Indoor Positioning Systems (IPS) have emerged as essential technologies for achieving accurate localization and navigation within enclosed environments where satellite-based systems, such as the Global Navigation Satellite System (GNSS), are unreliable. This article provides a comprehensive overview of the major IPS technologies, highlighting their operating principles, advantages, and limitations. The study examines a diverse range of positioning methods, including computer vision-based systems with dynamic tracking capabilities, pedestrian dead reckoning (PDR) solutions that function independently of external infrastructure, and communication signal–based approaches such as Ultra-Wideband (UWB), Radio Frequency Identification (RFID), Bluetooth Low Energy (BLE), Wi-Fi, and ZigBee. Each technology demonstrates distinct performance characteristics in terms of accuracy, cost efficiency, scalability, and energy consumption. By systematically comparing these approaches, this work identifies the contexts in which each technology performs optimally and discusses the trade-offs associated with their implementation. Furthermore, the paper synthesizes recent advancements that integrate artificial intelligence, machine learning, and sensor fusion techniques to enhance positioning precision and robustness under complex indoor conditions. The findings of this review aim to assist researchers, engineers, and practitioners in selecting the most appropriate IPS solution for specific application domains, facilitating informed decision-making in designing effective and reliable indoor localization systems.

This paper presents an observer approach for a low-cost foot-mounted inertial pedestrian navigation. The approach is based on the theory of invariant observer design, which enables the observer to estimate the navigation state with high accuracy even in the presence of noise and uncertainties. The navigation state is modeled as an element of the matrix Lie group of double direct isometries, SE 2 (3), which is a mathematical representation of the space such as position, velocity and attitude, in which the pedestrian moves. The model includes accelerometers and rate-gyros biases, which are commonly found in low-cost inertial sensors. Next, the design matrix based on the Invariant Extended Kalman Filter (IEKF) framework is derived. The proposed system is designed to operate using only Zero Velocity Updates (ZVU) as the aiding information, which is then shown to be left-invariant measurements.

A new Zero Velocity Interval (ZVI) detector is proposed and investigated in this article to augment the computation of a pedestrian’s position. In a low-cost pedestrian navigation system, the position of a pedestrian can be computed by using inertial navigation algorithms. The low-cost pedestrian navigation system employs an inertial measurement unit (IMU) comprised of accelerometer and gyroscope sensors to record acceleration and attitude rate. These measurements, when integrated mathematically, yield velocity and position. Similarly, attitude rate changes to attitude. The algorithm will then be able to figure out the changes in pedestrian position with the proper attitude. However, due to its low-cost nature, these sensors are built in such a way that their measurements are easily corrupted by noise, and once integrated mathematically, the measurement error grows exponentially. Zero Velocity Update (ZUPT) algorithm is frequently used to limit these errors. It works by detecting the ZVI that occur when the foot is on the ground. Assuming that the foot on the ground should have zero velocity, any remaining velocity measurements detected during this period are considered an error and are fed back to the navigation algorithm for correction. To detect the ZVI, a few commonly used detectors, namely Angular Rate Energy (ARE), Acceleration Moving Variance (MV), Acceleration Magnitude (MAG), and Generalized Likehood Ratio Test (GLRT) were revisited. These detectors were tested with real-world datasets of walking pedestrians. Then, a new detector, the Angular Rate Moving Variance (ARMV) detector, is proposed, and its performance is compared to that of the existing detectors.

Several objectives that service providers have to achieve are to determine the increase or decrease in the price change due to the change in service quality and the amount of service quality value. Multi-service wireless Internet pricing schemes that apply the quality of the bandwidth advantage are designed to take into account the need of ISPs to provide highquality services to users and increase their revenue, considering the limited bandwidth of the resources. The modified model is an improvement of the original model by adding variables and parameters to the multiple service network model by specifying the base price for QoS (alpha) and premium quality (β) as variables, parameters, and service class load factor, Pregnancy basis factor and differentiation factor. The models are solved by the program Lingo 18.0 to get the best solution. The results prove that the modified model is the best and yields the best profit for the service provider when the cost of all changes in quality of service is increased and the variable α and β is set as constant or variable. Keywords—Optimal solution; multi service network; wireless pricing scheme; bandwidth QoS attribute

This paper presents a Dynamic Gradient Pattern (DGP) based on Kalman filtering technique for urban road users tracking. DGP technique is proposed to enhance rigid object descriptive ability for improved verification. DGP descriptor along with weighted centroid was integrated with a Kalman filtering framework to enhance data association robustness and tracking accuracy. To handle multiple objects tracking, a DGP verification approach is addressed based on normalized Bhattacharyya distance. The proposed technique achieves a closer trajectory for rigid body movement. The DGP descriptor can discriminate the objects correctly, and it overcomes the partial occlusion and misdetection by verifying object location using the normalized Bhattacharyya distance between DGP features. Experimental evaluation is performed on urban videos that include a slow-motion temporary stop and partial occlusion. The experimental results demonstrate that the detecting and tracking accuracy are above 98.08% and 97.70% respectively.

This paper seeks to develop a new proposed pricing plan. We aim to address the multiple bottlenecks in the various QoS network scheme as an improved model by comparing the original model to the first and second modifications. We will achieve this by looking at the total cost that the customer will pay, the price charged for cost recovery or diversification by allowing the user to choose the QoS that best suits their budget and preference. Depending on the principle that increasing the quality leads to an increase in the price. The results obtained are from the Lingo 18 program which shows an improvement in the original problem by noting an increase in profits between the original model and the first and second modification.

The use of a low-cost MEMS-based Inertial Measurement Unit (IMU) provides a cost-effective approach for navigation purposes. Foot-mounted IMU is a popular option for indoor inertial pedestrian navigation, as a small and light MEMS-based inertial sensor can be tied to a pedestrian's foot or shoe. Without relying on GNSS or other external sensors to enhance navigation, the foot-mounted pedestrian navigation system can autonomously navigate, relying solely on the IMU. This is typically performed with the standard strapdown navigation algorithm in a Kalman filter, where Zero Velocity Updates (ZVU) are used together to restrict the error growth of the low-cost inertial sensors. ZVU is applied every time the user takes a step since there exists a zero velocity condition during stance phase. While velocity and correlated attitude errors can be estimated correctly using ZVUs, heading error is not because it is unobservable. In this paper, weextend our previous work to correct the heading error by aiding it using Multiple Polygon Areas (MPA) with adaptive weighting factor. We termed the approach as Adaptive Cardinal Heading Aided Inertial Navigation (A-CHAIN). We formulated an adaptive weighting factor applied to measurement noise to enhance measurement confidence. We then incorporated MPA heading into the algorithm, whereas multiple buildings with the same orientation are grouped together and assigned a specific heading information as a priori. Results shown that against the original CHAIN, the proposed Adaptive-CHAIN improved the position accuracy by more than five-fold.

This letter considers optimal control of a quadrotor unmanned aerial vehicles (UAV) using the discrete-time, finite-horizon, linear quadratic regulator (LQR). The state of a quadrotor UAV is represented as an element of the matrix Lie group of double direct isometries, SE 2 (3). The nonlinear system is linearized using a left-invariant error about a reference trajectory, leading to an optimal gain sequence that can be calculated offline. The reference trajectory is calculated using the differentially flat properties of the quadrotor. Monte-Carlo simulations demonstrate robustness of the proposed control scheme to parametric uncertainty, state-estimation error, and initial error. Additionally, when compared to an LQR controller that uses a conventional error definition, the proposed controller demonstrates better performance when initial errors are large.

Automatic vehicle detection in urban traffic surveillance is an important and urgent issue, since it provides necessary information for further processing. Conventional techniques utilize either motion segmentation or appearance-based detection, which involves either poor adaptation or high computation. The complexity of urban traffic scenarios lies in slow motion temporarily stopped or parked vehicles, dynamic background, and sudden illumination variations. In this paper, a new motion segmentation technique is proposed based on spatio-temporal background–foreground bimodal. The temporal background information is modeled using a weighted sigma–delta estimation, cumulative frame differencing is used to model the foreground pixels, and the spatial correlation between neighboring pixels is utilized to combine both background and foreground models. The median of consecutive frame difference adapts sudden illumination variation, update background model, and reinitialize foreground model. Comparative experimental results for typical urban traffic sequences show that the proposed technique achieves robust and accurate segmentation, which improves adaptation, reduce false detection, and satisfy real-time requirements.

The haze phenomenon exerts a degrading effect that decreases contrast and causes colour shifts in outdoor images. The presence of haze in digital images is bothersome, unpleasant, and, occasionally, even dangerous. The atmospheric light scattering (ALS) model is widely used to restore hazy images. In this model, two unknown parameters should be estimated: airlight and scene transmission. The quality of dehazed images depends considerably on the accuracy of both estimates. Classic methods typically determine airlight based on the brightest pixels in an image. However, in the traffic scene context, this estimate is compromised when other light sources, such as vehicle headlights from the opposite direction, are present. Transmission estimation is usually more complicated. Hence, the complexity of the overall dehazing process is dependent on this estimate. To address this issue, this study proposes a framework for constant-time airlight and linear transmission estimation. This framework consists of two methods: airlight by image integrals (ALII), which is utilized to estimate the airlight value in real time with high accuracy, and bounded transmission (BT), which is proposed for the linear and simplified estimation of transmission maps. To evaluate the proposed framework, three image datasets are used: (1) seven images that are gathered from the works of existing methods (called the global dataset); (2) the synthetic foggy road image database (FRIDA), which is a synthetically generated dataset for simulating different bad weather conditions; and (3) a dataset of images that were extracted from videos in Malaysia (IV-M), which consists of images that were extracted from traffic video sequences, which were captured in various weather conditions from 2014 to 2016 in Malaysia. Experimental results show that the proposed framework is at least seven times faster than existing methods. In addition, the ANOVA test proves that the quality of the dehazed images is statistically similar to or better than the image quality that was achieved using existing methods.

Vehicle detection is a fundamental step in urban traffic surveillance systems, since it provides necessary information for further processing. Conventional techniques utilize either background subtraction or foreground appearance-based detection, which involves either poor adaptation or high computation. The complexity of urban traffic scenarios lies in pose and orientation variations, slow or temporarily stopped vehicles and sudden illumination variations. In this work, a foreground-background bimodal is proposed to adapt for scene variation and complexity. Cumulative frame differencing and sigma-delta estimation are used to model foreground and background respectively. A correction feedback updates each model iteratively and recursively based on the detection mask of the other model. Variance update for sigma-delta estimation was limited to update background temporal activities, while cumulative frame differencing account for moving foreground by discarding limited background variations. Comparative experimental results for typical urban traffic sequences show that the proposed technique achieves robust and accurate detection, which improves adaptation, reduce false detection and satisfy real-time requirements.

We propose a multi-class mechanism for Optical Code Division Multiplexing (OCDM), Wavelength Division Multiplexing (WDM) Optical Packet Switch (OPS) architecture capable of supporting Quality of Service (QoS) transmission. OCDM/WDM has been proposed as a competitive hybrid switching technology to support the next generation optical Internet. This paper addresses performance issues in the slotted OPS networks and proposed four differentiation schemes to support Quality of Service. In addition, we present a comparison between the proposed schemes as well as, a simulation scheduler design which can be suitable for the core switch node in OPS networks. Using software simulations the performance of our algorithm in terms of losing probability, the packet delay, and scalability is evaluated.

Objectives: As a result of signal transmission in WiMAX too long distance, and because there are many request packets arrive at WiMAX gateway request handovers, this cost a congestion which may result in loss or delay in packet time sensitive. The objectives of this paper are to investigate the delay when packets with real time sensitive handover in WiMAX gateway and to measure and enhance the handover performance. Methods/Statistical Analysis: To overcome this problem, and achieve the objectives is propose a new scheme Resource Reservation Protocol & Random Early Detection with Weighted Fair Queuing (RR-WFQ) in order to analyze and enhance delay for real time packet in WiMAX gateway. This schema applies all Resource Reservation Protocol (RSVP), Random Early Detection (RED), and Weighted Fair Queuing (WFQ) as a Scheduling, RED as a mechanism and RSVP as a signaling protocol. Findings: The proposed scheme enhances the QoS by minimizing the delay in WiMAX gateway and BS. The scheme enhances the delay in all queues in a gateway, and video conferencing. The result of the scheme shows an enhancement delay in the WiMAX gateway. Application/ Improvements: Mathematical analysis and modeling have been done using queuing theory. Simulation has been done using OPNET simulation. Simulation results for the new approach confirms that has minimized delay in WiMAX gateway and BS the gateway and enhances the QoS. Keywords: Signaling Protocol, Proposed RR-WFQ Scheme, Queuing Management, Resource Reservation Protocol, WIMAX Scheduling

Recently, wireless sensor networks are beginning to be deployed at an accelerated pace. It is amazing to expect that the world will be covered with wireless sensor networks that are accessible via the Internet. This allows one to think of the Internet as a physical network. Wireless sensor networks are the base of wide range applications related environmental monitoring, transportation, national security, health care, surveillance, and military. In this article, recent contributions addressing energy-efficient coverage problems are surveyed through static wireless sensor networks. This article conveys the problem of maximizing networks lifetime for coverage and connectivity in wireless sensor networks. For providing sensing coverage to a set of points, target points; and for providing communication among the active sensor to transmit data at all times within the network, static sensor nodes should be randomly deployed in the region. Sensor nodes are energy-consumed and so only a minimum set of sensor nodes requires to be activated at any given time. Sensor nodes are respectively activated to achieve maximum lifetime of the network. Hence, the algorithm serves as an energy-efficient solution toward ensuring connected coverage in wireless sensor networks. It is worth mentioning that the optimal solution to the problem is NP-Complete that stimulates the need to discover efficient heuristic solutions.

Outdoor images that are captured in bad weather conditions have low contrast and infidelity colours. Under the turbid medium conditions such as haze, mist, fog and drizzle, the light which reaches to the sensor is attenuated by atmospheric particles. These atmospheric phenomena degrade the contrast intensity of outdoor images based on haze density. In this research, we present new method to improve both the intensity and fine details of outdoor scene images. The RGB (Red, Green and Blue) input image is converted to the HSI (Hue Saturation Intensity) colour space and the density of the haze is estimated. Then, we use Contrast Limited Adaptive Histogram Equalization (CLAHE) technique to enhance the degraded intensity based on the estimation of the density of the haze. Our method is effective in a wide range of weather conditions and under different levels of visibility.

Motion segmentation is a fundamental step for vehicle detection especially in urban traffic surveillance systems. Temporal frame differencing is the simplest and fastest technique that is used to identify foreground moving vehicles from static background scene. Conventional techniques utilize background modelling and subtraction, which involves poor adaptation under slow or temporarily stopped vehicles. To address this problems cumulative frame differencing (CFD) is proposed. Dynamic threshold value based on the standard deviation of CFD is used to estimate global variance of the motion accumulated variations of pixel intensity. The tests of the proposed technique achieve robust and accurate vehicle segmentation, which improves detection of slow motion, temporary and long term stopped vehicles, moreover, it enables the real-time capability.

The key issue of network performance in photonic packet switches is a Packet contention. A proper contention scheme must be used to avoid early packet switching congested. In the Optical Packet Switching Architecture using Fiber Delay Line (FDL) has been used with Optical Code Division Multiplexing and Wavelength Division Multiplexing (OCDM/WDM) techniques to solve the contention resolution, the packet delay becomes an important issue that needs to be reduced, as well as node cost. In this paper, we optimized the number of FDLs needed to be used in switching node where the total cost associated to the employment of FDLs is minimized subject to performance requirements. The optimal number of buffers is determined by simulation experiments. The result shows that 64 shared Feed-Back buffers and 64 shared Feed-Forward buffers are the optimum buffers for the hybrid OCDM/WDM technique.

Enhancing images that are plagued with weather related conditions; such as haze, fog and rain poses a challenging problem due to its ill-posed nature, which means the unknowns that need to be found are more than the equations that we have. To address such challenges, a fast yet robust method is proposed in this paper where unknowns in the light scattering model are estimated based on physically sound assumptions. Light scattering model describes the formation of those phenomena in an image as a combination of airlight and the original scene, where this combination is controlled by how much transmission value present at the scene's point. The transmission value determines how much of the original scene's intensity were attenuated and how much airlight was added. The attenuation term of the light scattering model causes the reduction in contrast and the airlight term causes the effect of color shift. In this paper, Intensity Deterioration Ratio (IDR) and Saturation Deterioration Ratio (SDR) are proposed, where the former can be used to estimate the reduction of contrast and so gives a clue about the attenuation term, and the latter to estimate the reduction of chromaticity in a scene which gives a clue about the airlight term. IDR and SDR are therefore used to give us a new insight in using the light scattering model when enhancing images.