A Recent Survey on Internet of Things (IoT) Communication Protocols
TL;DR: Various communication protocols, namely Zigbee, Bluetooth, Near Field Communication (NFC), LoRA, etc. are presented, and the difference between different communication protocols is provided.
Abstract: Internet of Things (IoT) consists of sensors embed with physical objects that are connected to the Internet and able to establish the communication between them without human intervene applications are industry, transportation, healthcare, robotics, smart agriculture, etc. The communication technology plays a crucial role in IoT to transfer the data from one place to another place through Internet. This paper presents various communication protocols, namely Zigbee, Bluetooth, Near Field Communication (NFC), LoRA, etc. Later, it provides the difference between different communication protocols. Finally, the overall discussion about the communication protocols in IoT.
Citations
More filters
TL;DR: A review of near and remote sensor networks in the agriculture domain is presented along with several considerations and challenges and an IoT-based smart solution for crop health monitoring is proposed, which is comprised of two modules.
Abstract: Internet of Things (IoT)-based automation of agricultural events can change the agriculture sector from being static and manual to dynamic and smart, leading to enhanced production with reduced human efforts. Precision Agriculture (PA) along with Wireless Sensor Network (WSN) are the main drivers of automation in the agriculture domain. PA uses specific sensors and software to ensure that the crops receive exactly what they need to optimize productivity and sustainability. PA includes retrieving real data about the conditions of soil, crops and weather from the sensors deployed in the fields. High-resolution images of crops are obtained from satellite or air-borne platforms (manned or unmanned), which are further processed to extract information used to provide future decisions. In this paper, a review of near and remote sensor networks in the agriculture domain is presented along with several considerations and challenges. This survey includes wireless communication technologies, sensors, and wireless nodes used to assess the environmental behaviour, the platforms used to obtain spectral images of crops, the common vegetation indices used to analyse spectral images and applications of WSN in agriculture. As a proof of concept, we present a case study showing how WSN-based PA system can be implemented. We propose an IoT-based smart solution for crop health monitoring, which is comprised of two modules. The first module is a wireless sensor network-based system to monitor real-time crop health status. The second module uses a low altitude remote sensing platform to obtain multi-spectral imagery, which is further processed to classify healthy and unhealthy crops. We also highlight the results obtained using a case study and list the challenges and future directions based on our work.
267 citations
Cites background from "A Recent Survey on Internet of Thin..."
...Table 3 shows the comparison of all mentioned wireless communication protocols [8]....
[...]
...4, there is an adaptation layer between the network layer and the MAC layer [8]....
[...]
...Each protocol has its own specifications depending on the bandwidth, number of free channels, data rate, battery timing, price and other factors [8]....
[...]
...Basically, it was developed for personal area networks by the ZigBee alliance [8]....
[...]
TL;DR: A Convolutional Neural Network composed of three convolutional layers, two maxpool, and three fully-connected layers as a deep learning model is proposed and its performance is evaluated using three open data sets and against extant research.
Abstract: Human falls are a global public health issue resulting in over 373 million severe injuries and 646,000 deaths yearly Falls result in direct financial cost to health systems and indirectly to society productivity Unsurprisingly, human fall detection and prevention are a major focus of health research In this article, we consider deep learning for fall detection in an IoT and fog computing environment We propose a Convolutional Neural Network composed of three convolutional layers, two maxpool, and three fully-connected layers as our deep learning model We evaluate its performance using three open data sets and against extant research Our approach for resolving dimensionality and modelling simplicity issues is outlined Accuracy, precision, sensitivity, specificity, and the Matthews Correlation Coefficient are used to evaluate performance The best results are achieved when using data augmentation during the training process The paper concludes with a discussion of challenges and future directions for research in this domain
149 citations
Additional excerpts
...The IoT devices communicate with the fog devices through wireless technologies, such as IEEE 802.11, Zigbee, and Bluetooth Low Energy [16]....
[...]
...11, Zigbee, and Bluetooth Low Energy [16]....
[...]
TL;DR: This article considers possible fog computing applications and potential enabling technologies towards sustainable smart cities in the IoT environments and different caching techniques and the use of Unmanned Aerial Vehicles, and various Artificial Intelligence and Machine Learning techniques in caching data for fog-based IoT systems are comprehensively discussed.
109 citations
TL;DR: This study proposes a new network forensics framework, called a Particle Deep Framework (PDF), which describes the digital investigation phases for identifying and tracing attack behaviors in IoT networks, and results reveal a high performance of the proposed framework for discovering and tracing cyber-attack events compared with the other techniques.
96 citations
01 Mar 2020
TL;DR: This paper has prescribed farming systems based on the embedded systems, IoT and wireless sensor networks for agri-farm field and livestock farms and describes the future scopes of relevant technologies in smart farming.
Abstract: With the exponential growth of the human race that means the growth of population, the conventional or ancient farming methods are becoming unable to cope with the growth with satisfaction. Hence advanced farming methods are much needed to approach the necessity of foods of this growing number of people. In the recent few years, smart farming systems based on embedded systems and the Internet of Things (IoT) getting attraction and popularity among people to enhance food production for people. This paper has prescribed farming systems based on the embedded systems, IoT and wireless sensor networks for agri-farm field and livestock farms. This paper includes the description of systems with the electronic circuitry of the systems, used network protocols and smart distant monitoring systems for PCs and Smartphones, etc. Later it includes some propositions and finally, the paper concludes with describing the future scopes of relevant technologies in smart farming.
82 citations
References
More filters
01 Jan 2013
TL;DR: Results obtained show that support vector machine can be successfully used for diagnosing diabetes disease and the machine learning method focus on classifying diabetes disease from high dimensional medical dataset is successful.
Abstract: Diabetes mellitus is one of the most serious health challenges in both developing and developed countries. According to the International Diabetes Federation, there are 285 million diabetic people worldwide. This total is expected to rise to 380 million within 20 years. Due to its importance, a design of classifier for the detection of Diabetes disease with optimal cost and better performance is the need of the age. The Pima Indian diabetic database at the UCI machine learning laboratory has become a standard for testing data mining algorithms to see their prediction accuracy in diabetes data classification. The proposed method uses Support Vector Machine (SVM), a machine learning method as the classifier for diagnosis of diabetes. The machine learning method focus on classifying diabetes disease from high dimensional medical dataset. The experimental results obtained show that support vector machine can be successfully used for diagnosing diabetes disease.
130 citations
01 Jan 2019
TL;DR: The recommendation system has been built on the type of genres that the user might prefer to watch and the approach adopted to do so is content-based filtering using genre correlation.
Abstract: A recommendation system is a system that provides suggestions to users for certain resources like books, movies, songs, etc., based on some data set. Movie recommendation systems usually predict what movies a user will like based on the attributes present in previously liked movies. Such recommendation systems are beneficial for organizations that collect data from large amounts of customers, and wish to effectively provide the best suggestions possible. A lot of factors can be considered while designing a movie recommendation system like the genre of the movie, actors present in it or even the director of the movie. The systems can recommend movies based on one or a combination of two or more attributes. In this paper, the recommendation system has been built on the type of genres that the user might prefer to watch. The approach adopted to do so is content-based filtering using genre correlation. The dataset used for the system is Movie Lens dataset. The data analysis tool used is R.
87 citations
TL;DR: This paper deals with measuring the Air Quality using Mq135 sensor along with Carbon Monoxide CO using MQ7 sensor using Machine Learning analysis and proviing a reducement of the cost of components versus the state of the art.
Abstract: This paper deals with measuring the Air Quality using MQ135 sensor along with Carbon Monoxide CO using MQ7 sensor. Measuring Air Quality is an important element for bringing awareness to take care of the future generations and for a healthier life. Based on this, Government of India has already taken certain measures to ban Single Stroke and Two Stroke Engine based motorcycles which are emitting high pollution. We are trying to implement a system using IoT platforms like Thingspeak or Cayenne in order to bring awareness to every individual about the harm we are doing to our environment. Already, New Delhi is remarked as the most pollution city in the world recording Air Quality above 300 PPM. We have used easiest platform like Thingspeak and set the dashboard to public such that everyone can come to know the Air Quality at the location where the system is installed. Machine Learning analysis brings us a lot of depth in understanding the information that we obtained from the data. Moreover, we are proviing a reducement of the cost of components versus the state of the art.
42 citations
TL;DR: This work proposes Energy and Delay Aware Data aggregation in Routing Protocol (EDADA-RPL) for IoT, which offers good performance in terms of network lifetime, delay, and packet delivery ratio.
Abstract: Energy conservation is one of the most critical problems in Internet of Things (IoT). It can be achieved in several ways, one of which is to select the optimal route for data transfer. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized routing protocol for IoT. The RPL changes its path frequently while transmitting the data from source to the destination, due to high data traffic in dense networks. Hence, it creates data traffic across the nodes in the networks. To solve this issue, we propose Energy and Delay Aware Data aggregation in Routing Protocol (EDADA-RPL) for IoT. It has two processes, namely parent selection and data aggregation. The process of parent selection uses routing metric residual energy (RER) to choose the best possible parent for data transmission. The data aggregation process uses the compressed sensing (CS) theory in the parent node to combine data packets from the child nodes. Finally, the aggregated data transmits from a downward parent to the sink. The sink node collects all the aggregated data and it performs the reconstruction operation to get the original data of the participant node. The simulation is carried out using the Contiki COOJA simulator. EDADA-RPL’s performance is compared to RPL and LA-RPL. The EDADA-RPL offers good performance in terms of network lifetime, delay, and packet delivery ratio.
39 citations
TL;DR: A fuzzy logic based energy aware routing protocol (FLEARPL), which considers the routing metrics load, residual energy (RER) and expected transmission count (ETX) for the best route selection and improves the network lifetime by 10-12% and packet delivery ratio by 2-5%.
Abstract: Maximizing the network lifetime is one of the major challenges in Low Power and Lossy Networks (LLN). Routing plays a major role in LLN, for minimizing the energy consumption across the network nodes. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized routing protocol for LLN. Though, RPL fulfilled the necessity of LLN, several issues like increasing the energy efficiency, quality of service and the network lifetime are to be focused. In LNN, the inefficient route selection results in increased network traffic, energy depletion and packet loss ratio across the network. In this paper, we propose a fuzzy logic based energy aware routing protocol (FLEARPL), which considers the routing metrics load, residual energy (RER) and expected transmission count (ETX) for the best route selection. FLEA-RPL applies fuzzy logic over these metrics, to select the best route to transfer the network data efficiently. The COOJA simulator is used to assess the efficiency of the proposed FLEA-RPL. The FLEA-RPL protocol is compared with similar protocol standard RPL, MRHOF (ETX) based RPL (MRHOFRPL) and FL-RPL. The simulation result shows that FLEA-RPL improves the network lifetime by 10-12% and packet delivery ratio by 2-5%.
38 citations