When integrating the Internet of Things (IoT) with Unmanned Aerial Vehicles (UAVs) occurred, tens of applications including smart agriculture have emerged to offer innovative solutions to modernize the farming sector. This paper aims to present a low-cost platform for comprehensive environmental parameter monitoring using flying IoT. This platform is deployed and tested in a real scenario on a farm in Medenine, Tunisia, in the period of March 2020 to March 2021. The experimental work fulfills the requirements of automated and real-time monitoring of the environmental parameters using both under- and aboveground sensors. These IoT sensors are on a farm collecting vast amounts of environmental data, where it is sent to ground gateways every 1 h, after which the obtained data is collected and transmitted by a drone to the cloud for storage and analysis every 12 h. This low-cost platform can help farmers, governmental, or manufacturers to predict environmental data over the geographically large farm field, which leads to enhancement in crop productivity and farm management in a cost-effective, and timely manner. Obtained experimental results infer that automated and human-made sets of actions can be applied and/or suggested, due to the innovative integration between IoT sensors with the drone. These smart actions help in precision agriculture, which, in turn, intensely boost crop productivity, saving natural resources.

https://www.mdpi.com/2071-1050/13/11/5908/pdf

A Low-Cost Platform for Environmental Smart Farming Monitoring System Based on IoT and UAVs

Dynamically adjusting the time duration of a node to stay active in one data collection period, i.e., duty cycle, is an efficient strategy to save energy and prolong the lifetime of network. In this paper, we propose a novel Adaptation Duty Cycle Control (ADCC) scheme based on feedback signals for wireless sensor networks (WSNs) which can reduce to-sink data transmission delay while lifetime is also improved. In ADCC, every node adaptively adjusts its duty cycle by comparing its own energy consumption with the largest energy consumption of the entire packet delivery flow, which is stored in a feedback ACK packet generated by sink node. Since in WSNs, a huge number of sensor nodes in the area far from the sink node have much remaining energy when network dies, even up to 90 %, these nodes have much larger duty cycles in ADCC compared with previous schemes, therefore the data transmission delay can be reduced to a great extent. Additionally, ADCC provides a largest-energy notification mechanism in order to determine the appropriate duty cycle of nodes in each data collection flow according to the application-dependent requirements. Comparing with the Wake on Idle, Dual-QCon and the Same Duty Cycle (SDC) schemes, ADCC can reduce the delay by more than 59.5 % under the same network lifetime, or increase the lifetime by 32.8 %–63.4 % under the same delay requirements, while also increase energy efficiency as much as 43.1 %.

Distributed duty cycle control for delay improvement in wireless sensor networks

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.

/pdf/a-framework-for-agricultural-pest-and-disease-monitoring-3wyxcub72z.pdf

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

DDC: Dynamic duty cycle for improving delay and energy efficiency in wireless sensor networks

Wireless Sensor Network (WSN) can be considered as scale-free weighted network which reflects the existing form and dynamic characteristic, such as application of mobile control and so on. Multicast communication is an effective way to save energy, bandwidth, cost and other resources in WSN, so we propose an Extended Ad hoc On-Demand Distance Vector (EAODV) routing method based on distributed minimum transmission (DMT) multicast routing for WSN in this paper. According to the causes of uncertain information of WSN such as random load, data fusion or other impact factors, the DMT-based EAODV routing method chooses the forwarding routes which can connect more multicast receivers to solve routing optimization problem. The method proposes a dynamic strategy to change the multicast tree structure based on the characteristic of node mobility of WSN. Experimental results demonstrate that the method effectively improves the efficiency of multicast routing. The obvious advantage and novelty of the proposed method is its good performance in package loss rate, throughput, energy consumption and delay.

Extended AODV routing method based on distributed minimum transmission (DMT) for WSN

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 Rechargeable Wireless Sensor Networks (R-WSNs), in order to achieve the maximum data collection rate it is critical that sensors operate in very low duty cycles because of the sporadic availability of energy. A sensor has to stay in a dormant state in most of the time in order to recharge the battery and use the energy prudently. In addition, a sensor cannot always conserve energy if a network is able to harvest excessive energy from the environment due to its limited storage capacity. Therefore, energy exploitation and energy saving have to be traded off depending on distinct application scenarios. Since higher data collection rate or maximum data collection rate is the ultimate objective for sensor deployment, surplus energy of a node can be utilized for strengthening packet delivery efficiency and improving the data generating rate in R-WSNs. In this work, we propose an algorithm based on data aggregation to compute an upper data generation rate by maximizing it as an optimization problem for a network, which is formulated as a linear programming problem. Subsequently, a dual problem by introducing Lagrange multipliers is constructed, and subgradient algorithms are used to solve it in a distributed manner. At the same time, a topology controlling scheme is adopted for improving the network's performance. Through extensive simulation and experiments, we demonstrate that our algorithm is efficient at maximizing the data collection rate in rechargeable wireless sensor networks.

https://www.mdpi.com/1424-8220/16/8/1201/pdf

Maximum Data Collection Rate Routing Protocol Based on Topology Control for Rechargeable Wireless Sensor Networks

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

Demin Gao

Papers

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

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

Maximum Data Collection Rate Routing Protocol Based on Topology Control for Rechargeable Wireless Sensor Networks

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