840 resources related to Machine-to-machine Communications
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2020 IEEE International Conference on Industrial Technology (ICIT)
ICIT focuses on industrial and manufacturing applications of electronics, controls, communications, instrumentation, and computational intelligence.
The scope of this conference will include the following fields of interests: Antenna Systems, Propagation, and RF Design, Signal Transmission and Reception, Spectrum Sharing, Spectrum Management, and Cognitive Radio, Multiple Antenna Systems and Cooperative Communications, Radio Access Technology and Heterogeneous Networks, Green Communications and Networks, IoT, M2M, Sensor Networks, and Ad-Hoc Networking, Wireless Networks: Protocols, Security and Services , Positioning, Navigation and Mobile Satellite System, Unmanned Aerial Vehicle Communications, Vehicular Networks, and Telematics, Electric Vehicles, Vehicular Electronics, and Intelligent Transportation, Future Trends and Emerging Technologies
The 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) aims to provide a forum that brings together International researchers from academia and practitioners in the industry to meet and exchange ideas and recent research work on all aspects of Information and Communication Technologies including Computing, communication, IOT, LiDAR, Image Analysis, wireless communication and other new technologies
Advances in pervasive systems and infrastructures: middleware systems and services; large-scale data management for pervasive computing; clouds, cloudlets, and fog computing; device-to-device coordination; Internet of Thing.Theoretical and analytical models and algorithms: context modeling and reasoning; adaptive and context-aware computing; activity recognition; programing paradigmsDomain-specific challenges and novel applications: pervasive technologies for healthcare, sport, smart homes and buildings, smart manufacturing (Industry 4.0), smart citiesIntersections of pervasive computing and communications with other research areas: social networks; urban/mobile crowd sensing & intelligence; opportunistic networks; big data.New techniques for user-level concerns: participatory and social sensing; trust, security, and privacy; user interfaces, interaction, and persuasionTechnological innovations: architectures, protocols, and technologies for pervasive communications
There have been a lot of trials to apply information and communication technology (ICT) to other industrial sectors such as green convergence, smart screen & appliances, next generation broadcasting & media, mobile convergence networks, and other ICT convergence applications and services, all under the name of "ICT convergence." ICTC is a unique global premier event for researchers, industry professionals, and academics, which aims at interacting with and disseminating information on the latest developments in the emerging industrial convergence centered around the information and communication technologies. More specifically, it will address challenges with realizing ICT convergence over the various industrial sectors, including the infrastructures and applications in wireless & mobile communication, smart devices & consumer appliances, mobile cloud computing, green communication, healthcare and bioinformatics, Internet of Things (IoT), M2M, Security, and intelligent transportation.
No periodicals are currently tagged "Machine-to-machine Communications"
Radio Protocols for LTE and LTE-Advanced, None
Towards 5G: Applications, Requirements and Candidate Technologies, None
Future wireless networks are expected to be highly heterogeneous, with the co‐existence of macrocells and small cells as well as the provisioning for device‐to‐device communication. In such heterogeneous and multi‐tier systems, centralized radio resource allocation and interference management schemes will not be scalable. Therefore, distributed resource allocation schemes will need to be designed. However, designing such distributed schemes is one ...
Internet of Things and Data Analytics Handbook, None
The Internet of Things (IoT) is a natural extension and evolution of Supervisory Control and Data Acquisition (SCADA). Early SCADA systems were deployed to monitor specific production processes, and separate systems were employed to manage assets, coordinate maintenance operations, optimize supply chains and other business operations. Today's enterprises are frequently composed of a patchwork of these legacy information systems. One ...
2015 International Conference on Information and Communication Technology Convergence (ICTC), 2015
Machine-to-Machine (M2M) communication, a promising technology enables ubiquitous connection between massive autonomous devices without human intervention. M2M communication is one of the core technologies supporting data exchange among sensing devices, processing devices, and actuating devices to facilitate various M2M applications (e.g., telematics, smart metering, smart grid, and home/factory/city automation). Machine Type Communication (MTC) is a new service defined by the ...
2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), 2018
We put forward a proposal for a new kind of framework for IOT networks, which can help to quickly setup, build and deploy dedicated IOT networks (we called them "ecosystems") for research or surveillance purposes.The proposal includes three components: the software which implements the framework itself, the machine-to-machine learning (M2M) component of the network, and the customization choice of opting ...
Big Data Panelist - Ritu Chadha: 2016 Technology Time Machine
Brain Panel Introduction - Paul Sadja: 2016 Technology Time Machine
Linear Regression: Intro to Machine Learning Workshop - IEEE Region 4 Presentation
Shannon to Machine Learning: ML & DL for 5G - Erik Stauffer - B5GS 2019
Women Making the Future Panelist - Kathy Herring Hyashi: 2016 Technology Time Machine
Tech Super Stars Panelist - Stuart Elby: 2016 Technology Time Machine
Brain Panelist - Jan Rabaey: 2016 Technology Time Machine
Panel Q&A - Big Data: 2016 Technology Time Machine
Keynote - AT&T's Alicia Abella: 2016 Technology Time Machine
Big Data Panelist - AJ Bubb: 2016 Technology Time Machine
Recap of Day 1 - Roberto Saracco: 2016 Technology Time Machine
Tech Super Stars Panelist - Joe Herkert: 2016 Technology Time Machine
Tech Super Stars Panelist - John Graham: 2016 Technology Time Machine
Brain Panelist - Jack Gallant: 2016 Technology Time Machine
Keynote on Machine Learning: Andrea Goldsmith - B5GS 2019
Ultra Reliable Low Latency Communication for 5G New Radio - Rapeepat Ratasuk - 5G Technologies for Tactical and First Responder Networks 2018
Women Making the Future Panelist - Meredith Perry: 2016 Technology Time Machine
Opening Address - Karen Bartleson: 2016 Technology Time Machine
Prospects and Challenges for GHz to THz Technologies/Architectures for Future Wireless Communications pt.2
Future wireless networks are expected to be highly heterogeneous, with the co‐existence of macrocells and small cells as well as the provisioning for device‐to‐device communication. In such heterogeneous and multi‐tier systems, centralized radio resource allocation and interference management schemes will not be scalable. Therefore, distributed resource allocation schemes will need to be designed. However, designing such distributed schemes is one of the fundamental research challenges for 5G multi‐tier cellular wireless networks. After a brief overview of 5G cellular systems, this chapter highlights three novel approaches that can be used to solve the distributed‐resource allocation problems in future heterogeneous networks. Specifically, we utilize the concepts of stable matching, factor‐graph‐based message passing, and distributed auctions and show their effectiveness in obtaining distributed solutions to the resource allocation problem. To this end, a brief qualitative comparison in terms of performance metrics, such as complexity, convergence, and signaling overhead, is presented.
The Internet of Things (IoT) is a natural extension and evolution of Supervisory Control and Data Acquisition (SCADA). Early SCADA systems were deployed to monitor specific production processes, and separate systems were employed to manage assets, coordinate maintenance operations, optimize supply chains and other business operations. Today's enterprises are frequently composed of a patchwork of these legacy information systems. One of the concepts central to the IoT is Machine to Machine (M2M) communication. It is one of the advances that made the Programmable Logic Controllers (PLC) such a revolutionary invention. By giving machines the ability to make decisions and perform actions based on nothing more than information provided to them by other machines, SCADA technology completely redefined the processes involved in industries like manufacturing, water treatment, oil and gas, and much more. Task automation driven by mechanically autonomous devices has improved the speed, efficiency, and quality of industrial processes.
Machine-to-Machine (M2M) communication, a promising technology enables ubiquitous connection between massive autonomous devices without human intervention. M2M communication is one of the core technologies supporting data exchange among sensing devices, processing devices, and actuating devices to facilitate various M2M applications (e.g., telematics, smart metering, smart grid, and home/factory/city automation). Machine Type Communication (MTC) is a new service defined by the 3GPP to enable M2M devices to communicate with each other over LTE-Advanced cellular networks. For huge numbers of MTC devices, one of the challenging issues is to provide an efficient way for multiple access in the network. In this paper, we propose a novel scheme named Access Time Distribution (ATD) that can distribute access time to reduce a random access overload. The ATD scheme can be an effective solution that can alleviate the overload problem.
We put forward a proposal for a new kind of framework for IOT networks, which can help to quickly setup, build and deploy dedicated IOT networks (we called them "ecosystems") for research or surveillance purposes.The proposal includes three components: the software which implements the framework itself, the machine-to-machine learning (M2M) component of the network, and the customization choice of opting either for a centralized compute & storage workstation or a block-chain powered decentralized server. The paper emphasizes on the possible implementation of a centralized IOT "ecosystem" and leaves the decentralized version for future discussion. The network is adhoc in nature to allow for devices for an easy "connect-&-transmit" process. Given a centralized compute-storage server, a user setups multiple devices like cell phones, camera-microphone enabled smartphones, high quality video recorders, and registers the specific devices to the central server which we call "Control". Once these devices are registered, the Control deploys the built M2M model to learn about the data stream as generated by the registered and now live endpoints of the ecosystem built.The M2M model can be more robust alternative for typical anomaly detection models. In addition to this, the data stream can be studied and analysed for specific patterns during specific periods corresponding to exceptional situations like device malfunctions. We introduce a concept of a trust factor for each of the live devices in ecosystem. For a device to have a low degree of trust, the model will be sensitive to anomalies in data stream from that device. For devices with high trust, the model will assume a stance of variable doubt based on its data stream.
We present an asynchronous (QDI) FFT design for low-power M2M communication. The design achieves low power by having efficient memory controls, twiddle multiplication, and allowing all subsystems in this nested butterfly architecture to run only as fast as they need to run. For a 10MHz input data rate, our 128-point, 16-bit, radix-23 FFT design consumes only 5.9nJ of energy at Vdd=1V in a 65nm technology.
A significant amount of research has been conducted on adapting 3GPP Long Term Evolution (LTE) and LTE-Advanced (LTE-A) random access to be more efficient for machine-to-machine (M2M) devices because of the huge number of such devices that may reside in each LTE/LTE-A cell. However, there are other attributes of M2M applications that can be used as the basis of independent efficiency improvements. One characteristic which has been overlooked thus far is the spatial and temporal correlations that often exist in the activity of neighboring M2M devices belonging to the same M2M application. In this paper, we illustrate how these correlations can be exploited by coordinating the preambles to be used by neighboring M2M devices to reduce the number of collisions during LTE-A random access, particularly in wireless sensor network (WSN) type applications. The technique is referred to as proximity coordinated random access (PCRA). Through simulation of an example local preamble coordination algorithm that can be executed autonomously by randomly deployed devices of the same M2M application, we demonstrate an increase in the efficiency of the random access process.
This paper proposes a channel state information (CSI) weighting combining scheme for a successive interference canceller (SIC). In general, SIC performance is degraded by the estimation errors in CSI caused by interference and temporal channel variations. This paper introduces a scheme to appropriately weight and combine pre-CSI, defined as CSI estimated for transmitting power control (TPC), and post-CSI, defined as CSI estimated in the SIC process. The combined CSI is more robust against interference and temporal channel variation because pre-CSI and post-CSI resist the former and the latter, respectively. Simulation results show that the combined CSI improves the number of simultaneous transmitting terminals (directly related to interference) 17% (vs. post-CSI) and 5% (vs. pre-CSI), and the allowable environmental speed (directly related to temporal channel variation) 50% (vs. pre-CSI).
How to enhance the Machine-to-Machine (M2M) communication with improved software services becomes an essential issue in the future fifth generation (5G) systems. Considering the enormous number of connected devices located in a specific direction relative to the base station, Millimeter-Wave (mmWave) communication with the beamforming technology plays a crucial role in meeting the high data transmission requirements of the devices. To achieve the requirements, it is necessary for the devices to integrate the Discontinuous Reception (DRX) mechanism with the mmWave communication. We propose a Beam- Aware DRX mechanism that is adaptive to a periodic beam pattern in mmWave communication. Compared with the original LTE DRX mechanism in mmWave communication.
Billions of machine-to-machine (M2M) devices are expected to enable various Internet of Things (IoT) applications, and a major portion of them is through wireless connections. Research activities are accelerating in wireless technologies on both ISM and cellular bands. However, supports from regulators on the TV white space (TVWS) spectrum for license exempt use could potentially unlash the M2M uptake for further development of various IoT applications. The nature of UHF frequencies and wide band TVWS operations pose significant challenges for conventional antennas integrating to compact sensor nodes for low power long range M2M communications. This paper presents a framework of sensor node enabled by a miniaturised planar antenna at TWVS for IoT applications such as smart meters and smart car parks.
No standards are currently tagged "Machine-to-machine Communications"