System assessment of WUSN using NB-IoT UAV-aided networks in potato crops

Unmanned Aerial Vehicles (UAV) are part of precision agriculture; also, their impact on fast deployable wireless communication is offering new solutions and systems never envisioned before such as collecting information from underground sensors by using low power Internet of Things (IoT) technologies. In this paper, we propose a (Narrow Band IoT) NB-IoT system for collecting underground soil parameters in potato crops using a UAV-aided network. To this end, a simulation tool implementing a gateway mounted on a UAV using NB-IoT based access network and LTE based backhaul network is developed. This tool evaluates the performance of a realistic scenario in a potato field near Bogota, Colombia, accounting for real size packets in a complete IoT application. While computing the wireless link quality, it allocates access and backhaul resources simultaneously based on the technologies used. We compare the performance of wireless underground sensors buried in dry and wet soils at four different depths. Results show that a single drone with 50 seconds of flight time could satisfy more than 2000 sensors deployed in a 20 hectares field, depending on the buried depth and soil characteristics. We found that an optimal flight altitude is located between 60 m and 80 m for buried sensors. Moreover, we establish that the water content reduces the maximum reachable buried depth from 70 cm in dry soils, down to 30 cm in wet ones. Besides, we found that in the proposed scenario, sensors & x2019; battery life could last up to 82 months for above ground sensors and 77 months for the deepest buried ones. Finally, we discuss the influence of the sensor & x2019;s density and buried depth, the flight service time and altitude in power-constrained conditions and we propose optimal configuration to improve system performance

Zenneck Waves in Decision Agriculture: An Empirical Verification and Application in EM-Based Underground Wireless Power Transfer

In this article, the results of experiments for the observation of Zenneck surface waves in sub GHz frequency range using dipole antennas are presented. Experiments are conducted over three different soils for communications distances of up to 1 m. This empirical analysis confirms the existence of Zenneck waves over the soil surface. Through the power delay profile (PDP) analysis, it has been shown that other subsurface components exhibit rapid decay as compared to the Zenneck waves. A potential application of the Zenneck waves for energy transmission in the area of decision agriculture is explored. Accordingly, a novel wireless through-the-soil power transfer application using Zenneck surface waves in electromagnetic (EM) based wireless underground communications is developed

Opportunities for improving irrigation efficiency with quantitative models, soil water sensors and wireless technology

Increasingly serious shortages of water make it imperative to improve the efficiency of irrigation in agriculture, horticulture and in the maintenance of urban landscapes. The main aim of the current review is to identify ways of meeting this objective. After reviewing current irrigation practices, discussion is centred on the sensitivity of crops to water deficit, the finding that growth of many crops is unaffected by considerable lowering of soil water content and, on this basis, the creation of improved means of irrigation scheduling. Subsequently, attention is focused on irrigation problems associated with spatial variability in soil water and the often slow infiltration of water into soil, especially the subsoil. As monitoring of soil water is important for estimating irrigation requirements, the attributes of the two main types of soil water sensors and their most appropriate uses are described. Attention is also drawn to the contribution of wireless technology to the transmission of sensor outputs. Rapid progress is being made in transmitting sensor data, obtained from different depths down the soil profile across irrigated areas, to a PC that processes the data and on this basis automatically commands irrigation equipment to deliver amounts of water, according to need, across the field. To help interpret sensor outputs, and for many other reasons, principles of water processes in the soil–plant system are incorporated into simulation models that are calibrated and tested in field experiments. Finally, it is emphasized that the relative importance of the factors discussed in this review to any particular situation varies enormously

Signals in the Soil: Subsurface Sensing

In this chapter, novel subsurface soil sensing approaches are presented for monitoring and real-time decision support system applications. The methods, materials, and operational feasibility aspects of soil sensors are explored. The soil sensing techniques covered in this chapter include aerial sensing, in-situ, proximal sensing, and remote sensing. The underlying mechanism used for sensing is also examined as well. The sensor selection and calibration techniques are described in detail. The chapter concludes with discussion of soil sensing challenges

Custom wireless sensor for monitoring grazing of free-range cattle

Scope and Method of Study: The purpose of this study was to develop a wireless sensor device capable of sensing cattle grazing activity. This included design and build of a miniaturized PCB, sensor specification, data processing, and experimental validation. Experiments were conducted in cooperation with the Oklahoma State University, Animal Science and Biosystems Engineering departments. The primary objective of this study was to provide information supporting the use of an accelerometer sensor for monitoring free-range cattle grazing activity. A wireless sensor platform was also developed for sensor and wireless communication development needs. Secondary objectives included exploring alternative applications, such as monitoring cattle waste excretion events, and identifying wireless network functionality for agricultural environments.Findings and Conclusions: During this study, parameters for using an accelerometer based grazing sensor were established relative to the head motion of grazing cattle. Initially, a survey of literature and video analysis of foraging livestock animals were conducted, where 0.5-8 bites/sec was confirmed as animal bite rate range. The preliminary video analysis provided guidelines for establishing a sensing strategy. Sensor data processing algorithm development and sampling rate selection were driven by video provided characteristics and sensor platform capability. The Fast Fourier Transform (FFT) was selected as the core component of the sensor's algorithm. The FFT was able to characterize grazing motions because of the animal's near-continuous periodic head movements. At least five bite cycles and a 32 Hz sampling rate were required for proper algorithm implementation. A sample size of 256 data points were collected for each accelerometer axis, and proved to be adequate for the FFT computations. A revised sample rate of 21.74 Hz was presented once the FFT was implemented in firmware. This new rate retained well performing FFT calculations based on the understanding that bite rates faster than 4 bites/sec were due to nibbling and partial bites. The FFT's Spectral power was binned and stored for the purpose of data compression and reduced wireless transmissions.The wireless sensor device platform was built using the CC1010 microcontroller/transceiver IC. The CC1010 provided integrated features commendable for fast FFT processing and conservative PCB layout design. The radio was configured for robust operation by using a 915 MHz carrier frequency, Manchester encoding, and 64 kHz frequency spread. A small, helical, and omnidirectional antenna was mounted directly to the PCB. Link budget was estimated to be 81 dBm, which equated to a 282 m (925 ft) transmission distance in optimum conditions. The device's dimensions were 19.6 mm (0.77 in) X 71.8 mm (2.83 in) X 11.0 mm (0.43 in). A custom PVC enclosure was used to house the device. For deploying experiments, the enclosure was fastened to a standard nylon turnout halter. A miniature GPS logger was also attached to the halter, which allowed for constructing grazing maps.Additionally, the proposed wireless sensor device was used to detect cattle urination and defecation events. This was accomplished by attaching the device to an animal's tail and sensing its elevated movements. Tilt measurements in the z-axis (front-to-back) direction provided the most prominent evidence of a distinct tail movement pattern during excretion events. A pattern recognition strategy was shown as a viable sensing method.An outline for a multilevel-networked system was also generated. This included cellular and internet communications, along with a customized application software for base/node management

Latitude, longitude, and beyond:mining mobile objects' behavior

Rapid advancements in Micro-Electro-Mechanical Systems (MEMS), and wireless communications, have resulted in a surge in data generation. Mobility data is one of the various forms of data, which are ubiquitously collected by different location sensing devices. Extensive knowledge about the behavior of humans and wildlife is buried in raw mobility data. This knowledge can be used for realizing numerous viable applications ranging from wildlife movement analysis, to various location-based recommendation systems, urban planning, and disaster relief. With respect to what mentioned above, in this thesis, we mainly focus on providing data analytics for understanding the behavior and interaction of mobile entities (humans and animals). To this end, the main research question to be addressed is: How can behaviors and interactions of mobile entities be determined from mobility data acquired by (mobile) wireless sensor nodes in an accurate and efficient manner? To answer the above-mentioned question, both application requirements and technological constraints are considered in this thesis. On the one hand, applications requirements call for accurate data analytics to uncover hidden information about individual behavior and social interaction of mobile entities, and to deal with the uncertainties in mobility data. Technological constraints, on the other hand, require these data analytics to be efficient in terms of their energy consumption and to have low memory footprint, and processing complexity