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モノのインターネット (IoT) :2017-2027年

Internet of Things (IoT) 2017-2027

発行 IDTechEx Ltd. 商品コード 300642
出版日 ページ情報 英文 169 Slides
納期: 即日から翌営業日
本日の銀行送金レート: 1USD=113.38円で換算しております。
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モノのインターネット (IoT) :2017-2027年 Internet of Things (IoT) 2017-2027
出版日: 2017年01月31日 ページ情報: 英文 169 Slides

当レポートでは、モノのインターネット (IoT) の展望を調査し、IoTの用途の例、成長推進因子・阻害因子の分析、MCU技術、関連技術の動向、ハードウェア企業の取り組み、主な市場動向、各種市場予測などをまとめています。

第1章 エグゼクティブサマリー・総論

  • 定義・範囲
  • IoTインフラの例
  • IoTとIoPの比較
  • 潜在的用途の例
  • IoTのバリューチェーンとbias vs IoP
  • IoTの市場機会とサプライヤーの例
  • 期待と空論
  • より大きなビジョン
  • 妥協と新たな課題
  • メガトレンドとIoT
  • IoTの障壁
  • システムおよびソフトウェアの課題
  • ハードウェア
  • IoT開発への投資:推移と予測
  • 大量の産業規格
  • 市場予測
    • 出荷数
    • ユニット価格
    • 出荷額
    • IoTシステム
    • 関連市場の予測・データ
    • EV・48Vマイルドハイブリッド
    • IDTechEx EV・48Vマイルドハイブリッド
    • オンロードレベル4/5自動運転車
    • 10カ年予測:農業用ロボット・ドローン
    • IoTウェアラブルデバイス市場:医療用

第2章 イントロダクション

  • IoTとは
  • 用途の例:ウェアラブルIoT
  • IoTの目標
  • IoTへの注目の理由
  • 障壁
  • 異議・不確定性

第3章 コアマイクロコントローラユニットMCU技術

  • 製造
  • 消費電力の最適化
  • 低消費電力バッテリーバックアップ
  • MCUアーキテクチャ
  • MCUコンポーネント:メモリー
  • MCUコンポーネント:IO
  • MCUコプロセッサー:DSP
  • MCUコプロセッサー:FPGA
  • MCUコプロセッサー:PLD・CPLD
  • MCUソフトウェア:OS
  • MCUソフトウェア:プログラミング言語
  • ケーススタディ:Texas Instruments MSP430G2333

第4章 隣接技術

  • センサー
    • IMU
    • GPS
    • 深度カメラ
  • 通信

第5章 ハードウェア企業

  • 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、など

第6章 各種動向

  • ベンチマーキングと将来のIoT
  • WANの選択肢:LoRaWAN・LoRa Alliance
  • eRIC
  • MCUアーキテクチャ動向:ARM
  • オープンソースハードウェア&システム
  • ムーアの法則
  • 価格の平準化
  • その他の動向



By 2027, tens of billions of smart objects will be internet enabled with IP addresses.

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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All report purchases include up to 30 minutes telephone time with an expert analyst who will help you link key findings in the report to the business issues you're addressing. This needs to be used within three months of purchasing the report.

Table of Contents


  • 1.1. Definitions and scope
  • 1.2. A natural next stage
  • 1.3. IoT infrastructure
  • 1.4. IoT contrasted with IoP
  • 1.5. IoT value chain and bias vs IoP
  • 1.6. Potential applications examples
  • 1.7. Examples of IoT opportunities and suppliers
  • 1.8. Hype and nonsense
  • 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
    • 1.13.5. Lower power ICs, more frugal node activation
  • 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 excluding batteries
    • 1.14.13. Energy harvesting EH choice
    • 1.14.14. 1Wi-Fi harvesting
  • 1.15. Investment in IoT development 2014-2020
  • 1.16. Industry standards ferment and SIGfox, NBIOT etc contention
  • 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$)
    • 1.17.3. Internet of Things forecasts 2017-2027 - node market value ex-factory (US$ 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


  • 2.1. 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
    • 2.6.9. Local government
    • 2.6.10. Smart home
    • 2.6.11. Bottom line
  • 2.7. Automotive IoT in more detail
    • 2.7.1. Introduction
    • 2.7.2. Sensors: IoT potential for insight, safety, performance
    • 2.7.3. Automobiles mapping pollution
    • 2.7.4. Automobile and smart home
    • 2.7.5. Cars as an IoT subscription service
    • 2.7.6. Some trends resulting
    • 2.7.7. Recent Acquisitions and mergers in automotive IoT
  • 2.8. Impediments
  • 2.9. Disagreements and uncertainty
  • 2.10. System and node operational improvements
    • 2.10.1. Overview of advances proceeding
    • 2.10.2. Lower power ICs and different design approach facilitate low power EH adoption
    • 2.10.3. Node to Node or Big Data?


  • 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


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


  • 5.1. 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...


  • 6.1. 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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