Search Results (247)

Recently, high-precision WSN (wireless sensor network) ranging and positioning algorithms based on RSSI (Received Signal Strength Indicator) in complex indoor environments have become a popular research topic. This is because RSSI is easy to obtain and more suitable for the large-scale deployment of WSNs. However, WSN ranging and positioning algorithms using RSSI are severely affected by the presence of noise and multipath effects in complex indoor environments. To reduce multipath effects, a multi-channel ranging algorithm was developed. This algorithm must obtain accurate initial parameter values or the target–reference distance in advance; otherwise, it will fall into local optima. We propose an environment-adaptive algorithm for multi-channel ranging optimization based on an innovative evolutionary model with multiple objectives and an existing adaptive extended Kalman filter. This novel model includes a newly created objective function of the relationship between weighted multi-channel RSSI and node distance, which allows it to achieve globally optimal results without requiring extensive training to obtain accurate initial parameter values or the target–reference distance beforehand. Extensive simulations and experiments show that our proposed algorithm always has much higher ranging accuracy than the existing algorithm, regardless of whether the multi-channel RSSI is regular or the number of paths matches. Full article

With the development of technologies and the growing need for accurate positioning inside buildings, the localization method based on Received Signal Strength Indicator (RSSI) fingerprinting is becoming increasingly popular. Its popularity is explained by the relative simplicity of implementation, low cost and the ability to use existing wireless infrastructure. This review article covers all the key aspects of building such systems: from the wireless communication technology and the creation of a radiomap to data preprocessing methods and model training using machine learning (ML) and deep learning (DL) algorithms. Specific recommendations are provided for each stage that can be useful for both researchers and practicing engineers. Particular attention is paid to such important issues as RSSI signal instability, the impact of multipath propagation, differences between devices and system scalability issues. In conclusion, the review highlights the most promising areas for further research. For smart cities, the approaches and recommendations presented in the review contribute to the development of urban services by combining indoor positioning systems with IoT platforms for automation, transport and energy management. Full article

Integrated sensing and communication (ISAC) is considered a key enabler for the future Internet of Things (IoT), as it enables wireless networks to simultaneously support high-capacity data transmission and precise environmental sensing. Indoor localization, as a representative sensing service in ISAC, has attracted considerable research attention. Nevertheless, its performance is largely constrained by the quality and granularity of the collected data. In this work, we propose an attention-based framework for cost-efficient indoor fingerprint localization that exploits extended trajectory map construction through a novel trajectory-based data augmentation (TDA) method. In particular, fingerprints at unmeasured locations are synthesized using a conditional Wasserstein generative adversarial network (CWGAN). A path generation algorithm is employed to produce diverse trajectories and construct the extended trajectory map. Based on this map, a multi-head attention model with direction-constrained auxiliary loss is then applied for accurate mobile device localization. Experiments in a real 5G indoor environment demonstrate the system’s effectiveness, achieving an average localization error of 1.09 m and at least 34% higher accuracy than existing approaches. Full article

Indoor wireless networks face increasing challenges in maintaining stable coverage and performance, particularly with the widespread use of high-frequency Wi-Fi and growing demands from smart home devices. Traditional methods to improve signal quality, such as adding access points, often fall short in dynamic environments where user movement and physical obstructions affect signal behavior. In this work, we propose a system that leverages existing Internet of Things (IoT) devices to perform real-time user localization and network adaptation using fine-grained Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) measurements. We deploy multiple ESP-32 microcontroller-based receivers in fixed positions to capture wireless signal characteristics and process them through a pipeline that includes filtering, segmentation, and feature extraction. Using supervised machine learning, we accurately predict the user’s location within a defined indoor grid. Our system achieves over $82\%$ accuracy in a realistic laboratory setting and shows improved performance when excluding redundant sensors. The results demonstrate the potential of communication-based sensing to enhance both user tracking and wireless connectivity without requiring additional infrastructure. Full article

Device-to-device (D2D) communication enhances network efficiency by enabling direct, low-latency links between nearby users or devices. While most existing research assumes that D2D connections occur with the nearest neighbor, this assumption often fails in real-world scenarios—such as dense indoor environments, smart buildings, and industrial IoT deployments—due to factors like channel variability, physical obstructions, or limited user participation. In this paper, we investigate the performance implications of connecting to the n-th nearest neighbor in a cellular network supporting underlay D2D communication. Using a stochastic geometry framework, we derive and analyze key performance metrics, including the coverage probability and average data rate, for both D2D and cellular links under proximity-aware connection strategies. Our results reveal that non-nearest-neighbor associations are not only common but sometimes necessary for maintaining reliable connectivity in highly dense or constrained spaces. These findings are directly relevant to IoT-enhanced localization systems, where fallback mechanisms and adaptive pairing are essential for communication resilience. This work contributes to the development of proximity-aware and spatially adaptive D2D frameworks for next-generation smart environments and 5G-and-beyond wireless networks. Full article

The adoption of wireless medical technologies in hospital environments is often limited by cellular coverage issues, especially in indoor areas with complex structures. This study presents a detailed radio spectrum measurement campaign conducted at the Cova da Beira University Hospital Center (CHUCB), using the NARDA SRM-3006 and R&S^®TSME6 equipment. The signal strength and quality of 5G NR, LTE, UMTS, and NB-IoT technologies were evaluated. Critical coverage gaps were identified, particularly at points 17, 19, and 21. Results revealed that operators MEO and NOS dominate coverage, with MEO providing better 5G NR coverage and NOS excelling in LTE signal quality. Based on the results, the localized installation of femtocells is proposed to improve coverage in these areas. The approach was designed to be scalable and replicable, with a planned application at Cumura Hospital (Guinea-Bissau), reinforcing the applicability of the solution in contexts with limited infrastructure. This work provides both technical and clinical contributions to achieving ubiquitous cellular coverage in healthcare settings. Full article

Accurate and scalable indoor localization is a key enabler of intelligent automation in smart environments and industrial systems. In this paper, we present SelfLoc, a self-supervised indoor localization system that combines IEEE 802.11az Round Trip Time (RTT) and Received Signal Strength Indicator (RSSI) data to achieve fine-grained positioning using commodity Wi-Fi infrastructure. Unlike conventional methods that depend heavily on labeled data, SelfLoc adopts a contrastive learning framework to extract spatially discriminative and temporally consistent representations from unlabeled wireless measurements. The system integrates a dual-contrastive strategy: temporal contrasting captures sequential signal dynamics essential for tracking mobile agents, while contextual contrasting promotes spatial separability by ensuring that signal representations from distinct locations remain well-differentiated, even under similar signal conditions or environmental symmetry. To this end, we design signal-specific augmentation techniques for the physical properties of RTT and RSSI, enabling the model to generalize across environments. SelfLoc also adapts effectively to new deployment scenarios with minimal labeled data, making it suitable for dynamic and collaborative industrial applications. We validate the effectiveness of SelfLoc through experiments conducted in two realistic indoor testbeds using commercial Android devices and seven Wi-Fi access points. The results demonstrate that SelfLoc achieves high localization precision, with a median error of only 0.55 m, and surpasses state-of-the-art baselines by at least 63.3% with limited supervision. These findings affirm the potential of SelfLoc to support spatial intelligence and collaborative automation, aligning with the goals of Industry 4.0 and Society 5.0, where seamless human–machine interactions and intelligent infrastructure are key enablers of next-generation smart environments. Full article

In contemporary wireless communication systems, achieving precise localization of communicating devices and accurate channel estimation is crucial for enhancing operational efficiency and reliability. This study introduces a novel approach that integrates the localization task and channel estimation into a single framework. We present a multi-task neural network architecture capable of simultaneously estimating channels from multiple base stations in a blind manner while estimating user terminal coordinates in given indoor environments. This approach exploits the relationship between channel characteristics and spatial information, using the same channel state information (CSI) data to perform both tasks with a single model. We evaluate the proposed solution, assessing its effectiveness across differing antenna spacing configurations and indoor test environments using both WiFi and 5G orthogonal frequency-division multiplexing (OFDM) systems. The results show performance benefits, achieving comparable channel estimation results to other studies while simultaneously providing a localization estimate, resulting in reduced model overhead while leveraging spatial context. The presented system demonstrates potential to improve the efficiency of communication systems in real-world applications, aligning with the goals of emerging integrated sensing and communication (ISAC) systems. Results based on experimental data using the proposed solution show a 50th percentile localization error of 1.62 m for 3-tap channels and 0.89 m for 10-tap channels. Full article

Combustible gas leakage remains a critical safety concern in industrial and indoor environments, necessitating the development of detection systems that are both accurate and practically deployable. This study presents a wireless gas detection system that integrates a gas sensor array, a low-power microcontroller with Zigbee-based communication, and a Back Propagation (BP) neural network optimized via a sequential hybrid strategy. Specifically, Particle Swarm Optimization (PSO) is employed for global parameter initialization, followed by Dung Beetle Optimization (DBO) for local refinement, jointly enhancing the network’s convergence speed and predictive precision. Experimental results confirm that the proposed PSO-DBO-BP model achieves high correlation coefficients (above 0.997) and low mean relative errors (below 0.25%) for all monitored gases, including hydrogen, carbon monoxide, alkanes, and smog. The model exhibits strong robustness in handling nonlinear responses and cross-sensitivity effects across multiple sensors, demonstrating its effectiveness in complex detection scenarios under laboratory conditions within embedded wireless sensor networks. Full article

Accurate indoor localization in wireless sensor networks remains a non-trivial challenge, particularly in complex environments characterized by signal variability and multipath propagation. This study presents a ZigBee-based localization approach that integrates multi-stage preprocessing of received signal strength indicator (RSSI) data with a reliability-aware extension of the maximum likelihood estimation (MLE) algorithm. To improve measurement stability, a hybrid filtering framework combining Kalman filtering, Dixon’s Q test, Gaussian smoothing, and mean averaging is applied to reduce the influence of noise and outliers. Building on the filtered data, the proposed method introduces a noise and link quality indicator (LQI)-based dynamic weighting mechanism that adjusts the contribution of each distance estimate during localization. The approach was evaluated under simulated and semi-physical non-line-of-sight (NLOS) indoor conditions designed to reflect practical deployment scenarios. While based on a limited set of representative test points, the method yielded improved positioning consistency and achieved an average accuracy gain of 11.7% over conventional MLE in the tested environments. These results suggest that the proposed method may offer a feasible solution for resource-constrained localization applications requiring robustness to signal degradation. Full article

In the contemporary field of wireless sensing, passive sensing leveraging channel state information (CSI) has found widespread applications across diverse scenarios, including behavior recognition, keystroke recognition, breath detection, and indoor localization. To ensure optimal sensing performance, wireless devices often collect a substantial number of CSI packets. However, when these packets need to be transmitted to a server or the cloud for time series analysis, the transmission load on the passive sensing system escalates rapidly, thereby impeding the system’s real-time performance. To address this challenge, we introduce the KCS algorithm, a novel compressed sensing (CS) algorithm grounded in K-Singular Value Decomposition (KSVD). The primary objective of the KCS algorithm is to enable the efficient transmission of CSI data. Departing from the use of a universal sparse matrix in traditional CS, the KCS algorithm constructs an overcomplete sparse matrix. This construction not only substantially bolsters the sparse representation capacity but also fine-tunes the compression performance. By doing so, it ensures the secure and efficient transmission of data. We applied the KCS algorithm to human behavior recognition and prediction. The experimental outcomes reveal that even when the volume of CSI data is reduced by 90%, the system still attains an average accuracy of 90%. This showcases the effectiveness of the KCS algorithm in balancing data compression and recognition performance, offering a promising solution for realistic applications where efficient data transmission and accurate sensing are crucial. Full article

Localization plays a crucial role in wireless sensor networks (WSNs) and it has sparked significant research interest. GPSs provide quite accurate positioning estimations, but they are ineffective indoors and in environments like underwater. Power usage and cost are further disadvantages, and so many alternatives have been proposed. Many works in the literature still base localization on RSSI measurements and often rely on methods to mitigate the effects of fluctuations in values, so it is important to know real values of RSSIs measured using common devices. This work presents the main localization techniques and exploits a real testbed to collect and evaluate RSSI measurements. An accuracy evaluation and a comparison among several localization techniques are also provided. Full article

In response to the urgent demands of the Internet of Things for precise indoor target positioning and information interaction, this paper proposes a multi-band analog radio-over-fiber mobile fronthaul system. The objective is to obtain the target’s location in indoor environments while integrating remote beamforming capabilities to achieve wireless access to the targets. Vector signals centered at 3, 4, 5, and 6 GHz for indoor positioning and centered at 30 GHz for wireless access are generated centrally in the distributed unit (DU) and fiber-distributed to the active antenna unit (AAU) in the multi-band analog radio-over-fiber mobile fronthaul system. Target positioning is achieved by radiating electromagnetic waves indoors through four omnidirectional antennas in conjunction with a pre-trained neural network, while high-speed wireless communication is realized through a phased array antenna (PAA) comprising four antenna elements. Remote beamforming for the PAA is implemented through the integration of an optical true time delay pool in the multi-band analog radio-over-fiber mobile fronthaul system. This integration decouples the weight control of beamforming from the AAU, enabling centralized control of beam direction at the DU and thereby reducing the complexity and cost of the AAU. Simulation results show that the average accuracy of localization classification can reach 86.92%, and six discrete beam directions are achieved via the optical true time delay pool. In the optical transmission layer, when the received optical power is 10 dBm, the error vector magnitudes (EVMs) of vector signals in all frequency bands remain below 3%. In the wireless transmission layer, two beam directions were selected for verification. Once the beam is aligned with the target device at maximum gain and the received signal is properly processed, the EVM of millimeter-wave vector signals remains below 11%. Full article

Positioning information greatly enhances the convenience of people’s lives and the efficiency of societal operations. However, due to the impact of complex indoor environments, GNSS signals suffer from multipath effects, blockages, and attenuation, making it difficult to provide reliable positioning services indoors. Smartphone indoor positioning and navigation is a crucial technology for enabling indoor location services. Nevertheless, relying solely on a single positioning technique can hardly achieve accurate indoor localization. We reviewed several main methods for indoor positioning using smartphone sensors, including Wi-Fi, Bluetooth, cameras, microphones, inertial sensors, and others. Among these, wireless medium-based positioning methods are prone to interference from signals and obstacles in the indoor environment, while inertial sensors are limited by error accumulation. The fusion of multi-source sensors in complex indoor scenarios benefits from the complementary advantages of various sensors and has become a research hotspot in the field of pervasive indoor localization applications for smartphones. In this paper, we extensively review the current mainstream sensors and indoor positioning methods for smartphone multi-source sensor fusion. We summarize the recent research progress in this domain along with the characteristics of the relevant techniques and applicable scenarios. Finally, we collate and organize the key issues and technological outlooks of this field. Full article

This article describes the design, implementation, and evaluation of an indoor localization system based on Received Signal Strength Indicator (RSSI) measurements in wireless sensor networks. While the majority of the literature uses the IEEE 802.15 standard for this type of system, all of the measurements in this study were performed using a test bench operating under the IEEE 802.11ax standard in the 2.4 GHz band. RSSI is widely used due to its simplicity and availability; however, its accuracy is limited by signal attenuation, electromagnetic interference, and environmental variability. To mitigate these limitations, the present work proposes the implementation of advanced techniques, including weighted averages and positioning algorithms such as Min–Max, Maximum Likelihood, and trilateration, aiming to achieve an accuracy of 2 m in controlled conditions. The design also included a specialized test bench to calculate the coordinates and estimate the location of unknown nodes using anchor node positioning. This approach combines the simplicity of RSSI with optimized algorithms, providing a robust and practical solution for indoor localization. The results validate the system’s effectiveness and highlight its potential for future applications in real-world environments, opening new possibilities for optimizing wireless sensor networks and addressing the current challenges in localization systems. Full article