Interoperable Real-Time Symbolic Programming for Smart Environments

The Internet of Things (IoT) is a framework of interconnected computing devices mechanical and digital machines, internationally identifiable physical objects (or things) or people that are have unique identity and the ability to transfer data over a network without human-to-human or human-to-computer interaction., their combination with the Internet, and their representation in the digital world. The accessibility and availability of cheap components of IoT devices enables a extensive range of applications and provide smart environments. These devices perform actuating and sensing tasks and identified through unique addresses. The IoT devices are connected to the Internet and expected to use the Constrained Application Protocol (CoAP) at the application layer as a main web transfer protocol. Message Queuing Telemetry Transport (MQTT) does not enforce the use of a particular security approach for its applications, but instead leaves that to the application designer. Therefore, IoT solutions can be based on application context and specific security requirements. MQTT is a Client Server publish/subscribe messaging transport protocol. It is lightweight, open, uncomplicated, and designed to make implementation more easier. These characteristics of MQTT make it perfect for use in most of the situations, including communication in Machine to Machine (M2M) and Internet of Things (IoT). In IOT there is major use of Wireless Sensor Networks (WSN) which connects virtual world to physical world. In this paper focus is given to application layer of IOT. In application layer two important protocols are MQTT and CoAP. Security mechanism is proposed in the paper for these protocols.

Edge computing is proposed to solve the problem of centralized cloud computing caused by a large number of IoT (Internet of Things) devices. The IoT protocols need to be modified according to the edge computing paradigm, where the edge computing devices for analyzing IoT data are distributed to the edge networks. The MQTT (Message Queuing Telemetry Transport) protocol, as a data distribution protocol widely adopted in many international IoT standards, is suitable for cloud computing because it uses a centralized broker to effectively collect and transmit data. However, the standard MQTT may suffer from serious traffic congestion problem on the broker, causing long transfer delays if there are massive IoT devices connected to the broker. In addition, the big data exchange between the IoT devices and the broker decreases network capability of the edge networks. The authors in this paper propose a novel MQTT with a multicast mechanism to minimize data transfer delay and network usage for the massive IoT communications. The proposed MQTT reduces data transfer delays by establishing bidirectional SDN (Software Defined Networking) multicast trees between the publishers and the subscribers by means of bypassing the centralized broker. As a result, it can reduce transmission delay by 65% and network usage by 58% compared with the standard MQTT.

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 this paper we build an MQTT(Message Queue Telemetry Transportation) broker on Amazon Web Service(AWS). The MQTT broker has been utilized as a platform to provide the Internet of Things(IoT) services which monitor and control room temperatures, and sense, alarm, and suppress fire. Arduino was used as the IoT end device connecting sensors and actuators to the platform via Wi-Fi channel. We created smart home scenario and designed IoT massages satisfying the scenario requirement. We also implemented the smart some system in hardware and software, and verified the system operation. We show that MQTT and AWS are good technical candidates for small IoT business applications.

The Internet of Things (IoT) is playing a key role in consumer and business environments. Due to the sensitivity of the information IoT devices collect and share, and the potential impact a data breach can have in people’s lives, securing communication and access to data in IoT has become a critical feature. Multiple application layer protocols are used nowadays in IoT, with the Constrained Application Protocol (CoAP) and the Message Queue Telemetry Transport (MQTT) being two of the most widely popular. In this paper, we propose a solution to increase the security of both CoAP and MQTT based on the distributed Usage Control (UCON) framework. The inclusion of UCON provides dynamic access control to the data shared using these protocols. This occurs by monitoring mutable attributes related to the local protocol nodes and also by sharing data values between remote nodes via the distributed instances of UCON. We present the architecture and the workflow of our approach together with a real implementation for performance evaluation purposes.

The Internet of Things (IoT) is growing rapidly as more and more household appliances, sensors and actuators are connected to the internet and communicate with each other. Because these IoT devices usually have only limited processing and communication capabilities, an efficient communication protocol is required to reduce the protocol overhead. The Message Queue Telemetry Transport (MQTT) protocol provides such properties. MQTT is a publish/subscribe protocol, where each client can subscribe to a message topic in order to receive all messages published under that topic. The payload of a message (i.e., the content) can be encrypted to hide private information. However, to forward messages, the topic of a message needs to be read by the broker and thus cannot be encrypted and might reveal private information. This paper presents methods to avoid this problem. It features and evaluates a one-time password approach to provide a fully obfuscated communication method for MQTT topics. Thereby, the user tracking and the generation of profiles is prevented.

Message Queuing Telemetry Transport (MQTT) is a popular communication protocol used to interconnect devices with considerable network restraints, such as those found in Internet of Things (IoT). MQTT directly impacts a large number of devices, but the software security of its server ("broker") implementations is not well studied. In this paper, we design, implement, and evaluate a novel fuzz testing model for MQTT. The fuzzer combines aspects of mutation guided fuzzing and generation guided fuzzing to rigorously exhaust the MQTT protocol and identify vulnerabilities in servers. We introduce Markov chains for mutation guided fuzzing and generation guided fuzzing that model the fuzzing engine according to a finite Bernoulli process. We implement "response feedback", a novel technique which monitors network and console activity to learn which inputs trigger new responses from the broker. In total, we found 7 major vulnerabilities across 9 different MQTT implementations, including 6 zero-day vulnerabilities and 2 CVEs. We show that when fuzzing these popular MQTT targets, our fuzzer compares favorably with other state-of-the-art fuzzing frameworks, such as BooFuzz and AFLNet.

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.

Messaging protocols for the Internet of Things (IoT) play a crucial role in facilitating efficient product creation and waste reduction, and in enhancing agricultural process efficiency within the realm of smart greenhouses. Publish–subscribe (pub-sub) systems improve communication between IoT devices and cloud platforms. Nevertheless, IoT technology is required to effectively handle a considerable volume of subscriptions or topic adjustments from several clients concurrently. In addition, subscription throughput is an essential factor of the pub-sub mechanism, as it directly influences the speed at which messages may be sent to subscribers. The primary focus of this paper pertains to a performance assessment of the proposed message categorization architecture for the Message Queue Telemetry Transport (MQTT) broker. This architecture aims to establish a standardized approach to pub-sub topics and generate new topics with various performance characteristics. We also standardize the form of MQTT protocol broker topic categorization and payload based on greenhouse specifications. The establishment of topic classification enhances the operational effectiveness of the broker, reduces data volume, and concurrently augments the number of messages and events transmitted from the greenhouse environment to the central server on a per-second basis. Our proposed architecture is validated across multiple MQTT brokers, including Mosquitto, ActiveMQ, Bevywise, and EMQ X, showing enhanced flexibility, extensibility, and simplicity while maintaining full compatibility with greenhouse environments. Key findings demonstrate significant improvements in performance metrics. The message processing time for the proposed Active MQ broker was increased approximately five-fold across all QoS levels compared to the original. Subscription throughput for the Bevywise MQTT Route 2.0 broker at QoS0 reached 1453.053, compared to 290.610 for the original broker. The number of messages in the Active MQ broker at QoS0 surged from 394.79 to 1973.95. These improvements demonstrate the architecture’s potential for broader IoT applications in pub-sub systems.

Ensuring the security of networks is a significant hurdle in the rollout of the Internet of Things (IoT). A widely used protocol in the IoT ecosystem is message queuing telemetry transport (MQTT), which is based on the published-subscribe model. IoT manufacturers are expected to expand their usage of the MQTT protocol, which is expected to increase the number of cyber security threats against the protocol. IoT settings are crucial to overcoming scalability and computing resource issues and minimizing the characteristics needed for categorization. Machine learning (ML) is extensively used in traffic categorization and intrusion detection. This study proposes a ML-based network traffic detection model (MLNTDM) to enhance IoT application layer attack detection. The proposed architecture for the MQTT protocol is evaluated based on its effectiveness in detecting malicious attacks and how these affect various MQTT brokers. This study focuses on low-power-consuming ML algorithms for detecting IoT botnet offenses and identifying typical attacks and their responses. With this framework, each network flow provides information that can help identify the source of generated traffic and network assaults. Results from our approach, as shown in the experiment, prove more accuracy.

Energy systems are now incorporating Internet of Things technology to make better monitoring and management of energy possible. This research study analyzes the design and implementation of a secure and smart monitoring network for hybrid energy systems using two of the most widely known Internet of Things protocols and AI, MQTT (Message Queuing Telemetry Transport) and CoAP (Constrained Application Protocol). Traditional energy monitoring has latency issues, low scalability. This paper discusses an enhancement over this with MQTT and CoAP, aiming at increasing overall performance and scalability within hybrid energy frameworks. Methodologies are developed to implement the application with IoT sensors and to collect data from MQTT and CoAP protocols for further processing in the Python programming language. To demonstrate these improvements in efficiency and reliability, the results are compared against those obtained from traditional systems. This work investigates how Internet of Things (IoT) and AI can be deployed in hybrid energy systems with advanced communication protocols to enhance observability as well as control. A total of many enhancements have been added to this system: latency by 35% has been minimized, data transmission efficiency of 96.87%, a reliability increase of 28%, 25% enhancement in scalability, and 20% reduction in energy consumption.

The Internet of Things (IoT), which connects billions of gadgets to expedite operations and enhance our lives, has completely changed the way we interact with our environment. With MQTT (Message Queuing Telemetry Transport) emerging as a popular communication protocol within the IoT ecosystem, the vast proliferation of networked devices has, however, presented serious security challenges. In order to strengthen MQTT security, this study suggests using an improved symmetric algorithm.Existing MQTT implementations frequently rely on simple security safeguards, making them susceptible to dangers like data manipulation, eavesdropping, and unauthorised access. Our research presents a novel symmetric algorithm designed to meet the particular needs of MQTT communication as a defence against these weaknesses. In order to protect the confidentiality and integrity of data transferred between IoT devices and brokers, this algorithm provides powerful encryption mechanisms. Additionally, it optimises resource usage to take into account the limitations of IoT devices, which frequently have constrained computational and memory resources.

The Internet of Things (IoT) has become widely used in recent years in a wide range of applications, such as, weather condition monitoring, transportation, smart homes, smart cities, smart farm, etc. The ecosystem of the IoT is also vast, including from sensor and hardware devices up to cloud-computing. An approach that is getting more and more attention in the IoT ecosystem is the edge-computing and one of its fundamental pieces of equipment is the edge-computing gateway (GTW), which can working as a data-processing device nearer to the things and as a bridge to the Internet, as well. The most important features for these GTWs must be robustness and efficiency and a very popular solution is to use low-cost Raspberry Pi card-size computers. Considering protocol solution, Message Queue Telemetry Transport (MQTT) communication protocol has been considered one of the most applicable to IoT because of its low-power capability. In this context, this paper describes a study about the performance evaluation of a low-power member of the Raspberry Pi family, the Raspberry Pi Zero W, working as an IoT gateway and running MQTT. The experimental results show its performance using as metrics: the processor temperature, the CPU usage level, and rate of MQTT received messages under different Quality of Services (QoS).

Design and implementation of an open-Source IoT and blockchain-based peer-to-peer energy trading platform using ESP32-S2, Node-Red and, MQTT protocol

The fast growth of the Internet of Things (IoT) has made this technology one of the most promising paradigms of recent years. Wireless Sensor Networks (WSNs) are one of the most important challenges of the Internet of things. These networks are made up of devices with limited processing power, memory, and energy. The constrained nature of WSNs makes it necessary to have specific restricted protocols to work in these environments. In this paper, we present an energy consumption and network traffic study of the main IoT application layer protocols, the Constrained Application Protocol (CoAP), and the version of Message Queue Telemetry Transport (MQTT) for sensor networks (MQTT_SN). The simulations presented evaluate the performance of these protocols with different network configurations.