Samson Ooko - Academia.edu (original) (raw)

Papers by Samson Ooko

The advancements in the Internet of Things (IoT) and Machine Learning (ML) have enabled significa... more The advancements in the Internet of Things (IoT) and Machine Learning (ML) have enabled significant improvements in Predictive Maintenance (PdM) in industries, providing economic benefits by reducing equipment downtime and maintenance costs. Traditional ML approaches, however, require more computational resources and are often limited to cloud-based processing, leading to increased costs and high latencies. Tiny Machine Learning (TinyML) offers a novel solution by enabling ML models to run on low-power, resource-constrained devices at the edge, facilitating real-time, ondevice inference. This review analyzes TinyML applications in PdM, highlighting the technology's potential to transform industrial maintenance practices. We explore the differences between TinyML and standard ML, discuss the economic and operational advantages of adopting PdM, and present practical case studies where TinyML has been successfully implemented. In addition, we address the challenges facing TinyML, including hardware limitations and the need for specialized algorithms. Our findings indicate that while TinyML is a promising technology for PdM, further research is needed to overcome these challenges and fully realize its potential. This review contributes to understanding TinyML's role in industrial PdM and outlines a roadmap for future research and development in this emerging field.

Moi University, 2019

Many learning institutions are increasing deploying wirelesses networks due to its advantages. Se... more Many learning institutions are increasing deploying wirelesses networks due to its advantages. Securing wireless networks is always a challenge, there is therefore a need to come up with lasting solutions towards ensuring that these networks are secured. This study focused on finding out underlying insecurity issues with the aim of proposing the best solutions for mitigating the threats. From the review of literature relating to security of wireless networks it was evident that this problem cuts across all industries necessitating the need for the study. The objectives of the study were: To investigate security threats associated wireless networks in universities and to propose measure that can be put in place to ensure secure university networks. The study was based on the Game Theory that was pioneered by Princeton mathematician John Von Neumann. A qualitative research design was used with data being collected using interviews, observation and analysis of documentations and related researches. A review of data collected from different universities in Kenya was analyzed to find out the vulnerabilities identified and thereafter solutions to mitigating the network security problems proposed.

2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC), 2013

Mobile Cloud Computing (MCC) is a new paradigm for mHealth applications. It has the potential to ... more Mobile Cloud Computing (MCC) is a new paradigm for mHealth applications. It has the potential to leverage mobile platforms limitations, improve capacity, reliability, and accessibility of mHealth services, benefit mHealth research, and change the face of healthcare information technology. However, as with any innovation, the integration of cloud computing with mobile computing should be evaluated before its widespread adoption. The aim of this paper is to explore the main limitations of mobile platforms, and identify the opportunities and challenges of MCC. This paper discusses the concept of MCC and its current state in mHealth, and evaluates the opportunities and challenges of this computing paradigm from various aspects including contextual, technical, technological, management, security, and quality aspects.

2021 IEEE Globecom Workshops (GC Wkshps), 2021

The integration of Internet of Things (IoT) and Machine Learning (ML) is driving the emergence of... more The integration of Internet of Things (IoT) and Machine Learning (ML) is driving the emergence of TinyML, a subset of ML for which models are constrained to be executed on low resource embedded devices. Currently, most IoT applications in Africa are based on cloud-centric architecture making it difficult to enjoy ML-driven intelligence due to connectivity, energy, and cost challenges making tinyML an ideal solution to such challenges. This paper presents an introduction to TinyML and discusses its general applications, solutions to concerns on the impact of Artificial Intelligence (AI) in the achievement of the sustainable development goals (SDGs), and the Information Technology for Development (ICT4D) TinyML technology requirements. TinyML applications classifications are highlighted with specific examples from Africa. Different challenges for the adoption of tinyML in Africa have also been presented with the opportunities that can arise from such challenges being shown. This paper shows how tinyML has great potential in Africa where the use of Embedded systems and AI is still underexploited.

2021 International Conference on Computing and Communications Applications and Technologies (I3CAT)

2021 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT)

The use of Internet of Things (IoT) is fast growing and more objects need a connection to the int... more The use of Internet of Things (IoT) is fast growing and more objects need a connection to the internet to extend their capabilities. 6LoWPAN introduces the IPv6 usage to connect IEEE 802.15.4 networks providing a large address space to enable more devices to connect to the internet. Despite the advantages, there are also privacy and security issues that need to be mitigated. This review outlines the 6LoWPAN network architecture, the protocol stack, and its advantages and applications. The security and privacy issues have also been analyzed with solutions and recommendations given.

The advancements in the Internet of Things (IoT) and Machine Learning (ML) have enabled significa... more The advancements in the Internet of Things (IoT) and Machine Learning (ML) have enabled significant improvements in Predictive Maintenance (PdM) in industries, providing economic benefits by reducing equipment downtime and maintenance costs. Traditional ML approaches, however, require more computational resources and are often limited to cloud-based processing, leading to increased costs and high latencies. Tiny Machine Learning (TinyML) offers a novel solution by enabling ML models to run on low-power, resource-constrained devices at the edge, facilitating real-time, ondevice inference. This review analyzes TinyML applications in PdM, highlighting the technology's potential to transform industrial maintenance practices. We explore the differences between TinyML and standard ML, discuss the economic and operational advantages of adopting PdM, and present practical case studies where TinyML has been successfully implemented. In addition, we address the challenges facing TinyML, including hardware limitations and the need for specialized algorithms. Our findings indicate that while TinyML is a promising technology for PdM, further research is needed to overcome these challenges and fully realize its potential. This review contributes to understanding TinyML's role in industrial PdM and outlines a roadmap for future research and development in this emerging field.

Moi University, 2019

Many learning institutions are increasing deploying wirelesses networks due to its advantages. Se... more Many learning institutions are increasing deploying wirelesses networks due to its advantages. Securing wireless networks is always a challenge, there is therefore a need to come up with lasting solutions towards ensuring that these networks are secured. This study focused on finding out underlying insecurity issues with the aim of proposing the best solutions for mitigating the threats. From the review of literature relating to security of wireless networks it was evident that this problem cuts across all industries necessitating the need for the study. The objectives of the study were: To investigate security threats associated wireless networks in universities and to propose measure that can be put in place to ensure secure university networks. The study was based on the Game Theory that was pioneered by Princeton mathematician John Von Neumann. A qualitative research design was used with data being collected using interviews, observation and analysis of documentations and related researches. A review of data collected from different universities in Kenya was analyzed to find out the vulnerabilities identified and thereafter solutions to mitigating the network security problems proposed.

2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC), 2013

Mobile Cloud Computing (MCC) is a new paradigm for mHealth applications. It has the potential to ... more Mobile Cloud Computing (MCC) is a new paradigm for mHealth applications. It has the potential to leverage mobile platforms limitations, improve capacity, reliability, and accessibility of mHealth services, benefit mHealth research, and change the face of healthcare information technology. However, as with any innovation, the integration of cloud computing with mobile computing should be evaluated before its widespread adoption. The aim of this paper is to explore the main limitations of mobile platforms, and identify the opportunities and challenges of MCC. This paper discusses the concept of MCC and its current state in mHealth, and evaluates the opportunities and challenges of this computing paradigm from various aspects including contextual, technical, technological, management, security, and quality aspects.

2021 IEEE Globecom Workshops (GC Wkshps), 2021

The integration of Internet of Things (IoT) and Machine Learning (ML) is driving the emergence of... more The integration of Internet of Things (IoT) and Machine Learning (ML) is driving the emergence of TinyML, a subset of ML for which models are constrained to be executed on low resource embedded devices. Currently, most IoT applications in Africa are based on cloud-centric architecture making it difficult to enjoy ML-driven intelligence due to connectivity, energy, and cost challenges making tinyML an ideal solution to such challenges. This paper presents an introduction to TinyML and discusses its general applications, solutions to concerns on the impact of Artificial Intelligence (AI) in the achievement of the sustainable development goals (SDGs), and the Information Technology for Development (ICT4D) TinyML technology requirements. TinyML applications classifications are highlighted with specific examples from Africa. Different challenges for the adoption of tinyML in Africa have also been presented with the opportunities that can arise from such challenges being shown. This paper shows how tinyML has great potential in Africa where the use of Embedded systems and AI is still underexploited.

2021 International Conference on Computing and Communications Applications and Technologies (I3CAT)

2021 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT)

The use of Internet of Things (IoT) is fast growing and more objects need a connection to the int... more The use of Internet of Things (IoT) is fast growing and more objects need a connection to the internet to extend their capabilities. 6LoWPAN introduces the IPv6 usage to connect IEEE 802.15.4 networks providing a large address space to enable more devices to connect to the internet. Despite the advantages, there are also privacy and security issues that need to be mitigated. This review outlines the 6LoWPAN network architecture, the protocol stack, and its advantages and applications. The security and privacy issues have also been analyzed with solutions and recommendations given.