Demonstrating MQTT+

In the last few years, the Message Queueing Telemetry Transport (MQTT) publish/subscribe protocol emerged as the de facto standard communication protocol for IoT, M2M and wireless sensor networks applications. Such popularity is mainly due to the extreme simplicity of the protocol at the client side, appropriate for low-cost and resource-constrained edge devices. Other nice features include a very low protocol overhead, ideal for limited bandwidth scenarios, the support of different Quality of Services (QoS) and many others. However, when an edge device is interested in performing processing operations over the data published by multiple clients, the use of MQTT may result in high network bandwidth usage and high energy consumption for the end devices, which is unacceptable in resource constrained scenarios. To overcome these issues, we propose in this paper MQTT+, which provides an enhanced protocol syntax and enrich the pub/sub broker with data filtering, processing and aggregation functionalities. MQTT+ is implemented starting from an open source MQTT broker and evaluated in different application scenarios.

Sensors and actuators play a vital role in realizing the Internet of Things (IoT) applications. The data transmission capability from edge devices to servers and vice-versa enables IoT applications to be more realistic and effective. However, one of the major challenges to IoT applications is power management at the edge devices. The power consumption of the edge devices depends on the amount of data transferred through the network in a defined interval. Data size and its applicability to the receiving device are controllable parameters, and consequently, energy efficiency can be improved. This work aims to establish an IoT architecture using a message queuing telemetry transport (MQTT) protocol that effectively handles the data transmission in the network. It also provides a solution for the elimination of data redundancy; hence, the network was effectively utilized, and the energy consumption was reduced in the edge devices. The proposed architecture was demonstrated with a potentiometer as a data generator and MQTT as a message broker. Further, the transmitted data was received at the edge devices and its performance was analyzed. Based on the results, it was verified that the client was able to receive the data without any loss in terms of precision. Data redundancy was avoided by disabling the data transmission whenever the nodeMCU identifies the constant sensor value. This retains the power at the edge devices and data transmission devices.

One of the main application scenario that the upcoming 5G systems are expected to support is Internet of Things (IoT) traffic backhauling. With the exponential increase of devices, it follows the critical challenge of handling the huge data volume that they produce. Terrestrial networks may not be sufficient for that, or not always available. Satellites then come into play, and they can provide the ubiquitous coverage targeted by 5G. On the other side, aggregation can be an efficient strategy to reduce the amount of traffic, and use network resources in an efficient manner. Building on that, in this work, we consider a hybrid IoT-satellite network architecture for collecting message queuing telemetry transport (MQTT) traffic. We design an advanced filter, namely, MQTT message filter aggregator (MQTT-MFA), that performs MQTT topics aggregation in close proximity of MQTT Publishers before data are sent over the satellite link. The developed MQTT-MFA implementation leverages on MQTT bridge, and it is compliant with Mosquitto, an open-source MQTT broker. Extensive simulation results have demonstrated that MQTT-MFA requires only 10% of the TCP/IP overhead generated in a baseline architecture (where the bridge is not set up) and 1/4 number of bytes to transmit the same amount of information, when compression of the data payload is also enabled.

The Internet of Things (IoT) becomes the future of a global data field in which the embedded devices communicate with each other, exchange data and making decisions through the Internet. IoT could improve the quality of life in smart cities, but a massive amount of data from different smart devices could slow down or crash database systems. In addition, IoT data transfer to Cloud for monitoring information and generating feedback that will lead to high delay in infrastructure level. Fog Computing can help by offering services closer to edge devices. In this paper, we propose an efficient system architecture to mitigate the problem of delay. We provide performance analysis like response time, throughput and packet loss for MQTT (Message Queue Telemetry Transport) and HTTP (Hyper Text Transfer Protocol) protocols based on Cloud or Fog servers with large volume of data from emulated traffic generator working alongside one real sensor . We implement both protocols in the same architecture, with low cost embedded devices to local and Cloud servers with different platforms. The results show that HTTP response time is 12.1 and 4.76 times higher than MQTT Fog and Cloud based located in the same geographical area of the sensors respectively. The worst case in performance is observed when the Cloud is public and outside the country region. The results obtained for throughput shows that MQTT has the capability to carry the data with available bandwidth and lowest percentage of packet loss. We also prove that the proposed Fog architecture is an efficient way to reduce latency and enhance performance in Cloud based IoT.

In the vision of Internet of Things (IoT), everyday objects such as domestic appliances, actuators and embedded systems of any kind soon will be connected with each other using Internet. Especially, with the emerging trends and devices evolving in this technology, choosing an optimized communication protocol for a particular application scenario is highly essential. Hence, in this work an analysis of existing communication protocols namely IETF Constrained Application Protocol (CoAP), IBM Message Queuing Telemetry Transport (MQTT) and W3C HyperText Transfer Protocol (HTTP) was carried out to understand their significance and classify them based on the application scenario. To perform a real-time analysis of these protocols, a test environment for inventory management system is designed. It involves sensors and communication boards, which generates data about the availability status of the every product. Various test cases were executed on all the three protocols by varying the request types in the process of retrieving the sensor data. To justify the performance of each protocol, metrics such as response time, latency and throughput are determined and analyzed. Finally, using the obtained results the protocols were classified and justified that each protocol works optimized for particular request type.

With the boom of Industry 4.0 for industrial automation, the data exchange between the different industrial devices has tremendously increased through local (edge) and cloud computing. Nowadays, several communication protocols are available to realize data acquisition in the industry. As a result, understanding the practical capabilities of each communication protocol arises as an essential issue in optimizing the data acquisition and storage in industrial plants. This study aims to develop a software-based test environment to evaluate the performance of two widely used communication protocols within the scope of the Industrial Internet of Things (IIoT). These contemplated protocols are Open Platform Communications Unified Architecture (OPC UA) and Message Queuing Telemetry Transport (MQTT). With the help of the developed test environment, several performance metrics, like packet overhead, latency, packet loss ratio, and CPU utilization, are evaluated for different application scenarios where the varying number of clients and subscribers are considered. Derived results indicate the stronger parts of each protocol under varying communication configurations.

A lightweight three factor authentication framework for IoT based critical applications

The development of energy and cost efficient IoT nodes is very important for the successful deployment of IoT solutions across various application domains. This paper presents energy models, which will enable the estimation of battery life, for both time-based and event-based low-cost IoT monitoring nodes. These nodes are based on the low-cost ESP8266 (ESP) modules which integrate both transceiver and microcontroller on a single small-size chip and only cost about $2. The active/sleep energy saving approach was used in the design of the IoT monitoring nodes because the power consumption of ESP modules is relatively high and often impacts negatively on the cost of operating the nodes. A low energy application layer protocol, that is, Message Queue Telemetry Transport (MQTT) was also employed for energy efficient wireless data transport. The finite automata theory was used to model the various states and behavior of the ESP modules used in IoT monitoring applications. The applicability of the models presented was tested in real life application scenarios and results are presented. In a temperature and humidity monitoring node, for example, the model shows a significant reduction in average current consumption from 70.89 mA to 0.58 mA for sleep durations of 0 and 30 minutes, respectively. The battery life of batteries rated in mAh can therefore be easily calculated from the current consumption figures.

In the process of large-scale machine-to-machine (M2M) communication in the IoT, the Request/Response mode applied by the traditional network system can be no longer applicable due to which high network performance demand and poor expansion ability. With the characteristics of weak coupling and asynchronous communication, Publish/Subscribe mode had become an important platform in the new generation of large distributed application system. This article summarized the research status of publish/subscribe systems. According to the application scenario of data transmission of low-voltage electrical equipment, this paper discusses the realization method of data transmission system based on Message Queuing Telemetry Transport (MQTT) technology. By analyzing the characteristics and simulation of MQTT protocol, it is verified that the subscription publishing model can effectively organize and publish information in the mobile computing environment and has good effectiveness.

Monitoring environmental conditions can be done effectively with the help of the Internet of Things (IOT) sensor network. Massive data generated by IOT sensor networks presents technological hurdles in terms of storage, processing, and querying. A Hadoop cloud platform is suggested as a fix for the issue. The data processing platform makes it possible for one node's work to be shared with others employing the time and workload genetic algorithm (TWLGA), which lowers the risk of software and hardware compatibility while simultaneously increasing the efficiency of a single node. For the experiment, a Hadoop cluster platform employing the TWLGA scheduling algorithm is built, and its performance is assessed. The outcomes demonstrate that processing huge volumes of data from the IOT sensor network is acceptable for the Hadoop cloud platform .

The Edge-Cloud Continuum represents a paradigm shift in distributed computing, seamlessly integrating resources from cloud data centers to edge devices. However, orchestrating services across this heterogeneous landscape poses significant challenges, as it requires finding a delicate balance between different (and competing) objectives, including service acceptance probability, offered Quality-of-Service, and network energy consumption. To address this challenge, we propose leveraging Multi-Objective Reinforcement Learning (MORL) to approximate the full Pareto Front of service orchestration policies. In contrast to conventional solutions based on single-objective RL, a MORL approach allows a network operator to inspect all possible “optimal” trade-offs, and then decide a posteriori on the orchestration policy that best satisfies the system’s operational requirements. Specifically, we first conduct an extensive measurement study to accurately model the energy consumption of heterogeneous edge devices and servers under various workloads, alongside the resource consumption of popular cloud services. Then, we develop a set-based MORL policy for service orchestration that can adapt to arbitrary network topologies without the need for retraining. Illustrative numerical results against selected heuristics show that our MORL policy outperforms baselines by 30% on average over a broad set of objective preferences, and generalizes to network topologies up to 5x larger than training.

The number of Machine-to-Machine (M2M) devices has increased massively in the last few years and will continue to increase in the years to come. Spectrum utilisation efficiency and energy efficiency are the main challenges and design goals for M2M networks. Cognitive radio (CR) is a promising technology that can address these challenges. In this paper, the authors have proposed and developed an energy efficient mechanism to reduce energy consumption in Cognitive M2M (CM2M) networks. Their solution guarantees the throughput and reliability constraints for CM2M Devices (CM2MDs). The proposed mechanism can reduce energy consumption in CM2M networks by exploiting efficient sensing and accessing schemes for CM2MDs. The authors further develop sleep-mode/switching and accessing techniques to work efficiently with the proposed mechanism. The simulation results show that the proposed mechanism guarantees a desirable throughput and reduces overall energy consumption in the network.

This systematic literature review presents a comprehensive analysis of contemporary developments in energy-efficient transformer design tailored for integration within modern smart grid infrastructures. As the global energy landscape transitions toward decentralized, intelligent, and low-carbon systems, the operational expectations for transformers have expanded considerably. No longer limited to conventional voltage regulation, transformers must now address fluctuating load profiles, reverse power flows from distributed energy resources, harmonic distortion, and integration with digital monitoring and control frameworks. This review systematically evaluates the current state of research by analyzing 87 peer-reviewed articles published between 2013 and 2022, selected through a rigorous process guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 protocol. The review investigates several key dimensions of transformer innovation, including the adoption of amorphous and nanocrystalline core materials for core loss reduction, the deployment of high-temperature superconducting windings for enhanced load efficiency, and the rapid evolution of solid-state transformer architectures enabling bidirectional and high-frequency operation. It also explores the application of IoT and wireless sensor networks in condition monitoring, contributing to improved predictive maintenance and reduced lifecycle costs. Furthermore, the analysis highlights the convergence of technical innovation with environmental priorities, as evidenced by the increasing use of biodegradable ester fluids, modular eco-designs, and life-cycle assessment (LCA) frameworks aimed at reducing carbon footprints and improving recyclability. In addition to synthesizing advancements, the review identifies underexplored areas in the existing literature, such as the scarcity of long-term performance evaluations, limited empirical testing under fault or abnormal conditions, integration complexities in hybrid designs, and region-specific barriers in infrastructure and policy adoption. By consolidating these insights, the review provides an essential reference point for researchers, engineers, utility planners, and policymakers seeking to optimize transformer performance, support regulatory compliance, and enable the evolution of sustainable, resilient, and future-ready power distribution systems.

In traditional cloud computing research, it is often considered that the network resources between edge devices and cloud platform are sufficient, and the symmetry between the upward link from edge devices to the cloud platform and the downward link from cloud platform to edge devices is definite. However, in many application scenarios, the network resources between edge devices and cloud platform might be limited, and the link symmetry may not be guaranteed. To solve this problem, network relay nodes are introduced to realize the data transmission between edge devices and the cloud platform. The environment in which network relay nodes that can cooperate with the cloud platform is called cloud network collaborative environment (CNCE). In CNCE, how to optimize data transmission from edge devices to cloud platform through relay nodes has become one of the most important research topics. In this paper, we focus on the following two influencing factors that previous studies ignored: (1) the multi-link and multi-constraint transmission process; and (2) the timely resource state of the relay node. Taking these factors into consideration, we design a novel data transmission scheduling algorithm, called ant colony based transmission scheduling approach (ACTS). First, we propose a multi-link optimization mechanism to optimize the constraint limits. This mechanism divides the transmission into two links called the downlink relay link and uplink relay link. For the downlink relay link, we use the store-and-forward method for the optimization. For the uplink relay link, we use the min–min method for the optimization. We use the ant colony algorithm for the overall optimization of the two links. Finally, we improve the pheromone update rule of the ant colony algorithm to avoid the algorithm from falling into a local optimum. Extensive experiments demonstrate that our proposed approach has better results in transmission efficiency than other advanced algorithms.

Federated learning (FL) is one of most promising distributed machine learning frameworks to strengthen data privacy and security by making. Specifically, in the FL, the multiple clients are involved to train their deep learning models (i.e., local training) and then aggregate them as a global model at the server. However, due to the heterogeneous nature of multiple distributed clients, it is challenging to implement and deploy the FL framework over various clients. By adopting the cloud native technologies such as containers, and Kubernetes, we design and implement the Kubernetes enabled Federated Learning Platform (KubeFL). The KubeFL helps the deployment of the FL over multiple clients via Docker containers and Kubernetes. To do this, in the proposed KubeFL, each FL framework implemented by the PyTorch is deployed on the Docker container in the Pod at each client (i.e., Node in the Kubernetes) while the one is also conducted at the server (i.e., Master in the Kubernetes). We evaluate its performance through extensive measurement-based analysis on the commercial NVIDIA Jetson TX2 edge devices under various practical configurations.