Routing Methodology Research Articles

Multipath Routing with Dynamic Load Balancing for Enhanced Energy Efficiency in Zone-Based Wireless Ad Hoc Networks

Mobile Ad hoc Networks (MANETs) consist of independent, self-organized mobile nodes capable of wirelessly communicating with each other in a reliable and secure manner. One of the major challenges in MANETs is energy consumption, as the lifetime of a node's battery is limited. This limitation can affect both network connectivity and the lifetime of individual nodes. Various protocols, proposed by the IETF and other solution providers, have aimed to optimize bandwidth, transmission quality, and power control, but none have significantly improved energy consumption. In response to these challenges, the proposed protocol is the Dynamic Load Balancing in Multipath Energy-Consuming Routing Protocol (DLB-MERP) for Wireless Ad hoc Networks. DLB-MERP can be viewed as an extension of the AOMDV protocol and is classified as a zone-based routing protocol, though it incorporates superior load balancing and energy management techniques. It introduces the concept of virtual zones to manage and distribute energy consumption in an organized way. The protocol selects communication paths based on the availability of node energy and power levels. A leader node is defined within each zone, chosen based on its energy level, load, and channel strength, to optimize energy usage. This method increases network resiliency through multipath routing, thereby enhancing overall network performance and extending network lifetime. DLB-MERP effectively addresses the limitations of existing energy-aware routing methodologies by balancing energy consumption, leading to prolonged network operation.

Reaching the efficiency limit of arbitrary polarization transformation with non-orthogonal metasurfaces

Polarization transformation is at the foundation of modern applications in photonics and quantum optics. Notwithstanding their applicative interests, basic theoretical and experimental efforts are still needed to exploit the full potential of polarization optics. Here, we reveal that the coherent superposition of two non-orthogonal eigen-states of Jones matrix can improve drastically the efficiency of arbitrary polarization transformation with respect to classical orthogonal polarization optics. By exploiting metasurface with stacking and twisted configuration, we have implemented a powerful configuration, termed “non-orthogonal metasurfaces”, and have experimentally demonstrated arbitrary input-output polarization modulation reaching nearly 100% transmission efficiency in a broadband and angle-insensitive manner. Additionally, we have proposed a routing methodology to project independent phase holograms with quadruplex circular polarization components. Our results outline a powerful paradigm to achieve extremely efficient polarization optics, and polarization multiplexing for communication and information encryption at microwave and optical frequencies.

Aircraft routing using dynamic programming and reinforcement learning: A customer-centric approach

Over the past few decades, the airline industry has evolved into a sophisticated business, with conventional objectives such as operational efficiency, effective fleet assignment, flight punctuality, and equitable crew scheduling being the primary focus. Nevertheless, given the growing accessibility to customer feedback (either through public forums or private data), it is crucial to make business decisions that cater to the customers’ requirements in addition to conventional goals. Within aircraft operations, there is a series of decisions to be made, such as flight scheduling, fleet assignment, aircraft routing, and crew scheduling. After designating a fleet type to each flight leg, airlines ascertain the sequence of flight legs, or strings, to be operated by individual aircrafts, while complying with obligatory maintenance checks. This constitutes the aircraft routing problem.We introduce a novel approach to aircraft routing problem, by infusing traditional methodologies with insightful customer feedback to create a more responsive and customer-centric model. Unlike conventional techniques of constructing routes, in our work, we construct maintenance feasible strings. Traditional aircraft routing methodologies typically concentrate on minimizing propagated delay. However, by integrating customer feedback, more effective prioritization to minimize delays for flight legs with higher customer dissatisfaction can be achieved. We propose an optimization model that strives to minimize propagated delay while prioritizing flights based on customer feedback, thus resulting in customer-aware routes. To solve the proposed optimization model, we design an iterative alternating optimization scheme where feasible strings are first constructed from the pool of available flight legs using a string generation procedure, followed by solving an integer linear optimization problem over only the feasible set of strings obtained in the first stage. To construct feasible strings of flights at each iteration, we use two different methods. The first method is based on a dynamic programming approach which is useful for mean delay information. To handle practical cases where delays are uncertain, we propose a reinforcement learning (RL) approach to construct the strings. We provide experiments of our proposed methods on synthetic and real data sets and demonstrate the effectiveness of our RL model in comparison to dynamic programming based model. Our experiments affirm that routing decisions informed by customer feedback prioritize minimizing delays for specific flight legs, resulting in routes that are inherently customer-friendly.

Mathematical Modeling and Analysis of Energy Aware Probabilistic Distribution Based Cluster Head Selection Algorithm for Wireless Sensor Networks

From a mathematical perspective, Wireless Sensor Networks (WSNs) are increasingly recognized for their utility across multiple sectors such as environmental oversight, healthcare monitoring, and process automation within industries. A paramount obstacle within WSNs is the management of energy efficiency, given that the sensor nodes are typically powered by batteries with finite energy reserves. The criticality of developing efficient routing protocols cannot be overstated, as they are instrumental in extending the operational lifespan of the network and guaranteeing dependable data communication. This study is centered around the mathematical modelling and performance evaluation of an innovative routing methodology that integrates machine learning algorithms to augment energy efficiency within WSNs. The novel routing strategy dynamically adjusts its operations in response to the immediate environmental and network states, aiming to minimize energy expenditure and elevate the network's overall efficiency. Through comprehensive numerical simulations, this research scrutinizes the efficacy of the machine learning-enhanced routing protocol against conventional routing methodologies, accentuating its advantages in energy savings and reliability in data transmission. The simulation framework encompasses a variety of network configurations, traffic distributions, and environmental contexts, employing metrics such as energy utilization, network longevity, packet delivery ratio, and latency to offer an in-depth examination of the machine learning-based routing approach's performance. Findings from the simulations affirm the algorithm's enhanced energy efficiency, which contributes to prolonged operation of sensor nodes and steadfast data communication across dynamically changing network landscapes. The implications of this study highlight the transformative potential of machine learning in redefining routing protocol design and optimization within energy-restricted WSNs. By elevating both energy efficiency and network functionality, this research marks a significant stride towards realizing sustainable and dependable WSNs, paving the way for their broader application in essential services.

Trajectory Mining and Routing: A Cross-Sectoral Approach

Trajectory data holds pivotal importance in the shipping industry and transcend their significance in various domains, including transportation, health care, tourism, surveillance, and security. In the maritime domain, improved predictions for estimated time of arrival (ETA) and optimal recommendations for alternate routes when the weather conditions deem it necessary can lead to lower costs, reduced emissions, and an increase in the overall efficiency of the industry. To this end, a methodology that yields optimal route recommendations for vessels is presented and evaluated in comparison with real-world vessel trajectories. The proposed approach utilizes historical vessel tracking data to extract maritime traffic patterns and implements an A* search algorithm on top of these patterns. The experimental results demonstrate that the proposed approach can lead to shorter vessel routes compared to another state-of-the-art routing methodology, resulting in cost savings for the maritime industry. This research not only enhances maritime routing but also demonstrates the broader applicability of trajectory mining, offering insights and solutions for diverse industries reliant on trajectory data.

Experience in assessing the abundance and spatial distribution of geese on the Taimyr Peninsula using the aerial survey method

The Taimyr Peninsula is a key region for reproduction of waterfowl of European and Asian wintering populations. The uniqueness of Taimyr is that birds migrate there from both the west and the east, following five out of eight global flyways: East Atlantic, Black Sea/Mediterranean, West Asian/East African, Central Asian, and East Asian/Australasian. The study develops the topic of conservation ornithology and sustainable use of biological resources raised at the First All-Russian Ornithological Congress by Dr. Evgeny Syroechkovsky. Russia lacks the system of state monitoring and assessment of waterfowl populations, in particular with the use of aerial survey methods. In 2019, counts of geese in the tundra zone of the Taimyr Peninsula were carried out from the board of an ultralight aircraft specially designed for aerial surveys. New data on the abundance and spatial distribution of geese were obtained. We substantiated the methodology for the survey flight routing with no transects and the use of GIS, remote sensing, and generalized additive models (GAM) for interpreting the results and extrapolating estimates of goose abundance. A database of the results of aerial surveys was compiled (http://rggsurveys.ru). According to our estimate, for the first time made by statistical modeling for the tundra zone of the Taimyr Peninsula, more than 1.5 million White-fronted geese, about 450000 Bean geese, 78400 Red-breasted geese, and 43000 Lesser White-fronted geese nest and moult on Taimyr. Comparison of these figures with modern estimates of the world populations of these species demonstrates the key role of the Taimyr Peninsula in maintaining populations of rare and economically important Anseriformes.

Adaptive graphical routing methodology for reducing traffic overhead in wireless sensor networks

Adaptive graphical routing methodology for reducing traffic overhead in wireless sensor networks

Cu-Doped SrTiO3 Nanostructured Catalysts for CO2 Conversion into Solar Fuels Using Localised Surface Plasmon Resonance

Carbon dioxide valorisation is one of the most discussed topics amongst researchers; indeed, finding a way to significantly reduce CO2 concentration in the atmosphere is crucial in order to mitigate climate change effects in the next decades. In this study, SrTiO3-supported Cu nanoparticles are exploited as Localised Surface Plasmon Resonance (LSPR)-mediated catalysts for CO2 reduction. The materials were prepared via sol–gel citrate route methodology, inserting Cu as a dopant in the perovskite structure; reducing treatments at different temperatures were performed to promote copper atom exsolution, thus forming nanostructures upon the surface. The perovskitic structure was confirmed via ex situ and operando XRD analysis, while compositional analysis was carried out through XPS and EDS; SEM and TEM images revealed morphological changes with different reducing treatments, and bulk reducibility was analysed with H2-TPR, revealing different Cu species in the material. Band gap analysis via DRS showed the successful incorporation of copper in the perovskite, affecting the light absorption properties. Finally, catalytic tests showed that copper nanoparticles play a role in CO2 activation with sunlight, proving that LSPR could be exploited for catalytic means.

Efficient Distance and Connectivity Based Traffic Density Stable Routing Protocol for Vehicular Ad Hoc Networks

Vehicle ad-hoc networks (VANETs) have become a prominent research topic in recent years due to rapid dynamic topology, high vehicle mobility, frequent link failures and significant delay constraints. The mobility of the vehicle nodes increases, and the overhead of control traffic due to the high dynamics of the VANET. Efficient routing algorithms are necessary for VANETs to ensure reliable transmission (VANETs). This work proposes a Traffic Density Stable Routing Protocol based on Connection- and Distance (TDSRP-DC) to avoid data packet collisions at intersections and an adaptive routing schedule based on the selection at every instant. Our approach is based on vehicle-to-vehicle communication. Ground vehicles identify the most appropriate next junction and transfer the data packet to the receiver to find the optimal multi-hop route. It relies on transferring the data between all vehicles to estimate real-time traffic fluctuations sporadically. Network formation, neighbor realization, fitness value prediction, and routing methodology are the conceptual process needed in this research. Valid parameters for finding the optimum path in the conceptual model include node distance, node speed, node azimuth, link stability, and link reliability. The suggested solution’s simulation was performed in comparison to traditional algorithms. Compared to existing TFOR algorithms on complex traffic, the proposed TDSRP-DC provides tremendous improvements based on packet delivery rate (10%) and performance (35%). The demonstrated results and comparisons illustrate the proposed routing protocol’s significance with enhanced quality of service performance.

Simulating Smart Cities Under Different Scenarios and Factors

In this research, we suggest building or utilizing a virtual environment of a smart city where fixed and mobile sensors are arranged in a certain way, using particular movement models and techniques for transmitting and sharing data via the virtual city. This proposal proposes to carry out several experiments for various movement models, such as:(Individual Mobility Model,Exponential movement model and Rayleigh Flight movement model), A specific data distribution (Gaussian, Littice, Power-law, Exponential) and a certain data routing methodology (Spray and Wait, Probabilistic Flooding, and Epidemic) were used to examine the impact of this on the use of network resources. In this proposal, we also want to look at the usage of various resources, including electricity and data. A movement model, an item distribution strategy, and a data routing protocol were all combined to create each experiment that was intended to be conducted in this proposal. All submitted experiments will ultimately be compared to one another, and recommendations that assist the research and design community in conserving network resources will be made. the findings show that the traffic models that were used with mobile nodes are considered one of the important factors in determining the consumption of network resources. In addition to the influence of traffic models on the consumption of network resources, algorithms and methods of routing data also have an impact. The Human Mobility Model, which accurately expresses human movement behavior, is considered one of the most stable models in terms of performance.

A Design and Challenges in Energy Optimizing CR-Wireless Sensor Networks

Background: The progress of the Cognitive Radio-Wireless Sensor Network is being influenced by advancements in wireless sensor networks (WSNs), which significantly have unique features of cognitive radio technology (CR-WSN). Enhancing the network lifespan of any network requires better utilization of the available spectrum as well as the selection of a good routing mechanism for transmitting informational data to the base station from the sensor node without data conflict. Aims: Cognitive radio methods play a significant part in achieving this, and when paired with WSNs, the above-mentioned objectives can be met to a large extent. Methods:: A unique energy-saving Distance- Based Multi-hop Clustering and Routing (DBMCR) methodology in association with spectrum allocation is proposed as a heterogeneous CR-WSN model. The supplied heterogeneous CR-wireless sensor networks are separated into areas and assigned a different spectrum depending on the distance. Information is sent over a multi-hop connection after dynamic clustering using distance computation. Results: The findings show that the suggested method achieves higher stability and ensures the energy-optimizing CR-WSN. The enhanced scalability can be seen in the First Node Death (FND). Additionally, the improved throughput helps to preserve the residual energy of the network which helps to address the issue of load balancing across nodes. Conclusions: Thus, the result acquired from the above findings shows that the proposed heterogeneous model achieves the enhanced network lifetime and ensures the energy optimizing CR-WSN.

Fault-Tolerant Routing for Wireless Sensor Networks using a Gaussian Network

Abstract: Wireless Sensor Networks (WSNs) are utilised for sensing various physical and environmental variables like temperature, pressure, motion, and pollution. A component of a network to continue operating even when some sensor nodes fail is regarded as fault tolerance in wireless sensor networks. Due to battery life deadlines and the challenges associated with charging or replacing failing nodes, the deployment of sensor nodes in the target area is done in a dense manner to maximise coverage and connection. This paper implements a hybrid fault-tolerant routing methodology to address the problem of fault tolerance in hierarchical topology for wireless sensor networks (WSNs). The network location is partitioned into small square grids, with a Gaussian integer serving as each grid's cluster head. A Gaussian network is created through communicating these cluster heads together. Discovering the Gaussian network's shortest path and multi-path routing, the project implements a hybrid Fault-tolerant Clustering routing protocol based on Gaussian network for Wireless Sensor Network (FCGW). The purpose of FCGW is to improve fault tolerance and reduce energy consumption for Wireless Sensor Networks. Experimental results using MATLAB shows that FCGW protocol has high data reliability. In addition, the FCGW protocol consumes lesser energy in the network compared to other protocols namely FT-LEACH, HEED and PSO-UFC

Learn global and optimize local: A data-driven methodology for last-mile routing

In last-mile routing, the task of finding a route is often framed as a Traveling Salesman Problem to minimize travel time and associated cost. However, solutions stemming from this approach do not match the realized paths as drivers deviate due to navigational considerations and preferences. To prescribe routes that incorporate this tacit knowledge, a data-driven model is proposed that aligns well with the hierarchical structure of delivery data wherein each stop belongs to a zone — a geographical area. First, on the global level, a zone sequence is established as a result of a minimization over a cost matrix which is a weighted combination of historical information and distances (travel times) between zones. Subsequently, within zones, sequences of stops are determined, such that, integrated with the predetermined zone sequence, a full solution is obtained.The methodology is particularly promising as it propels itself within the top-tier of submissions to the Last-Mile Routing Research Challenge while maintaining an elegant decomposition that ensures a feasible implementation into practice. The concurrence between prescribed and realized routes underpins the adequateness of a hierarchical breakdown of the problem, and the fact that drivers make a series of locally optimal decisions when navigating. Furthermore, experimenting with the balance between historical information and distance exposes that historic information is pivotal in deciding a starting zone of a route. The experiments also reveal that at the end of a route, historical information can best be discarded, making the time it takes to return to the station the primary concern.

Public transport access to drug treatment before and during COVID-19: Implications for the opioid epidemic.

Public transport disruptions caused by the COVID-19 pandemic had wide-ranging impacts on the ability of individuals to access health care. Individuals with opioid use disorder represent an especially vulnerable population due to the necessity of frequent, supervised doses of opioid agonists. Focused on Toronto, a major Canadian city suffering from the opioid epidemic, this analysis uses novel realistic routing methodologies to quantify how travel times to individuals\220 nearest clinics changed due to public transport disruptions from 2019 to 2020. Individuals seeking opioid agonist treatment face very constrained windows of access due to the need to manage work and other essential activities. We find that thousands of households in the most materially and socially deprived neighbourhoods crossed 30 and 20-minute travel time thresholds to their nearest clinic. As even small changes to travel times can lead to missed appointments and heighten the chances of overdose and death, understanding the distribution of those most impacted can help inform future policy measures to ensure adequate access to care.

A Constraint-Based Routing and Charging Methodology for Battery Electric Vehicles With Deep Reinforcement Learning

Electric vehicle route planning (EVRP) is generated from the collaborative operation of smart grids and intelligent transportation systems. It has become an essential issue for the widespread use of battery electric vehicles. However, the existed EVRP solutions are of either high computational complexity or low efficiency for large-scale problems. Towards such issues, we propose an efficient Deep Reinforcement Learning based methodology for constraint-based routing while simultaneously considering electric vehicles’ charging policies. We design a two-layer model to find near-optimal solutions and handle the broad range of problem instances according to the rewards and the preset feasibility rules. The first layer is to approximate a sequence of consecutive actions in reality and correspondingly produce a minimum time-consuming feasible route without re-training for each new problem instance. The second layer is to generate a charging scheme along the previously generated feasible path. The proposed methodology is independent of the road network topology and the electric vehicles’ types. Besides, the convergence of value function in the presented model for EVRP is studied. The experiment shows that our methodology outperforms the traditional ones in computation time with comparable solution quality. Moreover, the obtained model can be directly applied to treat other problem instances on various road networks without re-training procedures.

Real-time safest route identification: Examining the trade-off between safest and fastest routes

Several studies have shown that crash risk is a dynamic quantity that is frequently changing with considerable spatial and temporal variations. Recent advances in safety evaluation techniques such as using extreme value theory (EVT) models provided the opportunity to use traffic conflict data obtained from road user trajectories to estimate real time safety metrics. These metrics can aggregate crash risk along a certain route based on the duration of exposure to unsafe road conditions. This paper applies a Bayesian hierarchal extreme value theory model to trajectories obtained from a drone dataset from Athens, Greece, to develop a safest route algorithm capable of informing users about the safest route in an urban network in real time. The study area selected consists of a rectangular grid made up of 102 signalized and unsignalized intersections. The dynamic crash risk for each link in the network was obtained and used to identify the safest route between any origin–destination pair and the corresponding fastest route. The safest routes were then compared to the fastest routes and were found to be 22% safer on average, resulting in an 11% increased travel time. Moreover, the safest route was identical to the fastest route in 23% of the origin–destination pairs analyzed and had an average similarity of 54% in terms of links. Recognizing the trade-off between safety and mobility, a multi-objective routing methodology was proposed which balances travel time and crash risk using a weighted preference for safety. This work has considerable potential for improving the safety of all road users and may also be used for fleet routing applications as part of multi-objective routing systems.

Literature Survey on Automatic Pipe Routing

Piping systems are common in many architectures and designing such systems is often a complex task. Design automation of piping systems is therefore a universal research subject. Nonetheless, these piping systems are often still designed by hand as a result of their complexity. Consequently, costs associated with piping design are high, especially for large-scale architectures like ships and chemical plants. The goal of automatic pipe routing is to reduce the design time and associated costs of a piping system by automating the routing of these pipes. This survey provides an overview of the current state of automatic pipe routing literature to assist researchers and practitioners to further the study of automatic pipe routing. This is done by pinpointing and explaining the most important obstacles that stand in the way of making a full-scale automatic pipe routing method. The barriers that are analyzed are related to both model representation and optimization complexity. Finally, a synthesis table of research papers on automatic pipe routing is provided based on the handling of the aforementioned barriers and other general features of automatic pipe routing methodology. The survey concludes by discussing directions for further research.

New Energy Efficient Routing Protocol in Wireless Sensor Networks Using Firefly Algorithm

Energy constraint has become the major challenge in designing wireless sensor networks. Network lifetime is considered the most substantial metric in these networks. The routing technique is one of the best choices for maintaining a network lifetime. This paper demonstrates the implementation of the new methodology of routing in WSN using Firefly swarm intelligence. Energy consumption is the dominant issue in wireless sensor network routing. For network cutoff avoidance while maximizing net lifetime, energy exhaustion must be balanced. Balancing energy consumption is the key feature for the rising net lifetime of WSNs. This routing technique involves the determination of the optimal route from the node toward the sink to make energy exhaustion balance in the network and at the same time maximize network throughput and lifetime. The proposed technique show that it is better than other some routing techniques like Dijkstra routing, Fuzzy routing, and ant colony (ACO) routing technique. Results demonstrate that the proposed routing technique has beat the three routing techniques in throughput and extend net lifetime.

Energy-efficient routing protocol on public roads using real-time traffic information

Wireless technology has been significantly expanding to perform automotive services related to enhancing road networks and increasing traffic safety. Internet of Vehicles (IoV), as a part of wireless sensor technology, has succeeded in making considerable progress in promoting and protecting human lives from the damage caused by traffic accidents. The road statistical reports demonstrated that utilizing data dissemination techniques in IoV to share timely information about the vehicle’s position with drivers plays a key in avoiding accidents occurrence. This type of technique enables data propagation in a collaboratively and efficient manner to boost real-time performance. However, several limitations have arisen in network bandwidth usage and power consumption, as well as communication reliability with increasing packet loss probability. The main objective of this study is to present a new vehicular architecture that supports the integration of Software-Defined networks and fog computing for enhancing VANET performance. An energy-efficient and QoS-aware routing technique allowing for control of excessive dissemination of data traffic across the networks is also proposed. The simulation results show that the proposed routing protocol introduced an accurately controlling methodology in the transmission rate of data packets and communication reliability of VANET. There is a 50–60% enhancement of the whole power consumption and network throughput, depending on the implementation of the proposed geographical routing methodology.

Flood Routing of Tigris River in Baiji Station and Makhoul Dam Reservoir under Supposed Operation of the Dam

The digital elevation models (DEM) used for geometric analysis of the reservoir, as essential requirement in the study of flood routing in the reservoir and in Baiji station on Tigris river, the volume of storage, volume of island, positive and negative planner area and surface areas, residual capacity, reservoir average depth, and average thickness of the island, were estimated, at each hypothetical level from 108-160 meters above sea level, the geometric elements of storage volume and residual capacity are used directly for flood routing.
 The assumptions that have been assumed as basis for flood routing methodologies are:
 
 The input discharges to Baiji station before assuming the existence of the dam is the same input discharges in the reservoir after the assumption of existence and operating of the dam.
 The input discharge in Baiji station after construction of the dam is the same output discharge from gates.
 The presence of initial reserve before the arrival of flood wave, as the reservoir is not empty before the flood.
 Neglecting the drawn amount of water between the dam and Baiji station for irrigation and industrial purposes because of their small value.
 The outflow from the gates was assumed for each operating scenario, the water level should not reach the critical one in the reservoir and Baiji gaging station.
 If the river discharge in Baiji station not reach the destructive (critical) level, means that the level not reach the critical level in the other stations in downstream (Tikrit, Samarra, ... etc.).
 
 Two mathematical models were used for flood routing purpose, the first is a relationship of discharge-level to predict the level in Baiji station depended on outflow from gates, and the second is the relationship of storage-level to predict the level in the reservoir depended on storage volume when the leave of the flood wave, an algorithm and flow chart were developed to describe and explain the steps of the flood routing program, which can be modified and applied for any dam reservoir in the world, and it is used in current study, also to calculate inflow discharges then inflow volumes, either outflow discharges may assumed for eleven operating scenarios (at 11 supposed levels to receive the flood wave) and predict the equivalent level in Baiji, and the change in storage, and then the accumulated volume and the equivalent level in reservoir, when the end of flood wave.
 The optimum levels in the reservoir as well as in Baiji station depends on outflow discharge from the reservoir outlets, the predicted levels ranged between (103.83-105.75) meters above sea level in the station, resulting by outflow from the gates ranged from (1000-4500) m3/sec for each supposed scenario, knowing that these outflows within the discharge capacity of gates.
 So, the routing reveal to not get flood in Baiji because the levels are not arrival to the critical case, and the results confirm the non-arrivals of the level in the reservoir to a critical situation, according to these scenarios the water level ranging between (150.53-151.90) meters above sea level, and the equivalent storage volumes ranging from (4252686180-4518813396) m3 in the reservoir, in condition of the dam operator apply the current supposed operation system.