Performance-Oriented UWB RTLS Decision-Making Approach
Abstract
:1. Introduction
- The ability to identify and differentiate identical objects [1];
- Intended source of energy (batteries, recharging, etc.) [8];
- Availability [7];
- Robustness [22];
- Complexity [22];
- Environmental criteria [8];
- Battery properties [10];
- Privacy/control how information is collected and used [7];
- Buildings’ infrastructure impact [5] and
- Effect of random errors caused by signal interference and reflection [5].
- Definition of needs for RTLS (What RTLS will be used for?);
- Analysis of RTLS market (check if existing systems satisfy needs);
- Synthesis of previous steps (necessary for the definition of alternatives);
- Weighting criteria (group and fuzzy Analytic Hierarchy Process proposed by Budak and Ustundag [24]),
- Ranking of alternatives (TOPSIS method).
2. Materials and Methods
- Describes the topic (e.g., explanation of concepts and contains definitions);
- The experiment was performed by using UWB RTLS technology and discussing the accuracy of the results;
- The article contains novel information or was published in 2011 or later.
3. Real-Time Location Systems and Their Basic Properties
- Accuracy of less than 10 cm;
- Identification—in the case of several tags (transmitters) in the locating network, each tag has a known identity to have an informative overview when tracking locations in the application;
- Low energy consumption—operation of the system requires a small amount of energy;
- The uninterrupted connection between devices—when many devices in the room emit a signal, signal interference occurs. This disrupts communication, which can lead to distorted measurements. Among comparable technologies, UWB RTLSs prove to be the most reliable;
- Security—the system offers strong security against intrusions into the system;
- Low latency—fast data processing allows a short time between the movement of the transmitter in real space and the displayed movement in the application).
3.1. UWB RTLS Components and Operation
3.2. Positioning Methods
4. Results
4.1. Real-Time Location Systems Overview
4.2. Proposed UWB RTLS Selection Methodology
- the list of required properties that must be defined before the selection of UWB RTLS to determine its functionality;
- the list of constraints that have a decisive influence on the selection of the system even if it meets all system requirements;
- some additional features that may affect the user experience and assist in deciding when more than one system has met both requirements and constraints.
Selection Settings | Description | |
---|---|---|
Determining Required Properties | ||
(1) | The environment in which the system will be set up |
|
(2) | Properties of tracked items |
|
(3) | Type of data required/accuracy |
|
(4) | Software that will use the data captured |
|
Determining constraints | ||
(1) | Cost |
|
(2) | Compatibility |
|
Additional features | ||
(1) | Implementation time |
|
(2) | User experience |
|
4.3. Explanation of Selection Required Properties That Define the System’s Functionality (Phase 1)
4.3.1. The Environment in Which the System Will Be Set Up
4.3.2. Properties of Tracked Items
4.3.3. Type of Data Required/Accuracy
4.3.4. Software That Will Use the Data Captured
4.4. Explanation of Selection Settings That Define Constraints (Phase 1)
4.4.1. Cost
4.4.2. Compatibility
4.5. Explanation of Selection Settings That Define the Additional Features (Phase 2)
5. Discussion
5.1. The Work Environment in Which the System Will Be Set Up
5.2. Properties of Tracked Objects
5.3. Type of Data Required/Accuracy
5.4. Software That Will Use the Data Captured
5.5. Cost
5.6. Compatibility
6. Validation of Selection Methodology for RTLS UWB Variant/Provider Selection
6.1. Data Entry Form
6.2. Validation
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Cwikla, G.; Grabowik, C.; Kalinowski, K.; Paprocka, I.; Banas, W. The initial considerations and tests on the use of real time locating system in manufacturing processes improvement. IOP Conf. Ser. Mater. Sci. Eng. 2018, 400, 042013. [Google Scholar] [CrossRef]
- Kim, H.; Han, S. Accuracy Improvement of Real-Time Location Tracking for Construction Workers. Sustainability 2018, 10, 1488. [Google Scholar] [CrossRef]
- Dardari, D.; Closas, P.; Djuric, P.M. Indoor Tracking: Theory, Methods, and Technologies. IEEE Trans. Veh. Technol. 2015, 64, 1263–1278. [Google Scholar] [CrossRef]
- ISO/IEC 24730-1:2006; Information Technology—Real-Time Locating Systems (RTLS)—Part 1: Application Program Interface (API). ISO: Geneva, Switzerland, 2006. Available online: https://www.iso.org/standard/38840.html (accessed on 15 May 2022).
- Wu, H.; Marshall, A.; Yu, W. Path Planning and Following Algorithms in an Indoor Navigation Model for Visually Impaired. In Proceedings of the Second International Conference on Internet Monitoring and Protection (ICIMP 2007), San Jose, CA, USA, 1–5 July 2007; p. 38. [Google Scholar] [CrossRef]
- Boulos, M.N.K.; Berry, G. Real-time locating systems (RTLS) in healthcare: A condensed primer. Int. J. Health Geogr. 2012, 11, 25. [Google Scholar] [CrossRef] [PubMed]
- Alarifi, A.; Al-Salman, A.M.; Alsaleh, A.; Alnafessah, A.; Al-Hadhrami, S.; Al-Ammar, M.; Al-Khalifa, H. Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances. Sensors 2016, 16, 707. [Google Scholar] [CrossRef] [PubMed]
- Jachimczyk, B. Real-Time Locating Systems for Indoor Applications the Methodological Customization Approach; Blekinge Tekniska Högskol: Karlskrona, Sweden, 2019. [Google Scholar]
- Hammerin, K.; Streitenberger, R. RTLS—The Missing Link to Optimizing Logistics Management? Jönköping University, School of Engineering: Jönköping, Sweden, 2019. [Google Scholar]
- Rácz-Szabó, A.; Ruppert, T.; Bántay, L.; Löcklin, A.; Jakab, L.; Abonyi, J. Real-Time Locating System in Production Management. Sensors 2020, 20, 6766. [Google Scholar] [CrossRef]
- Tran, T.A.; Ruppert, T.; Abonyi, J. Indoor Positioning Systems Can Revolutionise Digital Lean. Appl. Sci. 2021, 11, 5291. [Google Scholar] [CrossRef]
- Zafarzadeh, M.; Hauge, J.B.; Wiktorsson, M. Real-time data gathering in production logistics: A research review on applications and technologies affecting environmental and social sustainability. In Proceedings of the 6th International EurOMA Sustainable Operations and Supply Chains Forum, Gothenburg, Sweden, 18–19 March 2019. [Google Scholar]
- Barbieri, L.; Brambilla, M.; Trabattoni, A.; Mervic, S.; Nicoli, M. UWB Localization in a Smart Factory: Augmentation Methods and Experimental Assessment. IEEE Trans. Instrum. Meas. 2021, 70, 1–18. [Google Scholar] [CrossRef]
- Caso, G.; Le, M.; De Nardis, L.; Di Benedetto, M.G. Performance Comparison of WiFi and UWB Fingerprinting Indoor Positioning Systems. Technologies 2018, 6, 14. [Google Scholar] [CrossRef] [Green Version]
- Botler, L.; Spork, M.; Diwold, K.; Romer, K. Direction Finding with UWB and BLE: A Comparative Study. In Proceedings of the 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Delhi, India, 10–13 December 2020; pp. 44–52. [Google Scholar] [CrossRef]
- Otim, T.; Díez, L.E.; Bahillo, A.; Lopez-Iturri, P.; Falcone, F. Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy. Electronics 2019, 8, 1351. [Google Scholar] [CrossRef]
- Frankó, A.; Vida, G.; Varga, P. Reliable Identification Schemes for Asset and Production Tracking in Industry 4.0. Sensors 2020, 20, 3709. [Google Scholar] [CrossRef] [PubMed]
- Gharat, V.; Colin, E.; Baudoin, G.; Richard, D. Indoor performance analysis of LF-RFID based positioning system: Comparison with UHF-RFID and UWB. In Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, Japan, 18–21 September 2017; pp. 1–8. [Google Scholar] [CrossRef]
- Adler, S.; Schmitt, S.; Wolter, K.; Kyas, M. A survey of experimental evaluation in indoor localization research. In Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Banff, AB, Canada, 13–16 October 2015; pp. 1–10. [Google Scholar] [CrossRef]
- Deak, G.; Curran, K.; Condell, J. A survey of active and passive indoor localisation systems. Comput. Commun. 2012, 35, 1939–1954. [Google Scholar] [CrossRef]
- Mazhar, F.; Khan, M.G.; Sällberg, B. Precise Indoor Positioning Using UWB: A Review of Methods, Algorithms and Implementations. Wirel. Pers. Commun. 2017, 97, 4467–4491. [Google Scholar] [CrossRef]
- Zuin, S.; Calzavara, M.; Sgarbossa, F.; Persona, A. Ultra Wide Band Indoor Positioning System: Analysis and testing of an IPS technology. IFAC-PapersOnLine 2018, 51, 1488–1492. [Google Scholar] [CrossRef]
- Gladysz, B.; Santarek, K. An approach to RTLS selection. DEStech Trans. Eng. Technol. Res. 2018, 13–18. [Google Scholar] [CrossRef]
- Budak, A.; Ustundag, A. Fuzzy decision making model for selection of real time location systems. Appl. Soft Comput. 2015, 36, 177–184. [Google Scholar] [CrossRef]
- Asosheh, A.; Khanifar, H. A technology selection method: Hospital location detection system. In Proceedings of the 5th International Symposium on Telecommunications, Tehran, Iran, 4–6 December 2010; pp. 992–999. [Google Scholar] [CrossRef]
- Zafari, F.; Papapanagiotou, I.; Christidis, K. Microlocation for Internet-of-Things-Equipped Smart Buildings. IEEE Internet Things J. 2016, 3, 96–112. [Google Scholar] [CrossRef]
- Spachos, P.; Papapanagiotou, I.; Plataniotis, K.N. Microlocation for Smart Buildings in the Era of the Internet of Things: A Survey of Technologies, Techniques, and Approaches. IEEE Signal Process. Mag. 2018, 35, 140–152. [Google Scholar] [CrossRef]
- Dahlman, G.; Omara, J. Real Time Location System for Indoor Environment; Malmö Universitet, Faculty of Technology and Society Computer Engineering: Malmö, Sweden, 2019. [Google Scholar]
- Luoh, L. ZigBee-based intelligent indoor positioning system soft computing. Soft Comput. 2014, 18, 443–456. [Google Scholar] [CrossRef]
- Biwas, I. Advantages of Using Ultra Wideband (UWB) Technology for Indoor Positioning. 2018. Available online: https://www.pathpartnertech.com/advantages-of-using-ultra-wideband-uwb-technology-for-indoor-positioning/ (accessed on 6 May 2022).
- Sewio. Product. n.d. Available online: https://www.sewio.net/ (accessed on 6 May 2022).
- Gajšek, B.; Šinko, S. RTLS potential for changes in the tooling industry business model towards smart factory. In Business Logistics in Modern Management; University of Osijek, Faculty of Economics: Osijek, Croatia, 2021; Volume 21, pp. 385–401. [Google Scholar]
- Subedi, S.; Pyun, J.-Y. Practical Fingerprinting Localization for Indoor Positioning System by Using Beacons. J. Sens. 2017, 2017, 9742170. [Google Scholar] [CrossRef]
- Grünerbl, A.; Bahle, G.; Lukowicz, P.; Hanser, F. Using Indoor Location to Assess the State of Dementia Patients: Results and Experience Report from a Long Term, Real World Study. In Proceedings of the Seventh International Conference on Intelligent Environments, Nottingham, UK, 25–28 July 2011; pp. 32–39. [Google Scholar] [CrossRef]
- Leser, R.; Schleindlhuber, A.; Lyons, K.; Baca, A. Accuracy of an UWB-based position tracking system used for time-motion analyses in game sports. Eur. J. Sport Sci. 2014, 14, 635–642. [Google Scholar] [CrossRef] [PubMed]
- Porto, S.M.C.; Arcidiacono, C.; Giummarra, A.; Anguzza, U.; Cascone, G. Localisation and identification performances of a real-time location system based on ultra wide band technology for monitoring and tracking dairy cow behaviour in a semi-open free-stall barn. Comput. Electron. Agric. 2014, 108, 221–229. [Google Scholar] [CrossRef]
- Maalek, R.; Sadeghpour, F. Accuracy assessment of ultra-wide band technology in locating dynamic resources in indoor scenarios. Autom. Constr. 2016, 63, 12–26. [Google Scholar] [CrossRef]
- Zhai, C.; Zhuo, Z.; Qin, Z.; Jia, M.; Hannu, T.; Lirong, Z.; Lida, X. A 2.4-GHz ISM RF and UWB hybrid RFID real-time locating system for industrial enterprise Internet of Things. Enterp. Inf. Syst. 2017, 11, 909–926. [Google Scholar] [CrossRef]
- Jimenez Ruiz, A.R.; Seco Granja, F. Comparing Ubisense, BeSpoon, and DecaWave UWB Location Systems: Indoor Performance Analysis. IEEE Trans. Instrum. Meas. 2017, 66, 2106–2117. [Google Scholar] [CrossRef]
- Naghdi, S.; Tjhai, C.; O’Keefe, K. Assessing a UWB RTLS as a Means for Rapid WLAN Radio Map Generation. In Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Nantes, France, 24–27 September 2018; pp. 1–5. [Google Scholar] [CrossRef]
- Musa, A.; Han, H.; Nugraha, G.D.; Choi, D.; Seo, S.; Kim, J. A Design of Indoor RTLS by Use of the UWB-WSN based Two Reference Points. In Proceedings of the 2nd International Conference on Applied Electromagnetic Technology (AEMT), Lombok, Indonesia, 9–12 April 2018; pp. 29–33. [Google Scholar] [CrossRef]
- Pudlovskiy, V.; Chugunov, A.; Kulikov, R. Investigation of Impact of UWB RTLS Errors on AGV Positioning Accuracy. In Proceedings of the International Russian Automation Conference (RusAutoCon), Sochi, Russia, 8–14 September 2019; pp. 1–5. [Google Scholar] [CrossRef]
- Hindermann, P.; Nüesch, S.; Früh, D.; Rüst, A.; Gygax, L. High precision real-time location estimates in a real-life barn environment using a commercial ultra wideband chip. Comput. Electron. Agric. 2020, 170, 105250. [Google Scholar] [CrossRef]
- Hulka, K.; Strniste, M.; Prycl, D. Accuracy and reliability of Sage Analytics tracking system based on UWB technology for indoor team sports. Int. J. Perform. Anal. Sport 2020, 20, 800–807. [Google Scholar] [CrossRef]
- Zabalegui, P.; De Miguel, G.; Goya, J.; Moya, I.; Mendizabal, J.; Adín, I. Residual based fault detection and exclusion methods applied to Ultra-Wideband navigation. Measurement 2021, 179, 109350. [Google Scholar] [CrossRef]
- Venkata Krishnaveni, B.; Suresh Reddy, K.; Ramana Reddy, P. Indoor Positioning and Tracking by Coupling IMU and UWB with the Extended Kalman Filter. IETE J. Res. 2022, 1–10. [Google Scholar] [CrossRef]
Technology | Wi-Fi | Bluetooth | Zigbee | RFID | UWB | |
---|---|---|---|---|---|---|
Passive | Active | |||||
Advantages | -Different devices could be used (any device on which Wi-Fi is enabled) -Receiving signal at different access points -No additional hardware needed -Low energy consumption | -Easy to install tags -Small size of tags -Easy to integrate into mobile devices -Precise positioning over short distances -Less energy than other | -Low energy consumption -Secure networking (data is encrypted) | -No need for energy sources -Smaller, lighter tags -Cheap | -Long read range -Strong data signal -Battery life is long | -Track large number of items at large distances -Long read ranges -Low latency (measurements can be made up to 100 times per second) -High-speed data transmission -Tags have unique identity -Low energy consumption -Security -Uninterrupted connections between devices |
Disadvantages | -More tags used simultaneously could deteriorate the quality of data transmission -Uses a large amount of bandwidth and high energy usage | -Works only on short distances | -Tag battery replacement cost -Unsecure communication | -Weaker data signal strength | -Tags need to be in range of the reader | -Tag or battery replacement cost -Signals can be blocked by large metallic objects |
Type of data | -Exact coordinates | -Exact coordinates | -Zone based localization | -Zone based localization: Provides only the simple judgment of whether item is entering a certain area or not | -Exact coordinates | |
Read range | Up to 1 km | 0–25 m | 100 m | 10 m | 100 m | 100 m |
Accuracy | 5–15 m | 10 m | 10 m | 1 m | 10 m | 10–30 cm |
Authors | System Used | Environment | Measurement Conditions | Results | |
---|---|---|---|---|---|
1 | A. Grünerbl, G. Bahle, P. Lukowicz & H. Friedrich (2011) [34] | Ubisense | Realistic settings in the nursing home | Tags hung around the dementia patients at the nursing home. They accompanied them in their daily tasks. 6 anchors were used, covering the common area and hallway—areas were divided into 10 semantic areas. | 92.6 % correct recognitions of areas (14-days period) |
2 | R. Leser, A. Schleindlhuber, K. Lyons & A. Baca (2014) [35] | Ubisense | Realistic settings during the basketball training | Players wore tags on the top of their heads or on the top of their shoulder. 6 anchors were used to cover the basketball court (height of about 5 m). | Average measurement error 14.70 m with SD 9.29 m |
3 | S.M.C. Porto, C. Arcidiacono, A. Giummarra, U. Anguzza & G. Cascone (2014) [36] | Ubisense | Realistic settings within a dairy house | Tags were fixed in the animals’ ears. 4 anchors were used, covering the selected area of the barn (height of 3.78 m). | Mean error of measurement: Metric A: 0.56 ± 0.46 m Metric B: 0.52 ± 0.36 m Metric C: 0.18 ± 0.20 m |
4 | R. Maalek & F. Sadeghpour (2015) [37] | Ubisense | Laboratory (with metallic surfaces) | 8 anchors were used. (1) Tags were attached to the top of the model train that could drive with 3 different speed levels. (2) Tags were attached to the shoulders and top of the hardhat (3) Static and dynamics tags were used to experiment the impact of tags on accuracy. Laboratory was therefore divided into different areas. | (1) Average relative error (%): 34% in 2D and 25% in 3D (2) Average accuracy for single tag 0.17 m in 2D and 0.3 m in 3D; for three tags 0.29 m in 2D and 0.47 m in 3D (3) Average accuracy: 0.149 m in 2D and 0.370 in 3D |
5 | C. Zhai, Z. Zou, Q. Zhou, J. Mao, Q. Chen, H. Tenhunen, L. Zheng & L. Xu (2017) [38] | / | Realistic settings in the office environment | 4 anchors were used, covering the office (5 × 4 m). | Average error RMSE is 0.0476 m |
6 | A. R. Jiménez Ruiz & F. Seco Granja (2017) [39] | Ubisense BeSpoon DecaWave | Industrial warehouse indoor space | 6 anchors were used for each system on 2.32 m on 2.48 m on 2.60 m Tag was attached to the mobile node. | Average errors are: 0.49 m 0.71 m 1.93 m |
7 | S. Naghdi, C. Tjhai & K. O’Keefe (2018) [40] | / | Empty room | 4 anchors were used to cover the room (4 × 11 m). The user was asked to walk with the mobile device. | Locations errors smaller than 1.5 m |
8 | A. Musa, H. Han, G. D. Nugraha, D. Choi, S. Seo & J. Kim (2018) [41] | DecaWave Evaluation Kits | Empty room | 2 anchors were used. | Average error RMSE is about 0.1279 m |
9 | G. Cwikla, C. Grabowik, K. Kalinowski, I. Paprocka & W. Banas (2018) [1] | Ubisense | Laboratory | 4 anchors were used. | Average measurement error 0.15–0.3 m |
10 | S. Zuin, M. Calzavara, F. Sgarbossa & A. Persona (2018) [22] | / | Underground garage | 6 anchors were used to cover the room (700 m2). Tags were given to the person who had to hold them in his hands. They were walking at the speed (1) 1.5 m/s and (2) 2 m/s. | For 70 % of measurements, errors were lower than 0.40 m for dynamic test Accuracy for statics test reaches 0.06 m or even 0.03 m |
11 | B. Jachimczyk (2019) [8] | / | Room equipped with basic office furniture | 6 anchors to cover the room 5.13 × 4.5 m × 2 m. | Average measurement error 0.5 m for TDOA + AOA and 0.3 m for TDOA |
12 | T. Otim, L. E. Díez, A. Bahillo, P. Lopez-Iturri & F. Falcone (2019) [16] | Decawave | Laboratory with computers, closets, and chairs | 4 anchors were used to cover the room (78 m2) at height 1.72 m. Tags were placed on a tripod and seven body parts: the forehead, right arm, right ankle, chest, right wrist, right thigh, and right hand. | The most minor error mean was 0.12 m (SD 0.06) on tripod and the most significant 2.46 m (SD 1.66) on chest |
13 | V. Pudlovskiy, A. Chugonov & R. Kulikov [42] | Decawave | Laboratory | Single anchor to seven anchors. | Systematic error of position estimation 0.115 m |
14 | P. Hindermann, S. Nüesch, D. Früh, A. Rüst & L. Gygax (2020) [43] | DecaWave | Barn | 4 anchors were used (height of 3.5 m). Tag hardware was designed to be attached to a neck collar; tags were positioned at three different heights of approximately (1) 0.5, (2) 1.5 and (3) 2.2 m above ground at each position. | Average measurement error (1) 0.29 m, (2) 0.02 m and (3) 2.13 m |
15 | K. Hulka, M. Strniste & D. Prycl (2020) [44] | Sage Analytics | Realistic settings basketball court | 6 anchors were used to cover the court (28 m in length). Tags were attached to the person who (1) walked, (2) jogged and (3) sprinted. | Measurement error between 0.34 and 1.76 m |
16 | P. Zabalegui, G. De Miguel, J. Goya, I. Moya, J. Mendizabal & I. Adín (2021) [45] | / | Outside laboratory (Automatic mock train infrastructure on the roof) | 3 anchors were used. Tag was attached to the roof of the electric train and a railway simulating the shape of an eight, but without crossing the rails. | Average measurement error 0.19 m |
17 | L. Barbieri, M. Brambilla, A. Trabattoni, S. Mervic & M. Nicoli (2021) [13] | DecaWave (1) Sewio (2) Ubisense (2) | Laboratory (1) Industry (2) | 3 and 4 anchors were used | Average measurement error 0.4 m |
18 | B. Gajšek & S. Šinko (2021) [32] | / | (1) Realistic settings in tool shop (2) Laboratory | 5 anchors were used. (1) Tag was moving around the shop chaotically. (2) Tag was placed around the laboratory on 243 pre-setup points. | 72.93% correct detections of the tags inside the defined workplaces |
19 | B. Venkata Krishnaveni, K. Suresh Reddy & P. Ramana Reddy (2022) [46] | / | Simulation | (1) 4 anchors at different heights were used (2) 4 anchors at the same height were used (3) 5 anchors at different heights were used | Average measurement error 0.26 m 0.33 m 0.64 m |
Company: | ||||
For what purposes you will use location data? | ||||
What exactly do you want to get from an RTLS system? Circle the required or add your own | -Coordinates in real-time -Insight into location/movement history -Visualization (heat map, spaghetti diagram, display of movements in the floor plan) -Rotation of the item tracked -Changes in speed -Exposure to pressure -Changes in temperature -Other: | |||
The required accuracy of the reported location of the tracked item: | [m] | |||
The need for location in 3D space: | Yes | No | ||
Anchor placement options: Circle the required or add your own | -Wall mounting -Ceiling mounting -Pillar mounting -Other: | |||
Climatic factors of the environment | Which of the following is present in the premises where you would like to implement localization? | -Dust: -Acids: -Oils: -Bases: -Steam: -Other: | ||
Minimum temperature in the room: | °C | |||
Maximum temperature in the room: | °C | |||
Humidity: | Low | Normal | High | |
Dimensions of space and layout | Number of rooms: | |||
Width of rooms: | [m] | |||
Length of rooms: | [m] | |||
Are there any metal objects in the room? If yes, which ones? | Yes | No | ||
Are there any partition walls and pillars in the room? If yes, which ones? | Yes | No | ||
Maximum working center height: | [m] | |||
Ceiling height: | [m] | |||
What items will be tracked? | ||||
How often would it be necessary to report the location of the object (consider the speed of movements)? Circle the required or add your own | Less than in a second Every second Every minute Every hour Less often than every hour Other: | |||
Will one or more objects be tracked at same time? | One | Several | ||
Number of tracked items and the corresponding number of tags: | ||||
Items’ properties in case that they are objects | Brief description of items that will be tracked (materials from which they are made): | |||
Are the items on which tag will be attached magnetic? | Yes | No | ||
Are the individual pieces packed in boxes during transport around the company or do they travel independently? | Packed | Independently | ||
Width of item (minimum and maximum in case that items are different): | [m] | |||
Length of item (minimum and maximum in case that items are different): | [m] | |||
Height of item (minimum and maximum in case that items are different): | [m] | |||
Items’ properties in case that they are people | A brief description of the activity you would like to follow (what is being done, is the activity predominantly sedentary or moving, are people wearing hats, are they wearing clothes with pockets, etc.): | |||
Where do you think the tag would bother the person you follow the least (on which part of the body): | ||||
Where do you think the tag would bother the person you follow the most (on which part of the body): | ||||
Needed data | List of software that will use the data captured with RTLS: | |||
Data type needed for additional analysis of the data captured if this is applicable: | ||||
Compatibility with existing equipment | Existing hardware in company: | |||
Existing operating systems in company: | ||||
Time adjustments | The deadline by which we would like the RTLS to be implemented: | |||
The time we can devote to training employees: | days/weeks | |||
User experiences | Learning difficulty | Easy | Medium | Hard |
Is user friendliness of software important? | Yes | No | ||
Additional features, specific for company: |
Costs constraints | The maximum expected cost for RTLS implementation: | € |
The maximum expected cost for connecting inhouse software solutions with RTLS: | € | |
Expected ROI: | Time unit | |
Maximum expected variable costs yearly (maintenance, batteries, rental of licenses, etc.): | € |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Šinko, S.; Marinič, E.; Poljanec, B.; Obrecht, M.; Gajšek, B. Performance-Oriented UWB RTLS Decision-Making Approach. Sustainability 2022, 14, 11456. https://doi.org/10.3390/su141811456
Šinko S, Marinič E, Poljanec B, Obrecht M, Gajšek B. Performance-Oriented UWB RTLS Decision-Making Approach. Sustainability. 2022; 14(18):11456. https://doi.org/10.3390/su141811456
Chicago/Turabian StyleŠinko, Simona, Enej Marinič, Blaž Poljanec, Matevž Obrecht, and Brigita Gajšek. 2022. "Performance-Oriented UWB RTLS Decision-Making Approach" Sustainability 14, no. 18: 11456. https://doi.org/10.3390/su141811456