Special issue on distributed computing and artificial intelligence

4:1! Google’s artificial intelligence (AI) program, AlphaGo, has won Go Master Lee Sedol in a best-of-five competition held in Korean March 9−15, 2016. Seen by many as a landmark moment for AI, the outcome did not come as a surprise, considering the excellent combination of 1920 CPUs with sophisticated AI algorithms, including neural networks and Monte Carlo tree search (Gibney, 2016; Silver et al., 2016). Indeed, research on distributed computing and artificial intelligence (DCAI) has matured during the last decade and many effective applications are now deployed, performing an increasingly important role in modern computer science, including the two most hyped technologies: Internet of Things and Big Data. Indeed, it is fair to say that the application of artificial intelligence in distributed environments is becoming an essential element of high added value and economic potential

Special Issue on International Journal of Imaging and Robotics

This paper presents a brief summary of the post-proceedings of the International Symposium on Distributed Computing and Artificial Intelligence (DCAI 2014) and the Workshop on Intelligent Systems for Context-based Information Fusion (ISCIF) held in Salamanca in June from 4th to 6th, 2014. This special issue presents a selection of the best papers selected from those that were accepted on the symposium focused on image processing and robotics

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

From the Queue to the Quality of Service Policy: A Middleware Implementation

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-02481-8_61Quality of service policies in communications is one of the current trends in distributed systems based on middleware technology. To implement the QoS policies it is necessary to define some common parameters. The aim of the QoS policies is to optimize the user defined QoS parameters. This article describes how to obtain the common QoS parameters using message queues for the communications and control components of communication. The paper introduces the Queue-based Quality of Service Cycle concept for each middleware component. The QoS parameters are obtained directly from the queue parameters, and Quality of Service Policies controls directly the message queues to obtain the user-defined parameters values.The middleware architecture described in this article is a part of the coordinated project SIDIRELI: Distributed Systems with Limited Resources. Control Kernel and Coordination. Education and Science Department, Spanish Government. CICYT: MICINN: DPI2008-06737-C02-01/02.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2009). From the Queue to the Quality of Service Policy: A Middleware Implementation. En Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. Springer Verlag (Germany). 432-437. doi:10.1007/978-3-642-02481-8_61S432437Aurrecoechea, C., Campbell, A.T., Hauw, L.: A Survey of QoS Architectures. Multimedia Systems Journal, Special Issue on QoS Architecture 6(3), 138–151 (1998)OMG. Data Distribution Service for Real-Time Systems, v1.1. Document formal/2005-12-04 (December 2005)Botts, M., Percivall, G., Reed, C., Davidson, J.: OGC®. Sensor Web Enablement: Overview And High Level Architecture, OpenGIS Consortium Inc (2006)Poza, J.L., Posadas, J.I., Simó, J.E.: QoS-based middleware architecture for distributed control systems. In: International Symposium on Distributed Computing and Artificial Intelligence, Salamanca (2008)Vogel, A., Kerherve, B., von Bochmann, G., Gecsei, J.: Distributed Multi-media and QoS: A Survey 2(2), 10–19 (1995)Crawley, E., Nair, R., Rajagopalan, B.: RFC 2386: A Framework for QoS-based Routing in the Internet, pp. 1–37, XP002219363 (August 1998)ITU-T Recommendation E.800 (0894). Terms and Definitions Related to Quality of Service and Network Performance Including Dependability (1994)Stuck, B.W., Arthurs, E.: A Computer & Communications Network Performance Analysis Primer. Prentice Hall, Englewood Cliffs (1984)Jain, R.: The art of Computer Systems Performance Analysis. John Wiley & Sons Inc., New york (1991)Coulouris, G., Dollimore, J., Kindberg, T.: Distributed Systems. Concepts and Design, 3rd edn. Addison Wesley, Madrid (2001)Jung, J.-l.: Quality of Service in Telecommunications Part II: Translation of QoS Pa-rameters into ATM Performance Parameters in B-ISDN. IEEE Comm. Mag., pp. 112–117 (August 1996)Wohlstadter, E., Tai, S., Mikalsen, T., Rouvellou, I., Devanbu, P.: GlueQoS: Middleware to Sweeten Quality-of-Service Policy Interactions. In: ICSE, 26th International Conference on Software Engineering (ICSE 2004) (2004

Editor’s Note

As the Internet of Things (IoT) further develops and expands to the Internet of Everything (IoE), high-speed multimedia streaming data processing, analysis, and shorter response times are increasingly becoming the demands of today. Driven by the Internet of Things (IoT), a new computing paradigm, Edge computing, is currently developing rapidly. Compared with traditional centralized generalpurpose computing, Edge computing is a distributed architecture. The operations of applications, data and services are moved from the central node of the network to the edge nodes on the network logic for processing. Under this structure, the analysis of data and the generation of knowledge are closer to the source of the data, so it is more suitable for processing. However, with the rapid development of 5G, IoT and other services and scenarios, there are more and more intelligent terminal devices. Multimedia streaming processing in IoT becomes a very prominent problem. To overcome this problem, the adoption of intelligent Edge or Artificial Intelligence (AI) powered Edge computing (Edge-AI) can achieve the goals of lower cost, higher security, lower latency, and ease of management. Recently, many network modeling methods, computing algorithms, and signal processing technologies have been successfully developed and applied to multimedia streaming processing in IoT with Edge Intelligence. A total of 13 papers are presented in this special issue for the purpose of collecting the latest developments and results on this research topic. We divide them into three categories: production and life applications, security, and text and image processing

Multi-agent simulations for emergency situations in an airport scenario

This paper presents a multi-agent framework using Net- Logo to simulate humanand collective behaviors during emergency evacuations. Emergency situationappears when an unexpected event occurs. In indoor emergency situation, evacuation plans defined by facility manager explain procedure and safety ways tofollow in an emergency situation. A critical and public scenario is an airportwhere there is an everyday transit of thousands of people. In this scenario theimportance is related with incidents statistics regarding overcrowding andcrushing in public buildings. Simulation has the objective of evaluating buildinglayouts considering several possible configurations. Agents could be based onreactive behavior like avoid danger or follow other agent, or in deliberative behaviorbased on BDI model. This tool provides decision support in a real emergencyscenario like an airport, analyzing alternative solutions to the evacuationprocess.Publicad

Fog-enabled Edge Learning for Cognitive Content-Centric Networking in 5G

By caching content at network edges close to the users, the content-centric networking (CCN) has been considered to enforce efficient content retrieval and distribution in the fifth generation (5G) networks. Due to the volume, velocity, and variety of data generated by various 5G users, an urgent and strategic issue is how to elevate the cognitive ability of the CCN to realize context-awareness, timely response, and traffic offloading for 5G applications. In this article, we envision that the fundamental work of designing a cognitive CCN (C-CCN) for the upcoming 5G is exploiting the fog computing to associatively learn and control the states of edge devices (such as phones, vehicles, and base stations) and in-network resources (computing, networking, and caching). Moreover, we propose a fog-enabled edge learning (FEL) framework for C-CCN in 5G, which can aggregate the idle computing resources of the neighbouring edge devices into virtual fogs to afford the heavy delay-sensitive learning tasks. By leveraging artificial intelligence (AI) to jointly processing sensed environmental data, dealing with the massive content statistics, and enforcing the mobility control at network edges, the FEL makes it possible for mobile users to cognitively share their data over the C-CCN in 5G. To validate the feasibility of proposed framework, we design two FEL-advanced cognitive services for C-CCN in 5G: 1) personalized network acceleration, 2) enhanced mobility management. Simultaneously, we present the simulations to show the FEL's efficiency on serving for the mobile users' delay-sensitive content retrieval and distribution in 5G.Comment: Submitted to IEEE Communications Magzine, under review, Feb. 09, 201