arXiv subjects

Dario Pompili

Publications and source records attributed to Dario Pompili.

At least 37 records · Page 2Linked to original sources

arXiv2019

UW-SVC: Scalable Video Coding Transmission for In-network Underwater Imagery Analysis

Underwater imagery has enabled numerous civilian applications in various domains, ranging from academia to industry, and from industrial surveillance and maintenance to environmental protection and behavior of marine creatures studies. The accumulation of litter and plastic debris at the seafloor and the bottom of rivers are extremely harmful for the aquatic life. We propose a solution for this problem using a team of Autonomous Underwater Vehicles (AUVs) to exchange the recorded video in order to reconstruct the seafloor regions of interest. However, underwater video transmission is a challenge in the harsh environment in which radio-frequency waves are absorbed for distances above a few tens of meters, optical waves require narrow laser beams and suffer from scattering and ocean wave motion, and acoustic waves, while long range, provide a very low bandwidth and unreliable channel for communication. In our solution, the scalable coded video of each vehicle is shared with a selected group of receiving videos, pseudo-multicasting, through the acoustic channel. Presented evaluations, including both simulations and experiments, confirm the efficiency and flexibility of the proposed solution using acoustic software-defined modems.

eess.SP↗

arXiv2019

Low-power All-analog Circuit for Rectangular-type Analog Joint Source Channel Coding

A low-complexity all-analog circuit is proposed to perform efficiently Analog Joint Source Channel Coding (AJSCC), which can compress two or more sensor signals into one with controlled distortion while also being robust against wireless channel impairments. The idea is to realize the rectangular-type AJSCC using Voltage Controlled Voltage Sources (VCVS). The proposal is verified by Spice simulations as well as breadboard and Printed Circuit Board (PCB) implementations. Results indicate that the design is feasible for low-complexity systems like persistent wireless sensor networks requiring low circuit power.

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arXiv2019

Towards Low-power Wearable Wireless Sensors for Molecular Biomarker and Physiological Signal Monitoring

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.

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arXiv2019

Towards Ultra-low-power Realization of Analog Joint Source-Channel Coding using MOSFETs

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.

cs.ET↗

arXiv2019

Improved Circuit Design of Analog Joint Source Channel Coding for Low-power and Low-complexity Wireless Sensors

To enable low-power and low-complexity wireless monitoring, an improved circuit design of Analog Joint Source Channel Coding (AJSCC) is proposed for wireless sensor nodes. This innovative design is based on Analog Divider Blocks (ADB) with tunable spacing between AJSCC levels. The ADB controls the switching between two types of Voltage Controlled Voltage Sources (VCVS). LTSpice simulations were performed to evaluate the performance of the circuit, and the power consumption and circuit complexity of this new ADB-based design were compared with our previous parallel-VCVS design. It is found that this improved circuit design based on ADB outperforms the design based on parallel VCVS for a large number of AJSCC levels (>= 16), both in terms of power consumption as well as circuit complexity, thus enabling persistent and higher temporal/spatial resolution environmental sensing.

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arXiv2019

Analog Signal Compression and Multiplexing Techniques for Healthcare Internet of Things

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.

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arXiv2019

Towards Wireless Health Monitoring via Analog Signal Compression based Biosensing Platform

Wireless all-analog biosensor design for concurrent microfluidic and physiological signal monitoring is presented in this work. The key component is an all-analog circuit capable of compressing two analog sources into one analog signal by Analog Joint Source-Channel Coding (AJSCC). Two circuit designs are discussed, including the stacked-Voltage Controlled Voltage Source (VCVS) design with the fixed number of levels, and an improved design, which supports a flexible number of AJSCC levels. Experimental results are presented on the wireless biosensor prototype, composed of Printed Circuit Board (PCB) realizations of the stacked-VCVS design. Furthermore, circuit simulation and wireless link simulation results are presented on the improved design. Results indicate that the proposed wireless biosensor is well suited for sensing two biological signals simultaneously with high accuracy, and can be applied to a wide variety of low-power and low-cost wireless continuous health monitoring applications.

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arXiv2019

Secure Mobile Technologies for Proactive Critical Infrastructure Situational Awareness

Trustworthy operation of our national critical infrastructures, such as the electricity grid, against adversarial parties and accidental failures requires constant and secure monitoring capabilities. In this paper, Eyephone is presented to leverage secure smartphone sensing and data acquisition capabilities and enable pervasive sensing of the national critical infrastructures. The reported information by the smartphone users will notify the control center operators about particular accidental or malicious remote critical infrastructure incidents. The reporting will be proactive regarding potentially upcoming failures given the system's current risky situation, e.g., a tree close to fall on a power grid transmission line. The information will include various modalities such as images, video, audio, time and location. Eyephone will use system-wide information flow analysis and policy enforcement to prevent user privacy violations during the incident reportings. A working proof-of-concept prototype of Eyephone is implemented. Our results show that Eyephone allows secure and effective use of smartphones for real-time situational awareness of our national critical infrastructures.

cs.CR↗

arXiv2019

Energy-efficient Wireless Analog Sensing for Persistent Underwater Environmental Monitoring

The design of sensors or "things" as part of the new Internet of Underwater Things (IoUTs) paradigm comes with multiple challenges including limited battery capacity, not polluting the water body, and the ability to track continuously phenomena with high temporal/spatial variability. We claim that traditional digital sensors are incapable to meet these demands because of their high power consumption, high complexity (cost), and the use of non-biodegradable materials. To address the above challenges, we propose a novel architecture consisting of a sensing substrate of dense analog biodegradable sensors over which lies the traditional Wireless Sensor Network (WSN). The substrate analog biodegradable sensors perform Shannon mapping (a data-compression technique) using just a single Field Effect Transistor (FET) without the need for power-hungry Analog-to-Digital Converters (ADCs) resulting in much lower power consumption, complexity, and the ability to be powered using only sustainable energy-harvesting techniques. A novel and efficient decoding technique is also presented. Both encoding/decoding techniques have been verified via Spice and MATLAB simulations accounting for underwater acoustic channel variations.

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arXiv2019

Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data

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 positive/normal situations (e.g., right turn, going straight), some of which (e.g., U-turn) could be as rare as anomalous situations. 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 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 approaches.

cs.LG↗

arXiv2019

HCFContext: Smartphone Context Inference via Sequential History-based Collaborative Filtering

Mobile context determination is an important step for many context aware services such as location-based services, enterprise policy enforcement, building or room occupancy detection for power or 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 or 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.

cs.LG↗

arXiv2019

Argus: Smartphone-enabled Human Cooperation via Multi-Agent Reinforcement Learning for Disaster Situational Awareness

Argus exploits a Multi-Agent Reinforcement Learning (MARL) framework to create a 3D mapping of the disaster scene using agents present around the incident zone to facilitate the rescue operations. The agents can be both human bystanders at the disaster scene as well as drones or robots that can assist the humans. The agents are involved in capturing the images of the scene using their smartphones (or on-board cameras in case of drones) as directed by the MARL algorithm. These images are used to build real time a 3D map of the disaster scene. Via both simulations and real experiments, an evaluation of the framework in terms of effectiveness in tracking random dynamicity of the environment is presented.

cs.CY↗

arXiv2019

In-network Collaboration for CDMA-based Reliable Underwater Acoustic Communications

Achieving high throughput and reliability in underwater acoustic networks for transmitting distributed and large volume of data is a challenging task due to the bandwidth-limited and unpredictable nature of the acoustic channel. In a multi-node network, such as in the Internet of Underwater Things (IoUT), communication link efficiency varies dynamically: if the channel is not in good condition, e.g., when in deep fade, channel coding techniques may fail to deliver the information even with multiple retransmissions. Hence, an efficient and agile collaborative strategy is required to allocate appropriate resources to the communication links based on their status. The proposed solution adjusts the physical and link-layer parameters collaboratively for a Code Division Multiple Access (CDMA)-based underwater network. An adaptive Hybrid Automatic Repeat Request (HARQ) solution is employed to guarantee reliable communications against errors in poor links. Results were validated using data collected from the LOON testbed-hosted at the NATO STO Centre for Maritime Research and Experimentation (CMRE) in La Spezia, Italy-and from the REP18-Atlantic sea trial conducted in Sept'18 in Portuguese water.

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arXiv2017

Elastic-Net: Boosting Energy Efficiency and Resource Utilization in 5G C-RANs

Current Distributed Radio Access Networks~(D-RANs), which are characterized by a static configuration and deployment of Base Stations~(BSs), have exposed their limitations in handling the temporal and geographical fluctuations of capacity demands. At the same time, each BS's spectrum and computing resources are only used by the active users in the cell range, causing idle BSs in some areas/times and overloaded BSs in other areas/times. Recently, Cloud Radio Access Network~(C-RAN) has been introduced as a new centralized paradigm for wireless cellular networks in which---through virtualization---the BSs are physically decoupled into Virtual Base Stations~(VBSs) and Remote Radio Heads~(RRHs). In this paper, a novel elastic framework aimed at fully exploiting the potential of C-RAN is proposed, which is able to adapt to the fluctuation in capacity demand while at the same time maximizing the energy efficiency and resource utilization. Simulation and testbed experiment results are presented to illustrate the performance gains of the proposed elastic solution against the current static deployment.

cs.NI↗

arXiv2017

CollabLoc: Privacy-Preserving Multi-Modal Localization via Collaborative Information Fusion

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 using an Android app.

cs.NI↗

arXiv2017

Understanding the Computational Requirements of Virtualized Baseband Units using a Programmable Cloud Radio Access Network Testbed

Cloud Radio Access Network (C-RAN) is emerging as a transformative architecture for the next generation of mobile cellular networks. In C-RAN, the Baseband Unit (BBU) is decoupled from the Base Station (BS) and consolidated in a centralized processing center. While the potential benefits of C-RAN have been studied extensively from the theoretical perspective, there are only a few works that address the system implementation issues and characterize the computational requirements of the virtualized BBU. In this paper, a programmable C-RAN testbed is presented where the BBU is virtualized using the OpenAirInterface (OAI) software platform, and the eNodeB and User Equipment (UEs) are implemented using USRP boards. Extensive experiments have been performed in a FDD downlink LTE emulation system to characterize the performance and computing resource consumption of the BBU under various conditions. It is shown that the processing time and CPU utilization of the BBU increase with the channel resources and with the Modulation and Coding Scheme (MCS) index, and that the CPU utilization percentage can be well approximated as a linear increasing function of the maximum downlink data rate. These results provide real-world insights into the characteristics of the BBU in terms of computing resource and power consumption, which may serve as inputs for the design of efficient resource-provisioning and allocation strategies in C-RAN systems.

cs.NI↗

arXiv2017

Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks

Mobile-Edge Computing (MEC) is an emerging paradigm that provides a capillary distribution of cloud computing capabilities to the edge of the wireless access network, enabling rich services and applications in close proximity to the end users. In this article, a MEC enabled multi-cell wireless network is considered where each Base Station (BS) is equipped with a MEC server that can assist mobile users in executing computation-intensive tasks via task offloading. The problem of Joint Task Offloading and Resource Allocation (JTORA) is studied in order to maximize the users' task offloading gains, which is measured by the reduction in task completion time and energy consumption. The considered problem is formulated as a Mixed Integer Non-linear Program (MINLP) that involves jointly optimizing the task offloading decision, uplink transmission power of mobile users, and computing resource allocation at the MEC servers. Due to the NP-hardness of this problem, solving for optimal solution is difficult and impractical for a large-scale network. To overcome this drawback, our approach is to decompose the original problem into (i) a Resource Allocation (RA) problem with fixed task offloading decision and (ii) a Task Offloading (TO) problem that optimizes the optimal-value function corresponding to the RA problem. We address the RA problem using convex and quasi-convex optimization techniques, and propose a novel heuristic algorithm to the TO problem that achieves a suboptimal solution in polynomial time. Numerical simulation results show that our algorithm performs closely to the optimal solution and that it significantly improves the users' offloading utility over traditional approaches.

cs.NI↗

arXiv2017

Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges

Mobile Edge Computing (MEC) is an emerging paradigm that provides computing, storage, and networking resources within the edge of the mobile Radio Access Network (RAN). MEC servers are deployed on generic computing platform within the RAN and allow for delay-sensitive and context-aware applications to be executed in close proximity to the end users. This approach alleviates the backhaul and core network and is crucial for enabling low-latency, high-bandwidth, and agile mobile services. This article envisages a real-time, context-aware collaboration framework that lies at the edge of the RAN, constituted of MEC servers and mobile devices, and that amalgamates the heterogeneous resources at the edge. Specifically, we introduce and study three strong use cases ranging from mobile-edge orchestration, collaborative caching and processing and multi-layer interference cancellation. We demonstrate the promising benefits of these approaches in facilitating the evolution to 5G networks. Finally, we discuss the key technical challenges and open-research issues that need to be addressed in order to make an efficient integration of MEC into 5G ecosystem.

cs.NI↗