A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

Cost-Effective Implementation of a Temperature Traceability System Based on Smart RFID Tags and IoT Services

[EN] This paper presents the design and validation of a traceability system, based on radio frequency identification (RFID) technology and Internet of Things (IoT) services, intended to address the interconnection and cost-implementation problems typical in traceability systems. The RFID layer integrates temperature sensors into RFID tags, to track and trace food conditions during transportation. The IoT paradigm makes it possible to connect multiple systems to the same platform, addressing interconnection problems between different technology providers. The cost-implementation issues are addressed following the Data as a Service (DaaS) billing scheme, where users pay for the data they consume and not the installed equipment, avoiding the big initial investment that these high-tech solutions commonly require. The developed system is validated in two case scenarios, one carried out in controlled laboratory conditions, monitoring chopped pumpkin. Another case, carried out in a real scenario, monitors oranges sent from Valencia, Spain to Cork, Ireland.Urbano, O.; Perles, A.; Pedraza, C.; Rubio-Arraez, S.; Castelló Gómez, ML.; Ortolá Ortolá, MD.; Mercado Romero, R. (2020). Cost-Effective Implementation of a Temperature Traceability System Based on Smart RFID Tags and IoT Services. Sensors. 20(4):1-19. https://doi.org/10.3390/s20041163119204Aung, M. M., & Chang, Y. S. (2014). Traceability in a food supply chain: Safety and quality perspectives. Food Control, 39, 172-184. doi:10.1016/j.foodcont.2013.11.007Bosona, T., & Gebresenbet, G. (2013). Food traceability as an integral part of logistics management in food and agricultural supply chain. Food Control, 33(1), 32-48. doi:10.1016/j.foodcont.2013.02.004Bechini, A., Cimino, M. G. C. A., Marcelloni, F., & Tomasi, A. (2008). Patterns and technologies for enabling supply chain traceability through collaborative e-business. Information and Software Technology, 50(4), 342-359. doi:10.1016/j.infsof.2007.02.017Badia-Melis, R., Mishra, P., & Ruiz-García, L. (2015). Food traceability: New trends and recent advances. A review. Food Control, 57, 393-401. doi:10.1016/j.foodcont.2015.05.005Timestrip Visual Indicators of Time and Temperaturehttps://timestrip.com/Storøy, J., Thakur, M., & Olsen, P. (2013). The TraceFood Framework – Principles and guidelines for implementing traceability in food value chains. Journal of Food Engineering, 115(1), 41-48. doi:10.1016/j.jfoodeng.2012.09.018Pizzuti, T., Mirabelli, G., Sanz-Bobi, M. A., & Goméz-Gonzaléz, F. (2014). Food Track & Trace ontology for helping the food traceability control. Journal of Food Engineering, 120, 17-30. doi:10.1016/j.jfoodeng.2013.07.017Landt, J. (2005). The history of RFID. IEEE Potentials, 24(4), 8-11. doi:10.1109/mp.2005.1549751Costa, C., Antonucci, F., Pallottino, F., Aguzzi, J., Sarriá, D., & Menesatti, P. (2012). A Review on Agri-food Supply Chain Traceability by Means of RFID Technology. Food and Bioprocess Technology, 6(2), 353-366. doi:10.1007/s11947-012-0958-7Mainetti, L., Mele, F., Patrono, L., Simone, F., Stefanizzi, M. L., & Vergallo, R. (2013). An RFID-Based Tracing and Tracking System for the Fresh Vegetables Supply Chain. International Journal of Antennas and Propagation, 2013, 1-15. doi:10.1155/2013/531364Figorilli, S., Antonucci, F., Costa, C., Pallottino, F., Raso, L., Castiglione, M., … Menesatti, P. (2018). A Blockchain Implementation Prototype for the Electronic Open Source Traceability of Wood along the Whole Supply Chain. Sensors, 18(9), 3133. doi:10.3390/s18093133Aguzzi, J., Sbragaglia, V., Sarriá, D., García, J. A., Costa, C., Río, J. del, … Sardà, F. (2011). A New Laboratory Radio Frequency Identification (RFID) System for Behavioural Tracking of Marine Organisms. Sensors, 11(10), 9532-9548. doi:10.3390/s111009532Donelli, M. (2018). An RFID-Based Sensor for Masonry Crack Monitoring. Sensors, 18(12), 4485. doi:10.3390/s18124485De Souza, P., Marendy, P., Barbosa, K., Budi, S., Hirsch, P., Nikolic, N., … Davie, A. (2018). Low-Cost Electronic Tagging System for Bee Monitoring. Sensors, 18(7), 2124. doi:10.3390/s18072124Corchia, L., Monti, G., & Tarricone, L. (2019). A Frequency Signature RFID Chipless Tag for Wearable Applications. Sensors, 19(3), 494. doi:10.3390/s19030494Zuffanelli, S., Aguila, P., Zamora, G., Paredes, F., Martin, F., & Bonache, J. (2016). A High-Gain Passive UHF-RFID Tag with Increased Read Range. Sensors, 16(7), 1150. doi:10.3390/s16071150Monteleone, S., Sampaio, M., & Maia, R. F. (2017). A novel deployment of smart Cold Chain system using 2G-RFID-Sys temperature monitoring in medicine Cold Chain based on Internet of Things. 2017 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI). doi:10.1109/soli.2017.8120995Zou, Z., Chen, Q., Uysal, I., & Zheng, L. (2014). Radio frequency identification enabled wireless sensing for intelligent food logistics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 372(2017), 20130313. doi:10.1098/rsta.2013.0313Azzarelli, J. M., Mirica, K. A., Ravnsbæk, J. B., & Swager, T. M. (2014). Wireless gas detection with a smartphone via rf communication. Proceedings of the National Academy of Sciences, 111(51), 18162-18166. doi:10.1073/pnas.1415403111Pies, M., Hajovsky, R., & Ozana, S. (2014). Wireless measurement of carbon monoxide concentration. 2014 14th International Conference on Control, Automation and Systems (ICCAS 2014). doi:10.1109/iccas.2014.6987843Azzara, A., Bocchino, S., Pagano, P., Pellerano, G., & Petracca, M. (2013). Middleware solutions in WSN: The IoT oriented approach in the ICSI project. 2013 21st International Conference on Software, Telecommunications and Computer Networks - (SoftCOM 2013). doi:10.1109/softcom.2013.6671886Ribeiro, A. R. L., Silva, F. C. S., Freitas, L. C., Costa, J. C., & Francês, C. R. (2005). SensorBus. Proceedings of the 3rd international IFIP/ACM Latin American conference on Networking - LANC ’05. doi:10.1145/1168117.1168119Sulc, V., Kuchta, R., & Vrba, R. (2010). IQRF Smart House - A Case Study. 2010 Third International Conference on Advances in Mesh Networks. doi:10.1109/mesh.2010.17Porkodi, R., & Bhuvaneswari, V. (2014). The Internet of Things (IoT) Applications and Communication Enabling Technology Standards: An Overview. 2014 International Conference on Intelligent Computing Applications. doi:10.1109/icica.2014.73EPC Radio-Frequency Identity Protocols. Generation-2 UHF RFIDhttps://www.gs1.org/sites/default/files/docs/epc/uhfc1g2_2_0_0_standard_20131101.pdfUusitalo, M. (2006). Global Vision for the Future Wireless World from the WWRF. IEEE Vehicular Technology Magazine>, 1(2), 4-8. doi:10.1109/mvt.2006.283570Sung, J., Lopez, T. S., & Kim, D. (2007). The EPC Sensor Network for RFID and WSN Integration Infrastructure. Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PerComW’07). doi:10.1109/percomw.2007.113Chunxiao Fan, Zhigang Wen, Fan Wang, & Yuexin Wu. (2011). A middleware of Internet of Things (IoT) based on ZigBee and RFID. IET International Conference on Communication Technology and Application (ICCTA 2011). doi:10.1049/cp.2011.0765Centenaro, M., Vangelista, L., Zanella, A., & Zorzi, M. (2016). Long-range communications in unlicensed bands: the rising stars in the IoT and smart city scenarios. IEEE Wireless Communications, 23(5), 60-67. doi:10.1109/mwc.2016.7721743Hai Liu, Bolic, M., Nayak, A., & Stojmenovic, I. (2008). Taxonomy and Challenges of the Integration of RFID and Wireless Sensor Networks. IEEE Network, 22(6), 26-35. doi:10.1109/mnet.2008.4694171Bertolini, M., Bevilacqua, M., & Massini, R. (2006). FMECA approach to product traceability in the food industry. Food Control, 17(2), 137-145. doi:10.1016/j.foodcont.2004.09.013Zhang, M., & Li, P. (2012). RFID Application Strategy in Agri-Food Supply Chain Based on Safety and Benefit Analysis. Physics Procedia, 25, 636-642. doi:10.1016/j.phpro.2012.03.137Engels, D. W., Kang, Y. S., & Wang, J. (2013). On security with the new Gen2 RFID security framework. 2013 IEEE International Conference on RFID (RFID). doi:10.1109/rfid.2013.6548148SINIEV: Un Centro Inteligente De Control De Tránsito Y Transporte Que Beneficiaría A Todo El Paíshttps://revistadelogistica.com/actualidad/siniev-un-centro-inteligente-de-control-de-transito-y-transporte-que-beneficiara-a-todo-el-pais/Tentzeris, M. M., Kim, S., Traille, A., Aubert, H., Yoshihiro, K., Georgiadis, A., & Collado, A. (2013). Inkjet-printed RFID-enabled sensors on paper for IoT and “Smart Skin” applications. ICECom 2013. doi:10.1109/icecom.2013.6684749Vega, F., Pantoja, J., Morales, S., Urbano, O., Arevalo, A., Muskus, E., … Hernandez, N. (2016). An IoT-based open platform for monitoring non-ionizing radiation levels in Colombia. 2016 IEEE Colombian Conference on Communications and Computing (COLCOM). doi:10.1109/colcomcon.2016.7516379Yang, K., & Jia, X. (2011). Data storage auditing service in cloud computing: challenges, methods and opportunities. World Wide Web, 15(4), 409-428. doi:10.1007/s11280-011-0138-0Alfian, G., Rhee, J., Ahn, H., Lee, J., Farooq, U., Ijaz, M. F., & Syaekhoni, M. A. (2017). Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system. Journal of Food Engineering, 212, 65-75. doi:10.1016/j.jfoodeng.2017.05.008Chen, R.-Y. (2015). Autonomous tracing system for backward design in food supply chain. Food Control, 51, 70-84. doi:10.1016/j.foodcont.2014.11.004Song, J., Wei, Q., Wang, X., Li, D., Liu, C., Zhang, M., & Meng, L. (2018). Degradation of carotenoids in dehydrated pumpkins as affected by different storage conditions. Food Research International, 107, 130-136. doi:10.1016/j.foodres.2018.02.024Montesano, D., Rocchetti, G., Putnik, P., & Lucini, L. (2018). Bioactive profile of pumpkin: an overview on terpenoids and their health-promoting properties. Current Opinion in Food Science, 22, 81-87. doi:10.1016/j.cofs.2018.02.003Rubio-Arraez, S., Capella, J. V., Castelló, M. L., & Ortolá, M. D. (2016). Physicochemical characteristics of citrus jelly with non cariogenic and functional sweeteners. Journal of Food Science and Technology, 53(10), 3642-3650. doi:10.1007/s13197-016-2319-4Carmona, L., Alquézar, B., Marques, V. V., & Peña, L. (2017). Anthocyanin biosynthesis and accumulation in blood oranges during postharvest storage at different low temperatures. Food Chemistry, 237, 7-14. doi:10.1016/j.foodchem.2017.05.07

Towards an Autonomous Industry 4.0 Warehouse: A UAV and Blockchain-Based System for Inventory and Traceability Applications in Big Data-Driven Supply Chain Management

[Abstract] Industry 4.0 has paved the way for a world where smart factories will automate and upgrade many processes through the use of some of the latest emerging technologies. One of such technologies is Unmanned Aerial Vehicles (UAVs), which have evolved a great deal in the last years in terms of technology (e.g., control units, sensors, UAV frames) and have significantly reduced their cost. UAVs can help industry in automatable and tedious tasks, like the ones performed on a regular basis for determining the inventory and for preserving item traceability. In such tasks, especially when it comes from untrusted third parties, it is essential to determine whether the collected information is valid or true. Likewise, ensuring data trustworthiness is a key issue in order to leverage Big Data analytics to supply chain efficiency and effectiveness. In such a case, blockchain, another Industry 4.0 technology that has become very popular in other fields like finance, has the potential to provide a higher level of transparency, security, trust and efficiency in the supply chain and enable the use of smart contracts. Thus, in this paper, we present the design and evaluation of a UAV-based system aimed at automating inventory tasks and keeping the traceability of industrial items attached to Radio-Frequency IDentification (RFID) tags. To confront current shortcomings, such a system is developed under a versatile, modular and scalable architecture aimed to reinforce cyber security and decentralization while fostering external audits and big data analytics. Therefore, the system uses a blockchain and a distributed ledger to store certain inventory data collected by UAVs, validate them, ensure their trustworthiness and make them available to the interested parties. In order to show the performance of the proposed system, different tests were performed in a real industrial warehouse, concluding that the system is able to obtain the inventory data really fast in comparison to traditional manual tasks, while being also able to estimate the position of the items when hovering over them thanks to their tag’s signal strength. In addition, the performance of the proposed blockchain-based architecture was evaluated in different scenarios.Xunta de Galicia; ED431C 2016-045Xunta de Galicia; ED431G/01Agencia Estatal de Investigación de España; TEC2016-75067-C4-1-

Global traceability

The use of Ultra High Frequency (UHF) Radio Frequency Identification (RFID) in supply chain management (SCM) systems was a big source for optimism. However, the expected rapid industry adoption of RFID did not take place. This research explores some of the existing challenges and obstacles to RFID adoption, such as the lack of consistent UHF spectrum regulations for RFID or the absence of standards that promote integration with Automatic Identification and Data Capture (AIDC) media. As a conclusion, in this project we suggest some solutions to these challenges in the use of multi-frequency RFID tags that can be read at more that one frequency or novel migration strategies and standards that would help expand the industry.Outgoin

ICT tools for data management and analysis to support decisional process oriented to sustainable agri-food chains

Il settore agroalimentare sta affrontando delle sfide globali. La prima riguarda sfamare la popolazione mondiale che nel 2050, secondo le proiezioni delle Nazioni Unite, raggiungerà quota 9,3 miliardi di persone. La seconda sfida riguarda la richiesta da parte dei consumatori di prodotti ottenuti da filiere agroalimentari sempre più sostenibili, sicure e trasparenti. In particolare, l’Agricoltura sostenibile è una tecnica di gestione in grado di preservare la diversità biologica, la produttività, la capacità di rigenerazione, la vitalità e l’abilità alla funzione di un ecosistema agricolo, assicurandone, oggi e in futuro, le funzioni ecologiche, economiche e sociali a livello locale, nazionale ed globale, senza danneggiare altri ecosistemi. Quindi, per fronteggiare la sfida dell’agricoltura sostenibile, gli agricoltori devono aumentare la qualità e la quantità della produzione, riducendo l’impatto ambientale attraverso nuovi strumenti e nuove strategie di gestione. Questo lavoro analizza l’integrazione nel settore agroalimentare di alcune tecnologie e metodologie ICT per l’acquisizione, gestione e analisi dei dati, come la tecnologia RFID (Radio Frequency IDentification), i FMIS (Farm Management Information Systems), i DW (Data Warehouse) e l’approccio OLAP (On-Line Analytical Processing). Infine, l’adozione delle tecnologie ICT da parte di vere aziende è stata valutata attraverso un questionario. Al riguardo dell’adozione delle tecnologie RFID, questo lavoro analizza l’opportunità di trasferimento tecnologico relativo al monitoraggio e controllo dei prodotti agroalimentari tramite l’utilizzo di sensori innovativi, intelligenti e miniaturizzati. Le informazioni riguardanti lo stato del prodotto sono trasferite in tempo reale in wireless, come previsto dalla tecnologia RFID. In particolare, due soluzioni RFID sono state analizzate, evidenziando vantaggi e punti critici in confronto ai classici sistemi per assicurare la tracciabilità e la qualità dei prodotti agroalimentari. Quindi, questo lavoro analizza la possibilità di sviluppare una struttura che combina le tecnologie della Business Intelligence con i principi della Protezione Integrata (IPM) per aiutare gli agricoltori nel processo decisionale, andando a diminuire l’impatto ambientale ed aumentare la performance produttiva. L’IPM richiede di utilizzare simultaneamente diverse tecniche di protezione delle colture per il controllo dei parassiti e patogeni tramite un approccio ecologico ed economico. Il sistema di BI proposto è chiamato BI4IPM e combina l’approccio OLTP (On-Line Transaction Processing) con quello OLAP per verificare il rispetto dei disciplinari di produzione integrata. BI4IPM è stato testato con dati provenienti da vere aziende olivicole pugliesi. L’olivo è una delle principali colture a livello globale e la Puglia è la prima regione produttrice in Italia, con un gran numero di aziende che generano dati sull’IPM. Le strategie di protezione delle colture sono correlate alle condizioni climatiche, considerando la forte relazione tra clima, colture e parassiti. Quindi, in questo lavoro è presentato un nuovo e avanzato modello OLAP che integra il GSI (Growing Season Index), un modello fenologico, per comparare indirettamente le aziende agricole dal punto di vista climatico. Il sistema proposto permette di analizzare dati IPM di diverse aziende agricole che presentano le stesse condizioni fenologiche in un anno al fine di individuare best practices e di evidenziare e spiegare pratiche differenti adottate da aziende che lavorano in differenti condizioni climatiche. Infine, è stata effettuata un’indagine al fine di capire come le aziende agricole della Basilicata si raggruppano in funzione del livello di innovazione adottato. È stato utilizzato un questionario per domandare alle aziende se adottano strumenti ICT, ed eventualmente in quale processo produttivo o di management vengano usati. È stata quindi effettuata un’analisi cluster sui dati raccolti. I risultati mostrano che, usando il metodo di clustering k-means, appaiono due gruppi: gli innovatori e gli altri. Mentre, applicando la rappresentazione boxlot, si ottengono 3 gruppi: innovatori, utilizzatori precoci e ritardatari.The Agri-Food sector is facing global challenges. The first issue concerns feeding a world population that in 2050, according to United Nations projections, will reach 9.3 billion people. The second challenge is the request by consumers for high quality products obtained by more sustainable, safely and clear agri-food chains. In particular, the Sustainable agriculture is a management strategy able to preserve the biological diversity, productivity, regeneration capacity, vitality and ability to function of an agricultural ecosystem, ensuring, today and in the future, significant ecological, economic and social functions at the local, national and global scales, without harming other ecosystems. Therefore, to face the challenge of the sustainable agriculture, farmers need to increase quality and quantity of the production, reducing the environmental impact through new management strategies and tools. This work explores the integration of several ICT technologies and methodologies in the agri-food sector for the data acquisition, management and analysis, such as RFID technology, Farm Management Information Systems (FMIS), Data Warehouse (DW) and On-Line Analytical Processing (OLAP). Finally, the adoption of the ICT technologies by real farms is evaluated through a survey. Regarding the adoption of the RFID technology, this work explores an opportunity for technology transfer related to the monitoring and control of agri-food products, based on the use of miniaturized, smart and innovative sensors. The information concerning to the state of the product is transferred in real time in a wireless way, according to the RFID technology. In particular, two technical solutions involving RFID are provided, highlighting the advantages and critical points referred to the normal system used to ensure the traceability and the quality of the agri-food products. Therefore, this work explores the possibility of developing a framework that combines business intelligence (BI) technologies with Integrated Pest Management (IPM) principles to support farmers in the decisional process, thereby decreasing environmental cost and improving production performance. The IPM requires the simultaneous use of different crop protection techniques to control pests through an ecological and economic approach. The proposed BI system is called BI4IPM, and it combines on-line transaction processing (OLTP) with OLAP to verify adherence to the IPM technical specifications. BI4IPM is tested with data from real Apulian olive crop farms. Olive tree is one of the most important crop at global scale and Apulia is the first olive-producing region in Italy, with a huge amount of farms that generate IPM data. The crop protection strategies are correlated to the climate conditions considering the very important relation among climate, crops and pests. Therefore, in this work is presented a new advanced OLAP model integrating the Growing Season Index (GSI), a phenology model, to compare indirectly the farms by a climatic point of view. The proposed system allows analysing IPM data of different farms having the same phenological conditions over a year to understand some best practices and to highlight and explain different practices adopted by farms working in different climatic conditions. Finally, a survey aimed at investigating how Lucania' farms cluster according to the level of innovation adopted was performed. It was used a questionnaire for asking if farms adopt ICTs tools and, in case, what type they involved in managing and/or production processes. It has been done a cluster analysis on collected data. Results show that, using k-means clustering method, appear two clusters: innovators, remaining groups. While, using boxplot representation, clustered three groups: innovators, early adopters and laggards

Cyber-physical systems in food production chain

The article reviews the state-of-the-science in the field of cyber-physical systems (CPSs). CPSs are intelligent systems that include physical, biological and computational components using engineering networks. CPSs are able to integrate into production processes, improve the exchange of information between industrial equipment, qualitatively transform production chains, and effectively manage business and customers. This is possible due to the ability of CPSs to manage ongoing processes through automatic monitoring and controlling the entire production process and adjusting the production to meet customer preferences. A comprehensive review identified key technology trends underlying CPSs. These are artificial intelligence, machine learning, big data analytics, augmented reality, Internet of things, quantum computing, fog computing, 3D printing, modeling and simulators, automatic object identifiers (RFID tags). CPSs will help to improve the control and traceability of production operations: they can collect information about raw materials, temperature and technological conditions, the degree of food product readiness, thereby increasing the quality of food products. Based on the results, terms and definitions, and potential application of cyber-physical systems in general and their application in food systems in particular were identified and discussed with an emphasis on food production (including meat products).The article reviews the state-of-the-science in the field of cyber-physical systems (CPSs). CPSs are intelligent systems that include physical, biological and computational components using engineering networks. CPSs are able to integrate into production processes, improve the exchange of information between industrial equipment, qualitatively transform production chains, and effectively manage business and customers. This is possible due to the ability of CPSs to manage ongoing processes through automatic monitoring and controlling the entire production process and adjusting the production to meet customer preferences. A comprehensive review identified key technology trends underlying CPSs. These are artificial intelligence, machine learning, big data analytics, augmented reality, Internet of things, quantum computing, fog computing, 3D printing, modeling and simulators, automatic object identifiers (RFID tags). CPSs will help to improve the control and traceability of production operations: they can collect information about raw materials, temperature and technological conditions, the degree of food product readiness, thereby increasing the quality of food products. Based on the results, terms and definitions, and potential application of cyber-physical systems in general and their application in food systems in particular were identified and discussed with an emphasis on food production (including meat products)

Investigative Studies Of Embedded Assembly Line Automation System With Dual Rfid Platform

The lack of control and outdated inventory system have increased the management complexity of factory production lines, especially by the increase of sales and demand in the industry. An unmanageable system in the assembly line leads to inefficiency problems in tracking the volume of the product. The objective of this research is to develop a new design of embedded dual RFID architecture (passive and active systems) into a single system to track and monitor the product delivery process at the assembly lines in the industries. A new combination of 2.4 GHz ZigBee-based RFID operating in wireless sensor network platform is proposed as the solution to the product management problem. Meanwhile, the proposed system involved hardware and software designs which were embedded with the passive RFID reader at Ultra High Frequency (UHF). Results from the experiments conducted showed that the embedded system namely Passive and Active RFID (PAR) produced better overall performance compared to the standalone which Passive RFID (PR) system. The indoor range test was measured from 0 up to 60 m distance. The measurements obtained at 1 m and 60 m of transmission range are -33 dB and -51 dB respectively. It was also observed that embedded system has better signal strength value 7.84 % compared to the standalone system at 60 m. For the highest power level, which is level 4 (10 dBm) it is found that only 0.02 dB of signal loss occurred and matches 99.8 % to the theoretical value for PAR system. The throughput values for the embedded are between 12 kbps to 29 kbps for 17 bytes of data per packet. In the latency test, the embedded PAR system has better and therefore lower delay of 10.9 %, 40.6 % and 74.7 % for up to 3 tags compared to the standalone system. Experimental studies using Design of Experiment (DOE) were also developed using factorial and statistical data analysis to validate the eligibility of the proposed system to be applied in industrial environment and requirements. The factorial analysis on the effects on the conveyor speed, product orientation, tag orientation, type of tags, linear distance and type of product materials had been studied in DOE experiments for guidelines to the industry. The percentage of successful product detection indicates a very high prediction at 97.8 %. The proposed path loss model also provides the estimation of wireless distance and number of assembly lines required for establishing an efficient product management system. From the path loss model at distance 10 m the RSSI value for the NLOS indoor environment of assembly line gave -72 dBm

Sensor-based ICT Systems for Smart Societies

L'abstract è presente nell'allegato / the abstract is in the attachmen