Indoor localization of Unmanned Vehicles is critical for tasks requiring low latency and low power consumption. Channel State Information (CSI) systems provide a promising approach by capturing fine-grained signal features, but often suffer from limited accuracy in complex environments or high computational cost. To address this, we propose MambaLoc, an indoor localization framework that balances efficiency and precision by leveraging Mamba architectures with cross-modal knowledge distillation. In offline training, a Transformer based on Ultra-Wideband (UWB) data acts as a teacher to guide the CSI-based Mamba student, improving robustness to multipath effects without relying on UWB during inference. The distillation process is further enhanced using a Gaussian Mixture Model (GMM) in both teacher and student, enabling effective transfer of UWB-derived spatial representations. Evaluations on real-world environments show that MambaLoc reduces MAE by 22.53% over a baseline Mamba, while cutting FLOPs by 65.1% and inference time by 58% compared to a Transformer model, making it a practical and deployable solution for UV localization.

Despite significant advances in autonomous driving, cross-city deployment remains challenging due to variations in traffic patterns, urban driving environments, and local regulations that require expensive retraining for each new location. Existing end-to-end driving systems trained on specific cities exhibit poor generalization when deployed elsewhere, limiting scalability. In this paper, we propose GeoDrive, a meta-learning framework that enables rapid adaptation to new cities with minimal local data. We combine Model-Agnostic Meta-Learning (MAML) with spatial context from OpenStreetMap to learn generalized driving policies that rapidly adapt to city-specific traffic dynamics. GeoDrive integrates a Large Language Model (LLM) module for interpretable decision-making that incorporates region-specific regulations and behavioral patterns. Experimental results show 18% and 32% improvements in trajectory prediction for intra-city and cross-city settings, respectively.

Brain-Computer Interfaces (BCIs), e.g., Neuralink, are evolving to enable seamless interaction and control across various domains, such as assistive systems and virtual environments. While conventional BCI applications treat users as control units that transmit commands to devices, our vision introduces a paradigm shift toward the novel concept of Human-as-a-Sensor (HaaS), where users themselves function as intelligent multimodal sensing agents. HaaS leverages BCIs to extract contextual spatial information from brain signals as individuals naturally interact with their environment. By decoding neural activity, HaaS complements the limitations of traditional sensors and offers unique advantages for spatially-aware systems. We explore a range of opportunities enabled by HaaS for enhancing spatial awareness and outline multi-disciplinary research challenges in realizing our vision. We also present a feasibility study on using HaaS to track human mobility, showcasing the promise of HaaS for powering future spatially-aware systems.

Schistosomiasis remains a significant public health challenge in Africa, where it is considered endemic, particularly in areas with inadequate access to clean water. This study focuses on mitigating the spread of schistosomiasis by monitoring the physicochemical parameters of water sources to detect the proliferation of infected snails, which serve as the intermediate host for the parasite. We propose a system that leverages sensing technology to monitor water physicochemical parameters, e.g., temperature (Temp), pH, and electrical conductivity (EC) and uses AI to predict their future values, alongside the dynamics of the infected snail population, which are crucial for schistosomiasis transmission. By forecasting these trends, the system facilitates proactive interventions, such as water treatment and sanitation improvements. To accomplish this, we leverage FNet, a model that effectively captures temporal features through discrete Fourier transforms (DFT), delivering Transformer-level accuracy while being more resource-efficient. While FNet is originally tailored for language processing tasks, we adapt it for time series analysis by employing a quantization technique. This approach is particularly advantageous in contexts where computational and operational efficiency is critical, making it well-suited for environments with limited infrastructure or financial flexibility. Our evaluation demonstrates that the FNet model outperforms transformers in terms of resource efficiency while maintaining similar performance, making it a promising tool for real-time monitoring and early detection of schistosomiasis risk. The results highlight the potential of this approach to enhance disease control efforts and contribute to improved public health outcomes in endemic regions.

Machine learning and deep learning are becoming more popular in the area of indoor localization leveraging the Wi-Fi Received signal strength demonstrating high performance and scalability. However, such models usually perform poorly in noisy environments, leading to reduced trust in their reliability. In this work, we propose a novel framework that enhances trust in AI-based localization by using explainable AI techniques for automatic real time auditing of model behavior. Our approach detects and corrects any unusual behavior on-the-fly. Experiments in a testbed and two challenging real-world settings demonstrate that our framework effectively stabilizes performance and maintains trust in the localization models.

WiFi-based indoor localization is essential but often hampered by its dependence on specific environments and network configurations. We propose a novel solution using Language Models, like BERT, to overcome these limitations. Our approach transforms WiFi RSSI data into a language-like format, enabling the system to generalize across different buildings and AP setups. Key to our method is the Virtual Space Mapper, which standardizes user and Access Point (AP) locations into a common coordinate system, and then the system adapts BERT to predict locations within this virtual space. This design allows for robust, accurate localization without the need for extensive recalibration. Our results show significant improvements in adaptability and accuracy, making our approach a scalable, flexible alternative to traditional WiFi localization methods.

Indoor localization is critical for enabling a wide range of location-based services such as navigation, security, and contextual computing in complex indoor environments. Despite significant advances, the deployment of indoor localization systems in real-world settings remains limited due to challenges posed by non-independent and identically distributed (non-IID) data and device heterogeneity. In this paper, we propose SimDeep, a novel Federated Learning (FL) framework designed to tackle these challenges. SimDeep introduces a Similarity Aggregation Strategy to aggregate model updates based on client similarity, thereby effectively addressing the non-IID issue. Experimental results demonstrate that SimDeep achieves 92.89% accuracy, outperforming traditional federated and centralized techniques, making it a promising solution for practical deployment.

Recent transportation mode detection systems propose leveraging signals from only the serving cell tower to ensure ubiquity and practical deployability across all phones. However, existing solutions employ limited statistical hand-engineered features and traditional machine learning classifiers, leading to low estimation accuracy.

Designing a globally ubiquitous positioning system that works seamlessly in all environments remains a critical area of ongoing research. While Global Navigation Satellite Systems (GNSS), such as GPS, are the predominant technology for global outdoor positioning, they fail in areas with physical obstructions (e.g., dense urban regions and indoors) and are prone to jamming. These limitations significantly restrict their global accessibility and dependability, highlighting the need for supplementary positioning technologies.

WiFi fingerprinting systems for indoor localization have improved drastically with the advent of deep learning. However, these systems are often designed to be used on the same testbeds they are trained on, making generalizations to unknown testbeds hard. Additionally, any changes to Access Point configurations can drastically impact system performance. These systems often require collecting new data, retraining, and/or fine-tuning, which are time-consuming and costly. To address these issues, we propose a novel localization framework that can adapt to varying environments without recalibration. This is achieved by utilizing a "virtual space" via Graph Neural Networks, enhancing adaptability and system performance.

Floor identification has gained much attention due to the increasing demand for indoor location-based services, especially prompt emergency response services. Leveraging Cellular signals for floor identification has been of recent interest due to the pervasiveness of cellular technology. However, all current systems rely on information from multiple cell towers concurrently, which is inaccessible in most phones and thus severely limits their deployability.

Indoor localization systems have become increasingly important in a wide range of applications, including industry, security, logistics, and emergency services. However, the growing demand for accurate localization has heightened concerns over privacy, as many localization systems rely on active signals that can be misused by an adversary to track users’ movements or manipulate their measurements. This paper presents PassiFi, a novel passive Wi-Fi time-based indoor localization system that effectively balances accuracy and privacy. PassiFi uses a passive WiFi Time Difference of Arrival (TDoA) approach that ensures users’ privacy and safeguards the integrity of their measurement data while still achieving high accuracy. The system adopts a fingerprinting approach to address multi-path and non-line-of-sight problems and utilizes deep neural networks to learn the complex relationship between TDoA and location. Evaluation in real-world testbed demonstrates PassiFi’s exceptional performance, surpassing traditional multilateration by 128%, achieving sub-meter accuracy on par with state-of-the-art active measurement systems, all while preserving privacy.

We propose SoLoc, a lightweight probabilistic fingerprinting-based technique for energy-free device-free indoor localization. The system harnesses photovoltaic currents harvested by the photovoltaic cells in smart environments for simultaneously powering digital devices and user positioning. The basic principle is that the location of the human interferes with the lighting received by the photovoltaic cells, thus producing a location fingerprint on the generated photocurrents. To ensure resilience to noisy measurements, SoLoc constructs probability distributions as a photovoltaic fingerprint at each location. Then, we employ a probabilistic graphical model for estimating the user location in the continuous space. Results show that SoLoc can localize the user at sub-meter accuracy in a real indoor environment.

Recently, deep learning-based localization systems have become one of the most promising techniques due to their accuracy in complex environments. However, these techniques require large amounts of data for training. Obtaining such data is usually a tedious and time-consuming process, which hinders their practical deployment. In this paper, we propose a data augmentation framework for deep learning-based localization systems. The basic idea is to use a conditional Generative Adversarial Network that is able to learn the complex structures in the original training data and then generate high-quality synthetic data that matches the original data distribution. Evaluation of the proposed data augmentation framework in a real testbed shows that our technique can increase the average localization accuracy by 22.2% compared to the case of not using data augmentation. This demonstrates the promise of the proposed framework for enhancing deep learning-based localization systems.

Indoor localization is a key component of pervasive and mobile computing. Due to the widespread use of WiFi technology, WiFi fingerprinting is one of the most widely utilized approaches for indoor localization. Despite advancements in WiFi-based positioning approaches, existing solutions necessitate a dense deployment of access points, time-consuming manual fingerprinting, and/or special hardware.

Quantum computing provides a new way for approaching problem solving, enabling efficient solutions for problems that are hard on classical computers. With researchers around the world showing quantum supremacy and the availability of cloud-based quantum computers, quantum computing is becoming a reality. In this paper, we explore the different directions of the use of quantum computing for location tracking and spatial systems. Specifically, we show an example for the expected gain of using quantum computing for localization by providing an efficient quantum algorithm for RF fingerprinting localization. The proposed quantum algorithm has a complexity that is exponentially better than its classical algorithm version, both in space and running time. We further discuss both software and hardware research challenges and opportunities that researchers can build on to explore this exciting new domain.

Location services are one of the most used applications today on mobile devices. The vast majority of localization systems propose solutions for locating the user in a 2D single floor environment. However, accurate estimation of the user's floor level, in tall multistory buildings, is a crucial basis for many applications, especially for emergency services.

Location-based services have undergone massive improvements over the last decade. Despite intense efforts in industry and academia, a pervasive infrastructure-free localization is still elusive. Towards making this possible, cellular-based systems have recently been proposed due to the wide-spread availability of the cellular networks and their support by commodity cellphones. However, these systems only consider locating the user in a 2D single floor environment, which reduces their value when used in multi-story buildings.

Robust and accurate indoor localization has been the goal of several research efforts over the past decade. Towards achieving this goal, WiFi fingerprinting-based indoor localization systems have been proposed. However, fingerprinting involves significant effort; especially when done at high density; and needs to be repeated with any change in the deployment area. While a number of recent systems have been introduced to reduce the calibration effort, these still trade overhead with accuracy.

The demand for a ubiquitous and accurate indoor localization service is continuously growing. Despite the pervasive nature of cellular-based solutions, their localization quality depends on the number of cell towers provided by the phone, which is typically limited. Specifically, according to the standard, any cell phone can receive signal strength information from up to seven cell towers. However, the majority of cell phones usually return only the associated cell tower information, significantly limiting the amount of information available to the location determination algorithm, degrading its performance.

A WiFi-based landmark (LM) is a unique point in the physical space that has a repeatable and identifiable WiFi signature as sensed by a mobile device. We present Lighthouse, a new class of WiFi landmarks based on concepts from computational geometry theory that can be leveraged to provide worldwide robust and accurate location based services (LBSs). The proposed Lighthouse landmarks have the nice properties of being abundant in space, hardware-and carry position-independent, can be computed efficiently from a single scan, are confined to a small area of space, do not restrict the user movement path, and do not require any calibration. We show that the positioning error of the Lighthouse landmarks is bounded and present the different extensions that allow it to handle practical situations including the noisy wireless channel, different AP transmit powers, obstacles in the environment, among others. We further present efficient algorithms for extracting them from WiFi scans. We have implemented and evaluated Lighthouse using thousands of surveys collected from different cities worldwide over a six months period. Our results show that Lighthouse's landmarks are one order of magnitude more frequent in the environment compared to the other state-of-the-art WiFi-based landmarks. In addition, the median accuracy of determining the LMs location is less than 3.6 meters. This accuracy is robust over time, different phones hardware, phone carrying positions, and parameters configurations; highlighting the promise of Lighthouse landmarks for enabling the next generation LBSs on a worldwide scale.

Recent years have witnessed fast growth in outdoor location-based services. While GPS is considered a ubiquitous localization system, it is not supported by low-end phones, requires direct line of sight to the satellites, and can drain the phone battery quickly.

A WiFi-based landmark (LM) is a unique point in the physical space that has a repeatable and identifiable WiFi signature as sensed by a mobile device. We present Lighthouse, a new class of WiFi landmarks based on concepts from computational geometry theory that can be leveraged to provide robust and accurate location based services (LBSs). Our experimental results show that Lighthouse's landmarks are one order of magnitude more frequent in the environment compared to the other state-of-the-art WiFi-based landmarks. In addition, the median accuracy of determining the LMs location is 3.4 meters which is robust under different conditions; highlighting the promise of Lighthouse landmarks for enabling the next generation LBSs.

Designing indoor and outdoor spaces to become accessible for people with disabilities is of paramount importance. Accessibility leads to improvements in human rights and business outcomes; due to inclusion of a broader range of the population. For example, adding braille writing to signs and installing ramps allow visually-impaired people and the wheel-chaired to navigate on their own.

WiFi fingerprinting is one of the mainstream technologies for indoor localization. However, it requires an initial calibration phase during which the fingerprint database is built manually by site surveyors. This process is labour intensive, tedious, and needs to be repeated with any change in the environment. While a number of recent systems have been introduced to reduce the calibration effort through RF propagation models and/or crowdsourcing, these still have some limitations. Other approaches use the recently developed iBeacon technology as an alternative to WiFi for indoor localization. However, these beacon-based solutions are limited to a small subset of high-end phones.

Map matching has been used to reduce the noisiness of the location estimates by aligning them to the road network on a digital map. A growing number of applications, e.g. energy-efficient localization and cellular provider side localization, depend on the availability of only sparse and coarse-grained positioning data; leading to a challenging map matching process.

We present the design, implementation, and evaluation of NaviRize: a novel system that can provide calibration-free, accurate RF-based indoor localization that works in realistic environments on heterogeneous devices. NaviRize is based on the space partitioning of the installed RF Transmitters (RFTXs) and exploits constraints based on the mutual Received Signal Strength(RSS) relation between each pair of RFTXs to further enhance the estimated user location. Evaluation of NaviRize shows that it can achieve a consistent median accuracy of 2.8m under different scenarios.

We present PassActiv, a mobile sensing system for the automatic activity recognition of railway passengers. Our key observations show that certain passengers' activities (e.g., purchasing tickets, etc) present identifiable signatures on one or more cell-phone sensors which can be leveraged to automatically recognize those activities. Evaluation of PassActiv through a field experiment in major train and subway stations in Japan shows that PassActiv can detect different activities accurately with at most 3% false positive rate and 4% false negative rate for all types of passengers' activities.

Although GPS has been considered a ubiquitous outdoor localization technology, we are still far from a similar technology for indoor environments. While a remarkable effort has been put in developing indoor location determination systems, they are still isolated efforts that are tailored to specific deployments. A truly ubiquitous indoor positioning system is envisioned to be deployed on a large scale worldwide, with minimum overhead, to work with heterogeneous devices, and to allow users to roam seamlessly from indoor to outdoor environments. Such a system will enable a wide set of applications including worldwide seamless direction finding between indoor locations, enhancing first responders' safety by providing anywhere localization and floorplans, and providing a richer environment for location-aware social networking applications.

We present the SnapNet, a system that provides accurate real-time map matching for cellular-based trajectories. Such coarse-grained trajectories introduce new challenges to map matching including (1) input locations that are far from the actual road segment (errors in the orders of kilometers), (2) back-and-forth transitions, and (3) highly sparse input data. SnapNet addresses these challenges by applying extensive preprocessing steps to remove the noisy locations and to handle the data sparseness. At the core of SnapNet is a novel incremental HMM algorithm that combines digital map hints and a number of heuristics to reduce the noise and provide real-time estimation. Evaluation of SnapNet in different cities covering more than 100km distance shows that it can achieve more than 90% accuracy under noisy coarse-grained input location estimates. This maps to over 97% and 34% enhancement in precision and recall respectively when compared to traditional HMM map matching algorithms. Moreover, SnapNet has a low latency of 1.2ms per location estimate.

We present Dejavu, a system that uses standard cell-phone sensors to provide accurate and energy-efficient outdoor localization suitable for car navigation. Our analysis shows that different road landmarks have a unique signature on cell-phone sensors; For example, going inside tunnels, moving over bumps, going up a bridge, and even potholes all affect the inertial sensors on the phone in a unique pattern. Dejavu employs a dead-reckoning localization approach and leverages these road landmarks, among other automatically discovered abundant virtual landmarks, to reset the accumulated error and achieve accurate localization. To maintain a low energy profile, Dejavu uses only energy-efficient sensors or sensors that are already running for other purposes.

The existence of a worldwide indoor floorplans database can lead to significant growth in location-based applications, especially for indoor environments. In this paper, we present CrowdInside: a crowdsourcing-based system for the automatic construction of buildings floorplans. CrowdInside leverages the smart phones sensors that are ubiquitously available with humans who use a building to automatically and transparently construct accurate motion traces. These accurate traces are generated based on a novel technique for reducing the errors in the inertial motion traces by using the points of interest in the indoor environment, such as elevators and stairs, for error resetting. The collected traces are then processed to detect the overall floorplan shape as well as higher level semantics such as detecting rooms and corridors shapes along with a variety of points of interest in the environment.