TIME-DOMAIN AND FREQUENCY-DOMAIN REFLECTOMETRY TYPE SOIL MOISTURE SENSOR PERFORMANCE AND SOIL TEMPERATURE EFFECTS IN FINE- AND COARSE-TEXTURED SOILS

The performances of six time-domain reflectometry (TDR) and frequency-domain reflectometry (FDR) type soil moisture sensors were investigated for measuring volumetric soil-water content (θv) in two different soil types. Soil-specific calibration equations were developed for each sensor using calibrated neutron probe-measured θv. Sensors were also investigated for their performance response in measuring θv to changes in soil temperature. The performance of all sensors was significantly different (P\u3c0.05) than the neutron probe-measured θv, with the same sensor also exhibiting variation between soils. In the silt loam soil, the 5TE sensor had the lowest root mean squared error (RMSE) of 0.041 m3/m3, indicating the best performance among all sensors investigated. The performance ranking of the other sensors from high performance to low was: TDR300 (High Clay Mode), CS616 (H) and 10HS, SM150, TDR300 (Standard Mode), and CS616 (V) (H: horizontal installation and V: vertical installation). In the loamy sand, the CS616 (H) performed best with an RMSE of 0.014 m3/m3 and the performance ranking of other sensors was: 5TE, CS616 (V), TDR300 (S), SM150, and 10HS. When θv was near or above field capacity, the performance error of most sensors increased. Most sensors exhibited a linear response to increase in soil temperature. Most sensors exhibited substantial sensitivity to changes in soil temperature and the θv response of the same sensor to high vs. normal soil temperatures differed significantly between the soils. All sensors underestimated θv in high temperature range in both soils. The ranking order of the magnitude of change in θv in response to 1°C increase in soil temperature (from the lowest to the greatest impact of soil temperature on sensor performance) in silt loam soil was: SM150, 5TE, TDR300 (S), 10HS, CS620, CS616 (H), and CS616 (V). The ranking order from lower to higher sensitivity to soil temperature changes in loamy sand was: 10HS, CS616 (H), 5TE, CS616 (V), SM150, and TDR300 (S). When the data from all sensors and soils are pooled, the overall average of change in θv for a 1°C increase in soil temperature was 0.21 m3/m3 in silt loam soil and -0.052 m3/m3 in loamy sand. When all TDR- and FDR-type sensors were pooled separately for both soils, the average change in θv for a 1°C increase in soil temperature for the TDR- and FDR-type sensors was 0.1918 and -0.0273 m3/m3, respectively, indicating that overall TDR-type sensors are more sensitive to soil temperature changes than FDR-type sensors when measuring θv

Limit overturning moment chuck

Limited memory capacity is one of the major constraints in Delay Tolerant Wireless Sensor Networks. Efficient management of the memory is critical to the performance of the network. This paper proposes a novel buffer management algorithm, SmartGap, a Quality of Information (QoI) targeted buffer management algorithm. That is, in a wireless sensor network that continuously measures a parameter which changes over time, such as temperature, the value of a single packet is governed by an estimation of its contribution to the recreation of the original signal. Attractive features of SmartGap include a low computational complexity and a simplified reconstruction of the original signal. An analysis and simulations in which the performance of SmartGap is compared with the performance of several commonly used buffer management algorithms in wireless sensor networks are provided in the paper. The simulations suggest that SmartGap indeed provides significantly improved QoI compared the other evaluated algorithms.QC 20150605</p

Energy Consumption and Latency Analysis for Wireless Multimedia Sensor Networks

Energy and bandwidth are limited resources in wireless sensor networks, and communication consumes significant amount of energy. When wireless vision sensors are used to capture and transfer image and video data, the problems of limited energy and bandwidth become even more pronounced. Thus, message traffic should be decreased to reduce the communication cost. In many applications, the interest is to detect composite and semantically higher-level events based on information from multiple sensors. Rather than sending all the information to the sinks and performing composite event detection at the sinks or control-center, it is much more efficient to push the detection of semantically high-level events within the network, and perform composite event detection in a peer-to-peer and energy-efficient manner across embedded smart cameras. In this paper, three different operation scenarios are analyzed for a wireless vision sensor network. A detailed quantitative comparison of these operation scenarios are presented in terms of energy consumption and latency. This quantitative analysis provides the motivation for, and emphasizes (1) the importance of performing high-level local processing and decision making at the embedded sensor level and (2) need for peer-to-peer communication solutions for wireless multimedia sensor networks

Topology Analysis of Wireless Sensor Networks for Sandstorm Monitoring

Sandstorms are serious natural disasters, which are commonly seen in the Middle East, Northern Africa, and Northern China.In these regions, sandstorms have caused massive damages to the natural environment, national economy, and human health. To avoid such damages, it is necessary to effectively monitor the origin and development of sandstorms. To this end, wireless sensor networks (WSNs) can be deployed in the regions where sandstorms generally originate so that sensor nodes can collaboratively perform sandstorm monitoring and rapidly convey the observations to remote administration center. Despite the potential advantages, the deployment of WSNs in the vicinity of sandstorms faces many unique challenges, such as the temporally buried sensors and increased path loss during sandstorms. Consequently, the WSNs may experience frequent disconnections during the sandstorms. This further leads to dynamically changing topology. In this paper, a topology analysis of the WSNs for sandstorm monitoring is performed. Four types of channels a sensor can utilize during sandstorms are analyzed, which include air-to-air channel, air-to-sand channel, sand-to-air channel, and sand-to-sand channel. Based on the channel model solutions, a percolation-based connectivity analysis is performed. It is shown that if the sensors are buried in low depth, allowing sensor to use multiple types of channels improves network connectivity. Accordingly, much smaller sensor density is required compared to the case, where only terrestrial air channels are used. Through this topology analysis a WSN architecture can be deployed for very efficient sandstorm monitoring

Topology Analysis of Wireless Sensor Networks for Sandstorm Monitoring

Sandstorms are serious natural disasters, which are commonly seen in the Middle East, Northern Africa, and Northern China.In these regions, sandstorms have caused massive damages to the natural environment, national economy, and human health. To avoid such damages, it is necessary to effectively monitor the origin and development of sandstorms. To this end, wireless sensor networks (WSNs) can be deployed in the regions where sandstorms generally originate so that sensor nodes can collaboratively perform sandstorm monitoring and rapidly convey the observations to remote administration center. Despite the potential advantages, the deployment of WSNs in the vicinity of sandstorms faces many unique challenges, such as the temporally buried sensors and increased path loss during sandstorms. Consequently, the WSNs may experience frequent disconnections during the sandstorms. This further leads to dynamically changing topology. In this paper, a topology analysis of the WSNs for sandstorm monitoring is performed. Four types of channels a sensor can utilize during sandstorms are analyzed, which include air-to-air channel, air-to-sand channel, sand-to-air channel, and sand-to-sand channel. Based on the channel model solutions, a percolation-based connectivity analysis is performed. It is shown that if the sensors are buried in low depth, allowing sensor to use multiple types of channels improves network connectivity. Accordingly, much smaller sensor density is required compared to the case, where only terrestrial air channels are used. Through this topology analysis a WSN architecture can be deployed for very efficient sandstorm monitoring

Internet of Things in Water Management and Treatment

The goal of the water security IoT chapter is to present a comprehensive and integrated IoT based approach to environmental quality and monitoring by generating new knowledge and innovative approaches that focus on sustainable resource management. Mainly, this chapter focuses on IoT applications in wastewater and stormwater, and the human and environmental consequences of water contaminants and their treatment. The IoT applications using sensors for sewer and stormwater monitoring across networked landscapes, water quality assessment, treatment, and sustainable management are introduced. The studies of rate limitations in biophysical and geochemical processes that support the ecosystem services related to water quality are presented. The applications of IoT solutions based on these discoveries are also discussed

Current Advances in Internet of Underground Things

The latest developments in Internet of Underground Things are covered in this chapter. First, the IOUT Architecture is discussed followed by the explanation of the challenges being faced in this paradigm. Moreover, a comprehensive coverage of the different IOUT components is presented that includes communications, sensing, and system integration with the cloud. An in-depth coverage of the applications of the IOUT in various disciplines is also surveyed. These applications include areas such as decision agriculture, pipeline monitoring, border control, and oil wells

Internet of Things in Agricultural Innovation and Security

The agricultural Internet of Things (Ag-IoT) paradigm has tremendous potential in transparent integration of underground soil sensing, farm machinery, and sensor-guided irrigation systems with the complex social network of growers, agronomists, crop consultants, and advisors. The aim of the IoT in agricultural innovation and security chapter is to present agricultural IoT research and paradigm to promote sustainable production of safe, healthy, and profitable crop and animal agricultural products. This chapter covers the IoT platform to test optimized management strategies, engage farmer and industry groups, and investigate new and traditional technology drivers that will enhance resilience of the farmers to the socio-environmental changes. A review of state-of-the-art communication architectures and underlying sensing technologies and communication mechanisms is presented with coverage of recent advances in the theory and applications of wireless underground communications. Major challenges in Ag-IoT design and implementation are also discussed

Autonomous Irrigation Management in Decision Agriculture

In this chapter, the important application of autonomous irrigation management in the field decision agriculture is discussed. The different types of sensor-guided irrigation systems are presented that includes center pivot systems and drip irrigation systems. Their sensing and actuator components are with detailed focus on real-time decision-making and integration to the cloud. This chapter also presents irrigation control systems which takes, as an input, soil moisture and temperature from IOUT and weather data from Internet and communicate with center pivot based irrigation systems. Moreover, the system architecture is explored where development of the nodes including sensing and actuators is presented. Finally, the chapter concludes with comprehensive discussion of adaptive control systems, software, and visualization system design