The rapid expansion of the Internet of Things (IoT) has created a growing demand for large-scale sensor deployment. However, the high cost of physical sensors limits the scalability and coverage of sensor networks, making fine-grained sensing difficult. Inductive Spatio-Temporal Kriging (ISK) addresses this challenge by introducing virtual sensors that infer measurements from physical sensors, typically using graph neural networks (GNNs) to model their relationships. Despite its promise, current ISK methods often rely on standard message-passing and generic architectures that fail to effectively capture spatio-temporal features or represent virtual nodes accurately. Additionally, existing graph construction techniques suffer from sparse and noisy connections, further hindering performance. To address these limitations, we propose DarkFarseer, a novel ISK framework with three key innovations. First, the Style-enhanced Temporal-Spatial architecture adopts a temporal-then-spatial processing scheme with a temporal style transfer mechanism to enhance virtual node representations. Second, Regional-semantic Contrastive Learning improves representation learning by aligning virtual nodes with regional component patterns. Third, the Similarity-Based Graph Denoising Strategy mitigates the influence of noisy edges by leveraging temporal similarity and regional structure. Extensive experiments on real-world datasets demonstrate that DarkFarseer significantly outperforms state-of-the-art ISK methods.

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.

Missing values are prevalent in multivariate time series forecasting, especially when certain variables are entirely missing, posing a significant challenge to traditional methods. Two-stage models that combine imputation and forecasting methods are prone to introduce bias and lead to error accumulation. In contrast, existing end-to-end forecasting models with missing data recovery components fail to ensure data consistency when missing variables. Diffusion models, known for their robust generative capabilities, prefer to generate consistent results based on the available observations. Consequently, we propose a novel end-to-end denoising diffusion model named DiffMissing for multivariate time series forecasting with variable missing. DiffMissing employs a contextual conditional encoder to extract local and global contextual information from the observed variables, assisting the denoising network in generating missing variables that align with the true distribution. Meanwhile, the dynamic interaction between observed and missing variables is modeled through a carefully designed adaptive sparse attention, ensuring consistency among variables. Extensive experimental results show that DiffMissing outperforms existing methods in prediction performance on multiple real-world datasets, especially in the high missing rate scenario of 90%, where the MAE is improved by an average of 9.90% over the best baseline.

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.

The need for accurate and scalable modeling of 5G and beyond networks has recently emerged, driven by the reliance on virtual network functions (VNFs) and network slicing. Unfortunately, traditional network modeling approaches fail to capture 5G network behavior since they disregard the domainspecific challenges of modeling 5G networks. We propose vChainNet, the first end-to-end network modeling framework that provides accurate, scalable, and generalizable per-VNF and slice-level modeling for 5G and beyond networks. vChainNet introduces a modular, sequence-to-sequence deep learning architecture that models each VNF independently and composes the per-VNF models for end-to-end slice delay prediction. Our system design overcomes key domain-specific challenges in modeling 5G networks, including the functional variability of VNFs, VNFs' stochastic behavior, and the scale introduced by network densification. To address these challenges, vChainNet tunes a lightweight model composed of over$\mathbf{9 5 \%}$fewer parameters than state-of-the-art models, fuses domain-specific manually-engineered features with automatic feature extraction, and optimizes a distribution-based loss function during model training. Our results show that vChainNet achieves an accuracy improvement up to 10.86 % compared to state-of-the-art traditional network models, while providing a speedup of up to 53.33 times over common packet-level simulators. Furthermore, vChainNet's reliance on tuning a lightweight model allows it to generalize to unseen VNF types without extra hyperparameter tuning efforts. These results demonstrate the accuracy, scalability, and generalization ability of vChainNet for modeling 5G and beyond networks.

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.

All available cellular-based floor identification systems require information from multiple cell towers simultaneously, a feature absent in almost all phones, thus constraining their practicality. To address this limitation, we propose CellFloor, the first floor identification system to achieve high accuracy using only the serving cell tower while being regulatory-compliant. Based on recent advances in NLP, CellFloor builds a domain-specific edge-deployed large language model to identify the floor given a sequence of serving tower signal measurements. Our novel NLP-inspired approach allows CellFloor to extract rich contextual patterns from the signals, overcoming the limited information available when only the serving tower is used. Moreover, CellFloor employs recent advances in deep generative models to improve robustness against serving tower signal variations, enhancing floor identification accuracy. Our extensive evaluation of CellFloor shows consistently re-markable accuracy on multiple real testbeds, where it accurately estimates the exact floor at least 99.49% of the time using only the serving tower. This accuracy is superior to state-of-the-art (SOTA) systems, even when they use all available towers. Furthermore, unlike CellFloor, most SOTA systems fail to meet regulatory requirements when restricted to using just the serving tower, which is the only information available from the majority of current phones in the market. CellFloor also maintains regulatory compliance under different challenging conditions, including using only 20% of the available training data.

In this paper, we design a system to generate Pseudo Ground Control Points (PGCPs) using standard low-cost widely available GNSS receivers in a crowd-sourcing manner. We propose a number of GNSS points filters that removes different causes of errors and biases, and design a linear regression height estimator leading to high-accuracy PGCP elevations. Evaluation of our system shows that the PGCPs can achieve a median accuracy of 22.5 cm in 25 metropolitan areas in the USA.

Accurate floor localization in a multi-story environment is an important but challenging task. Among the current floor localization techniques, fingerprinting is the mainstream technology due to its accuracy in noisy environments. To achieve accurate floor localization in a building with many floors, we have to collect sufficient data on each floor, which needs significant storage and running time; preventing fingerprinting techniques from scaling to support large multi-story buildings, especially on a worldwide scale.In this paper, we propose a quantum algorithm for accurate multi-story localization. The proposed algorithm leverages quantum computing concepts to provide an exponential enhancement in both space and running time compared to the classical counterparts. In addition, it builds on an efficient binary-neuron implementation that can be implemented using fewer qubits compared to the typical non-binary neurons, allowing for easier deployment with near-term quantum devices. We implement the proposed algorithm on a real IBM quantum machine and evaluate it on three real indoor testbeds. Results confirm the exponential saving in both time and space for the proposed quantum algorithm, while keeping the same localization accuracy compared to the traditional classical techniques, and using half the number of qubits required for other quantum localization algorithms.

Effective access points (APs) selection is a crucial step in localization systems. It directly affects both localization accuracy and computational efficiency. Classical APs selection algorithms are usually computationally expensive, hindering the deployment of localization systems in a large worldwide scaleIn this paper, we introduce a quantum APs selection algorithm for large-scale localization systems. The proposed algorithm leverages quantum annealing to eliminate redundant and noisy APs. We explain how to formulate the APs selection problem as a quadratic unconstrained binary optimization (QUBO) problem, suitable for quantum annealing, and how to select the minimum number of APs that maintain the same overall localization system accuracy as the complete APs set. Based on this, we further propose a logarithmic-complexity algorithm to select the optimal number of APs.We implement our quantum algorithm on a real D-Wave Systems quantum machine and assess its performance in a real test environment for a floor localization problem. Our findings reveal that by selecting fewer than 14% of the available APs in the environment, our quantum algorithm achieves the same floor localization accuracy as utilizing the entire set of APs and a superior accuracy over utilizing the reduced dataset by classical APs selection counterparts. Moreover, the proposed quantum algorithm achieves more than an order of magnitude speedup over the corresponding classical APs selection algorithms, emphasizing the efficiency of the proposed quantum algorithm for large-scale localization systems.

Smartphones have seamlessly integrated into our lives, serving as versatile tools for myriad tasks. Leveraging their ubiquitous presence and advanced sensors, recent research aims to replace costly dedicated hardware with smartphone-based solutions. In this poster, we introduce SmartScale, a novel system that estimates objects' weight using smartphone sensors. Our approach combines the smartphone's accelerometer and vibration motor to form the foundation of our methodology. By orchestrating the interplay between controlled oscillations and motion sensitivity, we detect dynamic responses to varying loads with precision. SmartScale leverages Recurrent Neural Networks to accurately compute mass, eliminating the need for specialized equipment and making weight detection accessible and cost-effective. To validate our approach, we created a dataset of a diverse range of weights across various phone models, achieving a 92% accuracy. This finding not only demonstrates the efficacy of our approach but also emphasizes smartphones' evolution from communication devices to intelligent assistants, reshaping our interaction with the world.

Mobile technologies, particularly smartphones, have become integral to human life, influencing nearly every aspect of daily activities. Their ubiquity and advanced capabilities have led to them being a solution to a wide range of problems. Recent research has focused on leveraging the existing sensors in smartphones to supplant more costly and less accessible dedicated hardware. In this vein, we introduce MobiScale, a novel system designed to measure the weight of light objects using the built-in accelerometer and vibration motor of smartphones. This system utilizes advanced feature extraction techniques to effectively capture the temporal and spectral characteristics of the weight data. The core of MobiScale is its CNN-LSTM architecture, which processes these features to estimate weights accurately. The implementation of various regularization techniques has been vital in enhancing the system's ability to generalize, thereby improving its performance. Tested across multiple devices and under various conditions, MobiScale has shown consistent and precise performance, achieving a mean square error of just 12 grams in weight estimation, underscoring its potential as a versatile and reliable tool for weight measurement using smartphone technology.

Locating the persons moving through an environment without the necessity of them being equipped with special devices has become vital for many applications, including security, IoT, healthcare, etc. Existing device-free indoor localization systems commonly rely on the utilization of received signal strength indicator (RSSI) and WiFi channel state information (CSI) techniques. However, the accuracy of RSSI is adversely affected by environmental factors like multipath interference and fading. Additionally, the lack of standardization in CSI necessitates the use of specialized hardware and software. In this article, we present TimeSense, a deep learning-based multiperson device-free indoor localization system that addresses these challenges. TimeSense leverages Time of Flight information acquired by the fine-time measurement protocol of IEEE 802.11-2016 standard. Specifically, the measured round trip time between the transmitter and receiver is influenced by the dynamic changes in the environment induced by human presence. TimeSense effectively detects this anomalous behavior using a stacked denoising auto-encoder model, thereby estimating the user’s location. The system incorporates a probabilistic approach on top of the deep learning model to ensure seamless tracking of the users. The evaluation of TimeSense in two realistic environments demonstrates its efficacy, achieving a median localization accuracy of 1.57 and 2.65 meters. This surpasses the performance of state-of-the-art techniques by 49% and 103% in the two testbeds.

The recent release of the third-generation partnership project, Release 17, calls for sub-meter cellular positioning accuracy with reduced latency in the calculation. To provide such high accuracy on a worldwide scale, leveraging the received signal strength (RSS) for positioning promises ubiquitous availability in the current and future equipment. RSS Fingerprint-based techniques have shown great potential for providing high accuracy in both indoor and outdoor environments. However, fingerprint-based positioning faces the challenge of providing a fast matching algorithm that can scale worldwide. In this paper, we propose a cosine similarity-based quantum algorithm for enabling fingerprint-based high accuracy and worldwide positioning that can be integrated with the next generation of 5G and 6G networks and beyond. By entangling the test RSS vector with the fingerprint RSS vectors, the proposed quantum algorithm has a complexity that is exponentially better than its classical version as well as the state-of-the-art quantum fingerprint positioning systems, both in the storage space and the running time. We implement the proposed quantum algorithm and evaluate it in a cellular testbed on a real IBM quantum machine. Results show the exponential saving in both time and space for the proposed quantum algorithm while keeping the same positioning accuracy compared to the traditional classical fingerprinting techniques and the state-of-the-art quantum algorithms.

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.

WiFi localization systems have become increasingly popular owing to the ubiquity of WiFi-enabled devices. However, traditional WiFi networks were not originally designed for localization purposes, and changes to the Access Point locations can significantly drop localization performance. Moreover, these systems are unable to generalize models trained in one building to other buildings due to dependencies on building characteristics and their WiFi configurations. To address this challenge, we propose GlobLoc: the first global indoor positioning system that can function in any environment in a plug-and-play manner without requiring recalibration. Towards this end, we introduce a novel concept called the “virtual environment” in which users are tracked rather than the physical counterpart. This enables the localization model trained on one environment to function in another, regardless of changes occurring in the network environment, as they share the same virtual environment. Our experiments demonstrate that GlobLoc outperforms existing state-of-the-art propagation- and fingerprinting-based systems by at least 65%. This superior accuracy is due to its robustness to changes in the WiFi environment and its adaptability to different building configurations.

Quantum computing is a fast-developing field that has the ability to tackle complex problems that conventional computers struggle with. Recently, quantum fingerprinting localization has been introduced, which allows for the implementation of large-scale location determination systems across the globe. In this paper, we introduce QRadar, a localization system that uses quantum similarity fingerprints based on the Euclidean similarity metric, which is an early and widely used measure. The computational complexity of QRadar is exponentially superior to classical systems. We explain how to generate the quantum fingerprint, how to encode the received signal strength (RSS) measurements as quantum particles, and we describe the quantum algorithm for computing the Euclidean similarity distance between the online RSS measurements and the fingerprint ones. Additionally, we investigate various sources of errors in quantum machines and how they affect the accuracy of QRadar. We then discuss how to choose an appropriate quantum machine to minimize localization errors.We installed QRadar on a real IBM Quantum Experience machine. The results we obtained from both the installation and simulations conducted on two real test sites confirm that QRadar can accurately determine the estimated location with a significant improvement in processing time when compared to conventional classical methods.

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.

The need for a ubiquitous and accurate transportation mode estimation system has recently risen. Unfortunately, GPS-based and inertial sensor-based solutions lack this needed ubiquity and large-scale deployability, especially in developing countries. Thus, novel systems have proposed leveraging the more ubiquitous cellular technology. However, these systems either require cell tower locations or rely on information from multiple towers, which limits their deployability.We propose AutoSense, a ubiquitous and easily deployable transportation mode estimation system that works on all phones by relying on handover and received signal strength (RSS) information from only the serving cell tower. AutoSense offers a novel domain-specific deep learning-based system to perform automatic feature extraction and time-series processing. Our system handles several challenges, including limitations in cellular data, lack of location information, overfitting, and information decay in long-term dependencies. We extensively evaluate AutoSense using a real-world public dataset composed of 395 hours of data collected over seven months. Our results show that, compared to state-of-the-art systems, AutoSense can achieve enhancements in average precision and recall of 12.36% and 14.93%, respectively, while providing a highly ubiquitous and deployable solution using only the serving cell tower information.

We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its discernible signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for classification. We evaluate SolarGest with both conventional opaque solar cells as well as emerging see-through transparent cells. Our experiments demonstrate that SolarGest achieves 99% for six gestures with a single cell and 95% for fifteen gesture with a$2\times 2$solar cell array. The power measuement study suggests that SolarGest consume 44% less power compared to light sensor based systems.

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.

Fingerprinting is one of the mainstream technologies for localization. However, it needs significant storage overhead and running time, preventing it from scaling to support world-wide indoor/outdoor localization.Quantum computing has the potential to revolutionize computation by making some classically intractable problems solvable on quantum computers. In this paper, we propose a quantum fingerprint-based localization algorithm for enabling large-scale location tracking systems, envisioning future era of location tracking and spatial systems. Specifically, we propose a quantum algorithm that provides an exponential enhancement of both the space and running time complexity compared to the traditional classical systems. We give the details of how to build the quantum fingerprint, how to encode the received signal strength (RSS) measurements in quantum particles, and finally; present a quantum algorithm for calculating the cosine similarity between the online RSS measurements and the fingerprint ones.Results from deploying our algorithm in three real testbeds on IBM Quantum Experience machines confirm the ability of our quantum system to get the same accuracy as the classical one but with the potential exponential saving in both space and running time.

We propose a novel Visible Light Positioning (VLP) method, called Iris, that uses light spectral information (LSI) to localize humans completely passively in the sense that it neither requires the user to carry any device, nor does it require any modifications to existing lighting infrastructure. Iris localizes a user based on the interference they produce on the LSI recorded at an array of spectral sensors embedded in the environment. We design a deep neural network that can effectively learn location fingerprints directly from the sensor LSI data and predict locations accurately under varying lighting conditions. We prototype Iris using a commercial-off-the-shelf light spectral sensor, AS7265x, which can measure light intensity over 18 different wavelength channels. We benchmark Iris against the state-of-the-art passive VLPs that rely on conventional photo-sensors capable of measuring only a single light intensity value aggregated over the entire visible spectrum. Our evaluations over two typical indoor environments, a 25 m2 one-bedroom apartment and a 13m × 8m office space, demonstrate that Iris can significantly reduce both the localization errors and the number of required sensors, while increasing robustness against changes in environmental lighting.

In this paper, we demonstrate OmniCells: a cellular-based indoor localization system designed to combat the device heterogeneity problem. OmniCells is a deep learning-based system that leverages cellular measurements from one or more training devices to provide consistent performance across unseen tracking phones. In this demo, we show the effect of device heterogeneity on the received cellular signals and how this leads to performance deterioration of traditional localization systems. In particular, we show how OmniCells and its novel feature extraction methods enable learning a rich and device-invariant representation without making any assumptions about the source or target devices. The system also includes other modules to increase the deep model's generalization and resilience to unseen scenarios.

In this paper, we investigate the impacts of location on the spectral distribution of received light, i.e., the intensity of light for different wavelengths, in indoor environments. Our findings show that, even when using the same light source, different locations exhibit slightly different spectral distribution due to reflections from their localised environment containing different materials or colours. Based on this observation, we present Spectral-Loc, a novel indoor localization method that employs light spectrum information to detect the device's position. Because spectrum sensors are increasingly being used in new products and applications, such as white balance in smartphone photography, Spectral-Loc can be quickly implemented without the need for extra hardware or infrastructure. We used a commercially available light spectrum sensor, the AS7265x, to prototype Spectral-Loc, which can measure light intensity over 18 different wavelength sub-bands. We benchmark the localization accuracy of Spectral-Loc against the conventional light intensity sensors that provide only a single intensity value. Our evaluations in two indoor areas, a meeting room and a large office, show that using light spectral information considerably decreases the localization error for different percentiles.

Earables have been increasingly gaining attention from consumers and manufacturers alike due to their small footprint, ease of use, and the added accessibility they bring. However, the limited interface of these devices, usually being a single button or force-sensor, inhibits their potential. Earables can provide a much richer experience by extending the ways in which users can interact with them. In this paper, we present EarGest, a novel earable-based hand gesture recognition system that does not require calibration or training, and works with commercially available BLE-enabled earphones and devices. Our proposed system is unique in its ability to leverage Bluetooth to detect hand motion near the ear to recognize gestures. By harnessing information from BLE connections between the wireless earphones and a host device, we accurately detect and classify different hand gestures performed by users, while also determining discrete levels of hand speed. EarGest operates without interfering with the regular functionality of the earphones and introduces minimal energy overhead on the host device. We implement a prototype of the system using eSense, a multi-sensory earable platform, and evaluate it in different scenarios and settings. Results show that our system can detect and classify seven near-ear hand gestures with an accuracy up to 98.5%, as well as identify hand motion speed with 96% accuracy.

Due to the noisy indoor radio propagation channel, radio frequency (RF)-based location determination systems usually require a tedious calibration phase to construct an RF fingerprint of the area of interest. This fingerprint varies with the used mobile device, changes of the transmit power of smart access points (APs), and dynamic changes in the environment; requiring re-calibration of the area of interest; which reduces the technology ease of use. In this paper, we presentIncVoronoi: a novel system that can provide low-overhead accurate RF-based indoor localization that works in realistic environments. The basic idea is that the relative relation between the received signal strength from two APs at a certain location reflects the relative distance from this location to the respective APs. Building on this,IncVoronoiincrementally reduces the user ambiguity region based on refining the Voronoi tessellation of the area of interest.IncVoronoialso includes a number of modules to efficiently run in realtime as well as to handle practical deployment issues including the noisy wireless environment, obstacles in the environment, heterogeneous devices hardware, and smart APs. We analyze the complexity ofIncVoronoiand further deploy it on different Android phones using the iBeacons and WiFi technologies in a university campus and a commercial building. Evaluation ofIncVoronoiwith a side-by-side comparison with traditional fingerprinting techniques shows that it can achieve a consistent median accuracy less than 1.9m under different scenarios with a low beacon density of one beacon every 22m$^2$. Compared to fingerprinting techniques, whose accuracy degrades by at least 156 percent, this accuracy comes with no training overhead and is robust to the different user devices, different transmit powers, and over temporal changes in the environment. This highlights the promise ofIncVoronoias a next generation indoor localization system.

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.

The ability to accurately evaluate the performance of location determination systems is crucial for many applications. Typically, the performance of such systems is obtained by comparing ground truth locations with estimated locations. However, these ground truth locations are usually obtained by clicking on a map or using other worldwide available technologies like GPS. This introduces ground truth errors that are due to the marking process, map distortions, or inherent GPS inaccuracy.In this paper, we present a theoretical framework for analyzing the effect of ground truth errors on the evaluation of localization systems. Based on that, we design two algorithms for computing the real algorithmic error from the validation error and marking/map ground truth errors, respectively. We further establish bounds on different performance metrics.Validation of our theoretical assumptions and analysis using real data collected in a typical environment shows the ability of our theoretical framework to correct the estimated error of a localization algorithm in the presence of ground truth errors. Specifically, our marking error algorithm matches the real error CDF within 4%, and our map error algorithm provides a more accurate estimate of the median/tail error by 150%/72% when the map is shifted by 6m.

Accessibility maps are key to support individuals with disabilities to actively participate in the society. The Americans with Disabilities Act (ADA) defines minimum requirements for roads and other public accommodation spaces to be accessible. Yet, it is sufficient to have one accessible route in a place, and available digital-maps lack accessibility information to help finding that accessible route.In this paper, we present the AccessMap system to automatically extend road-maps with accessibility semantics. It enables indoor and outdoor spaces to be automatically marked as visually-impaired and/or wheel-chaired accessible/inaccessible. AccessMap passively crowdsources measurements from sensors available in the users' smartphones to detect accessibility semantics. It employs a probabilistic framework to build and update the map with the semantics. Evaluation of AccessMap in different countries shows that it can passively detect a wide-range of accessibility semantics with high precision and recall (on average around 89.8% and 86.3% respectively). Furthermore, its probabilistic crowdsourcing framework increases the generated map’s average precision and recall to 98.7% and 99% with as few as seven encounters per semantic.

We present DynamicSLAM: an indoor localization technique that eliminates the need for the daunting calibration step. DynamicSLAM is a novel Simultaneous Localization And Mapping (SLAM) framework that iteratively acquires the feature map of the environment while simultaneously localizing users relative to this map. Specifically, we employ the phone inertial sensors to keep track of the user's path. To compensate for the error accumulation due to the low-cost inertial sensors, DynamicSLAM leverages unique points in the environment (anchors) as observations to reduce the estimated location error. DynamicSLAM introduces the novel concept of mobile human anchors that are based on the encounters with other users in the environment, significantly increasing the number and ubiquity of anchors and boosting localization accuracy. We present different encounter models and show how they are incorporated in a unified probabilistic framework to reduce the ambiguity in the user location. Furthermore, we present a theoretical proof for system convergence and the human anchors ability to reset the accumulated error. Evaluation of DynamicSLAM using different Android phones shows that it can provide a localization accuracy with a median of 1.1m. This accuracy outperforms the state-of-the-art techniques by 55 percent, highlighting DynamicSLAM promise for ubiquitous indoor localization.

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.

WiFi time of flight (ToF) measurement has been supported recently by the wireless LAN protocols to improve WiFi localization. Specifically, the IEEE 802.11-2016 standard has a fine-time measurement (FTM) protocol that can be used to measure the WiFi signal round trip time (RTT). In this paper, we present the design and implementation of WiNar, a WiFi RTT-based indoor location determination system that combines the advantages of both fingerprint and ranging-based techniques to overcome the different challenges of indoor environments. Using commercial-off-the-shelf access points and mobile phones, WiNar leverages the propagation time of the wireless signal with a fingerprinting model to address the multipath, non-line-of-sight, signal attenuation, and interference challenges of the indoor environments. Moreover, when leveraging the round trip time measurements, WiNar does not require clock synchronization between the transmitter and the receiver. We discuss the different components of the system and its implementation on the Android operating system. Our results show that WiNar has a sub-meter localization accuracy with an average localization error of less than 0.86 meters for two different testbeds. This accuracy outperforms the performance of the traditional signal strength (RSS) fingerprinting technique by at least 38% and ranging-based multi-lateration technique by at least 148%. Finally, our system is also robust to heterogeneous devices.

The demand for a ubiquitous and accurate indoor localization service is continuously growing. Cellular-based systems are a good candidate to provide such ubiquitous service due to their wide availability worldwide. One of the main barriers for accuracy is the large number of models of cell phones, which results in variations of the measured received signal strength (RSS), even at the same location and time. In this paper, we propose OmniCells, a deep learning-based system that leverages cellular measurements from one or more training devices to provide a consistent performance across unseen tracking phones. Specifically, OmniCells uses a novel approach to multi-task learning based on autoencoders that allows it to learn a rich and device-invariant RSS representation without any assumptions about the source or target devices. OmniCells also incorporates different modules to boost the system’s accuracy with RSS relative difference-based features and to improve the deep model’s generalization and robustness. Evaluation of OmniCells in two realistic testbeds using different Android phones with different form factors and cellular radio hardware shows that OmniCells can achieve a consistent median localization accuracy when tested on different phones. This is better than the state-of-the-art indoor cellular-based systems by at least 101%.

We present MagStroke, a system that provides a more flexible mechanism for providing textual input to smart devices with small screens. The key idea is that MagStroke leverages the effect of a magnet worn on the user's hand on a nearby device's magnetometer to enable the use of larger virtualized keyboards. While typing a certain key using a virtual standard keyboard, the hand and fingers of the user, with the attached magnet, move in a unique motion and thus a unique pattern is formed in the device's embedded magnetometer sensor readings. To realize MagStroke, we address several challenges including handling background magnetic noise, keystroke detection and pattern segmentation, feature selection, and training overhead reduction. We implement a proof-of-concept prototype using off-the-shelf mobile devices and extensively evaluate the different modules in our system. Our results show that MagStroke can achieve a word recognition accuracy of 95% with a 46% reduction in training overhead, highlighting its promise as a ubiquitous off-the-shelf magnetic-based keyboard.

We propose DeepNar, a deep learning-based indoor localization system that leverages WiFi signals time of flight (ToF) as environment features, to provide accurate and robust indoor localization. DeepNar leverages the fine-time measure¬ment (FTM) protocol in the recent IEEE 802.11-2016 standard to measure WiFi signal round trip time (RTT). Our system combines the advantages of fingerprinting and ranging-based techniques by providing a deep learning model along with a probabilistic framework that captures the complex relation between the propagation times of the WiFi signals heard by the mobile phone and its location. By leveraging the signals RTT, collected using commercial-off-the-shelf access points and mobile phones, DeepNar overcomes the different challenges of indoor environments such as the multipath interference, non-line-of-sight transmissions, signal attenuation, and interference. Moreover, DeepNar does not require clock synchronization between the transmitter and the receiver. Our system is composed of various components that handle outlier detection, avoids over-training, and accommodates heterogeneous devices. We implement and evaluate DeepNar over two testbeds. Our results show that DeepNar has a sub-meter localization accuracy with a median error less than 0.75m. This accuracy outperforms ranging-based multi-lateration technique by at least 182% and traditional signal strength (RSS) fingerprinting techniques by more than 119% and 33% in both testbeds considered.

In this article, we recognize multiple human activities in an Internet-of-Things (IoT) network using differential channel state information (CSI) of the available wireless fidelity (Wi-Fi) signals. Different human activities in the Wi-Fi environment lead to multipath fading, resulting in a change of CSI for each activity. This CSI is sensed by smart IoT devices, such as smartphones, tablets, and laptops for activity recognition. The use of differential CSI mitigates the offset and background noise. Another advantage of the proposed method is that it eliminates the requirement of traditional wearable activity recognition sensors, such as gyroscope, pedometers, and accelerometers. A long short-term memory (LSTM) model is used for automatic feature extraction and classification of human activities from the differential CSI. Training the LSTM model with the phase of differential denoised CSI significantly improves the classification accuracy. The results show a good tradeoff between model complexity and classification accuracy, thereby ensuring better performance as compared to the previous state-of-the-art methods.

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.

We design a system, SolarGest, which can recognize hand gestures near a solar-powered device by analyzing the patterns of the photocurrent. SolarGest is based on the observation that each gesture interferes with incident light rays on the solar panel in a unique way, leaving its distinguishable signature in harvested photocurrent. Using solar energy harvesting laws, we develop a model to optimize design and usage of SolarGest. To further improve the robustness of SolarGest under non-deterministic operating conditions, we combine dynamic time warping with Z-score transformation in a signal processing pipeline to pre-process each gesture waveform before it is analyzed for classification. We evaluate SolarGest with both conventional opaque solar cells as well as emerging see-through transparent cells. Our experiments with 6,960 gesture samples for 6 different gestures reveal that even with transparent cells, SolarGest can detect 96% of the gestures while consuming 44% less power compared to light sensor based systems.

Robust and accurate indoor localization has been the goal of several research efforts over the past decade. Due to the ubiquitous availability of WiFi indoors, many indoor localization systems have been proposed relying on WiFi fingerprinting. However, due to the inherent noise and instability of the wireless signals, the localization accuracy usually degrades and is not robust to dynamic changes in the environment.We present WiDeep, a deep learning-based indoor localization system that achieves a fine-grained and robust accuracy in the presence of noise. Specifically, WiDeep combines a stacked denoising autoencoders deep learning model and a probabilistic framework to handle the noise in the received WiFi signal and capture the complex relationship between the WiFi APs signals heard by the mobile phone and its location. WiDeep also introduces a number of modules to address practical challenges such as avoiding over-training and handling heterogeneous devices.We evaluate WiDeep in two testbeds of different sizes and densities of access points. The results show that it can achieve a mean localization accuracy of 2.64m and 1.21m for the larger and the smaller testbeds, respectively. This accuracy outperforms the state-of-the-art techniques in all test scenarios and is robust to heterogeneous devices.

Developing countries suffer from traffic congestion, poorly planned road/rail networks, and lack of access to public transportation facilities. This context results in an increase in fuel consumption, pollution level, monetary losses, massive delays, and less productivity. On the other hand, it has a negative impact on the commuters feelings and moods. Availability of real-time transit information - by providing public transportation vehicles locations using GPS devices - helps in estimating a passenger's waiting time and addressing the above issues. However, such solution is expensive for developing countries. This paper aims at designing and implementing a crowd-sourced mobile phones-based solution to estimate the expected waiting time of a passenger in public transit systems, the prediction of the remaining time to get on/off a vehicle, and to construct a real time public transit schedule.Trans-Sense has been evaluated using real data collected for over 800 hours, on a daily basis, by different Android phones, and using different light rail transit lines at different time spans. The results show that Trans-Sense can achieve an average recall and precision of 95.35% and 90.1%, respectively, in discriminating lightrail stations. Moreover, the empirical distributions governing the different time delays affecting a passenger's total trip time enable predicting the right time of arrival of a passenger to her destination with an accuracy of 91.81%. In addition, the system estimates the stations dimensions with an accuracy of 95.71%.

A ubiquitous outdoor localization system that is easy to deploy and works equally well for all mobile devices is highly-desirable. The GPS, despite its high accuracy, cannot be reliably used for this purpose since it is not available on low-end phones nor in areas with low satellite coverage. The application of classical fingerprinting approaches, on the other hand, is prohibited by excessive maintenance and deployment costs. In this paper, we propose Crescendo, a cellular network-based outdoor localization system that does not require calibration or infrastructure support. Crescendo builds on techniques borrowed from computational geometry to estimate the user's location. Specifically, given the network cells heard by the mobile device it leverages the Voronoi diagram of the network sites to provide an initial ambiguity area and incrementally reduces this area by leveraging pairwise site comparisons and visible cell information. Evaluation of Crescendo in both an urban and a rural area using real data shows median accuracies of 152m and 224m, respectively. This is an improvement over classical techniques by at least 18% and 15%, respectively.

Recently, deep learning-based positioning systems have gained attention due to their higher performance relative to traditional methods. However, obtaining the expected performance of deep learning-based systems requires large amounts of data to train model. Obtaining this data is usually a tedious process which hinders the utilization of such deep learning approaches. In this paper, we introduce a number of techniques for addressing the data collection problem for deep learning-based cellular localization systems. The basic idea is to generate synthetic data that reflects the typical pattern of the wireless data as observed from a small collected dataset. Evaluation of the proposed data augmentation techniques using different Android phones in a cellular localization case study shows that we can enhance the performance of the localization systems in both indoor and outdoor scenarios by 157% and 50.5%, respectively. This highlights the promise of the proposed techniques for enabling deep learning-based localization systems.

We present WiGest: a system that leverages changes in WiFi signal strength to sense in-air hand gestures around the user's mobile device. WiGest uses standard WiFi equipment, with no modifications, and requires no training for gesture recognition. The system identifies different RSS change primitives, from which we construct mutually-independent gesture families. These families can be mapped to distinguishable application actions. More fine-grained features can also be recognized for the detected primitives using CSI. WiGest addresses various challenges including cleaning the noisy signals, gesture type and attribute detection, reducing false positives due to interfering humans, and adapting to changing signal polarity. We implement a proof-of-concept prototype using off-the-shelf devices and extensively evaluate the system in two different environments. Our results show that WiGest detects the basic primitives with an accuracy of 87.5 percent using one AP, including through-the-wall non-line-of-sight scenarios, which increases to 96 percent using three overheard APs. Additionally, when evaluating the system using a multi-media player application, we achieve an accuracy of 96 percent. This accuracy is robust to the presence of other interfering humans, highlighting WiGest's ability to enable future ubiquitous hands-free gesture-based interaction with mobile devices.

Mapping and navigation applications are now considered popular services for mobile phones users. However, despite the fact that people spend most of their time indoors, these applications are still limited to indoor spaces due to the lack of large-scale indoor floorplan databases. In this paper, we present JustWalk: a crowd intelligence-based system for the automatic construction of buildings floorplans. JustWalk employs a participatory sensing approach using smartphones that are ubiquitously available with users who visit a building to automatically and transparently construct accurate motion traces. These accurate traces are generated by reducing the errors in the inertial motion traces by using the points of interest in the indoor environment (e.g., elevators and stairs, etc.) for error resetting. The collected traces are then processed by means of different mathematical and image processing techniques 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. In comparison to other approaches, our system depends only on the users walking patterns and motion traces. It does not require any explicit inputs or obtrusive actions (e.g., taking photos). Experimental evaluation of JustWalk using different android phones in three testbeds shows that it achieves high accuracy for the point of interest detection (0.6 percent false positive and 1 percent false negative rates). In addition, the proposed error resetting technique leads to more than 12 times enhancement in the median distance error compared to the state-of-the-art. Moreover, the detailed floorplan can be accurately estimated with a relatively small number of traces. This number is amortized over the number of users visiting the building. Finally, we show that JustWalk has a small energy footprint on cell-phones, and could be generalized to other buildings; highlighting its promise as a ubiquitous indoor mapping service.

Routing protocols in multi-hop cognitive radio networks (CRNs) can be classified into two main categories: local and global routing. Local routing protocols aim at decreasing the overhead of the routing process while exploring the route by choosing, in a greedy manner, one of the direct neighbors. On the contrary, global routing protocols choose the optimal route by exploring the whole network to the destination paying the flooding overhead cost. In this paper, we propose a primary user-aware$k$-hop routing scheme where$k$is the discovery radius. This scheme can be plugged into any CRN routing protocol to adapt, in real time, to network dynamics like the number and activity of primary users. The aim of this scheme is to cover the gap between local and global routing protocols for CRNs. It is based on balancing the routing overhead and the route optimality, in terms of primary users avoidance, according to a user-defined utility function. We analytically derive the optimal discovery radius ($k$) that achieves this target. Evaluations on NS2 with a side-by-side comparison with traditional CRNs protocols show that our scheme can achieve the user-defined balance between the route optimality, which in turn reflected on throughput and packet delivery ratio, and the routing overhead in real time.

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.

Device-free localization (DFL) is an emerging technology for estimating the position of a human or object that is not equipped with any electronic tag, nor participate actively in the localization process. Similar to device-based localization, the initial phase in DFL is to build the fingerprint database which is usually done manually using site surveying. This process is tedious, time-consuming, and vulnerable to environmental dynamics. Motivated by the recent advances in the Internet of Things (IoT), this paper introduces RadioGrapher; a system that automates the process of device-free fingerprint calibration in IoT environments. RadioGrapher leverages the device-based locations of entities in the area of interest in a crowd-sensing manner, aided with Fresnel zones of the wirelessly connected IoT devices to automatically construct a device-free fingerprint. Experimental evaluation of RadioGrapher in an IoT testbed using multiple entities shows that it can construct DFL fingerprints with high accuracy. Moreover, its median localization accuracy is comparable to that of manual fingerprinting. This comes with no calibration overhead, highlighting the promise of RadioGrapher as a crowdsourcing device-free fingerprint constructor in IoT environments.

We present CrowdMeter: a participatory system that leverages the sensed data collected from users' phones during their daily train commutes to gauge the real-time congestion level in railway stations. CrowdMeter tracks the passenger's position in the station as well as identifies her context (e.g., waiting for a train, buying a ticket) along her trajectory from the station's entrance to the train. Therefrom, CrowdMeter extracts novel features, based on the user's location and context, from the phone sensors. These features capture the passenger's behavior (e.g., the walking pattern) and the ambient environment characteristics (e.g., the ambient sound) that can indicate the surrounding congestion level along the passenger's route in a railway station. Finally, the system highlights each area of the station with a specific color (green, amber, red) that corresponds to one of a three congestion levels (low, medium, high).Evaluation of CrowdMeter through a field experiment in 10 different train stations in Japan shows that it can infer the congestion levels accurately, highlighting its promise as a ubiquitous travel-support service.

The high cost of setting up a wireless testbed prevents many institutes from equipping a lab with the required devices, especially in the developing countries. Remotely accessible testbeds provide a solution to this problem by allowing researchers to access the wireless testbeds through the Internet. In this work, we present the design, architecture and evaluation of WiPi: a low-cost remotely-accessible testbed that is built from heterogeneous devices and supports next-generation applications. WiPi efficiently utilizes available off-the-shelf computing nodes, such as standard laptops and Raspberry Pis, to increase the testbed resource utilization. Multiple features including users' isolation, resource pooling, ease of user experience, power efficiency, multiple application domains, efficient disk utilization, and disk protection are implemented as part of the testbed. Evaluation of WiPi using Software Defined Radios as the network interface over a mixed network of laptops and Raspberry Pis show that WiPi can support a different range of wireless networking applications, highlighting its suitability as a low-cost heterogeneous remotely accessible testbed.

Recently, door access control with Internet of Things has become increasingly popular in the field of security. However, conventional approaches such as video-based or biological information-based cannot satisfy the requirements of personal privacy protection in the modern society. Hence, a wireless signal-based technique which does not need users to carry any devices, called device-free have been introduced in recent years to detect and identify persons. In this paper, we present BLEDoorGuard, a wireless, invisible, and robust door access system which leverages received signal strength indicator from Bluetooth low energy (BLE) beacons to recognize a person who accesses a door. We evaluated BLEDoorGuard in two real-world scenarios: the first is an office with a key lock, and the second is a meeting room with swipe card access. We exploit the characteristics of use of BLE for person identification and propose a two-step algorithm with multiple classifiers. We demonstrate that BLEDoorGuard is capable of identifying the actual user during door access with an accuracy of 69% and 62% among groups of 6 and 10 people, respectively.

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.

We introduce a mobile sensing technique to detect a nearby active television, the channel it is tuned to, and whether it is receiving this channel over the air or not. This technique can find applications in tracking TV viewership, second screen services and advertising, as well as improving the efficiency of TV white space spectrum usage. The technique uses a three-stage detection process: It first uses a Gaussian mixture model on audio recordings from mobile phones to detect likely TV sounds in the area. It then correlates the recording with known TV channel audio to identify the channel and improve detection robustness. Finally, it applies a latency analysis to determine whether programming is received over-the-air or through alternate means such as cable or satellite TV. Our system is evaluated using diverse datasets that take into account different realistic scenarios of indoor environments for several users. The results show that the system can achieve an area under the curve (AUC) of 0.9979 and a false negative rate of 0.0132.

We present the SnapNet system, which provides accurate real-time map matching for cellular-based trajectory traces. Such traces are characterized by input locations that are far from the actual road segment, errors on the order of kilometers, back-and-forth transitions, and highly sparse input data. SnapNet applies a series of filters to handle the noisy locations and an interpolation stage to address the data sparseness. At the core of SnapNet is a novel incremental HMM algorithm that combines digital map hints in the estimation process and a number of heuristics to reduce the noise and provide real-time estimations. Evaluation of SnapNet using actual traces from different cities covering more than 400 km shows that it can achieve a precision and recall of more than 90% under noisy coarse-grained input location estimates. This maps to over 97% and 34% enhancement in precision and recall, respectively, when compared to the traditional HMM map-matching algorithms. Moreover, SnapNet has a latency of 0.58 ms per location estimate.

Digital maps have become a part of our daily lives with a growing number of commercial and free map services. However, these services still have a huge potential for enhancement with rich semantic information to support a large class of mapping applications. In this paper, we present Map++, a system that leverages commodity off-the-shelf smartphones in a crowd-sensing approach to automatically enrich digital maps with different road semantics like tunnels, bumps, bridges, footbridges, crosswalks, road capacity, among others. Our analysis shows that the smartphones sensors, whether with a user riding a vehicle or walking, get affected by the different road features which can be mined to extend the features of both free and commercial mapping services. Map++ leverages these detected features and employs a probabilistic framework that can handle the heterogeneity and uncertainty in the crowd-sensed data to update the digital maps. We present the design and implementation of Map++ and evaluate it in four cities. Our evaluation shows that we can detect different map features accurately with 4 percent false positive and 8 percent false negative rates for in-vehicle traces, and 3 percent false positive and 4 percent false negative rates for pedestrian traces. Moreover, we show that Map++ has a small energy footprint on the cell-phones, highlighting its promise as a ubiquitous digital maps enriching service.

Existing Location-based social networks (LBSNs), e.g., Foursquare, depend mainly on GPS or cellular-based localization to infer users' locations. However, GPS is unavailable indoors and cellular-based localization provides coarse-grained accuracy. This limits the accuracy of current LBSNs in indoor environments, where people spend 89 percent of their time. This in turn affects the user experience, in terms of the accuracy of the ranked list of venues, especially for the small screens of mobile devices, misses business opportunities, and leads to reduced venues coverage. In this paper, we present CheckInside: a system that can provide a fine-grained indoor location-based social network. CheckInside leverages the crowd-sensed data collected from users' mobile devices during the check-in operation and knowledge extracted from current LBSNs to associate a place with a logical name and a semantic fingerprint. This semantic fingerprint is used to obtain a more accurate list of nearby places as well as to automatically detect new places with similar signature. A novel algorithm for detecting fake check-ins and inferring a semantically-enriched floorplan is proposed as well as an algorithm for enhancing the system performance based on the user implicit feedback. Furthermore, CheckInside encompasses a coverage extender module to automatically predict names of new venues increasing the coverage of current LBSNs. Experimental evaluation of CheckInside in four malls over the course of six weeks with 20 participants shows that it can infer the actual user place within the top five venues 99 percent of the time. This is compared to 17 percent only in the case of current LBSNs. In addition, it increases the coverage of existing LBSNs by more than 37 percent.

Human respiratory rate is widely recognized as a vital measure of a patient's health and a primary indicator of several medical problems. Yet, it is usually ignored by medical practitioners due to limitations with available measurements techniques that are either performed through visual counting by trained personnel or using invasive and/or devices limited to medical facilities. We present Zephyr, a ubiquitous low-cost smartphone-based robust respiratory rate estimator. Our analysis shows that accelerometer and gyroscope measurements from a standard smartphone held on a person's chest is affected by her respiration cycle. Zephyr employs a series of signal processing modules to extract the breathing signal from the noisy inertial sensor measurements and handle noisy user movements. Furthermore, as part of Zephyr design, we propose a novel respiratory rate signal quality estimate that we leverage through a probabilistic framework to fuse the estimates from the different sensors, leading to a robust respiratory rate estimate. Implementation of the system on different off-the-shelf Android devices using more than 150 experiments with seven participants of different ages and genders under different typical settings with a side-by-side comparison to a commercial device shows that Zephyr can estimate the user's respiratory rate accurately with a median absolute error of 0.04 breaths per minute in realtime. In addition, Zephyr's quality-based probabilistic framework improves the respiratory rate estimation by more than 51% as compared to using any of the individual sensor measurements. This remarkable accuracy is achieved within 30 seconds of measurements, highlighting the viability of Zephyr as a ubiquitous low-cost respiratory rate estimator.

We present TransitLabel, a crowd-sensing system for automatic enrichment of transit stations indoor floorplans with different semantics like ticket vending machines, entrance gates, drink vending machines, platforms, cars' waiting lines, restrooms, lockers, waiting (sitting) areas, among others. Our key observations show that certain passengers' activities (e.g., purchasing tickets, crossing entrance gates, etc) present identifiable signatures on one or more cell-phone sensors. TransitLabel leverages this fact to automatically and unobtrusively recognize different passengers' activities, which in turn are mined to infer their uniquely associated stations semantics. Furthermore, the locations of the discovered semantics are automatically estimated from the inaccurate passengers' positions when these semantics are identified. We evaluate TransitLabel through a field experiment in eight different train stations in Japan. Our results show that TransitLabel can detect the fine-grained stations semantics accurately with 7.7% false positive rate and 7.5% false negative rate on average. In addition, it can consistently detect the location of discovered semantics accurately, achieving an error within 2.5m on average for all semantics. Finally, we show that TransitLabel has a small energy footprint on cell-phones, could be generalized to other stations, and is robust to different phone placements; highlighting its promise as a ubiquitous indoor maps enriching service.

One of the main methods for interacting with mobile devices today is the error-prone and inflexible touch-screen keyboard. This paper proposes MagBoard: a homomorphic ubiquitous keyboard for mobile devices. MagBoard allows application developers and users to design and print different custom keyboards for the same applications to fit different user's needs. The core idea is to leverage the triaxial magnetometer embedded in standard mobile phones to accurately localize the location of a magnet on a virtual grid superimposed on the printed keyboard. This is achieved through a once in a lifetime fingerprint. MagBoard also provides a number of modules that allow it to cope with background magnetic noise, heterogeneous devices, different magnet shapes, sizes, and strengths, as well as changes in magnet polarity. Our implementation of MagBoard on Android phones with extensive evaluation in different scenarios demonstrates that it can achieve a key detection accuracy of more than 91% for keys as small as 2cm × 2cm, reaching 100% for 4cm×4cm keys. This accuracy is robust with different phones and magnets, highlighting MagBoard promise as a homomorphic ubiquitous keyboard for mobile devices.

Due to the noisy indoor radio propagation channel, Radio Frequency (RF)-based location determination systems usually require a tedious calibration phase to construct an RF fingerprint of the area of interest. This fingerprint varies with the used mobile device, changes of the transmit power of smart access points (APs), and dynamic changes in the environment; requiring re-calibration of the area of interest; which reduces the technology ease of use. In this paper, we present IncVoronoi: a novel system that can provide zero-calibration accurate RF-based indoor localization that works in realistic environments. The basic idea is that the relative relation between the received signal strength from two APs at a certain location reflects the relative distance from this location to the respective APs. Building on this, IncVoronoi incrementally reduces the user ambiguity region based on refining the Voronoi tessellation of the area of interest. IncVoronoi also includes a number of modules to efficiently run in realtime as well as to handle practical deployment issues including the noisy wireless environment, obstacles in the environment, heterogeneous devices hardware, and smart APs. We have deployed IncVoronoi on different Android phones using the iBeacons technology in a university campus. Evaluation of IncVoronoi with a side-by-side comparison with traditional fingerprinting techniques shows that it can achieve a consistent median accuracy of 2.8m under different scenarios with a low beacon density of one beacon every 44m2. Compared to fingerprinting techniques, whose accuracy degrades by at least 156%, this accuracy comes with no training overhead and is robust to the different user devices, different transmit powers, and over temporal changes in the environment. This highlights the promise of IncVoronoi as a next generation indoor localization system.

Indoor localization using mobile sensors has gained momentum lately. Most of the current systems rely on an extensive calibration step to achieve high accuracy. We propose SemanticSLAM, a novel unsupervised indoor localization scheme that bypasses the need for war-driving. SemanticSLAM leverages the idea that certain locations in an indoor environment have a unique signature on one or more phone sensors. Climbing stairs, for example, has a distinct pattern on the phone's accelerometer; a specific spot may experience an unusual magnetic interference while another may have a unique set of Wi-Fi access points covering it. SemanticSLAM uses these unique points in the environment as landmarks and combines them with dead-reckoning in a new Simultaneous Localization And Mapping (SLAM) framework to reduce both the localization error and convergence time. In particular, the phone inertial sensors are used to keep track of the user's path, while the observed landmarks are used to compensate for the accumulation of error in a unified probabilistic framework. Evaluation in two testbeds on Android phones shows that the system can achieve 0.53 meters human median localization errors. In addition, the system can detect the location of landmarks with 0.83 meters median error. This is 62 percent better than a system that does not use SLAM. Moreover, SemanticSLAM has a 33 percent lower convergence time compared to the same systems. This highlights the promise of SemanticSLAM as an unconventional approach for indoor localization.

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 propose a primary user-aware k-hop routing scheme that can be plugged into any cognitive radio network routing protocol to adapt, in real time, to the environmental changes. The main use of this scheme is to make the compromise required between the route overhead and its optimality based on a user-defined utility function. We analytically derive the optimal discovery radius (k) that achieves this target. Evaluations on NS2 show that our scheme can enhance the current routing protocols in terms of throughput with minimal overhead.

Understanding crowd dynamics of large-scale events is crucial to deliver a pleasant experience for the participants. In this paper, we propose a novel hybrid participatory sensing approach to capture large group dynamics. Specifically, our approach is based on distributing a large number of tiny, wearable Bluetooth Low Energy (BLE) tags and few smartphones among the group members. We start by identifying the best configuration; in terms of transmit power and beaconing interval; for detecting BLE tags in indoor and outdoor environments. Then, as a case study, we deploy the system during the six main days of the Hajj pilgrimage in 2014, which is the world's largest annual religious gathering. We used the proposed hybrid participatory approach to collect the mobility data of pilgrim groups based on GPS location and co-occurring BLE tag detections. Our system provided up to 80% group-wise detectability in a single scan event. Moreover, 98% cumulative unique tags detectability is achieved in the whole event, leading to 0.74 million records. Analysis of the group dynamics revealed unexpected behavior and interesting findings. In particular, regional variations are found among different groups in the entry or exit times, duration of stay, and group cohesion; which are attributed to congestion, improper arrangements, or the nature of activities. Furthermore, we show sub-community clusters could be identified revealing clear biases based on the demographic characteristics. Based on this case study, we also give suggestions on using the proposed system for other large-scale events.

We present WiGest: a system that leverages changes in WiFi signal strength to sense in-air hand gestures around the user's mobile device. Compared to related work, WiGest is unique in using standard WiFi equipment, with no modifications, and no training for gesture recognition. The system identifies different signal change primitives, from which we construct mutually independent gesture families. These families can be mapped to distinguishable application actions. We address various challenges including cleaning the noisy signals, gesture type and attributes detection, reducing false positives due to interfering humans, and adapting to changing signal polarity. We implement a proof-of-concept prototype using off-the-shelf laptops and extensively evaluate the system in both an office environment and a typical apartment with standard WiFi access points. Our results show that WiGest detects the basic primitives with an accuracy of 87.5% using a single AP only, including through-the-wall non-line-of-sight scenarios. This accuracy increases to 96% using three overheard APs. In addition, when evaluating the system using a multi-media player application, we achieve a classification accuracy of 96%. This accuracy is robust to the presence of other interfering humans, highlighting WiGest's ability to enable future ubiquitous hands-free gesture-based interaction with mobile devices.

Availability of semantic-rich indoor floorplans; where places are labeled with their business names or categories; enables ubiquitous deployment of a wide range of indoor location-based services. In this paper, we present SemSense: a crowdsourcing-based system for automatic enrichment of indoor floorplans with semantic labels. SemSense exploits phone sensors data collected from users during their normal check-ins to location-based social networks (LBSNs) and combines them with data extracted from the LBSNs databases to associate a venue name with its location on an unlabeled floorplan. At the core of SemSense are different modules for handling incorrect location estimates, fake check-ins, as well as increasing the coverage of indoor venues by means of a novel category inference technique. Our experimental evaluation of SemSense using different Android phones in four malls in two cities shows that it can achieve a high semantic labeling accuracy of 87% using a relatively small number of check-ins at each venue in the presence of up to 50% erroneous check-ins. In addition, the proposed coverage extension technique leads to more than 27% enhancement in the places coverage ratio compared to the current LBSNs.

The exponential increase in mobile data demand, coupled with growing user expectation to be connected in all places at all times, have introduced novel challenges for researchers to address. Fortunately, the wide spread deployment of various network technologies and the increased adoption of multi-interface-enabled devices allow researchers to develop solutions for those challenges. Such solutions exploit available interfaces on these devices in both local and collaborative forms. These solutions, however, have faced a formidable deployment barrier. Therefore, in this paper, we present OSCAR, a multi-objective, incentive-based, collaborative, and deployable bandwidth aggregation system, designed to exploit multiple network interfaces on modern mobile devices. Oscar's architecture does not introduce any intermediate hardware nor require changes to current applications or legacy servers. This architecture estimates the interfaces characteristics and application requirements, schedules various connections and/or packets to different interfaces, and provides users with incentives for collaboration and bandwidth sharing. We formulate the OSCAR scheduler as a multi-objective scheduler that maximizes system throughput while achieving user-defined efficiency goals for both cost and energy consumption. We implement a small scale prototype of our OSCAR system, which we use to evaluate its performance. Our evaluation shows that we provide up to 150% enhancement in the throughput compared to current operating systems with only minor updates to the client devices.

Monitoring breathing rates and patterns helps in the diagnosis and potential avoidance of various health problems. Current solutions for respiratory monitoring, however, are usually invasive and/or limited to medical facilities. In this paper, we propose a novel respiratory monitoring system, UbiBreathe, based on ubiquitous off-the-shelf WiFi-enabled devices. Our experiments show that the received signal strength (RSS) at a WiFi-enabled device held on a person's chest is affected by the breathing process. This effect extends to scenarios when the person is situated on the line-of-sight (LOS) between the access point and the device, even without holding it. UbiBreathe leverages these changes in the WiFi RSS patterns to enable ubiquitous non-invasive respiratory rate estimation, as well as apnea detection.

Current outdoor localization techniques fail to provide the required accuracy for estimating the car's lane. In this paper, we present LaneQuest: a system that leverages the ubiquitous and low-energy inertial sensors available in commodity smart-phones to provide an accurate estimate of the car's current lane. LaneQuest leverages hints from the phone sensors about the surrounding environment to detect the car's lane. For example, a car making a right turn most probably will be in the right-most lane, a car passing by a pothole will be in a specific lane, and the car's angular velocity when driving through a curve reflects its lane. Our investigation shows that there are amble opportunities in the environment, i.e. lane “anchors”, that provide cues about the car's lane. To handle the ambiguous location, sensors noise, and fuzzy lane anchors; LaneQuest employs a novel probabilistic lane estimation algorithm. Furthermore, it uses an unsupervised crowd-sourcing approach to learn the position and lane-span distribution of the different lane-level anchors. Our evaluation results from implementation on different android devices and 260Km driving traces by 13 drivers in different cities shows that LaneQuest can detect the different lane-level anchors with an average precision and recall of more than 90%. This leads to an accurate detection of the exact car's lane position 80% of the time, increasing to 89% of the time to within one lane. This comes with a low-energy footprint, allowing LaneQuest to be implemented on the energy-constrained mobile devices.

Detecting the transportation mode of a user is important for a wide range of applications. While a number of recent systems addressed the transportation mode detection problem using the ubiquitous mobile phones, these studies either leverage GPS, the inertial sensors, and/or multiple cell towers information. However, these different phone sensors have high energy consumption, limited to a small subset of phones (e.g. high-end phones or phones that support neighbouring cell tower information), cannot work in certain areas (e.g. inside tunnels for GPS), and/or work only from the user side. In this paper, we present a transportation mode detection system, MonoSense, that leverages the phone serving cell information only. The basic idea is that the phone speed can be correlated with features extracted from both the serving cell tower ID and the received signal strength from it. To achieve high detection accuracy with this limited information, MonoSense leverages diversity along multiple axes to extract novel features. Specifically, MonoSense extracts features from both the time and frequency domain information available from the serving cell tower over different sliding widow sizes. More importantly, we show also that both the logarithmic and linear RSS scales can provide different information about the movement of a phone, further enriching the feature space and leading to higher accuracy. Evaluation of MonoSense using 135 hours of cellular traces covering 485 km and collected by four users using different Android phones shows that it can achieve an average precision and recall of 89.26% and 89.84% respectively in differentiating between the stationary, walking, and driving modes using only the serving cell tower information, highlighting MonoSense ability to enable a wide set of intelligent transportation applications.

Censoring has been proposed to be utilized in wireless distributed detection networks with a fusion center to enhance network performance in terms of error probability in addition to the well- established energy saving gains. In this paper, we further examine the employment of censoring in infrastructure-less cognitive radio networks, where nodes employ binary consensus algorithms to take global decisions regarding a binary hypothesis test without a fusion center to coordinate such a process. We show analytically - and verify by simulations - that censoring enhances the performance of such networks in terms of error probability and convergence times. Our protocol shows performance gains up to 46.6\% in terms of average error probability over its conventional counterpart, in addition to performance gains of about 48.7% in terms of average energy expenditure and savings up to 50% in incurred transmission overhead.

Device-free (DF) localization in WLANs has been introduced as a value-added service that allows tracking of indoor entities that do not carry any devices. Previous work in DF WLAN localization focused on the tracking of a single entity due to the intractability of the multi-entity tracking problem whose complexity grows exponentially with the number of humans being tracked. In this paper, we introduce ACE: a system that uses a probabilistic energy-minimization framework that combines a conditional random field with a Markov model to capture the temporal and spatial relations between the entities' poses. A novel cross-calibration technique is introduced to reduce the calibration overhead of multiple entities to linear, regardless of the number of humans being tracked. We design an efficient energy-minimization function that can be mapped to a binary graph-cut problem whose solution has a linear complexity on average and a third order polynomial in the worst case. We further employ clustering on the estimated location candidates to reduce outliers and obtain more accurate tracking in the continuous space. Experimental evaluation in two typical testbeds, with a side-by-side comparison with the state-of-the-art, shows that ACE can achieve a multi-entity tracking accuracy of less than 1.3 m. This corresponds to at least 11.8 percent, and up to 33 percent, enhancement in median distance error over the state-of-the-art DF localization systems. In addition, ACEcan estimate the number of entities correctly to within one difference error for 100 percent of the time. This highlights that ACE achieves its goals of having an accurate and efficient multi-entity indoors localization.

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.

Existing location-based social networks (LBSNs), e.g. Foursquare, depend mainly on GPS or network-based localization to infer users' locations. However, GPS is unavailable indoors and network-based localization provides coarse-grained accuracy. This limits the accuracy of current LBSNs in indoor environments, where people spend 89% of their time. This in turn affects the user experience, in terms of the accuracy of the ranked list of venues, especially for the small-screens of mobile devices; misses business opportunities; and leads to reduced venues coverage.

The exponential increase in mobile data demand coupled with the rapid deployment of various wireless access technologies have led to the proliferation of multi-interface enabled devices. As a result, researchers focused on exploiting the available interfaces on such devices in both solitary and coll

Ubiquity of Internet-connected and sensor-equipped portable devices sparked a new set of mobile computing applications that leverage the proliferating sensing capabilities of smart phones. For many of these applications, accurate estimation of the user heading, as compared to the phone heading, is o

This video presents a demo of indoor localization in multiple settings. In the demo, a user walks with a smartphone and the user's location is shown on the phone's screen in real time. Our system, called Unsupervised Indoor Localization (UnLoc) utilizes the sensor data from smartphones to learn "invisible landmarks" in the environment. Example landmarks could be a unique magnetic fluctuation experienced when the phone is near a water-cooler, or a distinct gyroscope rotation when the user turns a corner. We use these indoor "landmarks" to periodically reset the user's location. To track the user between these landmarks, we use an optimized variant of dead reckoning, ultimately leading to a robust location tracking system. We call our system UnLoc, since the landmarks are generated in an unsupervised manner, requiring no manual effort or floorplan of the building. The demo describes the high level intuitions, shows UnLoc in operation, and shares experiences from running UnLoc in various real-world environments.

Digital maps have become a part of our daily life with a number of commercial and free map services. These services have still a huge potential for enhancement with rich semantic information to support a large class of mapping applications. In this paper, we present Map++, a system that leverages standard cell-phone sensors in a crowdsensing approach to automatically enrich digital maps with different road semantics like tunnels, bumps, bridges, footbridges, crosswalks, road capacity, among others. Our analysis shows that cell-phones sensors with humans in vehicles or walking get affected by the different road features, which can be mined to extend the features of both free and commercial mapping services. We present the design and implementation of Map++ and evaluate it in a large city. Our evaluation shows that we can detect the different semantics accurately with at most 3% false positive rate and 6% false negative rate for both vehicle and pedestrian-based features. Moreover, we show that Map++ has a small energy footprint on the cell-phones, highlighting its promise as a ubiquitous digital maps enriching service.

Digital maps have become a part of our everyday lives as they are integrated into a wide range of map-based services like traffic estimation, navigation systems, and many more. These services still have a huge opportunity for enhancements with semantically richer maps to support a large class of new services. In this demo, we demonstrate the MAP++ crowd-sensing system for map semantics identification. Map++ leverages standard smart-phone sensors to automatically enrich digital maps with various road semantics such as bridges, crossroads, roundabouts, underpasses, among others. The goal of this demo is to showcase Map++ in action where it takes crowd-sensed motion traces and process them automatically in real-time to identify the map semantics. The demo also allows attendees to analyze the effect of the Map++ different parameters on system performance and road semantics.

Cognitive radios emerged as a solution for utilizing the spectrum which is considered a limited resource. Multi-hop routing in cognitive radio networks (CRNs) has been gaining increasing attention as it enables future large-scale CRNs. However, many existing protocols flood the network with control packets in route discovery phase, which leads to wasting bandwidth. In this paper, we introduce a location-aware probabilistic route discovery technique for CRNs that leverages gossiping with dynamic probabilities to reduce the flooding overhead without affecting the quality of the discovered routes. The proposed technique can be used on top of any routing protocol regardless of whether it relies on a common control channel or not. Evaluation of our technique through ns2 simulations for improving different classes of routing protocols shows a significant reduction in the number of control packets by up to 75% and an increase in throughput by up to 400%.

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.

Wide deployment of GSM based location determination systems is a critical step towards moving existing systems to the real world. The main barrier towards this critical step is the heterogeneity of existing types of cell phones which results in different readings of received signal strength. Specially, in the context of fingerprinting localization where offline phases are needed for system training and different types of phones may be used in the offline and the online phases. Therefore, a mapping function, that maps the RSSI values between different types of cell phones, is inevitably needed. A trivial solution is to build a radio map for each type of phone. Obviously, this solution can neither scale in terms of number of phone types nor fingerprint size. In this paper, we address this problem by proposing the following two-way approach: A mathematical approach that maps RSSI values of different types of phones using linear transformation with regression, or logging ratios of readings instead of absolute values. We have empirically evaluated the proposed approach on Android-based phones. Our experimental results show that applying our approach can improve location accuracy with at least 127.84% in multiple cell tower configuration and at least 22.11% in the single cell tower configuration compared to the state-of-the-art GSM localization systems.

With the ubiquity of WiFi-enabled smartphones, and large-scale access point deployment, WiFi-based localization is one of the most promising indoor localization systems. Existing WiFi localization solutions, however, exhibit high power demand due to the periodic updates required, which raises the barrier for deployment on mobile devices since battery life is a crucial resource. In this paper, we present an energy efficient architecture, GreenLoc, that leverages the numerous sensors existing on mobile devices, along with typical group mobility patterns, in order to lower the average energy cost for indoor localization. We also propose a cluster-based localization algorithm, integrate it with our GreenLoc architecture, and evaluate its performance via simulation and prototype implementation. Our results show up to 60% reduction in average energy consumed with a small penalty in accuracy that does not impact the performance of the range of applications we target.

Cognitive radio networks (CRNs) provide a solution to increase the utilization of the scarce radio frequency spectrum. Building testbeds for CRNs is one of the main challenges that can affect the wide deployability of such networks. In this paper, we present the design, implementation, and evaluation of CogFrame: a framework that facilitates the development of cost-efficient large-scale CRNs routing protocols testbeds. The framework allows the designers to focus on the CRNs routing protocols by abstracting the PHY and MAC layers while providing the necessary cross layer functionalities. CogFrame works with standard computers and WiFi cards to reduce the cost while allowing integration with other special hardware for more flexibility. In addition, CogFrame provides different modules for implementing and emulating complex scenarios such as regulatory authority policies, mobility management, and topology management. We benchmark the performance of CogFrame and compare it to standard ns-2 simulations and USRP2 implementations. In addition, we case study a location-aided routing protocol for CRNs using both CogFrame and ns-2 simulations. Our results highlight the ease of implementation, low-cost, and realistic replication of the CRN environment, showing the promise of CogFrame as a testbed for future CRNs implementations.

In this paper, we aim at protecting the privacy of the communicating parties while ensuring the authenticity of source nodes. In particular, we exploit intra-flow network coding to preserve the anonymity of communicating parties. Towards this objective, we propose an anonymity preservation scheme, namely closed group anonymity (CGA) that preserves the anonymity of the communicating parties via mixing their flows. Afterwards, we explore an instance of the Authentication-Privacy Trade-off in the context of Network Coding. We analyze the trade-off with the aid of the proposed anonymity scheme and a previously proposed Source Authentication Scheme using Network Coding (SANC). We present simulation results showing that the proposed algorithm successfully leverages network coding to preserve anonymity against traffic analysis attacks. Finally, we not only confirm the fundamental authentication-privacy trade-off in the context of intra-flow network coding but also parameterize it via introducing a tunable parameter to dynamically control, and potentially balance, this trade-off depending on the security provisions dictated by the operational scenario and application of interest.

Cognitive Radio Networks (CRNs) provide a solution to increase the utilization of the scarce radio frequency spectrum. Building testbeds for CRNs is one of the main challenges that can affect the wide deployability of such networks. Many testbeds were proposed for testing CRN routing protocols, but most of them are either pure simulations or require high deployment cost for buying and maintaining the needed hardware devices on a large scale. We propose CRESCENT: a modular framework for testing CRNs routing protocols with cost-efficient and large-scale deployability. CRESCENT is based on general purpose computers without the need for any special devices leveraging their built-in Wi-Fi interfaces as the default interface. The framework is built on top of Click modular router to ensure the ease of the development of new routing protocols while providing new modules that are essential for cognitive radio protocols.

Efficient scheduling of wireless resources has always been one of the most challenging tasks for wireless networks. To achieve throughput-optimality, traditional back-pressure algorithms calculate a maximal weight matching at each time slot. However, these algorithms need centralized scheduling with high complexity, and thus are not suitable for practical distributed implementations. A class of distributed queue-length-based CSMA algorithms have been proposed that achieve throughput optimality, which we refer to as regular throughput-optimal. These algorithms suffer from two problems: large delays, and temporal starvation. In this demo we demonstrate the operation of the v(t)-regulated CSMA algorithm that mitigates these two problems while provably retaining throughput optimality. The demo allows the participants to see the the performance advantage of v(t)-regulated CSMA over queue-length-based CSMA algorithms and change the different system parameters.

Device-free (DF) indoor localization has grasped great attention recently as a value-added service to the already installed WiFi infrastructure as it allows the tracking of entities that do not carry any devices nor participate actively in the localization process. Current approaches, however, require a relatively large number of wireless streams, i.e. transmitter-receiver pairs, which is not available in many typical scenarios, such as home monitoring. In this paper, we introduce MonoPHY as an accurate mono-stream device-free WLAN localization system. MonoPHY leverages the physical layer information of WiFi networks supported by the IEEE 802.11n standard to provide accurate DF localization with only one stream. In particular, MonoPHY leverages both the low-level Channel State Information and the MIMO information to capture the human effect on signal strength. Experimental evaluation in a typical apartment, with a side-by-side comparison with the state-of-the-art, shows that MonoPHY can achieve an accuracy of 1.36m. This corresponds to at least 48% enhancement in median distance error over the state-of-the-art DF localization systems using a single stream only.

We consider a cognitive radio setting in which a relay-assisted secondary link employs cooperative beamforming to enhance its throughput and to provide protection to the primary receiver from interference. We assume the presence of infinite buffers at both the primary and secondary transmitters and characterize the maximum stable throughput region exactly using the dominant system approach. Numerical examples are provided to give insights into the impact of power control on the stability region.

Current routing protocols for cognitive radio networks are severely affected by the frequent activity of primary users. Nodes that are in the interference range of an appearing primary user are not allowed to transmit, and therefore existing routes which utilize such nodes are obliged to undergo a route maintenance phase. This naturally provides other routes to the destination that may incur extra delay or increase packet queuing overhead. In this work, a novel route maintenance protocol is proposed that allows existing routes to endure the event of primary user presence by forming cooperative links between neighboring nodes and nulling out transmission at the primary receiver using cooperative beamforming. Our proposed protocol can be used in conjunction with any of the existing routing protocols, thus achieving modularity. Extensive simulations are done which prove that our proposed protocol outperforms existing route maintenance techniques in terms of end-to-end delay and loss ratio, with minimal incurred overhead.

We investigate the delay limited secrecy capacity of the flat fading channel under two different assumptions on the available transmitter channel state information (CSI). The first scenario assumes perfect prior knowledge of both the main and eavesdropper channel gains. Here, upper and lower bounds on the delay limited secrecy capacity are derived, and shown to be tight in the high signal-to-noise ratio (SNR) regime. In the second scenario, only the main channel CSI is assumed to be available at the transmitter where, remarkably, we establish the achievability of a non-zero delay-limited secure rate, for a wide class of channel distributions, with a high probability. In the two cases, our achievability arguments are based on a novel two-stage key-sharing approach that overcomes the secrecy outage phenomenon observed in earlier works.

The two-way wiretap channel is considered in this paper. Two legitimate users, Alice and Bob, wish to exchange messages securely in the presence of a passive eavesdropper Eve. In the full-duplex scenario, where each node can transmit and receive simultaneously, new achievable secrecy rate regions are obtained based on the idea of allowing the two users to jointly optimize their channel prefixing distributions and binning codebooks in addition to key sharing. The new regions are shown to be strictly larger than the known ones for a wide class of discrete memoryless and Gaussian channels. In the half-duplex case, where a user can only transmit or receive on any given degree of freedom, the idea of randomized scheduling is introduced and shown to offer a significant gain in terms of the achievable secrecy sum-rate. A practical setup is further developed based on a near field wireless communication scenario, and it is shown that one can exploit the two-way nature of the communication, via appropriately randomizing the transmit power levels and transmission schedule, to introduce significant ambiguity at a noiseless Eve.

The widespread usage of WLANs and mobile devices has fostered the interest in localization systems for wireless environments. The majority of research in the context of wireless-based localization systems has focused on device-based active localization, in which devices are attached to tracked entities. Recently, device-free passive localization (DfP) has been proposed where the tracked entity is neither required to carry devices nor to participate actively in the localization process. Previous studies have focused on small areas and/or controlled environments. In this paper, we present the design, implementation, and analysis of Nuzzer, a large-scale DfP localization system, which tracks entities in real environments, rich in multipath. We first present probabilistic techniques for DfP localization of a single entity and evaluate their performance both analytically and in typical office buildings. Our results show that Nuzzer gives location estimates with less than 2-meters median distance error. We then give an algorithm for estimating the number of entities in an area of interest and localizing them into coarse-grained zones to enhance the scalability of the system. This indicates the suitability of Nuzzer to a large number of application domains.

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.

We introduce Spot as an accurate and efficient system for multi-entity device-free (DF) detection and tracking. Current state-of-the-art systems focused on the tracking of a single entity because of the intractability of the multi-entity case that leads to exponential complexity. Spot provides a novel cross-calibration technique that reduces the overhead of multiple entities calibration from exponential to linear. Spot also captures the spatial relations between the entities' poses into a probabilistic energy minimization framework via a conditional random field model. The designed energy minimization function is solved by a binary graph-cut algorithm. We evaluate our system using a typical testbed and show that Spot can achieve a multi-entity median tracking error of less than 1.44m. This corresponds to 108.33% enhancement in median distance error over the state-of-the-art DF localization systems, which can only track a single entity. In addition, Spot can estimate the number of entities correctly to within one difference error with 92% accuracy. This highlights that Spot achieves its goals of having an accurate and efficient software-only DF tracking solution of multiple entities in indoor environments.

No abstract available.

We propose UnLoc, an unsupervised indoor localization scheme that bypasses the need for war-driving. Our key observation is that certain locations in an indoor environment present identifiable signatures on one or more sensing dimensions. An elevator, for instance, imposes a distinct pattern on a smartphone's accelerometer; a corridor-corner may overhear a unique set of WiFi access points; a specific spot may experience an unusual magnetic fluctuation. We hypothesize that these kind of signatures naturally exist in the environment, and can be envisioned as internal landmarks of a building. Mobile devices that "sense" these landmarks can recalibrate their locations, while dead-reckoning schemes can track them between landmarks. Results from 3 different indoor settings, including a shopping mall, demonstrate median location errors of 1:69m. War-driving is not necessary, neither are floorplans the system simultaneously computes the locations of users and landmarks, in a manner that they converge reasonably quickly. We believe this is an unconventional approach to indoor localization, holding promise for real-world deployment.

We propose UnLoc [1], an unsupervised indoor localization scheme that bypasses the need for war-driving. Our key observation is that certain locations in an indoor environment present an identifiable signature on one or more sensing dimensions. An elevator, for instance, imposes a distinct pattern on a smartphone's accelerometer; a specific spot may experience an unusual magnetic fluctuation. This form of urban sensing and activity recognition has already been demonstrated in literature [2, 3], but not yet applied in pure localization applications. We hypothesize that these kind of signatures naturally exist in the environment and can be envisioned as internal landmarks of a building. Mobile devices that "sense" these landmarks can recalibrate their locations, while dead-reckoning schemes can track them between landmarks. Neither war-driving nor floorplans are necessary - the system simultaneously computes the locations of users and landmarks, in a manner so that they converge reasonably quickly. We believe this is an unconventional approach to indoor localization, holding promise for real-world deployment.

WLAN Device-free passive (DfP) indoor localization is an emerging technology enabling the localization of entities that do not carry any devices nor participate actively in the localization process using the already installed wireless infrastructure. This technology is useful for a variety of applications such as intrusion detection, smart homes and border protection. We present the design, implementation and evaluation of RASID, a DfP system for human motion detection. RASID combines different modules for statistical anomaly detection while adapting to changes in the environment to provide accurate, robust, and low-overhead detection of human activities using standard WiFi hardware. Evaluation of the system in two different testbeds shows that it can achieve an accurate detection capability in both environments with an F-measure of at least 0.93. In addition, the high accuracy and low overhead performance are robust to changes in the environment as compared to the current state of the art DfP detection systems.

The widespread deployment of varying networking technologies, coupled with the exponential increase in end-user data demand, have led to the proliferation of multi-homed or multi-interface enabled devices. To date, these interfaces are mainly utilized one at a time based on network availability, cost, and user-choice. While researchers have focused on simultaneously leveraging these interfaces by aggregating their bandwidths, these solutions however, have faced a steep deployment barrier. In this demo, we demonstrate the OPERETTA optimal bandwidth aggregation system. OPERETTA aims to utilize all the available network interfaces on a mobile device by distributing the users traffic on them. It is implemented as a Layered Service Provider (LSP) in the Windows OS to intercept the network connections from the applications and schedule these connections on the most appropriate interfaces. The network interfaces are chosen with the goal to optimize the overall system power consumption while meeting a certain throughput constraint calculated based on a user defined utility. The demo shows the effect of applying different scheduling algorithms and user utility functions on the system throughput and energy consumption.

The widespread deployment of varying networking technologies, coupled with the exponential increase in end-user data demand, have led to the proliferation of multi-homed or multi-interface enabled devices. To date, these interfaces are mainly utilized one at a time based on network availability, cost, and user-choice. While researchers have focused on simultaneously leveraging these interfaces by aggregating their bandwidths, these solutions however, have faced a steep deployment barrier. In this paper, we propose a novel optimal, energy-efficient, and deployable bandwidth aggregation system (OPERETTA) for multiple interface enabled devices. OPERETTA satisfies three goals: achieving a user defined throughput level with optimal energy consumption over multiple interfaces, deployability without changes to current legacy servers, and leveraging incremental deployment to achieve increased performance gains. We present the OPERETTA architecture and formulate the optimal scheduling problem as a mixed integer programming problem yielding an efficient solution. We evaluate OPERETTA via implementation on the the Windows OS, and further verify our results with simulations on NS2. Our evaluation shows the tradeoffs between the energy and throughput goals. Furthermore, with no modifications to current legacy servers, OPERETTA achieves throughput gains up to 150% compared to current operating systems with the same energy consumption. In addition, with as few as 25% of the servers becoming OPERETTA enabled, OPERETTA performance reaches the throughput upper bound, highlighting its incremental deployment and performance gains.

Road traffic congestion estimation is a critical function that affects both developed and developing countries alike. In this paper, we present Monitor as a novel RF-based traffic detection and identification system. Compared to the current approaches for traffic estimation, Monitor is low-cost, does not disrupt traffic during installation, works for non-laned traffic, and does not require active user participation. Our approach is based on the fact that the presence of an object in an RF environment affects the signal strength, and hence can be used for detecting and identifying objects. We present the Monitor system architecture and how it uses statistical techniques, based on the mean and variance of the received RF signal strength, to both detect the presence of objects and differentiate between humans and cars to reduce the traffic estimation outliers. Implementation of Monitor on standard RF equipment shows its capability of detecting the presence of objects and identifying their type with high accuracy highlighting its promise for different vehicle-related applications.

Developing a robust and reliable vehicle detection and speed estimation system that alerts drivers about driving conditions and helps them avoid joining traffic jams is an important problem that has attracted lots of attention recently. In this paper, we introduce a novel RF-based vehicle motion detection and speed estimation system (ReVISE). Our system leverages the fact that the presence of objects in an RF environment affects the received signal strength and hence, can be used to detect and identify different characteristics of the objects in an area of interest. Our long-term vision for ReVISE is to leverage common wireless networks, such as WiFi or cellular, to detect the density of traffic and estimate the car speed based on the mobile devices carried by users. This gives us an edge over the current techniques for traffic estimation as we do not require any specialized hardware and the cellular signal strength information is available from all cell phones, providing large-scale ubiquitous traffic estimation. We present the design and analysis of ReVISE including its vehicle detection and speed estimation modules. The detection module can differentiate between an empty street, stationary cars, and moving cars based on a multi-class SVM approach that uses features from the RF signal strength. We also present two novel speed estimation techniques based on statistical and curve fitting approaches. Evaluation of ReVISE in a real testbed shows that the proposed techniques can detect vehicle motion with an accuracy of 100% and estimate the vehicle speed with an accuracy of 90% in typical streets. This highlights the feasibility and promise of using RF for vehicle detection and speed estimation.

The mission of tracking a pedestrian is valuable for many applications including walking distance estimation for the purpose of pervasive healthcare, museum and shopping mall guides, and locating emergency responders. In this paper, we show how accurate and ubiquitous tracking of a pedestrian can be performed using only the inertial sensors embedded in his/her mobile phone. Our work depends on performing dead reckoning to track the user's movement. The main challenge that needs to be addressed is handling the noise of the low cost low quality inertial sensors in cell phones. Our proposed system combines two novel contributions: a novel step count estimation technique and a gait-based accurate variable step size detection algorithm. The step count estimation technique is based on a lightweight finite state machine approach that leverages orientation-independent features. In order to capture the varying stride length of the user, based on his changing gait, we employ a multi-class hierarchical Support Vector Machine classifier. Combining the estimated number of steps with the an accurate estimate of the individual stride length, we achieve ubiquitous and accurate tracking of a person in indoor environments. We implement our system on different Android-based phones and compare it to the state-of-the-art techniques in indoor and outdoor testbeds with arbitrary phone orientation. Our results in two different testbeds show that we can provide an accurate step count estimation with an error of 5.72%. In addition, our gait type classifier has an accuracy of 97.74%. This leads to a combined tracking error of 6.9% while depending only on the inertial sensors and turning off the GPS sensor completely. This highlights the ability of the system to provide ubiquitous, accurate, and energy efficient tracking.

The widespread deployment of varying networking technologies, coupled with the exponential increase in end-user data demand, have all led to the proliferation of multi-homed or multi-interface enabled devices. To date, these interfaces are mainly utilized one at a time based on network availability, cost, and user-choice. Researchers have recently focused on leveraging these interfaces simultaneously by proposing solutions to aggregate their bandwidths in order to ultimately increase throughput and satisfy the end-user's growing demand on data. These solutions, however, have faced a steep deployment barrier due to various system design choices and heavy demand on energy. In this paper, we propose a novel Green and Deployable Bandwidth Aggregation System (G-DBAS) for multiple interface enabled devices. G-DBAS addresses a set of challenges including automatically estimating the characteristics of applications and scheduling various connections to different interfaces along with meeting different energy consumption goals set by users. We fully implement G-DBAS on the Windows OS and evaluate various scheduling strategies that we propose. Our implementation and simulation results show that G-DBAS can achieve the user energy-throughput goals while operating as an out-of-the-box standard Windows executable, highlighting its deployability and ease of use.

WLAN Device-free Passive (DfP) localization is an emerging technology that uses the widely deployed WiFi networks for detecting and localizing human presence within indoor environments. This paper presents an accurate and low-overhead technique for detecting human presence based on non-parametric statistical anomaly detection. This technique constructs profiles capturing the signal strength characteristics when no human is present within the area of interest and uses these profiles to identify any anomalies in the signal strength due to human motion activity. To adapt to changes in the environment, the constructed profiles are regularly updated by signal strength readings with low anomaly probability. Exponential smoothing is then used to reduce the effect of noisy readings in order to enhance the detection accuracy. Our work proved to be more robust and accurate than other DfP detection techniques, achieving a high detection accuracy of 4.7% miss detection rate and 3.8% false alarm rate, while requiring minimal deployment overhead.

Many routing protocols have been proposed for wireless sensor networks (WSNs). However, only few of them addressed secure routing. This work contributes in solving secure and reliable routing problems by presenting a secure and reliable multipath routing protocol (MSR) for WSNs. MSR consists of three key components: on-demand multipath routing, enhanced passive acknowledgment, and erasure coding. On-demand routing enhances the security and reliability of the routing protocol while enhanced passive acknowledgment is used to both enhance the security of the protocol and reduce its overhead. In addition, erasure coding is further used to increase the protocol security and reliability. We present the details of the protocol along with a study of major security attacks in WSNs and determines the level of MSR's resistance against these attacks. The evaluation of the proposed protocol using the NS2 simulator shows that it can significantly enhance the security of WSNs under different attacker models.

In this paper, we present the design, implementation, and evaluation of a system that automatically constructs accurate radio maps for device-free WLAN localization systems. The system is capable of generating deterministic and probabilistic radio maps for localization systems. Our system uses 3D ray tracing enhanced with the uniform theory of diffraction (UTD) to model the electric field behavior and the human shadowing effect. We present our system architecture and describe the details of its different components. We also propose an optional module, location-0 correction, that can significantly enhances the system accuracy and reduces its dependence on the 3D model details by using just one signal strength sample. Our experiments in a real testbed show that the predicted signal strength differs from the measurements by a maximum average absolute error of 2.77 dB achieving a maximum localization error of 3.13m and 2.84m for both the deterministic and probabilistic radio maps, respectively. In addition, the results show that our system is not sensitive to the 3D model details.

In this paper, we present a practical and provably secure two-way wireless communication scheme in the presence of a passive eavesdropper. The scheme implements a randomized scheduling and power allocation mechanism, where each legitimate node transmits in random time slots and with random transmit power. Such randomization results in ambiguity at the eavesdropper with regard to the origin of each transmitted frame. The scheme is analyzed in a time-varying binary block erasure channel model and secrecy outage probabilities are derived and empirically evaluated. The scheme is implemented over an IEEE 802.15.4-enabled Sun SPOT sensor motes. The results show that the proposed scheme achieves significant secrecy gains with a vanishing outage probability, at the expense of slight decrease in throughput, even when the eavesdropper is equipped with a receive power based classifier and is located too close to the transmitter node.

Research in location determination for GSM phones has gained interest recently as it enables a wide set of location based services. RSSI-based techniques have been the preferred method for GSM localization on the handset as RSSI information is available in all cell phones. Although the GSM standard allows for a cell phone to receive signal strength information from up to seven cell towers, many of today's cell phones are low-end phones, with limited API support, that gives only information about the associated cell tower. In addition, in many places in the world, the density of cell towers is very small and therefore, the available cell tower information for localization is very limited. This raises the challenge of accurately determining the cell phone location with very limited information, mainly the RSSI of the associated cell tower. In this paper we propose a Hidden Markov Model based solution that leverages the signal strength history from only the associated cell tower to achieve accurate GSM localization. We discuss the challenges of implementing our system and present the details of our system and how it addresses the challenges. To evaluate our proposed system, we implemented it on Android-based phones. Results for two different testbeds, representing urban and rural environments, show that our system provides at least 156% enhancement in median error in rural areas and at least 68% enhancement in median error in urban areas compared to current RSSI-based GSM localization systems.

Human motion has strong impact on the received signal strength (RSS) of indoor wireless links that can be exploited for variance-based device-free positioning. In this paper, we investigate the effect of human motion on the variance of the RSS of wireless local area networks (WLAN) operating at 2.4 GHz. Using measurements, the RSS variance for human in-place motion is determined as a function of the human position in a corridor setting. We provide ray tracing and empirical models to capture this effect. The accuracy of the different models is compared under different scenarios. Furthermore, we investigate the effect of having multiple-entities in the same area of interest on the RSS variance and provide models for it.

Routing in cognitive networks is a challenging problem due to the primary users' (PU) activities and mobility. Multipath routing is a general solution to improve reliability of connections. Existing multipath routing metrics for traditional wireless networks do not take into account PUs' activities. This work introduces a new routes selection metric for multipath routing in cognitive networks. Routes closeness, the proposed metric, favors routes that are not close to each other. Selecting non-close routes makes them less vulnerable to PUs' activities as it would be less possible for an active mobile PU to interrupt all the routes at the same time. We describe our new routes closeness metric along with a routing protocol that make use of it to enhance the performance in cognitive networks. Simulation results show the effectiveness of the new metric in increasing connection reliability and end-to-end throughput to up to 48% compared to other algorithms, especially with increasing PUs' mobility and secondary user's (SUs) density.

This paper develops a novel framework for sharing secret keys using the Automatic Repeat reQuest (ARQ) protocol. We first characterize the underlying information theoretic limits, under different assumptions on the channel spatial and temporal correlation function. Our analysis reveals a novel role of “dumb antennas” in overcoming the negative impact of spatial correlation on the achievable secrecy rates. We further develop an adaptive rate allocation policy, which achieves higher secrecy rates in temporally correlated channels, and explicit constructions for ARQ secrecy coding that enjoy low implementation complexity. Building on this theoretical foundation, we propose a unified framework for ARQ-based secrecy in Wi-Fi networks. By exploiting the existing ARQ mechanism in the IEEE 802.11 standard, we develop security overlays that offer strong security guarantees at the expense of only minor modifications in the medium access layer. Our numerical results establish the achievability of nonzero secrecy rates even when the eavesdropper channel is less noisy, on the average, than the legitimate channel, while our Linux-based prototype demonstrates the efficiency of our ARQ overlays in mitigating all known, passive and active, Wi-Fi attacks at the expense of a minimal increase in the link setup time and a small loss in throughput.

Context-aware applications have been gaining huge interest in the last few years. With cell phones becoming ubiquitous computing devices, cell phone localization has become an important research problem. In this paper, we present CellSense, a probabilistic RSSI-based fingerprinting location determination system for GSM phones. We discuss the challenges of implementing a probabilistic fingerprinting localization technique in GSM networks and present the details of the CellSense system and how it addresses the challenges. To evaluate our proposed system, we implemented CellSense on Android-based phones. Results for two different testbeds, representing urban and rural environments, show that CellSense provides at least 23.8% enhancement in accuracy in rural areas and at least 86.4% in urban areas compared to other RSSI-based GSM localization systems. This comes with a minimal increase in computational requirements. We also evaluate the effect of changing the different system parameters on the accuracy-complexity tradeoff.

Adding location to the available information enables a new category of applications. With the constrained battery on cell phones, energy-efficient localization becomes an important challenge. In this paper we introduce a low-energy calibration- free localization scheme based on the available internal sensors in many of today's phones. We start by energy profiling the different sensors that can be used for localization. Based on that, we propose GAC: a hybrid GPS/accelerometer/compass scheme that depends mainly on using the low-energy accelerometer and compass sensors and uses the GPS infrequently for synchronization. We implemented our system on Android-enabled cell phones and evaluated it in both highways and intra-city driving environments. Our results show that the proposed hybrid scheme has an exponential saving in energy, with a linear loss in accuracy compared to the GPS accuracy. We also evaluate the effect of the different parameters on the energy-accuracy tradeoff.

In many hybrid wireless sensor networks' applications, sensor nodes are deployed in hostile environments where trusted and un-trusted nodes co-exist. In anchor-based hybrid networks, it becomes important to allow trusted nodes to gain full access to the location information transmitted in beacon frames while, at the same time, prevent un-trusted nodes from using this information. The main challenge is that un-trusted nodes can measure the physical signal transmitted from anchor nodes, even if these nodes encrypt their transmission. Using the measured signal strength, un-trusted nodes can still tri-laterate the location of anchor nodes. In this paper, we propose HyberLoc, an algorithm that provides anchor physical layer location privacy in anchor-based hybrid sensor networks. The idea is for anchor nodes to dynamically change their transmission power following a certain probability distribution, degrading the localization accuracy at un-trusted nodes while maintaining high localization accuracy at trusted nodes. Given an average power constraint, our analysis shows that the discretized exponential distribution is the distribution that maximizes location uncertainty at the un-trusted nodes. Detailed evaluation through analysis, simulation, and implementation shows that HyperLoc gives trusted nodes up to 3.5 times better localization accuracy as compared to un-trusted nodes.

WLAN RSS-based localization has been a hot research topic for the last years. To obtain high accuracy in the noisy wireless channel, WLAN location determination systems usually use a calibration phase, where a radio map, capturing the signal strength signatures at different locations in the area of interest, is built. The radio map construction process takes a lot of time and effort, reducing the value of WLAN localization systems. In this paper, we propose 3D ray tracing as a way for automatically generating a highly accurate radio map. We compare this method to previously used propagation modeling-based methods like the Wall Attenuation Factor and 2D ray tracing models. We evaluate the performance of each method and its computational cost in a typical residential environment. We also examine the sensitivity of the localization accuracy to inaccurate material parameters. Our results quantify the accuracy- complexity trade-off of the different proposed techniques with 3D ray tracing giving the best localization accuracy compared to measurements with acceptable computational requirements on a typical PC.

The 11 papers in this special issue on mission critical networking are divided into three categories: quality of service issues (three papers); security issues (four papers); and configuration and data collection issues (four papers).

This paper develops a new physical layer framework for secure two-way wireless communication in the presence of a passive eavesdropper, i.e., Eve. Our approach achieves perfect information theoretic secrecy via a novel randomized scheduling and power allocation scheme. The key idea is to allow Alice and Bob to send symbols at random time instants. While Alice will be able to determine the symbols transmitted by Bob, Eve will suffer from ambiguity regarding the source of any particular symbol. This desirable ambiguity is enhanced, in our approach, by randomizing the transmit power level. Our theoretical analysis, in a 2-D geometry, reveals the ability of the proposed approach to achieve relatively high secure data rates under mild conditions on the spatial location of Eve. These theoretical claims are then validated by experimental results using IEEE 802.15.4-enabled sensor boards in different configurations, motivated by the spatial characteristics of Wireless Body Area Networks (WBAN).

In this paper we propose distributed storage algorithms for large-scale wireless sensor networks. Assume a wireless sensor network with n nodes that have limited power, memory, and bandwidth. Each node is capable of both sensing and storing data. Such sensor nodes might disappear from the network due to failures or battery depletion. Hence it is desired to design efficient schemes to collect data from these n nodes. We propose two distributed storage algorithms (DSA's) that utilize network flooding to solve this problem. In the first algorithm, DSA-I, we assume that the total number of sensors is known to each sensor in the network. We show that this algorithm is efficient in terms of the encoding and decoding operations. Furthermore, every node utilizes network flooding to disseminate its data throughout the network using a mixing time of approximately O(n). In the second algorithm, DSA-II, we assume that the total number of nodes is not known to every sensor; hence dissemination of the data does not depend on n. The encoding operations in this case take O(C mu2), where mu is the mean degree of the network graph and C is a system parameter. We evaluate the performance of the proposed algorithms through analysis and simulation. We show that the performance of the proposed algorithms matches the derived theoretical results.

In this paper, the delay limited secrecy capacity of the flat fading channel is investigated under two different assumptions on the available transmitter channel state information (CSI). The first scenario assumes perfect prior knowledge of both the main and eavesdropper channel gains. Here, upper and lower bounds on the secure delay limited capacity are derived and shown to be tight in the high signal-to-noise ratio (SNR) regime (for a wide class of channel distributions). In the second scenario, only the main channel CSI is assumed to be available at the transmitter. Remarkably, under this assumption, we establish the achievability of non-zero secure rate (for a wide class of channel distributions) under a strict delay constraint. In the two cases, our achievability arguments are based on a novel two-stage approach that overcomes the secrecy outage phenomenon observed in earlier works.

Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Traditionally, clustering algorithms aim at generating a number of disjoint clusters that satisfy some criteria. In this paper, we formulate a novel clustering problem that aims at generating overlapping multihop clusters. Overlapping clusters are useful in many sensor network applications, including intercluster routing, node localization, and time synchronization protocols. We also propose a randomized, distributed multihop clustering algorithm (KOCA) for solving the overlapping clustering problem. KOCA aims at generating connected overlapping clusters that cover the entire sensor network with a specific average overlapping degree. Through analysis and simulation experiments, we show how to select the different values of the parameters to achieve the clustering process objectives. Moreover, the results show that KOCA produces approximately equal-sized clusters, which allow distributing the load evenly over different clusters. In addition, KOCA is scalable; the clustering formation terminates in a constant time regardless of the network size.

First responders need to be able to have the most amount of situational awareness of the operations they perform while not being overwhelmed by all of the information that may be made available to them. This paper presents the Rover integration and fusion platform to alleviate the fusing of multiple information sources, which may not be known prior to the beginning of an operation. Rover can aid fusing sources by providing as much contextual information which often will be forgotten when designing programs, mapping contextual information into a view in which programs and users can use, and automate well-known and designed tasks. All of this occurs while deploying Rover to first responder incident scenes without the need for an existing network infrastructure prior to an incident.

Knowledge of node's locations is an essential requirement for many applications of wireless sensor networks. A major problem with multi-hop location discovery is the accumulated error. In this paper, we analyze the effect of reflection errors and present the Multi-hop Relative Location Estimation (MRLE) algorithm that estimates relative node's positions with low error margins. We capture the impact of different parameters on the accuracy of the estimated position and introduce a new metric, called the CLIQUE factor, that has a very dominant effect on the accuracy of the estimated positions. We further highlight means for trading accuracy for energy consumption and/or computational overhead.

Typical location determination systems require the presence of a physical device that is attached to the person that is being tracked. In addition, they usually require the tracked device to participate actively in the localization process. In this paper, we introduce the concept of Device-free Passive (DfP) localization. A DfP system is envisioned to be able to detect, track, and identify entities that do not carry any device, nor participate actively in the localization process. The system works by monitoring and processing changes in the received physical signals at one or more monitoring points to detect changes in the environment. Applications for DfP systems include intrusion detection and tracking, protecting outdoor assets, such as pipelines, railroad tracks, and perimeters.We describe the DfP system's architecture and the challenges that need to be addressed to materialize a DfP system. We show the feasibility of the system by describing algorithms for implementing different functionalities of a DfP system that works with nominal WiFi equipment. We present two techniques for intrusion detection and a technique for tracking a single intruder. Our results show that the system can achieve very high probability of detection and tracking with very few false positives. We also identify different research directions for addressing the challenges of realizing a DfP system.

Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Most of the published clustering algorithms strive to generate the minimum number of disjoint clusters. However, we argue that guaranteeing some degree of overlap among clusters can facilitate many applications, like inter-cluster routing, topology discovery and node localization, recovery from cluster head failure, etc. We formulate the overlapping multi-hop clustering problem as an extension to the k-dominating set problem. Then we propose MOCA; a randomized distributed multi-hop clustering algorithm for organizing the sensors into overlapping clusters. We validate MOCA in a simulated environment and analyze the effect of different parameters, e.g. node density and network connectivity, on its performance. The simulation results demonstrate that MOCA is scalable, introduces low overhead and produces approximately equal-sized clusters.

This paper presents the design, implementation and evaluation of the PinPoint location determination system. PinPoint is a distributed algorithm that enables a set of $n$ nodes to determine the RF propagation delays between every pair of nodes, from which the inter-node distances and hence the spatial topology can be readily determined. PinPoint does not require any calibration of the area of interest and thus is rapidly deployable. Unlike existing time-of-arrival techniques, PinPoint does not require an infrastructure of accurate clocks (e.g., GPS) nor does it incur the o(n2) message exchanges of "echoing" techniques. PinPoint can work with nodes having inexpensive crystal oscillator clocks, and incurs a constant number of message exchanges per node to determine the location of $n$ nodes. Each node's clock is assumed to run reliably but asynchronously with respect to the other nodes, i.e., they can run at slightly different rates because of hardware (oscillator) inaccuracies. PinPoint provides a mathematical way to compensate for these clock differences in order to arrive at a very precise timestamp recovery that in turn leads to a precise distance determination. Moreover, each node is able to determine the clock characteristics of other nodes in its neighborhood allowing network synchronization. We present a prototype implementation for PinPoint and discusses the practical issues in implementing the mathematical framework and how PinPoint handles the different sources of error affecting its accuracy. Evaluation of the prototype in typical indoor and outdoor environments shows that PinPoint gives an average accuracy of four to six feet, in different environments, allowing PinPoint to support accurate rapidly deployable localization scenarios.

Abstract: Recent years have witnessed a growing interest in the application of wireless sensor networks in unattended environments. Nodes in such applications are equipped with limited energy supply and need careful management in order to extend their lifetime. In order to conserve energy, many of the routing protocols proposed for wireless sensor networks reduce the number of transmitted packets by pursuing in-network data aggregation. Almost all of the aggregation schemes presented in the literature strive to save sensor’s energy while considering unconstrained data traffic. However, aggregation extends the queuing delay at the relay nodes and can thus complicate the handling of latencyconstrained data. In this paper, we analyze the conditions for effective aggregation of data traffic that is subject to end-to-end delay constraints. We present an algorithm for achieving maximal possible energy saving through data aggregation while meeting the desired level of timeliness. A Weighted Fair Queuing based mechanism for packet scheduling is employed at each node in order to perform service differentiation and ensure bounded delay for constrained traffic. The performance of the proposed approach is qualified via simulation. 1.

WLAN location determination systems are gaining increasing attention due to the value they add to wireless networks. In this paper, we present a multivariate analysis technique for enhancing the performance of WLAN location determination systems by taking the correlation between samples from the same access point into account. We show that the autocorrelation between consecutive samples from the same access point can be as high as 0.9. Giving a sequence of correlated signal strength samples from an access point, the technique estimates the user location based on the calculated probability of this sequence from the multivariate distribution. We use a linear autoregressive model to derive the multivariate distribution function for the correlated samples. Using analytical analysis, we show that the proposed technique provides better location accuracy over previous techniques especially for the highly correlated samples in a typical WLAN environment. Implementation of the technique in the Horus WLAN location determination system shows that the average system accuracy is increased by more than 64%. This significant enhancement in the accuracy of WLAN location determination systems helps increase the set of context-aware applications implemented on top of these systems.

We present the design and implementation of the Horus WLAN location determination system. The design of the Horus system aims at satisfying two goals: high accuracy and low computational requirements. The Horus system identifies different causes for the wireless channel variations and addresses them to achieve its high accuracy. It uses location-clustering techniques to reduce the computational requirements of the algorithm. The lightweight Horus algorithm helps in supporting a larger number of users by running the algorithm at the clients.We discuss the different components of the Horus system and its implementation under two different operating systems and evaluate the performance of the Horus system on two testbeds. Our results show that the Horus system achieves its goal. It has an error of less than 0.6 meter on the average and its computational requirements are more than an order of magnitude better than other WLAN location determination systems. Moreover, the techniques developed in the context of the Horus system are general and can be applied to other WLAN location determination systems to enhance their accuracy. We also report lessons learned from experimenting with the Horus system and provide directions for future work.

WLAN location determination systems add to the value of a wireless network by providing the user location without using any extra hardware. Current systems return the estimated user location from a set of discrete locations in the area of interest, which limits the accuracy of such systems to how far apart the selected points are. In this paper, we present two techniques to estimate the user location in the continuous physical space, namely the center of mass technique and time averaging technique. We test the performance of the two techniques in the context of the Horus WLAN location determination system under two different testbeds. Using the center of mass technique, the performance of the Horus system is enhanced by more than 13% for the first testbed and more than 6% for the second testbed. The time-averaging technique enhances the performance of the Horus system by more than 24% for the first testbed and more than 15% for the second testbed. The techniques are general and can be applied to any of the current WLAN location determination systems to enhance their accuracy. Moreover, the two techniques are independent and can be applied together to further enhance the accuracy of the current WLAN location determination systems

We present an autoregressive model for modelling samples autocorrelation from the same access point in WLAN location determination systems. Our work is in the context of the Horus system, which is a probabilistic WLAN location determination system. We show that the autocorrelation between consecutive samples from the same access point can be as high as 0.9. Using our model, we describe a technique to use multiple signal strength samples from each access point, taking the high autocorrelation into account, to achieve better accuracy. Implementation of the technique in the Horus system shows that the average system accuracy is increased by more than 50%. Our results show that assuming independence of samples from the same access point can lead to degraded performance as the number of samples used in the estimation algorithm is increased, due to the wrong independence assumption. We also discuss how to incorporate the new technique with other algorithms for enhancing the performance of WLAN location determination systems.

We present a WLAN location determination technique, the Joint Clustering technique, that uses: (1) signal strength probability distributions to address the noisy wireless channel, and (2) clustering of locations to reduce the computational cost of searching the radio map. The Joint Clustering technique reduces computational cost by more than an order of magnitude, compared to the current state of the art techniques, allowing non-centralized implementation on mobile clients. Results from 802.11-equipped iPAQ implementations show that the new technique gives user location to within 7 feet with over 90% accuracy.

To limit the radio map size and the time required to build the radio map, current WLAN location determination system do not handle small-scale variations. This contributes to most of the estimation error in the current systems. We propose a general technique, the perturbation technique, to handle the small-scale variations problem. The system uses user history to detect small-scale variations and then perturbs the signal strength vector entries to overcome it. The results obtained show that the accuracy can be increased by more than 8%. Moreover, the worst-case error is enhanced by more than 60%. We also show that the perturbation technique can help in enhancing the accuracy due to temporal variations in case of change in the environment conditions, thus increasing the accuracy of the current WLAN location determination systems beyond their limits.

We propose and analyze formal models for coordination functions of the 802.11 MAC layer using systems of communicating machines. We model the basic DCF (CSMA/CA) protocol, the DCF (distributed coordination function) protocol with RTS/CTS (MACA), and the PCF (point coordination function) protocol. Analyses show the following safety results: the CSMA/CA protocol is free from deadlocks and non-executable transitions; the MACA has a potential livelock, but is free from deadlocks and non-executable transitions; the PCF protocol is free from deadlocks and non-executable transitions. We also show that liveness is guaranteed in the PCF protocol.

While traditional routing protocols try to minimize the end-to-end delay or maximize the throughput, most energy-aware routing protocols for wireless sensor networks try to extend the life time of the network by minimizing the energy consumption sacrificing other performance metrics. We introduce a new energy-aware routing protocol that tries to minimize the energy consumption and, at the same time, maintain good end-to-end delay and throughput performance. The new algorithm is based on a constrained shortest-path algorithm. We compare the new algorithm with some traditional routing and energy-aware routing algorithms. The results show that the new algorithm performance is acceptable under all performance metrics and presents a performance balance between the traditional routing algorithms and the energy-aware routing algorithms. The constraint value can be chosen to achieve different performance objectives for different sensor network missions.

Banyan networks are commonly used as interconnection structures in ATM switches. This paper is concerned with the replication technique which was applied to the standard banyan networks. We apply this technique to the plane interconnected parallel network (PIPN) which is a switch introduced previously as a better banyan-based interconnection structure. The normalized throughput of unbuffered and buffered replicated PIPN is analyzed analytically under uniform traffic model. We apply the simulation technique to verify the analytical results under the uniform traffic model and to study the performance of different heterogeneous traffic models. The performance is shown to increase significantly when the replicated PIPN is used which supports the idea of using this switch as a new high-performance ATM switch.