International Journal of Reconfigurable and Embedded Systems (IJRES)
The centre of gravity of the computer industry is now moving from personal computing into embedded computing with the advent of VLSI system level integration and reconfigurable core in system-on-chip (SoC). Reconfigurable and Embedded systems are increasingly becoming a key technological component of all kinds of complex technical systems, ranging from audio-video-equipment, telephones, vehicles, toys, aircraft, medical diagnostics, pacemakers, climate control systems, manufacturing systems, intelligent power systems, security systems, to weapons etc. The aim of IJRES is to provide a vehicle for academics, industrial professionals, educators and policy makers working in the field to contribute and disseminate innovative and important new work on reconfigurable and embedded systems. The scope of the IJRES addresses the state of the art of all aspects of reconfigurable and embedded computing systems with emphasis on algorithms, circuits, systems, models, compilers, architectures, tools, design methodologies, test and applications.
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<![CDATA[Modeling of chimp optimization algorithm node localization scheme in wireless sensor networks ]]>
<![CDATA[Arunachalam, Sripriya]]>;
<![CDATA[Vijaya Kumar, Ashok Kumar]]>;
<![CDATA[Reddy, Desidi Narsimha]]>;
<![CDATA[Pathipati, Harikrishna]]>;
<![CDATA[Priyadarsini, Nethala Indira]]>;
<![CDATA[Babu Ramisetti, Lova Naga]]>
<![CDATA[ International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijres.v14.i1.pp221-230
<![CDATA[For smart environments in the digital age, wireless sensor networks (WSNs) are needed. Node localization (NL) in WSNs is complicated for recent researchers. WSN localization focuses on finding sensor nodes (SNs) in two dimensions. WSN NL provides decision-making information in packets sent to base stations. This article describes modeling of chimp optimization algorithm node localization system in wireless sensor networks (MCOANL-WSN). The MCOANL-WSN approach uses metaheuristic optimization to locate unknown network nodes. To simulate chimpanzees' cooperative hunting behavior, the MCOANL-WSN approach includes chimp optimization algorithm (COA) into the NL process. The system uses mathematical modeling to represent node collaboration to improve placements. COA-based localization is being proposed for dynamically responding to resource-constrained and dynamic WSNs. Wide-ranging simulations may assess the MCOANL-WSN system's scalability, energy efficiency, and localization accuracy. The findings demonstrate the superiority of the new modeling method over current NL schemes in improving WSN reliability and efficiency in various applications.]]>
<![CDATA[Central processing unit load reduction through application code optimization and memory management ]]>
<![CDATA[Bhadrayya, Sowmya Kandiga]]>;
<![CDATA[Ravishankar, Vishwas Bangalore]]>
<![CDATA[ International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijres.v14.i1.pp79-88
<![CDATA[Central processing unit (CPU) loading refers to the amount of processing power a CPU uses to execute a given set of commands or perform an exact task. Higher CPU load can lead to slower, sluggish performance, reduced lifespan, and reduced system stability. Using the CPU Load trace results, the performance bottlenecks can be identified and suitable methods can be adopted to reduce the load on the CPU. For an ideal embedded system, the CPU should be in idle state for around 70% of CPU usage time. In this paper, three types of optimization techniques are implemented, which include application code optimization, memory management, and implementing interrupt-driven data transfer. Application code can be optimized by getting rid of redundant code, duplicate functions and function inlining, function cloning which reduces the size of the code with increase in reusability. By moving the data, variables to data tightly coupled memory (DTCM) and instructions, functions to instruction tightly coupled memory (ITCM), the speed of the CPU increases which reduces the load on CPU. The conventional polling method which increases the CPU load can be reduced by implementing the same in interrupt-driven data transfer. The load on the CPU has reduced from 89.53% to 29.58%.]]>
<![CDATA[FPGA implementation of artificial neural network for PUF modeling ]]>
<![CDATA[Mispan, Mohd Syafiq]]>;
<![CDATA[Ishak, Mohammad Haziq]]>;
<![CDATA[Jidin, Aiman Zakwan]]>;
<![CDATA[Mohd Nasir, Haslinah]]>
<![CDATA[ International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijres.v14.i1.pp200-207
<![CDATA[Field-programmable gate array (FPGA) is a prominent device in developing the internet of things (IoT) application since it offers parallel computation, power efficiency, and scalability. The identification and authentication of these FPGAbased IoT applications are crucial to secure the user-sensitive data transmitted over IoT networks. Physical unclonable function (PUF) technology provides a great capability to be used as device identification and authentication for FPGAbased IoT applications. Nevertheless, conventional PUF-based authentication suffers a huge overhead in storing the challenge-response pairs (CRPs) in the verifier’s database. Therefore, in this paper, the FPGA implementation of the Arbiter-PUF model using an artificial neural network (ANN) is presented. The PUF model can generate the CRPs on-the-fly upon the authentication request (i.e., by a prover) to the verifier and eliminates huge storage of CRPs database in the verifier. The architecture of ANN (i.e., Arbiter-PUF model) is designed in Xilinx system generator and subsequently converted into intellectual property (IP). Further, the IP is programmed in Xilinx Artix-7 FPGA with other peripherals for CRPs generation and validation. The findings show that the Arbiter-PUF model implementation on FPGA using the ANN technique achieves approximately 98% accuracy. The model consumes 12,196 look-up tables (LUTs) and 67 mW power in FPGA.]]>
<![CDATA[Design of agrivoltaic system with internet of things control for chili fruit classification using the neural network method ]]>
<![CDATA[Wanayumini, Wanayumini]]>;
<![CDATA[Satria, Habib]]>;
<![CDATA[Rosnelly, Rika]]>
<![CDATA[ International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijres.v14.i1.pp176-183
<![CDATA[Agriculture is a leading sector in the economy as well as the most dominant provider of employment for the Indonesian people. The fertile soil factor allows various types of fruit to be grown, including chilies. However, complex problems make chili farmers have limitations in implementing conventional farming systems. Therefore, the development of an agrivoltaic system with internet of things (IoT) integrated sensors on chili plants can help farmers more easily control, add vitamins, fertilizers, and provide plant nutrients that can be done automatically periodically based on a real-time clock schedule. This system also operates using photovoltaic (PV) as a pumping machine for water circulation. Other technologies such as mini smart cameras are also being developed to monitor and take pictures of chilies which will later be converted using the graphical user interface (GUI) application for segmentation. The method used in this chili fruit classification uses an artificial neural network in classifying ripe, raw, and rotten chilies. The classification results obtained an R value of 0.9, which means it is close to a value of 1 in the suitability of the chili image. Therefore, farmers will find it easier to sort the chilies that will be harvested.]]>
<![CDATA[Video surveillance system based on artificial vision and fog computing for the detection of lethal weapons ]]>
<![CDATA[Yauri, Ricardo]]>;
<![CDATA[Monterrey, José]]>
<![CDATA[ International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijres.v14.i1.pp191-199
<![CDATA[Citizen insecurity in underdeveloped third world countries is aggravated by poor management of arms control and illegal trafficking, which requires information technology solutions in intelligent video surveillance systems for the detection of lethal weapons. The literature review highlights the need for an intelligent video surveillance system to combat high crime, using fog computing, which processes data in real time for the detection of weapons and other crimes. Furthermore, at an international level, solutions based on artificial intelligence and deep learning are being implemented for object recognition and weapons detection. Therefore, this paper describes the design of an intelligent video surveillance system based on artificial vision, fog and edge computing to detect lethal weapons in domestic environments, performing weapon classification and data transmission to police centers. The intelligent video surveillance system allows detecting lethal weapons and operates in three stages: an edge node with a Raspberry Pi 4; a detection algorithm based on a convolutional neural network with YOLOv5; and streaming tagged images to a security unit via WhatsApp. The main conclusion is that the system achieved a precision greater than 0.85 and a quick and efficient response in sending alerts, representing a scalable and effective solution against home burglary.]]>
<![CDATA[Optimizing resource allocation in job shop production systems with seasonal demand patterns ]]>
<![CDATA[Hammedi, Salah]]>;
<![CDATA[Elmeliani, Jalloul]]>;
<![CDATA[Nabli, Lotfi]]>
<![CDATA[ International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijres.v14.i1.pp12-25
<![CDATA[Job shop production systems that encounter seasonal demand patterns in the manufacturing industry are the subject of this article's exploration of the complex challenges of resource allocation. A nuanced understanding of each product's unique production processes, resource requirements, and lead times is necessary for the inherent complexity of job shop production, which characterized by diverse product lines. Resource reallocation becomes more complicated due to seasonal demand patterns, which require manufacturers to seamlessly transition resources between products and adjust strategies dynamically throughout the year. This article explores potential optimization techniques by drawing on insights from related studies on reliability monitoring and Petri nets. Strategically managing resource allocation is highlighted due to its significant impact on a company's competitiveness, adaptability to market changes, and overall financial performance. In the paper, there is a proposed architecture for resource allocation that combines data-driven insights, workforce planning, inventory management, machine allocation, lean principles, and technology integration. Effective strategies for reallocating resources are highlighted through the presentation of case studies and best practices, which include accurate demand forecasting and flexible workforce planning. The final section of the article emphasizes the holistic approach required to navigate the complexities of seasonal demand patterns and achieve sustained competitiveness and customer satisfaction.]]>
<![CDATA[Performance comparison of indoor navigation and obstacle avoidance methods for low-cost implementation in wheelchairs ]]>
<![CDATA[Ashwathnarayan, Satish Bhogannahalli]]>;
<![CDATA[Arsa, Deekshitha]]>;
<![CDATA[Yerriyuru Narasimhaiah, Sharath Kumar]]>;
<![CDATA[Anchan, Shreyas]]>;
<![CDATA[Prasath, Giri]]>
<![CDATA[ International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijres.v14.i1.pp100-108
<![CDATA[Wheelchairs are a huge support for the movement of people who have disabilities. The wheelchairs that were traditionally moved using manual effort have given way to powered and smart wheelchairs with various controlling methods. When powered wheelchairs are used indoors, navigation and avoiding obstacles become challenging and tricky for a disabled user. To address these challenges there have been implementations of expensive and high-end systems to make the wheelchair move autonomously but as a result such a wheelchair is not economically viable for many users. Thus, there is a need for an alternative low cost method for users to be able to navigate and move in an indoor environment. The paper reviews low-cost methods for implementing indoor navigation systems, weighing their performances to validate if these methods can be used as a viable alternative to the high-cost systems for autonomous navigation in an indoor environment.]]>
<![CDATA[Integration of K-Means and Silhouette score for energy efficiency of wireless sensor networks ]]>
<![CDATA[Hilmani, Adil]]>;
<![CDATA[Sabri, Yassine]]>;
<![CDATA[Maizate, Abderrahim]]>;
<![CDATA[Aouad, Siham]]>;
<![CDATA[Koundi, Mohammed]]>
<![CDATA[ International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijres.v14.i1.pp26-34
<![CDATA[In wireless sensor networks (WSNs), optimizing energy consumption, and ensuring efficient data transmission are crucial for network longevity and performance. This paper introduces an enhanced clustering technique for WSNs that aims to extend network lifetime and ensure reliable data delivery. Instead of regular K-Means clustering, we integrate the Silhouette score method to evaluate cluster quality and decide the optimal number of clusters. This improves how nodes are grouped together in the network. Additionally, we strategically select routing paths from cluster heads to the base station that minimize energy drainage. Comprehensive simulations show our dual optimization approach outperforms standard K-Means in terms of energy efficiency, stable network organization and effective data transmission and overall, the proposed improvements to clustering and routing significantly advance energy-constrained WSNs toward more sustainable and dependable real-world applications.]]>
<![CDATA[An internet of things-driven smart key system with real-time alerts: innovations in hotel security ]]>
<![CDATA[Jaya, Putra]]>;
<![CDATA[Fikri, Ryan]]>;
<![CDATA[Samala, Agariadne Dwinggo]]>;
<![CDATA[Sanjaya, Dimas]]>
<![CDATA[ International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijres.v14.i1.pp145-156
<![CDATA[This paper presents an innovative smart key system designed to enhance the safety and convenience of hotel guests. The system employs an iterative, agile approach encompassing the phases of requirement analysis, design, implementation, and testing. Key components of the input circuitry include limit switches, RFID-RC522 and SW420 vibration sensors, which collectively gather data. This data is processed using an Arduino Uno microcontroller and integrated with internet of things (IoT) technology. On the output side, the system incorporates a solenoid lock and is capable of promptly notifying users via Telegram in response to unauthorized access attempts. Importantly, the system can distinguish between vibrations caused by unauthorized entry and those from legitimate usage. Rigorous testing validates its efficacy, issuing Telegram alerts promptly when detecting security breaches. This technological advancement significantly enhances hotel room security, providing an intelligent real-time solution. The fusion of IoT, Arduino microcontroller, and precise sensor configuration underscores the system's reliability, setting new benchmarks for security in the hospitality sector. The comprehensive approach detailed in this paper offers valuable insights applicable to a wide range of security applications.]]>
<![CDATA[Analysing feature selection: impacts towards forecasting electricity power consumption ]]>
<![CDATA[Malik, Azman Ab]]>;
<![CDATA[Tao, Lyu]]>;
<![CDATA[Allias, Noormadinah]]>;
<![CDATA[Hamzah, Irni Hamiza]]>
<![CDATA[ International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijres.v14.i1.pp265-272
<![CDATA[This study focuses on the development of electrical power forecasting based on electricity usage in Wuzhou, China. To develop a forecasting model, the important features need to be identified. Therefore, this study investigates the performance of the feature selection method, focusing on the mutual information as a filter and random forest as a wrapper-based feature selection. From the experiment, six features have been chosen, whereby both feature selection methods chose almost identical features. Later, the selected features are trained and tested with common machine learning models, namely random forest regressor, support vector regression (SVR), k-nearest neighbor (KNN) regressor, and extreme gradient boosting (XGBoost) regressor. The performances of the feature selections tested on each of the models are measured in terms of mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R²). Findings from the experiment revealed that XGBoost outperform the other machine learning models with RMSE 0.9566 and R² indicated with 0.2561. However, SVR outperformed XGBoost and other model by obtaining MAE 0.6028. It can be concluded that the performance of filter-based outperformed the embedded feature selection.]]>
