A WSN Framework for Privacy Aware Indoor Location

The lack of physical exercise is among the most relevant factors in developing health issues, and strategies to incentivize active lifestyles are key to preventing these issues. The PLEINAIR project developed a framework for creating outdoor park equipment, exploiting the IoT paradigm to build “Outdoor Smart Objects” (OSO) for making physical activity more appealing and rewarding to a broad range of users, regardless of their age and fitness. This paper presents the design and implementation of a prominent demonstrator of the OSO concept, consisting of a smart, sensitive flooring, based on anti-trauma floors commonly found in kids playgrounds. The floor is equipped with pressure sensors (piezoresistors) and visual feedback (LED-strips), to offer an enhanced, interactive and personalized user experience. OSOs exploit distributed intelligence and are connected to the Cloud infrastructure by using a MQTT protocol; apps have then been developed for interacting with the PLEINAIR system. Although simple in its general concept, several challenges must be faced, related to the application range (which called for high pressure sensitivity) and the scalability of the approach (requiring to implement a hierarchical system architecture). Some prototypes were fabricated and tested in a public environment, providing positive feedback to both the technical design and the concept validation.

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“…Further recent works on the application of smart flooring to indoor localization can be found in Ref. [ 22 ]. In Ref.…”

Section: Methodsmentioning

confidence: 99%

IoT Smart Flooring Supporting Active and Healthy Lifestyles

Cocconcelli

Matrella

Mora

et al. 2023

Sensors

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“…The outcomes of the payload were utilized to generate one‐time MAC addresses among the communication pairs. In 2022, Tošić et al 31 have suggested a WSN outline for the privacy‐aware indoor location. A protocol for performing computations and querying in WSN was introduced in this approach, which needs to explore additional algorithms for tracking and detecting the user.…”

Section: Related Workmentioning

confidence: 99%

A source location privacy protocol‐based energy‐efficient and link‐reliable multi‐scale bifurcated deep Capsnet routing in social Internet of Things

Mariappan Sakthivel,

Subramanian,

Syed Mohammadu

et al. 2024

Int J Communication

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SummarySource location privacy is a developing research topic in the social Internet of Things. Source location privacy holds paramount importance in security critical wireless sensor network applications like tracking and monitoring. Several methods have been proposed for source location privacy in the social Internet of Things, but the existing methods have some issues such as improper path selection, the transmission of duplicate messages, and low network lifetime. To overcome these issues, a source location privacy protocol based on energy‐efficient and link‐reliable multi‐scale bifurcated deep Capsnet routing in the social Internet of Things is proposed in this manuscript. At first, the optimal route for the source is selected with the help of energy‐efficient and link‐reliable routing, this method helps to avoid improper path selection. To estimate the quality of the selected optimal path, the multi‐scale bifurcated deep Capsnet is applied. The introduced method is executed in MATLAB. The introduced method's performance is estimated with the aid of several performances evaluating metrics like sensitivity, energy consumption, network lifetime, safety period, and delay.

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