Abdu Gumaei, Mabrook Al‐Rakhami, Mohammad Mehedi Hassan, Pasquale Pace · 7 authors
Nowadays, drones are not just deployed for defense and military establishments, but they are widely used in many applications such as natural disaster monitoring, soil and crop analysis, road and traffic surveillance, and consumer product delivery. Some information, such as drone identification and flight modes, can be transmitted to other drones. This information can be shared between drones by using radio frequency (RF) signals and through 5G networks. Recently, few studies have been proposed to use deep neural networks (DNNs) on RF signals for identifying drones and detecting their flight modes, such as off, on and connected, flying, hovering, and video recording. However, transmitting RF signals between drones and 5G nodes needs to be secure and decentralized; in addition, the performance of identification and detection needs to be more accurate. In this article, we introduce a framework that combines a blockchain with a deep recurrent neural network (DRNN) and edge computing for 5G-enabled drone identification and flight mode detection. In the proposed framework, raw RF signals of different drones under several flight modes are remotely sensed and collected on a cloud server to train a DRNN model and then distribute the trained model on edge devices for detecting drones and their flight modes. Blockchain is used in the proposed framework for data integrity and securing data transmission. The DRNN model is evaluated on a public dataset, called DroneRF. Experimental evaluation results show that the DRNN model of the proposed framework can detect drones and their flight modes from real RF signals with high accuracy as compared to recent related work.
Intelligent UAV-based monitoring systems are becoming an essential apparatus for crowd monitoring as they have proven to be viable and cost-effective solutions. Applications of such systems may include detecting antisocial and abnormal behavior among a crowd to ensure public safety and security, especially during periods of pandemic or social unrest when technology is aimed at replacing the human factor to ensure scalability and reduce risk. On the other hand, the modern architectures of autonomous UAV-based systems requires processing the captured information at the edge and cloud facilities, which requires transmission and/or retransmission of the captured data. This vulnerability of data security during transmission may compromise the benefits of the technology. Therefore, there is need for an effective strategy to achieve a secure architecture that takes into consideration the limited computing capabilities onboard the UAV agents and the distributed nature of the system. Blockchain, as a distributed network technology, will provide a safe, transparent, and efficient network system for UAV systems. Therefore, this article proposes a drone-swarm-aided distributed monitoring system in a blockchain-powered network. In the proposed monitoring mechanism, the security protocol and encryption algorithm are applied to ensure the security of each stage of the system, so as to realize the cooperative drone swarm to reliably perform monitoring tasks. The blockchain technology is introduced to achieve tamper-proof monitoring log recording and support group decision making of monitoring transactions.
Modern times have brought about increased criminal activity, and in an ever-connected world, numerous crimes go un-rectified. Car theft, while a crime, is also the basis for other crimes to be committed. In the world of smart things and ubiquitous computing surveillance cameras with small onboard computers are everywhere, from car dashboard cameras to street cameras. These devices or networks of devices are mainly referenced after a crime was committed supporting after-the-fact events rather than supporting proactive handling of potential crimes. In this paper, we present a proactive blockchain-based distributed platform to detect, verify, and track suspicious vehicles that can be a base for a heinous crime. The proposed platform takes advantage of the under-utilized resources in already-present surveillance equipment. The platform allows remote and local processing video feeds in search of suspicious vehicles, allowing police to act quickly following the identification and verification of a stolen car being detected depending on the computational power of the user's device. The platform will verify the findings by group voting using a blockchain base shared between all devices, and the subsequent tracking of the suspicious vehicle by tasking other cameras running the platform to actively scan for the vehicle. This will free up some of the limited resources our police possess, a big resource being time, and allowing these now-freed resources to be reallocated into other areas. Our system evaluation and performance show indications of prompt responses in detecting stolen cars, helping police catch the stolen cars sooner.
Multiobject tracking is a basic task in video analysis. Due to the strict requirements on efficiency and resource consumption, most of the applications on edge devices are online or near-online methods. Besides motion modeling, appearance information is also widely used for tracking. However, the influence of occlusion is usually ignored. In this article, spatial-temporal co-occurrence constraints (STCCs) features are introduced to resist occlusions by exploring the rich spatial and temporal information of tracklets. In addition, a novel blockchain-based near-online framework called co-occurrence constraints tracklet tracker (CoCTs) is proposed for cross-camera tracking. It inherits the advantages of the blockchain technology in sharing information. Based on blockchain, an efficient association mechanism and a reliable information sharing method are introduced. Experimental results show that CoCT performs high computational efficiency and low resource consumption. In the edge computing environment, it achieves real-time performance on cross-camera tracking. On the MOT17 benchmark, our method shows the state-of-the-art results compared with other online trackers.
Unmanned aerial vehicles (UAVs) are expected to be extensively used as an integral part in the future generations of communication networks, to provide ubiquitous connectivity. The mobile nature of UAVs make them a tempting candidate to provide seamless connectivity in environments where the installation of conventional terrestrial base stations (BS) is not feasible. Nonetheless, there are major deployment issues related to optimal placement of UAV-mounted base stations (UAV-BSs) due to limited number of UAV-BSs, limited energy availability and trade-off between coverage area and its altitude. In this paper, we address UAV-BSs placement issues by proposing a novel Machine learning (ML) based intelligent deployment mechanism. More specifically, for intelligent deployment of UAV-BSs based on energy, computational power, nature of available data and criticality of the scenario, we use two different approaches: Support Vector Machine (SVM) and Deep Learning (DL), which is composed of sequential time series learning process. Moreover, to address the security and privacy challenges emanating from the wireless connectivity and untrusted broadcast nature of UAV-BSs, we propose a Blockchain-based novel information-sharing scheme. To evaluate the performance of our combined secure and intelligent proposed approach, we have improved energy consumption by almost twice in contrast with the normal deployment of UAV-BSs.
Meng Li, F. Richard Yu, Pengbo Si, Ruizhe Yang · 6 authors
Recently, the development of the internet of Things (ioT) provides plenty of opportunities and challenges in various fields. As an essential part of ioT, machine-to-machine (M2M) communications open a novel way that machine-type communication devices (MTCDs) are connected and communicated without any human intervention. However, when ioT infrastructures are destroyed, network services will be disrupted. Then it is difficult for the MTCDs located in remote areas to restore communication by themselves immediately. To cope with these problems, in this article, we introduce some promising technologies such as unmanned aerial vehicles (UAV), blockchain and mobile edge computing (MEC) to ensure data transmission, security and reliability in damaged M2M communications networks. Meanwhile, we propose a joint optimization framework to maximize both data computation capacity and throughput of blockchain systems, and formulate it as a Markov decision process (MDP). in order to solve the dynamic and complicated optimization problem, dueling deep Q-network (DQN) is adopted, so that the optimal selection and decision can be made to achieve maximum system rewards. Simulation results with different system parameters show that our proposed framework can improve the system performance effectively compared to the existing schemes. Finally, open research issues and challenges are discussed for UAV-assisted M2M communications.
National security is a top priority to mitigate intrusions and criminal acts. Governments require robust national surveillance system that can cover all geographical areas, including the blind spots that may hold violence and criminal incidents' triggers i.e. malls, stadiums, airports, and other key sites. Integrating existing surveillance infrastructures rather than creating centralized solutions will have great potential on scalability as well as providing more liberal framework that is not run by a single point of control. However, this definitely requires establishing secure communication and mutual trust amongst these entities, which is a real challenge. Towards this end, we propose an efficient smart surveillance architecture that combines machine learning and Blockchain technologies to facilitate the exchange of relevant surveillance events as admitted transactions into a permissioned Hyperledger fabric Blockchain. We conducted comprehensive analysis to demonstrate the feasibility of blockchain and the efficiency of the machine learning-based face recognition and matching for real-time surveillance of suspects using heterogeneous surveillance infrastructure. The proposed architecture proved scalability and real-time behavior after putting the system through multiple test cases. With very high matching accuracy, and end-to-end latency of less than 12.8 seconds, the system proves to be scalable, and fast enough for a smart surveillance use case.
Abstract With the exponential growth in the number of vital infrastructures such as nuclear plants and transport and distribution networks, these systems have become more susceptible to coordinated cyberattacks. One of the effective approaches used to strengthen the security of these infrastructures is the use of unmanned aerial vehicles (UAVs) for surveillance and data collection. However, UAVs themselves are prone to attacks on their collected sensor data. Recently, blockchain (BC) has been proposed as a revolutionary technology that can be integrated within Internet of things (IoT) to provide a desired level of security and privacy. However, the integration of BC within IoT networks, where UAV's sensors constitute a major component, is extremely challenging. The major contribution of this study is twofold:(1) survey the security issues for UAV's collected sensor data, define the security requirements for such systems, and identify ways to address them; and (2) propose a novel BC‐based solution to ensure the security of and the trust between the UAVs and their relevant ground control stations. Our implementation results and analysis show that using UAVs as means for protecting critical infrastructure is greatly enhanced through the utilization of trusted BC‐based unmanned aerial systems.
Muhammad Zahid Khan, Muhammad Zahid Khan, Muhammad Zahid Khan, Muhammad Usman Khan · 6 authors
Studies have been actively conducted on analyzing the driver's behavior inside the vehicle premises. Moreover, the transmission of the tempered proof multimedia content is also a major point of interest for the research community. At present, most of the techniques for detecting the distracted behavior of the driver is based on the detection of different face attributes like eyes and head posture etc, by using the traditional hand crafted features. In this paper we propose the deep learning based algorithm using the Convolution Neural Network. The proposed algorithm is independent of feature extraction of the specific parts, instead, it automatically picks the best features specific to the problem. We have utilized the State Form Distracted Driver Detection dataset to train our proposed algorithm. Furthermore, this paper also proposes a secure and tempered proof multimedia transaction. Original video data may be edited and fabricated with the false information. Multimedia blockchain can be helpful in tackling this problem. We have used Secure Hashing Algorithm (SHA‐256) algorithm for extracting the hashes of multimedia content. By utilizing the blockchain, we safely transmit the tempered proof video data coming from inside the vehicle, automatically detecting abnormal activities with our deep learning based algorithm. So, this paper combines the deep learning algorithms with blockchain techniques which is novel in research. Comparison between the results of proposed algorithm with the current state of the art work shows that proposed algorithm outperforms by achieving 86.02% accuracy on the test data.
Rong Wang, Wei‐Tek Tsai, Juan He, Can Liu · 6 authors
In recent decades, video surveillance systems have become an indispensable management tool for cities. Managers can grasp the information of the scene without visiting the scene. Through the surveillance system, the effect of management and supervision can be improved, and the probability of major accidents can be reduced. However, with the advent of the Internet of Things (IoT) era, video surveillance systems will face challenges such as massive equipment access, massive data, insufficient bandwidth, vulnerable to attack, and real-time monitoring difficulties. Based on the analysis of the research and application status of video surveillance system, we propose a video surveillance system based on permissioned blockchains (BCs) and edge computing. The system uses permissioned BCs, edge computing, InterPlanetary File System (IPFS) technology and convolution neural networks (CNNs). The edge computing is used to achieve large-scale wireless sensor information acquisition and data processing. IPFS storage service is used to realize massive video data storage, and CNNs technology is used to realize real-time monitoring.
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.
Jun 1, 2018·2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
Pierluigi Gallo, Suporn Pongnumkul, Uy Quoc Nguyen
The growing demand for safety in urban environments is supported by monitoring using video surveillance. The need to analyze multiple video-flows from different cameras deployed around the city by heterogeneous owners introduces vulnerabilities and privacy issues. Video frames, timestamps, and camera settings can be digitally manipulated by malicious users; the positions of cameras, their orientation and their mechanical settings can be physically manipulated. Digital and physical manipulations may have several effects, including the change of the observed scene and the potential violation of neighbors' privacy. To face these risks, we introduce BlockSee, a blockchain-based video surveillance system that jointly provides validation and immutability to camera settings and surveillance videos, making them readily available to authorized users in case of events. The encouraging results obtained with BlockSee pave the way to new distributed city-wide monitoring systems.