Embeded Besed Monitoring and Controlling Energy Efficient Wireless Sensor Network System Using Distance Vector Protocol

No Abstrac

Monitoring for Precision Agriculture using Wireless Sensor Network-A review

This paper explores the potential of WSN in the area of agriculture in India. Aiming at the sugarcane crop, a multi-parameter monitoring system is designed based on low-power ZigBee wireless communication technology for system automation and monitoring. Real time data is collected by wireless sensor nodes and transmitted to base station using zigbee. Data is received, saved and displayed at base station to achieve soil temperature, soil moisture and humidity monitoring. The data is continuously monitored at base station and if it exceeds the desired limit, a message is sent to farmer on mobile through GSM network for controlling actions. The implementation of system software and hardware are given, including the design of wireless node and the implementation principle of data transmission and communication modules. This system overcomes the limitations of wired sensor networks and has the advantage of flexible networking for monitoring equipment, convenient installation and removing of equipment, low cost and reliable nodes and high capacity

A Data Collecting Strategy for Farmland WSNs using a Mobile Sink

To the characteristics of large number of sensor nodes, wide area and unbalanced energy consumption in farmland Wireless Sensor Networks, an efficient data collection strategy (GCMS) based on grid clustering and a mobile sink is proposed. Firstly, cluster is divided based on virtual grid, and the cluster head is selected by considering node position and residual energy. Then, an optimal mobile path and residence time allocation mechanism for mobile sink are proposed. Finally, GCMS is simulated and compared with LEACH and GRDG. Simulation results show that GCMS can significantly prolong the network lifetime and increase the amount of data collection, especially suitable for large-scale farmland Wireless Sensor Networks

Wireless sensor network for precision agriculture: Design, Performance Modeling and Evaluation, and Node Localization

The use of wireless sensor networks is essential for the implementation of information and control technologies in precision agriculture. In this thesis, we address the challenges associated with the design of such a network system. We present our design of the network stack for a wireless sensor network used for a precision agriculture application where sensors periodically collect environmental data from spatially distributed locations in the farm-field. The physical (PHY) layer in our network allows multiple power modes in both receive and transmit operations for the purpose of achieving energy savings. We design our medium access control (MAC) layer which uses these multiple power modes to save energy during the wake-up synchronization phase. The network layer is designed to custom fit the needs of the application, namely, reliable collection of data and minimization of the energy consumption. The design of various protocol layers involves a cross-layer design strategy. We present analytical models and simulation studies to compare the energy consumption of our MAC protocol with that of the popular duty-cycle based S-MAC protocol and show that our protocol has better energy efficiency as well as latency in a periodic data collection application operating over a multi-hop network of sensor nodes. We also study the problem of sensor node localization for a hybrid wireless sensor network, with nodes located both underground (sensor nodes) and above-ground (satellite nodes). We consider two types of ranging measurements (received signal strength and time of arrival) from unmodulated signals transmitted between neighboring sensor nodes and between satellite nodes and sensor nodes. The problems are formulated with the goal of parameter estimation of the joint distribution of the received signal strength and time of arrival of the received signals. First, we arrive at power fading models for various communication scenarios in our network to model the received signal strength in terms of the propagation distance and hence, the participating nodes\u27 location coordinates. We account for the various signal degradation effects such as fading, reflection, transmission, and interference between two signals arriving along different paths. With the same goal, we derive statistical models for the measured time of arrival with the parameters governed by the sensor nodes\u27 location coordinates. The probability distribution of the detected time of arrival of a signal is derived based on rigorous statistical analysis. Then, we formulate maximum likelihood optimization problems to estimate the nodes\u27 location coordinates using the derived statistical models. The results are validated through the implementation of the proposed sensor localization approach in Python using the SciPy optimization package. We also present a sensitivity analysis of the estimates with respect to the soil complex permittivity and magnetic permeability. The contributions of this work are threefold. We present the system design of a wireless sensor network for use in a large scale deployable periodic data collection application. Next, we develop a thorough performance evaluation of the energy efficiency, throughput and latency of the system and compare with a traditional duty cycle based approach. Finally, we formulate maximum likelihood estimation based frameworks involving received signal power as well as latency measurements to solve the problem of sensor node localization based on relatively cheaper received signal strength measurements and more accurate time of arrival measurements for nodes deployed in multiple physical media (air and soil), and accounting for multi-path effects, signal loss and delays, and Gaussian and Rician fading

A Systematic Review of IoT Solutions for Smart Farming

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.info:eu-repo/semantics/publishedVersio

무선 통신 기반의 스마트 관개 모니터링 시스템

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 안성훈.농업은 개발 도상국들의 경제적 중추임에도 불구하고 대부분의 개발 도상국에서는 자동화된 장비나 데이터 모니터링 등의 지능형 시스템이 거의 적용되지 못한 상태에서 인력에 의해 농업의 모든 과정을 수행하고 있다. 관개는 농작물의 생산성에 결정적 영향을 미치는 필수적인 농업 공정중 하나로서, 연중 강우량의 변동에 대한 대응을 위하여 대부분의 농촌지역에는 농업용수 관개 시스템의 구축을 위해 노력하고 있다. 하지만, 이러한 인력에 의한 농업 방법에서의 관개 시스템은 스마트 센서를 이용한 모니터링 및 제어 등의 기술적 요소가 적용되지 못하여 효율적인 수자원의 활용이 제한되고 이로 인해 농작물의 생산성 또한 낮은 실정이다. 본 논문에서는 개발 도상국의 농촌 지역에서 적용 가능한 무선통신(RF: Radio Frequency) 기반의 스마트 관개 모니터링 시스템 및 요금 선불 시스템을 제안한다. 본 연구는 탄자니아 아루샤(Arusha) 지역의 응구루도토(Ngurudoto) 마을을 대상으로 수행되었다. 본 연구에서 제안하는 시스템은 기상 데이터와 토양 수분 데이터를 하이브리드로 분석하여 농업 용수의 소요를 모니터링한다. 하드웨어 시스템은 기상 측정 컨트롤러, 토양 수분 센서, 수류 센서, 솔레노이드 밸브 및 요금 선불 시스템 등으로 구성된다. 시스템의 각 센서는 무선 통신을 통해 서버로 수집된 데이터를 전송하도록 구축되었는데, 이러한 무선 통신 시스템 아키텍처는 인터넷의 운용이 제한되는 네트워크 오지 지역에 적합하도록 설계되었다. 수집된 데이터에 대한 분석 및 예측은 데이터 분석 알고리즘을 통해 수행되는데, 이를 통하여 농장에 용수를 공급할 시기 및 수량과 함께 요구되는 전력량이 자동으로 판단된다. 한편, 선불시스템은 데이터 분석 결과에 기반하여 용수 사용자가 용수를 공급받기 전에 비용을 우선 지불하도록 개발되었다. 본 시스템의 모든 센서에서 수집된 정보는 실시간으로 모니터링되도록 그래픽 기반의 사용자 인터페이스를 활용하여 정보를 제공한다. 본 연구를 통하여 개발된 무선 통신 기반 스마트 관개 모니터링 시스템은 사용자 중심의 편의성과 경제적인 관개 및 모니터링 시스템을 제공하여 개발 도상국의 경제적 기반인 농업 분야의 발전에 긍정적인 영향을 미칠것으로 기대한다.Agriculture is the backbone of the economy of most developing countries. In these countries, agriculture or farming is mostly done manually with little integration of machinery, intelligent systems and data monitoring. Irrigation is an essential process that influences crop production. The fluctuating amount of rainfall per year has led to the adaption of irrigation systems in most farms. This manual type of farming has proved to yield fair results, however, due to the absence of smart sensors monitoring methods and control, it has failed to be a better type of farming and thus leading to low harvests and draining water sources. In this paper, we introduce an RF (Radio Frequency) based Smart Irrigation Meter System and a water prepayment system in rural areas of Tanzania. Specifically, Ngurudoto area in Arusha region where it will be used as a case study for data collection. The proposed system is hybrid, comprising of both weather data (evapotranspiration) and soil moisture data. The architecture of the system has on-site weather measurement controllers, soil moisture sensors buried on the ground, water flow sensors, solenoid valve, and a prepayment system. These sensors send data to the server through wireless RF based communication architecture, which is suitable for areas where the internet is not reliable and, it is interpreted and decisions and predictions are made on the data by our data analysis algorithm. The decisions made are, when to automatically irrigate a farm and the amount of water and the power needed. Then, the user has to pay first before being supplied with water. All these sensors and water usage are monitored in real time and displaying the information on a custom built graphical user interface. The RF-based smart irrigation monitoring system has both economical and social impact on the developing countries' societies by introducing a convenient and affordable means of Irrigation system and autonomous monitoring.Chapter 1. Introduction 1 Chapter 2 Background of the study and Literature review 3 1.1.Purpose of Research 17 Chapter 3. Requirements and System Design 21 3.1. Key Components 21 3.1.1. System Architecture 21 3.1.2. The Smart Irrigation Meter 22 3.1.2. Parts of Smart Irrigation Meter 23 3.1.3. The pre-paid system and the monitoring device 26 3.2. The Monitoring Application and Cloud Server. 27 Chapter 4. Experiment Setup 30 4.1. Testing Location 30 4.2. Hardware & Software Setup 31 Chapter 5 Results and Analysis 36 5.1 Optimization and anomaly detection algorithm 36 5.1.1 Dynamic Regression Model 36 5.1.2 Nave classifier algorithm for anomaly detection. 38 Chapter 6. Conclusion 44 References 46 초 록 49Maste

Design and development of an SDN robotic system with intelligent openflow IOT testbeds for power assessment, prediction and fault management

This thesis was submitted for the award of Docctor of Philosophy and was awarded by Brunel University LondonCurrent wind turbine and power grid industry have relatively little research and development with regards to implementing novel communication network and intel- ligent system to overcome issues that pertain to network failure and lack of monitor- ing. Wind turbine location could be a big concern when it comes to identifying an efficient location for future wind turbine and the impact of a site with non-efficient meteorological parameters can result in relocation of a wind turbine and revenue- loss. Unplanned wind turbine shutdowns that are considered to be one of the major revenue-loss factors of a modern wind farm business. Typically, the unplanned wind turbine shutdown is a result of sensors fail due to harsh environment challenges that prevent hardware status from being available on the monitoring system. The above mentioned research problems pertain to wind turbine site assessment and predic- tion of power. In this thesis, a novel programmable software-defined robotics and IoT testbeds are proposed with the fusion of Artificial Intelligence and optimiza- tion methods to solve specific problems related to wind turbine site assessment and fault management. The site selection process is implemented using proposed aerial and ground robotic systems that are incorporated with Software-Defined Networks and OpenFlow switching capabilities. A second stage development of the system is proposing a prediction platform that run on the aerial robot cluster using neural net- works optimization regression techniques. To overcome the unplanned wind turbine network outage, an IoT micro cloud cluster system is proposed that act as immedi- ate fail-over platform to provide continuous health readings of the wind turbine to ensure the turbine in question will not get shutdown unnecessarily. The proposed system help in minimizing revenue-loss caused by stopping a wind turbine from op- eration and help maintain generated power stability on the grid. Additionally, since large wind farms require an agile and scalable management of selecting the most efficient wind turbine location install. Thus, a softwarized cognitive routing proto- col is proposed. The group of quadcopters is a redundant failover Software-Defined Network/OpenFlow system that can cover every single way point of the farm land. Although, power consumption is essential for the continuity the service, a Software- Defined charging system testbed is proposed that uses inductive power transfer wit

Internet of Things Applications in Precision Agriculture: A Review

The goal of this paper is to review the implementation of an Internet of Things (IoT)-based system in the precision agriculture sector. Each year, farmers suffer enormous losses as a result of insect infestations and a lack of equipment to manage the farm effectively. The selected article summarises the recommended systematic equipment and approach for implementing an IoT in smart farming. This review's purpose is to identify and discuss the significant devices, cloud platforms, communication protocols, and data processing methodologies. This review highlights an updated technology for agricultural smart management by revising every area, such as crop field data and application utilization. By customizing their technology spending decisions, agriculture stakeholders can better protect the environment and increase food production in a way that meets future global demand. Last but not least, the contribution of this research is that the use of IoT in the agricultural sector helps to improve sensing and monitoring of production, including farm resource usage, animal behavior, crop growth, and food processing. Also, it provides a better understanding of the individual agricultural circumstances, such as environmental and weather conditions, the growth of weeds, pests, and diseases