| T2T Research Group | J. Hong |
| Internet-Draft | Y-G. Hong |
| Intended status: Informational | ETRI |
| Expires: September 12, 2019 | J-S. Youn |
| DONG-EUI Univ | |
| March 11, 2019 |
Problem Statement of IoT integrated with Edge Computing
draft-hong-iot-edge-computing-02
This document describes new challenges such as strict latency, constrained network bandwidth and devices, intermittent connectivity, privacy and security, for IoT services originated from the IoT environmental changes. In order to address those new challenges, the integration of Edge computing and IoT has been emerged as a promising solution. This document discribes the concept of IoT integrated with Edge computing as well as its use cases. It also discusses benefits and challenges of Edge computing. The direction of Edge computing for IoT should be discussed in the IETF/IRTF.
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Nowadays, most IoT services are based on Cloud computing since it can provide virtually unlimited storage and processing power. The integration of IoT with Cloud computing brings many advantages such as flexibility, efficiency, and ability to store and use data.
However, the IoT environment is changing in such a way that vast amounts of data are created at edge/local networks and about a half of data is stored, processed, analyzed and acted upon close to the data producer. Thus, emerging IoT services introduce new challenges that cannot be addressed by today's centralized Cloud computing models alone.
In this document, we describe new challenges for emerging IoT services such as strict latency, constrained network bandwidth, constrained devices, uninterrupted services with intermittent connectivity, privacy and security due to the IoT environmental changes.
In order to address those new challenges for IoT services, the integration of Edge computing with IoT has been emerged as a promising solution. In this document, we describe the concept of IoT integrated with Edge computing as well as its use cases to discuss the benefits and challenges of Edge computing mainly focused on IoT data. The purpose of this document is to bring up the issues of Edge computing for IoT services in IETF/IRTF.
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in [RFC2119].
Since the phrase 'Internet of Things (IoT)' was coined by Kevin Ashton in 1999 working on Radio-frequency identification (RFID) technology at the Auto-ID Center of the Massachusetts Institute of Technology (MIT) [Ashton], the concept of IoT has been that things connected to the Internet can send and receive information collected by sensors without human intervention, where things are various embedded systems such as home appliances, mobile equipment, wearable devices, etc. IoT has become one of the notable innovations playing an important role in our daily lives [Lin].
IoT is generally characterized by real world small things that are widely distributed but have limited storage and processing power. On the other hand, Cloud computing is a predominant technology which has virtually unlimited capacity in terms of storage and processing power. Thus, the IoT with Cloud computing has been recognized as an efficient way to overcome those IoT issues [Botta].
The integration of IoT with Cloud computing brings many advantages such as flexibility, efficiency, and capability to store and use IoT data since Cloud computing has been a mature technology used to provide computing services or IoT data storage over the Internet.
Now with IoT, we will reach the era of post-Clouds where unprecedented volume and variety of data will be generated by things at edge/local networks and many applications will be deployed on the edge netwoks to consume these IoT data. Some of the applications may need very short response times, some may contain personal data, and others may generate vast amounts of data. Today's Cloud based service models are not suitable for these applications.
It is predicted that by 2019, 45% of the data created in IoT will be stored, processed, analyzed and acted close to, or at the edge of the network and about 50 billion devices will connect to the Internet by 2020 [Evans]. So, moving all data from edge/local networks to the cloud data center may not be an efficient way anymore to process vast amounts of data.
In Cloud computing, users traditionally only consumed IoT data through Cloud services. Now, however, users are also producing IoT data with their mobile devices. This change requires more functionality at edge/local networks [Shi].
As the IoT environment is changing in such a way that vast amounts of data are created at edge/local networks and about a half of IoT data is stored, processed, analyzed and acted close to the IoT data producer, the emerging IoT services introduce new challenges that cannot be addressed by today's centralized Cloud computing models alone [Chiang].
Many industrial control systems, such as manufacturing systems, smart grids, oil and gas systems, etc., often require end-to-end latency between the sensor and control node remains within a few milliseconds and some other IoT applications may require latency below a few tens of milliseconds [Weiner]. These requirements for latency are difficult to achieve by today's Cloud services.
With an exponential rate, IoT data is generated by the massive things connected into the Internet [Kelly] and extremely high network bandwidth is required to send all the data to the cloud. Since 90% of the IoT data generated by the endpoints will be stored and processed locally rather than in the cloud, sending all the IoT data to the cloud is often unnecessary. Or sometimes it is prohibited due to regulations and data privacy concerns.
Many IoT things such as sensors, data collectors, actuators, controllers, cars, drones, etc., have very limited hardware resources. Many constrained IoT things cannot rely solely on their limited resources to meet all their computing needs. It is not practical to require everyone to interact directly with the cloud. This is because these interactions require resource-intensive processing and complex protocols.
Cloud services will have difficulty providing uninterrupted services to devices and systems such as vehicles, drones, and oil rigs that have intermittent network connectivity to the cloud.
When IoT services are deployed at home, personal information can be learned from detected usage data. For example, one can easily guess whether a home is empty by reading its electricity or water usage. In this case, the way to support services without exposing personal information is a challenge.
When IoT data is sent to the cloud which is the end point in the traditional end-to-end communication system, privacy of the data is a challenge since it may travel across multiple routers to the cloud.
As described in section 4, new challenges for supporting emerging IoT services exist and Edge computing is one of the candidates to satisfy these challenges. The concept of Edge computing is very intuitive. The definition of Edge computing from ISO is 'Form of distributed computing in which significant processing and data storage takes place on nodes which are at the edge of the network' [ISO_TR]. And the similar concept of Fog computing from Open Fog Consortium is 'A horizontal, system-level architecture that distributes computing, storage, control and networking functions closer to the users along a cloud-to-thing continuum' [OpenFog]. Based on these definitions, we can summarize a general philosophy of Edge computing as "Distribute the required functions close to users and data".
As an aspect of IoT, Edge computing can provide many capabilities for IoT services because IoT systems are based on sensors and actuator devices in edge area and IoT data generated from sensors and actuator devices are gathered through a gateway [ISO_TR]. Besides on IoT data, other functions such as computing, control and network functions are also very remarkable to support IoT services. In this document, we will first concentrate on IoT data's aspect since the benefit of Edge computing with IoT data is very big in use cases.
As tremendous IoT sensors, IoT actuators, and IoT devices are connected to the Internet, IoT data volume from these things are expected to increase explosively. And it is expected that much of this high volume of IoT data is produced and/or consumed within edge/local networks, not to traverse through cloud networks. Until now, most IoT data generated by IoT things is transferred and accumulated in a remote server and storage of IoT data in a remote server is expensive in transmission and storage. To mitigate the cost of transmission and storage, it is required to divide IoT data into two types of data; one is stored in edge/local networks and the other is stored in cloud networks. The effect of Edge computing is revealed with the handling IoT data in edge/local networks.
Until now, most network equipment such as routers, gateways, and switches just forward data delivered from other network devices without reading or modifying the content. In end-to-end communication, data is acknowledged and proceed at a final corresponding node. This is a typical usage of cloud computing and a client-server communication. But, in the IoT environment, some IoT data will be transferred to a cloud network and some will be delivered to an edge node. The main reason of this separation is to provide real-time processing and security enhancement in IoT. Although there are many new technologies to reduce the delay and transmission time, it is not easy to guarantee real-time processing. The typical use case of this requirement is industrial Internet and smart factory. Even though there are also several solutions to provide security in IoT, the more basic rule is not to expose the privacy data to public networks. If we separate IoT data into private and non-private data, and keep private data within an edge/local network not to expose them in a public network, the security and privacy in IoT cna be addressed by the separation.
If it is possible to separate IoT data in edge/local networks and cloud networks, Edge computing can do more functions with IoT data in edge/local networks. Because Edge computing has the capabilities to handle IoT data in edge/local networks, it is also possible to analyze IoT data to provide enhanced IoT services such as intelligence. To analyze IoT data in an edge/local network, it is required to have comparatively processing performance and this requirement is not obstacle to deploy Edge computing due to the development of H/W and S/W.
If we consider new challenges of IoT services, not only the big volume of IoT data but also the massive number of IoT things can be a critical problem. Even though, we acknowledge this future problem, the Internet architecture originally has the capability of scalability and it will mitigate scalability issue in the IoT environment. But, we cannot estimate the number of IoT things in the future and we cannot guarantee the Internet architecture still sustain the scalability issue in the IoT environment. Edge computing will separate the scalability domain into edge/local networks and outside network (e.g., cloud networks) and this separation of scalability domain can provide more efficient way to tackle the massive number of IoT things.
Because Edge computing can handle IoT data in an edge area and store the IoT data in an edge node, and proceed IoT data if it is needed, it can also separate the management domain into two parts. Edge Computing can concentrate on management of IoT things in an edge area and cooperate with the management of other outside networks.
At an Edge computing discussion in IETF/IRTF meetings, the motivation for IoT Edge computing is describe as follows; [IETF_Edge]
As we described at previous sections, the above motivation for IoT Edge computing could directly be benefits of Edge computing in the IoT environment. The above motivation for IoT Edge computing is mainly related to IoT data and other motivation for IoT Edge computing can exist as other aspects of networking and communication.
In spite of its benefits, Edge computing in IoT services has challenges such as programmability, naming, data abstraction, service management, privacy and security and optimization metrics.
Edge computing can support IoT services independently of Cloud computing. However, Edge computing is increasingly connected to Cloud computing in most IoT systems for processing and storaging data. Thus, the relationship of Edge Computing to Cloud Computing is also another challenge of Edge Computing in IoT [ISO_TR].
When we consider the implementation and deployment of Edge computing, it can be mainly referred to an IoT Gateway. The role of an IoT Gateway is to provide multiple accesses to the heterogeneous IoT devices/sensors, handling IoT data and delivering the IoT data to the final destinations such as cloud networks. Similar to an IoT Gateway, an Edge computing architecture as an edge computing node provides downside connectivity to IoT sensors and devices (southbound connectivity) and upside connectivity to cloud networks (northbound connectivity). Also, the architecture provides the function of data storage. Beside these functions, the Edge computing architecture should provide the computing functions, such as data processing, data analyzing, and additional function of intelligence.
+---------------------------+
| |
| Cloud networks |
| |
+------------+--------------+
|
|
+----------------------+-----------------------+
| | |
| +---------------+---------------+ |
| | | |
| | Edge gateway function | |
| | (Northbound) | |
| | | |
| +---------------+---------------+ |
| | |
| +---------------+---------------+ |
| | | |
| | Edge computing function | |
| | (Storage, Processing, | |
| | Analyzing, Intelligence) | |
| | | |
| +---------------+---------------+ |
| | |
| +---------------+---------------+ |
| | | |
| | Edge networking function | |
| | (Southbound) | |
| | | |
| +-------------------------------+ |
| |
| Edge computing node |
+-----+-------+------+-------+-------+-------+-+
| | | | | |
| | | | | |
+---+----+ | +---+----+ | +---+----+ |
|Sensor 1| | |Sensor 2| .|.. |Sensor n| |
+--------+ | +--------+ | +--------+ |
| | |
| | |
+----+---+ +-----+--+ +-----+--+
|Device 1| |Device 2| .... |Device n|
+--------+ +--------+ +--------+
Figure 1: Architecture of IoT integrated with Edge computing
It is expected that the Edge computing architecture will play an important role to deploy new IoT services with integration to big data and AI services.
In traditional construction domain, there are many heavy equipment and machineries and dangerous elements. Even though human pay attention to risk elements, it is not easy to avoid them. If some accidents are happened in a construction site, it causes a loss of lives and property. Thus, there have been many trials in a construction area to protect lives and property.
Measurements of noise, vibration, and gas in a construction area are recorded on a remote server and reported to an inspector. Today, data produced bu such measurements is collected by a gateway in a construction area and transferred to a remote server. This incurs transmission cost, e.g. over a LTE connection, and storage cost, e.g. when using Amazon Web Services. When an inspector wants to investigate some accidents, he checks the information stored in a server.
If we deploy Edge computing in a construction area, the sensor data can be processed and analyzed in a gateway located within or near a construction area. And with the help of a statistical analysis or machine learning technologies, we can predict future accidents in advance and this prediction can be used as an alarm in a construction area and a notification to an inspector.
To determine the exact cause of some accident, not only sensor data but also audio and video data are transferred to a remote server or cloud networks. In this case, the data volume of audio and video is quite big and the cost of transmission can be a problem. If Edge computing can predict the time of accident, it can reduce the data volume of transmission; in general period, it can transmit the audio and video data with a low resolution/degree and in emergent period, it transmits the audio and video data with a high resolution/degree. By adjusting the resolution/degree of audio and video data, it can reduce transmission cost significantly.
In future smart cities, Smart grids will be critical in ensuring availability and efficiency for energy saving and control in city-wide electricity management. Edge computing is expected to play a significant role in those systems to improve transmission efficiency of electricity, react and restore for power disturbances, reduce operation cost, reuse renewable energy effectively, save energy of electricity for future usage, and so on. In addition, Edge computing can help monitoring power generation and power demands, and making electrical energy storage decisions in the Smart grid system.
The Water system is one of the most important aspects for building smart city. Effective use of water, and cost-effective and environment-friendly treatment of water are critical for water control and management. This can be facilitated by Edge computing in Smart water systems, to help monitor water consumption, transportation, prediction of future water use, and so on. For example, water harvesting and ground water monitoring will be supported from Edge computing. Also, a Smart water system is able to analyze collected information related to water control and management, control the reduction of water losses and improve the city water system through Edge computing.
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| [RFC2119] | Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/RFC2119, March 1997. |