Graph Trilateration for Indoor Localization in Sparsely Distributed Edge Computing Devices in Complex Environments Using Bluetooth Technology

Feasibility of assessing cognitive impairment via distributed camera network and privacy-preserving edge computing

INTRODUCTION

Mild cognitive impairment (MCI) involves cognitive decline beyond normal age and education expectations. It correlates with decreased socialization and increased aimless motion. We aim to automate detection of these behaviors for improved longitudinal monitoring.

METHODS

We used a privacy-preserving distributed camera network to collect data from MCI patients in an indoor space. Movement and social interaction features were developed using this data to train machine learning algorithms to differentiate between higher and lower cognitive functioning MCI groups.

RESULTS

A Wilcoxon rank-sum test showed significant differences between high- and low-functioning cohorts in the movement and social interaction features. Despite the absence of data linking each person's identity to their specific level of cognitive decline, a machine learning model using key features achieved 71% accuracy.

DISCUSSION

We show that an edge computing-based privacy-preserving camera network can differentiate between levels of cognitive impairment based on movements and social interactions during group activities.

Highlights

Movement and social interaction features showed significant differences in high- and low-functioning cohorts.Significant features included linear path lengths, walking speed, direction change and velocity entropies, and number of group formations, among others.Differences were observed despite the presence of healthy individuals and the lack of individual identifiers.Data were collected using a 39-camera privacy-preserving edge computing network covering a 1700-m2 indoor space.

Adaptive Network Model for Assisting People with Disabilities through Crowd Monitoring and Control

Here, we present an effective application of adaptive cooperative networks, namely assisting disables in navigating in a crowd in a pandemic or emergency situation. To achieve this, we model crowd movement and introduce a cooperative learning approach to enable cooperation and self-organization of the crowd members with impaired health or on wheelchairs to ensure their safe movement in the crowd. Here, it is assumed that the movement path and the varying locations of the other crowd members can be estimated by each agent. Therefore, the network nodes (agents) should continuously reorganize themselves by varying their speeds and distances from each other, from the surrounding walls, and from obstacles within a predefined limit. It is also demonstrated how the available wireless trackers such as AirTags can be used for this purpose. The model effectiveness is examined with respect to the real-time changes in environmental parameters and its efficacy is verified.

A Self-Localization Algorithm for Mobile Targets in Indoor Wireless Sensor Networks Using Wake-Up Media Access Control Protocol

Indoor localization of a mobile target represents a prominent application within wireless sensor network (WSN), showcasing significant values and scientific interest. Interference, obstacles, and energy consumption are critical challenges for indoor applications and battery replacements. A proposed tracking system deals with several factors such as latency, energy consumption, and accuracy presenting an innovative solution for the mobile localization application. In this paper, a novel algorithm introduces a self-localization algorithm for mobile targets using the wake-up media access control (MAC) protocol. The developed tracking application is based on the trilateration technique with received signal strength indication (RSSI) measurements. Simulations are implemented in the objective modular network testbed in C++ (OMNeT++) discrete event simulator using the C++ programming language, and the RSSI values introduced are based on real indoor measurements. In addition, a determination approach for finding the optimal parameters of RSSI is assigned to implement for the simulation parameters. Simulation results show a significant reduction in power consumption and exceptional accuracy, with an average error of 1.91 m in 90% of cases. This method allows the optimization of overall energy consumption, which consumes only 2.69% during the localization of 100 different positions.