{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T05:07:56Z","timestamp":1778908076115,"version":"3.51.4"},"reference-count":25,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology","award":["NSTC 114-2622-E-027-015"],"award-info":[{"award-number":["NSTC 114-2622-E-027-015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Integrating sensors with wireless communication capabilities into smart wireless sensing devices allows us to form a wireless sensing network. This network works in conjunction with monitors to display and control parameters at different locations or in the environment. By deploying a wireless sensing network, the system can interact with the user by sending notifications when necessary, based on the environmental conditions and user activities detected by the wireless sensors, and make corresponding adjustments to or control the environment. The advancement and widespread adoption of the internet have enabled the development of this technology. Wireless sensors are widely used in product positioning and environmental monitoring management, making the management of complex products more accurate. The Monitor and Control System (MCS), which combines network cameras and wireless sensors with neural network technology and fuzzy control systems, improves the existing positioning method and enhances positioning accuracy. Product management, which comprises comprehensive digital services and is facing serious staff shortages, has turned to digital payment to reduce labor costs. This experiment was simulated using Network Simulator 2 (NS2). In the sensing system part, the application of a ZigBee network and its status were explored, and interference was analyzed. Information on network interference simulations and their impact on normal services was compiled for network management purposes. Using NS2 network simulation, this study utilizes ZigBee with different neuron nodes and different training times to find the best network model, compares various queuing mechanisms and functions as a network interference intrusion detection system, and explores its node defense capabilities in cases of interference. Node Density: Node density is typically determined by the number of nodes in the simulation area and the size of the scene. Low Density: Sparse node distribution, prone to network partitioning, is suitable for testing latency-tolerant networks (DTNs) or route discovery capabilities. High Density: It entails dense node distribution, severe signal interference, and packet collisions. It is suitable for testing MAC layer collision prevention mechanisms (such as CSMA\/CA) and the scalability of outing protocols. Configuration Method: the \u201cset Dest\u201d tool is used in a Tcl script to generate a mobile scene file, defining the number of nodes, range (X, Y), and time to be more significant in product management.<\/jats:p>","DOI":"10.3390\/s26103096","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T07:50:15Z","timestamp":1778745015000},"page":"3096","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of Smart Sensors in Commodity Management"],"prefix":"10.3390","volume":"26","author":[{"given":"Chao-Kong","family":"Chung","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9600-5812","authenticated-orcid":false,"given":"Meng-Yun","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2442-2983","authenticated-orcid":false,"given":"Guo-Ming","family":"Sung","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,14]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"The impact of management control systems (MCS) on organizations performance a literature review","volume":"24","author":"Alkhafaji","year":"2018","journal-title":"J. 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