With the development of information technology, Internet-of-Things (IoT) and low-altitude remote-sensing technology represented by Unmanned Aerial Vehicles (UAVs) are widely used in environmental monitoring fields. In agricultural modernization, IoT and UAV can monitor the incidence of crop diseases and pests from the ground micro and air macro perspectives, respectively. IoT technology can collect real-time weather parameters of the crop growth by means of numerous inexpensive sensor nodes. While depending on spectral camera technology, UAVs can capture the images of farmland, and these images can be utilize for analyzing the occurrence of pests and diseases of crops. In this work, we attempt to design an agriculture framework for providing profound insights into the specific relationship between the occurrence of pests/diseases and weather parameters. Firstly, considering that most farms are usually located in remote areas and far away from infrastructure, making it hard to deploy agricultural IoT devices due to limited energy supplement, a sun tracker device is designed to adjust the angle automatically between the solar panel and the sunlight for improving the energy-harvesting rate. Secondly, for resolving the problem of short flight time of UAV, a flight mode is introduced to ensure the maximum utilization of wind force and prolong the fight time. Thirdly, the images captured by UAV are transmitted to the cloud data center for analyzing the degree of damage of pests and diseases based on spectrum analysis technology. Finally, the agriculture framework is deployed in the Yangtze River Zone of China and the results demonstrate that wheat is susceptible to disease when the temperature is between 14 °C and 16 °C, and high rainfall decreases the spread of wheat powdery mildew.

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A Framework for Agricultural Pest and Disease Monitoring Based on Internet-of-Things and Unmanned Aerial Vehicles.

The electrocardiogram (ECG) signal can easily be affected by various types of noises while being recorded, which decreases the accuracy of subsequent diagnosis. Therefore, the efficient denoising of ECG signals has become an important research topic. In the paper, we proposed an efficient ECG denoising approach based on empirical mode decomposition (EMD), sample entropy, and improved threshold function. This method can better remove the noise of ECG signals and provide better diagnosis service for the computer-based automatic medical system. The proposed work includes three stages of analysis: (1) EMD is used to decompose the signal into finite intrinsic mode functions (IMFs), and according to the sample entropy of each order of IMF following EMD, the order of IMFs denoised is determined; (2) the new threshold function is adopted to denoise these IMFs after the order of IMFs denoised is determined; and (3) the signal is reconstructed and smoothed. The proposed method solves the shortcoming of discarding the first-order IMF directly in traditional EMD denoising and proposes a new threshold denoising function to improve the traditional soft and hard threshold functions. We further conduct simulation experiments of ECG signals from the MIT-BIH database, in which three types of noise are simulated: white Gaussian noise, electromyogram (EMG), and power line interference. The experimental results show that the proposed method is robust to a variety of noise types. Moreover, we analyze the effectiveness of the proposed method under different input SNR with reference to improving SNR ( ) and mean square error ( ), then compare the denoising algorithm proposed in this paper with previous ECG signal denoising techniques. The results demonstrate that the proposed method has a higher and a lower . Qualitative and quantitative studies demonstrate that the proposed algorithm is a good ECG signal denoising method.

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An Efficient ECG Denoising Method Based on Empirical Mode Decomposition, Sample Entropy, and Improved Threshold Function

Hybrid metaheuristic algorithm for optimal cluster head selection in wireless sensor network

Wildfire is a sudden and hazardous natural disaster. Currently, many schemes based on optical spectrum analysis have been proposed to detect wildfire, but obstacles in forest areas can decrease the efficiency of spectral monitoring, resulting in a wildfire detection system not being able to monitor the occurrence of wildfire promptly. In this paper, we propose a novel wildfire detection system using sound spectrum analysis based on the Internet of Things (IoT), which utilizes a wireless acoustic detection system to probe wildfire and distinguish the difference in the sound between the crown and the surface fire. We also designed a new power supply unit: tree-energy device, which utilizes the biological energy of the living trees to generate electricity. We implemented sound spectrum analysis on the data collected by sound sensors and then combined our classification algorithms. The results describe that the sound frequency of the crown fire is about 0–400 Hz, while the sound frequency of the surface fire ranges from 0 to 15,000 Hz. However, the accuracy of the classification method is affected by some factors, such as the distribution of sensors, the loss of energy in sound transmission, and the delay of data transmission. In the simulation experiments, the recognition rate of the method can reach about 70%.

https://www.mdpi.com/1424-8220/19/23/5093/pdf

Wildfire Detection Using Sound Spectrum Analysis Based on the Internet of Things.

Deployment of sensor nodes is one of the crucial factors in mobile wireless sensor networks for improving the performance of the network. The network's lifetime primarily depends on the consumed energy and area coverage by the sensor nodes. The efficiency of mobile wireless sensor networks increases by the efficient deployment of the sensors. Coverage and energy consumption mainly depends on the effective deployment schemes of sensors. This article presents an energy-efficient coverage optimization technique with the help of the Voronoi-Glowworm Swarm Optimization-K-means algorithm. In this approach, Glowworm Swarm Optimization, K-means algorithm, and Voronoi cell structure enhance the coverage area with a minimum number of active nodes. This approach considers optimum sensing radius calculation for efficient sensor deployment. Furthermore, the proposed method improves the lifetime of the deployed network by decreasing the consumed energy by the deployed sensor nodes using multi-hop transmission and the sleep-wake mechanism. The simulation result shows that area coverage is achieved by the proposed method up to 99.99% with the optimum number of active sensor nodes.

Energy-efficient coverage optimization in wireless sensor networks based on Voronoi-Glowworm Swarm Optimization-K-means algorithm

Low and dynamic duty cycles cause that the E2E delay for packet delivery is more critical in energy-harvesting wireless sensor networks (EH-WSNs). The traditional routing protocols are constrained by the in-technology communication paradigm, where Wi-Fi devices can talk to the Wi-Fi devices only, and so on for ZigBee or wireless technology. This is, however, not necessary by recent advances in cross-technology communication (CTC). The CTC enables ZigBee nodes to be coordinated by a Wi-Fi node without any hardware changes or gateway equipment, which sheds the light on more efficient routing protocols design. In this paper, we introduce a new routing protocol based on a CTC technique called RowBee. RowBee takes the advantages of coordination from the Wi-Fi node to assist the ZigBee nodes for establishing routing paths and allows nodes to choose their duty cycles freely with finer duty-cycle granularity. A simple yet effective method is employed so that the ZigBee nodes are coordinately waked up simultaneously according to the beacons broadcasted by the Wi-Fi nodes. We implement RowBee based on a USRP-N210 and MICAz hybrid platform, and the experimental results show that RowBee can reduce the E2E delay greatly.

/pdf/rowbee-a-routing-protocol-based-on-cross-technology-57ehzjldr9.pdf

RowBee: A Routing Protocol Based on Cross-Technology Communication for Energy-Harvesting Wireless Sensor Networks

For rechargeable wireless sensor networks, limited energy storage capacity, dynamic energy supply, low and dynamic duty cycles cause that it is unpractical to maintain a fixed routing path for packets delivery permanently from a source to destination in a distributed scenario. Therefore, before data delivery, a sensor has to update its waking schedule continuously and share them to its neighbors, which lead to high energy expenditure for reestablishing path links frequently and low efficiency of energy utilization for collecting packets. In this work, we propose the maximum data generation rate routing protocol based on data flow controlling technology. For a sensor, it does not share its waking schedule to its neighbors and cache any waking schedules of other sensors. Hence, the energy consumption for time synchronization, location information and waking schedule shared will be reduced significantly. The saving energy can be used for improving data collection rate. Simulation shows our scheme is efficient to improve packets generation rate in rechargeable wireless sensor networks.

http://tsp.techscience.com/uploads/attached/file/20190428/20190428051458_20151.pdf

Maximum Data Generation Rate Routing Protocol based on Data Flow Controlling Technology for Rechargeable Wireless Sensor Networks

In Energy-Harvesting Wireless Sensor Networks (EH-WSNs), sensors have to continuously adapt their duty cycle to regulate energy consumption according to available energy. Therefore, it is unpractical to preserve an invariable working-schedule for a sensor and relay on a fixed routing path for a long time. For achieving packets forwarding effectively with low transmission latency, asynchronous and synchronous Medium Access Control (MAC) protocols are adopted widely regulating energy consumption in EH-WSNs. Since they bear a clear set of advantages and disadvantages, we propose a communication scheme that hybridizes asynchronous and synchronous MAC protocols for reducing average End-to-End delay in EH-WSNs, where a sensor will switch between the asynchronous and synchronous systems according to its energy supplement and practical scenario for adapting to its duty cycle. Our algorithm is suitable for not only low traffic load, but also high/periodic traffic situation. Especially, when working-schedules of sensors are adjusted dynamically depending on the energy environment, our algorithm provides better performance in EH-WSNs. Through extensive simulation and experiments, we demonstrate that our algorithm is efficient in reducing data transmission latency in EH-WSNs.

Shuo Zhang

Papers

RowBee: A Routing Protocol Based on Cross-Technology Communication for Energy-Harvesting Wireless Sensor Networks

Maximum Data Generation Rate Routing Protocol based on Data Flow Controlling Technology for Rechargeable Wireless Sensor Networks

HAS-MAC: A Hybrid Asynchronous and Synchronous Communication System for Energy-Harvesting Wireless Sensor Networks