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The Internet of Things (IoT) technology has begun to proliferate in recent years, which simultaneously increases the number of attacks. Owing to the massive volume and multi-dimensional data in IoT, anomaly detection leads to low prediction accuracy and a high false alarm rate. Further, there is a deficit of real-world test datasets for anomaly detection. This work aims to generate a novel real-time anomaly detection dataset and proposes an efficient anomaly detection model using an Improved Grey Wolf Optimization (IGWO)-enabled Long Short-Term Memory (LSTM) network in IoT edge scenarios. Dataset generation is carried out using a testbed setup containing Raspberry Pi 4 and sensors connected by a lightweight Message Queuing Telemetry Transport (MQTT) protocol. An autoencoder is used for feature reduction as it can investigate the input characteristics without sacrificing vital information. The LSTM classifier parameters, such as learning rate, optimizer, and batch size, are tuned precisely using IGWO techniques. The experimental results disclose that the proposed model achieves an accuracy of 99.11% for the testbed dataset, which is better than recent models. To confirm the generalizability of our model, the CICIDS 2017, DS2OS, and MQTTset standard datasets are applied explicitly. The developed model outcomes are statistically verified using the Wilcoxon signed rank test.

Connected and Autonomous Vehicles (CAVs) will be provided with multiple sensing and connectivity options as well as embedded computing and decision-making capabilities. The resulting technological landscape paves the way for the deployment of a plethora of innovative applications involving different stakeholders, such as insurance companies, car repairs, car manufacturers and public authorities. In such a context it is crucial to collect data in an efficient manner, not to burden the vehicle itself and the network infrastructure, while also providing an interoperable data sharing among all the involved players. The Digital Twin (DT) concept can play a key role to properly retrieve, store and share data as well as to exploit them to monitor, predict and improve the vehicle safety and driving experience. This work proposes a comprehensive framework which encompasses the presence of an edge-based DT interacting with the vehicle and the remote applications. It leverages properly specified interfaces and semantic models for different types of data provided by on-board sensing and learning capabilities. A Proof-of-Concept (PoC) has been developed to assess the practicality of the proposal and its performance in terms of communication and computation footprint under a variety of settings.

The MQTTset dataset has been extensively investigated for enhancing anomaly detection in IoT-based systems, with a focus on identifying Denial of Service (DoS) attacks. The research addresses a critical gap in MQTT traffic anomaly detection by proposing the incorporation of the ‘source’ attribute from PCAP files and utilizing hand-crafted feature engineering techniques. Various filtering methods, including data conversion, attribute filtering, handling missing values, and scaling, are employed. Anomalies are categorized and prioritized based on frequency of occurrence, with a specific emphasis on DoS attacks. The study compares the performance of the decision tree and its eight variant models (ID3, C4.5, Random Forest, CatBoost, LightGBM, XGBoost, CART, and Gradient Boosting) for anomaly detection in IoT-based systems. Evaluation metrics such as prediction accuracy, F1 score, and computational times (training and testing) are utilized. Hyperparameter fine-tuning techniques like grid search and random search are applied to enhance model performance, accuracy, and reduce computational costs. Results indicate that the benchmark Decision Tree model achieved 92.57% accuracy and a 92.38% F1 score with training and testing times of 2.95 seconds and 0.86 seconds, respectively. The Feature Engineering (Modified) dataset demonstrated a substantial improvement, reaching 98.56% accuracy and a 98.50% F1 score, with comparable training and testing times of 0.70 seconds and 0.02 seconds. Furthermore, the Modified Decision Tree Algorithm significantly improved accuracy to 99.27%, F1 score to 99.26%, and reduced training time to 0.73 seconds and testing time to 0.14 seconds. The research contributes valuable insights into feature engineering and guides the selection of effective approaches for anomaly detection in IoT-based systems, providing early threat warnings and enhancing overall system security and reliability.

Smart homes are becoming increasingly popular worldwide, and they are mainly based on Internet of Things (IoT) technologies to enable their functionality. However, because IoT devices have limited computing power and resources, implementing strong security measures is difficult, making the use of intrusion detection systems (IDS) an appropriate option. In this study, we propose an optimized model with high performance for intrusion detection in Message Queue Telemetry Transport protocol (MQTT)-based IoT networks for smart homes. This is done by studying 22 Machine Learning (ML) algorithms based on an extended two-stage evaluation approach that includes several aspects for optimizing and validating the performance to find the ideal model. Based on the empirical evaluation, the Generalized Linear Model (GLM) classifier with the random over-sampling technique produced the best detection performance with 100% accuracy and an f-score of 100%, outperforming previous studies. This study also investigated the influence of automatic feature engineering techniques on the performance of algorithms. With the automatic feature engineering technique, the performance increased by up to 38.9%, and the time required to classify the attacks decreased by up to 67.7%. This shows that automatic feature engineering can improve performance and reduce detection time.

This study proposes the development of a contactless emergency assistance system designed for individuals with severe physical disabilities (tetraplegia and no voice but normal mouth movements) to address the limitations of traditional emergency bells in sudden emergencies. The core technology of the system includes artificial intelligence facial recognition and fuzzy motion algorithms to identify facial movements. After the auxiliary signal is triggered, the system uses Message Queuing Telemetry Transport (MQTT) technology to activate warning lights and speakers. The signal is then transmitted to the LINE application on a computer or smartphone platform via Wi-Fi, notifying caregivers to provide timely assistance. Experimental results show that the system has a user-friendly interface and an accuracy rate of about 96.69%. This study is the successful development of an emergency assistance system controllable by quadriplegic patients. The one-to-many alarm device can establish a safe net, effectively help individuals with severe physical disabilities proactively seek help, reduce the risk of accidents, and alleviate caregiver shortages. For quadriplegics, this technology offers a unique alternative with significant advantages over commercially available manually operated or voice command call bells.

As the Internet of Things (IoT) industry grows, the risk of network protocol security threats has also increased. One protocol that has come under scrutiny for its security vulnerabilities is MQTT (Message Queuing Telemetry Transport), which is widely used. To address this issue, an automated execution program called fuzz has been developed to verify the security of MQTT brokers. This program is provided with various random and unexpected input data and monitored for different responses, such as acknowledgments, crashes, failures, or memory leaks. To generate a significant number of realistic MQTT protocols, we have proposed a Generative Adversarial Networks (GAN)-based protocol fuzzer called SGANFuzz. Our experimental results show that SGANFuzz has successfully detected 6 vulnerabilities among 7 MQTT implementations, including 3 CVE bugs. Compared to the state-of-the-art fuzzing tools, SGANFuzz has proven to be the most efficient fuzzing tool in terms of vulnerability detection and has expanded the feedback coverage by receiving more unique network responses from MQTT brokers.

The Internet of Things (IoT) is a network of smart sensory objects. These objects collect environmental data and communicate to exchange this gathered data without human intervention. IoT has evolved as a revolutionary technology for over a decade. It is currently, widely applied in a variety of applications like Internet of Vehicles (IoV), Internet of Healthcare Things (IoHT), and the Internet of Everything (IoE). IoHT is one of the most beneficial and significant IoT applications. The quality of the connection in an IoT network is governed by the application layer communication protocol. Application type has a significant impact on the preference of protocol selection. In this paper, the performance of CoAP and MQTT-SN is compared. Based on the performance analysis it was concluded that in case of high traffic conditions network congestion occurs on the medical broker of MQTT-SN, which lowers the network efficiency. A framework is proposed to achieve reliability and time optimization in larger sensor-based healthcare networks. An adaptive switching-based data communication model has been designed. In the proposed model, we have enabled IoHT network to run two application protocols in parallel. The adaptive switching algorithm allows to switch between the two available protocols based on the status of the network condition. Network simulation has been performed using NODE-RED. This tool is used to test the proposed framework in different scenarios. In the end it was concluded that during network congestion conditions at medical broker of MQTT-SN, the adaptive switching algorithm allows the network to switch to CoAP connection for the time efficient and reliable transmission in the IoHT network.

MQTT for IoT communication requires multiple brokers to aggregate traffic from localized areas. However, routing mechanisms among these brokers have yet to be specified. In this paper, the Distributed MQTT broker by data Link look-up for Traffic reduction (DMLT) is proposed as a new routing mechanism. This mechanism is aimed at layer-2 based control. It provides cooperation with MQTT and the Spanning Tree Protocol and invokes a traffic reduction and simplified transfer by an independent flow. We evaluate the performance of the DMLT method and compare the DMLT method and the conventional method in terms of traffic volume. According to the experimental results, a reduction in traffic volume is confirmed. We also build and verify a prototype of a wide-area DMLT (WDMLT) system, which is a method suitable for large-scale deployment of DMLT. Finally, the IoT DEP registered as an international standardization and its application to the proposed method is explained.

Message Queuing Telemetry Transport (MQTT) is expected to be the de facto messaging IoT standard. Therefore, MQTT must achieve efficient security. Nevertheless, the most significant drawback of the MQTT is its lack of protection mechanisms. Meanwhile, the existing approaches have added processing overhead to the devices and are still vulnerable to various attacks. Therefore, this research work presented an integrated scheme known as the Robust Security Scheme (RSS) to protect the MQTT against any exploitations that might result in sophisticated cyberattacks. The proposed RSS employs two cryptosystems: 1) a dynamic variant of the Advanced Encryption Standard (D-AES); and 2) Key-Policy Attribute-Based Encryption (KP-ABE). RSS introduces a new design architecture of the symmetric AES algorithm to encrypt the MQTT payload called D-AES. Additionally, the second part of the proposed hybrid cryptosystem is KP-ABE, which is utilized to cipher the private key of the proposed D-AES to avoid the computation overhead of bilinear maps. The performance of the proposed RSS is measured in terms of processing time and traffic overhead. Additionally, the security aspects are evaluated in terms of balance, avalanche effect, and hamming distance and compared to the existing works in a testbed environment. Results revealed that the proposed D-AES is more promising with improvements than the standard AES algorithm. The proposed scheme achieves polymorphism while maintaining interoperability. RSS exhibited improvements over the standard AES algorithm by 8.75%, 10.45%, and 6.81% in terms of balance, avalanche effect, and hamming distance, respectively.

Machine-to-machine (M2M) communication is a key implementation technology for future Industrial Internet of Things (IIoT) applications. The requirements for flexibility, efficiency, and cross-platform compatibility in intermodule communication between the connected machines raise challenges for the M2M messaging mechanism. For the loom message transmission mechanism, it is necessary to have certain flexibility and meet certain M2M communication requirements to understand each other. In this paper, we analyze and study the data model, data exchange language, MQTT protocol and logo resolution technology of looms to address the current problems of low data transmission efficiency, poor flexibility and lack of flexibility in the network. By establishing the data model of looms and classifying them by topic, selecting JSON format as the transmission format of data, and using MQTT protocol to realize data transmission between loom networks, an MQTT-based messaging mechanism is established. The data involved in the communication of the loom equipment are grouped according to the communication requirements, and only the loom data required by the application are subscribed. At the same time, the hardware and software parts of the communication module are designed with ESP32 as the core, so that it can complete the functions of loom network communication, data analysis, format conversion and so on. In addition, the real-time requirements of the loom network communication and the network requirements for future intelligent manufacturing must be met. This paper uses the BP neural network to intelligently optimize the loom network, and the existing problems related to performance indices are analyzed. The original loom network is optimized by integrating the RSSI, QoS requirements, transmission priority, real-time communication and data transmission volume of nodes. In the meantime, the particle swarm optimization algorithm is introduced to optimize the BP neural network to avoid the problem that it easily can fall into the local optimal solution and effectively improving the network optimization efficiency. Finally, the feasibility of this method is proven by using the experimental platform, which can solve the problems of low network transmission efficiency and difficult network optimization of looms and provide an effective technical example of loom information transmission and network optimization for industry.

Internet of Things is a widely adopted and pervasive technology, but also one of the most relevant in cyber-security, given the volume and sensitivity of shared data and the availability of affordable but insecure products. In this paper, we propose a novel cyber-threat exploiting the Message Queue Telemetry Transport (MQTT) protocol to implement a tunneling attack. In IoT networks, sensitive and critical information are exchanged between devices or external systems to perform data analysis. For this reason, a tunneling threat could be adopted by a malicious user to steal information. In this context, a tunneling system based on MQTT can be considered since this communication protocol could be allowed to pass through enterprise firewalls because it is widely adopted in this IoT world. An attacker can exploit the MQTT protocol for various purposes such as steal information or access to not-allowed websites/servers. In particular in this work, we contribute in two main points: initially we demonstrate how the proposed threat is able to encapsulate messages through the MQTT protocol, by also comparing it with other tunneling systems exploiting different communication protocols. Obtained results show that exploiting MQTT for tunneling purposes is a good choice, compared to other communication protocols, especially for payloads up to 3000 bytes. Then, we propose and validate an initial machine learning based approach able to detect the proposed MQTT tunnel, by comparing different detection algorithms tested with and without a hyperparameter optimization, in terms of accuracy, F1 score and Receiver Operating Characteristic (ROC) curve. In this case, obtained results show that some algorithms are able to identify the attack, with an accuracy exceeding 95%, while others lack of such capability.

Common use cases in the Industrial Internet of Things (IIoT) deploy massive amounts of sensors and actuators that communicate with each other or to a remote cloud. While they form too large and too volatile networks to run on ultra-reliable, time-synchronized low-latency channels, participants still require reliability and latency guaranties. We elaborate this for safety-critical use cases. This paper focuses on the effects of networking protocols for industrial communication services. It analyzes and compares the traditional Message Queuing Telemetry Transport for Sensor Networks (MQTT-SN) with the Constrained Application Protocol (CoAP) as a current IETF recommendation, and also with emerging Information-centric Networking (ICN) approaches, which are ready for deployment. Our findings indicate a rather diverse picture with a large dependence on deployment: Publish-subscribe protocols are more versatile, whereas ICN protocols are more robust in multi-hop environments. MQTT-SN competitively claims resources on congested links, while CoAP politely coexists on the price of its performance. [ABSTRACT FROM AUTHOR]

As the number of Internet of Things (IoT) devices proliferates, the magnitude and velocity of data continues to increase rapidly. IoT systems rely primarily on using messaging protocols for exchanging IoT data and there exists several protocols or frameworks that support distinct types of messaging patterns. Given that IoT devices typically have limited computational resources and processing power, choosing a lightweight, reliable, scalable, interoperable, extensible and secure messaging protocol becomes a very challenging task. As a result, it is not uncommon that IoT systems may employ multiple messaging protocols for supporting device heterogeneity and different message exchange patterns. In addition, basic similarities among existing several messaging protocols or frameworks that exist today for exchanging IoT data within IoT systems suggest the potential of interoperability. Given that IoT systems help facilitate the interconnectivity among distributed, heterogeneous entities, interoperability among existing messaging protocols will play an increasingly important role in simplifying the development and deployment of IoT systems. In this paper, we present a comprehensive review of the existing messaging protocols that can be used in deploying IoT systems. Throughout this paper, we highlight the protocols' distinctive approaches and applicability of using them across various IoT environments. In addition, we highlight challenges, strengths and weaknesses of these messaging protocols in the context of IoT.

Currently, one of the main challenges that the world's big cities are facing is the security problem of their citizens and specifically the disappearance of people (even more in Latin America countries). Thus, to address this problem, a software system called SiGPro has been developed, which allows notifications for the early location of missing persons, using some technological tools, being one of the most prominent application of the MQTT protocol frequently used in the Internet of Things (IoT). SiGPro applies a MQTT-Bridge for hierarchical server communication and developing the software system for real-time performance. SiGPro innovates a Progressive Notification Mechanism, not considered in other similar projects and being one of the main contributions of this research; it delivers notifications depending on the disappearance distance in order to not saturate the end user. In addition, SiGPro was deployed in three cities in Ecuador, (being Quito city the MQTT-broker), where more than 140 users participated in each city and in different test scenarios. Time and distances of disappearance solution were obtained, taking into account the total anonymity of the victim. Finally, SigPro has shown that increasing crowdsensing (number of users), reduces the distance at which the missing person has been located.

Nowadays billions of smart devices or things are present in Internet of Things (IoT) environments, such as homes, hospitals, factories, and vehicles, all around the world. As a result, the number of interconnected devices is continuously and rapidly growing. These devices communicate with each other and with other services using various communication protocols for the transportation of sensor or event data. These protocols enable applications to collect, store, process, describe, and analyze data to solve a variety of problems. IoT also aims to provide secure, bi-directional communication between interconnected devices, such as sensors, actuators, microcontrollers or smart appliances, and corresponding cloud services. In this paper we analyze the growth of M2M protocol research (MQTT, AMQP, and CoAP) over the past 20 years, and show how the growth in MQTT research stands out from the rest. We also gather relevant application areas of MQTT, as the most widespread M2M/IoT protocol, by performing a detailed literature search in major digital research archives. Our quantitative evaluation presents some of the important MQTT-related studies published in the past five years, which we compare to discuss the main features, advantages, and limitations of the MQTT protocol. We also propose a taxonomy to compare the properties and features of various MQTT implementations, i.e. brokers and libraries currently available in the public domain to help researchers and end-users to efficiently choose a broker or client library based on their requirements. Finally, we discuss the relevant findings of our comparison and highlight open issues that need further research and attention.

The current number of working mothers has greatly increased. Subsequently, baby care has become a daily challenge for many families. Thus, most parents send their babies to their grandparents' house or to baby care houses. However, the parents cannot continuously monitor their babies' conditions either in normal or abnormal situations. Therefore, an Internet of Things-based Baby Monitoring System (IoT-BBMS) is proposed as an efficient and low-cost IoT-based system for monitoring in real time. We also proposed a new algorithm for our system that plays a key role in providing better baby care while parents are away. In the designed system, Node Micro-Controller Unit (NodeMCU) Controller Board is exploited to gather the data read by the sensors and uploaded via Wi-Fi to the AdaFruit MQTT server. The proposed system exploits sensors to monitor the baby's vital parameters, such as ambient temperature, moisture, and crying. A prototype of the proposed baby cradle has been designed using Nx Siemens software, and a red meranti wood is used as the material for the cradle. The system architecture consists of a baby cradle that will automatically swing using a motor when the baby cries. Parents can also monitor their babies' condition through an external web camera and switch on the lullaby toy located on the baby cradle remotely via the MQTT server to entertain the baby. The proposed system prototype is fabricated and tested to prove its effectiveness in terms of cost and simplicity and to ensure safe operation to enable the baby-parenting anywhere and anytime through the network. Finally, the baby monitoring system is proven to work effectively in monitoring the baby's situation and surrounding conditions according to the prototype.

Network covert channels are a part of the information hiding research area that deals with the secret transfer of information over communication networks. Covert channels can be utilized, for instance, for data leakage and stealthy malware communications. While data hiding in communication networks has been studied within the last years for several major communication protocols, currently no work is available that investigates covert channels for the publish-subscriber model. To fill this gap, we present the first comprehensive study of covert channels in a protocol utilizing the publish-subscriber model, i.e., the Message Queuing Telemetry Transport (MQTT) protocol which is widely deployed in Internet of Things (IoT) environments. In particular, we describe seven direct and six indirect covert channels and we evaluate and categorize them using the network information hiding patterns approach. Finally, in order to prove that MQTT-based covert channels are practically feasible and effective, we implement the chosen data hiding scheme and perform its experimental evaluation.

Home automation systems have attracted considerable attention with the advancement of communications technology. A smart home (SH) is an Internet of Things (IoT) application that utilizes the Internet to monitor and control appliances using a home automation system. Lack of IoT technology usage, unfriendly user interface, limited wireless transmission range, and high costs are the limitations of existing home automation systems. Therefore, this study presents a cost-effective and hybrid (local and remote) IoT-based home automation system with a user-friendly interface for smartphones and laptops. A prototype called IoT@HoMe is developed with an algorithm to enable the monitoring of home conditions and automate the control of home appliances over the Internet anytime and anywhere. This system utilizes a node microcontroller unit (NodeMCU) as a Wi-Fi-based gateway to connect different sensors and updates their data to Adafruit IO cloud server. The collected data from several sensors (radio-frequency identification, ultrasonic, temperature, humidity, gas, and motion sensors) can be accessed via If This Then That (IFTTT) on users' devices (smartphones and/or laptops) over the Internet regardless of their location. A set of relays is used to connect the NodeMCU to homes under controlled appliances. The designed system is structured in a portable manner as a control box that can be attached for monitoring and controlling a real house. The proposed IoT-based system for home automation can easily and efficiently control appliances over the Internet and support home safety with autonomous operation. IoT@HoMe is a low cost and reliable automation system that reduces energy consumption and can notably provide convenience, safety, and security for SH residents.

In this article, we focus on the usage of MQTT (Message Queuing Telemetry Transport) within Connected Vehicles (CVs). Indeed, in the original version of MQTT protocol, the broker is responsible “only” for sending received data to subscribers; abstracting then the underlying mechanism of data exchange. However, within CVs context, subscribers (i.e., the processing infrastructure) may be overloaded with irrelevant data, in particular when the requirement is real or near real-time processing. To overcome this issue, we propose MQTT-CV; a new variant of MQTT protocol, in which the broker is able to perform local processing in order to reduce the workload at the infrastructure; i.e., filtering data before sending them. In this article, we first validate formally the correctness of MQTT-CV protocol (i.e., the three components of the proposed protocol are correctly interacting), through the use of Promela language and its system verification tool; the model checker SPIN. Secondly, using real-world data provided by our car manufacturer partner, we have conducted real implementation and experiments. The obtained results show the effectiveness of our approach in term of data workload reduction at the processing infrastructure. The mean improvement, besides the fact that it is dependent of the target application, was in general about 10 times less in comparison to native MQTT protocol. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new Internet of Things (IoT) solution based on a Plug-and-Play (PnP) architecture, aiming to stimulate new paths set by the concept of the Smart Environment. In this sense, the IoT ecosystem is considered, and the layers are presented from the higher level of software to the lowest level of sensors/actuators. We extend the state of art solutions since our proposal features are transparent in both hardware and software domains, supporting the identification of sensors through an easy and intuitive tool responsible for creating and managing all resources and logic programming in an IoT environment. To show the main contributions of this research, we will introduce the architecture, adding the IEEE 1451 standard, based on well-defined message protocols. The contribution of this work encompasses all PnP levels in the IoT architecture, from the platform to the user interface, which are different from all approaches currently existent in state of the art. Moreover, a proof of concept is described at the end of this article. [ABSTRACT FROM AUTHOR]

Message Queuing Telemetry Transport (MQTT) is a widespread protocol for topic-based publish-subscribe architectures supporting IoT and social networks applications. MQTT brokers are logical entities that couple publishers and subscribers and play a critical role in such architectures. MQTT brokers can be implemented either as standalone servers or in a cluster configuration. Clusters of brokers increase reliability, availability and overall performance, since operations can be highly parallelized among the brokers that form the cluster. The load-balancing strategy in a cluster usually consists in connecting an incoming client to a randomly selected broker. This random-attach strategy, it is very simple, but generates a significant amount of inter-broker traffic, as we demonstrate through theoretical and experimental evaluations. Inter-broker traffic is an overhead for the system and it increases the CPU load of the brokers, compromising the scaling behavior of the whole cluster. Indeed, we found that a linear increase of the number of brokers forming a cluster does not necessarily provide an equivalent linear gain in performance, and such a scaling penalty can be surprisingly significant, in the order of 40%. To solve this issue and improve performance, we propose a novel load-balancing strategy that envisages the use of multiple MQTT sessions per client to reduce inter-broker traffic and that can be implemented by means of a greedy algorithm. We show feasibility and effectiveness of our strategy for IoT and social-network applications by means of simulations and real measurements. The resulting scaling penalty is reduced to 10%. [ABSTRACT FROM AUTHOR]

Due to the constant evolution of the technologies related to the Internet of Things and the great expansion of the protocols and devices that possibilitate their use, there is a need to enforce and to approach the study of its concepts and applications to the students of electronic engineering and technical university careers. In this work, a weather station is designed and built based on low-cost technologies, whose data can be visible through an online platform designed for this purpose. The weather station is oriented to be used in laboratory practices and for teaching of subjects related to IoT, sensing platforms, communication platforms, etc. The general objective of the low-cost weather station platform is to provide a didactic, portable and simple design for replication in classroom in order to provide a whole system from the beginning to the students. Thanks to the use of this low cost platform it was possible to perform better approach to this technology obtaining great improvements in the learning process of the students. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is a prominent paradigm applied to several areas ranging from medicine to industrial networks. It aims at connecting to the Internet of million of daily used objects. Important challenges in this area consist in to propose mechanisms and protocols that meet security, device interoperability, quality of service and energy-efficiency requirements. Particularly, the Message Queue Telemetry Transport (MQTT) protocol has been prospected in order to provide efficient communication at the application layer for the IoT. This survey aims to present the fundamentals, tools and future directions related to MQTT protocol and its variation tailored for sensor networks, called MQTT-SN. We discuss such protocols comparing to other current IoT application layer protocols, such as Constrained Application Protocol (CoAP). Additionally, we present tools so as to support practical experimentation and simulation. Particularly, we carry out pactical experiments to observe the communication delay between MQTT and CoAP. Finally, the open issues and challenges in this area are examined. [ABSTRACT FROM AUTHOR]

The Internet of Things is a technological paradigm that can be described as the set of elements that have small-embedded systems. This consisting of sensors, microprocessors, interconnection modules and actuators, which communicate with each other, to share useful information and improve processes. However, there are a large number of solutions from different vendors, both licensed and open source, that when deployed together may present problems, mainly due to a lack of standardization. In this paper we propose the use of OPC UA as middleware between MQTT and cross-platform Web applications. In addition, we develop a data management platform. Among the results obtained, it is possible to show the advantages of using MQTT and OPC UA, since it provides greater efficiency in the response time and security of the application. [ABSTRACT FROM AUTHOR]

Increasing cost and demand of energy has led many organizations to find smart ways for monitoring, controlling and saving energy. A smart Energy Management System (EMS) can contribute towards cutting the costs while still meeting energy demand. The emerging technologies of Internet of Things (IoT) and Big Data can be utilized to better manage energy consumption in residential, commercial, and industrial sectors. This paper presents an Energy Management System (EMS) for smart homes. In this system, each home device is interfaced with a data acquisition module that is an IoT object with a unique IP address resulting in a large mesh wireless network of devices. The data acquisition System on Chip (SoC) module collects energy consumption data from each device of each smart home and transmits the data to a centralized server for further processing and analysis. This information from all residential areas accumulates in the utility?s server as Big Data. The proposed EMS utilizes off-the-shelf Business Intelligence (BI) and Big Data analytics software packages to better manage energy consumption and to meet consumer demand. Since air conditioning contributes to 60% of electricity consumption in Arab Gulf countries, HVAC (Heating, Ventilation and Air Conditioning) Units have been taken as a case study to validate the proposed system. A prototype was built and tested in the lab to mimic small residential area HVAC systems1. [ABSTRACT FROM PUBLISHER]