virtualization framework to support connected objects sensor event processing and reasoning by providing a semantic overlay of underlying IoT cloud.
SenaaS:An Event-driven Sensor Virtualization Approach for Internet of Things Cloud Sarfraz Alam, Mohammad M. R. Chowdhury, Josef Noll University Graduate Center, UNIK Kjeller, Norway {sarfraz, mohammad, josef}@unik.no Abstract—In this paper, we propose an Internet of Things (IoT) virtualization framework to support connected objects sensor event processing and reasoning by providing a semantic overlay of underlying IoT cloud. The framework uses the sensor-as-aservice notion to expose IoT cloud’s connected objects functional aspects in the form of web services. The framework uses an adapter oriented approach to address the issue of connectivity with various types of sensor nodes. We employ semantic enhanced access polices to ensure that only authorized parties can access the IoT framework services, which result in enhancing overall security of the proposed framework. Furthermore, the use of eventdriven service oriented architecture (e-SOA) paradigm assists the framework to leverage the monitoring process by dynamically sensing and responding to different connected objects sensor events. We present our design principles, implementations, and demonstrate the development of IoT application with reasoning capability by using a green school motorcycle (GSMC) case study. Our exploration shows that amalgamation of e-SOA, semantic web technologies and virtualization paves the way to address the connectivity, security and monitoring issues of IoT domain. Index Terms—Connected Objects, Internet of Things, SOA, Sensor, Semantic Technologies, Virtualization
I. I NTRODUCTION Web 3.0 is being transformed from connecting people and services to connecting objects (things). Today, a lot of devices and objects are emerged with sensors, enabling them to sense real-time information from the environment, and coupling this information with the web. This leads to a promising Internet of things (IoT) concept that allows connectivity of anything from anywhere at anytime. IoT creates a new digital ecosystem by amalgamating different technologies and standards, allowing different key players of industry to be part of it further. New business opportunities have been opened for retail, logistics, food, health, energy, smart home, and transportation sectors. For instance, IBM utilized the IoT for Norwegian Sea oil platforms by implementing a service, which gathers real-time information from the bottom of Sea so that a better decision can be made in order to drill down to the Sea. Though IoT possesses benefits for society and business, but it still lacks many technological issues which needs be addressed. First of all, IoT does not provide any registry mechanism for publishing service information publicly that is hosted on sensor. In [1], authors present a web-based repository, containing information about services from various sources and can be easily aggregated in the form of mashup. Secondly, different
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sensors and devices comprise with different data formats and models, thus causing IoT to exhibit deficiency in discovering and composing diversified services. Thirdly, IoT deficient in handling service invocation that sensor triggers with the occurrence of an event. The interaction between events and services are absent in current IoT clouds. Moreover, authorized access to IoT cloud sensor data and services without relaxing user privacy is also a challenging task. In this paper, we propose a semantic enhanced IoT virtualization framework to address the aforementioned challenges. The framework uses the Sensor-as-a-service (SenaaS) approach that does not only foster use of virtualization in IoT domain, but it also exposes IoT cloud’s sensors capabilities and data in the form of services. The paper makes the following contributions: ∙ We propose a virtualized IoT framework, realizing the event-driven service oriented architecture (SOA) in IoT domain. ∙ We design ontology to contrive a semantic overlay of underlying IoT cloud. ∙ We present a policy-based service access mechanism and delineate polices in terms of semantic rules. The rest of the paper is structured as follows. Section 2 discusses related work. Section 3 outlines our SenaaS and semantic service access approach. Section 4 provides details of the proposed framework. Section 5 considers a use case where proposed approach is employed to realize the eventdriven SOA. Section 6 describes our prototype implementation, including framework ontology, and sensor platform used for the test deployment. We outline objectives achieved by our design and some of the key open issues in section 7. II. R ELATED W ORK Recent years have seen an explosion of IoT environment. It is anticipated as the next generation internet. Several research initiatives are going on in the area of IoT. However, IoT is tightly coupled with the sensor technologies because in most cases they provide the sensing, actuating and communicating capabilities to the IoT. Therefore, Sensor technologies have become pivotal to influence the physical objects in the creation and usage of future services. Example of such innovation services is Nike+iPod iPhone application [2], which records workout information and tracks burned calories with the help of accelerometer embedded into Nike jogger. The iPhone
application acquires exercises and burned-calories information from accelerometer and presents on iPhone. Application also publishes this information on social network so that social network friends engage in challenges with each other. Different EU projects, including [3] and [4] tried to develop a system that creates new services and applications for end users and enterprises by elevating the physical world to the digital world. However, these projects are successful to a certain extend to address issues such as heterogeneity, flexibility, sensor description and capability. Recently, Sensor Web Enablement (SWE) [5] provides a suite of standards, including sensor model language (SensorML) [5] that provides metadata model in XML format to describe sensor, its capabilities and measurement process. SWE model is inadequate in nature due to unintended meaning of XML and XML Schema. Semantic technologies have been used to provide intended meanings in many domain such as medical, telecommunication, search, to name a few. The use of semantic technology for capabilities description in mobile industry has already been explored in standardization [6] and research work [7][8]. Similarly, several system [10] and vocabularies [9] [11] have been developed for addressing the research challenges in sensor network domain. For instance, Horan et al. used the semantic technologies for describing capabilities of wireless transducer network [12]. Jie and Feng proposed a hierarchical structure sensor information system based on the semantic web technology [13]. They implemented common domain ontology to represent real-time data from sensors, but the ontology is represented in UML diagrams. In addition, the system is not flexible enough that could adapt according to different business scenarios and their changes. Haung and Javed proposed Semantic Web Architecture for Sensor Networks (SWASN), focusing on sensor data and inference over sensor data for wide range of WSNs [14]. Eid et al. used ontologies to formalize the sensors for searching distributed and heterogeneous sensor network data [15]. The study is mainly focusing on modeling sensor taxonomy hierarchy and sensor data ontology. However, the ontology lacks the description of sensor services. Most of the aforementioned research works either focus only on vocabulary or sensor network framework. Whereas, we imply a holistic approach, considering the complete IoT environment and provide a semantic overlay of underneath infrastructure that enable the framework to offer IoT capabilities and data in the form of web services. III. A PPROACH A. Sensor-as-a-Service (SenaaS) Our approach is based on sensor-as-a-service (SenaaS) notion. SenaaS exposes functional aspects of sensor as services by hiding technical details of sensors from the user. The approach assists in specifying, creating, managing, discovering and delivering sensor functionalities and capabilities as services. To date, sensor-world is facing a lot of challenges such as sensor modeling, sensor orchestration, security and interoperability. However, we have seen that service-world has
already experienced similar challenges on service level, and service-world has already developed a number of standardized solutions to address such challenges. Therefore, the key idea is to address the sensor-world challenges by lifting one step up and transferring them into service-world challenges so that one can exploit all the existing service-world standards to cope with sensor-world challenges We propose a semantic mediation overlay for sensor using existing standards and mechanisms for knowledge representation, modeling and reasoning in order to enable SenaaS notion. The approach uses Sensor Web Enablement suite particularly SensorML, one of the most widely used sensor modeling language, as baseline technology. The use of SensorML ensures the interoperability with existing sensor infrastructure. It translates the SensorML description of connected objects into OWL [24] description using SonsorOntology (see section 5), thus enabling automated reasoning capabilities over connected objects description. This not only expose SenosrML both physical and non-physical process as autonomous semanticenhanced sensor service by incorporating domain knowledge but it also enables service composition. B. Semantic Access Authorization Model This paper also proposes a semantic access authorization model composed of a formal knowledge base and access policy. The knowledge base consists of concepts, properties (linking concepts), and instances of the concepts. Policy contains the constraints which are being formulated using the components of the knowledge base. Access decision is achieved through execution of policies. A policy execution environment derives the authorization decisions. Fig. 1 illustrates the overview of the proposed semantic access authorization model.
Figure 1: Semantic access, authorization model Semantic technologies are used to implement the knowledge base and the policies. Semantics mean the explicit interpretation of domain knowledge to make the machine processing more intelligent, adaptive and efficient [17]. Such interpretations are critical for decision making. IV. I OT V IRTUALIZATION F RAMEWORK The Fig. 2 depicts the high level view of the IoT virtualization framework. The framework targets the IoT cloud, consisting tens or hundreds of sensor, actuators, connected objects and devices instead of thousands of nodes. Furthermore, these nodes can
Figure 2: High level view of IoT virtualization framework
be consisted of different classes of devices, including resourceconstrained and resource-rich devices. The framework is capable of getting information from different sources and makes it available for novel services in from of virtual services. It also provides web service interface for functional aspects of IoT cloud’s connected objects. Moreover, it maintains the catalogue of all available sensors and infrastructure services and act as a delegator between the service requester and real-world IoT cloud. The main driver for the virtualization framework is re-usability of sensor information for a variety of novel service, both for owners and providers. A. Functional Architecture Fig. 3 depicts the functional architecture of the IoT virtualization framework.
due to the nature of request/response. This classic demandbased passive approach is not suitable for IoT clouds because all IoT clouds are event-driven that means events play a vital role in IoT cloud. Making sense of IoT events and performing a course of action in response to these events is the highly demanding capability of any IoT service framework. Here, we will provide the detail of each layer of IoT virtualization framework. The real-world access layer provides an interface with underlying IoT cloud. It implies an adapter oriented approach to address the technical diversity regarding sensor types and communication mechanisms. One of the main goals of this layer is to get real-world information and carry it to the upper layer for further processing. It receives the sensor events and dispatches them to an event manager by using a callback message pattern, where messages are sent asynchronously to the receivers that later process the messages and take appropriate action. This layer can also transfers action messages from upper layer and then selects appropriate adapters to deliver it to the IoT cloud’s connected objects and actuators. The semantic overlay provides the semantic model of underlying IoT cloud by maintaining an IoT ontology, the sensor ontology, and an event ontology and the service access policies. It facilitates CRUD (create, read, update and delete) operation on knowledge base. The layer is capable of importing any sensor system description in SensorML and translates it to OWL description using the sensor ontology and the mediation rules. It supports both persistent and inmemory storage. The in-memory caching mechanism keeps the last observation of IoT cloud’s connected objects in order to boost the performance of the framework. It also provides policy-based semantic authorizations (see section 6 for detail). The goal of the service virtualization layer is to expose the functional aspects of underlying IoT cloud and information in the form of services. The layer aims at delivering requester the information they look for based on their access rights. The layer performs various tasks: It queries knowledge base for virtual service and translates them into semantic enhanced web service description. Further, it generates micro formats of available web services for publishing them on social network sites in order to increase the visibility of available sensor service. Through its service orchestrator, the layer can compose service based on the available services description. This layer is also responsible for notifying all the subscribers of a specific sensor event.
Figure 3: IoT virtualization framework functional architecture
V. C ASE S TUDY
The architecture is composed of three layers: (i) the Realworld access layer, (ii) the Semantic Overlay layer, and (iii) the Service Virtualization layer. It follows the event- driven SOA [18] concept to address all the capabilities needed to respond to the dynamics of a real-time IoT infrastructure. SOA exercises a classic request/response communication pattern, and relationship between service and its consumer is synchronous
We present a green school motorcycle (GSMC) [19] case study to validate the concept of SenaaS and rule based service access, and composition. The GSMC is classified as heavy motorcycle, and it requires a standard motorcycle license in most of the European countries. The GSMC is equipped with 3G/GPRS modem, GPS receiver, Ethernet, embedded system with 1G RAM, 1GHz processor, Ubuntu embedded Linux, and 7 inch LCD.
The GSMC capabilities make it uniquely identifiable, addressable and available at anytime from anywhere. In addition, the GSMC is equipped with some on-board sensors such location. Based on our proposed framework, we integrated the Sun SPOT sensor platform with the GSMC and exposed the Sun SPOT sensor (i.e., temperature, accelerometer, light) and on-board sensors in the form of semantic services, thus enabling the notion of sensor as service (SenaaS). The GSMC holds different type of information such as status of battery, charging information and route information. The key actors compelling in such information are: (i) GMSC owner, (ii) battery manufacturer, (iii) charging outlet providers, (iv) energy grid company, and (v) friends. The proposed framework ensures that only authorized actors (of GSMC use case) can access the services. Following scenarios further clarify the access authorization aspect of the GSMC use case for every service type. Owner of the GSMC by default can access all the GMSC IoT SenaaS services. With explicit consents of the owner, authorized friends can even access these services. Battery status service can be monitored by the owner and the battery manufacturer. Recommendation service will be only available for the owner and authorized friends. As a state of the art future advancement service, energy grid company can only access the smart grid feed service. In this paper, we will focus on battery related information (i.e., available capacity and change vs. discharge rate) and motion detection. Research conducted in [20][21] shows that temperature has a profound impact on battery capacity. According to these studies, reduction of battery capacity towards lower temperature is very significant. For instance, reducing the temperature to 10 yields 8% reduced capacity, and for polar temperatures of -20 the battery capacity is reduced by 30%. Therefore, it is extremely beneficial to know the battery capacity especially in winter. Three services are needed to compose virtual battery capacity service. The temperature service (TS) receives the environment temperature from on-board installed temperature sensor and keeps it in the cache. The loading factor (LFS) service takes the temperature from TS and performs the mathematically calculation () to determine the battery capacity. Later, the notification service (NTS) gets information about all the subscribers and notifies them either using SMS service or push notification service. For motion detection only one service (i.e., MDS) is needed that gets the motion indication from the accelerometer. VI. P ROTOTYPE I MPLEMENTATION
relationship between the two instances. A property belongs to a domain and has a range. Syntactically, a domain links a property to a class and range links a property to either a class or a data range [4]. From an instance point of view, a property relates instances from the domain with the instances from the range. The real actors of a practical use case scenario (e.g. individuals) are defined through instances and they belong to the classes. Among the different ontology languages [23], this work uses the Web Ontology Language (OWL) [24]. Fig. 4 illustrates breakdown of the knowledge base used here containing classes (nodes), subclasses, instances of classes or subclasses (is), properties (edge between nodes) and data values.
Figure 4: Breakdown of knowledge base, containing core concepts The ontology is developed around the core concept of IOT, which is composed of one or more instances of ConnectedObjects concept, which in turns is composed of multiple instances of Sensor concept. The ontology provides shared vocabulary for describing concepts including Actors, Sensor, Sensing capabilities and virtual services. It also defines a set of attributes (i.e., owl:DatatypeProperty)and relationship (i.e., owl:ObjectProperty), which holds between different concepts. Following is the formal definition used in developing the ontology. Definition 1: 𝑜𝑤𝑙 : 𝑠𝑢𝑏𝐶𝑙𝑎𝑠𝑠𝑂𝑓 : 𝑆𝐶 (𝐶1 ) ⊆ 𝑆𝐶 (𝐶2 ) , Semantic scope of C2 is narrow than that of C1. VirtualService class has 3 different subclassess 𝑉 𝑖𝑟𝑡𝑢𝑎𝑙𝑆𝑒𝑟𝑣𝑖𝑐𝑒 ⊆
This section outlines the underlying formalism and implementation details of the GSMC use case prototype.
{𝑀 𝑜𝑛𝑖𝑡𝑜𝑟𝑖𝑛𝑔, 𝑅𝑒𝑐𝑜𝑚𝑚𝑒𝑛𝑑𝑎𝑡𝑖𝑜𝑛, 𝑆𝑒𝑛𝑎𝑎𝑆, 𝑆𝑂𝑇 𝐴𝐴𝑑𝑣𝑎𝑛𝑐𝑒𝑑}
A. Framework Knowledge base
Definition 2: 𝑃 (𝑖1 , 𝑖2 ) states that 𝑖1 related with 𝑖2 through property 𝑃 .
The Ontology, which is defined as formal and explicit representation of knowledge, is used for representing knowledge base. The ontology is a set of classes C, properties P and instances i. The key concepts of the domain are defined through classes. In ontology a property establishes a
𝑏𝑒𝑙𝑜𝑛𝑔𝑇 𝑂 (𝐵𝑜𝑏, 𝑂𝑤𝑛𝑒𝑟) 𝑒𝑚𝑏𝑒𝑑𝑂𝑛 (𝑆𝑝𝑜𝑡1, 𝐺𝑆𝑀 𝐶) 𝑝𝑢𝑏𝑙𝑖𝑠ℎ𝑒𝑟𝑂𝑓 (𝑆𝑝𝑜𝑡1, 𝑏𝑒𝑙𝑜𝑤𝑍𝑒𝑟𝑜𝑇 𝑒𝑚𝑝𝐸𝑣𝑒𝑛𝑡)
Table I: Access polices for different services Services
Polices represented by SWRL
SenaaS
𝐴𝑐𝑡𝑜𝑟(?𝐴) ∧ 𝑏𝑒𝑙𝑜𝑛𝑔𝑇 𝑜(?𝐴, ?𝐴𝑇 ) ∧ 𝑆𝑒𝑛𝑎𝑎𝑆(?𝑆) ∧ 𝑎𝑙𝑙𝑜𝑤𝑒𝑑𝑇 𝑜(?𝑆, ?𝐴𝑇 ) → 𝑐𝑎𝑛𝐴𝑐𝑐𝑒𝑠𝑠𝑇 𝑜(?𝐴, ?𝑆) 𝐴𝑐𝑡𝑜𝑟(?𝐴) ∧ 𝑏𝑒𝑙𝑜𝑛𝑔𝑇 𝑜(?𝐴, ?𝐴𝑇 ) ∧ 𝑀 𝑜𝑛𝑖𝑡𝑜𝑟𝑖𝑛𝑔(?𝑀 ) ∧ 𝑎𝑙𝑙𝑜𝑤𝑒𝑑𝑇 𝑜(?𝑀, ?𝐴𝑇 ) → 𝑐𝑎𝑛𝐴𝑐𝑐𝑒𝑠𝑠𝑇 𝑜(?𝐴, ?𝑀 ) 𝐴𝑐𝑡𝑜𝑟(?𝐴) ∧ 𝑏𝑒𝑙𝑜𝑛𝑔𝑇 𝑜(?𝐴, ?𝐴𝑇 ) ∧ 𝑅𝑒𝑐𝑜𝑚𝑚𝑒𝑛𝑑𝑎𝑡𝑖𝑜𝑛(?𝑅) ∧ 𝑎𝑙𝑙𝑜𝑤𝑒𝑑𝑇 𝑜(?𝑅, ?𝐴𝑇 ) → 𝑐𝑎𝑛𝐴𝑐𝑐𝑒𝑠𝑠𝑇 𝑜(?𝐴, ?𝑅) 𝐴𝑐𝑡𝑜𝑟(?𝐴) ∧ 𝑏𝑒𝑙𝑜𝑛𝑔𝑇 𝑜(?𝐴, ?𝐴𝑇 ) ∧ 𝑆𝑂𝑇 𝐴𝐴𝑑𝑣𝑎𝑛𝑐𝑒𝑑(?𝑆𝐴) ∧ 𝑎𝑙𝑙𝑜𝑤𝑒𝑑𝑇 𝑜(?𝑆𝐴, ?𝐴𝑇 ) → 𝑐𝑎𝑛𝐴𝑐𝑐𝑒𝑠𝑠𝑇 𝑜(?𝐴, ?𝑆𝐴)
Monitoring Recommendation SOTA Advanced
Definition 3: sensor platform, which in turns enhance the interoperability. 𝑖1 , 𝑖2 , ....𝑖𝑛 : 𝑆𝐶(𝐶1 ), 𝑖𝑛𝑠𝑡𝑎𝑛𝑐𝑒𝑠 𝑖1 , 𝑖2 , ....𝑖𝑛 𝑏𝑒𝑙𝑜𝑛𝑔 𝑡𝑜 𝑐𝑙𝑎𝑠𝑠 𝐶1 This paper uses a static hard coded approach to select the Following are example of different instances: appropriate communication adapter to communicate with the real-world IoT infrastructure. However, we are planning to {𝐵𝑜𝑏, 𝐴𝑙𝑖𝑐𝑒, 𝐴𝐴𝐵𝑎𝑡𝑡𝑒𝑟𝑦𝐶𝑜𝑚𝑝𝑎𝑛𝑦, 𝐵𝐵𝐺𝑟𝑖𝑑𝐶𝑜𝑚𝑝𝑎𝑛𝑦} enhance the framework with automated selection of adapter for : 𝐴𝑐𝑡𝑜𝑟𝑠 different connected devices based on semantic representation of communication adapters. {𝑂𝑤𝑛𝑒𝑟, 𝐹 𝑟𝑖𝑒𝑛𝑑, 𝐵𝑎𝑡𝑡𝑒𝑟𝑦𝑀 𝑎𝑛𝑢𝑓 𝑎𝑐𝑡𝑢𝑟𝑒𝑟} : 𝐴𝑐𝑡𝑜𝑟𝑇 𝑦𝑝𝑒 Despite the slow uptick of applicability of semantic tech{𝐺𝑆𝑀 𝐶, 𝐺𝑆𝑀 𝐶𝑂𝑤𝑛𝑒𝑟𝑀 𝑜𝑏𝑖𝑙𝑒} : 𝐶𝑜𝑛𝑛𝑒𝑐𝑡𝑒𝑑𝑂𝑏𝑗𝑒𝑐𝑡𝑠 nologies in IoT domain, our solution tries to make existing {𝑆𝑝𝑜𝑡1, 𝑆𝑝𝑜𝑡2} : 𝑆𝑒𝑛𝑠𝑜𝑟𝑠 IoT environment more interoperable using service virtualiza{𝑇 𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒, 𝐴𝑐𝑐𝑒𝑙𝑒𝑟𝑜𝑚𝑒𝑡𝑒𝑟, 𝐿𝑜𝑐𝑎𝑡𝑖𝑜𝑛} : 𝑆𝑒𝑛𝑎𝑎𝑆 tion and semantic description. With service virtualization the framework acts as entry point for the outer world and provides {𝐵𝑎𝑡𝑡𝑒𝑟𝑦𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦} : 𝑀 𝑜𝑛𝑖𝑡𝑜𝑟𝑖𝑛𝑔 interface for functional aspects of IoT cloud’s connected objects capabilities and data by hiding details of underlying B. Access Polices sensor and actuator platform, hardware and software environOntology through OWL lacks the required expressivity for ment from the consumer of these interfaces. Having semantic granting access permissions. Addition of rules with OWL overlay of IoT cloud, it is very easy to query about the using SWRL [16] can enhance the expressivity of OWL [17]. connected device and its services. However, the ontology used In this paper the authorization policies are specified with for the semantic overlay should be simple and lightweight in terms of complexity as we have seen the success of friendSWRL. Table 1 described the authorized actors for each GSMC of-a-friend (FOAF) [25] and semantically-interlinked online service. Following these assumptions, we formulated four communities (SIOC) [26] in social networks. To restrict access to different types of services by different different access policies that correspond four different types actors, we proposed semantically enhanced access control of GSMC service. The SWRL representation of the access mechanism in this framework. Over the years, researchers policies are as follows: are working to bring the science of semantics to access The polices are evaluated using reasoner and rule engine in control. The popular Role-based Access Control (RBAC) [27], order to get the authorizations. In this outlook, we use well Attribute-based Access Control (ABAC) [28], and Contextknown Pellet API [22] for reasoning and policy evaluation. aware Access Control (CWAC) [29] models have already been VII. D ISCUSSION AND F UTURE W ORK semantically extended [30], [31], [32]. Adding semantics has The proposed IoT virtualization framework demonstrate been found to facilitate high level specification of access how sensor based services can be enabled in resource con- rights and constraints in the access control models. The use strained connected object scenario. To realize this, the proto- of semantic technologies provides flexibility to add complex typical implementation used a real-life battery driven motor constraints as well. Researchers lately supported the idea of cycle (GMSC) which has built-in sensors and is remotely using OWL and SWRL to represent policy [33]. Well-defined accessible through Internet. In order to address the dynamics semantics, expressiveness of condition and extensibility are of real-time IoT, the paper follows e-SOA principle that some of the crucial policy specification criteria [34]. Accordadds complex event processing, allowing the framework to ing Coi et al., use of OWL and SWRL for formal specification dynamically sense and respond to different events trigger by of policies supports these criteria [34]. Supporting the notion different connected objects of IoT cloud. The combined effect of adding semantics to policy, we realized the access policy of event- dispatcher and event-manager makes the system an of the model using OWL and SWRL. active response based asynchronous system in addition to a Our ongoing and future work includes the development passive demand based synchronous system. of IoT framework services micro-formats for advertising on Our exploration showed that adapter oriented approach social network sites. A real time performance analysis of the could be used to mitigate the diversity issues related to proposed framework is yet to be performed.
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