MIPRO 2013, May 20-24, 2013, Opatija, Croatia
Abstraction and Semantics support in M2M communications Vanesa Čačković, Željko Popović Ericsson Nikola Tesla d.d. Krapinska 45, Zagreb, Croatia e-mail:
[email protected],
[email protected]
Abstract - In line with the global horizontalization trends in M2M solutions, it is predicted that considerable percentage of the M2M service platforms will be designed to accommodate multiple verticals. Data and information management tools will be of increasing importance. This includes capabilities to access and capture data, but more prominently how to handle data and information in a structured way. Semantic annotation of data and means to add contextual information will be needed. Data models will evolve both as domain specific models and as common models. Instrumental will be tools to link these models, and also technologies to perform reasoning and knowledge e.g. based on evolved semantic web technologies. This paper will give an overview of current work in standardization bodies on semantic support for M2M data: how to discover, interpret and use the M2M data from different sources, without any kind of prior knowledge of that. This is essential to offer high-level M2M horizontal services and to develop open markets for M2M data.
I.
INTRODUCTION TO M2M
Over the last few years there has been much talk about Machine-to-Machine (M2M) communications. Machineto-Machine (M2M) communications by the definition are technological solutions and deployments allowing Machines and Devices to communicate with each other, with no (or little) human interactions [1]. Although the concept is not new, M2M is now at a rapid expansion. The reason for that is mainly in the technological advancement resulting with availability of applicable communication technologies, affordable devices and embedded computing with sensing capabilities. There is also an emerging need for M2M applications from across industries and society, driven mainly from enterprise or government needs. Enterprises focus on cost reduction and efficiency while government focus is on sustainability, safety and socioeconomic impacts. The telecom industry is increasingly looking at M2M as a new source for revenue when on many markets revenue growth is stalling [2]. Although the M2M market is much alive and growing, there are still a lot of open questions like fragmentation of solutions, network misalignment, security, privacy, service capabilities, testing and certification of devices, etc. that need to be overcome before the M2M market can reach its full potential. Therefore, the next logical step is the standardization of the M2M domain. [3]. Without the proper standards there is no interoperability both within and between domains. Within a domain, standards provide
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cost efficient realizations of solutions. Between domains, the interoperability ensures cooperation between the engaged domains [3]. Current M2M markets are still highly fragmented. Various vertical M2M solutions have been designed independently and separately for different applications, which inevitably impacts or even impedes large-scale M2M deployment. Standardization bodies have the long term intent to drive the M2M domain towards a horizontal system and business orientation. The reason for belief in horizontalization is that it provides the most efficient structure for the involved industries [7]. The European Telecommunications Standards Institute (ETSI) formed an M2M Technical Committee responsible for developing standards for Machine to Machine Communications. The group aim is to provide an end-toend view of Machine to Machine standardization, and to co-operate closely with ETSI's activities on Next Generation Networks, and also with the work of the The 3rd Generation Partnership Project (3GPP) standards initiative for mobile communication technologies. The Release 1 of the M2M functional architecture has been published in October 2011 [7]. Release 1 of ETSI M2M defines a Service Capability Layer (SCL) which is enabling transport of M2M data between devices or gateways and network applications. Release 1 provides an abstraction layer hiding the heterogeneity of M2M access networks and provides means for secure data transport. By design choice, the ETSI M2M Rel.1 SCL is handling only data containers without any knowledge of the data contained. The advantages of this approach are a clean separation of data transport from data handling and focus on the generic, commonly needed functions of an SCL thus avoiding applications-specific functionality to be included into the ETSI M2M standards. While Rel. 1 of ETSI M2M already opens a lot of opportunities in the M2M area, there are a number of limitations: • The common-place vertically integrated, but isolated M2M applications are now replaced by M2M applications which are re-using a common data transport, but which are still vertically integrated and isolated from each other • Device and application need to agree beforehand on a common definition of the exchanged containers as well as on the contained data. This makes re-use of M2M data across different applications difficult.
• There are only very limited functions in Rel.1 to discover which data are available in an SCL • There is no support in the SCL to enable an open market of data, e.g. in which data owner publish (sell) their data and independent data users provide applications that make use of the data • Limited chances for ETSI M2M compliant platform providers to enable value-added services re-using M2M data • Limited opportunities for treating different kinds of M2M data with different Quality-of-Service or by charging differently for them For operators and providers of an ETSI M2M compliant platform this is limiting their ability for offering new and innovative business models [4]. This paper will provide an overview of current work on semantic support for M2M data: how to discover, interpret and use the M2M data from different sources, without any kind of prior knowledge of that. Section two will provide the description of key features of M2M communication and existing technology solutions, section three will provide the overview of standardization activities on semantics in M2M, section four will provide an explanation what constitutes semantic information and how can it be used in M2M communication whilst the last fifth chapter will give a conclusion. II.
M2M TECHNOLOGY SOLUTIONS
The main capability of any M2M system is the remote monitoring and control of real world properties of different environments and places (air quality, mines, cities) or of a diverse set of assets (buildings, vehicles, goods, other infrastructures). Although the general purpose is the same, the service requirements of M2M services can vary significantly [6]. Connected consumer electronics will have similar (demanding) QoS requirements as mobile multimedia services for mobile broadband. On the contrary, sensors, meters and connected devices have low QoS requirements, but may have very stringent requirements on energy efficient operation and cost. Other M2M services may impose other requirements, like high reliability and low delay for industrial control applications and smart grid; high reliability, low delay and high mobility for automotive applications, etc. Since the M2M services can be so diverse, today's most M2M solutions are highly specialized stove-pipes with hardcoded integration of devices into the applications. A. Sensor and Actuator service functionality It is predicted that in following years a considerable percentage of the M2M service platforms will be designed to accommodate multiple verticals, a trend that we already see today. In order to accommodate multiple verticals there have to be certain horizontalization points [2]. One horizontalization point is at the level of M2M device access (device access reuse by multiple applications). The M2M Service layer is expected to become a resource management layer: The web resource
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abstraction will be extended to sensors, actuators, processing software and sensor data storage (current examples include Pachube and SENSEI, [9], [10]). The next horizontalization step is to enable sensor data reuse by multiple applications and the ability of multiple applications to send commands to certain actuators. This requires a common data model or linked data models for heterogeneous sensor data or actuator commands. So by 2016 it is predicted that there will be data transformation functions in the sensor and actuator service layer since it is expected that there will still be competing sensor technologies for the same application area (e.g. ZigBee, Bluetooth for Health) or different sensor technologies with different sensor models for different application areas that the service layer has to accommodate. However by 2016 there will only be a few cases of verticals that share information from the same sensors (e.g. electric vehicle car sensors for utility and automotive) and therefore their data models need to be at least linked [4]. The sensors collect information and the actuators provide control capabilities for certain real world entities of interest (a room is a real world entity of interest and the sensors and actuators are the resources). By 2016 the information model that describes the entities of interest (people, places, things) will be decoupled from the information model of the information sources (sensors) and actuation targets (actuators) related to an entity of interest. Due to mobility of resources and entities in certain application scenarios, the relationship between the entities and the relevant resources (sensor S is in room R) as well as entities themselves (e.g. person P is in room R) will be dynamic and their relationship explicitly declared using semantic technologies such as Resource Description Framework (RDF). Nevertheless application logic that uses the resource and entity of interest information models will be hardcoded, i.e. there will be no artificial intelligence to process semantically annotated relationships. We might also see some standardized relationships for resources and entities the same way as there are standardized representations for relationship between people (e.g. friend-of-a-friend ontology). The Semantic Sensor Network (SSN) incubator group of The World Wide Web Consortium (W3C) already has started this effort. B. Data and information management Data and information can come from any conceivable source. They include sensors, measurements and observations made by various ICT systems, explicit and implicit user inputs from e.g. social network sites, or other static or dynamic databases. All in all, data and information processing is about efficient tools for handling, understanding and making use of data and information as increased knowledge of for making decisions or executing actions. It should be noted that tools are needed both for handling individual unique data items (“small data”) as well as large amounts of data (“big data”). The creation of a data source and development of any application making use of the data already is or will be decoupled. This is in particular true for sensor networks
where deployment traditionally has been done with a single application in mind. This decoupling of usage from data source requires a semantic description of the data. Raw sensor data (e.g. temperature) will not be specific to a vertical application. A sensor data model should provide a stepwise mapping to higher abstractions. The first step is to make a solely sensor related semantic tagging of the data. Secondly, domain specific information should be added explaining the context of the data, e.g. health. Subsequent steps could include specifications of application specific information. Work in this direction has been conducted by e.g. Open Geospatial Consortium in Sensor Web Enablement as well as the Semantic Sensor Network incubator group in W3C [11], [12]. In addition to domain specific information models, there will also be general ontologies and common vocabularies which can link together information from different domains. The Linked Open Data project [13] provides information and tools for linking data from different domains. The basic technologies used Language to describe the ontologies are RDF and Web Ontology (OWL). In the development of domain specific ontologies common vocabularies will be increasingly accepted and used.
application infrastructure which is built reusing existing standards and can serve multiple applications. Business application 1 Business application 2 Multi Application
Business application n
Horizontal Multi-Service Platform
data Common Application Infrastructure
Technology Independent
Transport network
Reuse of Existing Standards
End to End
Gateway
The current semantic web technologies build on the notion of adding new information will not change previous conclusions. However, for dynamic knowledge creation, extensions to the current Semantic Web Technologies are needed. III.
M2M Service Capabilities Resouce Based
Device 1
Device 2
...
Device n
Figure 1. ETSI M2M vision
STANDARDIZATION NEEDS
As already mentioned in the introduction, the current M2M related standards and technologies landscape is highly fragmented. The fragmentation can be seen across different applied domains where there is very little or no reuse of technologies beyond basic communications or networking standards. Even within a particular applied sector, a number of competing standards and technologies are used and promoted. The standards landscape related to M2M is very large. The Global Standards Collaboration Machine-Machine Task Force (GSC MSTF) identifies 143 organizations with a direct or indirect interest in M2M standardization [4]. The entire ecosystem of solution providers and users would greatly benefit from less fragmentation and should strive towards use of a common set of basic tools. This would provide faster time to market, economy of scale and reduce overall costs. To avoid creation of competing M2M standards Global One M2M Initiative has been formed [15]. Its goal is to develop one globally agreed M2M specification with initial focus on Service Layer and consolidation of current M2M Service Layer standards activities into the One M2M initiative. The work in One M2M will be based on ETSI M2M vision that can be simplified shown as in Figure 1. There are essentially three elements of the M2M value chain: the device, the network and the application. The goal is to connect multiple devices through technology independant network to common horizontal
ETSI Release 2 M2M standard should also propose some default way how syntactic and semantic information could be documented by vertical industries. Work on support of semantics in M2M is still in state of early draft: „ETSI TR 101 584 V0.4.0 Machine to Machine Communications (M2M); Study on Semantic support for M2M Data“[5]; however it is announced that the standard will be available in next few months time frame. This Study on Semantic support for M2M Data will analyze benefit, feasibility and potential requirements for the support of semantic information on application related M2M Resources in the M2M system. It will create use cases that illustrate provisioning and usage of such semantic information and that demonstrate the benefit for the M2M ecosystem. It will also investigate on the kind and amount of semantic information that would become available in the M2M system and investigate discovery mechanisms for semantic information in the ETSI M2M System [5]. IV.
SEMANTIC IN M2M COMMUNICATION
At the core of providing M2M applications is the collection, storage and processing of data and information from different M2M devices for subsequent use by the M2M applications themselves. Those functionalities are implemented in M2M Service Enablement (SE) layer which can simplified be shown as in Figure 2. The main assumption behind the architecture in Figure 2 is that it follows a service oriented design. That translates to the fact that different services can be used by the service users as stand alone services. The minimum set of functions
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needed for a meaningful M2M SE Platform contains the Data Access and Messaging, B2B Service Access and the Security and Policies. Information Services are part of SE (as opposed to raw data services) and include services that offer high level information about devices, entities of interest or processed raw data. Information Services contain domain specific data services that facilitate the processing of raw data into information (e.g. context information) meaningful to the service users. Service Provider
...
Mapping Table
Device Specifications
Service Provider
B2B service
Data Model
Data Mapping
Data Processing
Data Collector
Data Storage
B2B service access
Security and Policies
Internal Services
Data Services
Information Services
Data access and messaging
Custom User Services
Figure 3. Data mapping function
To support the data mapping, a mapping table needs to be created to link the unified data model with specific sensor and device descriptions. To do the mapping, the semantics of the collected data need to be understood. The original sensor data or device descriptions can be obtained either manually or automatically fetched from manufacturers. It is then compared with unified data model to create a mapping table for each sensor and device. This step is done at the device setup phase.
System Services
Data access and messaging sensors
Data Access
gateways
When the data are collected at runtime, the data mapping function identifies the data source and find the relevant mapping information from the mapping table to map the data into our unified data model.
databases
Figure 2. M2M Sevice Enablement architecture
The measured or observed information about physical phenomena and the context information about entities of the real world needs to be modelled in order to enable the interpretation of this information in the M2M SE as well as by consumer applications and services. Semantic annotation of the data (for example, with domain knowledge) can provide machine-interpretable descriptions on what the data represents, where it originates from, how it can be related to its surroundings, who is providing it, and what are the quality, technical, and non-technical attributes. Data collected from sensors normally have different formats and do not include semantic information in the data. To be able to efficiently use the data by various applications, the data need to be mapped to a standard format and tagged with semantic information. Data mapping function could look like shown on figure 3:
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From the application side, it only needs to know the defined data model with specified interface to query for information. The applications will not need to know the device specifications and format used by each sensor and device manufacturers. When the sensors are replaced by other models, only the mapping table needs to be updated. The applications are not affected. Example of using semantic technologies in M2M communication would be a sensor measuring and setting a room temperature on demand of an end user. Devices and sensors register through network gateway which writes their data into semantic database. Every time the sensor sends the temperature, the gateway writes it into the repository, and matches it with desired temperature inside the room where sensor is located. Implementation of semantic annotation of M2M data enables opening of the secondary M2M market. While the primary M2M market means vertically connected sensor/applications which target a specific business need, re-using of data collected in primary maket with semantic information which help to understand the original data opens up the secondary M2M market. Data can be in this way shared among different verticals and re-used by different applications.
By providing means to understand M2M data, the available business models can be greatly enhanced. For example, through offering additional semantic information about the data, platform provider can enable (and potentially charge for) the discovery of devices and data by semantic specification. Another possible business that can be provided would be to provide derived information from the provided raw data through intelligent processing, e.g. analysing the data, aggregating data across many different data sources, or to provide interpreted data as an additional service. Adding semantic information to a system can be done in different ways. The possibilites are yet not standardized, but several options are proposed in early draft [5]. Although the question of adding semantic information in M2M data is under standardization, there are still many questions left unaswered. For example: How exactly should data be annotated in order to be understandable? Should data be universally understandable, or only inside specific vertical domains? Where is the borderline? What is the best way to create extensible annotations, i.e. from minimal semantic information (simple ontology) towards more elaborate? Where do the semantic descriptions (ontologies) come from and who defines the ontologies? Will there be defined fixed catalogue of ontologies? All of those questions will have to be answered and standardized in the future work in order to reach the interoperability full scale of M2M applications. V.
[7] [8] [9]
[10] [11] [12] [13]
[14]
[15] [16] [17]
[18]
[19] [20]
[21]
CONCLUSION
[22]
Recently various studies as well as standardization bodies identified the importance of semantic technologies in M2M communication. Semantics can provide machineinterpretable meta-data and annotations and help describing different attributes of M2M devices and data. Implementing standardized semantic technologies into M2M communication system incerases overall interoperability and opens up new business models. However, semantics also come with an overhead and standards will need to look for a trade-off between efficiency and expressability.
[23] [24]
[25]
[26]
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