Wireless sensor networks (WSNs) have demonstrated research and developmental interests in numerous fields, like communication, agriculture, industry, smart health, monitoring, and surveillance. In the area of agriculture production, IoT-based WSN has been used to observe the yields condition and automate agriculture precision using various sensors. These sensors are deployed in the agricultural environment to improve production yields through intelligent farming decisions and obtain information regarding crops, plants, temperature measurement, humidity, and irrigation systems. However, sensors have limited resources concerning processing, energy, transmitting, and memory capabilities that can negatively impact agriculture production. Besides efficiency, the protection and security of these IoT-based agricultural sensors are also important from malicious adversaries. In this article, we proposed an IoT-based WSN framework as an application to smart agriculture comprising different design levels. Firstly, agricultural sensors capture relevant data and determine a set of cluster heads based on multi-criteria decision function. Additionally, the strength of the signals on the transmission links is measured while using signal to noise ratio (SNR) to achieve consistent and efficient data transmissions. Secondly, security is provided for data transmission from agricultural sensors towards base stations (BS) while using the recurrence of the linear congruential generator. The simulated results proved that the proposed framework significantly enhanced the communication performance as an average of 13.5% in the network throughput, 38.5% in the packets drop ratio, 13.5% in the network latency, 16% in the energy consumption, and 26% in the routing overheads for smart agriculture, as compared to other solutions.

https://www.mdpi.com/1424-8220/20/7/2081/pdf

An Energy Efficient and Secure IoT-Based WSN Framework: An Application to Smart Agriculture

Secure and energy-efficient framework using Internet of Medical Things for e-healthcare.

An intelligent and secure smart watering system using fuzzy logic and blockchain

Advances in the Internet of Things (IoT) are helping to make water management smarter and optimizing consumption in the smart agriculture industry. This article proposes a new topology of sensor nodes based on the use of inexpensive and highly efficient components, such as water level, soil moisture, temperature, humidity, and rain sensors. Additionally, to guarantee good performance of the system, the used transmission module is based on LoRa LPWAN technology. The design of the main circuit board of the system is optimized by combining two layers and implementing software optimization. The overall sensor network is developed and tested in the research lab, and real farms can be controlled by users manually or automatically using the mobile application. Experimental results are produced by testing sensor and communication link effectiveness, and are subsequently validated in the field through a one-week measurement campaign.

Smart Agriculture Using IoT Multi-Sensors: A Novel Watering Management System

Along with the development of the Internet of Things (IoT), waste management has appeared as a serious issue. Waste management is a daily task in urban areas, which requires a large amount of labour resources and affects natural, budgetary, efficiency, and social aspects. Many approaches have been proposed to optimize waste management, such as using the nearest neighbour search, colony optimization, genetic algorithm, and particle swarm optimization methods. However, the results are still too vague and cannot be applied in real systems, such as in universities or cities. Recently, there has been a trend of combining optimal waste management strategies with low-cost IoT architectures. In this paper, we propose a novel method that vigorously and efficiently achieves waste management by predicting the probability of the waste level in trash bins. By using machine learning and graph theory, the system can optimize the collection of waste with the shortest path. This article presents an investigation case implemented at the real campus of Ton Duc Thang University (Vietnam) to evaluate the performance and practicability of the system’s implementation. We examine data transfer on the LoRa module and demonstrate the advantages of the proposed system, which is implemented through a simple circuit designed with low cost, ease of use, and replace ability. Our system saves time by finding the best route in the management of waste collection.

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Waste Management System Using IoT-Based Machine Learning in University

An analysis of energy efficiency in Wireless Sensor Networks (WSNs) applied in smart agriculture

Moodle is a widely deployed distance learning platform that provides numerous opportunities to enhance the learning process. Moodle’s importance in maintaining the continuity of education in states of emergency and other circumstances has been particularly demonstrated in the context of the COVID-19 virus’ rapid spread. However, there is a problem with personalizing the learning and monitoring of students’ work. There is room for upgrading the system by applying data mining and different machine-learning methods. The multi-agent Observer system proposed in our paper supports students engaged in learning by monitoring their work and making suggestions based on the prediction of their final course success, using indicators of engagement and machine-learning algorithms. A novelty is that Observer collects data independently of the Moodle database, autonomously creates a training set, and learns from gathered data. Since the data are anonymized, researchers and lecturers can freely use them for purposes broader than that specified for Observer. The paper shows how the methodology, technologies, and techniques used in Observer provide an autonomous system of personalized assistance for students within Moodle platforms.

Multi-agent system observer: Intelligent support for engaged e-learning

IEEE 802.15.4 standard is widely used in the field of low-power, low-cost and low-bit rate communications. In this paper, we analyze performance of an IEEE 802.15.4 network operating in 2450 MHz ISM (Industry, Science and Medicine) band in the presence of κ–μ fading, interference and additive white Gaussian noise. A novel analytical expression for the chip error probability evaluation of an IEEE 802.15.4 wireless channel has been derived. Its correctness is validated by Monte Carlo simulations. Capitalizing on the derived CEP expression, the symbol error probability and the packet error probability are also evaluated for various values of the κ–μ fading channel parameters and signal-to-interference-to-noise ratios.

Performance Analysis of an IEEE 802.15.4 Network Operating Under κ–μ Fading, Interference and AWGN

The output signal of a noncoherent dual branch receiver with EGC combiner in presence of Nakagami fading and additive white Gaussian noise will be considered in this paper. The PDF of the receiver branch signal as well as the PDF of the EGC combiner output signal will be calculated and displayed in this paper. The mentioned probability density functions will provide an necessary assumption for determination of the other important receiver characteristics and parameters. Ill. 6, bibl. 5 (in English; summaries in English, Russian and Lithuanian).

/pdf/output-signal-characteristics-of-a-post-detection-egc-114rav512o.pdf

Output Signal Characteristics of a Post-detection EGC Combiner with Two ASK Input Signals in the Presence of Fading and Gaussian Noise

This paper presents a wireless macrodiversity communication system consisting of a receiver for macrodiversity selection combining (SC) receiver and two microdiversity SC receivers operating over correlated Gamma shadowed Rayleigh multipath fading environment in the presence of co-channel interference subject to Rayleigh short-term fading. First, we derive expression for cumulative distribution function of output signals of the both microdiversity SC receivers, and then capitalizing on it, we evaluate outage probability of the macrodiversity reception. The obtained probability outage results, both numerical and those obtained by simulation, as well as, the influence of Gamma long-term severity parameter and the correlation coefficient, are graphically illustrated and discussed.

/pdf/outage-performance-of-macrodiversity-reception-in-the-2agraapq4u.pdf

Miloš Banđur

Papers

An analysis of energy efficiency in Wireless Sensor Networks (WSNs) applied in smart agriculture

Multi-agent system observer: Intelligent support for engaged e-learning

Performance Analysis of an IEEE 802.15.4 Network Operating Under κ–μ Fading, Interference and AWGN

Output Signal Characteristics of a Post-detection EGC Combiner with Two ASK Input Signals in the Presence of Fading and Gaussian Noise

Outage Performance of Macrodiversity Reception in the Presence Rayleigh Short-Term Fading and Co-channel Interference