT interoperability as a means of facilitating services orchestration in IoT. The research proposes a platform that allows heterogeneous devices to collaborate thereby enabling dynamic service orchestration. The platform provides a common framework for representing semantics allowing for a consistent information exchange format. The information is stored and presented in an ontology thereby preserving semantics and making the information comprehensible to machines allowing for automated addressing, tracking and discovery as well as information representation, storage, and exchange. Process mining techniques were used to discover service orchestrations. Process mining techniques enabled the analysis of runtime behavior of service orchestrations and the semantic breakdown of the service request and creation in real time. This enabled the research to draw observations that led to conclusions presented in this work. The research noted that the use of semantic technologies facilitates interoperability in heterogeneous devices and can be implemented as a means to bypass challenges presented by differences in IoT “things”.
- Full Text:
- Date Issued: 2016
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