LISA: Lightweight context-aware IoT service architecture

Gochhayat, Sarada Prasad; Kaliyar, Pallavi; Conti, Mauro; Tiwari, Prayag; Prasath, V. B. Surya; Gupta, Deepak; Khanna, Ashish

doi:10.1016/j.jclepro.2018.12.096

Cited by 44 publications

(21 citation statements)

References 20 publications

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“…Data loss in neighboring nodes may occur during this data transfer due to the location of the sensors, limited battery conditions, rules of routing algorithms, etc. Data transfer process in WSN consists of 4 stages [40]. These stages are data collection, data processing, data packaging and data transfer.…”

Section: Methodsmentioning

confidence: 99%

Deep learning techniques of losses in data transmitted in wireless sensor networks

Ersoy¹,

Aksoy²

2021

Turk J Elec Eng & Comp Sci

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Wireless sensor network systems are frequently used today along with the rapidly developing technology. Wireless sensor networks, which form the basis of the Internet of Things, have a wide user base in the world. For example, wireless sensor networks are used in many areas from education to health, from military applications to home applications. It enables the data obtained from the sensors to be transferred between nodes with the help of end-to-end wireless protocols. In parallel with the increasing number of nodes in WSN, data traffic density also increases. Due to the limitations of the WSN network, lost packet rates also increase with increasing data traffic. In this study, a data set was created by examining the data transfers of different amounts of WSN nodes placed in different places. The effects of the number of sensors and the distance between them were obtained from the data set. In this study, a data set was created by collecting the data from the sensor nodes placed at 1500m x 1500m intervals in the ns-3 discrete event emulator program. Today, with the rapid development of technology, deep learning methods which are one of the artificial intelligence methods, are also used in WSN. In this study, the loss rate in the transferred data packets was tried to be estimated with the highest accuracy by using Deep Belief Network (DBN), Recurrent Neural Network (RNN) and Deep Neural Network (DNN) deep learning techniques over the obtained dataset. Of these three deep learning methods, DNN deep learning method was found to accurately estimate the loss rate in the transferred data packets with an accuracy rate of 88.50%.

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Section: Methodsmentioning

confidence: 99%

Deep learning techniques of losses in data transmitted in wireless sensor networks

Ersoy¹,

Aksoy²

2021

Turk J Elec Eng & Comp Sci

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“…The animal information is also helpful for the study of the welfare of the animals, and the concept of animal rights (Andersen and Winter, 2017). The technologies for the Internet of Things (Gochhayat et al, 2019;Kumar et al, 2019;Bebortta et al, 2020;Qian et al, 2020) are also widely used in different domains for understanding the functionality and structure of the brain and address some problems in human daily life. De Albuquerque et al (2017) investigate the applications of brain computer interface systems.…”

Section: Related Workmentioning

confidence: 99%

Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model

Zhu

Zeng

Wang

et al. 2020

Front. Hum. Neurosci.

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Large-scale neuroscience literature call for effective methods to mine the knowledge from species perspective to link the brain and neuroscience communities, neurorobotics, computing devices, and AI research communities. Structured knowledge can motivate researchers to better understand the functionality and structure of the brain and link the related resources and components. However, the abstracts of massive scientific works do not explicitly mention the species. Therefore, in addition to dictionary-based methods, we need to mine species using cognitive computing models that are more like the human reading process, and these methods can take advantage of the rich information in the literature. We also enable the model to automatically distinguish whether the mentioned species is the main research subject. Distinguishing the two situations can generate value at different levels of knowledge management. We propose SpecExplorer project which is used to explore the knowledge associations of different species for brain and neuroscience. This project frees humans from the tedious task of classifying neuroscience literature by species. Species classification task belongs to the multi-label classification which is more complex than the single-label classification due to the correlation between labels. To resolve this problem, we present the sequence-to-sequence classification framework to adaptively assign multiple species to the literature. To model the structure information of documents, we propose the hierarchical attentive decoding (HAD) to extract span of interest (SOI) for predicting each species. We create three datasets from PubMed and PMC corpora. We present two versions of annotation criteria (mention-based annotation and semantic-based annotation) for species research. Experiments demonstrate that our approach achieves improvements in the final results. Finally, we perform species-based analysis of brain diseases, brain cognitive functions, and proteins related to the hippocampus and provide potential research directions for certain species.

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“…The works presented in [19][20][21] suggest the idea of involving the end users into the process and service building to discover and control things for a better utilization of the applications, which can be interpreted as the DIY idea. Many IoT marketplaces [22][23][24][25] works have been presented in the recent past, and many context-aware virtual objects and virtual IoT services [26][27][28][29][30][31] works have also been proposed.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach of IoT Services Orchestration Based on Multiple Sensor and Actuator Platforms Using Virtual Objects in Online IoT App-Store

2019

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The information and communication technology (ICT) is witnessing a revolutionary era with the advancements in the Internet of Things (IoT). An IoT network is a combination of sensor and actuator networks, connected and communicating in certain ways to design and provide IoT services to the end users. These IoT services are created by mapping physical-world objects into virtual-world objects. In this work, we propose a novel approach of IoT services orchestration based on multiple sensor and actuator platforms using virtual objects in online IoT application-store (app-store). In this work, we focused on combining the concepts of do-it-yourself (DIY) IoT marketplace, virtual objects (VOs), and virtual services. We built a fusion IoT services platform on a previously proposed IoT application store. The IoT application store enables the sharing and discovery of IoT VOs, along with micro-services associated with each VO uploaded into the application store. The fusion IoT services platform enables the user to fetch the desired or all VOs from the IoT app store and map the available VOs to form the fusion IoT services. The user can either select all the available VOs and see all the possible services’ combinations or select the desired (DIY) services and customize the virtual services scope. The performance of the proposed fusion IoT services platform was evaluated on the basis of the service connection times, service response times with varying load of VOs, virtual users, and active platforms. The proposed idea also offers a sustainable solution by proposing the reuse of existing resources and reducing duplicate deployments, which can lessen the total cost of the physical networks’ deployment and maintenance. To the best of our knowledge, the proposed work is the first of its kind.

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LISA: Lightweight context-aware IoT service architecture

Cited by 44 publications

References 20 publications

Deep learning techniques of losses in data transmitted in wireless sensor networks

Deep learning techniques of losses in data transmitted in wireless sensor networks

Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model

A Novel Approach of IoT Services Orchestration Based on Multiple Sensor and Actuator Platforms Using Virtual Objects in Online IoT App-Store

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