Most machine learning proposals in the Internet of Things (IoT) are designed and evaluated on preprocessed data sets, where data acquisition and cleaning steps are often considered a black box. In addition, IoT environments have numerous challenges related to acquiring data from sensors, where sensitive data can be threatened by malicious users who seek to interfere with the communication channel or storage. Additionally, sensor data can also be affected by noise. Therefore, differentiating the type of threat/anomaly requires additional energy and computational resources. We propose to carry out data cleaning/anomaly techniques on the IoT device itself, not in the cloud servers but closer to the data source. Therefore, the IoT device sends trusted data to the Cloud. Among the benefits of this is the considerable reduction in the cost of implementation due to less movement of data between IoT devices and the Cloud. Consequently, we define three anomaly detection steps using smoothing filters, unsupervised learning, and deep learning techniques (i.e., hybrid model) to detect different variations of anomalies and threats while focusing on a small computational/memory footprint. The deployment of the hybrid model on AVR, Tensilica, and ARM microcontrollers showed that the last ones are an adequate target to implement the model because they best satisfy the necessary hardware requirements. The proposed model consumes 50 kB of Flash and 12 kB of RAM and processes data locally, achieving a bandwidth reduction of 60%. Finally, the hybrid model was tested in external data sets.

Nowadays, increasing air-pollution levels are a public health concern that affects all living beings, with the most polluting gases being present in urban environments. For this reason, this research presents portable Internet of Things (IoT) environmental monitoring devices that can be installed in vehicles and that send message queuing telemetry transport (MQTT) messages to a server, with a time series database allocated in edge computing. The visualization stage is performed in cloud computing to determine the city air-pollution concentration using three different labels: low, normal, and high. To determine the environmental conditions in Ibarra, Ecuador, a data analysis scheme is used with outlier detection and supervised classification stages. In terms of relevant results, the performance percentage of the IoT nodes used to infer air quality was greater than $90\%$. In addition, the memory consumption was 14 Kbytes in a flash and 3 Kbytes in a RAM, reducing the power consumption and bandwidth needed in traditional air-pollution measuring stations.

This paper proposes the solution of tasks set required for autonomous robotic group behavior optimization during the mission on a distributed area in a cluttered hazardous terrain. The navigation scheme uses the benefits of the original real-time technical vision system (TVS) based on a dynamic triangulation principle. The method uses TVS output data with fuzzy logic rules processing for resolution stabilization. Based on previous researches, the dynamic communication network model is modified to implement the propagation of information with a feedback method for more stable data exchange inside the robotic group. According to the comparative analysis of approximation methods, in this paper authors are proposing to use two-steps post-processing path planning aiming to get a smooth and energy-saving trajectory. The article provides a wide range of studies and computational experiment results for different scenarios for evaluation of common cloud point influence on robotic motion planning.

Agricultural production is on most countries’ national agenda because climate change affects crops, fruits, vegetables, and insect infestation. Therefore, achieving maximum production results is a challenge faced by professional growers, who have seen greenhouses as a very good option to guarantee these results. By using new technologies inside greenhouses, farmers can reduce the damaging effect of insects on plants and improve indoor cultivation through climate control. However, to efficiently manage agricultural fields and greenhouses today, farmers have to apply technologies in line Industry 4.0, such as: robots, Internet of Things devices, machine learning applications, and so on. In this context, deploying sensors plays a key role in collecting data and finding information supporting the farmer’s decision-making. As a feasible solution for small farms, this paper presents an autonomous robot that moves through greenhouse crop paths with previously-planned routes and can collect environmental data provided by a wireless sensor network, where the farmer does not have previous information about the crop. Here, an unsupervised learning algorithm is implemented to cluster the optimal, standard, and deficient sectors of a greenhouse to determine inappropriate growth patterns in crops. Finally, a user interface is designed to help farmers plan both the route and distance to be traveled by the robot while collecting information from the sensors to observe crop conditions.

This paper presents a theoretical analysis of possible jitter impact in application of numeric criterion for fastmeasurement of frequency by coincidence principle. The primary goal is the generation of a signal containing a known amount of each jitter components. This signal was used for testing signals with regular pulse trains. Initially, jitter components are analyzed and modeled individually. Next, sequences for combining different kinds of jitter are modeled, simulated and evaluated. Jitter model simulation in Matlab is utilized to show the independence of frequencymeasurement results on the total jitter present in the reference and desired pulse trains independently. A good agreement between previously introduced theory of fastmeasurement of frequency and simulation in jitter presence is verified; these results allows to engineers use the numeric criterion for fastmeasurement of frequency in spite to interactions among jitter components in various applications for frequency domain sensors