Pollution Context-Aware Representation in Vehicular Internet of Things for Smart Cities

2020 ◽  
pp. 23-39
Author(s):  
Twahirwa Evariste ◽  
Willie Kasakula ◽  
James Rwigema ◽  
Raja Datta
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Context Aware

scholarly journals A Survey of Context-Aware Messaging-Addressing for Sustainable Internet of Things (IoT)

2020 ◽  
Vol 12 (10) ◽  
pp. 4105
Author(s):  
Alaa Omran Almagrabi ◽  
Yasser D. Al-Otaibi
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Contextual Information ◽  
Spatial Models ◽  
Context Aware ◽  
Engineering Technology ◽  
Smart Transportation ◽  
Context Aware Computing ◽  
Iot Applications ◽  
Smart Healthcare

Nowadays, communication engineering technology is merging with the Internet of Things (IoT), which consists of numerous connected devices (referred to as things) around the world. Many researchers have shown significant growth of sensor deployments for multiple smart engineering technologies, such as smart-healthcare, smart-industries, smart-cities, and smart-transportation, etc. In such intelligent engineering technologies, sensors continuously generate a bunch of messages in the network. To enhance the value of the data in the messages, we must know the actuality of the data embedded inside the messages. For this purpose, the contextual information of the data creates a vital challenge. Recently, context-aware computing has emerged to be fruitful in dealing with sensor information. In the ubiquitous computing domain, location is commonly considered one of the most essential sources of context. However, whenever users or applications are concerned with objects, and their site or spatial relationships, location models or spatial models are necessary to form a model of the environment. This paper investigates the area of context-aware messaging and addressing services in diverse IoT applications. The paper examines the notion of context and the use of context within the data exchanged by the sensors in an IoT application for messaging and addressing purposes. Based on the importance and need for context of the information, we identify three critical categories of new IoT applications for context-aware messaging and addressing services: emergency applications, applications for guiding and reminding, and social networking applications. For this purpose, a representative range of systems is reviewed according to the application type, the technology being used, their architecture, the context information, and the services they provide. This survey assists the work of defining an approach for context-aware messaging services domain by discovering the area of context-aware messaging.

scholarly journals Models for Internet of Things Environments—A Survey

Information ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 487
Author(s):  
Ana Cristina Franco da Silva ◽  
Pascal Hirmer
Keyword(s):  
Internet Of Things ◽  
State Of The Art ◽  
Smart Cities ◽  
Self Organization ◽  
Context Aware ◽  
Sensors And Actuators ◽  
Relational Models ◽  
Comparative Survey ◽  
Smart Factories ◽  
The Internet Of Things

Today, the Internet of Things (IoT) is an emerging topic in research and industry. Famous examples of IoT applications are smart homes, smart cities, and smart factories. Through highly interconnected devices, equipped with sensors and actuators, context-aware approaches can be developed to enable, e.g., monitoring and self-organization. To achieve context-awareness, a large amount of environment models have been developed for the IoT that contain information about the devices of an environment, their attached sensors and actuators, as well as their interconnection. However, these models highly differ in their content, the format being used, for example ontologies or relational models, and the domain to which they are applied. In this article, we present a comparative survey of models for IoT environments. By doing so, we describe and compare the selected models based on a deep literature research. The result is a comparative overview of existing state-of-the-art IoT environment models.

scholarly journals C2IoT: A framework for Cloud-based Context-aware Internet of Things services for smart cities

2017 ◽  
Vol 110 ◽  
pp. 151-158 ◽  
Author(s):  
Soufiane Faieq ◽  
Rajaa Saidi ◽  
Hamid Elghazi ◽  
Moulay Driss Rahmani
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Context Aware

Everything of Internet of Things and Smart Cities Applications

2017 ◽  
Author(s):  
Mazin S. Al-Hakeem ◽  
Alaa H.Al-Hamami
Keyword(s):  
Internet Of Things ◽  
Smart Cities

The future of using Internet of Things (loTs) and Context-Aware Technology in E-learning

2020 ◽  
Author(s):  
Mohamed A. Amasha ◽  
Marwa F. Areed ◽  
Salem Alkhalaf ◽  
Rania A. Abougalala ◽  
Safaa M. Elatawy ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Context Aware ◽  
The Future ◽  
E Learning

scholarly journals Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 894-918
Author(s):  
Luís Rosa ◽  
Fábio Silva ◽  
Cesar Analide
Keyword(s):  
Internet Of Things ◽  
Mobile Networks ◽  
Smart Cities ◽  
Human Mobility ◽  
Rapid Development ◽  
Disruptive Technology ◽  
New Paradigm ◽  
Design And Optimization ◽  
High Level

The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches.

Robust Networking: Dynamic Topology Evolution Learning for Internet of Things

2021 ◽  
Vol 17 (3) ◽  
pp. 1-23
Author(s):  
Ning Chen ◽  
Tie Qiu ◽  
Mahmoud Daneshmand ◽  
Dapeng Oliver Wu
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Evolutionary Process ◽  
Small Error ◽  
Cyber Attacks ◽  
Communication Capacity ◽  
Error Ratio ◽  
The Internet Of Things ◽  
Evolution Learning

The Internet of Things (IoT) has been extensively deployed in smart cities. However, with the expanding scale of networking, the failure of some nodes in the network severely affects the communication capacity of IoT applications. Therefore, researchers pay attention to improving communication capacity caused by network failures for applications that require high quality of services (QoS). Furthermore, the robustness of network topology is an important metric to measure the network communication capacity and the ability to resist the cyber-attacks induced by some failed nodes. While some algorithms have been proposed to enhance the robustness of IoT topologies, they are characterized by large computation overhead, and lacking a lightweight topology optimization model. To address this problem, we first propose a novel robustness optimization using evolution learning (ROEL) with a neural network. ROEL dynamically optimizes the IoT topology and intelligently prospects the robust degree in the process of evolutionary optimization. The experimental results demonstrate that ROEL can represent the evolutionary process of IoT topologies, and the prediction accuracy of network robustness is satisfactory with a small error ratio. Our algorithm has a better tolerance capacity in terms of resistance to random attacks and malicious attacks compared with other algorithms.

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