Light Intensity Based IoT Device Positioning for Indoor Monitoring

Gaofei Sun, Xiaoshuang Xing, Zhenjiang Qian, Zhiguo Wang, Saide Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


With the prosperous deployment of IoT devices in recent years, more data are collected by ubiquitous sensors. Hence, there are two main challenges needed to be solved. Firstly, how to accommodate the communication links among these devices and collect their data effectively? Secondly, how to track or localize these devices and well organize the IoT network? The common way to track these devices is achieved by registering locations of these devices manually or RSSI measurement. However, these measures suffer from high complexity and inaccuracy, and cause boring reconfiguration process with shift of devices. In this framework, we proposed a novel and low cost IoT monitoring system with self-location awareness. The collected data by sensors, such as the light intensity, can be used for device localizations. By using the proposed algorithm, we derived the critical parameters for light curves, and interpolated for the predicted light intensity inside the whole room. The experiment results indicated the proposed algorithm is useful for inferring device location with low cost, which are suitable for device management without privacy information.

Original languageEnglish (US)
Title of host publicationSecurity, Privacy, and Anonymity in Computation, Communication, and Storage - 13th International Conference, SpaCCS 2020, Proceedings
EditorsGuojun Wang, Bing Chen, Wei Li, Roberto Di Pietro, Xuefeng Yan, Hao Han
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783030688509
StatePublished - 2021
Event13th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2020 - Nanjing, China
Duration: Dec 18 2020Dec 20 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12382 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2020

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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