IoT Technology, Applications and Challenges: A Contemporary Survey

Balaji, S.; Nathani, Karan; Santhakumar, R.

doi:10.1007/s11277-019-06407-w

Cited by 229 publications

(104 citation statements)

References 59 publications

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“…Humidity leaves a negative impact on the growth of plant leaves, photosynthesis, and pollination. Therefore, to sense the level of humidity in air, this sensor directly measures the temperature and moisture content in the air [107]. Grofit provides different climate monitoring devices that can monitor the air temperature, air humidity, and sun radiation.…”

Section: Humidity Sensormentioning

confidence: 99%

Role of IoT Technology in Agriculture: A Systematic Literature Review

et al. 2020

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The growing demand for food in terms of quality and quantity has increased the need for industrialization and intensification in the agriculture field. Internet of Things (IoT) is a highly promising technology that is offering many innovative solutions to modernize the agriculture sector. Research institutions and scientific groups are continuously working to deliver solutions and products using IoT to address different domains of agriculture. This paper presents a systematic literature review (SLR) by conducting a survey of IoT technologies and their current utilization in different application domains of the agriculture sector. The underlying SLR has been compiled by reviewing research articles published in well-reputed venues between 2006 and 2019. A total of 67 papers were carefully selected through a systematic process and classified accordingly. The primary objective of this systematic study is the collection of all relevant research on IoT agricultural applications, sensors/devices, communication protocols, and network types. Furthermore, it also discusses the main issues and challenges that are being investigated in the field of agriculture. Moreover, an IoT agriculture framework has been presented that contextualizes the representation of a wide range of current solutions in the field of agriculture. Similarly, country policies for IoT-based agriculture have also been presented. Lastly, open issues and challenges have been presented to provide the researchers promising future directions in the domain of IoT agriculture.

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Section: Humidity Sensormentioning

confidence: 99%

Role of IoT Technology in Agriculture: A Systematic Literature Review

et al. 2020

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“…I NTERNET of Things (IoT) is a key technology driving toward a new perspective of the world where almost all conceivable things are connected to a network for remote sensing and control [1]- [3]. The realization of the IoT is enabled by the integration of various technologies, including smart devices, wireless networking, cloud computing, and data analytics [1], [2].…”

Section: Introductionmentioning

confidence: 99%

A Q-Learning-Based Approach for Enhancing Energy Efficiency of Bluetooth Low Energy

Lopez-Estrada

Kim

2021

IEEE Access

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Bluetooth low energy (BLE) is a promising candidate technology for use in the Internet of Things (IoT) because of its ultra-low-power communication. Although BLE devices are designed to run on a small battery for a few years, several attempts have been made to extend BLE lifetime through various techniques. In particular, emerging approaches such as artificial intelligence (AI) can be utilized to further improve the BLE energy efficiency. For this purpose, this paper proposes a Q-learning-based scheduling algorithm for BLE. The proposed scheduling algorithm dynamically adjusts the key parameters that govern the operation of the BLE transmission scheme. These key parameters, namely, the length of connection interval and the number of packets to transmit during the interval, have a profound effect on energy efficiency and the quality of service (QoS) specified in terms of maximum latency. According to the framework of reinforcement learning, our Q-learning-based scheduling algorithm is appropriately constructed to simultaneously provide a longer network lifetime and satisfy the QoS requirement. The numerical results show that the proposed Q-learning-based approach significantly increases the network lifetime compared to alternative methods while meeting QoS requirements.

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“…The modern world and communications technology have shown in the business world, people who have access to data and better information will control the future. The internet of Things (IoT) is indeed the product of coverage and evolution of three internet elements, wireless technology, and Micro-electronic and mechanical devices [1,2]. Connectivity to the internet in the IoT has led to its application of all aspects of human life, including smart cities, smart water control environment, Security and emergencies, smart transportation, smart agriculture, industrial and health control, and so on.…”

Section: -Introductionmentioning

confidence: 99%

Selfish Node detection based on GA and Learning Automata in IoT

Nobahary¹,

Garakani²,

Khademzadeh³

et al. 2020

Preprint

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It is critical to increasing the network throughput on the internet of things with short-range nodes. Nodes prevent to cooperate with other nodes in the network are known as selfish nodes. Previous studies have done on the selfish nodes detection that leads to increase throughput and reduce the end to end delay. The proposed method for discovering the selfish node is based on genetic algorithm and learning automata. It consists of three phases of setup and clustering, the best routing selection based on genetic algorithm, and finally, the learning and update phase. For appropriate network performance, the clustering algorithm implemented in the first phase. Nodes are working together to send the data packet to the destination in the second phase, and the neighbor node selected for forwarding the data packet in which that node has a high value of fitness function, among others. In the third phase, each node monitors the performance of its neighbor nodes in forwarding the data packet and uses the learning automata system to identify the selfish nodes. By preventing to cooperate selfish nodes and decreasing the probability selection of selfish nodes, it increases the throughput in the network. The results of the simulation show that the detection accuracy of selfish nodes in comparison with the existing methods average 12%, and the false positive rate has decreased by 5%.

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IoT Technology, Applications and Challenges: A Contemporary Survey

Cited by 229 publications

References 59 publications

Role of IoT Technology in Agriculture: A Systematic Literature Review

Role of IoT Technology in Agriculture: A Systematic Literature Review

A Q-Learning-Based Approach for Enhancing Energy Efficiency of Bluetooth Low Energy

Selfish Node detection based on GA and Learning Automata in IoT

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