Biblio

Internet of Things (IoT) is to connect objects of different application fields, functionality and technology. These objects are entirely addressable and use standard communication protocol. Intelligent agents are used to integrate Internet of Things with heterogeneous low-power embedded resource-constrained networked devices. This paper discusses with the implemented real world scenario of smart autonomous patient management with the assistance of semantic technology in IoT. It uses the Smart Semantic framework using domain ontologies to encapsulate the processed information from sensor networks. This embedded Agent based Semantic Internet of Things in healthcare (ASIOTH) system is having semantic logic and semantic value based Information to make the system as smart and intelligent. This paper aims at explaining in detail the technology drivers behind the IoT and health care with the information on data modeling, data mapping of existing IoT data into different other associated system data, workflow or the process flow behind the technical operations of the remote device coordination, the architecture of network, middleware, databases, application services. The challenges and the associated solution in this field are discussed with the use case.

Social animals as found in fish schools, bird flocks, bee hives, and ant colonies are able to solve highly complex problems in nature. This includes foraging for food, constructing astonishingly complex nests, and evading or defending against predators. Remarkably, these animals in many cases use very simple, decentralized communication mechanisms that do not require a single leader. This makes the animals perform surprisingly well, even in dynamically changing environments. The collective intelligence of such animals is known as swarm intelligence and it has inspired popular and very powerful optimization paradigms, including ant colony optimization (ACO) and particle swarm optimization (PSO). The reasons behind their success are often elusive. We are just beginning to understand when and why swarm intelligence algorithms perform well, and how to use swarm intelligence most effectively. Understanding the fundamental working principles that determine their efficiency is a major challenge. This tutorial will give a comprehensive overview of recent theoretical results on swarm intelligence algorithms, with an emphasis on their efficiency (runtime/computational complexity). In particular, the tutorial will show how techniques for the analysis of evolutionary algorithms can be used to analyze swarm intelligence algorithms and how the performance of swarm intelligence algorithms compares to that of evolutionary algorithms. The results shed light on the working principles of swarm intelligence algorithms, identify the impact of parameters and other design choices on performance, and thus help to use swarm intelligence more effectively. The tutorial will be divided into a first, larger part on ACO and a second, smaller part on PSO. For ACO we will consider simple variants of the MAX-MIN ant system. Investigations of example functions in pseudo-Boolean optimization demonstrate that the choices of the pheromone update strategy and the evaporation rate have a drastic impact on the running time. We further consider the performance of ACO on illustrative problems from combinatorial optimization: constructing minimum spanning trees, solving shortest path problems with and without noise, and finding short tours for the TSP. For particle swarm optimization, the tutorial will cover results on PSO for pseudo-Boolean optimization as well as a discussion of theoretical results in continuous spaces.

Effective traffic light control algorithms are of central importance for reducing congestion. While the currently most effective algorithms rely on expensive infrastructure to obtain knowledge of the traffic state, within the COLOMBO project, low-cost adaptive traffic light controllers have been examined that rely on swarm intelligence principles and the exploitation of V2X data. The swarm-based traffic light controller exploits numerical values that are adapted by the principles of stigmergy and used to switch between lower-level traffic light control strategies. This algorithm has more than 100 parameters that determine its behavior. In our work, we have explored the automatic configuration of this traffic light controller. In fact, the possibility of automatically configuring the parameters of the swarm-based traffic light control algorithm in this case is instrumental for the development of such a method and the high performance reached by it.

Text mining has developed and emerged as an essential tool for revealing the hidden value in the data. Text mining is an emerging technique for companies around the world and suitable for large enduring analyses and discrete investigations. Since there is a need to track disrupting technologies, explore internal knowledge bases or review enormous data sets. Most of the information produced due to conversation transcripts is an unstructured format. These data have ambiguity, redundancy, duplications, typological errors and many more. The processing and analysis of these unstructured data are difficult task. But, there are several techniques in text mining are available to extract keywords from these unstructured conversation transcripts. Keyword Extraction is the process of examining the most significant word in the context which helps to take decisions in a much faster manner. The main objective of the proposed work is extracting the keywords from meeting transcripts by using the Swarm Intelligence (SI) techniques. Here Stochastic Diffusion Search (SDS) algorithm is used for keyword extraction and Firefly algorithm used for clustering. These techniques will be implemented for an extensive range of optimization problems and produced better results when compared with existing technique.

Many common cyberdefenses (like firewalls and intrusion-detection systems) are static, giving attackers the freedom to probe them at will. Moving-target defense (MTD) adds dynamism, putting the systems to be defended in motion, potentially at great cost to the defender. An alternative approach is a mobile resilient defense that removes attackers' ability to rely on prior experience without requiring motion in the protected infrastructure. The defensive technology absorbs most of the cost of motion, is resilient to attack, and is unpredictable to attackers. The authors' mobile resilient defense, Ant-Based Cyber Defense (ABCD), is a set of roaming, bio-inspired, digital-ant agents working with stationary agents in a hierarchy headed by a human supervisor. ABCD provides a resilient, extensible, and flexible defense that can scale to large, multi-enterprise infrastructures such as the smart electric grid.