Energy Efficient Data Collection in Smart Cities Using IoT

Wala, Tanuj; Chand, Narottam; Sharma, Ajay K.

doi:10.1007/978-3-030-40305-8_30

Cited by 11 publications

(5 citation statements)

References 52 publications

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“…This is one of the most crucial and complex tasks in the entire composition of smart cities as the data acquired from these sources is nonuniform and mostly unstructured. Wala et al [119] discuss the challenges faced by Wireless Sensor Networks (WSN) when collecting data from scores of sensors deployed all over the city. Efficient data collection through managing energy consumption, resource utilization, and minimizing transmission costs is necessary.…”

Section: Data Collection Layermentioning

confidence: 99%

“…Hsinchun et al [121] determined that analysis of big data helps us to gain insight into the information attained in real-time which can be used to make better decisions and enhance the performance of ICT applications in various domains like science and technology, smart health, e-commerce, smart-security and more. AI is an essential component in smart city applications since it helps to make spontaneous decisions even while encountering an unforeseen situation [119]. AI and its domains like machine learning and NLP come into the picture when this raw big data must be analyzed to produce smart outcomes.…”

Section: Problems In Smart Citymentioning

confidence: 99%

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Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

Tyagi

Bhushan

2023

Wireless Pers Commun

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Smart cities provide an efficient infrastructure for the enhancement of the quality of life of the people by aiding in fast urbanization and resource management through sustainable and scalable innovative solutions. The penetration of Information and Communication Technology (ICT) in smart cities has been a major contributor to keeping up with the agility and pace of their development. In this paper, we have explored Natural Language Processing (NLP) which is one such technical discipline that has great potential in optimizing ICT processes and has so far been kept away from the limelight. Through this study, we have established the various roles that NLP plays in building smart cities after thoroughly analyzing its architecture, background, and scope. Subsequently, we present a detailed description of NLP's recent applications in the domain of smart healthcare, smart business, and industry, smart community, smart media, smart research, and development as well as smart education accompanied by NLP's open challenges at the very end. This work aims to throw light on the potential of NLP as one of the pillars in assisting the technical advancement and realization of smart cities.

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Section: Data Collection Layermentioning

confidence: 99%

Section: Problems In Smart Citymentioning

confidence: 99%

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

Tyagi

Bhushan

2023

Wireless Pers Commun

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“…In addition to the previous technologies, IoT, and other techniques are used to reduce energy in smart cities (Wala et al , 2020). Mahapatra et al (2017) developed a unifying framework for IoT in smart homes to build a green and sustainable smart city.…”

Section: Related Workmentioning

confidence: 99%

An intelligent system for energy management in smart cities based on big data and ontology

Sayah

Kazar

Lejdel³

et al. 2020

SASBE

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PurposeThis research paper aims at proposing a framework based on semantic integration in Big Data for saving energy in smart cities. The presented approach highlights the potential opportunities offered by Big Data and ontologies to reduce energy consumption in smart cities.Design/methodology/approachThis study provides an overview of semantics in Big Data and reviews various works that investigate energy saving in smart homes and cities. To reach this end, we propose an efficient architecture based on the cooperation between ontology, Big Data, and Multi-Agent Systems. Furthermore, the proposed approach shows the strength of these technologies to reduce energy consumption in smart cities.FindingsThrough this research, we seek to clarify and explain both the role of Multi-Agent System and ontology paradigms to improve systems interoperability. Indeed, it is useful to develop the proposed architecture based on Big Data. This study highlights the opportunities offered when they are combined together to provide a reliable system for saving energy in smart cities.Practical implicationsThe significant advancement of contemporary applications (smart cities, social networks, health care, IoT, etc.) requires a vast emergence of Big Data and semantics technologies in these fields. The obtained results provide an improved vision of energy-saving and environmental protection while keeping the inhabitants’ comfort.Originality/valueThis work is an efficient contribution that provides more comprehensive solutions to ontology integration in the Big Data environment. We have used all available data to reduce energy consumption, promote the change of inhabitant’s behavior, offer the required comfort, and implement an effective long-term energy policy in a smart and sustainable environment.

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“…However, using a routing protocol to reduce the energy consumption in an IoT network implies that these devices must be connected which is not always realistically possible if the nodes are placed in isolated or harsh conditions. One of the solutions proposed to tackle the limitation of routing-based energyefficient solutions are the MDC approaches [4]. However, the most challenging part in mobile sink-based solutions is to determine and plan the trajectory of the mobile sink in order to efficiently gather data from the IoT nodes.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient and Context-aware Trajectory Planning for Mobile Data Collection in IoT using Deep Reinforcement Learning

Benhamaid

Lakhlef

Bouabdallah

2022

2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

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IoT networks are often composed of spatially distributed nodes. This is why mobile data collection (MDC) emerged as an efficient solution to gather data from IoT networks that tolerate delay. In this paper, we study the use of reinforcement learning (RL) to plan the data collection trajectory of a mobile node (MN) in cluster-based IoT networks. Most of the existing solutions use static methods. However, in a context where the MN has little information (no previous data set) about the environment and where the environment is subject to changes (cluster mobility, etc.), we want the MN to learn an energyefficient trajectory and adapt the trajectory to the significant changes in the environment. For that purpose, we will train two reinforcement learning (RL) algorithms: Q-learning and state-action-reward-state-action (SARSA) combined with deep learning (DL). This solution will allow us to maximize the collected data while minimizing the energy consumption of the MN. These algorithms will also adapt the trajectory of the MN to the significant changes in the environment.

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Energy Efficient Data Collection in Smart Cities Using IoT

Cited by 11 publications

References 52 publications

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

An intelligent system for energy management in smart cities based on big data and ontology

Energy-Efficient and Context-aware Trajectory Planning for Mobile Data Collection in IoT using Deep Reinforcement Learning

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