A Semantic-enabled Framework For Future Internet Of Things Applications

While the challenge of connecting Internet of Things (IoT) devices at the lowest layer has been widely studied, integrating and interoperating huge amounts of sensed data of heterogeneous IoT devices is becoming increasingly important because of the possibility of consuming such data in supporting many potential novel IoT applications. A common approach to processing and consuming IoT data is a centralized paradigm: sensor data is sent over the network to a comparatively powerful central server or a cloud service, where all processing takes place. However, this approach has some limitations as it requires devices to interact directly with a cloud which is not cost effective. First, it has high demands on the device's storage and computational capabilities. Second, as devices grow rapidly in a deployment area, sending all the data to a centralized cloud server requires high network bandwidth. Moreover, this often creates data privacy concerns as all raw data will be sent to a centralized place. To address the above limitations for building future Internet of Things applications, we present an early design of a novel framework that combines Internet of Things, Semantic Web, and Big Data concepts. We not only present the core components to build an IoT system, but also list existing alternatives with their merits. This framework aims to incorporate open standards to address the potential challenges in building future IoT applications. Therefore, our discussion revolves around open standards to build the framework, rather than proprietary standards

Uma Revisão Sistemática sobre Descrição Semântica na Internet das Coisas

A Internet das Coisas (Internet of Things - IOT) está criando um ecossistema de objetos conectados à Internet, porém que são heterogêneos em termos de recursos, características, natureza, protocolos, ações e tecnologias. Neste cenário, a descrição das coisas (Thing Description - TD) mostra-se como uma alternativa para descrever de forma padronizada os objetos da IOT e permitir, entre outras possibilidades, uma melhor interoperabilidade. Visto o crescente interesse de pesquisa e as oportunidades de aplicação da TD, este trabalho apresenta uma revisão sistemática deste tema, visando identificar áreas de aplicação, técnicas, tecnologias e resultados com o uso da descrição das coisas. 324 trabalhos foram identificados, dos quais 46 foram selecionados para análise, os quais foram classificados em seis diferentes áreas

Concevoir des applications internet des objets sémantiques

According to Cisco's predictions, there will be more than 50 billions of devices connected to the Internet by 2020.The devices and produced data are mainly exploited to build domain-specific Internet of Things (IoT) applications. From a data-centric perspective, these applications are not interoperable with each other.To assist users or even machines in building promising inter-domain IoT applications, main challenges are to exploit, reuse, interpret and combine sensor data.To overcome interoperability issues, we designed the Machine-to-Machine Measurement (M3) framework consisting in:(1) generating templates to easily build Semantic Web of Things applications, (2) semantically annotating IoT data to infer high-level knowledge by reusing as much as possible the domain knowledge expertise, and (3) a semantic-based security application to assist users in designing secure IoT applications.Regarding the reasoning part, stemming from the 'Linked Open Data', we propose an innovative idea called the 'Linked Open Rules' to easily share and reuse rules to infer high-level abstractions from sensor data.The M3 framework has been suggested to standardizations and working groups such as ETSI M2M, oneM2M, W3C SSN ontology and W3C Web of Things. Proof-of-concepts of the flexible M3 framework have been developed on the cloud (http://www.sensormeasurement.appspot.com/) and embedded on Android-based constrained devices.Selon les prévisions de Cisco , il y aura plus de 50 milliards d'appareils connectés à Internet d'ici 2020. Les appareils et les données produites sont principalement exploitées pour construire des applications « Internet des Objets (IdO) ». D'un point de vue des données, ces applications ne sont pas interopérables les unes avec les autres. Pour aider les utilisateurs ou même les machines à construire des applications 'Internet des Objets' inter-domaines innovantes, les principaux défis sont l'exploitation, la réutilisation, l'interprétation et la combinaison de ces données produites par les capteurs. Pour surmonter les problèmes d'interopérabilité, nous avons conçu le système Machine-to-Machine Measurement (M3) consistant à: (1) enrichir les données de capteurs avec les technologies du web sémantique pour décrire explicitement leur sens selon le contexte, (2) interpréter les données des capteurs pour en déduire des connaissances supplémentaires en réutilisant autant que possible la connaissance du domaine définie par des experts, et (3) une base de connaissances de sécurité pour assurer la sécurité dès la conception lors de la construction des applications IdO. Concernant la partie raisonnement, inspiré par le « Web de données », nous proposons une idée novatrice appelée le « Web des règles » afin de partager et réutiliser facilement les règles pour interpréter et raisonner sur les données de capteurs. Le système M3 a été suggéré à des normalisations et groupes de travail tels que l'ETSI M2M, oneM2M, W3C SSN et W3C Web of Things. Une preuve de concept de M3 a été implémentée et est disponible sur le web (http://www.sensormeasurement.appspot.com/) mais aussi embarqu

Low Latency Reliable Data Sharing Mechanism for UAV Swarm Missions

The use of Unmanned Aerial Vehicle (UAV) swarms is increasing in many commercial applications as well as military applications (such as reconnaissance missions, search and rescue missions). Autonomous UAV swarm systems rely on multi-node interhost communication, which is used in coordination for complex tasks. Reliability and low latency in data transfer play an important role in the maintenance of UAV coordination for these tasks. In these applications, the control of UAVs is performed by autonomous software and any failure in data reception may have catastrophic consequences. On the other hand, there are lots of factors that affect communication link performance such as path loss, interference, etc. in communication technology (WIFI, 5G, etc.), transport layer protocol, network topology, and so on. Therefore, the necessity of reliable and low latency data sharing mechanisms among UAVs comes into prominence gradually. This paper examines available middleware solutions, transport layer protocols, and data serialization formats. Based on evaluation results, this research proposes a middleware concept for mobile wireless networks like UAV swarm systems

Data Annotation and Ontology Provisioning for Semantic Applications in Virtualized Wireless Sensor Networks

In recent years, virtualization in Wireless Sensor Networks (WSNs) has become very popular for many reasons including efficient resource management, proper sharing and using the same WSN physical infrastructure by multiple applications and services. Semantic applications are very much pertinent to provide situational awareness to the end-users. Incorporating semantic applications in the virtualized WSNs can play a crucial role in providing contextual information to understand the situation, increase usability and interoperability. However, provisioning of semantic applications in virtualized WSNs remains as a big challenge. The reason is the data collected by the virtual sensors needs to be annotated in-network, and the pre-requisite of the data annotation process is to have an ontology that needs to be provisioned, i.e., developed, deployed and managed. Unfortunately, annotating sensor data and ontology provisioning in virtualized WSNs is not straightforward because of limited resources of sensors, on-demand creation of virtual sensors, and unpredictable lifetime. As the existing researches do not consider data annotation in virtualized WSN infrastructure level, these solutions are domain specific and lack of providing support for multiple applications. Moreover, the major drawback of the current ontology provisioning mechanisms requires domain experts to develop, deploy, and manage the ontologies in WSNs. This thesis aims to propose a solution for provisioning of multiple semantic applications in the virtualized WSNs. The main contribution of this thesis is twofold. First, we have proposed an architecture to annotate sensor data in the virtualized WSN infrastructure and defined an ontology in sensor domain to perform data annotation. Second, we have proposed an architecture for provisioning ontology in the virtualized WSNs that consists of an ontology provisioning center, an ontology-enabled virtualized WSN, and an ontology deployment protocol. The proposed architectures use overlay network as a foundation. We have built a proof-of-concept prototype for a semantic wildfire monitoring application in the cloud environment using the Google App Engine. In order to evaluate the viability of the proposed architecture, we have made performance measurement of the implemented prototype. We ran a simulation to justify our proposed ontology provisioning protocol

An Adaptive Mediation Framework for Workflow Management in the Internet of Things

Tärkavad värkvõrksüsteemid koosnevad arvukast hulgast heterogeensetest füüsilistest seadmetest, mis ühenduvad Internetiga. Need seadmed suudavad pidevalt ümbritseva keskkonnaga suhelda ja osana lõppkasutaja rakendusestest edendada valdkondi nagu tark kodu, e-tervis, logistika jne. Selleks, et integreerida füüsilisi seadmeid värkvõrgu haldussüssteemidega, on töövoo haldussüsteemid kerkinud esile sobiva lahendusena. Ent töövoo haldussüsteemide rakendamine värkvõrku toob kaasa reaalajas teenuste komponeerimise väljakutseid nagu pidev teenusavastus ja -käivitus. Lisaks kerkib küsimus, kuidas piiratud resurssidega värkvõrgu seadmeid töövoo haldussüsteemidega integreerida ning kuidas töövooge värkvõrgu seadmetel käivitada. Tööülesanded (nagu pidev seadmeavastus) võivad värkvõrgus osalevatele piiratud arvutusjõudluse ja akukestvusega seadmetele nagu nutitelefonid koormavaks osutuda. Siinkohal on võimalikuks lahenduseks töö delegeerimine pilve. Käesolev magistritöö esitleb kontekstipõhist raamistikku tööülesannete vahendamiseks värkvõrgurakendustes. Antud raamistikus modelleeritakse ning käitatakse tööülesandeid kasutades töövoogusid. Raamistiku prototüübiga läbi viidud uurimus näitas, et raamistik on võimeline tuvastama, millal seadme avastusülesannete pilve delegeerimine on kuluefektiivsem. Vahel aga pole töövoo käitamistarkvara paigaldamine värkvõrgu seadmetele soovitav, arvestades energiasäästlikkust ning käituskiirust. Käesolev töö võrdles kaht tüüpi töövookäitust: a) töövoo mudeli käitamine käitusmootoriga ning b) töövoo mudelist tõlgitud programmikoodi käitamine. Lähtudes katsetest päris seadmetega, võrreldi nimetatud kahte meetodit silmas pidades süsteemiressursside- ning energiakasutust.Emerging Internet of Things (IoT) systems consist of great numbers of heterogeneous physical entities that are interconnected via the Internet. These devices can continuously interact with the surrounding environment and be used for user applications that benefit human life in domains such as assisted living, e-health, transportation etc. In order to integrate the frontend physical things with IoT management systems, Workflow Management Systems (WfMS) have gained attention as a viable option. However, applying WfMS in IoT faces real-time service composition challenges such as continuous service discovery and invocation. Another question is how to integrate resource-contained IoT devices with the WfMS and execute workflows on the IoT devices. Tasks such as continuous device discovery can be taxing for IoT-involved devices with limited processing power and battery life such as smartphones. In order to overcome this, some tasks can be delegated to a utility Cloud instance. This thesis proposes a context-based framework for task mediation in Internet of Things applications. In the framework, tasks are modelled and executed as workflows. A case study carried out with a prototype of the framework showed that the proposed framework is able to decide when it is more cost-efficient to delegate discovery tasks to the cloud. However, sometimes embedding a workflow engine in an IoT device is not beneficial considering agility and energy conservation. This thesis compared two types of workflow execution: a) execution of workflow models using an embedded workflow engine and b) execution of program code translations based on the workflow models. Based on experiments with real devices, the two methods were compared in terms of system resource and energy usage

Join query enhancement processing (jqpro) with big rdf data on a distributed system using hashing-merge join technique

Semantic web technologies have emerged in the last few years across different fields of study and their data are still growing rapidly. Specifically, the increased data storage and publishing capabilities in standard open web formats have made the technology much more successful. So, the data have become readable by humans, and they can be processed on a computer. The demand for complex multiple RDF queries is becoming significant with the increasing number of RDF triples. Such complex queries occasionally produce many common subexpressions. It is therefore extremely challenging to reduce the amount of RDF queries and transmission time for a vast number of related RDF data. Moreover, Recent literature shows that join query processing of Big RDF data has introduced many problems with respect to execution time and throughput. The hash-based encoding induces low execution time, which takes a long time to load and hence does not load all graphs. This is because the Resource Description Framework (RDF) collects and analyses large data in swarms, thereby having to deal with the inherent challenge of efficient swarm storage. The effective storage and data retrieval, which could be applied to high amounts of possible schema-less data, has also proven exceedingly difficult for RDF data storage. For instance, it is particularly difficult to view semantic and SPARQL query languages, as well as huge and complex graph patterns. To address this problem, a Join Query Processing Model (JQPro) is introduced for Big RDF data. The objectives of this research are: (i) formulate plan generator algorithms for join query processing on the basis of the previous research. (ii) develop an enhancement model of Join Query Processing (JQPro) based on SPARQL and Hadoop MapReduce using hashing-merge join technique to process Big RDF Data. (iii) evaluate and compare the performance based on the execution time, throughput, and CPU utilization of the JQPro model with existing models. On the other hand, the throughput was employed to measure the units of information that a system can process in each time frame. In addition, the CPU utilization was used in the big join query processing as an important resource element particularly during the map, to reduce phases. Furthermore, the hash-join and Sort-Merge algorithms were used to generate the join query processing, and this was employed due to their capacity to allow for more data sets to be joined. Both processes were sorted by algorithms on join attributes and the sorted relations was merged. Therefore, the join column sorted the groups of datasets with the same value. The sort–merge–join algorithm sorts the datasets on the joining attribute and then searches for tuples by merging the two datasets. Then, a processing framework for RDF queries was introduced and the benchmark was used for performance evaluation. Finally, the validation was conducted by standard statistical analysis to validate and compare the performance of the JQPro model with current models. In addition, the synthetic benchmarks Lehigh University Benchmark (LUBM) and Waterloo SPARQL Diversity Test Suite (WatDiv) v06 were used for measurement. The experiment was carried out on three datasets ranging from 10 million to 1 billion RDF triples produced by the generator of WatDiv data with a scale factor of 10, 100 and 1000, respectively. A selective dataset for each experimental query was also used for the processing of RDFs with a LUBM benchmark in sizes 500, 1000 and 2000 million triples. The result revealed that there is a strong correlation between execution time and throughput with a strength of 99.9% percent as confirmed by the Pearson correlation coefficient. Furthermore, the findings show that the JQPro solution was comparable to gStore RDF-3X, RDFox and PARJ and the percentage of improved performance was 87.77% in terms of execution time. The CPU utilization was significantly increased by extensive mapping and reduced code computing. It is therefore inferred that the JQPro solution is timely and innovative, as it provides an efficient execution time and CPU utilization where users could perform better queries for Big RDF data processing in a seamless manne

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

Network-enabled sensing and actuation devices are key enablers to connect real-world objects to the cyber world. The Internet of Things (IoT) consists of the network-enabled devices and communication technologies that allow connectivity and integration of physical objects (Things) into the digital world (Internet). Enormous amounts of dynamic IoT data are collected from Internet-connected devices. IoT data are usually multi-variant streams that are heterogeneous, sporadic, multi-modal, and spatio-temporal. IoT data can be disseminated with different granularities and have diverse structures, types, and qualities. Dealing with the data deluge from heterogeneous IoT resources and services imposes new challenges on indexing, discovery, and ranking mechanisms that will allow building applications that require on-line access and retrieval of ad-hoc IoT data. However, the existing IoT data indexing and discovery approaches are complex or centralised, which hinders their scalability. The primary objective of this article is to provide a holistic overview of the state-of-the-art on indexing, discovery, and ranking of IoT data. The article aims to pave the way for researchers to design, develop, implement, and evaluate techniques and approaches for on-line large-scale distributed IoT applications and services