Publications

2023

1. ECOM: Epoch Randomness-Based Consensus Committee Configuration for IoT Blockchains

   Ronghua Xu, Deeraj Nagothu, and Yu Chen

   2023
2. A Virtual Community Healthcare Framework in Metaverse Enabled by Digital Twins

   Qian Qu, Han Sun, and Yu Chen

   In International Congress on Communications, Networking, and Information Systems 2023
3. RODAD: Resilience Oriented Decentralized Anomaly Detection for Urban Air Mobility Networks

   Sixiao Wei, Hui Huang, Genshe Chen, and 4 more authors

   In 2023 Integrated Communication, Navigation and Surveillance Conference (ICNS) 2023
4. Light-Weight Real-Time Senior Safety Monitoring using Digital Twins

   Qian Qu, Han Sun, and Yu Chen

   In Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation 2023
5. AUSOME: authenticating social media images using frequency analysis

   Nihal Poredi, Deearj Nagothu, and Yu Chen

   In Disruptive Technologies in Information Sciences VII 2023

2022

1. DeFakePro: Decentralized Deepfake Attacks Detection Using ENF Authentication

   Deeraj Nagothu, Ronghua Xu, Yu Chen, and 2 more authors

   IT Professional Sep 2022

   Abs

   Advancements in generative models, such as deepfake, allow users to imitate a targeted person and manipulate online interactions. It has been recognized that disinformation may cause disturbance in society and ruin the foundation of trust. This article presents DeFakePro, a decentralized consensus mechanism-based deepfake detection technique in online video conferencing tools. Leveraging electrical network frequency (ENF), an environmental fingerprint embedded in digital media recording affords a consensus mechanism design called proof-of-ENF (PoENF) algorithm. The similarity in ENF signal fluctuations is utilized in the PoENF algorithm to authenticate the media broadcasted in conferencing tools. By utilizing the video conferencing setup with malicious participants to broadcast deepfake video recordings to other participants, the DeFakePro system verifies the authenticity of the incoming media in both audio and video channels.

2. Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach

   Deeraj Nagothu, Ronghua Xu, Yu Chen, and 2 more authors

   Future Internet May 2022

   Abs

   With the fast development of Fifth-/Sixth-Generation (5G/6G) communications and the Internet of Video Things (IoVT), a broad range of mega-scale data applications emerge (e.g., all-weather all-time video). These network-based applications highly depend on reliable, secure, and real-time audio and/or video streams (AVSs), which consequently become a target for attackers. While modern Artificial Intelligence (AI) technology is integrated with many multimedia applications to help enhance its applications, the development of General Adversarial Networks (GANs) also leads to deepfake attacks that enable manipulation of audio or video streams to mimic any targeted person. Deepfake attacks are highly disturbing and can mislead the public, raising further challenges in policy, technology, social, and legal aspects. Instead of engaging in an endless AI arms race “fighting fire with fire”, where new Deep Learning (DL) algorithms keep making fake AVS more realistic, this paper proposes a novel approach that tackles the challenging problem of detecting deepfaked AVS data leveraging Electrical Network Frequency (ENF) signals embedded in the AVS data as a fingerprint. Under low Signal-to-Noise Ratio (SNR) conditions, Short-Time Fourier Transform (STFT) and Multiple Signal Classification (MUSIC) spectrum estimation techniques are investigated to detect the Instantaneous Frequency (IF) of interest. For reliable authentication, we enhanced the ENF signal embedded through an artificial power source in a noisy environment using the spectral combination technique and a Robust Filtering Algorithm (RFA). The proposed signal estimation workflow was deployed on a continuous audio/video input for resilience against frame manipulation attacks. A Singular Spectrum Analysis (SSA) approach was selected to minimize the false positive rate of signal correlations. Extensive experimental analysis for a reliable ENF edge-based estimation in deepfaked multimedia recordings is provided to facilitate the need for distinguishing artificially altered media content.

3. A Distributed Crawler for IoVT-based Public Safety Surveillance Exploring the Spatio-Temporal Correlation

   Deeraj Nagothu, Daniel Dimock, Adrian Kulesza, and 2 more authors

   In Sensors and Systems for Space Applications XV Jun 2022

   Abs

   Modern infrastructure development has led to a rise in deployed surveillance cameras to monitor remote locations and widespread infrastructures. In today’s networked surveillance environment, however, human operators are often overwhelmed with the huge amount of visual feeds, which causes poor judgment and delayed response to emergencies. This paper proposes a distributed crawler scheme (DiCrawler) for smart surveillance systems deployed on Internet of Video Things (IoVT). The IoVT camera nodes monitor continuous video input, track the object of interest while preserving privacy, and relay correlative information to targeted nodes for constant monitoring. Each IoVT node monitors the space inside its field of view (FoV) and notifies the neighboring nodes about the objects leaving the FoV and heading in their directions. A smart communication algorithm among IoVT nodes is designed to prevent network bandwidth bottlenecks and preserve computational power. The DiCrawler system can corroborate with human operators and assist with decision-making by raising alarms in case of suspicious behavior. The IoVT network is completely decentralized, using only peer-to-peer (P2P) communication. DiCrawler does not rely on a central server for any computations, preventing a potential bottleneck if hundreds of cameras were connected and constantly uploading data to a server. Each module is also in a compact form factor, making it viable to be mounted on traditional security surveillance cameras. Extensive experimental study on a proof-of-concept prototype validated the effectiveness of the DiCrawler design.

4. Evolution of Attacks on Intelligent Surveillance Systems and Effective Detection Techniques

   Deeraj Nagothu, Nihal Poredi, Yu Chen, and 3 more authors

   Aug 2022

   Abs

   Intelligent surveillance systems play an essential role in modern smart cities to enable situational awareness. As part of the critical infrastructure, surveillance systems are often targeted by attackers aiming to compromise the security and safety of smart cities. Manipulating the audio or video channels could create a false perception of captured events and bypass detection. This chapter presents an overview of the attack vectors designed to compromise intelligent surveillance systems and discusses existing detection techniques. With advanced machine learning (ML) models and computing resources, both attack vectors and detection techniques have evolved to use ML-based techniques more effectively, resulting in non-equilibrium dynamics. The current detection techniques vary from training a neural network to detect forgery artifacts to use the intrinsic and extrinsic environmental fingerprints for any manipulations. Therefore, studying the effectiveness of different detection techniques and their reliability against the defined attack vectors is a priority to secure the system and create a plan of action against potential threats.

5. Real-Time Elderly Monitoring for Senior Safety by Lightweight Human Action Recognition

   Han Sun, and Yu Chen

   In 2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT) Aug 2022

   Abs

   With an increasing number of elders living alone, care-giving from a distance becomes a compelling need, particularly for safety. Real-time monitoring and action recognition are essential to raise an alert timely when abnormal behaviors or unusual activities occur. While wearable sensors are widely recognized as a promising solution, highly depending on user’s ability and willingness makes them inefficient. In contrast, video streams collected through non-contact optical cameras provide richer information and release the burden on elders. In this paper, leveraging the Independently-Recurrent neural Network (IndRNN) we propose a novel Real-time Elderly Monitoring for senior Safety (REMS) based on lightweight human action recognition (HAR) technology. Using captured skeleton images, the REMS scheme is able to recognize abnormal behaviors or actions and preserve the user’s privacy. To achieve a high accuracy, the HAR module is trained and fine-tuned using multiple databases. An extensive experimental study verified that REMS system preforms action recognition accurately and timely. REMS meets the design goals as a privacy-preserving elderly safety monitoring system and possesses the potential to be adopted in various smart monitoring systems.

6. SAUSA: Securing Access, Usage, and Storage of 3D Point CloudData by a Blockchain-Based Authentication Network

   Ronghua Xu, Yu Chen, Genshe Chen, and 1 more author

   Future Internet Aug 2022
7. uDFL: A Secure Microchained Decentralized Federated Learning Fabric atop IoT Networks

   Ronghua Xu, and Yu Chen

   IEEE Transactions on Network and Service Management Aug 2022
8. Fairledger: a Fair Proof-of-Sequential-Work based Lightweight Distributed Ledger for IoT Networks

   Ronghua Xu, and Yu Chen

   In 2022 IEEE International Conference on Blockchain (Blockchain) Aug 2022
9. A Secure Dynamic Edge Resource Federation Architecture for Cross-Domain IoT Systems

   Ronghua Xu, Yu Chen, Xiaohua Li, and 1 more author

   In 2022 International Conference on Computer Communications and Networks (ICCCN) Aug 2022
10. 

    Robustness of Electrical Network Frequency Signals as a Fingerprint for Digital Media Authentication

    Nihal Poredi, Deeraj Nagothu, Yu Chen, and 4 more authors

    In 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP) Aug 2022

    HTML
11. Real-Time Elderly Monitoring for Senior Safety by Lightweight Human Action Recognition

    Han Sun, and Yu Chen

    In 2022 IEEE 16th International Symposium on Medical Information and Communication Technology (ISMICT) Aug 2022

    Abs

    With an increasing number of elders living alone, care-giving from a distance becomes a compelling need, particularly for safety. Real-time monitoring and action recognition are essential to raise an alert timely when abnormal behaviors or unusual activities occur. While wearable sensors are widely recognized as a promising solution, highly depending on user’s ability and willingness makes them inefficient. In contrast, video streams collected through non-contact optical cameras provide richer information and release the burden on elders. In this paper, leveraging the Independently-Recurrent neural Network (IndRNN) we propose a novel Real-time Elderly Monitoring for senior Safety (REMS) based on lightweight human action recognition (HAR) technology. Using captured skeleton images, the REMS scheme is able to recognize abnormal behaviors or actions and preserve the user’s privacy. To achieve a high accuracy, the HAR module is trained and fine-tuned using multiple databases. An extensive experimental study verified that REMS system preforms action recognition accurately and timely. REMS meets the design goals as a privacy-preserving elderly safety monitoring system and possesses the potential to be adopted in various smart monitoring systems.

12. LightMAN: A Lightweight Microchained Fabric for Assurance-and Resilience-Oriented Urban Air Mobility Networks

    Ronghua Xu, Sixiao Wei, Yu Chen, and 2 more authors

    Drones Aug 2022

2021

1. Authenticating Video Feeds Using Electric Network Frequency Estimation at the Edge

   Deeraj Nagothu, Yu Chen, Alexander Aved, and 1 more author

   EAI Endorsed Transactions on Security and Safety Feb 2021
2. DeFake: Decentralized ENF-Consensus Based DeepFake Detection in Video Conferencing

   Deeraj Nagothu, Ronghua Xu, Yu Chen, and 2 more authors

   In IEEE 23rd International Workshop on Multimedia Signal Processing Oct 2021
3. Detecting Compromised Edge Smart Cameras Using Lightweight Environmental Fingerprint Consensus

   Deeraj Nagothu, Ronghua Xu, Yu Chen, and 2 more authors

   In Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems Nov 2021

   Abs

   Rapid advances in the Internet of Video Things (IoVT) deployment in modern smart cities has enabled secure infrastructures with minimal human intervention. However, attacks on audio-video inputs affect the reliability of large-scale multimedia surveillance systems as attackers are able to manipulate the perception of live events. For example, Deepfake audio/video attacks and frame duplication attacks can cause significant security breaches. This paper proposes a Lightweight Environmental Fingerprint Consensus based detection of compromised smart cameras in edge surveillance systems (LEFC). LEFC is a partial decentralized authentication mechanism that leverages Electrical Network Frequency (ENF) as an environmental fingerprint and distributed ledger technology (DLT). An ENF signal carries randomly fluctuating spatio-temporal signatures, which enable digital media authentication. With the proposed DLT consensus mechanism named Proof-of-ENF (PoENF) as a backbone, LEFC can estimate and authenticate the media recording and detect byzantine nodes controlled by the perpetrator. The experimental evaluation shows feasibility and effectiveness of proposed LEFC scheme under a distributed byzantine network environment.

4. CriPI: An Efficient Critical Pixels Identification Algorithm for Fast One-Pixel Attacks

   Wei Quan, Deeraj Nagothu, Nihal Poredi, and 1 more author

   In Sensors and Systems for Space Applications XIV Apr 2021

   Abs

   Deep neural networks (DNN) have been studied intensively in recent years, leading to many practical applications. However, there are also concerns about the security problems and vulnerabilities of DNN. Studies on adversarial network development have shown that relatively more minor perturbations can impact the DNN performance and manipulate its outcome. The impacts of adversarial perturbations have led to the development of advanced techniques for generating image-level perturbations. Once embedded in a clean image, these perturbations are not perceptible to human eyes and fool a well-trained deep learning (DL) convolutional neural network (CNN) classifier. This work introduces a new Critical-Pixel Iterative (CriPI) algorithm after a thorough study on critical pixels’ characteristics. The proposed CriPI algorithm can identify the critical pixels and generate one-pixel attack perturbations with a much higher efficiency. Compared to a one-pixel attack benchmark algorithm, the CriPI algorithm significantly reduces the time delay of the attack from seven minutes to one minute with similar success rates.

5. Econledger: A proof-of-enf consensus based lightweight distributed ledger for iovt networks

   Ronghua Xu, Deeraj Nagothu, and Yu Chen

   Future Internet Apr 2021
6. Decentralized video input authentication as an edge service for smart cities

   Ronghua Xu, Deeraj Nagothu, and Yu Chen

   IEEE Consumer Electronics Magazine Apr 2021
7. Fed-ddm: A federated ledgers based framework for hierarchical decentralized data marketplaces

   Ronghua Xu, and Yu Chen

   In 2021 International Conference on Computer Communications and Networks (ICCCN) Apr 2021
8. Microchain: A Light Hierarchical Consensus Protocol for IoT Systems

   Ronghua Xu, Yu Chen, and Erik Blasch

   Apr 2021
9. Enable Fair Proof-of-Work (PoW) Consensus for Blockchains in IoT by Miner Twins (MinT)

   Qian Qu, Ronghua Xu, Yu Chen, and 2 more authors

   Future Internet Apr 2021

   HTML

2020

1. Smart Surveillance for Public Safety Enabled by Edge Computing

   Deeraj Nagothu, Ronghua Xu, Seyed Yahya Nikouei, and 2 more authors

   Edge Computing: Models, technologies and applications Apr 2020
2. Enabling Continuous Operations for UAVs with an Autonomous Service Network Infrastructure

   Michael Rosenberg, John Henry Burns, Deeraj Nagothu, and 1 more author

   In Sensors and Systems for Space Applications XIII Apr 2020

   Abs

   One of the major restrictions on the practical applications of unmanned aerial vehicles (UAV) is their incomplete self-sufficiency, which makes continuous operations infeasible without human oversights. The more oversight UAVs require, the less likely they are going to be commercially advantageous when compared to their alternatives. As an autonomous system, how much human interaction is needed to function is one of the best indicators evaluating the limitations and inefficiencies of the UAVs. Popular UAV related research areas, such as path planning and computer vision, have enabled substantial advances in the ability of drones to act on their own. This research is dedicated to in-flight operations, in which there is not much reported effort to tackle the problem from the aspect of the supportive infrastructure. In this paper, an Autonomous Service network infrastructure (AutoServe) is proposed. Aiming at increasing the future autonomy of UAVs, the AutoServe system includes a service-oriented landing platform and a customized communication protocol. This supportive AutoServe infrastructure will autonomize many tasks currently done manually by human operators, such as battery replacement. A proof-of-concept prototype has been built and the simulation experimental study validated the design.

3. Collaborative Multi-Object Tracking as an Edge Service Using Transfer Learning

   Han Sun, Yu Chen, Alexander Aved, and 1 more author

   In 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) Apr 2020

   Abs

   Video cameras have been pervasively deployed in Smart Cities and timely processing of the huge amount of video data brings up new challenges. Each individual camera is only able to cover a limited range due to the effective monitoring distances and the complicated layout of a building or an area. Once the object of interest moves away from the camera field of view, the system may lose object tracking. When the object appears and is captured by another camera, object detection, feature abstraction, and behavior classification require an association with other video data exploitation results. Hence, pragmatic approaches for object re-verification (or re-acquisition) are needed so as to not infer the object as a new track. In this paper, we propose a novel Collaborative Multi-Object Tracking system as an Edge service (CoMOTE) based on transfer learning technology. A transfer learning-based human detection and tracking method is introduced, which allocates most of the computing tasks on edge devices. An edge server is adopted to integrate information from edge cameras for decision-making to enable multi-view surveillance. The CoMOTE scheme not only reduces the dependence on a central server but also leads to a lower communication cost. Implemented on edge nodes (Raspberry Pi 4) and tested with WildTrack multi-view video database, extensive experimental results verify that the CoMOTE prototype achieved the design goal and outperforms multiple state-of-the-art methods.

4. Blendsps: A blockchain-enabled decentralized smart public safety system

   Ronghua Xu, Seyed Yahya Nikouei, Deeraj Nagothu, and 2 more authors

   Smart Cities Apr 2020
5. BIT: A blockchain integrated time banking system for community exchange economy

   Ronghua Xu, Zhe Zhai, Yu Chen, and 1 more author

   In 2020 IEEE International Smart Cities Conference (ISC2) Apr 2020
6. Hybrid blockchain-enabled secure microservices fabric for decentralized multi-domain avionics systems

   Ronghua Xu, Yu Chen, Erik Blasch, and 3 more authors

   In Sensors and Systems for Space Applications XIII Apr 2020
7. Poster: MicroFL: A Lightweight, Secure-by-Design Edge Network Fabric for Decentralized IoT Systems

   Ronghua Xu, Yu Chen, and Jian Li

   In The Network and Distributed System Security Symposium (NDSS) Apr 2020
8. Decentralized access control for IoT based on blockchain and smart contract

   Ronghua Xu, Yu Chen, and Erik Blasch

   Apr 2020
9. Collaborative Multi-Object Tracking as an Edge Service Using Transfer Learning

   Han Sun, Yu Chen, Alexander Aved, and 1 more author

   In 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) Apr 2020

   Abs

   Video cameras have been pervasively deployed in Smart Cities and timely processing of the huge amount of video data brings up new challenges. Each individual camera is only able to cover a limited range due to the effective monitoring distances and the complicated layout of a building or an area. Once the object of interest moves away from the camera field of view, the system may lose object tracking. When the object appears and is captured by another camera, object detection, feature abstraction, and behavior classification require an association with other video data exploitation results. Hence, pragmatic approaches for object re-verification (or re-acquisition) are needed so as to not infer the object as a new track. In this paper, we propose a novel Collaborative Multi-Object Tracking system as an Edge service (CoMOTE) based on transfer learning technology. A transfer learning-based human detection and tracking method is introduced, which allocates most of the computing tasks on edge devices. An edge server is adopted to integrate information from edge cameras for decision-making to enable multi-view surveillance. The CoMOTE scheme not only reduces the dependence on a central server but also leads to a lower communication cost. Implemented on edge nodes (Raspberry Pi 4) and tested with WildTrack multi-view video database, extensive experimental results verify that the CoMOTE prototype achieved the design goal and outperforms multiple state-of-the-art methods.

2019

1. A Distributed Agent-Based Framework for a Constellation of Drones in a Military Operation

   A.H. Fitwi, D. Nagothu, Y. Chen, and 1 more author

   In Proc. - Winter Simul. Conf. Apr 2019

   Abs

   \textcopyright 2019 IEEE. A seamless communication capability is important in military operations. Likewise, enhanced security, increased capacity, and robust communication mechanisms are vital for humanitarian and disaster-response operations. Often, a system of wide-band satellites is employed for real-time exchange of information and over-the-horizon control, but the communications are prone to denial of service (DoS) attacks, and delayed redeployment. Hence, a swarm of drones could be deployed in mission-critical operations in times of urgency for a secured and robust distributed-intercommunication which is essential for survivability and successful completion of missions. In this paper, a distributed-agent-based framework for secure and reliable information exchange between drones in a constellation is proposed. The framework comprises a mechanism for path planning simulation and estimation, a flexible network architecture for improved client-server(C/S) and peer-to-peer (P2P) connectivity, as well as agents for identity authentications and secure communications. The framework has been simulated and verified with results showing promise.

2. Detecting Malicious False Frame Injection Attacks on Surveillance Systems at the Edge Using Electrical Network Frequency Signals

   Deeraj Nagothu, Y. Yu Chen, Erik Blasch, and 2 more authors

   Sensors (Basel). Apr 2019

   Abs

   Over the past few years, the importance of video surveillance in securing national critical infrastructure has significantly increased, with applications including the detection of failures and anomalies. Accompanied by the proliferation of video is the increasing number of attacks against surveillance systems. Among the attacks, False Frame Injection (FFI) attacks that replay video frames from a previous recording to mask the live feed has the highest impact. While many attempts have been made to detect FFI frames using features from the video feeds, video analysis is computationally too intensive to be deployed on-site for real-time false frame detection. In this paper, we investigated the feasibility of FFI attacks on compromised surveillance systems at the edge and propose an effective technique to detect the injected false video and audio frames by monitoring the surveillance feed using the embedded Electrical Network Frequency (ENF) signals. An ENF operates at a nominal frequency of 60 Hz/50 Hz based on its geographical location and maintains a stable value across the entire power grid interconnection with minor fluctuations. For surveillance system video/audio recordings connected to the power grid, the ENF signals are embedded. The time-varying nature of the ENF component was used as a forensic application for authenticating the surveillance feed. The paper highlights the ENF signal collection from a power grid creating a reference database and ENF extraction from the recordings using conventional short-time Fourier Transform and spectrum detection for robust ENF signal analysis in the presence of noise and interference caused in different harmonics. The experimental results demonstrated the effectiveness of ENF signal detection and/or abnormalities for FFI attacks.

3. A Microservice-Enabled Architecture for Smart Surveillance Using Blockchain Technology

   D. Nagothu, R. Xu, S.Y. Nikouei, and 1 more author

   In 2018 IEEE Int. Smart Cities Conf. ISC2 2018 Apr 2019

   Abs

   \textcopyright 2018 IEEE. While the smart surveillance system enhanced by the Internet of Things (IoT) technology becomes an essential part of Smart Cities, it also brings new concerns in security of the data. Compared to the traditional surveillance systems that is built following a monolithic architecture to carry out lower level operations, such as monitoring and recording, the modern surveillance systems are expected to support more scalable and decentralized solutions for advanced video stream analysis at the large volumes of distributed edge devices. In addition, the centralized architecture of the conventional surveillance systems is vulnerable to single point of failure and privacy breach owning to the lack of protection to the surveillance feed. This position paper introduces a novel secure smart surveillance system based on microservices architecture and blockchain technology. Encapsulating the video analysis algorithms as various independent microservices not only isolates the video feed from different sectors, but also improve the system availability and robustness by decentralizing the operations. The blockchain technology securely synchronizes the video analysis databases among microservices across surveillance domains, and provides tamper proof of data in the trustless network environment. Smart contract enabled access authorization strategy prevents any unauthorized user from accessing the microservices and offers a scalable, decentralized and fine-grained access control solution for smart surveillance systems.

4. A Study on Smart Online Frame Forging Attacks against Video Surveillance System

   D. Nagothu, J. Schwell, Y. Chen, and 2 more authors

   In Proc. SPIE - Int. Soc. Opt. Eng. Apr 2019

   Abs

   \textcopyright 2019 SPIE. Video Surveillance Systems (VSS) have become an essential infrastructural element of smart cities by increasing public safety and countering criminal activities. A VSS is normally deployed in a secure network to prevent the access from unauthorized personnel. Compared to traditional systems that continuously record video regardless of the actions in the frame, a smart VSS has the capability of capturing video data upon motion detection or object detection, and then extracts essential information and send to users. This increasing design complexity of the surveillance system, however, also introduces new security vulnerabilities. In this work, a smart, real-time frame duplication attack is investigated. We show the feasibility of forging the video streams in real-time as the camera’s surroundings change. The generated frames are compared constantly and instantly to identify changes in the pixel values that could represent motion detection or changes in light intensities outdoors. An attacker (intruder) can remotely trigger the replay of some previously duplicated video streams manually or automatically, via a special quick response (QR) code or when the face of an intruder appear in the camera field of view. A detection technique is proposed by leveraging the real-time electrical network frequency (ENF) reference database to match with the power grid frequency.

5. Real-Time Index Authentication for Event-Oriented Surveillance Video Query Using Blockchain

   S.Y. Nikouei, R. Xu, D. Nagothu, and 3 more authors

   In 2018 IEEE Int. Smart Cities Conf. ISC2 2018 Apr 2019

   Abs

   \textcopyright 2018 IEEE. Information from surveillance video is essential for situational awareness (SAW). Nowadays, a prohibitively large amount of surveillance data is being generated continuously by ubiquitously distributed video sensors. It is very challenging to immediately identify the objects of interest or zoom in suspicious actions from thousands of video frames. Making the big data indexable is critical to tackle this problem. It is ideal to generate pattern indexes in a real-Time, on-site manner on the video streaming instead of depending on the batch processing at the cloud centers. The modern edge-fog-cloud computing paradigm allows implementation of time sensitive tasks at the edge of the network. The on-site edge devices collect the information sensed in format of frames and extracts useful features. The near-site fog nodes conduct the contextualization and classification of the features. The remote cloud center is in charge of more data intensive and computing intensive tasks. However, exchanging the index information among devices in different layers raises security concerns where an adversary can capture or tamper with features to mislead the surveillance system. In this paper, a blockchain enabled scheme is proposed to protect the index data through an encrypted secure channel between the edge and fog nodes. It reduces the chance of attacks on the small edge and fog devices. The feasibility of the proposal is validated through intensive experimental analysis.

6. Exploration of blockchain-enabled decentralized capability-based access control strategy for space situation awareness

   Ronghua Xu, Yu Chen, Erik Blasch, and 1 more author

   Optical Engineering Apr 2019
7. Blendsm-ddm: Blockchain-enabled secure microservices for decentralized data marketplaces

   Ronghua Xu, Gowri Sankar Ramachandran, Yu Chen, and 1 more author

   In 2019 IEEE international smart cities conference (ISC2) Apr 2019
8. Blendmas: A blockchain-enabled decentralized microservices architecture for smart public safety

   Ronghua Xu, Seyed Yahya Nikouei, Yu Chen, and 2 more authors

   In 2019 IEEE International Conference on Blockchain (Blockchain) Apr 2019
9. Decentralized autonomous imaging data processing using blockchain

   Ronghua Xu, Sherry Chen, Lixin Yang, and 2 more authors

   In Multimodal Biomedical Imaging XIV Apr 2019

2018

1. Blendcac: A smart contract enabled decentralized capability-based access control mechanism for the iot

   Ronghua Xu, Yu Chen, Erik Blasch, and 1 more author

   Computers Apr 2018
2. Constructing trustworthy and safe communities on a blockchain-enabled social credits system

   Ronghua Xu, Xuheng Lin, Qi Dong, and 1 more author

   In Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services Apr 2018
3. Blendcac: A blockchain-enabled decentralized capability-based access control for iots

   Ronghua Xu, Yu Chen, Erik Blasch, and 1 more author

   In 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) Apr 2018
4. Real-time human objects tracking for smart surveillance at the edge

   Ronghua Xu, Seyed Yahya Nikouei, Yu Chen, and 4 more authors

   In 2018 IEEE international conference on communications (ICC) Apr 2018
5. A federated capability-based access control mechanism for internet of things (iots)

   Ronghua Xu, Yu Chen, Erik Blasch, and 1 more author

   In Sensors and Systems for Space Applications XI Apr 2018
6. Smart surveillance as an edge network service: From harr-cascade, svm to a lightweight cnn

   Seyed Yahya Nikouei, Yu Chen, Sejun Song, and 3 more authors

   In 2018 ieee 4th international conference on collaboration and internet computing (cic) Apr 2018
7. A microservice-enabled architecture for smart surveillance using blockchain technology

   Deeraj Nagothu, Ronghua Xu, Seyed Yahya Nikouei, and 1 more author

   In 2018 IEEE international smart cities conference (ISC2) Apr 2018
8. Real-time index authentication for event-oriented surveillance video query using blockchain

   Seyed Yahya Nikouei, Ronghua Xu, Deeraj Nagothu, and 3 more authors

   In 2018 IEEE International Smart Cities Conference (ISC2) Apr 2018
9. Blendcac: A blockchain-enabled decentralized capability-based access control for iots

   Ronghua Xu, Yu Chen, Erik Blasch, and 1 more author

   In 2018 IEEE International conference on Internet of Things (iThings) and IEEE green computing and communications (GreenCom) and IEEE cyber, physical and social computing (CPSCom) and IEEE Smart Data (SmartData) Apr 2018
10. Real-time human detection as an edge service enabled by a lightweight cnn

    Seyed Yahya Nikouei, Yu Chen, Sejun Song, and 3 more authors

    In 2018 IEEE International Conference on Edge Computing (EDGE) Apr 2018