Coverage strategy for heterogeneous nodes in wireless sensor network based on temporal variability of farmland

Ako se razmotre različiti vremenski uvjeti na poljoprivrednom zemljištu, stopa pokrivenosti bežične mreže senzora (WSN - Wireless Sensor Network) obično je mala. Mogu se pojaviti problemi slijepih točaka i prenatrpanosti vrućim točkama. Predlažemo strategiju pokrivenosti za heterogene čvorove u WSN na temelju vremenskih promjena koje utječu na obradivost, a koja predviđa ključne čvorove primjenom modela predviđanja ključnog čvora u skladu s vremenskim promjenama na imanju. Uvođenjem obnovljivih čvorova energije, mogu se odrediti položaji heterogenih čvorova u mreži. Zadatak se ponovo prebacuje na heterogene čvorove ovisno o zaostaloj energiji čvorova te se stanje čvorova u mreži dinamički podešava. Simulacija pokazuje da CSHN može učinkovito smanjiti stopu propadanja čvora i potrošnju energije mreže i tako produžiti opstojnost mreže te ujednačiti pokrivenost i potrošnju energije mreže.Given temporal variability of farmland, the coverage rate of wireless sensor network (WSN) is usually low. There may arise the problems of blind spot area and congestion of hot spots. We propose a coverage strategy for heterogeneous nodes in WSN based on temporal variability of farmland, which predicts the key nodes using key node prediction model according to temporal variability of farmland environment. Through introduction of renewable energy nodes, the positions of heterogeneous nodes in the network can be determined. The task is re-allocated to the heterogeneous nodes depending on the residual energy of nodes, and the state of nodes in the network is adjusted dynamically. Simulation shows that CSHN can effectively reduce the node death rate and network energy consumption, while prolonging the survival of network and equalizing coverage and network energy consumption

Optimal data collection in wireless sensor networks with correlated energy harvesting

We study the optimal data collection rate in a hybrid wireless sensor network where sensor data is collected by mobile sinks. In such networks, there is a trade-off between the cost of data collection and the timeliness of the data. We further assume that the sensor node under study harvests its energy from its environment. Such energy harvesting sensors ideally operate energy neutral, meaning that they can harvest the necessary energy to sense and transmit data, and have on-board rechargeable batteries to level out energy harvesting fluctuations. Even with batteries, fluctuations in energy harvesting can considerably affect performance, as it is not at all unlikely that a sensor node runs out of energy, and is neither able to sense nor to transmit data. The energy harvesting process also influences the cost vs. timeliness trade-off as additional data collection requires additional energy as well. To study this trade-off, we propose an analytic model for the value of the information that a sensor node brings to decision-making. We account for the timeliness of the data by discounting the value of the information at the sensor over time, we adopt the energy chunk approach (i.e. discretise the energy level) to track energy harvesting and expenditure over time, and introduce correlation in the energy harvesting process to study its influence on the optimal collection rate

Edge IoT Driven Framework for Experimental Investigation and Computational Modeling of Integrated Food, Energy, and Water System

As the global population soars from today’s 7.3 billion to an estimated 10 billion by 2050, the demand for Food, Energy, and Water (FEW) resources is expected to more than double. Such a sharp increase in demand for FEW resources will undoubtedly be one of the biggest global challenges. The management of food, energy, water for smart, sustainable cities involves a multi-scale problem. The interactions of these three dynamic infrastructures require a robust mathematical framework for analysis. Two critical solutions for this challenge are focused on technology innovation on systems that integrate food-energy-water and computational models that can quantify the FEW nexus. Information Communication Technology (ICT) and the Internet of Things (IoT) technologies are innovations that will play critical roles in addressing the FEW nexus stress in an integrated way. The use of sensors and IoT devices will be essential in moving us to a path of more productivity and sustainability. Recent advancements in IoT, Wireless Sensor Networks (WSN), and ICT are one lever that can address some of the environmental, economic, and technical challenges and opportunities in this sector. This dissertation focuses on quantifying and modeling the nexus by proposing a Leontief input-output model unique to food-energy-water interacting systems. It investigates linkage and interdependency as demand for resource changes based on quantifiable data. The interdependence of FEW components was measured by their direct and indirect linkage magnitude for each interaction. This work contributes to the critical domain required to develop a unique integrated interdependency model of a FEW system shying away from the piece-meal approach. The physical prototype for the integrated FEW system is a smart urban farm that is optimized and built for the experimental portion of this dissertation. The prototype is equipped with an automated smart irrigation system that uses real-time data from wireless sensor networks to schedule irrigation. These wireless sensor nodes are allocated for monitoring soil moisture, temperature, solar radiation, humidity utilizing sensors embedded in the root area of the crops and around the testbed. The system consistently collected data from the three critical sources; energy, water, and food. From this physical model, the data collected was structured into three categories. Food data consists of: physical plant growth, yield productivity, and leaf measurement. Soil and environment parameters include; soil moisture and temperature, ambient temperature, solar radiation. Weather data consists of rainfall, wind direction, and speed. Energy data include voltage, current, watts from both generation and consumption end. Water data include flow rate. The system provides off-grid clean PV energy for all energy demands of farming purposes, such as irrigation and devices in the wireless sensor networks. Future reliability of the off-grid power system is addressed by investigating the state of charge, state of health, and aging mechanism of the backup battery units. The reliability assessment of the lead-acid battery is evaluated using Weibull parametric distribution analysis model to estimate the service life of the battery under different operating parameters and temperatures. Machine learning algorithms are implemented on sensor data acquired from the experimental and physical models to predict crop yield. Further correlation analysis and variable interaction effects on crop yield are investigated

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

SDQIM: Software-Defined Quadcopter Inter-domain Management Protocol with Decentralized Inductive Power Transfer (DIPT) platform

Due to the flexible deployment, low-cost and management, unmanned aerial vehicles (UAV), quadcopters have attracted significant interest lately in expanding wireless communications to areas that lack to cellular coverage network. Currently, SDN is considered a viable alternative that improves network scalability and management. Formerly, we designed a novel software-defined quadcopter (SDQ) with mobile IoT vehicle [1] to measure the best efficient wind power location for effective future wind turbine placement. However, large wind farms require complex and scalable wind measurement platform that consists of multiple quadcopters and ground mobile nodes that operate under a decision-making softwarized protocol. To this end, this paper proposes a novel cognitive routing protocol called Software-Defined Quadcopter Inter-domain Management (SDQIM) Protocol. The SDQIM structure consists of multiple SDN controllers with failover capability. Our proposed protocol operate with multiple phases as each phase is used to cover a specific scenario that could occur in the inter-domain network. The SDQ-main will have a total overview of the network topology and the SDQ-slave will be responsible for communication management with all other local sub-domains. However, for large-scale farmland, quadcopter power level is essential for the continuity of the system services. Thus, we additionally propose a Decentralized Inductive Power Transfer (DIPT) platform that operates autonomously using software defined controlling approach. In essence, The DIPT platform is considered to be automated and low-cost approach that encompasses a custom-designed SDN controller for all inter-domain quadcopters. Our system has been implemented by using an experimental testbed. The experimental results showed that scalability of the SDQ system can be increased to cover large wind farms with uninterrupted power management platform

A Survey on Smart Agriculture: Development Modes, Technologies, and Security and Privacy Challenges

With the deep combination of both modern information technology and traditional agriculture, the era of agriculture 4.0, which takes the form of smart agriculture, has come. Smart agriculture provides solutions for agricultural intelligence and automation. However, information security issues cannot be ignored with the development of agriculture brought by modern information technology. In this paper, three typical development modes of smart agriculture (precision agriculture, facility agriculture, and order agriculture) are presented. Then, 7 key technologies and 11 key applications are derived from the above modes. Based on the above technologies and applications, 6 security and privacy countermeasures (authentication and access control, privacy-preserving, blockchain-based solutions for data integrity, cryptography and key management, physical countermeasures, and intrusion detection systems) are summarized and discussed. Moreover, the security challenges of smart agriculture are analyzed and organized into two aspects: 1) agricultural production, and 2) information technology. Most current research projects have not taken agricultural equipment as potential security threats. Therefore, we did some additional experiments based on solar insecticidal lamps Internet of Things, and the results indicate that agricultural equipment has an impact on agricultural security. Finally, more technologies (5 G communication, fog computing, Internet of Everything, renewable energy management system, software defined network, virtual reality, augmented reality, and cyber security datasets for smart agriculture) are described as the future research directions of smart agriculture

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

SAgric-IoT: an IoT-based platform and deep learning for greenhouse monitoring

The Internet of Things (IoT) and convolutional neural networks (CNN) integration is a growing topic of interest for researchers as a technology that will contribute to transforming agriculture. IoT will enable farmers to decide and act based on data collected from sensor nodes regarding field conditions and not purely based on experience, thus minimizing the wastage of supplies (seeds, water, pesticide, and fumigants). On the other hand, CNN complements monitoring systems with tasks such as the early detection of crop diseases or predicting the number of consumable resources and supplies (water, fertilizers) needed to increase productivity. This paper proposes SAgric-IoT, a technology platform based on IoT and CNN for precision agriculture, to monitor environmental and physical variables and provide early disease detection while automatically controlling the irrigation and fertilization in greenhouses. The results show SAgric-IoT is a reliable IoT platform with a low packet loss level that considerably reduces energy consumption and has a disease identification detection accuracy and classification process of over 90%

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Energy-harvesting-powered sensors are increasingly deployed beyond the reach of terrestrial gateways, where there is often no persistent power supply. Making use of the internet of drones (IoD) for data aggregation in such environments is a promising paradigm to enhance network scalability and connectivity. The flexibility of IoD and favorable line-of-sight connections between the drones and ground nodes are exploited to improve data reception at the drones. In this article, we discuss the challenges of online flight control of IoD, where data-driven neural networks can be tailored to design the trajectories and patrol speeds of the drones and their communication schedules, preventing buffer overflows at the ground nodes. In a small-scale IoD, a multi-agent deep reinforcement learning can be developed with long short-term memory to train the continuous flight control of IoD and data aggregation scheduling, where a joint action is generated for IoD via sharing the flight control decisions among the drones. In a large-scale IoD, sharing the flight control decisions in real-time can result in communication overheads and interference. In this case, deep reinforcement learning can be trained with the second-hand visiting experiences, where the drones learn the actions of each other based on historical scheduling records maintained at the ground nodes.This work was supported in part by the National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit under Grant UIDP/UIDB/04234/2020, and in part by the National Funds through FCT, under CMU Portugal Partnership under Project CMU/TIC/0022/2019 (CRUAV).info:eu-repo/semantics/publishedVersio

A Low-cost, Long-range, and Solar-based IoT Soil Quality Monitor

The project objective is to create a low-cost, long-range, and solar-based IoT soil quality monitoring system. The system must transmit packages of data gathered from separate nodes, consisting of two different types of sensors, to a centralized gateway receiver to be displayed to the user in an elegant and readable manner. The end goal of the project is to supplement produce grown by large agricultural bodies around the United States without the misuse of water resources. This report presents the need for this system, details the components of the system, and the rationale behind design choices. It serves as a comprehensive guide to all the work that has been completed, provides an outlook for future iterations, and demonstrates the viability of LoRa communication for low power packet sending in a rural environment