Smart Manhole Managing and Monitoring System Using IOT

Abstract

A smart city is the future goal for humanity to have cleaner and better amenities. When creating a smart city, smart subsurface infrastructure is a crucial component to consider. Monitoring the drainage system is critical to keeping the city clean and healthy. Because human monitoring is ineffective, drainage problems are handled slowly and take longer to resolve. The technology is designed to plot the co-ordinates of the manholes using GPS.The proposed approach is low-cost and requires little maintenance. The GPS will refresh the information every 5 minutes. When crossing a manhole, the driver is alerted that a manhole is ahead, allowing them to avoid it. The system employs a machine learning algorithm based on Q-Learning to calculate each state. If any of those parameters changes abruptly, the system connects to the admin office and notifies the situation via message wherever it has been posted. This study would be tremendously beneficial to society in terms of manhole maintenance. Implement a Reinforcement Learning algorithm, such as Q-Learning, that is based on reward and policy. A test data set will be used to assess the performance of the Q learning model.

Introduction

I. INTRODUCTION

Manhole Monitoring system parameters, including location, will be known in real time by Smart. The GPS system detects and collects all of these factors and data. Manhole's coordinates are being plotted on a Google map.  When it comes to the safety of human life, it is critical to avoid accidents. Many accidents are caused by manholes that are not visible to the driver's eyesight. If the co-ordinates of a manhole are plotted on a Google map, it will be easier to avoid manholes and drive safely across them. People's daily life are disrupted when drains become clogged during the rainy season. As a result, the municipal corporation should have a system in place that alerts authorities about sewage obstacles and their precise location.

When it comes to cleaning, clearing, and inspecting a drainage system, access points are an essential component. Metropolitan cities have implemented subsurface drainage systems, and the municipal corporation must keep the city clean. If sewage is not properly maintained, ground water becomes contaminated, resulting in dangerous diseases. Blockages in drains during the monsoon season disrupt the public's daily routine. As a result, the city corporation should have a facility that alerts officials to sewer obstructions and their precise position. It mostly recognises in the field of notifying people of gas explosions, increases in water level, and temperature levels. It makes use of IoT to create a drainage monitoring system in a highly automotive setting by using sensors to detect and deliver alerts to authorities via GSM and GPS module. This idea eliminates the disadvantages by putting water flow rate sensors at node intersections to detect drainage water blockage. When a node is blocked, the flow of drainage water varies, and when it exceeds the set value, an alert is displayed in the managing station. Other flaws are addressed by detecting temperature differences inside the manhole and alerting the management station. Because physically maintaining manholes is time-consuming and dangerous owing to the bad environmental conditions inside, the primary goal of this project is to offer a system that monitors water level, air temperature, water flow, and harmful gases. If the drainage becomes clogged and sewage water overflows, the sensors detect it and send a notification to the municipal. Going inside the manholes to assess their current state is so perilous. A remote alarm system is required to communicate data collected by sensors installed inside the manhole to the managing station in order to tackle all underground sanitation concerns. This contains power-supply components such as a controller, memory, transceiver, and battery.

II. LITERATURE SURVEY

As per [1] Unfortunate waste administration, along with other natural risks like separated framework (such potholes and open sewer vents) and stale water represents a serious danger to the wellbeing of neighborhood populaces. Brilliant waste administration frameworks are turning out to be more significant as the idea of "savvy urban communities" gains prominence. Manual observing calls for greater investment, cash, and exertion yet might be extraordinarily worked with by present day innovation.

One such option is to use the Web of Things (IoT) to screen such exercises; nonetheless, this includes the utilization of sensitive electrical hardware that are expensive to keep up with. Subsequently, this article subtleties the formation of a framework utilizing profound learning strategies that may essentially decrease the significant expenses of compelling ecological observing and give a hand in making the world a superior, more secure spot both now and later on.

As per [2] a change that makes the ongoing framework simpler to keep up with and subsequently less expensive. To accomplish this objective, the time expected to recognize the burdened locale might be extensively decreased. Continuous information, for example, water stream rate and harmful gas fixation not entirely settled by supplanting existing sewer vents with sensor based sewer vent covers at key spots. The approved help station gets this data before it becomes stopped up, considering convenient fix that decreases the probability of road flooding. While planning a savvy city, it means quite a bit to consider the subsurface framework. To keep a protected and solid local area, framework observing is pivotal. The shortcoming of manual checking implies that seepage issues take more time to determine and additional opportunity to research. The framework is intended to utilize a remote sensor network comprised of sensor hubs to diminish the seriousness of these issues. At the point when any sewer vent's level ascents past a specific limit, the proposed framework sends an email to the administration station by means of the Web of Things. This innovation diminishes the casualty chance of manual scroungers who clean the underground depletes and furthermore helps the overall population. Actual variables like as temperature, stickiness, water level and stream, hindrances, and whether a sewer vent top is open or shut are consequently detected and refreshed continuously by a sensor unit by means of the Web of Things. The outcome is a keen and automatic framework.

As per [3] there has been a shift towards utilizing many sorts of sensors, for example, gas identifying components and temperature estimating components, to distinguish issues. Assuming that there is a surprising change in any of these elements, the framework associates with the civil office and advises the suitable gatherings by message. Web of Things innovation is utilized to lay out the essential framework, and likewise, the fundamental upkeep. This article will be exceptionally helpful to society in keeping the sewer vent ready to rock 'n roll.

As indicated by [4] the application and configuration capability of seepage checking framework. Modules highlighting gas sensors, stream sensors, and NRF will be connected with Arduino microcontrollers and introduced in seepage sewer vents. Assuming a blockage is distinguished in the path interfacing two sewer vents, harmful gases are recognized, and the water level is observed, this information is all handed-off to the proper specialists so restorative measures can be taken. This can be all followed continuously by the framework.

As per [5] The gadget recognizes any development of the sewer vent cover and tells the Metropolitan Enterprise. Along these lines, the condition of the sewers is followed consistently, and when a crisis emerges, the Civil Organization is told of the nature and area of the issue inside the sewage organization. Utilizing a Repetitive Brain Organization with a Long Momentary Memory (LSTM) technique, we consistently caught information from the sensors, and presently we use this information to decide the limit of the sensor readings.

As per [6] make a system to stop open sewer vents in huge urban communities to diminish the gamble of mishaps. Man-opening covers might be checked for weakening utilizing sensors like slant sensors and weight sensors, with the accumulated information shipped off the important metropolitan specialists and neighborhood board individuals. Web of Things considers remote checking and routine adjusting. In the event that this venture is finished, it will help society enormously. The arrival of noxious gases could prompt a blast and the demise of anyone around. Mishaps happen assuming that the sewer vent cover's point at any point changes. Additionally, assuming any break in it, it might break. In this way, appropriate consideration of sewer vents is urgent. Before, occupants needed to report issues with their man-openings or call the corporate office to have them checked. Be that as it may, in the twenty-first 100 years, it isn't not difficult to go straightforwardly and genuinely analyze the man-openings.

As per [7] the Web of Things-based sewer vent well checking framework, which truly screens the state of sewer vent wells. Principal parts of the framework incorporate a lock well cover, caution terminal, server, and client for concentrated checking. Both the lock well cover and the caution terminal get on the well cover's status moving, transfer that information to the application layer through the organization, and alarm the client, who may then make the suitable move. The application layer houses both the server and the client for unified checking. The method is used by the three biggest media communications organizations to shield their ground links. It fills two needs: first, it guards organizations' resources and markets; second, it eliminates the quantity of lives lost and wounds supported because of missing sewer vent covers.

As per [8] utilizing compressive detecting (CS) to reduce requests on the ADC of a Web of Things-based MC checking framework to stretch its administration life. As an option in contrast to the computerized pseudo-irregular number generator (PRNG) frequently utilized in CS-based ADCs, we propose two low-power simple tumultuous oscillators.

Temperature estimations check the viability of the recommended frameworks. This study tackles circuit plan issues and upgrades framework execution for long haul and productive activity of IoT-based MC observing frameworks.

As per [9] The methodology utilize a Guileless Bayes classifier to sharpen down on missing sewer vent areas and remember them. There are fundamentally three sections to this task: 1) Distinguish sewer vents on streets, 2) Track down missing sewer vents, and 3) Transfer new facilitates to a Web of Things stage. MATLAB was what we used to handle the pictures. Utilizing an Arduino Mega and a SIM808 GSM modem, we had the option to gather the directions expected to add the missing sewer vent covers to BLYNK IOT.

As indicated by [10] accomplish the ideal of a "savvy city," which will give cleaner and better open administrations. While planning a "brilliant city," it's vital to consider the subsurface foundation. Watching out for the city's waste frameworks is pivotal to keeping up with its neatness and general wellbeing. Because of the failure of manual checking, waste issues are frequently not tended to until they have declined. The framework is based on a remote sensor organization, with sensor hubs, to manage this large number of issues. The recommended framework is constant, reasonable, Web of-Things-based, and it makes an impression on the administration station in the event that the level of any sewer vent ascends past a specific edge. The public advantages from this strategy, and the risk of mortality for the manual scroungers who clean the underground depletes is highly diminished.

III. OBJECTIVES

1. Plotting coordinates of Manhole on the Google map.
2. To design and develop an approach for Smart Manhole prediction using Machine learning approaches.
3. To develop a model for Smart Manhole detection in case of water level rise during a flood to avoid the life risks for human beings and to avoid vehicle damage also.
4. To explore and analyze proposed modules with machine learning techniques.

IV. PROPOSED METHODOLGY

Gather data from sensors, such as synthetic data and real-time environmental audit data. Data mining approaches such as data preprocessing, data cleaning, data gathering, outlier detection, and data conversion should be used. Following these phases, data was saved into a database called background knowledge, which is used for time testing. All data gathered has been stored in a global database utilizing a connection-oriented design. During testing, we read both testing and training data at the same time. Use Q-Learning to forecast the possibilities using a decision-making system. For water distribution, it uses a motor that turns on and off automatically based on the train policy. Finally provide the analysis accuracy with True positive and false negative of system.

Conclusion

This technology is completely different in terms of monitoring and maintaining the manhole system. In real time, it explains manhole identification. It uses Internet of Things technology to monitor numerous data such as location (GPS) and water level, as well as an ultrasonic sensor in the manhole. These parameters are regularly monitored and updated using GPS. When there is a flood, the water level rises to the level of the road, making drainage manholes impossible to see, which can be dangerous for anyone walking or driving through them.

References

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Copyright

Copyright © 2023 Sumit Sharma, Dr. Saniya Ansari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.