Design and analysis of collision reduction algorithms for LED-based indoor positioning with simulation and experimental validation

In this paper, we develop a low complexity indoor positioning system (IPS) and design a lightweight, low-cost, and wearable receiver for it. The accuracy of proximity-based LED IPS has been improved using overlap between LED beams but LED packets in the overlap region are subject to collisions. In this paper, we design collision handling algorithms for the IPS that considers building and lighting infrastructures. Mathematical analyses of the proposed algorithms are done and models for the probability of collisions are developed. The models, which are verified using simulations, are used to calculate the time required for position update called positioning time. Analysis of the positioning time is done for single and multiple receivers systems and validated with experimental measurements. Results show positioning error as low as 56 cm with a positioning time of about 300 ms for slotted unsynchronized systems and 500 ms for unslotted unsynchronized systems which makes the developed system pragmatic and appropriate for human positioning

Design of improved IR protocol for LED indoor positioning system

In this work, we design an infrared protocol (IRP) for light emitting diode (LED) based indoor positioning. The designed IRP compensates for the shortcomings of other existing protocols when applied to the multiple LED estimation indoor positioning model (MLEM). MLEM uses overlap of LED beams to increase accuracy of positioning. The overlap sets up a multipoint-to-point optical communication channel. The existing protocols which are designed for point-to-point links, when modified to suit the MLEM overlapping region, show a high positioning time between 3 s and 4.5 s. These values are not desirable for real time tracking. A new protocol is therefore designed to reduce the positioning time. The protocol is implemented in an experimental MLEM design using ATmega 328 microcontroller hardware. The experimental results show the new protocol reduces the positioning time to 0.5 s

LED-based indoor positioning system using novel optical pixelation technique

At present, about 47 million people worldwide have Alzheimer's disease (AD), and because there is no treatment currently available to cure AD, people with AD (PWAD) are cared for. The estimated cost of care for PWAD in 2016 alone is about $236 billion, which puts a huge burden on relatives of PWAD. This work aims to reduce this burden by proposing an inexpensive indoor positioning system that can be used to monitor PWAD. For the positioning, freeform lenses are used to enable a novel optically pixeled LED luminaire (OPLL) that focuses beams from LEDs to various parts of a room, thereby creating uniquely identifiable regions which are used to improve positioning accuracy. Monte Carlo simulation with the designed OPLL in a room with dimensions 5m × 5m × 3m is used to compute the positioning error and theoretical analysis and experiments are used to validate the time for positioning. Results show that by appropriate LED beam design, OPLL has a positioning error and time for positioning of 0.735 m and 187 ms which is 55.1% lower and 1.2 times faster than existing multiple LED estimation model proximity systems

Optical boundaries for LED-based indoor positioning system

Overlap of footprints of light emitting diodes (LEDs) increases the positioning accuracy of wearable LED indoor positioning systems (IPS) but such an approach assumes that the footprint boundaries are defined. In this work, we develop a mathematical model for defining the footprint boundaries of an LED in terms of a threshold angle instead of the conventional half or full angle. To show the effect of the threshold angle, we compare how overlaps and receiver tilts affect the performance of an LED-based IPS when the optical boundary is defined at the threshold angle and at the full angle. Using experimental measurements, simulations, and theoretical analysis, the effect of the defined threshold angle is estimated. The results show that the positional time when using the newly defined threshold angle is 12 times shorter than the time when the full angle is used. When the effect of tilt is considered, the threshold angle time is 22 times shorter than the full angle positioning time. Regarding accuracy, it is shown in this work that a positioning error as low as 230 mm can be obtained. Consequently, while the IPS gives a very low positioning error, a defined threshold angle reduces delays in an overlap-based LED IPS

Indoor localization based on multiple LEDs position estimation

This paper describes the simulation results and hardware implementation of an inexpensive, low-complexity LED based indoor positioning system. Localization by multiple LEDs estimation model (MLEM) approximates position of a mobile receiver by the acquisition of positional information from LED transmitters. Multiple LED orientation can either be with or without overlap. Receivers in a no-overlap LED orientation experience only single access while multiple access receivers are designed for orientations with overlaps. Single and multiple access systems were developed and implemented by the use of low cost ATMEG 328 microcontroller. Since multiple LEDs transmit data at the same wavelength and are asynchronous, overlap in multiple access system causes interference. The possibility of this interference is reduced by packet based pulse duration multiplexing (PDM) and a low duty cycle transmission protocol. By the use of MLEM, root mean square error in position estimation is reduced to about 1 percent of the length an indoor location. Experimental results show that overlap increases positional accuracy over a wider coverage region and that the multiple access system allows for a more reliable positioning

Fluid Mechanics Analysis of a Voice Coil Needle Free Jet Injector

This paper considers the analytic modelling of the voice coil actuated needle free jet injection. It first investigates the currently existing model for the injection and making some alterations based on the system behavior and basic definitions in physics. These alterations yielded a new set of equations which were solved using the Runge-Kutta-Felberg formular. Second order, forth order and fifth order curve fitting of the numerical model were performed in order to develop a polynomial that gives the direct non-differential pressure-time relationship for the needle free jet injection (developed, to the best knowledge of the authors, for the first time). This fifth order polynomial presented the least error, measured by the least of the norm of residuals. The model for the voice coil activator was developed by writing force balance equations based on Newton’s second law of motion. The transfer function of the motor was then used to investigate the behavior of the voice coil actuator. Finally, the two systems (voice coil motor and injection) were linked up by setting up an analogous electromechanical system to see the behavior of the voice coil motor in the injection. Where the mechanical parts represent the injection pressure profile. Keywords: Needle Free Jet Injection, Voice coil moto

Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless Telecommunication Performance

The pervasive nature of wireless telecommunication has made it the foundation for mainstream technologies like automation, smart vehicles, virtual reality, and unmanned aerial vehicles. As these technologies experience widespread adoption in our daily lives, ensuring the reliable performance of cellular networks in mobile scenarios has become a paramount challenge. Beamforming, an integral component of modern mobile networks, enables spatial selectivity and improves network quality. However, many beamforming techniques are iterative, introducing unwanted latency to the system. In recent times, there has been a growing interest in leveraging mobile users' location information to expedite beamforming processes. This paper explores the concept of contextual beamforming, discussing its advantages, disadvantages and implications. Notably, the study presents an impressive 53% improvement in signal-to-noise ratio (SNR) by implementing the adaptive beamforming (MRT) algorithm compared to scenarios without beamforming. It further elucidates how MRT contributes to contextual beamforming. The importance of localization in implementing contextual beamforming is also examined. Additionally, the paper delves into the use of artificial intelligence schemes, including machine learning and deep learning, in implementing contextual beamforming techniques that leverage user location information. Based on the comprehensive review, the results suggest that the combination of MRT and Zero forcing (ZF) techniques, alongside deep neural networks (DNN) employing Bayesian Optimization (BO), represents the most promising approach for contextual beamforming. Furthermore, the study discusses the future potential of programmable switches, such as Tofino, in enabling location-aware beamforming

Improving Throughput For Mobile Receivers Using Adaptive Beamforming

In anticipation of a rapid increase in wireless communication, MIMO is one key technology to be explored for 5G. Conventional approaches are unable to predict many of the key characteristics for MIMO channels, and more detailed methods suffer from significant computational complexity due to the number of antennas in a MIMO array. In this work, the beamforming performance for moving users in a large cell with effective channel throughput has been explored. The Glasgow University campus model is used to estimate channel properties when various beamforming techniques are implemented. The techniques explored are Maximum Ratio Transmission (MRT) (for transmitter), Equal Gain Combining (EGC), Selection Combining (SC), and Max Ratio Combining (MRC) (for receiver) beamforming in 3GPP Long Term Evolution (LTE). Throughput, received signal strength, and signal to noise interference ratio (SINR) are determined. By implementing the beamforming techniques, on average, we are able to improve the throughput from 9 Mbps to 14 Mbps. The best throughput/SINR has been observed with MRT-MRC in comparison to No-beamforming

Lessons learned: Symbiotic autonomous robot ecosystem for nuclear environments

Nuclear facilities have a regulatory requirement to measure radiation levels within Post Operational Cleanout (POCO) around nuclear facilities each year, resulting in a trend towards robotic deployments to gain an improved understanding during nuclear decommissioning phases. The UK Nuclear Decommissioning Authority supports the view that human-in-the-loop robotic deployments are a solution to improve procedures and reduce risks within radiation haracterisation of nuclear sites. We present a novel implementation of a Cyber-Physical System (CPS) deployed in an analogue nuclear environment, comprised of a multi-robot team coordinated by a human-in-the-loop operator through a digital twin interface. The development of the CPS created efficient partnerships across systems including robots, digital systems and human. This was presented as a multi-staged mission within an inspection scenario for the heterogeneous Symbiotic Multi-Robot Fleet (SMuRF). Symbiotic interactions were achieved across the SMuRF where robots utilised automated collaborative governance to work together where a single robot would face challenges in full characterisation of radiation. Key contributions include the demonstration of symbiotic autonomy and query-based learning of an autonomous mission supporting scalable autonomy and autonomy as a service. The coordination of the CPS was a success and displayed further challenges and improvements related to future multi-robot fleets