An IoT approach for context-aware smart traffic management using ontology

Goel, Deepti; Chaudhury, Santanu; Ghosh, Hiranmay

doi:10.1145/3106426.3106499

Cited by 23 publications

(25 citation statements)

References 10 publications

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“…In IoT, pattern recognition algorithms have been used in several works. This paper is interested in the application of the two main types of pattern recognition algorithms: classification [6][7][8][9][10][11][12][13][14][15], and clustering [16][17][18][19][20] algorithms. The survey in this section makes possible, on the one hand, to clear the scope of the current use of these algorithms and, on the other hand, to motivate the application of these algorithms to sustain of non-functional requirements, such as QoS in IoT platforms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In [13], the authors define a context-aware service framework for IoT based on ontologies, to regulate smooth traffic flow in smart cities by analyzing real-time traffic environment. The framework uses the sensors and IoT devices in the Smart Traffic System to capture the user's preferences and context information, which can be travel time, weather conditions or the driving patterns.…”

Section: Literature Reviewmentioning

confidence: 99%

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Experimental comparison of the diagnostic capabilities of classification and clustering algorithms for the QoS management in an autonomic IoT platform

Morales¹,

Ouedraogo²,

Aguilar³

et al. 2019

SOCA

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The IoT platforms must allow the communication between the Applications and Devices according to their non-functional requirements. One of the main non-functional requirements is the Quality of Service (QoS). In a previous work has been defined an Autonomic Internet of Things (IoT) platform for the QoS Management, based on the concept of autonomic cycle of data analysis tasks. In this platform have been defined two autonomic cycles, one based on a classification task that determines the current operational state to define the set of tasks to execute in the communication system to guarantee a given QoS. The other one is based on a clustering task that discovers the current operational state, and based on it, determines the set of tasks to be executed in the communication system. This paper analyzes the diagnostic capabilities of the system based on both approaches, using different metrics. For that, a real scenario has been considered, with simulations that have generated data to test both tasks. Each technique has different aspects to be considered for a correct QoS management in the context of IoT platforms. The classification technique can determine very well the learned operational states, but the clustering approach can carry out a more detailed description of the operational states. Additionally, due to the classification and clustering technique used, called LAMDA (Learning Algorithm for Multivariate Data Analysis), the paper analyzes the operational state profile determined by them, which is very useful in a diagnostic process. IntroductionThe classical components of an IoT ecosystem, according to the standards are [1][2]: Devices, Network, IoT platform, and Application. Devices are the responsible of the collection of the data, and in some case, the preprocessing of these data and the execution of specific tasks; the Applications exploit the advantage of a set of interconnected Devices in order to carry out actions in the environment; the Network allows the communication between the different components in the IoT platform, particularly, between the devices and applications; and finally, the IoT platform manages smart capabilities like the autonomy, security, among other things, and can support heterogeneous non-functional requirements of Applications and Devices. On the one hand, the IoT platforms are based on European Telecommunications Standards Institute (ETSI) SmartM2M and OneM2M [1][2] specifications, which define that an IoT platform consists mainly of two types of entities, IoT server(s) and gateway(s). These entities are implemented as Chain of Network Functions (NFs) that achieve a connection between applications and devices. Namely, NFs are processing functions, with defined functional behaviors and interfaces (e.g. a load balancing function applied on Internet protocol (IP) packets). On the other hand, these platforms are not able to sustain Quality of Service (QoS) to IoT missions critical Applications like remote surgery [34]. As per [34], QoS in IoT is one of the critical factors, which nee...

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Experimental comparison of the diagnostic capabilities of classification and clustering algorithms for the QoS management in an autonomic IoT platform

Morales¹,

Ouedraogo²,

Aguilar³

et al. 2019

SOCA

View full text Add to dashboard Cite

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“…Other examples constituting the use of procedural data include real‐time QoS‐aware health care data transmission (Satija et al, ), industrial process failure alerts (Xu, Sun, Wan, Liu, & Song, ), agricultural produce management across vast geographical regions (Brewster et al, ), and automated vehicular fault diagnosis (Rani et al, ). Priori : It is the knowledge that is deduced irrespective of experience or historical evidence, and purely on the basis of first principles. For example, the ability of a computing machine to identify between an integer and a string data type or traffic flow control on city roads during peak traffic hours (Goel et al, ). This class of knowledge discovery can be further divided into two types— Dispersed and Situated . Dispersed : A priori dispersed knowledge type in IoT has fragmented instantaneous information having no single source of truth but needs to assemble truths from various sources to arrive at a decision.…”

Section: Knowledge Discovery For Iotmentioning

confidence: 99%

“…Priori : It is the knowledge that is deduced irrespective of experience or historical evidence, and purely on the basis of first principles. For example, the ability of a computing machine to identify between an integer and a string data type or traffic flow control on city roads during peak traffic hours (Goel et al, ). This class of knowledge discovery can be further divided into two types— Dispersed and Situated . Dispersed : A priori dispersed knowledge type in IoT has fragmented instantaneous information having no single source of truth but needs to assemble truths from various sources to arrive at a decision.…”

Section: Knowledge Discovery For Iotmentioning

confidence: 99%

Knowledge discovery for enabling smart Internet of Things: A survey

Misra¹,

Mukherjee²,

Roy

2018

WIREs Data Min & Knowl

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The use of knowledge discovery on the Internet of Things (IoT) and its allied domains is undeniably one of the most indispensable ones, which results in optimized placement architectures, efficient routing protocols, device energy savings, and enhanced security measures for the implementation. The absence of knowledge discovery in IoT results in just an implementation of large‐scale sensor networks, which generates a huge amount of data, and which needs, an often under‐optimized, processing for actionable outputs. In this survey, we explore various domains of IoT for which knowledge discovery is inseparable from the application, and show how it benefits the overall implementation of the IoT architecture. This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Machine Learning Application Areas > Internet and Web‐Based Applications Algorithmic Development > Spatial and Temporal Data Mining

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“…In this way, we observe that the smart grid assess the transmission rate and the distance between network nodes through cognitive processes and draw a routing path to deliver RDF triple towards semantic reservoir [5], [6]. Therefore, the grid finds individual data transmission rate of a node Node T R = ∑…”

Section: Introductionmentioning

confidence: 99%

A Knowledge-Based Path Optimization Technique for Cognitive Nodes in Smart Grid

Qureshi

Siddiqui

Abbas

et al. 2018

2018 IEEE Global Communications Conference (GLOBECOM)

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The cognitive network uses cognitive processes to record data transmission rate among nodes and applies selflearning methods to trace data load points for finding optimal transmission path in the distributed computing environment. Several industrial systems, e.g., data-centers, smart grids, etc., have adopted this cognitive paradigm and retrieved the least HOP count paths for processing huge datasets with minimum resource consumption. Therefore, this technique works well in transmitting structured data such as 'XML', however, if the data is in unstructured format i.e. 'RDF', the transmission technique wraps it with the same layout of payload and eventually returns inaccuracy in calculating traces of data load points due to the abnormal payload layout. In this paper, we propose a knowledgebased optimal routing path analyzer (RORP) that resolves the transmission wrapping issue of the payload by introducing a novel RDF-aware payload-layout. The proposed analyzer uses the enhanced payload layout to transmit unstructured RDF triples with an append pheromone (footsteps) value through cognitive nodes towards the semantic reservoir. The grid performs analytics and returns least HOP count path for processing huge RDF datasets in the cognitive network. The simulation results show that the proposed approach effectively returns the least HOP count path, enhances network performance by minimizing the resource consumption at each of the cognitive nodes and reduces traffic congestion through knowledge-based HOP count analytics technique in the cognitive environment of the smart grid.

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An IoT approach for context-aware smart traffic management using ontology

Cited by 23 publications

References 10 publications

Experimental comparison of the diagnostic capabilities of classification and clustering algorithms for the QoS management in an autonomic IoT platform

Experimental comparison of the diagnostic capabilities of classification and clustering algorithms for the QoS management in an autonomic IoT platform

Knowledge discovery for enabling smart Internet of Things: A survey

A Knowledge-Based Path Optimization Technique for Cognitive Nodes in Smart Grid

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