International Journal of Sensors Wireless Communications and Control - Volume 12, Issue 8, 2022

MQTT is an open standard protocol promoted by OASIS and ISO, which allows devices to transport messages using the publish/subscribe model. MQTT is more prevalent than other application layer protocols of the Internet of Things (IoT) due to its lightweight nature, low bandwidth usage, application demand, etc. It is easy and straightforward to use the protocol, making it optimal for communication in resource-constrained situations such as machine-to-machine (M2M), Wireless Sensor Networks (WSNs), and in IoT circumstances in which the actuator and sensor nodes connect with applications through the MQTT message broker. A few review papers on MQTT protocol are available in the literature that focuses on broker details, comparison of IoT protocols, and limitations. In this paper, an overview of MQTT, existing survey work on MQTT, publication statistics, MQTT protocol performance evaluation, applications of MQTT, security issues of MQTT, comparison between MQTT and MQTT-SN, tools available or MQTT and available MQTT brokers to provide service are discussed. Graphs and comparison tables are presented to show the outcomes of the application and performance evaluation. The scope of this review paper is also to contribute a novel taxonomy of application layer protocols, their merits and demerits, correlation of MQTT with other application layer protocols, existing works of MQTT protocol to improve reliability, efficiency, security, issues, and challenges in MQTT, as well as future directions of MQTT.

Background: This research uses the Ethiopian HICE survey dataset. Predicting food insecurity is critical in presenting the household's situation to the appropriate agencies that take preventative and intervention measures. Objective: This research paper's primary goal is to predict households' food security status using ensemble learning models. Methods: We use five base classifiers and a voting strategy for ensemble classification to enhance the performance of different base classifiers. Backward feature elimination and hard and soft voting-based ensemble learning are used to evaluate household food security. The training set for the basic classifiers is composed of the features that have been selected. Each ML classifier makes its prediction about the class label with the help of an ensemble learning method. For making decisions, hard voting uses a simple majority, whereas soft vote employs a weighted probability. To determine the final prediction. Ethiopian household income, consumption, and expenditure dataset are used to test the proposed ensemble learning approach. The backward feature elimination approach improved the model's performance by removing irrelevant and redundant features. Random forest, gradient boosting, multi-layer perceptron, K-nearest Neighbor, and Extra Tree classifiers were used to predict the family's level of food security. Finally, the authors compare the accuracy of ensemble and base classifiers. Results: The experiment result shows that the RF classifier surpasses the other base and ensemble classifiers and scored 99.98% accuracy. Because a Random forest classifier is an ensemble learning classifier that uses several decision trees, the final prediction is computed based on the majority vote of the several trees. The comparison result of hard and soft voting reveals that soft voting outperforms hard voting before and after feature selection with accuracies of 99.79% and 99.77%, respectively. Conclusion: Based on the result obtained, ensemble learning plays a significant role in predicting household food security status and implementing hard and soft voting. The RF classifier surpasses the other base and ensemble classifiers with an accuracy of 99.98%. From ensemble methods, soft voting surpasses hard voting with an accuracy score of 99.79%.

Aim: Structured light is frequently selected for efficient and accurate depth imaging, and single-frame-based methods have been presented for real-time sensing or imaging dynamic objects. However, many existing single-frame-based methods do not provide sufficient range resolution. Even those capable of sufficient range resolution mostly result in insufficient signal-to-noise ratio or depend on spatially windowed uniqueness, where a larger window makes the identification trickier. Methods: This paper presents a novel method for rapid structured-light range sensing using a binary color stripe pattern. For accurate and reliable depth acquisition, we identify projected stripes by our stripe segmentation and unwrapping algorithms. For robust stripe detection, the color-stripe segmentation algorithm performs image upsizing, motion blurring, and color balancing. The binary stripe unwrapping algorithm consists of mean-speed walk unrolling, row-wise unrolling, and local median correction, and resolves the high-frequency color-stripe redundancy efficiently and reliably. Results: Experimental results show the effectiveness and reliability of the presented method. Conclusion: Even using an entry-level phone camera under a low-cost DLP projector produces highaccuracy results.

Background: Internet-of-things (IoT) has been developed for use in a variety of fields in recent years. The IoT network is embedded with numerous sensors that can sense data directly from the environment. The network's sensing components function as sources, observing environmental occurrences and sending important data to the appropriate data centers. When the sensors detect the stated development, they send the data to a central station. On the other hand, sensors have limited processing, energy, transmission, and memory capacities, which might have a detrimental influence on the system. Objectives: We have suggested an energy-efficient framework based on Swarm Intelligence in IoT. The idea behind using Swarm Intelligence is the probabilistic-based global search phenomena that suit well for IoT networks because of the randomization of nodes. Our framework considers the prominent metaheuristic concepts responsible for the overall performance of the IoT network. Our current research is based on lowering sensor energy consumption in IoT networks, resulting in a longer network lifetime. Methods: This study selects the most appropriate potential node in the IoT network to make it energy- efficient. It suggests a technique combining PSO's exploitation capabilities with the GWO's exploration capabilities to avoid local minima problems and convergence issues. The proposed method PSGWO is compared with the traditional PSO, GWO, Hybrid WSO-SA, and HABCMBOA algorithms based on several performance metrics in our research study. Results: The results of our tests reveal that this hybrid strategy beats all other ways tested, and the energy consumption rate of the proposed framework is decreased by 23.8% in the case of PSO, 20.2% in the case of GWO, 31.5% in the case of hybrid WSO-SA, and 29.6% in the case of HABC-MBOA, respectively. Conclusion: In this study, several performance parameters, including energy consumption, network lifetime, live nodes, temperature, and throughput, are taken into account to choose the best potential node for the IoT network. Using various simulations, the performance of the proposed algorithm was evaluated and compared to the metaheuristic techniques. Moreover, PSGWO is found to be improved, and the energy consumption rate is decreased.

Background: Event detection and monitoring applications involve highly populated sensor nodes in Wireless Sensor Networks (WSNs). Dense deployment of nodes leads to correlated sensor observations in the spatial and temporal domain. Most of the previous works focused on constant sensing radii for spatially correlated sensor observations. However, in real time scenario, the sensor nodes may have variable sensing coverage areas, which comprise a Heterogeneous WSN. Objective: To address this issue, we present an Enhanced Weighted Spatial Correlation Model for Heterogeneous sensor nodes in WSNs. Methods: The mathematical framework considers the spatial coordinates of sensor nodes, the distances between the sensor nodes, and their sensing coverage. Furthermore, the correlation coefficient is calculated in terms of overlapping areas for randomly deployed nodes. Performance of the correlation model is evaluated and analyzed in terms of event distortion function. In addition to this, a macro and micro-zone concept is introduced, wherein sensor information is weighted for better event estimation at the sink node. Moreover, dynamic weighing of nodes like Inverse, Shepard’s and Gaussian distance weighing algorithms are simulated and analyzed for minimal event distortion. Over and above, the system performance is evaluated for different approaches considering reporting nodes with and without clustering of sensor nodes for macro and microzone concept. Simulation results for the Enhanced Weighted Spatial Correlation Model developed are obtained using MATLAB software. Results: The comparative study shows an improved system performance in terms of minimal distortion obtained for non-clustered nodes; thereby reducing the computational complexity of cluster formation. Furthermore, the dynamic weighing algorithms outperform the existing fixed weighing algorithms for the correlation model with the lowest distortion function. Conclusion: Moreover, in the above algorithms, the event distortion gradually decreases and later becomes constant with the increase in the number of representative nodes. Hence, it illustrates that minimal distortion can be achieved by activating lesser number of representative nodes, thereby preserving the energy of other sensor nodes and increasing the lifetime of WSNs.