Abstract: The rapid expansion of the Internet of Things (IoT) and smart device ecosystems has accelerated the development and deployment of Machine-to-Machine (M2M) communication protocols. M2M communication enables autonomous data exchange between devices without human intervention, forming the backbone of modern IoT applications in industries such as healthcare, transportation, energy, and manufacturing. This comprehensive review provides an in-depth analysis of the most widely adopted M2M communication protocols, including HTTP, WebSocket, XMPP, AMQP, MQTT, CoAP, DDS, LPWAN and LwM2M. The paper explores their architectural principles, operational mechanisms, and core features, highlighting their respective strengths and limitations in various application scenarios. Comparative analysis is presented based on critical performance metrics such as scalability, latency, security, energy efficiency, interoperability, and Quality of Service (QoS). The review also discusses the challenges associated with protocol selection, integration, and deployment in resource-constrained environments. Furthermore, emerging trends such as the convergence of edge computing, 5G connectivity, and enhanced security frameworks are examined for their impact on the evolution of M2M communication. The paper concludes by outlining future research directions, including the need for adaptive and intelligent protocol frameworks capable of addressing dynamic application requirements. By synthesizing recent advancements and providing actionable insights, this review aims to guide researchers, engineers, and practitioners in selecting and implementing optimal M2M communication protocols for next-generation IoT systems.

Abstract: Clinical laboratories require accurate and continuous environmental monitoring to ensure the integrity of biological samples, reagents, and laboratory instruments. Environmental deviations, especially in temperature and air quality, can compromise test accuracy and violate regulatory standards such as ISO 15189. This paper presents the design and development of a smart IoT-based environmental monitoring system tailored for clinical lab environments. The system focuses on real-time monitoring of laboratory freezer temperature, ambient room temperature, humidity, and air quality. It utilizes ESP32 microcontrollers integrated with DS18B20 temperature sensors, DHT11 temperature and humidity sensors, and MQ-135 gas sensors to provide comprehensive environmental surveillance. Data is transmitted via Wi-Fi to a cloud-based dashboard, offering remote access, historical data logging, and customizable alert mechanisms for threshold breaches. To optimize energy consumption, the system incorporates power-efficient features such as deep sleep modes and adaptive sampling intervals. The prototype demonstrates consistent and accurate performance in a working laboratory setting. By offering real-time insights and automated alerts, the system minimizes the risks associated with manual monitoring and enhances compliance readiness. The proposed solution is low-cost, modular, and scalable, making it suitable for deployment in both large and resource-limited clinical laboratories.

Abstract: In the software development life cycle, testing plays a crucial role in identifying errors or bugs, ensuring the verification of requirement specifications, design, analysis, coding, and estimating the software's reliability. As software systems grow larger, the size of the test suite typically expands exponentially. However, conducting exhaustive testing is often impractical due to the challenges posed by combinatorial optimization problems, as well as factors such as cost, constraints, and limited resources. To alleviate the burden on software development, it becomes essential to streamline test suites. Generating an optimal number of test cases is imperative for expediting the overall software testing process. Pairwise testing techniques emerge as pivotal in this context, aiming to reduce the size of test suites. Existing literature highlights the effectiveness of varying the number of interactions among input parameters, significantly diminishing the need for extensive test data. Over the past decade, numerous test data generation strategies have been developed, differing in their support for various interaction levels—ranging from the minimum of two (pairwise) to t (t-way), where t can be any value greater than 2. Additionally, various means, such as Artificial Intelligence and Machine Learning, are employed to accelerate the testing process. A comprehensive literature review is crucial for advancing the development of superior test suite generation techniques. Such an examination reveals research gaps that can inspire new approaches from researchers. This paper aims to review prominent pairwise test suite generation techniques, evaluating their strengths and weaknesses. The literature review underscores that many techniques support pairwise interaction, some support t-way interaction, only a few endorse un-uniform interaction, and none accommodates dynamic interactions among input parameters. Notably, the increasing prevalence of Internet of Things (IoT) devices that receive audio and video (metadata) as input parameters lacks adequate test generation techniques supporting metadata. In addition to identifying pros and cons, this paper offers suggestions to guide future researchers in efficiently addressing combinatorial optimization problems and ensuring cost-effectiveness. The objective is to contribute to the evolution of robust techniques for generating test suites, laying the foundation for more effective and comprehensive software testing methodologies.