Context-Aware Environments for the Internet of Things

Cristea, Valentin; Dobre, Ciprian; Pop, Florin

doi:10.1007/978-3-642-34952-2_2

Cited by 29 publications

(19 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Distributed Systems composed out of such services are immensely valuable in applications such as the Internet of Things (IoT) [1], [2], [3]. The IoT has been predicted as a technology that will grow widely in the near future [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Composing Context-Aware Distributed Systems Using Qos and Trust Principles

Dimuthu¹,

Lahiru²,

Rajeev³

2016

STSC

View full text Add to dashboard Cite

The emergence of ubiquitous computing and the wide adoption of smart phones over the past few years require many Web Services to function in a context-aware manner. In such services, not only the functional attributes, but also the QoS attributes (e.g., response time) and the trust (i.e., the degree of compliance of a service to its specification) depend on the context of the services. Hence, when designing systems composed out of such services, it is important to consider the entire system execution context in addition to its functional and non-functional requirements. Achieving this goal requires the consideration of the context to trust-QoS dependencies, and interaction patterns between individual services. In this work, we tackle this challenge by first proposing a model that uses the context-QoS dependency information of individual services and inter-service interaction patterns to make predictions about the QoS and trust of compositions at the design phase. Then we apply the prediction model to select the optimum set of service to create the composed system. Our approach allows for better design and implementation decisions about composed systems in the early phases of the software lifecycle thereby reducing cost, time, and effort. The preliminary results show that the proposed model provides more accurate predictions and results in optimum composed systems than the prevalent approaches.

show abstract

Section: Introductionmentioning

confidence: 99%

Composing Context-Aware Distributed Systems Using Qos and Trust Principles

Dimuthu¹,

Lahiru²,

Rajeev³

2016

STSC

View full text Add to dashboard Cite

show abstract

“…This concept was first introduced in 1999 but the intensification of the implementation efforts was possible due to the confluence of a few factors: the increased performance of the hardware devices, wireless connectivity having spectacular performances and the possibility to control a wide range of simple and cheap devices with the aid of intelligent processes running in Cloud [2] [7]. In Internet of Things in 2020 A Roadmap for the Future [8], IoT is described as a "network composed of things/objects which have identities, virtual personalities which operates in intelligent spaces and use intelligent interfaces to connect and communicate with users having various environmental and social contexts".…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based Architecture for Farm Management

Mocanu

Cristea

Negru

et al. 2015

2015 20th International Conference on Control Systems and Computer Science

Self Cite

View full text Add to dashboard Cite

Cloud Computing technologies can fundamentally contribute to business processes in agriculture, through enhancing traditional farm management systems, by integrating processes like, monitoring, operation farm management, with cloud specific capabilities, which will lead to an increased productivity. Based on this idea we propose, in this paper, a cloud-based architecture that helps creating an integrated intelligent system for improving product quality and business development in the farms field. Farmers cloud access diverse set of information and data related to his farm, in an integrated and unified approach in order to take informed decisions or to be supported in decision taking process.

show abstract

“…7 In object-based modeling, classes are used to promote the concept of encapsulation and abstraction. 11,12 These approaches target to resolve many issues due to heterogeneity, interoperability, scale of experiment, etc., which ease the resource discovery. 8 The ontology-based technique makes use of semantic technology and reasoning engine.…”

mentioning

confidence: 99%

“…For the purpose, a number of the approaches exist, eg, supervised learning, unsupervised learning, fuzzy logic, rules, probabilistic, first-order predicate logic having their own pros and cons but no single approach is efficient. 11,12 These approaches target to resolve many issues due to heterogeneity, interoperability, scale of experiment, etc., which ease the resource discovery. 7 The resource discovery enables an end user to use knowledge without prior information that lead to a challenging task for data providers, publishers, and brokers, and is defined broadly based on the following domains: (i) identification, assessment, extraction, formatting and (ii) analysis, exploitation.…”

mentioning

confidence: 99%

Context‐aware search optimization framework on the internet of things

Bharti

Kumar

Saxena

2018

Concurrency and Computation

View full text Add to dashboard Cite

The resource discovery on IoT paradigm requires to be efficient with respect to modeling, storage, processing, and validation of the gathered data. These requirements face challenges like interoperability, heterogeneity, etc, with respect to exponentially growing interconnected resources across distinct application domains and drastically changing search metrics. It leads resource discovery to emerge as a non-linear constrained-specific problem that need to be linearized for its optimization with reduced complexity. Keeping the perspective, a context-aware search optimization framework on the internet of things is introduced, which targets knowledge presentation through schema, discovery via a multi-modal search algorithm, and its optimization through an Iterative Gradient Descent algorithm. The multi-modal search algorithm through keywords, value or spatial-temporal indices performs resource discovery by finding the suited matches as a search set from a search-space. The search set is further evaluated via the iterative gradient descent algorithm for optimization through the usage of iterative and convergence properties of the gradient descent. The search efficiency is tested using various objective functions and resources on MATLAB and is compared with Newton and Quasi-Newton methods. The obtained results depict the efficiency of the algorithm graphically with reference to the searching time, such as validate the system performance. KEYWORDS electronic toll plaza, Internet of Things, iterative gradient descent, multi-modal search, resource discovery, search optimization INTRODUCTIONThe Internet of Things (IoT) paradigm is expected to have almost 212 billion resources, as reported by Gartner, 1 which require to be identifiable to communicate and to interact with each other. These resources are defined with context, ie, a set of inter-related events that share logical and timing relation among each other to provide relevant information such as location and type to characterize them. 2 The events belong to various stages such as data collection, processing, modeling, storage, reasoning, and validation. Each stage with respect to context has their own needs, models, and techniques, which are to be studied thoroughly.For the Context Collection, the IoT platform requires various type of sensors that are compatible with different applications. For example, a temperature tracking system is designed that uses a client-server architecture having a Raspberry Pi and system-on-a-chip (SoC) device. It senses the temperature and forwards it to the end user using an Android mobile application. 3 Another novel service, Social Internet of Things framework (SIoT)is proposed to enhance intelligence, for dynamic IoT service discovery in smart spaces, and is based on a cognitive reasoning approach. 4Moving to the Context Modeling, there exist various techniques such as key-value, mark-up scheme, graphical, logic-based, and ontology-based to model the gathered data. The key-value-based modeling technique creates text or binary file a...

show abstract

Context-Aware Environments for the Internet of Things

Cited by 29 publications

References 30 publications

Composing Context-Aware Distributed Systems Using Qos and Trust Principles

Composing Context-Aware Distributed Systems Using Qos and Trust Principles

Cloud-Based Architecture for Farm Management

Context‐aware search optimization framework on the internet of things

Contact Info

Product

Resources

About