Sensors and IoT for Water Quality Monitoring: A Systematic Review of Technologies and Field Validation

With the help of IoT (Internet of Things) communication technologies, a variety of devices and objects can connect to one another and communicate, building a network of intelligent systems. Real-time process monitoring, control, and automation are made possible by these technologies, which make it easier for data and information to be transferred between equipment. IoT devices link to each other and share data using wireless communication protocols as Wi-Fi, Bluetooth, Zigbee, and cellular networks (2G, 3G, 4G, and 5G). These wireless technologies offer IoT installations across many environments flexibility, mobility, and scalability. IoT uses sensor networks to gather information from the real world. Devices contain embedded sensors that can measure and detect a variety of temperature, humidity, pressure, motion, and other environmental conditions. The information is transferred to one or more clouds. IoT systems commonly use cloud computing platforms to store, analyze, and analyze the large amount of data generated by connected devices. Cloud-based solutions offer the scalability, data storage, and computational power necessary for IoT applications, enabling real-time insights and wise decision-making. IoT devices are quickly utilizing edge computing capabilities to get past bandwidth limitations, latency, and privacy concerns. Edge devices like gateways and edge servers reduce reliance on cloud infrastructure by processing and analyzing data locally. Edge computing enables quicker response times, data filtering, and offline abilities. In order to ensure compatibility and simple connection across Internet of Things (IoT) networks and devices, numerous communication protocols and standards have been created. Examples include HTTP (Hypertext Transfer Protocol), CoAP (Constrained Application Protocol), and MQTT (Message Queuing Telemetry Transport). IoT communication systems consider important privacy and security considerations. Secure communication protocols, authentication methods, and encryption mechanisms safeguard data exchanged between devices. To address privacy issues, data anonymization, consent management, and adherence to privacy regulations are used. Analytics and artificial intelligence (AI) technologies combine with IoT connection technologies. Machine learning algorithms examine IoT data streams to produce insightful findings, identify patterns, and provide predictive capabilities. AI-driven insights enable intelligent automation while also enhancing operational efficiency and resource use.

To facilitate the successful deployments of the Internet of Things (IoT) applications, the support of secure and efficient communication protocol and architecture is inevitable. Owing to its lightweight and easiness, the Message Queue Telemetry Transport (MQTT) has become one of the most popular communication protocols in the Internet-of-Things (IoT). However, the security supports in the MQTT are very weak: it assumes the security support from the underlying Secure Sockets Layer (SSL). The weakness incurs several key drawbacks. One is the support of SSL capacities is a pressure for those resources-constrained devices. One another and very important one is the lack of the support of secure group communication. Without efficient and secure group communication support, the MQTT-based IoT systems would suffer from deteriorated computational and communication performance, especially when there are tons of IoT devices accessing the systems. In this paper, we design a secure MQTT group communication framework in which each MQTT application would periodically updates the group key and the data communication can be efficiently and securely encrypted by the group keys. Both our prototype system and the analysis show that our design can improve the performance of security, computation, and communication.

”A Novel MQTT Security framework In Generic IoT Model”

The explosive growth of the domain of the Internet of things (IoT) network devices has resulted in unparalleled ease of productivity, convenience, and automation, with Message Queuing Telemetry Transport (MQTT) protocol being widely recognized as an essential communication standard in IoT environments. MQTT enables fast and lightweight communication between IoT devices to facilitate data exchange, but this flexibility also exposes MQTT to significant security vulnerabilities and challenges that demand highly robust security. This paper aims to enhance the detection efficiency of an MQTT traffic intrusion detection system (IDS). Our proposed approach includes the development of a binary balanced MQTT dataset with an effective feature engineering and machine learning framework to enhance the security of MQTT traffic. Our feature selection analysis and comparison demonstrates that selecting a 10-feature model provides the highest effectiveness, as it shows significant advantages in terms of constant accuracy and superior training and testing times across all models. The results of this study show that the framework has the capability to enhance the efficiency of an IDS for MQTT traffic, with more than 96% accuracy, precision, recall, F1-score, and ROC, and it outperformed the most recent study that used the same dataset.

Agriculture involves various physical quantities that need to be monitored and controlled. IoT have several capabilities which are suitable for implementing Precise Agriculture. IoT architecture involves sensors, nodes and computing which can be edge, fog and cloud computing. In IoT there has been a need of communication between nodes, nodes and gateway and gateways to cloud. Different protocols are used at different layers of IoT architecture for communication. Those must be analysed for selecting appropriate protocol for an application. As IoT uses low power devices resources must be utilized properly. There has been a need of low bandwidth, low power communication protocols both in application and network layers to support heavy traffic in power constrained devices. In this paper detailed comparison is made between application layer protocols used in IoT namely MQTT and HTTP for their suitability in IoT applications. To control bandwidth not only energy efficient protocol and also pre-processing of data is required.

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.

The rise of the Internet of Things (IoT) over the past decade has revolutionized environmental monitoring systems, enabling real-time data collection and analysis. Since its development in 1999 by IBM and Eurotech, the Message Queuing Telemetry Transport (MQTT) protocol has gained popularity for its lightweight, low-power communication, particularly in constrained network environments. By 2023, MQTT was responsible for over 85% of IoT communication protocols used in sensor-based systems. This paper presents a real-time monitoring system designed to capture atmospheric parameters, including temperature, light intensity, and humidity. Using MQTT, the system transmits data to a central server, where it is stored in a database and visualized through a web interface. A key focus of this study is the evaluation of MQTT's data transmission speed, given its widespread use in IoT applications. The study conducts extensive tests under varying conditions, analyzing MQTT's transmission latency and throughput. Historical data from previous studies shows that MQTT can achieve latencies as low as 10 ms in optimized environments, making it a preferred protocol for time-sensitive applications. Our results further validate MQTT’s efficiency, demonstrating consistent data delivery speeds with average latencies below 50 ms across different scenarios. These findings confirm MQTT’s suitability for real-time environmental monitoring and its potential for broader IoT applications that require fast and reliable data transfer

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.

The main challenge for the Internet of Things (IoT) is to ensure interoperability between heterogeneous IoT entities. To support the interaction, intercommunication, and interoperability between these devices several solutions are proposed in the literature. The SDN (Software-defined Network) is one of these solutions to resolve the problem of the heterogeneous network used in IoT. To guarantee network interoperability, the SDN uses a centralized controller, which handles the entire network. The role of end devices in IoT is only forwarding data. The MQTT (Message Queuing Telemetry Transport) protocol is another solution for granting interoperability in IoT. Which is a publish/subscribe based messaging protocol that avoids direct connection between devices by relaying data through a central server called the broker. Combination of these two solutions to manage IoT devices makes it easy to add new devices without touching or changing the existing infrastructure. The new devices only need to communicate with the broker. Moreover, the Controller SDN is responsible for handling networks. Consequently, smart devices added don’t need to be compatible with the others. In this paper, we present the design and the implementation of a new IoT architecture, which is a combination of SDN technology and MQTT protocol. That enables heterogeneous IoT devices to be interoperable and interact without any problems. Our system utilizes the lightweight protocol MQTT with a new mechanism using several slave brokers and one master. The slaves manage the group of the end devices in the wireless IoT network, and the master broker installed in the SDN controller supervises the integral network. The SDN controller uses a multicast system to send MQTTdata across the external wireless network. As a result, that reduces transmission delay between wireless IoT network compared with the using of a standard MQTT.

The Internet of Things (IoT) deployment in emerging markets has increased dramatically, making security a prominent issue in IoT communication. Several protocols are available for IoT communication; among them, Message Queuing Telemetry Transport (MQTT) is pervasive in intelligent applications. However, MQTT is designed for resource-constrained IoT devices and, by default, does not have a security scheme, necessitating an additional security scheme to overcome its weaknesses. The security vulnerabilities in MQTT inherently lead to overhead and poor communication performance. Adding a lightweight security framework for MQTT is essential to overcome these problems in a resource-constrained environment. The conventional MQTT security schemes present a single trusted scheme and perform attribute verification and key generation, which tend to be a bottleneck at the server and pave the way for various security attacks. In addition to that, using the same secret key for an extended period and a flawed key revocation system can affect the security of MQTT. To address these issues, we propose an Improved Ciphertext Policy-Attribute-Based Encryption (ICP-ABE) integrated with a lightweight symmetric encryption scheme, PRESENT, to improve the security of MQTT. In this work, the PRESENT algorithm enables the secure sharing of blind keys among clients. We evaluated a previously proposed ICP-ABE scheme from the perspective of energy consumption and communication overhead. Furthermore, we evaluated the efficiency of the scheme using provable security and formal methods. The simulation results showed that the proposed scheme consumes less energy in standard and attack scenarios than the simple PRESENT, Key Schedule Algorithm (KSA)-PRESENT Secure Message Queue Telemetry Transport (SMQTT), and ECC-RSA frameworks, with a topology of 30 nodes. In general, the proposed lightweight security framework for MQTT addresses the vulnerabilities of MQTT and ensures secure communication in a resource-constrained environment, making it a promising solution for IoT applications in emerging markets.

Increased numbers of installed IoT devices and more complex building management algorithms make vital a secure, reliable, and cloud-based IoT platform, offering provisions for devices to communicate and react to predefined situations. This platform facilitates data acquisition, management, and interactions among IoT devices in order to exchange information including measurement data and control signals with controllers via a two-way communication mechanism. In this paper, an IoT platform to implement a device-supply management algorithm in a smart building, aiming to supply higher-priority devices from solar power and to maximize solar-power utilization, has been designed and implemented. Message Queue Telemetry Transport (MQTT), which is the state-of-the-art Internet of Things (IoT) protocol, has been adopted in this work to incorporate communications between the devices and the controller. MQTT publisher and subscriber are deployed in the Python programming language. A cloud-based data aggregation platform has been used with an interface to MATLAB, in which the device management algorithm runs. From the results, it could be observed that the IoT platform successfully achieves the goals of the designed device-supply management algorithm.

Over-the-air (OTA) updating is a critical mechanism for secure internet of things (IoT) systems for remotely updating the firmware (or keys) of IoT devices. Message queue telemetry transport (MQTT) is a very popular internet of things (IoT) communication protocol globally. Therefore, MQTT also becomes popular in facilitating the OTA mechanism in many IoT platforms, such as the Amazon IoT platform. In these IoT platforms, the MQTT broker acts as the message broker and as an OTA server simultaneously; in these broker-based OTA architectures, it is quite common that an IoT application manager not only uploads the new firmware/software to the broker but also delegates his signing authority on the firmware/software to the same broker. If the broker is secure and trusted, this OTA model works well; however, it incurs lots of security concerns if the broker is not fully trusted or if it is curious. Many MQTT deployments do not own their own brokers, but rely on a third-party broker, which sometimes is a freeware program or is maintained by a curious third party. Therefore, a secure OTA process should protect privacy against these brokers. This paper designs a novel MQTT-based OTA model in which an IoT application manager can fully control the OTA process through an end-to-end (E2E) channel. We design the model using MQTT 5.0’s new features and functions. The analysis shows that the new model greatly enhances security and privacy properties while maintaining high efficiency.

Sustained Internet of Things (IoT) deployment and functioning are heavily reliant on the use of effective data communication protocols. In the IoT landscape, the publish/subscribe-based Message Queuing Telemetry Transport (MQTT) protocol is popular. Cyber security threats against the MQTT protocol are anticipated to increase at par with its increasing use by IoT manufacturers. In particular, IoT is vulnerable to protocol-based Application layer Denial of Service (DoS) attacks, which have been known to cause widespread service disruption in legacy systems. In this paper, we propose an Application layer DoS attack detection framework for the MQTT protocol and test the scheme on legitimate and protocol compliant DoS attack scenarios. To protect the MQTT message brokers from such attacks, we propose a machine learning-based detection framework developed for the MQTT protocol. Through experiments, we demonstrate the impact of such attacks on various MQTT brokers and evaluate the effectiveness of the proposed framework to detect these malicious attacks. The results obtained indicate that the attackers can overwhelm the server resources even when legitimate access was denied to MQTT brokers and resources have been restricted. In addition, the MQTT features we have identified showed high attack detection accuracy. The field size and length-based features drastically reduced the false-positive rates and are suitable in detecting IoT based attacks.

Technologies such as Internet of Things (IoT) and big data have been widely adopted to improve quality of life. In this paper, an IoT platform is proposed to automate urban farming process by embodiment of IoT, big data and cloud computing. The IoT platform is designed so that users especially farmers can monitor the growing environment and the nutrient can be adjusted automatically without human intervention. The platform utilises pH, total dissolved solids (TDS), oxidation reduction potential (ORP) and temperature data to regulate the optimum concentration of the nutrient solutions. The growth rate of the plant is monitored continuously using camera. The proposed system uses a WiFi-based network and Message Queuing Telemetry Transport (MQTT) protocol to send sensor data from IoT device to server hosted in the cloud. Web and mobile based user application is included in the platform to allow users to monitor the urban farm anytime, anywhere.

Message Queuing Telemetry Transport (MQTT) is a standard messaging protocol for the Internet of Things (IoT). Among the various communication protocols used in IoT, MQTT stands unique because of its multiple advantages such as being efficient and light weight, reliability in message delivery and scalability to millions of things. However, the fact that the data privacy of the MQTT messages can be compromised while the data is in transit poses risks to the security mechanism. Attack scenarios related to MQTT have exposed multiple risks and vulnerabilities such as thousands of MQTT brokers being accessible over the default port, data privacy, authentication, data integrity, port obscurity, and botnet over MQTT. These risks and vulnerabilities undermine security mechanism which results in compromised IoT systems. Development Security and Operations (DevSecOps) aims at integrating security at every phase of the IoT lifecycle with enhanced automation, tools, and a process for determining security vulnerabilities at every stage. This results in a rapid and cost-effective IoT system which is enabled by proactive security mechanisms, threat prediction, threat detection, and alerting mechanisms. The aim of this work is to build a DevSecOps pipeline utilizing open source MQTT servers and brokers. A comparative study was performed to identify the risk posture provided by the DevSecOps pipeline across MQTT ports offering different combinations of security mechanisms. Firstly, threat modelling was conducted wherein the IoT system was analyzed at an architectural level from an attacker’s perspective and appropriate risk mitigation and defense mechanisms were accommodated into the design. The IoT system was then subjected to rigorous static and dynamic analysis followed by vulnerability scanning and third component checks. Penetration test cases and controls are automated to check threats and vulnerabilities like escalation of privileges, denial of service, spoofing, information disclosure, and repudiation. An alerting mechanism is also integrated into the system to monitor risks and vulnerabilities. Our proposed DevSecOps models achieves standard maturity in security systems with earlier threat prediction and detection.