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市場調查報告書

物聯網 (IoT) :2017-2027年

Internet of Things (IoT) 2017-2027

出版商 IDTechEx Ltd. 商品編碼 300642
出版日期 內容資訊 英文 148 Slides
商品交期: 最快1-2個工作天內
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物聯網 (IoT) :2017-2027年 Internet of Things (IoT) 2017-2027
出版日期: 2016年11月03日 內容資訊: 英文 148 Slides
簡介

本報告提供物聯網 (IoT)的展望調查,IoT的用途範例,成長推動因素、阻礙要素分析,MCU技術,相關技術趨勢,硬體設備企業的配合措施,主要的市場趨勢,各種市場預測等彙整資料。

第1章 摘要整理、總論

  • 定義、範圍
  • IoT基礎設施範例
  • IoT和IoP比較
  • 潛在性用途 範例
  • IoT的價值鏈和bias vs IoP
  • IoT的市場機會與供應商範例
  • 期待與空論
  • 基於大的願景
  • 妥協新的課題
  • 大趨勢與IoT
  • IoT的障礙
  • 系統及軟體的課題
  • 硬體設備
  • IoT開發的投資:變化與預測
  • 大量的產業規格
  • 市場預測
    • 出貨數
    • 單位價格
    • 出貨收益
    • IoT系統
    • 相關市場預測、資料
    • EV、48v輕度混合動力車
    • IDTechEx EV、48v輕度混合動力車
    • 公路式層級4/5自動駕駛車
    • 10年預測:農業用機器人、無人機
    • IoT穿戴式設備市場:醫療用

第2章 簡介

  • 所謂IoT
  • 用途範例:穿戴式IoT
  • IoT的目標
  • 對IoT的關注的理由
  • 障礙
  • 異議、不確定性

第3章 核心微控制器單位MCU技術

  • 製造
  • 消費電力最佳化
  • 低功率備用電池
  • MCU架構
  • MCU零組件:記憶體
  • MCU零組件:IO
  • MCU協處理器:DSP
  • MCU協處理器:FPGA
  • MCU協處理器:PLD、CPLD
  • MCU軟體:OS
  • MCU軟體:編程語言
  • 案例研究:Texas Instruments MSP430G2333

第4章 鄰接技術

  • 感測器
    • IMU
    • GPS
    • 深度相機
  • 通訊

第5章 硬體設備企業

  • Renesas Electronics
  • NXP+Freescale
  • Microchip+Atmel
  • Atmel
  • ST Microelectronics
  • Infineon Technologies
  • Texas Instruments (TI)
  • Cypress/Spansion
  • Samsung
  • Intel
  • Digispark
  • Arduino/Genuino
  • Apple
  • Google
  • Amazon
  • Raspberry Pi Foundation
  • Beagleboard等

第6章 各種趨勢

  • 基準與未來的IoT
  • WAN的選項:LoRaWAN、LoRa Alliance
  • eRIC
  • MCU架構趨勢:ARM
  • 開放原始碼硬體設備&系統
  • 摩爾定律
  • 價格的水平化
  • 其他趨勢

本網頁內容可能與最新版本有所差異。詳細情況請與我們聯繫。

目錄

Researched in late 2016 with ongoing updates, this unique report on the Internet of Things IoT has over 140 data filled pages including over 150 images. It is intended to assist investors, participants and intending participants in the value chain including developers and academics, interested government officials and users seeking the truth based on new investigation. The focus is on identifying genuine capabilities and needs from a commercial point of view.

The pages are mostly in the form of easily assimilated infograms, roadmaps and forecasts. The report is about nodes that sense, learn, gather data and initiate reports and action using IP addressed sensor nodes to process and send information. It is realistic and analytical not evangelical. We do not repeat the mantra about tens of billions of nodes being deployed in only a few years. The many analysts sticking to such euphoria ignore the fact that, contrary to their expectation, very little IoT was deployed in 2016. They are "bubble pushing" with their forecasts, predicting ever steeper takeoff, now a physical impossibility.

However, our ongoing global travel, interviews, conferences and research by our multi-lingual PhD level analysts located across the world does lead us to believe that a large market will eventually emerge but not primarily for nodes, where our price sensitivity analysis and experimentation shows commoditisation rapidly arriving. Indeed, as Cisco correctly notes, it is a pre-requisite for success. The money will lie in the systems, software and support examined in this study, though we also look closely at node design to reveal all the impediments to progress as well as the things coming right and the potential for enhanced functionality and payback. For example, the ongoing major breaches of internet security with small connected devices sit awkwardly with system and software manufacturers' claims year after year that they have cracked the problem.

The most primitive IoT nodes have an actuator and no sensor as with connected Raspberry Pi single board computers retrofitted to air conditioning for remote operation. We have talked to the CEO of Raspberry Pi, to systems and node suppliers, academics and many others and assessed their replies.

IoT centres around nodes collaborating for the benefit of humans without human intervention at the time. It does not include the Internet of People which is a renaming of the world of connected personal electronics operated by humans: this has completely different characteristics and it is cynical to conflate it with IoT, just as shovelling in RFID, all M2M, ZigBee and so on is unhelpful.

Nevertheless, we show how IoT nodes can be on people and quantify the appropriate part of wearables market because is relevant. The report explains further with a host of examples and options, even giving forecasts for agricultural robots following several respondents seeing agriculture as an important potential IoT market.

As IoT moves to higher volumes - billions rather than millions yearly - the nodes will typically not be hard wired: wireless nodes will have battery power and increasingly energy harvesting EH on-board because it will be impractical to change batteries. We consider the unsolved problem of suitable EH and the possibilities for solving it.

The largest potential applications will be multi-sensor so, for many reasons, component count will increase making cost reduction more difficult. We look at expenditure on IoT enabling technology which currently runs to billions of dollars yearly, mainly coming from governments and aspiring suppliers. However, we reveal how most of those reporting these and other IoT figures are puffing their data with things that may never be a part of the IoT scene such as sensor research in general.

Expenditure on buying and installing actual IoT networks is much more modest, contrary to heroic forecasts made by most analysts and manufacturers in the past. IDTechEx was disbelieving about the huge projections by others for the last four years and we have been proved right so far. Nevertheless, even our node forecasts have now been reduced in the light of what has happened, though our systems figures have been increased. It adds up to $20 billion in actual networks including nodes in ten years from now and rapid progress after that. See the number and dollar breakdown by application. Learn which players do what. What are now looking to be the important IoT applications and why? What are the important open source options at node and system level? What has come right lately that will boost IoT and what is still problematic? These and many other questions are answered.

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Table of Contents

1. EXECUTIVE SUMMARY AND CONCLUSIONS

  • 1.1. Definitions and scope
  • 1.2. A natural next stage
  • 1.3. IoT infrastructure example
  • 1.4. IoT contrasted with IoP
  • 1.5. Potential applications examples
  • 1.6. IoT value chain and bias vs IoP
  • 1.7. Examples of IoT opportunities and suppliers
  • 1.8. Hype and nonsense
    • 1.8.1. The dumb RFID Internet of Things
    • 1.8.2. Calling existing systems and protocols Internet of Things
  • 1.9. The bigger vision
  • 1.10. But wider deployment means compromises and new challenges
  • 1.11. Some megatrends favour IoT: others do not
  • 1.12. Impediments to IoT
  • 1.13. System and software issues
    • 1.13.1. Severe security breaches continue
    • 1.13.2. Choosing a low power WAN
    • 1.13.3. Sensor fusion
    • 1.13.4. Artificial intelligence: deep learning
  • 1.14. Hardware
    • 1.14.1. IoT nodes: basics
    • 1.14.2. System on a Chip (SoC)
    • 1.14.3. Microcontroller units (MCUs)
    • 1.14.4. Anatomy of a generic device
    • 1.14.5. Compute power
    • 1.14.6. How are microcontrollers used?
    • 1.14.7. Capabilities, limitations, application
    • 1.14.8. Beyond microcontrollers
    • 1.14.9. Single Board Computer SBC
    • 1.14.10. Internet of Things nodes
    • 1.14.11. New IoT formats: RFMod's BeanIoT
    • 1.14.12. IoT node with up to ten sensors and battery power: cost structure
    • 1.14.13. Energy harvesting EH choice
  • 1.15. Investment in IoT development 2014-2020
  • 1.16. Too many (or not enough) industry standards
  • 1.17. Market forecasts 2017-2027
    • 1.17.1. Internet of Things forecasts 2017-2027 - Numbers (Billions)
    • 1.17.2. Internet of Things forecasts 2017-2027 - Unit Price (US$)
    • 1.17.3. Internet of Things forecasts 2017-2027 - Market Value (US$ Billions)
    • 1.17.4. IoT systems globally 2017-2027 (US$ Billions)
    • 1.17.5. Allied market forecasts and data
    • 1.17.6. EV and 48V mild hybrid global forecasts number K 2017-2027
    • 1.17.7. IDTechEx EV and 48V mild hybrid global forecasts $ billion 2017-2027
    • 1.17.8. On-road Level 4/5 autonomous vehicles forecasts
    • 1.17.9. Ten-year market forecasts for all agricultural robots and drones segmented by type and/or function
    • 1.17.10. Ten-year market forecasts for agricultural robots and drones segmented by type and/or function
    • 1.17.11. Market for IoT wearable devices: medical

2. INTRODUCTION

  • 2.1. What is IoT?
  • 2.2. Example of possible applications: wearable IoT
  • 2.3. The IoT dream
  • 2.4. Many rename existing things without IP addresses as IoT: this is unhelpful
  • 2.5. Heroic forecasts retained despite a quiet 2016
  • 2.6. Why is IoT gaining attention?
    • 2.6.1. Primary driver
    • 2.6.2. New technology
    • 2.6.3. Oil and gas
    • 2.6.4. Manufacturing etc. Bosch view
    • 2.6.5. Utilities
    • 2.6.6. Transportation
    • 2.6.7. Automotive
    • 2.6.8. Retail
  • 2.7. Impediments
  • 2.8. Disagreements and uncertainty

3. CORE MICROCONTROLLER UNIT MCU TECHNOLOGIES

  • 3.1. Manufacture
  • 3.2. Optimising power consumption
  • 3.3. Low power battery backup
  • 3.4. MCU architectures
  • 3.5. MCU components: memory
  • 3.6. MCU components: IO
  • 3.7. MCU co-processors: DSPs
  • 3.8. MCU co-processors: FPGAs
  • 3.9. MCU co-processors: PLDs and CPLDs
  • 3.10. MCU software: Operating Systems
  • 3.11. MCU software: programming languages
  • 3.12. Case study: Texas Instruments MSP430G2333

4. ADJACENT TECHNOLOGIES

  • 4.1. Sensors
    • 4.1.1. Inertial measurement units (IMUs)
    • 4.1.2. Global Positioning System (GPS)
    • 4.1.3. Depth cameras
  • 4.2. Communications

5. HARDWARE PLAYERS

  • 5.1. Renesas Electronics
  • 5.2. NXP+Freescale
  • 5.3. Microchip+Atmel
  • 5.4. Atmel
  • 5.5. ST Microelectronics
  • 5.6. Infineon Technologies
  • 5.7. Texas Instruments (TI)
  • 5.8. Cypress/Spansion
  • 5.9. Samsung
  • 5.10. Intel
  • 5.11. Digispark
  • 5.12. Arduino/Genuino
  • 5.13. Apple
  • 5.14. Google
  • 5.15. Amazon
  • 5.16. Raspberry Pi Foundation
  • 5.17. Beagleboard
  • 5.18. Some more MCU prototyping boards...
  • 5.19. And many more SBCs...

6. TRENDS

  • 6.1. Benchmarking Clarifies the Future of Internet of Things
  • 6.2. Wide Area network choice - LoRaWAN and LoRa Alliance
  • 6.3. eRIC
  • 6.4. MCU architecture trends: ARM
  • 6.5. Open source hardware and systems
  • 6.6. Moore's Law
  • 6.7. Prices equilibrating
  • 6.8. Other MCU trends
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