Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Adaptive Data Aggregation with Mobile Agents and Evolutionary Computing based Clustering in Sparse Wireless Sensor Networks

The Information processing based on Data mining in WSN is at its starting stage, when compared to traditional machine learning in WSN. In order to solve a particular problem in WSN the researchers now a day were mainly focused on applying machine learning techniques. The Different researchers will have different assumptions, application scenarios and preferences in applying machine learning algorithms. These differences will result to a major challenge in allowing researchers to build upon each other’s work so that research results will accumulate in the community. Thus, a common architecture across the WSN machine learning community would be necessary in order to overcome these differences. The improvement or optimizing of the performance of the entire network in terms of energy conservation and network lifetime will be one of the major objectives in wireless sensor network. This paper will survey the Data Mining in WSN applications from two perspectives, namely the network associated issue and application associated issue. In the network associated issue, different machine learning algorithms applied in WSNs were used in order to improve network performance will be discussed. In application associated issue, machine learning methods that have been used for information processing in WSNs will be summarized

A Very Brief Introduction to Machine Learning With Applications to Communication Systems

Given the unprecedented availability of data and computing resources, there is widespread renewed interest in applying data-driven machine learning methods to problems for which the development of conventional engineering solutions is challenged by modelling or algorithmic deficiencies. This tutorial-style paper starts by addressing the questions of why and when such techniques can be useful. It then provides a high-level introduction to the basics of supervised and unsupervised learning. For both supervised and unsupervised learning, exemplifying applications to communication networks are discussed by distinguishing tasks carried out at the edge and at the cloud segments of the network at different layers of the protocol stack

Predilection Perspective of Peremptory Evaluation of Wireless Sensor Networks with Machine Learning Approach

Data mining based information processing in Wireless Sensor Network WSN is at its preliminary stage as compared to traditional machine learning and WSN Currently researches mainly focus on applying machine learning techniques to solve a particular problem in WSN Different researchers will have different assumptions application scenarios and preferences in applying machine learning algorithms These differences represent a major challenge in allowing researchers to build upon each other s work so that research results will accumulate in the community Thus a common architecture across the WSN machine learning community would be necessary One of the major objectives of many WSN research works is to improve or optimize the performance of the entire network in terms of energy conservation and network lifetime This paper will survey Data Mining in WSN application from two perspectives namely the Network associated issue and Application associated issue In the Network associated issue different machine learning algorithms applied in WSNs to enhance network performance will be discussed In Application associated issue machine learning methods that have been used for information processing in WSNs will be summarize

EDGE INTELLIGENCE-BASED SELF-HEALING NETWORK FOR LARGE-SCALE LOW POWER AND LOSSY NETWORK DEVICES WITH IOC

Techniques are presented herein that support the derivation, leveraging machine learning (ML) algorithms, of a cross correlation index of Key Performance Indicators (KPIs) – specific to wireless technologies such as, for example, Internet Protocol version 6 (IPv6) over Networks of Resource-constrained Nodes (6Lo), Wi-Fi, and Institute of Electrical and Electronics Engineers (IEEE) technical standard 802.15.4 variants – across multiple tenants within a cloud native multi-tenanted environment to proactively optimize routing performance and identify performance optimizations so that the network may be automatically self-healed. Aspects of the presented techniques provide intelligent fault management capabilities for a customer (having, for example, outdoor large-scale wireless sensor network (WSN) devices) thus reducing the difficulty and the cost of deployed device maintenance, support the generation of a working scheme for the field staff to repair faulty devices, provide a Vulnerability Scan Service (VSS) for connected mesh endpoints, and support the use of multi-tenant functionality for each vendor