Semantic web based architecture for managing hardware heterogeneity in wireless sensor network

Lecture Notes in Computer Science

et al. 2018

The development of the Semantic Web of Things (SWoT) is challenged by the nature of IoT architectures where constrained devices are connected to powerful cloud servers in charge of processing remotely collected data. Such an architectural pattern introduces multiple bottlenecks constituting a hurdle for scalability, and degrades the QoS parameters such as response time. This hinders the development of a number of critical and time-sensitive applications. As an alternative to this Cloudcentric architecture, Fog-enabled architectures can be considered to take advantage of the myriad of devices that can be used for partially processing data circulating between the local sensors and the remote Cloud servers. The approach developed in this paper is a contribution in this direction: it aims to enable rule-based processing to be deployed closer to data sources, in order to foster the implementation of semantic-enabled applications. For this purpose, we define a dynamic deployment technique for rule-based semantic reasoning on Fog nodes. This technique has been evaluated according to a strategy improving information delivery delay to applications. The implementation in Java based on SHACL rules has been executed on a platform containing a server, a laptop and a Raspberry Pi, and is evaluated on a smart building use case where both distribution and scalability have been considered.

Section: Rule Representation and Deploymentmentioning

confidence: 99%

Section: Rule Representation and Deploymentmentioning

confidence: 99%

Towards Cooperative Semantic Computing: A Distributed Reasoning Approach for Fog-Enabled SWoT

Lecture Notes in Computer Science

et al. 2018

“…To ensure a coherence between the KB of the different nodes, new production capability notifications are propagated upstream by nodes upon reception. As EDR relies on a local representation of the network for each node, when generating the notification to propagate, the node acts as a proxy, and claims to be the producer of the new observation type.…”

Section: The Edr Algorithmmentioning

confidence: 99%

Reasoning on the edge or in the cloud?

Internet Technology Letters

et al. 2018

The emergence of the Internet of Things (IoT) and the Semantic Web of Things is leading to intensive cloud processing executing reasoning rules over large volumes of enriched data. However, the role of the cloud in existing approaches as a central point for both data processing and provisioning reduces scalability and introduces latency. This paper sketches a new approach for rule-based reasoning, enabling distributed rule evaluation on edge nodes, and reducing the latency for IoT applications while avoiding the total dependence on a central node. This approach is evaluated in a simulated smart building. KEYWORDScloud computing, edge computing, rule-based reasoning, semantic web of things INTRODUCTIONThe Semantic Web of Things (SWoT) emerged from the interaction between the Internet of Things (IoT) and the Semantic Web (SW). It is driven by the need for interoperability at the core of the IoT, to which the expressiveness of the SW's formalisms is seen as a solution. 1 Integrating SW technologies into the IoT allows the transformation of raw data into rich, meaningful information that can be distributed with knowledge to applications, thus breaking the vertical silos of domain-specific development.In this configuration, applications both hold the business logic and are consumers of the information produced within the network. However, the SWoT is also challenged by the constrained and dynamic nature of the IoT. This does not comply to the traditional principles and application domains of the SW. Typical IoT deployments follow a hierarchical architecture captured in the Lower, Middle and Upper Node (LMU-N) architectural pattern, 2 a multitiered IoT architectural pattern where devices connect to cloud servers via intermediate gateways. The entry point to the network for applications is usually a powerful node, for example, a cloud server offering web services. Data collected by constrained nodes on the edge should therefore be moved upstream toward server nodes so that it can be processed and sent to relevant applications connected to the cloud. An application's business logic can be implemented by production rules, where high-level symptoms are inferred from lower-level data, for example, inferring the high-level event "Such a room is an uncomfortable place" from luminosity and temperature observations. These rules are generally applied in the cloud ( (3,4) ), forwarding only the result to applications in order to reduce bandwidth consumption and its processing load. However, this architecture creates a bottleneck, since the data must be concentrated in a single node, the cloud server, for processing. This leads to multiple issues. Firstly, it is not a scalable design, and it is unfit for large deployments, 5 such as in smart cities scenarii. Secondly, the cost of semantic reasoning increases rapidly with the size of the knowledge base (KB), 6 thus processing all rules on a growing data instance introduces a delay, which can be unacceptable for time-sensitive applications (ie, e-health, security, emergencies). Thir...

“…In [22], reasoning nodes act as proxy for the capabilities of legacy nodes unable to process enriched data. In EDR, each reasoning-enabled node n has a similar role, with respect to both its sensors and Lower(n) that are proxied toward U pper(n).…”

Section: B Knowledge Considered By Nodes Applying Edrmentioning

confidence: 99%

A Distributed Scalable Approach for Rule Processing: Computing in the Fog for the SWoT

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)

et al. 2018

The development of the Semantic Web of Things (SWoT) is challenged by the nature of IoT deployment architectures, where constrained devices collect data processed remotely by powerful Cloud servers. Such a deployment pattern introduces bottlenecks constituting a hurdle for scalability, and increases response time. This hinders the development of a number of critical and time-sensitive applications. Enabling the deployment of the Semantic Web stack closer to the constrained devices of the IoT may foster the development of time-sensitive interoperable applications, while reducing forwarding the user data to remote third party Cloud servers. The approach we develop in this paper is a contribution towards this direction, and aims to enable rulebased reasoning closer to sensors producing IoT data. For this purpose, we define a distributed scalable semantic processing algorithm by dynamically propagating deduction rules on Fog nodes. Our goal is to shorten the time needed to deliver high level information deduced from the collected data. This approach is evaluated on a smart building use case where both distribution and scalability have been considered.