Node Transmission Research Articles

Имитационная модель одноранговой беспроводной сенсорной сети

The article provides a description of the developed simulation model Mesh network. The simulation model is a tool for assessing the probabilistic and temporal characteristics of data delivery, as the main indicator of the quality of the service provided by the Mesh network. A mesh network is a complex object of modeling: the mobility of nodes, the lack of a fixed infrastructure and centralized control, the limitations of the wireless transmission medium and node resources are the main features that distinguish Mesh networks from wired communication networks. These features affect the choice of the data delivery route and the quality indicators of the service provided by the Mesh network. The simulation model takes into account the features of the Mesh network, in particular, the features of modeling the states and loading of network nodes, the length of the queue for service, and the change in the level of residual energy of the node are considered. With the help of a simulation experiment on a Mesh network model with given parameters and external influences, it is possible not only to evaluate the probabilistic and temporal characteristics of data delivery, but also, taking into account the acceptable values for the quality indicators of the provided service, recommend the structure of the Mesh networks.

Minimizing Energy Hole Problem Comparisons in Some Hierarchical WSN Routing Protocols

This paper study the efficient use of energy as a challenging task of designing these protocols. The energy holes are created due to non-uniform node distribution in the network when quickly drain the energy in a few nodes. It is studies removing energy holes by using sleep and awake mechanism for sensor nodes to save energy. This approach finds the maximum distance nodes to calculate the maximum energy for data. It intends to enumerate the dead nodes transmission, alive nodes, throughput, overhead and packet delivery ratio consumed by the entire network and affected by the network area changes. lastly, a brief performance analysis of Low Energy Adaptive Clustering Hierarchy (LEACH), Distributed Energy-Efficient Clustering (DEEC), Threshold sensitive Energy Efficient sensor Network protocol (TEEN) and Stable Election Protocol (SEP) is carried out considering metrics of the previous characteristics of wireless sensor network.

Prototype of implementation of hybrid visible light communication technology for tele controlling systems in archives warehouse

Paper is one type of archive that is stored in the Archives Building. Maintenance of paper type archives needs to be considered to avoid mold growth and discoloration due to cigarette smoke. Therefore, a telecontrolling device is needed that can maintain a stable air humidity at 30% - 55% and a temperature of 15 °C - 22 °C. However, devices with a frequency of 2.4 GHz should not be used simultaneously in the same area because it will cause interference that can reduce performance. In this study, a temperature tele controlling system and smoke detection in the archive warehouse were made using hybrid visible light communication technology to improve data transmission performance. The system consists of 1 transmitter node connected to DHT 22, MQ 2, LED laser and WiFi network, as well as 2 receiver nodes to control the fan and water pump. The data from the receiver will be passed through the photodiode and then processed by the microcontroller. If the temperature value reaches the threshold, the receiver node will send a command so that the fan will turn on automatically, then if the smoke value reaches the threshold, the water pump will turn on automatically. The temperature and smoke values ??can be monitored in real time through the android application. The test results show that the use of hybrid visible light communication technology in the tele controlling system makes the delivery performance better.

Statistical Model of Accurately Estimating Service Delay Behavior in Saturated IEEE 802.11 Networks Based on 2-D Markov Chain

To accurately estimate the service delay behavior of IEEE 802.11 networks, this paper comprehensively considers four main factors that affect the performance of IEEE 802.11 networks and establishes a service delay model with statistical characteristics. We analyzed the operation mechanism of 802.11 DCF, using the backoff stage and the backoff counter to portray the dynamic change characteristics of the system regarding the data frame transmission states. Afterward, we calculated the one-step transition probability of these states, establishing a 2-D Markov model, including the ICS procedure and the backoff procedure. Based on this model, we constructed steady-state equations to derive a relationship between the transmission probability and collision probability for each node transmission queue. By analyzing the ICS delay and the backoff delay, we obtained the probability generating function (PGF) of the average idle time. The analytical expressions of other service delays, such as the successful transmission time and collided transmission time, were derived to obtain the PGF of the total service delay. In the numerical simulation, we compared the first two statistical moments of the PGF with the Nav model, and it was found that our delay evaluation results were significantly better than the traditional evaluation results. The average service delay of the Nav model in all the scenarios was larger than that of the proposed model due to the lack of the ICS procedure in the Nav model. Since a DIFS duration is generally much shorter than a random backoff duration, our model saves the bandwidth and improves transmission efficiency.

Cooperative Communications Based on Deep Learning Using a Recurrent Neural Network in Wireless Communication Networks

In recent years, cooperative communication (CC) technology has emerged as a hotspot for testing wireless communication networks (WCNs), and it will play an important role in the spectrum utilization of future wireless communication systems. Instead of running node transmissions at full capacity, this design will distribute available paths across multiple relay nodes to increase the overall throughput. The modeling WCNs coordination processes, as a recurrent mechanism and recommending a deep learning-based transfer choice, propose a recurrent neural network (RNN) process-based relay selection in this research article. This network is trained according to the joint receiver and transmitter outage likelihood and shared knowledge, and without the use of a model or prior data, the best relay is picked from a set of relay nodes. In this study, we make use of the RNN to do superdimensional (high-layered) processing and increase the rate of learning and also have a neural network (NN) selection testing to study the communication device, find out whether or not it can be used, find out how much the system is capable of, and look at how much energy the network needs. In these simulations, it has been shown that the RNN scheme is more effective on these targets and allows the design to keep converged over a longer period of time. We will compare the accuracy and efficiency of our RNN processed-based relay selection methods with long short-term memory (LSTM), gated recurrent units (GRU), and bidirectional long short-term memory (BLSTM),which are all acronyms for long short-term memory methods.

Improving Energy Efficiency of Wireless Sensor Networks through Topology Optimization

- Relevance of the field of wireless sensor networks (WSNs) is increasing, and one of the most pressing challenges is in energy usage. This makes it a resource restraint type network for wireless sensor nodes that contain small unchangeable battery. Sensor network design has been influenced by and depends on the application by factors such as scalability, power consumption, environment etc. Most of the energy is used for communications among the three energy-saving activities: sensing, processing and communication. In this paper, an energy efficient (energy conserving) routing protocol called Wireless Sensor Network Energy Reduction Routing Coordinate Algorithm (WSNERRCA) is proposed. This provides a more efficient energy consumption pattern in WSN, by using eight straight line routing coordinate to sink. It transmits data within nodes transmission range (single-hop) and multi-hopping along routes (coordinates) thereby saving energy and optimizing delivery. The energy-model is simulated using NS-2 and the residual energy computed with the aid of AWK programming language coding. This model out-performed its counterpart (EEEWSNMIA) by 6%, as seen in recent research work published by Elsevier based on the criteria of conserving the highest energy of the sensor network with a hundred and twenty nodes while upholding optimally the QoS factors.

Delay-Optimal Coded Offloading for Distributed Edge Computing in Fading Environments

The rapid growth in scale and complexity of mobile applications fosters the development of the coded edge computing paradigm. By exploiting the redundancy in the encoded subtasks, coded edge computing enables collaborative transmission of multiple edge nodes and is promising for distributed computing in wireless fading environments. Nonetheless, to the best of our knowledge, due to challenges arising from the selection of the coding parameters, offloading strategy design for coded edge computing in general fading environments still remains open. With this consideration, the coded offloading problem is studied in this work and a delay-optimal coded offloading scheme is proposed. In particular, when the offloaded tasks are encoded by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(k,r)$ </tex-math></inline-formula> linear codes, transmission diversity gains can be obtained by performing edge node selection to mitigate fading. However, the corresponding optimization problem turns out to be a highly non-trivial non-linear mixed-integer programming. To this end, through in-depth analysis based on order statistics, it is found that the average processing delay of the offloaded tasks admits a favorable <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> -structure with respect to the coding parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> , under arbitrary fading distribution. This key theoretic result allows us to efficiently solve the original problem using monotonic optimization. Simulations are conducted to validate our analysis and corroborate the effectiveness of the proposed scheme.

PCube: Scaling LoRa Concurrent Transmissions with Reception Diversities

This article presents the design and implementation of PCube, a phase-based parallel packet decoder for concurrent transmissions of LoRa nodes. The key enabling technology behind PCube is a novel air-channel phase measurement technique that is able to extract phase differences of air-channels between LoRa nodes and multiple antennas of a gateway. PCube leverages the reception diversities of multiple receiving antennas of a gateway and scales the concurrent transmissions of a large number of LoRa nodes, even exceeding the number of receiving antennas at a gateway. As a phase-based parallel decoder, PCube provides a new dimension to resolve collisions and supports more concurrent transmissions by complementing time and frequency-based parallel decoders. PCube is implemented and evaluated with synchronized software defined radios and off-the-shelf LoRa nodes in both indoors and outdoors. Results demonstrate that PCube can substantially outperform state-of-the-art works in terms of aggregated throughput by 4.9× and the number of concurrent nodes by up to 5×. More importantly, PCube scales well with the number of receiving antennas of a gateway, which is promising to break the barrier of concurrent transmissions.

Adaptive Modulation and Coding for Underwater Acoustic Communications Based on Data-Driven Learning Algorithm

With the development of the underwater acoustic (UWA) adaptive communication system, energy-efficient transmission has become a critical topic in underwater acoustic (UWA) communications. Due to the unique characteristics of the underwater environment, the transmitter node will almost always have outdated channel state information (CSI), which results in low energy efficiency. In this paper, we take full advantage of bidirectional links and propose an adaptive modulation and coding (AMC) scheme that aims to maximize the long-term energy efficiency of a single link by jointly scheduling the coding rate, modulation order, and transmission power. Considering the complexity characteristics of UWA channels, we proposed a bit error ratio (BER) estimation method based on deep neural networks (DNN). The proposed network could realize channel estimation, feature extraction, and BER estimation by using a fixed pilot of the feedback link. Then, we design a channel classification method based on the estimated BERs of the modulation and coding scheme (MCS) and further model the UWA channels as a finite-state Markov chain (FSMC) with an unknown transition probability. Thus, we formulate the AMC problem as a Markov Decision Process (MDP) and solve it through a reinforcement learning framework. Considering the large state-action pairs, a double deep Q-network (DDQN) based scheme is proposed. Simulation results demonstrate that the proposed AMC scheme outperforms the fixed MCS with a perfect channel information state, and achieves near-optimal energy efficiency.

Entanglement Distribution with Minimal Memory Requirements Using Time-Bin Photonic Qudits

Generating multiple entangled qubit pairs between distributed nodes is a prerequisite for a future quantum internet. To achieve a practicable generation rate, standard protocols based on photonic qubits require multiple long-term quantum memories, which remains a significant experimental challenge. In this paper, we propose a novel protocol based on $2^m$-dimensional time-bin photonic qudits that allow for the simultaneous generation of multiple ($m$) entangled pairs between two distributed qubit registers and outline a specific implementation of the protocol based on cavity-mediated spin-photon interactions. By adopting the qudit protocol, the required qubit memory time is independent of the transmission loss between the nodes in contrast to standard qubit approaches. As such, our protocol can significantly boost the performance of near-term quantum networks.

A comprehensive survey on age of information in massive IoT networks

Ambient intelligence (AmI) represents the future vision of intelligent computing that can bring intelligence to our daily life through various domains. In such applications, AmI is often subject to the freshness of information collected, which is commonly quantified by a relatively newer metric called age of information (AoI). In the data aggregation and analytics for Internet-of-Things (IoT) AmI, AoI should be well managed, because information update should be as timely as possible to achieve optimal performances. AoI has been studied in various applications using different queuing policies, scheduling algorithms, and multiple access schemes, in which each component of communication and information systems are designed and analyzed to improve the AoI. This paper provides a comprehensive overview of literature on the AoI and its variants in large-scale networks. AoI in IoT systems depends on the arrival rate at the source nodes, queuing policy adopted at the nodes, the scheduling of nodes for information transmission and the access scheme adopted by the nodes. To better design and operate the AmI applications that require the freshness of information, we discuss the impacts of the queuing policy, stochastic modeling, scheduling, and multiple access schemes. In particular, non-orthogonal multiple access (NOMA), which is regarded as one of the key technologies in beyond 5G and 6G, and it hybrid version combined with the conventional orthogonal multiple access (OMA) are discussed in the context of AoI. In addition, we identify promising research opportunities in potential age-sensitive applications. Thus, compared to the existing surveys on AoI, this paper provide more practical and up-to-date design guidelines for the applications with the information freshness requirements.

M-T2F: A High-Efficient Contention Protocol for Wireless Networking in Cyber-Physical-Social Systems

Cyber-physical-social systems (CPSS) as an emerging computing paradigm are revolutionizing the relationship between humans, devices and physical environments and will be expected to have a fundamental impact and influence in our daily lives. In a typical CPSS scenario, plenty of nodes (or devices) often adopt wireless distributed contention protocols to transmit sensed environment states for decision making. Therefore, designing a high-efficient and collision-less contention protocol is of great importance for wireless networking. As is known, in the classic time-to-frequency (T2F) protocol, each node signals on one randomly chosen subcarrier for contention. Though reducing the conventional time-domain contention time, T2F leads to many contention collisions (i.e., multiple nodes choose the same subcarrier) when the number of nodes is large. Aiming at this challenge, in this paper, we propose M-T2F (where each node signals on multiple randomly chosen subcarriers for contention) and analyze its consensus achievement. Specifically, M-T2F supports more nodes for transmission but introduces less collision, benefiting from lots of multicarrier choice combinations. Since the subset relationship between subcarrier choices poses a challenge to reach a consensus among nodes, M-T2F enables all nodes to reach a consensus by excluding the subset relationship and meanwhile reduces the contention time by utilizing wireless broadcast characteristics. As a result, our designs significantly outperform related designs in terms of the collision probability, throughput, delay, and fairness. For instance, for a network of 50 nodes adopting 8 subcarriers, in our designs, the collision probability is as low as 0.1 (i.e., a reduction of 70%) and the throughput efficiency is up to 0.9 (i.e., an improvement of 34%). Extensive simulations verify the effectiveness of our designs and validate the accuracy of our theoretical analysis. This study is very helpful to achieve satisfactory quality of experience and quality of services for wireless networking in CPSS.

A Secure Long-Range Transceiver for Monitoring and Storing IoT Data in the Cloud: Design and Performance Study.

Due to the high demand for Internet of Things (IoT) and real-time data monitoring and control applications in recent years, the long-range (LoRa) communication protocols leverage technology to provide inter-cluster communications in an effective manner. A secure LoRa system is required to monitor and store IoT data in the cloud. This paper aims to report on the design, analysis, and performance evaluation of a low-cost LoRa transceiver interface unit (433 MHz band) for the real-time monitoring and storing of IoT sensor data in the cloud. We designed and analyzed a low-cost LoRa transceiver interface unit consisting of a LoRa communication module and Wi-Fi module in the laboratory. The system was built (prototype) using radially available hardware devices from the local electronics shops at about USD 150. The transmitter can securely exchange IoT sensor data to the receiver node at about 10 km using a LoRa Wi-Fi module. The receiver node accumulates the sensor data and stores it in the cloud for processing. The performance of the proposed LoRa transceiver was evaluated by field experiments in which two transmitter nodes were deployed on the rooftop of Auckland University of Technology's Tower building on city campus (New Zealand), and the receiver node was deployed in Liston Park, which was located 10 km away from the University Tower building. The manual incident field tests examined the accuracy of the sensor data, and the system achieved a data accuracy of about 99%. The reaction time of the transmitter nodes was determined by the data accumulation of sensor nodes within 2-20 s. Results show that the system is robust and can be used to effectively link city and suburban park communities.

Sparse Random Reconstruction of Data Loss With Low Redundancy in Wireless Sensor Networks for Mechanical Vibration Monitoring

Wireless sensor networks (WSNs) for condition monitoring of mechanical equipment have been shown effective for ensuring operational safety and reducing breakdown losses. For battery-supply sensor nodes, energy efficiency for data transmission of mass vibration signals has become a significant challenge. In this article, an efficient low redundant data method is designed and implemented in the WSN nodes for reliable transmission. The proposed method includes two stages. Stage 1 involves the delta-discrete cosine transform (delta-DCT) that reduces the temporal and frequency redundancy of the original data so that the transferred data can be downsized via range coding. In stage 2, the sparse random reconstruction is injected to increase the redundancy of the compressed data to guarantee reliable transmission. Experimental results demonstrate that the original data can be downsized to 50%–55%, and the compression data can be recovered via sparse reconstruction without any data loss.

Minimum‐variance recursive state estimation for complex networks with stochastic switching topologies and random quantization under try‐once‐discard protocol

SummaryThis article is concerned with the issue of minimum‐variance recursive state estimation (MVRSE) for a class of nonlinear dynamical complex networks (NDCNs) with stochastic switching topologies and random quantization under the try‐once‐discard (TOD) protocol. Two sequences of Bernoulli distributed random variables with given occurrence probabilities are utilized to characterize the stochastic switching manners of network topologies and the randomly occurring quantized output measurements, where the quantization effects are portrayed by the uniform quantizer. Moreover, the TOD protocol is adopted to arrange the order of the information transmission of network nodes so as to alleviate the communication burden and mitigate the network congestions. The focus of the MVRSE issue is to develop a novel state estimation algorithm such that, for all stochastic switching topologies, random quantization effects and TOD protocol, an optimized upper bound of the estimation error covariance is guaranteed by properly designing the estimator gain. In addition, the theoretical proof is derived, which illustrates that the state estimation error is exponentially mean‐square bounded under certain conditions. Meanwhile, we also present the related theoretical analysis, which discusses the impact caused by random quantization. Finally, a numerical experiment is utilized to show the validity of the novel MVRSE approach.

Buffer-Aided Relaying Strategies for Two-Way Wireless Networks

The energy and time efficiency of wireless sensor networks (WSNs) is frequently affected by the low reliability of their links. To mitigate the outage probability, cooperation topologies are used. However, these topologies have particular challenges since the relay consumes energy in assisting a foreign communication, and the successful transmission in each direction is conditioned to the availability of the two segments involved in the communication. To overcome the temporary unavailability of a link, the use of buffers in the relay has been proposed, but energy and time efficiency remain a challenge for basic configurations. We propose two-way buffer-aided relaying strategies that exploit the presence of buffers in the different nodes that participate in the communication, as well as the efficient use of buffer capacity. The proposed strategies make the decision to forward the messages in one of the communication directions or broadcast coded messages based on buffer and channel state information. Firstly, we evaluate the impact of considering the use of buffers in the transmitter nodes. Then, we propose and evaluate the impact of the full and joint use of the entire buffer capacity to assist communication in both directions. Finally, we evaluate the performance of a system that exploits both the use of full and joint buffering and the presence of buffering in the transmitter. The results show that better performance, in terms of outage probability, is obtained by the third strategy; since it allows the buffer capacity to be exploited to a greater extent in the most urgent direction at each moment, as well as to select the start of each transmission to a viable communication opportunity. This represents a notable benefit in terms of energy and time efficiency for WSNs since unnecessary transmission of information is avoided, the number of idle slots decreases, and the amount of information per unit of time and energy increases.

Population flow based spatial-temporal eigenvector filtering modeling for exploring effects of health risk factors on COVID-19

The COVID-19 pandemic has had great impact on human health and social economy. Several studies examined spatial and temporal patterns of health risk factors associated with COVID-19, but population flow spillover effect has not been sufficiently considered. In this paper, a population flow-based spatial-temporal eigenvector filtering model (FLOW-ESTF) was developed to consider spatial-temporal patterns and population flow connectivity simultaneously. The proposed FLOW-ESTF method efficiently improved model prediction accuracy, which could help the government aware of the infection risk level and to make suitable control policies. The selected population flow spatial-temporal eigenvector contributed most to modeling and the visualization of corresponding eigenvector set helped to explore the underlying spatial-temporal patterns and pandemic transmission nodes. The model coefficients could reflect how health risk factors contribute the modeling of state-level COVID-19 weekly increased cases and how their influence changed through time, which could help people and government to better aware the potential health risks and to adjust control measures at different stage. The extracted population flow spatial-temporal eigenvector not only represents influence of population flow and its spillover effects but also represents some possible omitted health risk factors. This could provide an efficient path to solve the problem of spatial and temporal autocorrelation in COVID-19 modeling and an intuitive way to discover underlying spatial patterns, which will partially compensate for the problems of insufficient consideration of potential risk variables and missing data.

Construction of Landscape Ecological Planning Evaluation Model Based on Sensor Network

The application of wireless sensor network (WSN) technology promotes the modernization of forestry. WSN application technology in forest areas is an important research topic for the sustainable development of forestry in China and is also a research hotspot for forestry ecological monitoring at present. The application of wireless sensor networks in forest areas, first of all, solves the problem of limited energy supply and low-latency data transmission in the forest environment. Due to the large area of the forest environment and uneven tree density, dynamic changes in forest height, easy to block the signal, and other characteristics, the forest environment is prone to node energy depletion fast, the network life cycle is short, and data transmission delays large dilemma. Second, the application of wireless sensor networks is usually centered on maximum data acquisition, but the contradiction between high data acquisition rate and limited energy supply is inevitable, so it is necessary to construct a maximum data acquisition rate model with limited energy supply as a constraint to guarantee the optimal acquisition conditions for wireless sensor networks. In this paper, from the application of wireless sensor network technology in forest environment, the research on the application of wireless sensor network in forestry is carried out around the analysis of energy self-collection permanent function of sensor nodes, sensor node transmission routing strategy, data collection, fusion, and fuzzy inference decision-making fire danger warning process, so as to provide a solution to the overall problem of forest fire warning based on rechargeable wireless sensor network. In this paper, we analyze the dynamic replenishment of energy in rechargeable wireless sensor networks and propose an energy-based transmission control protocol that effectively improves data transmission efficiency. In the rechargeable wireless sensor network, the network E2E (end-to-end) average delay time is calculated based on the number of nodes on the data transmission link. The research idea of this paper starts from the application technology of wireless sensor network in the forest environment, and the design of the energy self-collection permanent function of sensor nodes, sensor node transmission routing strategy, data collection, fusion, and fuzzy inference decision fire danger warning process are realized vertically.

Accurate Prediction of Tunnel Face Deformations in the Rock Tunnel Construction Process via High-Granularity Monitoring Data and Attention-Based Deep Learning Model

Monitoring and predicting the deformation of surrounding rocks in the rock tunnel construction process is of great significance. This study implemented a wireless sensor network (WSN), including gateway transmission, relay point, and sensor nodes, to obtain high granularity deformation data during construction. A transformer model is proposed, which considers the construction sequence into the positional embedding and has an attention module to deeply learn the high dimensionality correlation between the nearby deformation data and the tunnel face deformation. The attention-enhanced LSTM model and the LSTM model are also constructed to compare them with the performance of the transformer model. A site study conducted on a shallow buried tunnel section suggested an excellent performance of the proposed WSN system. The transformer model shows the best performance in terms of the model prediction results, which can extract more information from the time sequence data than the attention-enhanced LSTM and LSTM models. The proposed system has great value as guidance and reference for the construction of rock tunnel projects in complex and unfavourable geological conditions.

LoRa Network-Based System for Monitoring the Agricultural Sector in Andean Areas: Case Study Ecuador.

This article focuses on the development of a system based on the long-range network (LoRa), which is used for monitoring the agricultural sector and is implemented in areas of the Andean region of Ecuador. The LoRa network is applied for the analysis of climatic parameters by monitoring temperature, relative humidity, soil moisture and ultraviolet radiation. It consists of two transmitter nodes and one receiver node, a LoRa Gateway with two communication channels for data reception and one for data transmission, and an IoT server. In addition, a graphical user interface has been developed in Thinger.io to monitor the crops and remotely control the actuators. The research conducted contains useful information for the deployment of a LoRa network in agricultural crops located in mountainous areas above 2910 m.a.s.l., where there are terrains with irregular orography, reaching a coverage of 50 hectares and a range distance of 875 m to the farthest point in the community of Chirinche Bajo, Ecuador. An average RSSI of the radio link of −122 dBm was obtained in areas with a 15% slope and 130 m difference in height according to the Gateway, where the presence of vegetation, eucalyptus trees and no line-of-sight generated interference to the radio signal. The success rate of PDR packet delivery with an SF of nine, had a better performance, with values of no less than 76% and 92% in uplink and downlink respectively. Finally, the technological gap is reduced, since the network reaches places where traditional technologies do not exist, allowing farmers to make timely decisions in the production process in the face of adverse weather events.