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Anti-Loss Key Tag Using Bluetooth Smart


  • Department of ECM, K L University, Guntur - 522502, Andhra Pradesh, India


Objectives: The most common asset tracking systems used are RFID, Wi-Fi, Cellular, WSN, Robots, GPS etc. While using such techniques, the energy consumption will be increased; proximity range cannot be met and are costly. Statistical Analysis: So the developers started to build up a less complex, compact and energy efficient object tracker with the use of Bluetooth Low Energy. Findings: This paper aims at developing a Key Tag that identifies the location of the minute objects within the specified proximity area. The main components are: I) Bluetooth Low Energy, II) Smartphone. The key functions are performed by Bluetooth Low Energy (BLE) module and smart phone application. The important feature of this key tag is its low power consumption through the usage of Bluetooth Low energy that has the key feature of sleep mode for most of the time and only wakes up when the connection is initiated. Improvements: Low power consumption is achieved by limiting the actual connection times to a very few ms the results achieved through BLE analysis is presented and discussed here.


ARM Cortex M0, Bluetooth Low Energy, GATT, Key Tag, Proximity

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  • Asset Tracking Solution with BLE and Smartphone: An Energy/Position Accuracy Trade-Off. Date Accessed: 25/02/2016.
  • Balakrishnan D, Nayak A. An Efficient Approach for Mobile Asset Tracking Using Contexts. In IEEE Transactions on Parallel and Distributed Systems. 2012 Feb; 23(2): 211–-8.
  • Shangguan L, Zimu Zhou, Kebin Liu. Towards Accurate Object Localization with Smartphone. In IEEE Transactions on Parallel and Distributed Systems. 2014 Oct; 25(10): 2731–42.
  • Bisio I, Sciarrone A, Zappatore S. Asset Tracking Architecture with Bluetooth Low Energy Tags and Ad Hoc Smart Phone Applications. Networks and Communications (EuCNC), 2015 European Conference on, Paris: 2015.p.460–4.
  • Kevin Townsend, Carles Cufí, Akiba, Robert Davidson.Getting Started with Bluetooth Low Energy. 1st (Edn), CA, United States of America: O’Reilly Media;. 2014 Apr.
  • Bisio I, Lavagetto F, Marchese M, Sciarrone A. GPS/HPSand Wi-Fi Fingerprint-Based Location Recognition for Check-In Applications Over Smartphone in Cloud-Based LBSs. In: IEEE Transactions on Multimedia. 2013 Jun; 15(4): 858–69.
  • Comprehensive indoor remote tracking system. http:// 7129914. Date Accessed: 22/06/2015.
  • Mary Livinsa Z , Jaya shri S. Monitoring Moving Target and Energy Saving Localization Algorithm in Wireless Sensor Networks. Indian Journal of Science and Technology. 2016 Jan; 9(3):1–5
  • Yehong Chen, Pil Seong Park , Qian Gao . An Enhanced Model-based Tracking Algorithm with Dynamic Adjustment of Learning Parameters according to Online Performance Evaluation. Indian Journal of Science and Technology. 2015 Oct; 8(26):1–6.
  • Gaurav Verma , Himanshu Verma , Ipsita Singh , Aditya Vikram , Sheetal Singhal , AshishKumar , SandeepBanarw al, Khush ali Goel. Wireless Position Tracking of a DTMF based Mobile Robot using GSM and GPS. Indian Journal of Science and Technology. 2015 Aug; 8(17):1–6.
  • Kelepouris T, McFarlane D. Determining the value of asset location information systems in a manufacturing environment.International Journal of Production Economics.2010 Aug; 126(2):324–34.
  • Data sheet of NXP 50-MHz, 32-bit Cortex-M0™ microcontrollers LPC1100. /datasheet/LPC112X.pdf. Date accessed: 24/02/2015.
  • Introduction to Bluetooth Low Energy. accessed: 20/04/2014.


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