(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 13, No. 10, 2022 Performance Evaluation of Raspberry Pi as an IoT Edge Signal Processing Device for a Real-Time Flash Flood Forecasting System Aslinda Hassan 1 , Haniza Nahar 2 , Wahidah Md Shah 3 , Azlianor Abd-Aziz 4 , Sarah Afiqah Sahiran 5 , Nazrulazhar Bahaman 6 , Mohd Riduan Ahmad 7 , Isredza Rahmi A. Hamid 8 , and Muhammad Abu Bakar Sidik 9 Fakulti Teknologi Maklumat dan Komunikasi (FTMK), Universiti Teknikal Malaysia Melaka (UTeM), Jalan Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia 1,2,3,4,5,6 Fakulti Kejuruteraan Elektronik dan Kejuruteraan Komputer (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM), Jalan Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia 7 Fakulti Sains Komputer Dan Teknologi Maklumat, Universiti Tun Hussein Onn (UTHM), 86400 Parit Raja, Batu Pahat Johor, Malaysia 8 Department of Electrical Engineering, Faculty of Engineering, Universitas Sriwijaya (UNSRI), Palembang, Indonesia 9 Abstract—The Raspberry Pi has evolved in recent years into a popular, low-cost, tiny computer for a wide range of IoT ap- plications. Raspberry Pi is not only successful for data collection but also for data processing, including data storage and analysis. Thus, this study investigates the capability of Raspberry Pi as an edge processing device for capturing lightning strike signals in predicting flash flood locations. An electric and magnetic sensor (EMS) is connected to a Raspberry Pi in the experiment setup. The Raspberry Pi is then used to process digitised lightning signals. From the experiment, Raspberry Pi’s performance is measured using the performance metrics: central processing unit (CPU) usage and temperature. The results revealed that the Raspberry Pi could handle the real-time collection and processing of lightning signals from the EMSs without affecting the hardware capability. Keywords—Raspberry Pi; IoT; edge; performance I. I NTRODUCTION The Internet of Things (IoT) is changing how we live, work, travel and do business. It is also the cornerstone of a modern industrial revolution known as Industry 4.0 and the key to the digital transformation of businesses, communities, and society. An IoT ecosystem comprises web-enabled intelligent devices that use embedded systems, such as processors, sen- sors, and communication hardware. These intelligent devices collect, transmit and act on data they acquire from their environments. The devices then share the sensor data they acquire by connecting to an IoT gateway or other edge device, where the data is either transferred to the cloud for analysis or locally analysed. According to Priceconomics.com, the number of connected devices is projected to rise from 8.7 billion in 2012 to 50 billion in 2020 [1]. Huawei predicts that 100 billion connected devices will be used in every business and living area by 2025 [2]. Consequently, the data generated by the IoT is projected to reach 4.4 zettabytes by 2020 from just 0.1 zettabytes in 2013 [1]. The value of IoT goes further than data collection and real- time monitoring. Companies can gradually see the need to upload vast amounts of data to the cloud and support flexible resource management and visualised operations. They will also strive to process their data using machine learning and predic- tive analytics in order to introduce better technologies that will bring them success. Previously, placing all computational tasks on the cloud has proved to be an efficient way to process data since the power of cloud computing outperforms the capacity of the IoT. However, over the past few years, the significant increase of data generated by smart devices has put a strain on bandwidth utilisation [3]. Furthermore, digital traffic jams are almost anticipated, with the world estimated to generate up to 4.4 zettabytes of data by 2020. There is also the ”last mile” bottleneck problem. Essentially, the last mile defines the final networking segment, which connects an organisation’s local network to the Internet. Since all network traffic destined for a particular organisation is channelled through that connection, it can be a bottleneck in networking throughput [4]. Due to the miniaturisation of processing and storage tech- nology, current IoT devices have become more potent in col- lecting, storing, and processing data. This scenario has opened opportunities for organisations to optimise their networks and relocate more processing functions closer to where data is collected at the edge of the network. Gartner defines edge computing as a “part of a distributed computing topology where information processing is located close to the edge, where things and people produce or consume that information” [5]. In essence, edge computing brings computation and data storage closer to the smart devices rather than depending on a central location that might be thousands of kilometres away. Edge computing allows the data from the IoT devices to be analysed before being sent to the data centre. The main objective of edge computing is to prevent data, especially real- time data, from suffering latency issues that can affect the performance of an application [6]. Recent years have seen the development of Raspberry Pi as a popular, low-cost, tiny computer for several IoT applications. Raspberry Pi, referred to as a Single Board Computer (SBC), can run a complete operating system and has sufficient periph- erals like memory, central processing unit (CPU), and power to initiate execution without additional hardware. In the present www.ijacsa.thesai.org 841 | Page