Abstract

Drought, a natural disaster, occurs in all types of climate. Temperature, wind, relative humidity, rainfall intensity and rainfall duration play a significant part in the phenomenon of droughts. It affects natural sources of water and also can reduce the water supply, quality of water, crop yield, as well as the economy and social activities. Drought is an extreme event that affects natural resources, environment and society for a long time. Usually, drought occurs gradually; but it can be more terrible than floods. Meteorological, hydrological and agricultural are its broad types as described in Fig. [1]. Out of these, meteorological drought, defined as an abnormal shortage of precipitation, is the main cause of all other types of droughts. Drought index measures the levels of drought by acquiring data into a single numerical value from one or more hydrological variables. Several drought indices are found to assess the change in climatic variables. Those are Palmer Drought Severity Index, Crop Moisture Index, Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Reconnaissance Drought Index (RDI) [1]. Based on the drought studies, almost all drought indices use precipitation as the sole variable or with the other hydrological elements, as per the type of demand, which is indicated by WMO [2]. There have been many attempts over the last few decades to develop a new drought index on the basis of a climatological precipitation study [1, 3, 4]. Vicente-Serrano et al. [5] proposed a new evapotranspiration-based index which is Standardized Precipitation Evapotranspiration Index (SPEI) based on the calculation of Potential Evapotranspiration.

Abstract

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and dataintensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations.

Abstract

Random Number Generators (RNGs) are the solution for cryptographic applications to enhance hardware security. These RNGs ought to have three specific properties unpredictability, aperiodic, and good statistical criteria. This brief presents a True Random Number Generator (TRNG) based on Ring oscillators’ jitter with MLFSR. The MLFSR is augmented with a set of prime primitive polynomials, Boolean, and non-linear functions to attain a non-linear, unpredictable, and extended sequence period. Paper mainly focused on achieving high randomness by integrated the TRNG with a new promising crypto engine Keccak as a post-processing block; leads to extensively more security in data transfer, encryption keys, data authenticity of ICs, and IoT based applications. The RNG design is coded in Verilog HDL and implemented on the FPGA Zed board. Hashing is performed with a throughput of 2.4Gbps at 100 MHz either with RNG data or SHA data. Evaluated the randomness of the generated non-deterministic bitstreams (10 Mb) using the NIST 800–22 & Diehard test suite and successfully passed.

Abstract

In an Intelligent Precision Agriculture (IPA), several Internet of Things (IoT) smart devices and drones can be deployed to monitor an agricultural environment. The drones can be further utilized to collect the data from smart devices and send to the Ground Station Server (GSS). However, insecure communication among the smart devices, drones and the GSS make the IoT agriculture environment vulnerable to various potential attacks. For this goal, a new authentication and key management scheme for IoT-enabled IPA, called AKMS-AgriIoT, has been put forward with the private blockchain-based solution. The blocks formed with the encrypted transactions and their respective signatures by the GSS are mined by the cloud servers to verify and add the blocks in the private blockchain center. A detailed security analysis and comparative study reveal that the proposed AKMS-AgriIoT supports better security, and provides more functionality features, less communication costs and comparable computation costs as compared to other relevant schemes. In addition, the blockchain-based implementation on the proposed AKMS-AgriIoT has been also carried out.

Abstract

—Effective design of autopilots for fixed-wing unmanned aerial vehicles (UAVs) is still a great challenge, due to unmodeled effects and uncertainties that these vehicles exhibit during flight. Unmodeled effects and uncertainties comprise longitudinal/lateral cross-couplings, as well as poor knowledge of equilibrium points (trimming points) of the UAV dynamics. The main contribution of this article is a new adaptive autopilot design, based on uncertain Euler–Lagrange dynamics of the UAV and where the control can explicitly take into account under-actuation in the dynamics, reduced structural knowledge of cross-couplings and trimming points. This system uncertainty is handled via appropriately designed adaptive laws: stability of the controlled UAV is analyzed. Hardware-in-the-loop tests, comparisons with an Ardupilot autopilot and with a robustified autopilot validate the effectiveness of the control design, even in the presence of strong saturation of the UAV actuators.

Abstract

—Connected vehicle means providing different services, such as advanced driver-assistance systems (ADAS) from vehicles connected to the network. Vehicular ad-hoc networks (VANETs) can support vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications to realize connected vehicle. In VANETs, secure communication must be ensured, as otherwise it can lead to traffic accidents and human injuries. Recently, many studies on V2I authentication have been conducted to guarantee the security of V2I communications. However, recent V2I authentication protocols do not consider the handover situation, and it causes unnecessary computations. As vehicles have limited computing resources, unnecessary computation can lead to overload to the vehicles. In recent years, blockchain-based VANET is an active field of research because it can provide decentralization, data integrity and transparency. Using the strength of the blockchain technology, we design a blockchain-based handover authentication protocol for VANETs. In the proposed protocol, vehicles only perform lightweight computations in handover situations for efficiency of the network. We also conduct the formal analysis such as Burrows–Abadi–Needham (BAN) logic, Real-Or-Random (ROR) oracle model, and Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation to the proposed protocol. We simulate the proposed protocol using network simulator 3 (NS-3) to verify that the proposed protocol is practical. Finally, we compare the computational cost and security features of the proposed protocol with existing protocols to show that the proposed protocol is more secure and efficient.

Abstract

—Smart vehicles-enabled intelligent transportation system (ITS) supports a wide range of applications, such as, but not limited to, traffic planning and management, collision avoidance alert system, automated road speed enforcement, electronic toll collection, and real-time parking management, to name a few. However, it suffers from various types of security and privacy issues due to insecure communication among the entities over public channels. Therefore, an efficient and lightweight security mechanism is essential to protect the data that is both at rest as well as in transit. To this direction, we propose a public blockchain-envisioned secure communication framework for ITS (in short, called PBSCF-ITS). The proposed PBSCFITS guarantees access control and key management among the vehicle to vehicle, vehicle to road side unit, and road side unit to cloud server. We analyze the security of PBSCF-ITS to prove its resilience against various types of possible attacks. Furthermore, the performance of PBSCF-ITS with other related competing schemes has been compared. The obtained results illustrate that PBSCF-ITS outperforms the existing ones. Additionally, the pragmatic study of PBSCF-ITS is conducted to check its influence on various network related performance parameters, like number of mined blocks and transactions per block.

Abstract

Healthcare service providers store the patients’ electronic medical records in the cloud in order to provide high quality healthcare services. Patients’ sensitive data is typically encrypted before storing in the cloud. However, it gives rise to a new challenge, namely access control over encrypted data. Attribute-Based Encryption (ABE) is a promising cryptographic technique to achieve the fine grained access control on outsourced encrypted data. Traditional ABE schemes assume a fixed access policy that is not suitable for the present dynamic environment. Although some ABE schemes with a dynamic access control policy have been proposed in the literature, these schemes have not addressed forward security, backward security, and user revocation after a policy update. In this paper, we propose an ABE scheme that supports access policy updates, and also provides forward security, backward security and user revocation at the same time. We have formally shown that the proposed scheme is Chosen Plaintext Attack (CPA)-secure under the Decisional Bilinear Diffie–Hellman (DBDH) assumption. Finally, the performance analysis exhibits that the proposed scheme is efficient in communication and computation, and is also suitable for resource constrained devices.

Abstract

The rapid development of smartphone technology and the Internet services in mobile devices facilitates easy access to online social networking (OSN) sites anytime, anywhere. At the same time, this allures the adversaries to exploit the OSNs as a soft target for easy execution of various attacks that can quickly spread to a large number of users. In distributed denial-of-service (DDoS) attacks, an adversary aims to overwhelm the normal traffic of a targeted server with a flood of fake login messages so that the associated Internet service or website turns inoperable. In this paper, we propose a secure and lightweight authentication scheme (𝑃 𝑅𝐷𝑜𝑆) that resists DDoS and other security attacks in mobile OSN environments. We provide a multi-faceted solution towards the remedy of DDoS attacks in the OSN environment. After a certain threshold, the scheme discards further user login attempts and blocks an adversary who intends to overload the network server. We use the pre-loaded shadow identity and emergency key pairs, and a key-refilling strategy that rebuilds the essential synchronization between a blocked naive user and the OSN server. This technique restores the intended unlinkability property of the protocol. Using NS3 simulation, we study the impact of DDoS attackers on network throughput and network delay. Moreover, we validate and compare the proposed scheme against state-ofthe-art solutions using the real attacks and benign datasets. We use the Canadian Institute for Cybersecurity (CIC) DoS dataset 2017, which is generated by capturing the normal and DoS attack packets separately with subsequent pre-processed for testing. We also use the machine learning (ML) algorithms, such as K-Nearest Neighbor (KNN), Gaussian Naive Bayes, and Multilayer Perceptron (MLP) to demonstrate the performance of the proposed solution in a practical attack detection scenario. We observe that these algorithms provide 97.05%, 95.48%, and 96.6% DDoS attack detection accuracy, respectively.

Abstract

Movement is a universal response to music, with dance often taking place in social settings. Although previous work has suggested that socially relevant information, such as personality and gender, are encoded in dance movement, the generalizability of previous work is limited. The current study aims to decode dancers’ gender, personality traits, and music preference from music‑induced movements. We propose a method that predicts such individual difference from free dance movements, and demonstrate the robustness of the proposed method by using two data sets collected using different musical stimuli. In addition, we introduce a novel measure to explore the relative importance of different joints in predicting individual differences. Results demonstrated near perfect classification of gender, and notably high prediction of personality and music preferences. Furthermore, learned models demonstrated generalizability across datasets highlighting the importance of certain joints in intrinsic movement patterns specific to individual differences. Results further support theories of embodied music cognition and the role of bodily movement in musical experiences by demonstrating the influence of gender, personality, and music preferences on embodied responses to heard music.