Month: March 2017

Modeling the internet of things: a hybrid modeling approach using complex networks and agent-based models

Sensors, coupled with transceivers, have quickly evolved from technologies purely confined to laboratory test beds to workable solutions used across the globe. These mobile and connected devices form the nuts and bolts required to fulfill the vision of the so-called internet of things (IoT). This idea has evolved as a result of proliferation of electronic gadgets fitted with sensors and often being uniquely identifiable (possible with technological solutions such as the use of Radio Frequency Identifiers). While there is a growing need for comprehensive modeling paradigms as well as example case studies for the IoT, currently there is no standard methodology available for modeling such real-world complex IoT-based scenarios. Here, using a combination of complex networks-based and agent-based modeling approaches, ​we present a novel approach to modeling the IoT. Specifically, the proposed approach uses the Cognitive Agent-Based Computing (CABC) framework to simulate complex IoT networks. We demonstrate modeling of several standard complex network topologies such as lattice, random, small-world, and scale-free networks. To further demonstrate the effectiveness of the proposed approach, we also present a case study and a novel algorithm for autonomous monitoring of power consumption in networked IoT devices. We also discuss and compare the presented approach with previous approaches to modeling. Extensive simulation experiments using several network configurations demonstrate the effectiveness and viability of the proposed approach.


Modeling the internet of things: a hybrid modeling approach using complex networks and agent-based models
Komal Batool and Muaz A. Niazi
Complex Adaptive Systems Modeling 2017 5:4
DOI: 10.1186/s40294-017-0043-1


Network theory may explain the vulnerability of medieval human settlements to the Black Death pandemic

Epidemics can spread across large regions becoming pandemics by flowing along transportation and social networks. Two network attributes, transitivity (when a node is connected to two other nodes that are also directly connected between them) and centrality (the number and intensity of connections with the other nodes in the network), are widely associated with the dynamics of transmission of pathogens. Here we investigate how network centrality and transitivity influence vulnerability to diseases of human populations by examining one of the most devastating pandemic in human history, the fourteenth century plague pandemic called Black Death. We found that, after controlling for the city spatial location and the disease arrival time, cities with higher values of both centrality and transitivity were more severely affected by the plague. A simulation study indicates that this association was due to central cities with high transitivity undergo more exogenous re-infections. Our study provides an easy method to identify hotspots in epidemic networks. Focusing our effort in those vulnerable nodes may save time and resources by improving our ability of controlling deadly epidemics.


Network theory may explain the vulnerability of medieval human settlements to the Black Death pandemic
José M. Gómez & Miguel Verdú
Scientific Reports 7, Article number: 43467 (2017)


Levitation of heavy particles against gravity in asymptotically downward flows

In the fluid transport of particles, it is generally expected that heavy particles carried by a laminar fluid flow moving downward will also move downward.  We establish a theory to show, however, that particles can be dynamically levitated and lifted by such flows, thereby moving against the flow and against gravity, even when they are orders of magnitude denser than the fluid. We suggest that this counterintuitive effect has potential implications for the air-transport of water droplets and the lifting of sediments in water.


Levitation of heavy particles against gravity and against the flow
Jean-Regis Angilella, Daniel J. Case, Adilson E. Motter
Chaos 27, 031103 (2017)