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Distance-based Indoor Localization using Empirical Path Loss Model and RSSI in Wireless Sensor Networks Dwi Joko Suroso; Muhammad Arifin; Panarat Cherntanomwong
Journal of Robotics and Control (JRC) Vol 1, No 6 (2020): November
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.1638

Abstract

Wireless sensor networks (WSNs) have a vital role in indoor localization development. As today, there are more demands in location-based service (LBS), mainly indoor environments, which put the researches on indoor localization massive attention. As the global-positioning-system (GPS) is unreliable indoor, some methods in WSNs-based indoor localization have been developed. Path loss model-based can be useful for providing the power-distance relationship the distance-based indoor localization. Received signal strength indicator (RSSI) has been commonly utilized and proven to be a reliable yet straightforward metric in the distance-based method. We face issues related to the complexity of indoor localization to be deployed in a real situation. Hence, it motivates us to propose a simple yet having acceptable accuracy results. In this research, we applied the standard distance-based methods, which are is trilateration and min-max or bounding box algorithm. We used the RSSI values as the localization parameter from the ZigBee standard. We utilized the general path loss model to estimate the traveling distance between the transmitter (TX) and receiver (RX) based on the RSSI values. We conducted measurements in a simple indoor lobby environment to validate the performance of our proposed localization system. The results show that the min-max algorithm performs better accuracy compared to the trilateration, which yields an error distance of up to 3m.  By these results, we conclude that the distance-based method using ZigBee standard working on 2.4 GHz center frequency can be reliable in the range of 1-3m. This small range is affected by the existence of interference objects (IOs) lead to signal multipath, causing the unreliability of RSSI values. These results can be the first step for building the indoor localization system, which low-cost, low-complexity, and can be applied in many fields, especially indoor robots and small devices in internet-of-things (IoT) world’s today.
A Simulation-Based Study of Maze-Solving-Robot Navigation for Educational Purposes Ismu Rijal Fahmi; Dwi Joko Suroso
Journal of Robotics and Control (JRC) Vol 3, No 1 (2022): January
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v3i1.12241

Abstract

The point of education in the early stage of studying robotics is understanding its basic principles joyfully. Therefore, this paper creates a simulation program of indoor navigations using an open-source code in Python to make navigation and control algorithms easier and more attractive to understand and develop. We propose the maze-solving-robot simulation as a teaching medium in class to help students imagine and connect the robot theory to its actual movement. The simulation code is built for free to learn, improve, and extend in robotics courses or assignments. A maze-solving robot study case is then done as an example of implementing navigation algorithms. Five algorithms are compared, such as Random Mouse, Wall Follower, Pledge, Tremaux, and Dead-End Filling. Each algorithm is simulated a hundred times in every type of the proposed mazes, namely mazes with dead ends, loops only, and both dead ends and loops. The observed indicators of the algorithms are the success rate of the robots reaching the finish lines and the number of steps taken. The simulation results show that each algorithm has different characteristics that should be considered before being chosen. The recommendation of when-to-use the algorithms is discussed in this paper as an example of the output simulation analysis for studying robotics.
Performance Comparison of Several Range-based Techniques for Indoor Localization Based on RSSI Dwi Joko Suroso; Farid Yuli Martin Adiyatma; Ahmad Eko Kurniawan; Panarat Cherntanomwong
International Journal on Information and Communication Technology (IJoICT) Vol. 7 No. 1 (2021): June 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v7i1.550

Abstract

The classical rang-based technique for position estimation is still reliably used for indoor localization. Trilateration and multilateration, which include three or more references to locate the indoor object, are two common examples. These techniques use at least three intersection-locations of the references' distance and conclude that the intersection is the object's position. However, some challenges have appeared when using a simple power-to-distance parameter, i.e., received signal strength indicator (RSSI). RSSI is known for its fluctuated values when used as the localization parameter. The improvement of classical range-based has been proposed, namely min-max and iRingLA algorithms. These algorithms or methods use the approximation in a bounding-box and rings for min-max and iRingLA, respectively. This paper discusses the comparison performance of min-max and iRingLA with multilateration as the classical method. We found that min-max gives the best performance, and in some positions, iRingLA gives the best accuracy error. Hence, the approximation method can be promising for indoor localization, especially when using a simple and straightforward RSSI parameter.
Random Forest-based Fingerprinting Technique for Device-free Indoor Localization System Dwi Joko Suroso; Refa Rupaksi; Aditya Bagus Krisnawan; Nur Abdillah Siddiq
Indonesian Journal of Computing, Engineering, and Design (IJoCED) Vol. 3 No. 2 (2021): IJoCED
Publisher : Faculty of Engineering and Technology, Sampoerna University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35806/ijoced.v3i2.172

Abstract

The device-free indoor localization (DFIL) research is gaining attention due to the popularity of location-based service (LBS)-based advertisement. In DFIL, a user or an object does not need to bring any device to be localized. In this paper, we propose the Wi-Fi-based DFIL and the random forest algorithm for the fingerprint-based technique. The simple parameter commonly used in indoor localization is the Received Signal Strength Indicator (RSSI). We apply the fingerprint technique because of its reliability to handle the RSSI fluctuation and time-varying effect in a static indoor environment. We conducted an actual measurement campaign to observe the DFIL's implementation visibility. The DFIL system works by comparing the database fingerprint in an empty open office with the database in which a person is inside the measurement area without bringing any devices. Thus, we have the device-free RSSI database for fingerprint technique from both empty rooms and RSSI affected by a person inside the room. We validated the random forest algorithm results by comparing them with the k-nearest neighbor (kNN) and artificial neural network (ANN). The results show that our proposed system's accuracy is better than kNN and ANN with a mean error of 0.63 m than kNN with 0.80 m and ANN with 1.01 m. Meanwhile, the precision of the random forest is 0.63 m, whereas kNN and ANN are 0.67 m and 0.80 m, showing that the random forest performed better. We concluded that our simple DFIL system is visible to apply with acceptable accuracy performance.
Smart Folding and Floating Shelter Design for Disaster Mitigation with Natural Ventilation and UVC System Ndaru Nuridho Alfian; Damara Kartikasari; Nur Setyo Adi Widodo; Dwi Joko Suroso
International Journal of Disaster Management Vol 4, No 3 (2021): December
Publisher : TDMRC, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1690.899 KB) | DOI: 10.24815/ijdm.v4i3.22814

Abstract

The global COVID-19 outbreak has hit the world in the last two years. Indonesia itself recorded positive cases of COVID-19 of approximately 4 million cases as of September 15, 2021. In addition, the frequency of occurrence of natural disasters in Indonesia, which is relatively high every year, requires our collective attention. In early 2021, there have been several natural disasters, including floods in South Kalimantan, earthquakes in West Sulawesi, and others. If the impact of the natural disaster makes residents must do the evacuation, a proper shelter (evacuee camp) and prioritizes health protocols are needed. Therefore, this study discusses the design innovation of disaster response shelters in the form of smart folding and floating shelters designed for a shelter with a capacity of one family (4-5 people). This capacity limitation is to maintain health protocols and suppress the transmission of the Coronavirus in evacuation areas. Our designed shelter prepared in a compact form to facilitate evacuation mobility and can be implemented in all types of disasters with a folding and floating structure system (the shelter can float and be folded). The material used is light steel as the main structure and cork wall as a material that allows the shelter to float. We designed natural ventilation to regulate air circulation, integrated with an ultraviolet C (UVC) lamp. The UVC lamp is intended as a disinfectant against the Coronavirus. Thus, the application of natural ventilation and disinfection using UVC can provide a cleaner air supply. This air supply and circulation are shown in our simulation results using ANSYS Fluent. These results show that smart folding and floating shelter designs can be used for disaster mitigation.