The use of wireless and Internet of Things (IoT) devices is growing rapidly. Because of this expansion, nowadays, mobile apps are integrated into low-cost, low-power platforms. Low-power, inexpensive sensor nodes are used to facilitate this integration. Given that they self-organize, these systems qualify as IoT-based wireless sensor networks. WSNs have gained tremendous popularity in recent years, but they are also subject to security breaches from multiple entities. WSNs pose various challenges, such as the possibility of numerous attacks, their innate power, and their unfeasibility for use in standard security solutions. In this paper, to overcome these issues, we propose the secure encryption random permutation pseudo algorithm (SERPPA) for achieving network security and energy consumption. SERPPA contains a major entity known as a cluster head responsible for backing up and monitoring the activities of the nodes in the network. The proposed work performance is compared with other work based on secure IoT devices. The calculation metrics taken for consideration are energy, overheads, computation cost, and time consumption. The obtained results show that the proposed SERPPA is very significant in comparison to the existing works, such as GKA (Group Key Agreement) and MPKE (Multipath Key Establishment), in terms of data transfer rate, energy consumption and throughput.

To select a biomass crop type of the highest sustainability for the purpose of producing biofuel is recognized as a problem of the multi-criteria decision analysis (MCDA) type, as it comprises different conflicting criteria. To effectively address this problem, the present paper introduces a novel integrated approach using the complex proportional assessment (COPRAS) method under the intuitionistic fuzzy sets (IFSs). The proposed approach works based on the IFSs operators as well as an innovative process utilized in evaluating the attributes’ weights. To evaluate these weights, the subjective weights using the step-wise weight assessment ratio analysis (SWARA) model are integrated with the objective weights achieved using an entropy-based approach in order to attain more realistic weights. As MCDA problems inevitably suffer from different degrees of uncertainty, the proposed approach could be of great help to those who are required to make decisions in uncertain settings. The paper took into consideration a sustainable biomass crop selection problem to exemplify the effectiveness of the presented approach in handling real MCDA problems. Moreover, a sensitivity analysis with respect to the diverse values of the attributes is presented in order to assess the stability of the introduced model. This study reveals that the combination of the objective and subjective weights enhances the stability of the introduced approach with diverse attribute weights. Finally, the results of the introduced model are compared to some existing intuitionistic fuzzy information-based methods. The findings of the comparison confirm the efficiency of the present approach in performing the defined tasks under uncertain environments.

Unmanned aerial vehicles (UAVs) coupled with machine learning approaches have attracted considerable interest from academicians and industrialists. UAVs provide the advantage of operating and monitoring actions performed in a remote area, making them useful in various applications, particularly the area of smart farming. Even though the expense of controlling UAVs is a key factor in smart farming, this motivates farmers to employ UAVs while farming. This paper proposes a novel crop-monitoring system using a machine learning-based classification with UAVs. This research aims to monitor a crop in a remote area with below-average cultivation and the climatic conditions of the region. First, data are pre-processed via resizing, noise removal, and data cleaning and are then segmented for image enhancement, edge normalization, and smoothing. The segmented image was pre-trained using convolutional neural networks (CNN) to extract features. Through this process, crop abnormalities were detected. When an abnormality in the input data is detected, then these data are classified to predict the crop abnormality stage. Herein, the fast recurrent neural network-based classification technique was used to classify abnormalities in crops. The experiment was conducted by providing the present weather conditions as the input values; namely, the sensor values of temperature, humidity, rain, and moisture. To obtain results, around 32 truth frames were taken into account. Various parameters—namely, accuracy, precision, and specificity—were employed to determine the accuracy of the proposed approach. Aerial images for monitoring climatic conditions were considered for the input data. The data were collected and classified to detect crop abnormalities based on climatic conditions and pre-historic data based on the cultivation of the field. This monitoring system will differentiate between weeds and crops.

In recent years, AI-enabled aerial sensing has become an essential tool for agriculture, forestry, defense, and border protection agencies. This technology includes using unmanned aerial vehicles (UAVs), or drones, equipped with sensors and AI algorithms to collect and analyze real-time data. UAVs can monitor border regions for illegal activity, like smuggling or unlawful border crossings. AI algorithm people and the data collected by UAVs to detect and classify objects, such as vehicles or people, and identify suspicious behavior. This study explores the application of aerial monitoring systems integrated with artificial intelligence (AI) in agriculture, forestry, defense, and border protection. With the increasing need for efficient surveillance and security measures, the utilization of AI-driven aerial monitoring systems has gained significant attention. Overall, AI-enabled aerial sensing has the potential to revolutionize agriculture, forestry, defense, and border protection by providing real-time situational awareness and improving response times.