Abstract

The rapid proliferation of Internet of Things (IoT) devices and applications has resulted in an increasing demand for Low Power and Wide Area Network (LPWAN) solutions. The adoption of IoT networks still faces several challenges, despite the rapid advancement of low-power communication technology. Homogenizing this sector requires allowing interoperability between many technologies, which is now one of the largest obstacles. In this article, we present the design and implementation of the hybrid LPWAN architecture that can accomplish wide-area communication coverage and low-power consumption for IoT applications by leveraging two LPWAN technologies, Wireless Smart Ubiquitous Network (Wi-SUN) and Long Range (LoRa). In particular, LoRa is used for long-range communication, and Wi-SUN for a low-latency mesh network. Additionally, we implemented smart street light controlling system as a real-world deployment at the university campus to showcase the efficiency of the hybrid network. Our results demonstrate that the hybrid LPWAN architecture provides a better coverage and capacity while consuming less power than that of the LoRa or Wi-SUN network. The results of this study demonstrate the effectiveness of the proposed hybrid LPWAN architecture as a viable solution for next-generation IoT applications.

Abstract

Affordable healthcare, particularly for elderly persons, is an important goal with aging of the world population. Cloud-connected smart sensor systems are an important part of the healthcare ecosystem. In this letter, we present an affordable large area flexible pressure sensor array using a piezoresistive thin film. The dimensions of the sensor are 180 cm × 90 cm, according to the dimensions of a standard single bed mattress. The sensor array consists of eight vertical sensor strips with a high aspect ratio of 120, highest reported so far for a flexible piezoresistive sensor. The measured output of the sensors showed a high signal-to-noise ratio, with loaded and unloaded sensor resistances of 11 pm 3 Omega and 219.2 pm 0.5 kOmega, respectively. The fabrication process has been designed to be conducive to roll-to-roll manufacturing processes to increase scalability and reduce cost. With a sampling frequency of 10 Hz, the sensor array provides real-time information about user position and possible fall events.

Abstract

We investigate the phenomenon of transition to synchronization in the Sakaguchi-Kuramoto model in the presence of higher-order interactions and global order parameter adaptation. The investigation is done by performing extensive numerical simulations and low-dimensional modeling of the system. Numerical simulations of the full system show both continuous (second-order) as well as discontinuous transitions. The discontinuous transitions can either be associated with explosive (first-order) or tiered synchronization states depending on the choice of parameters. To develop an in depth understanding of the transition scenario in the parameter space we derive a reduced order model (ROM) using the Ott-Antonsen ansatz, the results of which closely match with those of the numerical simulations of the full system. The simplicity and analytical accessibility of the ROM help to conveniently unfold the transition scenario in the system having complex dependence on the parameters. Simultaneous analysis of the full system and the ROM clearly identifies the regions of the parameter space exhibiting different types of transitions. It is observed that the second-order transition is connected with a supercritical pitchfork bifurcation (PB) of the ROM. On the other hand, the discontinuous tiered transition is associated with multiple saddle-node (SN) bifurcations along with a supercritical PB and the first-order transition involves a subcritical PB alongside a SN bifurcation. Finally, the stability analysis of the different synchronization states of the system is performed analytically.

Abstract

This paper identifies the cryptocurrency market crashes and analyses its dynamics using the complex network. We identify three distinct crashes during 2017–20, and the analysis is carried out by dividing the time series into pre-crash, crash, and post-crash periods. Partial correlation based complex network analysis is carried out to study the crashes. Degree density (𝜌𝐷), average path length (̄𝑙), and average clustering coefficient (𝑐𝑐) are estimated from these networks. We find that both 𝜌𝐷 and 𝑐𝑐 are smallest during the pre-crash period, and spike during the crash suggesting the network is dense during a crash. Although 𝜌𝐷 and 𝑐𝑐 decrease in the postcrash period, they remain higher than pre-crash levels for the 2017–18 and 2018–19 crashes suggesting a market attempt to return to normalcy. We get ̄𝑙 is minimal during the crash period, suggesting a rapid flow of information. A dense network and rapid information flow suggest that during a crash uninformed synchronized panic sell-off happens. However, during the 2019– 20 crash, the values of 𝜌𝐷, 𝑐𝑐, and ̄𝑙 did not vary significantly, indicating minimal change in dynamics compared to other crashes. The findings of this study may guide investors in making decisions during market crashes.

Abstract

Our investigation centres on assessing the importance of a biased parameter (𝛼) in a multiplex Markov chain (MMC) model that is characterized by biased random walks in multiplex networks. We explore how varying complex network topologies affect the total multiplex imbalance as a function of biased parameter. Our primary finding is that the system demonstrates a gradual increase in total imbalance within both positive and negative regions of the biased parameter, with a consistent minimum value occurring at 𝛼 = −1. In contrast to the negative region, the total imbalance is consistently high when 𝛼 is significantly positive. We perform a detailed examination of four different network structures and establish three sets of multiplex networks. In each of these networks, the second layer consists of a Regular Random network, while the first layer is either a Barabási–Albert, Erdős-Rényi, or Watts Strogatz network, depending on the set. Our results demonstrate that the combination of Barabási–Albert and Random Regular Network exhibits the highest level of right saturation imbalance. Additionally, for left saturation imbalance, the Erdős–Rényi and Random Regular combination achieve a slightly higher value. We also observe that the total amount of imbalance at 𝛼 = −1 follows a decreasing trend as the size of the network of each layer increases. Furthermore, we are also able to illustrate that the second most significant eigenvalue of the supra-transition matrix exhibits a similar pattern in response to changes in the bias parameter, aligning with the overall system’s imbalance.

Abstract

We delve into the interplay between network’s symmetry and functioning for a generic class of dynamical systems. Primarily, we focus on a class of systems that characterize the spreading process, such as the spread of epidemics in complex networks, where the coupling configuration is nonlinear rather than diffusive. Through theoretical and numerical analysis, we establish a compelling connection between the symmetry of the graph and the trajectories followed by the dynamical processes for those nodes forming symmetry orbits and displaying identical eigenvector centrality. In particular, we are able to show that when the initial transitory states are removed, the symmetric group of nodes respond synchronously; nonetheless, they maintain a constant distance from each other and hence follow splay states. We have verified this phenomenon once more using two distinct kinds of networks. In one instance, every node takes part in nontrivial clusters. In the alternative scenario, we create symmetric orbits as per our target. The cluster nodes show splay states in both situations.

Abstract

Complexity is an important metric for appropriate characterization of different classes of irregular signals, observed in the laboratory or in nature. The literature is already rich in the description of such measures using a variety of entropy and disequilibrium measures, separately or in combination. Chaotic signal was given prime importance in such studies while no such measure was proposed so far, how complex were the extreme events when compared to non-extreme chaos. We address here this question of complexity in extreme events and investigate if we can distinguish them from non-extreme chaotic signal. The normalized Shannon entropy in combination with disequlibrium is used for our study and it is able to distinguish between extreme chaos and non-extreme chaos and moreover, it depicts the transition points from periodic to extremes via Pomeau-Manneville intermittency and, from small amplitude to large amplitude chaos and its transition to extremes via interior crisis. We report a general trend of complexity against a system parameter that increases during a transition to extreme events, reaches a maximum, and then starts decreasing. We employ three models, a nonautonomous Liénard system, 2-dimensional Ikeda map and a 6-dimensional coupled Hindmarh-Rose system to validate our proposition

Abstract

This paper is a short intervention in locating the concerns about labour in the present phase of digital transformation within the longer history of technological change. It engages with some key analytical frameworks in technology studies and development economics to identify the continuities in the present digital revolution with previous moments of equivalent technological change. It also highlights some crucial differences with the current digital turn. Finally, the paper uses available data and information to deliberate on the possible impact of the ongoing digital transformation on the employment question and the nature of work in India.

Abstract

We review the main applications of machine learning models that are not fully supervised in particle physics, i.e., clustering, anomaly detection, detector simulation, and unfolding. Unsupervised methods are ideal for anomaly detection tasks—machine learning models can be trained on background data to identify deviations if we model the background data precisely. The learning can also be partially unsupervised when we can provide some information about the anomalies at the data level. Generative models are useful in speeding up detector simulations—they can mimic the computationally intensive task without large resources. They can also efficiently map detector-level data to parton-level data (i.e., data unfolding). In this review, we focus on interesting ideas and connections and briefly overview the underlying techniques wherever necessary.

Abstract

Remote labs are a groundbreaking development in the education industry, providing students with access to laboratory education anytime, anywhere. However, most remote labs are costly and difficult to scale, especially in developing countries. With this as a motivation, this paper proposes a new remote labs (RLabs) solution that includes two use case experiments: Vanishing Rod and Focal Length. The hardware experiments are built at a low-cost by retrofitting Internet of Things (IoT) components. They are also made portable by designing miniaturised and modular setups. The software architecture designed as part of the solution seamlessly supports the scalability of the experiments, offering compatibility with a wide range of hardware devices and IoT platforms. Additionally, it can live-stream remote experiments without needing dedicated server space for the stream. The software architecture also includes an automation suite that periodically checks the status of the experiments using computer vision (CV). The software architecture is further assessed for its latency and performance. RLabs is qualitatively evaluated against seven non-functional attributes - affordability, portability, scalability, compatibility, maintainability, usability, and universality. Finally, user feedback was collected from a group of students, and the scores indicate a positive response to the students’ learning and the platform’s usability.