Dense Indoor Environments Research Articles

Evaluación de Interferencias en Escenarios Urbanos Interiores en Bandas Inferiores a 6 GHz

In an increasingly interconnected world, the unlicensed 2.4 and 5 GHz frequency bands have been essential for developing wireless applications such as Wi-Fi (IoT devices), home automation, and media streaming, among others. However, in highly populated urban environments, such as Guayaquil,Ecuador, the efficient administration of these spectrum swaths is a critical challenge that allows the development of smart and sustainable cities. The lack of comprehensive studies on interference in these bands in dense indoor environments causes a deterioration in network reliability, limiting the ability to use higher modulation schemes (MCS) which are essential for applications that require higher connection speeds. This study focuses on the precise collection of radio spectrum measurements in Guayaquil, specifically in the 2.4 and 5 GHz bands, to evaluate interference levels and channel availability in these densely urban environments. The results reveal more pronounced saturation in the 2.4 GHz band, underscoring the urgent need for more effective spectrum management in densely populated urban areas to ensure robust connectivity.

Evaluation of the Interference Performance of FMCW Radar Sensors in Dense Indoor Environments

Evaluation of the Interference Performance of FMCW Radar Sensors in Dense Indoor Environments

Tracking people across ultra populated indoor spaces by matching unreliable Wi-Fi signals with disconnected video feeds

Tracking in dense indoor environments where several thousands of people move around is an extremely challenging problem. In this paper, we present a system — DenseTrack for tracking people in such environments. DenseTrack leverages data from the sensing modalities that are already present in these environments — Wi-Fi (from enterprise network deployments) and Video (from surveillance cameras). We combine Wi-Fi information with video data to overcome the individual errors induced by these modalities. More precisely, the locations derived from video are used to overcome the localization errors inherent in using Wi-Fi signals where precise Wi-Fi MAC IDs are used to locate the same devices across different levels and locations inside a building. Typically, localization in dense environments is a computationally expensive process when done with just video data; hence hard to scale. DenseTrack combines Wi-Fi and video data to improve the accuracy of tracking people that are represented by video objects from non-overlapping video feeds. DenseTrack is a scalable and device-agnostic solution as it does not require any app installation on user smartphones or modifications to the Wi-Fi system. At the core of DenseTrack, is our algorithm — inCremental Association of Independent Variables under Uncertainty (CAIVU). CAIVU is inspired by the multi-armed bandits model and is designed to handle various complex features of practical real-world environments. CAIVU matches the devices reported by an off-the-shelf Wi-Fi system using connectivity information to specific video blobs obtained through a computationally efficient analysis of video data. By exploiting data from heterogeneous sources, DenseTrack offers an effective real-time solution for individual tracking in heavily populated indoor environments. We emphasize that no other previous system targeted nor was validated in such dense indoor environments. We tested DenseTrack extensively using both simulated data, as well as two real-world validations using data from an extremely dense convention center and a moderately dense university environment. Our simulation results show that DenseTrack achieves an average video-to-Wi-Fi matching accuracy of up to 90% in dense environments with a matching latency of 60 s on the simulator. When tested in a real-world extremely dense environment with over 500,000 people moving between different non-overlapping camera feeds, DenseTrack achieved an average match accuracy of 83% to within a 2-people distance with an average latency of 48 s.

Broadband Circular Polarised Printed Antennas for Indoor Wireless Communication Systems: A Comprehensive Review.

With the rapid changes in wireless communication systems, indoor wireless communication (IWC) technology has undergone tremendous development. Antennas are crucial components of IWC systems that transmit and receive signals within indoor environments. Thus, the development of indoor technology is highly dependent on the development of indoor antennas. However, indoor environments with limited space require the fewest indoor antenna units and the smallest indoor antenna sizes possible. Hence, indoor antennas with compact size and broad applications have become widely preferred. In an IWC system, circularly polarised (CP) antennas are generally important, especially in dense indoor environments, because compared with linearly polarised (LP) antennas, CP antennas reduce polarisation mismatch and multipath losses. This paper combs through the existing studies related to three-dimensional (3D) geometry (nonplanar) or waveguide indoor antennas and the two common approaches to two-dimensional (2D) geometry (planar) indoor antennas, namely, broadband CP printed monopole antennas (BCPPMAs) and broadband CP printed slot antennas (BCPPSAs). The advantages, disadvantages and limitations of previous works are highlighted as well. These research works are summarised, compared and analysed to understand the recent specifications of BCPPMAs and BCPPSAs to generate the most appropriate design structure suitable for current IWC systems.

Towards a fast and stable filter for RSSI-based handoff algorithms in dense indoor WLANs

In dense indoor wireless networks, handoffs occur frequently. The criteria to trigger handoffs are not defined by the IEEE 802.11 standard, thus being specific to each manufacturer’s implementation. Current handoff implementations typically use RSSI (Received Signal Strength Indicator) as a performance metric and commonly cause association instability in dense environments, a well-known problem referred to as the ping-pong effect. In this paper, we present a deep analysis of RSSI traces collected in dense indoor environments using the FIBRE testbed. Based on that, we conclude that the RSSI time series presents deep fast fades that occur frequently in bursts of small sizes, which can cause ping-pongs. Motivated by this behavior, we propose a new and simple filtering mechanism called Maximum which targets to eliminate these valleys in the RSSI time series. In a nutshell, this filter chooses the maximum RSSI value from a sliding window containing the last few RSSI samples of the series. We conduct simulations based on real RSSI traces from static and mobile scenarios to evaluate Maximum with respect to other filtering mechanisms found in the literature. Additionally, we present a simplified model of the behavior of Maximum that allows us to study the probability of unwanted handoffs as a function of the RSSI of the available access points. Our analysis reveals that Maximum is able to offer a better tradeoff between handoff triggering delay and stability in mobile scenarios, while also performing well in static scenarios, effectively avoiding the occurrence of ping-pongs in most cases.

Image Compression Using Chain Coding for Electronic Shelf Labels (ESL) Systems

One of the most important and recurring tasks in managing a store is to provide accurate, up-to-date price information to customers on the shelves. Manual updating of price tags has been a time-consuming, error-prone task with high labor costs. An Electronic shelf labels (ESL) system is becoming an attractive alternative for this task because of the dynamic-price-updating and customer’s product-evaluation-display features. A common ESL system configuration in a retail store includes thousands of battery-powered ESL tags that are mostly connected wirelessly in a dense indoor environment. Raising the success ratio of wireless communication is essential for the system’s viability due to its limited battery life. Most of the ESL traffic is the image data of goods that appear on the tags, and reducing the amount of the data is one of the most effective ways to enhance communication performance and reduce retransmission. This paper proposes an ESL image compression mechanism based on chain coding that utilizes ESL images’ characteristics. The performance results show that the proposed mechanism could compress the ESL images smaller and decompress faster.

Experimental analysis of received signals strength in Bluetooth Low Energy (BLE) and its effect on distance and position estimation

AbstractReceived Signal Strength Indicator (RSSI) is the measurement of the power in the radio signal and a parameter for distance‐based measurements. Bluetooth Low Energy (BLE) is an advance implementation for Internet of Things (IoT). BLE beacons‐based indoor positioning systems provide an easy and energy‐efficient, low deployment cost solution for wide variety of applications including smart phones. This paper presents an in‐depth experimental study of BLE RSSI in a dense indoor environment. Due to noise, fluctuations in RSSI occur, which produces distance estimation error, which ultimately affect position estimation accuracy. The main objective of this paper is to know the variations in RSSI and develop a radio propagation model in order to minimize the distance estimation and position estimation error. Based on our real‐time experimental analysis using BLE modules, there is an average 1.32‐m position estimation error in the presence of 10‐dBm variation in RSSI. Moreover, we also observed that environmental specific radio propagation constants greatly affect distance and position estimation accuracy in BLE modules.

Optimal trajectory generation and robust flatness–based tracking control of quadrotors

SummaryThis work proposes an optimal trajectory generation and a robust flatness–based tracking controller design to create a new performance guidance module for the quadrotor in dense indoor environments. The properties of the differential flatness, the B‐spline, and the direct collocation method are exploited to convert the constrained optimization problem into a nonlinear programming one, which can be easily resolved by a classic solver. After that, the obtained optimal reference trajectory is applied to the dynamic quadrotor model and two different flatness‐based controllers, namely, one based on feedback linearization and one based on feedforward linearization, are developed and compared to ensure the trajectory tracking despite the existence of disturbances and parametric uncertainties. Numerical simulation is executed to evaluate the proposed optimal trajectory generation approach and the robust tracking strategies. It turns out that the controller based on feedforward linearization outperforms the feedback linearization one in robustness and permits obtaining a performance guidance law for an uncertain quadrotor system.

Millimeter Wave Networked Wearables in Dense Indoor Environments

Supporting high data rate wireless connectivity among wearable devices in a dense indoor environment is challenging. This is primarily due to bandwidth scarcity when many users operate multiple devices simultaneously. The millimeter-wave (mmWave) band has the potential to address this bottleneck, thanks to more spectrum and less interference because of signal blockage at these frequencies. In this paper, we explain the potential and challenges associated with using mmWave for wearable networks. To provide a means for concrete analysis, we present a system model that admits easy analysis of dense, indoor mmWave wearable networks. We evaluate the performance of the system while considering the unique propagation features at mmWave frequencies, such as human body blockages and reflections from walls. One conclusion is that the non-isotropy of the surroundings relative to a reference user causes variations in system performance depending on the user location, body orientation, and density of the network. The impact of using antenna arrays is quantified through analytic closed-form expressions that incorporate antenna gain and directivity. It is shown that using directional antennas, positioning the transceiver devices appropriately, and orienting the human user body in certain directions depending on the user location result in gigabits-per-second achievable ergodic rates for mmWave wearable networks.

Aggressive flight of fixed-wing and quadrotor aircraft in dense indoor environments

In this paper, we describe trajectory planning and state estimation algorithms for aggressive flight of micro aerial vehicles in known, obstacle-dense environments. Finding aggressive but dynamically feasible and collision-free trajectories in cluttered environments requires trajectory optimization and state estimation in the full state space of the vehicle, which is usually computationally infeasible on realistic timescales for real vehicles and sensors. We first build on previous work of van Nieuwstadt and Murray and Mellinger and Kumar, to show how a search process can be coupled with optimization in the output space of a differentially flat vehicle model to find aggressive trajectories that utilize the full maneuvering capabilities of a quadrotor. We further extend this work to vehicles with complex, Dubins-type dynamics and present a novel trajectory representation called a “Dubins–Polynomial trajectory”, which allows us to optimize trajectories for fixed-wing vehicles. To provide accurate state estimation for aggressive flight, we show how the Gaussian particle filter can be extended to allow laser rangefinder localization to be combined with a Kalman filter. This formulation allows similar estimation accuracy to particle filtering in the full vehicle state but with an order of magnitude more efficiency. We conclude with experiments demonstrating the execution of quadrotor and fixed-wing trajectories in cluttered environments. We show results of aggressive flight at speeds of up to 8 m/s for the quadrotor and 11 m/s for the fixed-wing aircraft.