Ubiquitous Networks: A Need of Future World of Things

With an exponential increase in the number of applications and user demand, it is essential to respond to the query of users with fast services and networks used. This is possible only by the use of ubiquitous networks supporting mass media communications. The integration of advanced technologies such as Communication, Navigation and Sensing Services (CONASENSE) and Human Bond Communications (HBC) takes care of sensing, services, data, speed, cooperation, content, and cost of communication. The combination of Data, Technology, and Media used for intelligent computation and communication over the internet could serve the purpose, and that’s the urgent demand of growing networks marching towards a fusion of IoT and 5G leading to 6G. IoT with 5G will be the backbone of networks in the future generation network, adding the concept of virtualization at Anytime, Anywhere, Anything, and Anybody. The definition of ubiquitous technology considers it networked, wireless and mobile, to connect a more significant number of users and the world around them. The ubiquitous network connects the D2D, M2M, D2M and uses the ICT and Cloud-based technology to mitigate the QoS parameters. The paper’s primary contribution is the proposal of 6G enabling technologies and use cases to demonstrate the need and integration of various prime techniques as IoT++5G++Cloud++AI/ML. The technology road map and proposed C6-WISDOM model illustrate the fundamentals of enabling future ubiquitous networks (6G). It also focuses on the critical requirements of 6G technology in support of ubiquitous networks and identifies the present technologies integrated to provide vertical sustainable wireless networking solutions.

Semigroups for flows on limits of graphs

We use a version of the Trotter-Kato approximation theorem for strongly continuous semigroups in order to study ows on growing networks. For that reason we use the abstract notion of direct limits in the sense of category theory

Temporal Behavior of Local Characteristics in Complex Networks with Preferential Attachment-Based Growth

The study of temporal behavior of local characteristics in complex growing networks makes it possible to more accurately understand the processes caused by the development of interconnections and links between parts of the complex system that occur as a result of its growth. The spatial position of an element of the system, determined on the basis of connections with its other elements, is constantly changing as the result of these dynamic processes. In this paper, we examine two non-stationary Markov stochastic processes related to the evolution of Barabási–Albert networks: the first describes the dynamics of the degree of a fixed node in the network, and the second is related to the dynamics of the total degree of its neighbors. We evaluate the temporal behavior of some characteristics of the distributions of these two random variables, which are associated with higher-order moments, including their variation, skewness, and kurtosis. The analysis shows that both distributions have a variation coefficient close to 1, positive skewness, and a kurtosis greater than 3. This means that both distributions have huge standard deviations that are of the same order of magnitude as the expected values. Moreover, they are asymmetric with fat right-hand tails.

Discovering and Implementing Self-Sustaining Networks of Cooperation with General Collective Intelligence

Leveraging General Collective Intelligence or GCI, a platform with the potential to achieve an exponential increase in general problem-solving ability, a methodology is defined for finding potential opportunities for cooperation, as well as for negotiating and launching cooperation. This paper explores the mechanisms by which GCI enables networks of cooperation to be formed in order to increase outcomes of cooperation and in order to make that cooperation self-sustaining. And this paper explores why implementing a GCI for the first time requires designing an iterative process that self-assembles continually growing networks of cooperation.

The network structure of scientific revolutions

Philosophers of science have long postulated how collective scientific knowledge grows. Empirical validation has been challenging due to limitations in collecting and systematizing large historical records. Here, we capitalize on the largest online encyclopedia to formulate knowledge as growing networks of articles and their hyperlinked inter-relations. We demonstrate that concept networks grow not by expanding from their core but rather by creating and filling knowledge gaps, a process which produces discoveries that are more frequently awarded Nobel prizes than others. Moreover, we operationalize paradigms as network modules to reveal a temporal signature in structural stability across scientific subjects. In a network formulation of scientific discovery, data-driven conditions underlying breakthroughs depend just as much on identifying uncharted gaps as on advancing solutions within scientific communities.

A Survey on the Security of Low Power Wide Area Networks: Threats, Challenges, and Potential Solutions

Low power wide area network (LPWAN) is among the fastest growing networks in Internet of Things (IoT) technologies. Owing to varieties of outstanding features which include long range communication and low power consumption, LPWANs are fast becoming the most widely deployed connectivity standards in IoT domain. However, this promising network are exposed to various security and privacy threats and challenges. For reliable connectivity within networks, the security and privacy challenges need to be effectively addressed with proper mitigation protocol in place. In this paper, a comprehensive review on the security feature of LPWAN is presented. The paper mainly focuses on analyzing LPWAN’s key cybersecurity architecture and it present a significant emphasis on how the LPWAN is highly attractive to intruders and attackers. This paper aims at summarizing recent research works on key LPWAN security challenges such as replay attack, denial-of-service attack, worm hole attack, and eavesdropping attack, the effect of the attacks, and most importantly the various approaches proposed in the literature for the attacks’ mitigation. The paper concludes by highlighting major research gaps and future directions for the successful deployment of LPWAN.

A continuum model for the growth of dendritic actin networks

Polymerization of dendritic actin networks underlies important mechanical processes in cell biology such as the protrusion of lamellipodia, propulsion of growth cones in dendrites of neurons, intracellular transport of organelles and pathogens, among others. The forces required for these mechanical functions have been deduced from mechano-chemical models of actin polymerization; most models are focused on single growing filaments, and only a few address polymerization of filament networks through simulations. Here, we propose a continuum model of surface growth and filament nucleation to describe polymerization of dendritic actin networks. The model describes growth and elasticity in terms of macroscopic stresses, strains and filament density rather than focusing on individual filaments. The microscopic processes underlying polymerization are subsumed into kinetic laws characterizing the change of filament density and the propagation of growing surfaces. This continuum model can predict the evolution of actin networks in disparate experiments. A key conclusion of the analysis is that existing laws relating force to polymerization speed of single filaments cannot predict the response of growing networks. Therefore, a new kinetic law, consistent with the dissipation inequality, is proposed to capture the evolution of dendritic actin networks under different loading conditions. This model may be extended to other settings involving a more complex interplay between mechanical stresses and polymerization kinetics, such as the growth of networks of microtubules, collagen filaments, intermediate filaments and carbon nanotubes.