Living systems are associated with Social networks — networks made up of nodes, some of which may be more important in various aspects as compared to others. While different quantitative measures labeled as “centralities” have previously been used in the network analysis community to find out influential nodes in a network, it is debatable how valid the centrality measures actually are. In other words, the research question that remains unanswered is: how exactly do these measures perform in the real world? So, as an example, if a centrality of a particular node identifies it to be important, is the node actually important? The goal of this paper is not just to perform a traditional social network analysis but rather to evaluate different centrality measures by conducting an empirical study analyzing exactly how do network centralities correlate with data from published multidisciplinary network data sets. We take standard published network data sets while using a random network to establish a baseline. These data sets included the Zachary's Karate Club network, dolphin social network and a neural network of nematode Caenorhabditis elegans. Each of the data sets was analyzed in terms of different centrality measures and compared with existing knowledge from associated published articles to review the role of each centrality measure in the determination of influential nodes. Our empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify important nodes.

Agent-based modeling and simulation tools provide a mature platform for development of complex simulations. They however, have not been applied much in the domain of mainstream modeling and simulation of computer networks. In this article, we evaluate how and if these tools can offer any value-addition in the modeling & simulation of complex networks such as pervasive computing, large-scale peer-to-peer systems, and networks involving considerable environment and human/animal/habitat interaction. Specifically, we demonstrate the effectiveness of NetLogo - a tool that has been widely used in the area of agent-based social simulation.

Purpose: Cloud computing systems represent one of the most complex computing systems currently in existence. Current applications of Cloud involve extensive use of distributed systems with varying degree of connectivity and usage. With a recent focus on large-scale proliferation of Cloud computing, identity management in Cloud based systems is a critical issue for the sustainability of any Cloud-based service. This area has also received considerable attention from the research community as well as the IT industry. Numerous Cloud Identity Management Systems (IDMSs) have been proposed so far; however, most of those systems are neither widely accepted nor considered highly reliable due to their constraints in terms of scope, applicability and security. In order to achieve reliability and effectiveness in IDMs for Cloud, further extensive research needs to be carried out to critically examine Cloud based IDMSs and their level of security. Methods: In this work, we have holistically analyzed Cloud IDMSs to better understand the general as well as the security aspects of this domain. From the security perspective, we present a comprehensive list of attacks that occur frequently in Cloud based IDMSs. In order to alleviate those attacks, we present a well-organized taxonomy tree covering the most desired features essential for any Cloud-based IDMSs. Additionally, we have specified various mechanisms of realization (such as access control polices, encryption, self-service) against each of the features of Cloud IDMSs. We have further used the proposed taxonomy as an assessment criterion for the evaluation of Cloud based IDMSs. Results: Our in-depth analysis of various Cloud based IDMSs reveals that most of the systems do not offer support to all the essential features of Cloud IDMS and the ones that do, have their own certain weaknesses. None of the discussed techniques heuristically covers all the security features; moreover, they lack compliance to international standards which, understandably, undermines their credibility. Conclusion: Presented work will help Cloud subscribers and providers in understanding the available solutions as well as the involved risks, allowing them to make more knowledgeable decisions while selecting potential Cloud IDMSs that best suits their functional and security requirements.

In this paper, we present a novel formal agent-based simulation framework (FABS). FABS uses formal specification as a means of clear description of wireless sensor networks (WSNs) sensing a complex adaptive environment. This specification model is then used to develop an agent-based model of both the WSN as well as the environment. As proof of concept, we demonstrate the application of FABS to a boids model of self-organized flocking of animals monitored by a random deployment of proximity sensors.

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.