Abstract

The most significant and critical infrastructures, such as the electricity utilities, clean water facilities, nuclear plants and manufacturing industries are controlled and supervised by the industrial control systems. These systems undergo through a metamorphosis as a result of the Industry 4.0 revolution, which emphasises enhanced connectivity and flexibility with the Internet of Things (IoT) and cloud computing technologies. As the data is transferred across the Internet, Industry 4.0 communication can be easily attacked by launching different potential attacks. As a consequence, we attempt to propose a novel certificate-based access control and key establishment scheme for securing Industry 4.0 communication, called ACKS-IA. It offers access control and key establishment between smart industrial devices, as well as between a smart device and its associated cloud server. A formal security analysis of ACKS-IA through the broadly-accepted Burrows–Abadi– Needham (BAN) logic is provided. It confirms that ACKS-IA is secured and provides secure mutual authentication among the communication entities. The detailed informal security analysis and comparative study with the existing related schemes reveal that the proposed ACKS-IA is secured and efficient in terms of communication cost, computation cost, and security and functionality features including anonymity and untraceability as compared to other competing schemes. Finally, a real testbed implementation of ACKS-IA is provided to measure its effect on important performance attributes.

Abstract

This paper discusses the development of autopilot hardware for small fixed-wing air vehicles. Weight constraint is the critical factor in developing such hardware. The sensors and communication devices are selected based on the requirements and constraints of these air vehicles. The sensors used in the hardware are calibrated using a three-axis rotating platform. The software written in the autopilot hardware is flexible enough to incorporate complex estimation and control algorithms along with the hardware-in-loop simulations. Linear output feedback controllers are designed for fixed wing micro and nano air vehicles. Successful flight trials are conducted to demonstrate the utility of the autopilot hardware for small fixed-wing air vehicles

Abstract

The epidemic growth of the Internet of Things (IoT) objects have revolutionized Maritime Transportation Systems (MTS). Though, it becomes challenging for the centralized cloud-centric framework to fulfil the application requirements such as low latency and power utilization. The introduction of the distributed edge-centric framework has recently helped the IoT-enabled MTS to meet these requirements by manipulating the tasks at the edge of the networks. Despite the fact that MTS leverages mobile subscribers by overcoming inherent cloud computing limitations, data security and user privacy requirements in establishing the MTS setup are still non-trivial challenges. In this article, we develop a key agreement solution for mobile users to realize mutual authentication in a single round. Our protocol offers user anonymity to maintain user privacy, and it can prevent physical attacks by physically unclonable functions. Initially, the security analysis is conferred to substantiate our protocol’s security persistence or strength. Later, its performance correlation is observed under the assumption of diverse metrics in a predefined empirical setup. The meticulous performance correlation endorses the precedence of our protocol over specified related protocols.

Abstract

In recent years there has been rampant growth in the field of ML, AI, Big Data, IoT, cyber security, and cloud computing. These technologies have also integrated into the field of smart healthcare. The Internet of Medical Things (IoMT)-driven e-health is one such domain, which utilizes these modern technologies to provide cost-effective and secure healthcare care services to the patients [1]. It contain sensors, devices, actuators, etc. All the raw data is collected from these devices, which is prepossessed and refined to give useful insights. The medical professional can analyze and visualize critical health parameters such as ‘‘blood pressure (BP), heart rate, temperature, etc Based on the current status of the patient, alerts can be generated to doctors or the nursing staff in case of any eventuality. The integration of IoMT with e-health can enhance live tracking, monitoring and health services of patients [2], [3]. This data is regularly shared by the integrated devices and stored over the cloud server, where the healthcare professional can get all the diagnostics of patients. With the benefits of the technology, there are some security challenges. It can be architectural design flaws, a software bug, or a hardware backdoor that can lead to the compromise of the security of the systems. The cyber threat actor can gain authorized access to the system and paralyze all the networks at once. Weak control access, authentication and authorization protocols in the network may compromise the systems to the cyber attackers [4]. Attacks such as ransomware, rootkit, malware, denial of service (DoS), etc., can cause harm to the devices and tamper the healthcare data. Attackers can remotely access the smart healthcare devices and can use them as the botnet devices. With remote access, the attackers can redirect the network somewhere else and can huge network flooding [5], [6]. There have been a lot of high-impact cyber attacks happening to the e-health infrastructure. For example, a patient with the smart pacemaker monitors heart rate, temperature, and blood flow rate that can be critical to a heart alignment patient. Imagine this device gets compromised by a threat actor, it can create panic even worse and can cause death to the patient [7]. For mitigation of the cyber-attacks on all the critical infrastructure (i.e., e-health), cyber security comes into action. With threat modeling and hunting, we can actively search for any threat in the network, its source, and the severity of the threats. Once threats are found in the network, all the mitigation techniques can be used to secure the network from any intrusion. Cyber security provides a strong mechanism (i.e., authentication and key agreement) with all the tools and techniques to safeguard the e-health system [5], [6], [8]. Therefore, proper registration, authentication, authorization and intrusion detection schemes can be used to mitigate the various cyber attacks, i.e., ‘‘replay attempts, man-in-themiddle (MiTM) attacks, replay attacks, illegal session key computation, stolen verifier, etc., [8]

Abstract

This paper proposes a 162nW resistance-to-digital converter (RDC) for miniature battery-powered sensing systems. The RDC first converts input resistance to a pulse by charging a capacitor to a threshold voltage with a current proportional to the resistance. It compensates temperature sensitivity of the charging current by generating the threshold voltage with the same temperature dependency. Then, the circuit digitizes the pulse using an up-down counter that cancels temperature-dependent delay and offset of the low-power comparator in a digital Correlated Double Sampling (CDS) style. Designed in a 180 nm CMOS process, the proposed circuit achieves a figure-of-merit (FoM) of 0.845pJ/c.s. in simulation, with a conversion time of 50 ms for input resistance from to , while consuming 162nW at a supply voltage of 900 mV. Also, it obtains a temperature sensitivity of 26.9ppm/°C from −40 to 100°C

Abstract

This paper proposes a switched-capacitor network (SCN) based fractional bandgap voltage reference (BGR) circuit designed in 180nm CMOS process to achieve high accuracy and low power consumption for implantable biomedical applications. The design proposes a VEB generator that employs a 2x charge pump and an improved SCN to generate a temperature inde-pendent reference voltage (VREF) ’. A low-power clock generator circuit is proposed, which reduces the leakage current by 37 % compared to previous works, thereby reducing the circuit's power consumption to 18.5nW at typical conditions. The design works from a supply voltage of 0.5V and has a TC of 74. Sppm/ ∘C over a temperature range of 0−80∘C . The PSRR of the circuit is -62.9dB at 100Hz. Based on the Monte Carlo simulations of 500 samples, we obtain an untrimmed 3σ/μ of 2.6%. The design occupies an active area of 0.027mm

Abstract

Internet of Things (IoT)-based smart factories offer the manufacturing sectors a great opportunity to embrace the fourth industrial revolution (Industry 4.0). The real-time monitoring of manufacturing operations in an Industry 4.0 needs to be ensured by the deployed technologies, like Artificial Intelligence (AI) and Big Data analytics. The overall purpose is to improve the outcomes of the production process. However, Industry 4.0 becomes vulnerable to different potential attacks as the communication takes place via public environments. In this article, an authentication and key agreement method has been suggested to secure the communication that can occur in a Fog-based Industry 4.0 environment. The security proposal provides secure mutual authentication along with key establishment between various smart industrial devices and fog servers, as well as between fog servers and cloud servers. The security analysis and comparative study reveal that the proposed method can mitigate various potential attacks, and it also offers important security and functionality attributes as compared to those for other competing schemes.

Abstract

Denial-of-service (DoS) attacks are a major concern in the field of Internet technology, and they pose a difficult security challenge. Among the different types of DoS attacks, distributed denial-of-service (DDoS) attacks are especially concerning because they can quickly drain the resources of a target, disrupting their computational and communication capabilities without warning. In response to the severity of this problem, various defense strategies have been developed to defend against DDoS attacks. In this paper, a survey on the various DDoS attacks detection schemes is presented. An overview of the DDoS attack methodology is given. The information on different types of DDoS detection methods in SDN environment is also provided. Then a comparative study of different DDoS attack detection schemes is given, which contains the comparisons of different schemes in terms of their advantages, limitations, accuracy values, and time complexity.

Abstract

In the recent past, forest fires have increased due to the changing climate pattern. It is necessary to analyse and quantify various gaseous emissions so as to mitigate their harmful effects on air pollution. Satellite remote sensing data provides an opportunity to study the greenhouse gases in the atmosphere. The multispectral sensor of the Tropospheric Monitoring Instrument (Sentinel-5) is capable of recording the reflectance of wavelengths vital for measuring the atmospheric concentrations of methane, formaldehyde, aerosol, carbon monoxide, etc., at a spatial resolution of 0.01°. The present study utilized the Google Earth Engine (GEE) platform to study the emissions caused by forest fires in four districts of Uttarakhand State of India, which witnessed unprecedented fires in April-May 2021. All the datasets were ingested in GEE, which has the capability to analyse large datasets without the need to download them. The pre-fire period chosen was September 2020; the fire period was February-May 2021, and the post-fire period was June 2021. The variables chosen were aerosol absorbing index (AAI), carbon monoxide (CO) and nitrogen dioxide (NO2). The climate parameter temperature (Moderate Resolution Imaging Spectroradiometer Land Surface Temperature) and precipitation (from Climate Hazards Group InfraRed Precipitation (CHIRPS) Pentad) were also studied for the period mentioned. The results indicate a different trend for emissions in each district. For AAI, maximum emissions were noted in district Nainital

Abstract

Document summarization aims to create a precise and coherent summary of a text document. Many deep learning summarization models are developed mainly for English, often requiring a large training corpus and efficient pre-trained language models and tools. However, English summarization models for low-resource Indian languages are often limited by rich morphological variation, syntax, and semantic differences. In this paper, we propose GAE-ISumm, an unsupervised Indic summarization model that extracts summaries from text documents. In particular, our proposed model, GAE-ISumm uses Graph Autoencoder (GAE) to learn text representations and a document summary jointly. We also provide a manually-annotated Telugu summarization dataset TELSUM, to experiment with our model GAE-ISumm. Further, we experiment with the most publicly available Indian language summarization datasets to investigate the effectiveness of GAE-ISumm on other Indian languages. Our experiments of GAE-ISumm in seven languages make the following observations: (i) it is competitive or better than state-of-the-art results on all datasets, (ii) it reports benchmark results on TELSUM, and (iii) the inclusion of positional and cluster information in the proposed model improved the performance of summaries.