Reliable and persistent water monitoring is a challenging problem in smart underwater Internet of Things (UW IoT) due to its harsh, unexplored, and unpredictable nature. Given the need for high-resolution spatio–temporal sensing in such environments, traditional digital sensors are not suitable due to their high-cost, high-power consumption, and nonbiodegradable nature. Further, reliable and low-latency communication techniques that avoid data packet retransmissions, if the feedback is available, are crucial for reconstructing the phenomenon being monitored in a timely manner at the fusion center, such as a drone. To address the above challenges, we propose a novel architecture consisting of a substrate of densely deployed underwater all-analog biodegradable sensors that enable persistent sensing and continually transmitting data to the surface digital buoys. The analog nodes are designed to be energy efficient by implementing analog joint source-channel coding (JSCC), a low-complexity compression-communication technique, using biodegradable field effect transistors (FETs). We, then, propose a correlation-aware hybrid automatic repeat request (HARQ) technique to transmit data from the surface buoys to the fusion center. Such HARQ technique leverages JSCC and the redundancy in the buoy data (arising from the correlation of the phenomenon at the analog nodes) to avoid retransmissions, thus, saving energy and time.

Mobile context determination is an important step for many context-aware services such as location-based services, enterprise policy enforcement, building/room occupancy detection for power/HVAC operation, etc. Especially in enterprise scenarios where policies (e.g., attending a confidential meeting only when the user is in “Location X”) are defined based on mobile context, it is paramount to verify the accuracy of the mobile context. Most of the existing solutions rely on context obtained directly from sensors which could be hacked, noisy or insufficient, which cannot be relied upon for security applications. In this article, we take a different approach by modeling mobile context based on past context data of related users and considering its unique challenges such as missing features. To this end, we propose three models for modeling mobile context based on symbolic time series data of feature-value pairs—two stochastic models based on the theory of Hidden Markov Models (HMMs) and one model based on deep learning—personalized model(HPContext),collaborative filtering model((HCFContext)), anddeep learning model(DeepContext) to eventually replaceHPContext.HPContextandDeepContextpredict the current context using sequential history of the user's own past context observations; whileHCFContextenhances the former with collaborative filtering features, which enables it to predict the current context of the primary user based on the context observations of users related to the primary user, e.g., same team colleagues in the company, gym friends, family members, etc.DeepContextmodels mobile context based on symbolic (i.e., categorical valued rather than continuous-valued) time series data using deep learning techniques. These models are then used to determine the context of the primary user at the current instant or some timesteps in to the future. Each of the proposed models can also be used to enhance or complement the context obtained from sensors. Finally, these models are thoroughly validated on a real-life dataset.

With the increase in the use of unmanned aerial vehicles (UAVs)/drones, it is important to detect and identify causes of failure in real time for proper recovery from a potential crash-like scenario or postincident forensics analysis. The cause of crash could be either a fault in the sensor/actuator system, a physical damage/attack, or a cyber attack on the drone's software. In this work, we propose novel architectures based on deep convolutional and long short-term memory neural networks to detect (via autoencoder) and classify drone misoperations based on real-time sensor data. The proposed architectures are able to learn high-level features automatically from the raw sensor data. Empirical results show that our solution is able to detect (with over 90% accuracy) and classify various types of drone misoperations [with about 99% accuracy (simulation data) and up to 85% accuracy (experimental data)]. Furthermore, with the help of field programmable gate array-based hardware acceleration, we achieved a speedup of 40x ($\sim\!\text{2.6 ms}$) for detection, while consuming half the amount of power compared with onboard GPU devices, such as NVIDIA Jetson TX2.

The signal recovery performance of the rectangular-type Analog Joint Source-Channel Coding (AJSCC) is analyzed in this work for high and medium/low Signal-to-Noise Ratio (SNR) scenarios in a wireless sensing system with the Doppler channel. The analytical formulations of the Mean Square Error (MSE) performance are derived based on the geometry of the rectangular-type AJSCC on analog sensing and system with Doppler effects. The comprehensive listing of all cases in the three-dimensional geometric signal mapping curve is provided to obtain the theoretical formulations for the medium/low SNR scenario. Evaluation results indicate that there are optimal parameters in the rectangular-type AJSCC to minimize the signal recovery MSE for analog sensing and system with Doppler effects in both high and medium/low SNR scenarios. The analysis of the work provides insights on the optimization of the rectangular-type AJSCC in practical wireless sensing systems.

Mobile devices aggregate various types of data from sensitive corporate documents to personal content. While users desire to access this content on a single device via a unified user experience and through any mobile app, protecting this data is challenging. Even though different data types have different security and privacy needs, mobile operating systems include only a few, if any, functionalities for fine-grained data protection. We present SWIRLS, an Android-based mobile OS that provides a policy-based information-flow data protection abstraction for mobile apps to support BYOD (bring-your-own-device) use cases. SWIRLS attaches security policies to individual pieces of data and enforces these policies as the data flows through the device. Unlike current BYOD solutions like VMs that create duplication overload, SWIRLS provides a single environment to access content from different security contexts using the same applications while monitoring for malicious data leakage. SWIRLS leverages a two-level hybrid information flow tracking (IFT) mechanism to track both intra-application flows and a higher level IFT based on processes for application isolation. Our evaluation presents BYOD data protection use-cases such as limiting document sharing, preventing leakage based on document classification and security policies based on geo-fencing. SWIRLS only imposes a low battery consumption and performance overhead.

Mobile location-based services are important context-aware services that are more and more used for enforcing security policies, for supporting indoor room navigation, and for providing personalized assistance. However, a major problem still remains unaddressed-the lack of solutions that work across buildings while not using additional infrastructure and also accounting for privacy and reliability needs. A privacy-preserving, multi-modal, cross-building, collaborative localization platform is proposed based on Wi-Fi Received Signal Strength Indicator (RSSI) (existing infrastructure), Cellular RSSI, sound, light, and geo-magnetic levels, that enables sub-room level localization. The solution is fully based on mobile phones and existing Wi-Fi infrastructure, and has privacy inherently built into it via cryptographically-secured onion routing and perturbation/randomization techniques. It also exploits the idea of weighted collaboration to increase the reliability as well as to limit the effect of noisy devices (due to sensor noise/privacy). The solution has been analyzed in terms of latency overhead due to onion-routing, request load on phones, privacy-accuracy tradeoffs, optimum parameters, granularity, different classification algorithms using real location data collected at multiple indoor and outdoor locations via an Android application. The additional features other than Wi-Fi RSSI values are shown to increase the accuracy to a maximum of 15 percent, while considering Geo-magnetic field is shown to enhance the granularity from 2.5 m to ≈1 m, a 60 percent improvement.

With the increase in use of Unmanned Aerial Vehicles (UAVs)/drones, it is important to detect and identify causes of failure in real time for proper recovery from a potential crash-like scenario or post incident forensics analysis. The cause of crash could be either a fault in the sensor/actuator system, a physical damage/attack, or a cyber attack on the drone's software. In this paper, we propose novel architectures based on deep Convolutional and Long Short-Term Memory Neural Networks (CNNs and LSTMs) to detect (via Autoencoder) and classify drone mis-operations based on real-time sensor data. The proposed architectures are able to learn high-level features automatically from the raw sensor data and learn the spatial and temporal dynamics in the sensor data. We validate the proposed deep-learning architectures via simulations and realworld experiments on a drone. Empirical results show that our solution is able to detect (with over 90% accuracy) and classify various types of drone mis-operations (with about 99% accuracy (simulation data) and upto 85% accuracy (experimental data)).

Recently, much progress has been made in using Neural Networks (NNs) for important yet narrowly focused tasks such as image classification (e.g., VGG-Net, ResNet), playing complex games like GO or other Computer Vision (CV) tasks. While these achievements are impressive, they are either achieved on computers with virtually unlimited resources or with little regard to real-time actionability. In this paper, we propose to combine the ubiquity of low-resource mobile devices, e.g., drones, with approximate- and distributed-computing techniques in order to make these NN techniques deployable on resource-constrained devices as well as to provide realtime information about the environment. We target situational awareness, which involves sensing the crucial factors in a new environment on a real-time basis. Specifically, we introduce intelligence to a team of drones in the form of real-time detection of a suspect/weapon using local resources and suspect identification in an emergency situation. We validate our proposed methods using Microsoft AirSim simulator via both simulations and hardware-in-the-loop emulations.

Search and Rescue (SAR) is an important part of several applications of national and social interest. Existing solutions for search missions in both terrestrial and aerial domains are mostly limited to single agent and specific environments; however, search missions can significantly benefit from the use of multiple agents that can quickly adapt to new environments. In this paper, we propose a framework based on Multi-Agent Deep Reinforcement Learning (MADRL) that realizes the actor-critic framework in a distributed manner for coordinating multiple Unmanned Aerial Vehicles (UAVs) in the exploration of unknown regions. One of the original aspects of our work is that the actors represent simulated or actual UAVs exploring the environment in parallel instead of traditional computer threads. Also, we propose addition of Long Short Term Memory (LSTM) neural network layers to the actor and critic architectures to handle imperfect communication and partial observability scenarios. The proposed approach has been evaluated in a grid world and has been compared against other competing algorithms such as Multi-Agent Q-Learning, Multi-Agent Deep Q-Learning to show its advantages. More generally, our approach could be extended to image-based/continuous action space environments as well.

The research challenge of current wireless sensor networks (WSNs) is to design energy-efficient, low-cost, high-accuracy, self-healing, and scalable systems for applications such as environmental monitoring. Traditional WSNs consist of low density, power-hungry digital motes that are expensive and cannot remain functional for long periods on a single power charge. In order to address these challenges, a dumb-sensing and smart-processing architecture that splits sensing and computation capabilities is proposed. Sensing is exclusively the responsibility of analog substrate-consisting of low-power, low-cost all-analog sensors-that sits beneath the traditional WSN comprising of digital nodes, which does all the processing of the sensor data received from analog sensors. A low-power and low-cost solution for substrate sensors has been proposed using analog joint source-channel coding (AJSCC) realized via the characteristics of metal-oxide-semiconductor field-effect transistor (MOSFET). Digital nodes (receiver) also estimate the source distribution at the analog sensors (transmitter) using machine learning techniques so as to find the optimal parameters of AJSCC that are communicated back to the analog sensors to adapt their sensing resolution as per the application needs. The proposed techniques have been validated via simulations from MATLAB and LTSpice to show promising performance and indeed prove that our framework can support large-scale high density and persistent WSN deployment.

Corner cases are the main bottlenecks when applying Artificial Intelligence (AI) systems to safety-critical applications. An AI system should be intelligent enough to detect such situations so that system developers can prepare for subsequent planning. In this paper, we propose semi-supervised anomaly detection considering the imbalance of normal situations: In particular, driving data consists of multiple normal situations (e.g., right turn, going straight), some of which (e.g., U-turn) could be as rare as anomalous ones. Existing machine learning based anomaly detection approaches do not fare sufficiently well when applied to such imbalanced data. In this paper, we present a novel multi-task learning (LSTM autoencoder and predictor) based approach that leverages domain-knowledge (maneuver labels) for anomaly detection in driving data. We evaluate the proposed approach both quantitatively and qualitatively on 150 hours of real-world driving data and show improved performance over baseline/existing approaches.

Certain sensing applications such as Internet of Things (IoTs), where the sensing phenomenon may change rapidly in both time and space, requires sensors that consume ultra-low power (so that they do not need to be put to sleep leading to loss of temporal and spatial resolution) and have low costs (for high density deployment). A novel encoding based on Metal Oxide Semiconductor Field Effect Transistors (MOSFETs) is proposed to realize Analog Joint Source Channel Coding (AJSCC), a low-complexity technique to compress two (or more) signals into one with controlled distortion. In AJSCC, the y-axis is quantized while the x-axis is continuously captured. A power-efficient design to support multiple quantization levels is presented so that the digital receiver can decide the optimum quantization and the analog transmitter circuit is able to realize that. The approach is verified via Spice and MATLAB simulations.

Mobile context determination is an important step for many context-aware services such as location-based services, enterprise policy enforcement, building/room occupancy detection for power/HVAC operation, etc. Especially in enterprise scenarios where policies (e.g., attending a confidential meeting only when the user is in "Location X") are defined based on mobile context, it is paramount to verify the accuracy of the mobile context. To this end, two stochastic models based on the theory of Hidden Markov Models (HMMs) to obtain mobile context are proposed-personalized model (HPContext) and collaborative filtering model (HCFContext). The former predicts the current context using sequential history of the user's past context observations; the latter enhances HPContext with collaborative filtering features, which enables it to predict the current context of the primary user based on the context observations of users related to the primary user, e.g., same team colleagues in company, gym friends, family members, etc. Each of the proposed models can also be used to enhance/complement the context obtained from sensors. Furthermore, since privacy is a concern in collaborative filtering, a privacy-preserving method is proposed to derive HCFContext model parameters based on the concepts of homomorphic encryption. Finally, these models are thoroughly validated on a real-life dataset.

Achieving reliable and persistent environmental field estimation in Underwater Internet of Things (UW IoT) is a challenging problem. Given the need for high-resolution spatio-temporal sensing in such environment, traditional digital sensors are not suitable due to their high cost, high power consumption, and non-biodegradable nature. Further, reliable communication techniques that avoid retransmissions are crucial for reconstructing the phenomenon in a timely manner at the fusion center such as a drone. To address the above challenges, we propose a novel architecture consisting of a substrate of densely deployed underwater all-analog biodegradable sensors that continuously transmit data to the surface digital buoys. The analog nodes are designed to be energy efficient by implementing Analog Joint Source Channel Coding (AJSCC), a low-complexity compression-communication technique, using biodegradable Field Effect Transistors (FETs). We then propose a correlation-aware Hybrid Automatic Repeat Request (HARQ) technique to transmit data from the surface buoys to the fusion center. Such HARQ technique leverages redundancy in the buoy data (arising from the correlation of the phenomenon at the analog nodes) to avoid retransmissions, thus saving energy and time. The performance of the proposed analog sensor design and of the correlation-aware HARQ communication technique has been evaluated via simulations and shown to achieve the desired behavior.

Certain sensing applications such as Internet of Things (IoTs), where the sensing phenomenon may change rapidly in both time and space, require sensors that consume ultra-low power devices (so as to be able to collect data continuously and not lose temporal and spatial resolution) and have low costs (for high density deployment). A novel encoding based on Metal Oxide Semiconductor Field Effect Transistors (MOS-FETs) is proposed to realize Analog Joint Source Channel Coding (AJSCC), a low-complexity technique to compress two (or more) signals into one with controlled distortion. The approach is verified via Spice simulations and breadboard implementation.

Mobile phones provide an excellent opportunity for building context-aware applications. In particular, location-based services are important context-aware services that are more and more used for enforcing security policies, for supporting indoor room navigation, and for providing personalized assistance. However, a major problem still remains unaddressed--the lack of solutions that work across buildings while not using additional infrastructure and also accounting for privacy and reliability needs. In this paper, a privacy-preserving, multi-modal, cross-building, collaborative localization platform is proposed based on Wi-Fi RSSI (existing infrastructure), Cellular RSSI, sound and light levels, that enables room-level localization as main application (though sub room level granularity is possible). The privacy is inherently built into the solution based on onion routing, and perturbation/randomization techniques, and exploits the idea of weighted collaboration to increase the reliability as well as to limit the effect of noisy devices (due to sensor noise/privacy). The proposed solution has been analyzed in terms of privacy, accuracy, optimum parameters, and other overheads on location data collected at multiple indoor and outdoor locations.

A low-power wearable wireless sensor measuring both molecular biomarkers and physiological signals is proposed, where the former are measured by a microfluidic biosensing system while the latter are measured electrically. The low-power consumption of the sensor is achieved by an all-analog circuit implementing Analog Joint Source-Channel Coding (AJSCC) compression. The sensor is applicable to a wide range of biomedical applications that require real-time concurrent molecular biomarker and physiological signal monitoring.

Scalability is a major issue for Internet of Things (IoT) as the total amount of traffic data collected and/or the number of sensors deployed grow. In some IoT applications such as healthcare, power consumption is also a key design factor for the IoT devices. In this paper, a multi-signal compression and encoding method based on Analog Joint Source Channel Coding (AJSCC) is proposed that works fully in the analog domain without the need for power-hungry Analog-to-Digital Converters (ADCs). Compression is achieved by quantizing all the input signals but one. While saving power, this method can also reduce the number of devices by combining one or more sensing functionalities into a single device (called 'AJSCC device'). Apart from analog encoding, AJSCC devices communicate to an aggregator node (FPMM receiver) using a novel Frequency Position Modulation and Multiplexing (FPMM) technique. Such joint modulation and multiplexing technique presents three mayor advantages-it is robust to interference at particular frequency bands, it protects against eavesdropping, and it consumes low power due to a very low Signal-to-Noise Ratio (SNR) operating region at the receiver. Performance of the proposed multi-signal compression method and FPMM technique is evaluated via simulations in terms of Mean Square Error (MSE) and Miss Detection Rate (MDR), respectively.

A low-complexity and low-power all-analog circuit is proposed to perform efficiently Analog Joint Source Channel Coding (AJSCC). The proposed idea is to adopt Voltage Controlled Voltage Source (VCVS) to realize the rectangular-type mapping in AJSCC. The proposal is verified by Spice simulations as well as via breadboard and Printed Circuit Board (PCB) implementations. Field testing results indicate that the design is feasible for low-complexity and low-power systems such as wireless sensor networks for environmental monitoring.