13,577 research outputs found
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
IoT based intelligent irrigation support system for smart farming applications
India is an agricultural country with an ample amount of arable land that produces wide variety of crops. Growing population and urbanization puts up challenges: more and quality yield in limited area, effective utilization of water resources, inculcating technology with traditional mechanisms, to be faced. A crop irrigation management system with sensor data fetch, transfer and operate functionalities is proposed to meet the expectations. The system comprises of: sensing, data processing and actuator sections, with a network of ambient temperature and humidity at a height and, soil moisture sensor placed at the root zone of the subject. The sensor generated data is compressed and then sent to an FTP server for processing. At the server, a 2-layer Neural Network with 4-Inputs, plant growth, temperature, humidity and soil moisture is used for decision making that controls water supply, fertilizer spray, etc. and a plant is used as the test object. Results show that there is tolerable error in the reconstructed data and 62.5% and 67.5% compression is achieved for ambient temperature, humidity and soil moisture respectively. The decisions are only 2% erroneous when done using Neural Networks using this data. Thus, due to its good data handling, decision making capabilities for precise water usage, being portable and user-friendly, this system proves beneficial in home gardens, greenhouses
IoT and Blockchain for Smart Cities
Blockchain is a Distributed Ledger Technology (DLT) that makes it possible to secure any type of transaction. This is because the information stored on the Blockchain is immutable, impeding any type of fraud or modification of the data. It was first created for Bitcoin transactions; however, the research community has realized its potential quickly, and started using it for purposes other than cryptocurrency transactions. Blockchain may even be used to secure and provide reliability to the data being transmitted between computational systems, ensuring their immutability. Given the amount of data produced within a smart city, the use of Blockchain is imperative in smart cities, as it protects them from cyberattacks and fraud. Moreover, the transparency of the information stored on Blockchain means that it helps create a more just and democratic society
AIoT for Achieving Sustainable Development Goals
Artificial Intelligence of Things (AIoT) is a relatively new concept that involves the merging of Artificial Intelligence (AI) with the Internet of Things (IoT). It has emerged from the realization that Internet of Things networks could be further enhanced if they were also provided with Artificial Intelligence, enhancing the extraction of data and network operation. Prior to AIoT, the Internet of Things would consist of networks of sensors embedded in a physical environment, that collected data and sent them to a remote server. Upon reaching the server, a data analysis would be carried out which normally involved the application of a series of Artificial Intelligence techniques by experts. However, as Internet of Things networks expand in smart cities, this workflow makes optimal operation unfeasible. This is because the data that is captured by IoT is increasing in size continually. Sending such amounts of data to a remote server becomes costly, time-consuming and resource inefficient. Moreover, dependence on a central server means that a server failure, which would be imminent if overloaded with data, would lead to a halt in the operation of the smart service for which the IoT network had been deployed. Thus, decentralizing the operation becomes a crucial element of AIoT. This is done through the Edge Computing paradigm which takes the processing of data to the edge of the network. Artificial Intelligence is found at the edge of the network so that the data may be processed, filtered and analyzed there. It is even possible to equip the edge of the network with the ability to make decisions through the implementation of AI techniques such as Machine Learning. The speed of decision making at the edge of the network means that many social, environmental, industrial and administrative processes may be optimized, as crucial decisions may be taken faster.
Deep Intelligence is a tool that employs disruptive Artificial Intelligence techniques for data analysis i.e., classification, clustering, forecasting, optimization, visualization. Its strength lies in its ability to extract data from virtually any source type. This is a very important feature given the heterogeneity of the data being produced in the world today. Another very important characteristic is its intuitiveness and ability to operate almost autonomously. The user is guided through the process which means that anyone can use it without any knowledge of the technical, technological and mathematical aspects of the processes performed by the platform. This means that the Deepint.net platform integrates functionalities that would normally take years to implement in any sector individually and that would normally require a group of experts in data analysis and related technologies [1-322].
The Deep Intelligence platform can be used to easily operate Edge Computing architectures and IoT networks. The joint characteristics of a well-designed Edge Computing platform (that is, one which brings computing resources to the edge of the network) and of the advanced Deepint.net platform deployed in a cloud environment, mean that high speed, real-time response, effective troubleshooting and management, as well as precise forecasting can be achieved.
Moreover, the low cost of the solution, in combination with the availability of low-cost sensors, devices, Edge Computing hardware, means that deployment becomes a possibility for developing countries, where such solutions are needed most
Prioritizing policy tools to support development of IoT technologies in Iran
The Internet of Things is a new perspective on the information technology industry that encompasses all technical, social and economic concepts. Identifying priority application areas for this technology is one of the key points for its effective use. Governments also have a variety of tools for policy-making to support the development of this technology. Therefore, knowing which tool has a higher priority for support is a very important point that can not only prevent the loss of resources but also improve the speed of development. In this research, using the opinion of experts and using the TOPSIS method, an attempt has been made to identify the priority of IoT application areas as well as the priority of government support policy tools in these areas. The results of this research have shown that the important areas in this field respectively are Smart cities, Factories and industries, Shipping, Healthcare, Supply chain management, Buildings and houses and finally Agriculture and animal husbandry. Also Government policy tools respectively, in order of priority, are Financial and Investment Incentives, Flexible regulatory, Tax Exemption, Deploying IOT applications in E-government, Standards and Accreditation, Technology Infrastructure, Macro Policies, Application Infrastructure, Cybersecurity Regulation, Privacy Regulation
Sparrow Search Algorithm based BGRNN Model for Animal Healthcare Monitoring in Smart IoT
Rural regions rely heavily on agriculture for their economic survival. Therefore, it is crucial for farmers to implement effective and technical solutions to raise production, lessen the impact of issues associated to animal husbandry, and improve agricultural yields. Because of technological developments in computers and data storage, huge volumes of information are now available. The difficulty of extracting useful information from this mountain of data has prompted the development of novel approaches and tools, such as data mining, that can help close the informational gap. To evaluate data mining methods and put them to use in the Animal database to create meaningful connections was the goal of the suggested system. The study's primary objective was to develop an IoT-based Integrated Animal Health Care System. Various sensors were used as the research tool to collect physical and environmental data on the animals and their habitats. Temperature, heart rate, and air quality readings were the types of information collected. This research contributes to the field of health monitoring by introducing an Optimised Bidirectional Gated Recurrent Neural Network approach. The BiGRNN is an improved form of the Gated Recurrent Unit (GRU) in which input is sent both forward and backward through a network and the resulting outputs are connected to the same output layer. Since the BiGRNN method employs a number of hyper-parameters, it is optimised by means of the Sparrow Search Algorithm (SSA). The originality of the study is demonstrated by the development of an SSA technique for hyperparameter optimisation of the BiGRNN, with a focus on health forecasting. Hyperparameters like momentum, learning rate, and weight decay may all be adjusted with the SSA method. In conclusion, the results demonstrate that the suggested tactic is more effective than the current methods
Towards the internet of agents: an analysis of the internet of things from the intelligence and autonomy perspective
Recently, the scientific community has demonstrated a special interest in the process related to the integration of the agent-oriented
technology with Internet of Things (IoT) platforms. Then, it arises a novel approach named Internet of Agents (IoA) as an alternative
to add an intelligence and autonomy component for IoT devices and networks. This paper presents an analysis of the main benefits
derived from the use of the IoA approach, based on a practical point of view regarding the necessities that humans demand in their
daily life and work, which can be solved by IoT networks modeled as IoA infrastructures. It has been presented 24 study cases of the
IoA approach at different domains ––smart industry, smart city and smart health wellbeing–– in order to define the scope of these
proposals in terms of intelligence and autonomy in contrast to their corresponding generic IoT applications.En los Ăşltimos años, la comunidad cientĂfica ha mostrado un interĂ©s especial en torno al proceso de integraciĂłn de la tecnologĂa
orientada a agentes sobre plataformas de Internet de las Cosas (IoT, por sus siglas en inglĂ©s). Surge asĂ, un nuevo enfoque denominado
Internet de los Agentes (IoA, por sus siglas en inglĂ©s) como una alternativa para añadir un componente de inteligencia y autonomĂa
sobre los dispositivos y redes de IoT. El presente trabajo muestra un análisis de los principales beneficios derivados del uso del
enfoque del IoA, visto desde las actuales necesidades que el ser humano demanda en su trabajo y vida cotidiana, las cuales pueden
ser resueltas por redes de IoT modeladas como infraestructuras de IoA. Se plantea un total de 24 casos prácticos de aplicaciones de
IoA en diferentes dominios ––industria, ciudad, y salud y bienestar inteligente–– a fin de determinar el alcance de dichas aplicaciones
en tĂ©rminos de inteligencia y autonomĂa respecto a sus correspondientes aplicaciones genĂ©ricas de IoT.This study was founded by the Ecuadorian Ministry of
Higher Education, Science, Technology and Innovation
(SENESCYT)
IoT-Enabled Social Relationships Meet Artificial Social Intelligence
With the recent advances of the Internet of Things, and the increasing
accessibility of ubiquitous computing resources and mobile devices, the
prevalence of rich media contents, and the ensuing social, economic, and
cultural changes, computing technology and applications have evolved quickly
over the past decade. They now go beyond personal computing, facilitating
collaboration and social interactions in general, causing a quick proliferation
of social relationships among IoT entities. The increasing number of these
relationships and their heterogeneous social features have led to computing and
communication bottlenecks that prevent the IoT network from taking advantage of
these relationships to improve the offered services and customize the delivered
content, known as relationship explosion. On the other hand, the quick advances
in artificial intelligence applications in social computing have led to the
emerging of a promising research field known as Artificial Social Intelligence
(ASI) that has the potential to tackle the social relationship explosion
problem. This paper discusses the role of IoT in social relationships detection
and management, the problem of social relationships explosion in IoT and
reviews the proposed solutions using ASI, including social-oriented
machine-learning and deep-learning techniques.Comment: Submitted to IEEE internet of things journa
Methodologies for innovation and best practices in Industry 4.0 for SMEs
Today, cyber physical systems are transforming the way in which industries operate, we call this Industry 4.0 or the fourth industrial revolution. Industry 4.0 involves the use of technologies such as Cloud Computing, Edge Computing, Internet of Things, Robotics and most of all Big Data.
Big Data are the very basis of the Industry 4.0 paradigm, because they can provide crucial information on all the processes that take place within manufacturing (which helps optimize processes and prevent downtime), as well as provide information about the employees (performance, individual needs, safety in the workplace) as well as clients/customers (their needs and wants, trends, opinions) which helps businesses become competitive and expand on the international market.
Current processing capabilities thanks to technologies such as Internet of Things, Cloud Computing and Edge Computing, mean that data can be processed much faster and with greater security. The implementation of Artificial Intelligence techniques, such as Machine Learning, can enable technologies, can help machines take certain decisions autonomously, or help humans make decisions much faster. Furthermore, data can be used to feed predictive models which can help businesses and manufacturers anticipate future changes and needs, address problems before they cause tangible harm
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