Peter Y. Dibal

Work place: Department of Computer Engineering, University of Maiduguri, Nigeria



Research Interests: Data Structures and Algorithms, Computer systems and computational processes, Signal Processing, Interaction Design, Algorithm Design, Analysis of Algorithms


Peter. Y. Dibal has a PhD. in Communication Engineering from the Federal University of Technology Minna. He holds an MSc. degree in Electronics and Communications Engineering from Teesside University in the UK. His research interests include advanced signal processing algorithms design and implementation using Transaction Level Modeling, and RTL design of application-specific processors using VHDL. He has been consulting in modeling and simulations using MATLAB, Embedded C, and VHDL since 2013; University of Maiduguri ICT center and Nigerian Communications Commission (NCC) are some of organizations have consulted with. He is currently a senior lecturer in the department of Computer Engineering, University of Maiduguri. He has publications with Elsevier and other reputable international journals.

Author Articles
Development of IoT Cloud-based Platform for Smart Farming in the Sub-saharan Africa with Implementation of Smart-irrigation as Test-Case

By Supreme A. Okoh Elizabeth N. Onwuka Bala A. Salihu Suleiman Zubairu Peter Y. Dibal Emmanuel Nwankwo

DOI:, Pub. Date: 8 Apr. 2023

UN Department of Economics and Social Affairs predicted that the world population will increase by 2 billion in 2050 with over 50% from the Sub-Saharan Africa (SSA). Considering the level of poverty and food insecurity in the region, there is an urgent need for a sustainable increase in agricultural produce. However, farming approach in the region is primarily traditional. Traditional farming is characterized by high labor costs, low production, and under/oversupply of farm inputs. All these factors make farming unappealing to many. The use of digital technologies such as broadband, Internet of Things (IoT), Cloud computing, and Big Data Analytics promise improved returns on agricultural investments and could make farming appealing even to the youth. However, initial cost of smart farming could be high. Therefore, development of a dedicated IoT cloud-based platform is imperative. Then farmers could subscribe and have their farms managed on the platform. It should be noted that majority of farmers in SSA are smallholders who are poor, uneducated, and live in rural areas but produce about 80% of the food. They majorly use 2G phones, which are not internet enabled. These peculiarities must be factored into the design of any functional IoT platform that would serve this group. This paper presents the development of such a platform, which was tested with smart irrigation of maize crops in a testbed. Besides the convenience provided by the smart system, it recorded irrigation water saving of over 36% compared to the control method which demonstrates how irrigation is done traditionally.

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Performance Analysis of IoT Cloud-based Platforms using Quality of Service Metrics

By Supreme Ayewoh Okoh Elizabeth N. Onwuka Suleiman Zubairu Bala Alhaji Salihu Peter Y. Dibal

DOI:, Pub. Date: 8 Feb. 2023

There are several IoT platforms providing a variety of services for different applications. Finding the optimal fit between application and platform is challenging since it is hard to evaluate the effects of minor platform changes. Several websites offer reviews based on user ratings to guide potential users in their selection. Unfortunately, review data are subjective and sometimes conflicting – indicating that they are not objective enough for a fair judgment. Scientific papers are known to be the reliable sources of authentic information based on evidence-based research. However, literature revealed that though a lot of work has been done on theoretical comparative analysis of IoT platforms based on their features, functions, architectures, security, communication protocols, analytics, scalability, etc., empirical studies based on measurable metrics such as response time, throughput, and technical efficiency, that objectively characterize user experience seem to be lacking. In an attempt to fill this gap, this study used web analytic tools to gather data on the performance of some selected IoT cloud platforms. Descriptive and inferential statistical models were used to analyze the gathered data to provide a technical ground for the performance evaluation of the selected IoT platforms. Results showed that the platforms performed differently in the key performance metrics (KPM) used. No platform emerged best in all the KPMs. Users' choice will therefore be based on metrics that are most relevant to their applications. It is believed that this work will provide companies and other users with quantitative evidence to corroborate social media data and thereby give a better insight into the performance of IoT platforms. It will also help vendors to improve on their quality of service (QoS).

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