Biblio

Thefts problem in household needs to be anticipated with home security system. One of simple methods is using automatic solenoid door lock system, so that it is difficult to be duplicated and will reduce the chance of theft action when the house is empty. Therefore, a home security system prototype that can be accessed by utilizing biometric fingerprint, Radio Frequency Identification (RFID), and keypad sensors was designed and tested. Arduino Uno works to turn on the door lock solenoid, so door access will be given when authentication is successful. Experimental results show that fingerprint sensor works well by being able to read fingerprints perfectly and the average time required to scan a fingerprint was 3.7 seconds. Meanwhile, Radio Frequency Identification (RFID) sensor detects Electronic-Kartu Tanda Penduduk (E-KTP) and the average time required for Radio Frequency Identification (RFID) to scan the card is about 2.4 seconds. Keypad functions to store password to unlock the door which produces the average time of 3.7 seconds after 10 trials. Average time to open with multi-sensor is 9.8 seconds. However, its drawback is no notification or SMS which directly be accessed by a cellphone or website with Wi-Fi or Telegram applications allow homeowners to monitor their doors from afar as to minimize the number of house thefts.

In this paper, a novel composite right/left-handed transmission line (CRLH TL) 3-unit cell is presented for finding excellent time-delay (TD) efficiency of Chipless RFID's True-Time-Delay Lines (TTDLs). RFID (Radio Frequency Identification) is a non-contact automatic identification technology that uses radio frequency (RF) signals to identify target items automatically and retrieve pertinent data without the need for human participation. However, as compared to barcodes, RFID tags are prohibitively expensive and complex to manufacture. Chipless RFID tags are RFID tags that do not contain silicon chips and are therefore less expensive and easier to manufacture. It combines radio broadcasting technology with radar technology. Radio broadcasting technology use radio waves to send and receive voice, pictures, numbers, and symbols, whereas radar technology employs the radio wave reflection theory. Chipless RFID lowers the cost of sensors such as gas, temperature, humidity, and pressure. In addition, Chipless RFID tags can be used as sensors which are also required for security purposes and future IoT applications.

In recent years, body-worn RFID and NFC (near field communication) devices have become one of the principal technologies concurring to the rise of healthcare internet of thing (H-IoT) systems. Similarly, points of care (PoCs) moved increasingly closer to patients to reduce the costs while supporting precision medicine and improving chronic illness management, thanks to timely and frequent feedback from the patients themselves. A typical PoC involves medical sensing devices capable of sampling human health, personal equipment with communications and computing capabilities (smartphone or tablet) and a secure software environment for data transmission to medical centers. Hybrid platforms simultaneously employing NFC and ultra-high frequency (UHF) RFID could be successfully developed for the first sensing layer. An application example of the proposed hybrid system for the monitoring of acute myocardial infarction (AMI) survivors details how the combined use of NFC and UHF-RFID in the same PoC can support the multifaceted need of AMI survivors while protecting the sensitive data on the patient’s health.

The recent experience in the use of virtual reality (VR) technology has shown that users prefer Electromyography (EMG) sensor-based controllers over hand controllers. The results presented in this paper show the potential of EMG-based controllers, in particular the Myo armband, to identify a computer system user. In the first scenario, we train various classifiers with 25 keyboard typing movements for training and test with 75. The results with a 1-dimensional convolutional neural network indicate that we are able to identify the user with an accuracy of 93% by analyzing only the EMG data from the Myo armband. When we use 75 moves for training, accuracy increases to 96.45% after cross-validation.

Smart Phones being a revolution in this Modern era which is considered a boon as well as a curse, it is a known fact that most kids of the current generation are addictive to smartphones. The National Institute of Health (NIH) has carried out different studies such as exposure of smartphones to children under 12 years old, health risk associated with their usage, social implications, etc. One such study reveals that children who spend more than two hours a day, on smartphones have been seen performing poorly when it comes to language and cognitive skills. In addition, children who spend more than seven hours per day were diagnosed to have a thinner brain cortex. Hence, it is of great importance to control the amount of exposure of children to smartphones, as well as access to irregulated content. Significant research work has gone in this regard with a plethora of inputs features, feature extraction techniques, and machine learning models. This paper is a survey of the State-of-the-art techniques in detecting the age of the user using machine learning models on touch, keystroke dynamics, and sensor data.

Recent years have witnessed impressive advances in technology which led to the rapid growth of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) using numerous low-powered devices with a huge number of actuators and sensors. These devices gather and exchange data over the internet and generate enormous amounts of data needed to be secured. Although traditional cryptography provides an efficient means of addressing device and communication confidentiality, integrity, and authenticity issues, it may not be appropriate for very resource-constrained systems, particularly for end-nodes such as a simply connected sensor. Thus, there is an ascent need to use lightweight cryptography (LWC) providing the needed level of security with less complexity, area and energy overhead. In this paper, four lightweight cryptographic algorithms called PRESENT, LED, Piccolo, and SPARX were implemented over a Contiki-based IoT operating system, dedicated for IoT platforms, and assessed regarding RAM and ROM usage, power and energy consumption, and CPU cycles number. The Cooja network simulator is used in this study to determine the best lightweight algorithms to use in IoT applications utilizing wireless sensor networks technology.

Based on the campus wireless IPv6 network system, using WiFi contactless sensing and positioning technology and action recognition technology, this paper designs a new campus security early warning system. The characteristic is that there is no need to add new monitoring equipment. As long as it is the location covered by the wireless IPv6 network, personnel quantity statistics and personnel body action status display can be realized. It plays an effective monitoring supplement to the places that cannot be covered by video surveillance in the past, and can effectively prevent campus violence or other emergencies.

Recent concepts in defense herald an increasing degree of automation of future military systems, with an emphasis on accelerating sensing-to-decision loops at the tactical edge, reducing their network communication footprint, and improving the inference quality of intelligent components in the loop. These requirements pose resource management challenges, calling for operating-system-like constructs that optimize the use of limited computational resources at the tactical edge. This paper describes these challenges and presents IoBT-OS, an operating system for the Internet of Battlefield Things that aims to optimize decision latency, improve decision accuracy, and reduce corresponding resource demands on computational and network components. A simple case-study with initial evaluation results is shown from a target tracking application scenario.

Smart grids integrate computing and communication infrastructure with conventional power grids to improve situational awareness, control, and safety. Several technologies such as automatic fault detection, automated reconfiguration, and outage management require close network monitoring. Therefore, utilities utilize sensing equipment such as PMUs (phasor measurement units), smart meters, and bellwether meters to obtain grid measurements. However, the expansion in sensing equipment results in an increased strain on existing communication infrastructure. Prior works overcome this problem by exploiting the sparsity of power consumption data in the Haar, Hankel, and Toeplitz transformation bases to achieve sub-Nyquist compression. However, data-driven dictionaries enable superior compression ratios and reconstruction accuracy by learning the sparsifying basis. Therefore, this work proposes using dictionary learning to learn the sparsifying basis of smart meter data. The smart meter data sent to the data centers are compressed using a random projection matrix prior to transmission. These measurements are aggregated to obtain the compressed measurements at the primary nodes. Compressive sensing-based estimators are then utilized to estimate the system states. This approach was validated on the IEEE 33-node distribution system and showed superior reconstruction accuracy over conventional transformation bases and over-complete dictionaries. Voltage magnitude and angle estimation error less than 0.3% mean absolute percentage error and 0.04 degree mean absolute error, respectively, were achieved at compression ratios as high as eight.

This article describes an analysis of the key technologies currently applied to improve the quality, efficiency, safety and sustainability of Smart Grid systems and identifies the tools to optimize them and possible gaps in this area, considering the different energy sources, distributed generation, microgrids and energy consumption and production capacity. The research was conducted with a qualitative methodological approach, where the literature review was carried out with studies published from 2019 to 2022, in five (5) databases following the selection of studies recommended by the PRISMA guide. Of the five hundred and four (504) publications identified, ten (10) studies provided insight into the technological trends that are impacting this scenario, namely: Internet of Things, Big Data, Edge Computing, Artificial Intelligence and Blockchain. It is concluded that to obtain the best performance within Smart Grids, it is necessary to have the maximum synergy between these technologies, since this union will enable the application of advanced smart digital technology solutions to energy generation and distribution operations, thus allowing to conquer a new level of optimization.

Intelligent transportation systems, such as connected vehicles, are able to establish real-time, optimized and collision-free communication with the surrounding ecosystem. Introducing the internet of things (IoT) in connected vehicles relies on deployment of massive scale sensors, actuators, electronic control units (ECUs) and antennas with embedded software and communication technologies. Combined with the lack of designed-in security for sensors and ECUs, this creates challenges for security engineers and architects to identify, understand and analyze threats so that actions can be taken to protect the system assets. This paper proposes a novel STRIDE-based threat model for IoT sensors in connected vehicle networks aimed at addressing these challenges. Using a reference architecture of a connected vehicle, we identify system assets in connected vehicle sub-systems such as devices and peripherals that mostly involve sensors. Moreover, we provide a prioritized set of security recommendations, with consideration to the feasibility and deployment challenges, which enables practical applicability of the developed threat model to help specify security requirements to protect critical assets within the sensor network.

With the objective to eliminate the input current sensor in a totem-pole boost power factor corrector (PFC) for its low-cost design, a novel discretized sampling-based robust control scheme is proposed in this work. The proposed control methodology proves to be beneficial due to its ease of implementation and its ability to support high-frequency operation, while being able to eliminate one sensor and, thus, enhancing reliability and cost-effectiveness. In addition, detailed closed-loop stability analysis is carried out for the controller in discrete domain to ascertain brisk dynamic operation when subjected to sudden load fluctuations. To establish the robustness of the proposed control scheme, a detailed sensitivity analysis of the closed-loop performance metrics with respect to undesired changes and inherent uncertainty in system parameters is presented in this article. A comparison with the state-of-the-art (SOA) methods is provided, and conclusive results in terms of better dynamic performance are also established. To verify and elaborate on the specifics of the proposed scheme, a detailed simulation study is conducted, and the results show 25% reduction in response time as compared to SOA approaches. A 500-W boost PFC prototype is developed and tested with the proposed control scheme to evaluate and benchmark the system steady-state and dynamic performance. A total harmonic distortion of 1.68% is obtained at the rated load with a resultant power factor of 0.998 (lag), which proves the effectiveness and superiority of the proposed control scheme.

Recent approaches have proven the effectiveness of local outlier factor-based outlier detection when applied over traffic flow probability distributions. However, these approaches used distance metrics based on the Bhattacharyya coefficient when calculating probability distribution similarity. Consequently, the limited expressiveness of the Bhattacharyya coefficient restricted the accuracy of the methods. The crucial deficiency of the Bhattacharyya distance metric is its inability to compare distributions with non-overlapping sample spaces over the domain of natural numbers. Traffic flow intensity varies greatly, which results in numerous non-overlapping sample spaces, rendering metrics based on the Bhattacharyya coefficient inappropriate. In this work, we address this issue by exploring alternative distance metrics and showing their applicability in a massive real-life traffic flow data set from 26 vital intersections in The Hague. The results on these data collected from 272 sensors for more than two years show various advantages of the Earth Mover's distance both in effectiveness and efficiency.

Industry 4.0 is the latest industrial revolution primarily merging automation with advanced manufacturing to reduce direct human effort and resources. Predictive maintenance (PdM) is an industry 4.0 solution, which facilitates predicting faults in a component or a system powered by state-of-the- art machine learning (ML) algorithms (especially deep learning algorithms) and the Internet-of-Things (IoT) sensors. However, IoT sensors and deep learning (DL) algorithms, both are known for their vulnerabilities to cyber-attacks. In the context of PdM systems, such attacks can have catastrophic consequences as they are hard to detect due to the nature of the attack. To date, the majority of the published literature focuses on the accuracy of DL enabled PdM systems and often ignores the effect of such attacks. In this paper, we demonstrate the effect of IoT sensor attacks (in the form of false data injection attack) on a PdM system. At first, we use three state-of-the-art DL algorithms, specifically, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) for predicting the Remaining Useful Life (RUL) of a turbofan engine using NASA's C-MAPSS dataset. The obtained results show that the GRU-based PdM model outperforms some of the recent literature on RUL prediction using the C-MAPSS dataset. Afterward, we model and apply two different types of false data injection attacks (FDIA), specifically, continuous and interim FDIAs on turbofan engine sensor data and evaluate their impact on CNN, LSTM, and GRU-based PdM systems. The obtained results demonstrate that FDI attacks on even a few IoT sensors can strongly defect the RUL prediction in all cases. However, the GRU-based PdM model performs better in terms of accuracy and resiliency to FDIA. Lastly, we perform a study on the GRU-based PdM model using four different GRU networks with different sequence lengths. Our experiments reveal an interesting relationship between the accuracy, resiliency and sequence length for the GRU-based PdM models.

Edge computing (EC) is a new developing computing technology where data are collected, and analysed nearer to the edge or sources of the data. Cloud to the edge, intelligent applications and analytics are part of the IoT applications and technology. Edge computing technology aims to bring cloud computing features near to edge devices. For time-sensitive applications in cloud computing, architecture massive volume of data is generated at the edge and stored and analysed in the cloud. Cloud infrastructure is a composition of data centres and large-scale networks, which provides reliable services to users. Traditional cloud computing is inefficient due to delay in response, network delay and congestion as simultaneous transactions to the cloud, which is a centralised system. This paper presents a literature review on cloud-based edge computing technologies for delay-sensitive applications and suggests a conceptual model of edge computing architecture. Further, the paper also presents the implementation of QoS support edge computing paradigm in Python for further research to improve the latency and throughput for time-sensitive applications.

Cyber Physical Systems (CPS) are widely deployed and employed in many recent real applications such as automobiles with sensing technology for crashes to protect passengers, automated homes with various smart appliances and control units, and medical instruments with sensing capability of glucose levels in blood to keep track of normal body function. In spite of their significance, CPS infrastructures are vulnerable to cyberattacks due to the limitations in the computing, processing, memory, power, and transmission capabilities for their endpoint/edge appliances. In this paper, we consider a short systematic investigation for the models and techniques of cyberattacks and threats rate against Cyber Physical Systems with multiple subsystems and redundant elements such as, network of computing devices or storage modules. The cyberattacks are assumed to be externally launched against the Cyber Physical System during a prescribed operational time unit following stochastic distribution models such as Poisson probability distribution, negative-binomial probability distribution and other that have been extensively employed in the literature and proved their efficiency in modeling system attacks and threats.

With the popularization of mobile communication and sensing equipment, as well as the rapid development of location-aware technology and wireless communication technology, LBSs(Location-based services) bring convenience to people’s lives and enable people to arrange activities more efficiently and reasonably. It can provide more flexible LBS proximity detection query, which has attracted widespread attention in recent years. However, the development of proximity detection query still faces many severe challenges including query information privacy. For example, when users want to ensure their location privacy and data security, they can get more secure location-based services. In this article, we propose an efficient and privacy-protecting proximity detection framework based on location services: PD(Proximity Detection). Through PD, users can query the range of arbitrary polygons and obtain accurate LBS results. Specifically, based on homomorphic encryption technology, an efficient PRQ(polygon range query) algorithm is constructed. With the help of PRQ, PD, you can obtain accurate polygon range query results through the encryption request and the services provided by the LAS(LBS Agent Server) and the CS(Cloud Server). In addition, the query privacy of the queryer and the information of the data provider are protected. The correctness proof and performance analysis show that the scheme is safe and feasible. Therefore, our scheme is suitable for many practical applications.

Embedded systems in physically insecure environments are subject to additional security risk via capture by an adversary. A captured microchip device can be reverse engineered to recover internal buffer data that would otherwise be inaccessible through standard IO mechanisms. We consider an adversary who has sufficient ability to gain all internal bits and logic from a device at the time of capture as an unsolved threat. In this paper we present a novel sensing architecture that enhances embedded system security by randomly encoding sensed values. We randomly encode data at the time of sensing to minimize the amount of plaintext data present on a device in buffer memory. We encode using techniques that are unintelligible to an adversary even with full internal bit knowledge. The encoding is decipherable by a trusted home server, and we have provided an architecture to perform this decoding. Our experimental results show the proposed architecture meets timing requirements needed to perform communications with a satellite utilizing short-burst data, such as in remote sensing telemetry and tracking applications.

This paper presents a fully blind false data injection (FDI) attack against an industrial field-bus i.e. PROFINET that is widely used in Siemens distributed Input/Output (I/O) systems. In contrast to the existing academic efforts in the research community which assume that an attacker is already familiar with the target system, and has a full knowledge of what is being transferred from the sensors or to the actuators in the remote I/O module, our attack overcomes these strong assumptions successfully. For a real scenario, we first sniff and capture real time data packets (PNIO-RT) that are exchanged between the IO-Controller and the IO-Device. Based on the collected data, we create an I/O database that is utilized to replace the correct data with false one automatically and online. Our full attack-chain is implemented on a real industrial setting based on Siemens devices, and tested for two scenarios. In the first one, we manipulate the data that represents the actual sensor readings sent from the IO-Device to the IO-Controller, whereas in the second scenario we aim at manipulating the data that represents the actuator values sent from the IO-Controller to the IO-Device. Our results show that compromising PROFINET I/O systems in the both tested scenarios is feasible, and the physical process to be controlled is affected. Eventually we suggest some possible mitigation solutions to secure our systems from such threats.

Trust along digitalized supply chains is challenged by the aspect that monitoring equipment may not be trustworthy or unreliable as respective measurements originate from potentially untrusted parties. To allow for dynamic relationships along supply chains, we propose a blockchain-backed supply chain monitoring architecture relying on trusted hardware. Our design provides a notion of secure end-to-end sensing of interactions even when originating from untrusted surroundings. Due to attested checkpointing, we can identify misinformation early on and reliably pinpoint the origin. A blockchain enables long-term verifiability for all (now trustworthy) IoT data within our system even if issues are detected only after the fact. Our feasibility study and cost analysis further show that our design is indeed deployable in and applicable to today’s supply chain settings.

With the continuous development of edge computing, the application scope of mobile crowdsourcing (MCS) is constantly increasing. The distributed nature of edge computing can transmit data at the edge of processing to meet the needs of low latency. The trustworthiness of the third-party platform will affect the level of privacy protection, because managers of the platform may disclose the information of workers. Anonymous servers also belong to third-party platforms. For unreal third-party platforms, this paper recommends that workers first use the localized differential privacy mechanism to interfere with the real location information, and then upload it to an anonymous server to request services, called the localized differential anonymous privacy protection mechanism (LDNP). The two privacy protection mechanisms further enhance privacy protection, but exacerbate the loss of service quality. Therefore, this paper proposes to give corresponding compensation based on the authenticity of the location information uploaded by workers, so as to encourage more workers to upload real location information. Through comparative experiments on real data, the LDNP algorithm not only protects the location privacy of workers, but also maintains the availability of data. The simulation experiment verifies the effectiveness of the incentive mechanism.

Mobile devices have been widely used to deploy security-sensitive applications such as mobile payments, mobile offices etc. SM2 digital signature technology is critical in these applications to provide the protection including identity authentication, data integrity, action non-repudiation. Since mobile devices are prone to being stolen or lost, several server-aided SM2 cooperative signature schemes have been proposed for the mobile scenario. However, existing solutions could not well fit the high-concurrency scenario which needs lightweight computation and communication complexity, especially for the server sides. In this paper, we propose a SM2 cooperative signature algorithm (SM2-CSA) for the high-concurrency scenario, which involves only one-time client-server interaction and one elliptic curve addition operation on the server side in the signing procedure. Theoretical analysis and practical tests shows that SM2-CSA can provide better computation and communication efficiency compared with existing schemes without compromising the security.

In order to solve the problem of large data volume brought by the traditional Nyquist sampling theorem in radar signal detection, a compressive detection (CD) model based on compressed sensing (CS) theory is proposed by analyzing the sparsity of the radar target in the range domain. The lower sampling rate completes the compressive sampling of the radar signal on the range field. On this basis, the two-dimensional distribution of the Doppler unit is established by moving target detention moving target detention (MTD), and the detection of the target is achieved with the two-dimensional constant false alarm rate (2D-CFAR) detection algorithm. The simulation experiment results prove that the algorithm can effectively detect the target without the need for reconstruction signals, and has good detection performance.

To measure machinery vibration, a sensor system consisting of a 3-axis accelerometer, ADXL345, attached to a self-contained system-on-a-chip with integrated Wi-Fi capabilities, ESP8266, is a low-cost solution. In this work, we first show that in such a system, the widely used direct-read-and-send method which samples and sends individually acquired vibration data points to the server is not effective, especially using Wi-Fi connection. We show that the micro delays in each individual data transmission will limit the sensor sampling rate and will also affect the time of the acquired data points not evenly spaced. Then, we propose that vibration should be sampled in batches before sending the acquired data out from the sensor node. The vibration for each batch should be acquired continuously without any form of interruption in between the sampling process to ensure the data points are evenly spaced. To fill the data gaps between the batches, we propose the use of compressive sampling technique. Our experimental results show that the maximum sampling rate of the direct-read-and-send method is 350Hz with a standard uncertainty of 12.4, and the method loses more information compared to our proposed solution that can measure the vibration wirelessly and continuously up to 633Hz. The gaps filled using compressive sampling can achieve an accuracy in terms of mean absolute error (MAE) of up to 0.06 with a standard uncertainty of 0.002, making the low-cost vibration sensor node a cost-effective solution.

We address the problem of recovering signals from compressed measurements based on generative priors. Recently, generative-model based compressive sensing (GMCS) methods have shown superior performance over traditional compressive sensing (CS) techniques in recovering signals from fewer measurements. However, it is possible to further improve the performance of GMCS by introducing controlled sparsity in the latent-space. We propose a proximal meta-learning (PML) algorithm to enforce sparsity in the latent-space while training the generator. Enforcing sparsity naturally leads to a union-of-submanifolds model in the solution space. The overall framework is named as sparsity driven latent space sampling (SDLSS). In addition, we derive the sample complexity bounds for the proposed model. Furthermore, we demonstrate the efficacy of the proposed framework over the state-of-the-art techniques with application to CS on standard datasets such as MNIST and CIFAR-10. In particular, we evaluate the performance of the proposed method as a function of the number of measurements and sparsity factor in the latent space using standard objective measures. Our findings show that the sparsity driven latent space sampling approach improves the accuracy and aids in faster recovery of the signal in GMCS.