Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

In recent year, the Internet of Things (IoT) has drawn significant research attention. IoT is considered as a part of the Internet of the future and will comprise billions of intelligent communicating `things'. The future of the Internet will consist of heterogeneously connected devices that will further extend the borders of the world with physical entities and virtual components. The Internet of Things (IoT) will empower the connected things with new capabilities. In this survey, the definitions, architecture, fundamental technologies, and applications of IoT are systematically reviewed. Firstly, various definitions of IoT are introduced; secondly, emerging techniques for the implementation of IoT are discussed; thirdly, some open issues related to the IoT applications are explored; finally, the major challenges which need addressing by the research community and corresponding potential solutions are investigated.

The internet of things: a survey

Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of radio-frequency identification (RFID), and wireless, mobile, and sensor devices. A wide range of industrial IoT applications have been developed and deployed in recent years. In an effort to understand the development of IoT in industries, this paper reviews the current research of IoT, key enabling technologies, major IoT applications in industries, and identifies research trends and challenges. A main contribution of this review paper is that it summarizes the current state-of-the-art IoT in industries systematically.

Internet of Things in Industries: A Survey

Broadcasting is a common operation in a network to resolve many issues. In a mobile ad hoc network (MANET) in particular, due to host mobility, such operations are expected to be executed more frequently (such as finding a route to a particular host, paging a particular host, and sending an alarm signal). Because radio signals are likely to overlap with others in a geographical area, a straightforward broadcasting by flooding is usually very costly and will result in serious redundancy, contention, and collision, to which we call the broadcast storm problem. In this paper, we identify this problem by showing how serious it is through analyses and simulations. We propose several schemes to reduce redundant rebroadcasts and differentiate timing of rebroadcasts to alleviate this problem. Simulation results are presented, which show different levels of improvement over the basic flooding approach.

/pdf/the-broadcast-storm-problem-in-a-mobile-ad-hoc-network-2ovgqt6pyc.pdf

The broadcast storm problem in a mobile ad hoc network

The term ‘‘Internet-of-Things’’ is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. Internet-of-Things envisions a future in which digital and physical entities can be linked, by means of appropriate information and communication technologies, to enable a whole new class of applications and services. In this article, we present a survey of technologies, applications and research challenges for Internetof-Things.

/pdf/internet-of-things-vision-applications-and-research-4xfstaiv80.pdf

Internet of things: Vision, applications and research challenges

An architectural approach that meets high bandwidth requirements by introducing a communication architecture based on lightpaths, optical transmission paths in the network, is introduced. Since lightpaths form the building block of the proposed architecture, its performance hinges on their efficient establishment and management. It is shown that although the problem of optimally establishing lightpaths is NP-complete, simple heuristics provide near optimal substitutes for several of the basic problems motivated by a lightpath-based architecture. >

Lightpath communications: an approach to high bandwidth optical WAN's

Transmissions scheduling is a key design problem in packet radio networks, relevant to TDMA and CDMA systems. A large number of topology-dependent scheduling algorithms are available, in which changes of topology inevitably require recomputation of transmission schedules. The need for constant adaptation of schedules to mobile topologies entails significant, sometime insurmountable, problems. These are the protocol overhead due to schedule recomputation, performance penalty due to suspension of transmissions during schedule reorganization, exchange of control message and new schedule broadcast. Furthermore, if topology changes faster than the rate at which new schedules can be recomputed and distributed, the network can suffer a catastrophic failure. The authors propose a robust scheduling protocol which is unique in providing a topology transparent solution to scheduled access in multi-hop mobile radio networks. The proposed solution adds the main advantages of random access protocols to scheduled access. Similarly to random access it is robust in the presence of mobile nodes. Unlike random access, however, it does not suffer from inherent instability, and performance deterioration due to packet collisions. Unlike current scheduled access protocols, the transmission schedules of the proposed solution are independent of topology changes, and channel access is inherently fair and traffic adaptive. >

Making transmission schedules immune to topology changes in multi-hop packet radio networks

As the radio spectrum usage paradigm shifting from the traditional command and control allocation scheme to the open spectrum allocation scheme, wireless ad-hoc networks meet new opportunities and challenges. The open spectrum allocation scheme has potential to provide those networks more capacity, and make them more flexible and reliable. However, the freedom brought by the new spectrum allocation scheme introduces spectrum management and network coordination challenges. Moreover, wireless ad-hoc networks usually rely on a common control channel for operation. Such a control channel may, however, not always available in an open spectrum allocation scheme due to the interference and the need for coexistence with primary users of the spectrum. Instead, common channels most likely exist in a local area.In this paper, we propose a cluster-based framework to form a wireless mesh network in the context of open spectrum sharing. Clusters are constructed by neighbor nodes sharing local common channels, and the network is formed by interconnecting the clusters gradually. We identify issues in such a network and provide mechanisms for neighbor discovery, cluster formation, network formation, and network topology management. The unique feature of this network is its ability to intelligently adapt to the network and radio environment change.

CogMesh: A Cluster-Based Cognitive Radio Network

The implementation of optical packet-switched networks requires that the problems of resource contention, signalling and local and global synchronization be resolved. A possible optical solution to resource contention is based on the use of switching matrices suitably connected with optical delay lines. Signalling could be dealt with using subcarrier multiplexing of packet headers. Synchronization could take advantage of clock tone multiplexing techniques, digital processing for ultra-fast clock recovery, and new distributed techniques for global packet-slot alignment. To explore the practical feasibility and effectiveness of these key techniques, a consortium was formed among the University of Massachusetts, Stanford University, and GTE Laboratories. The consortium, funded by ARPA, has three main goals: investigating networking issues involved in optical contention resolution (University of Massachusetts), constructing an experimental contention-resolution optical (CRO) device (GTE Laboratories), and building a packet-switched optical network prototype employing a CRO and novel signaling/synchronization techniques (Stanford University). This paper describes the details of the project and provides an overview of the main results obtained so far.

Imrich Chlamtac

Papers

Internet of things: Vision, applications and research challenges

Lightpath communications: an approach to high bandwidth optical WAN's

Making transmission schedules immune to topology changes in multi-hop packet radio networks

CogMesh: A Cluster-Based Cognitive Radio Network

CORD: contention resolution by delay lines