Environmental Research 203 (2022) 111899

Available online 17 August 20210013-9351/© 2021 Elsevier Inc. All rights reserved.

Internet of things for secure surveillance for sewage wastewater treatment systems

Priyan Malarvizhi Kumar, Choong Seon Hong *

Department of Computer Science and Engineering, Kyung Hee University, South Korea

A R T I C L E I N F O

Keywords: Internet of things Surveillance system Sewage wastewater monitoring system

A B S T R A C T

IoT is a secure communication technology used to transfer data from a physical entity to a device with intelligent analysis tools through a wireless channel. The wastewater treatment method extracts pollutants and transforms them into effluents added to the water supply with minimal environmental effects or recovered directly. The major issue is monitoring the disposal of sewage in the treatment plants. Hence, this paper, Surveillance-based Sewage Wastewater Monitoring System (SSWMS) with IoT, has been proposed for monitoring wastewater treatment and improving water quality. A smart water sensor enabled by IoT monitors water quality, water pressure, and water temperature and quantifies water dynamics to map water flow through the entire treatment facility. The proposed method calculates the wastewater treatment facility’s effectiveness and ensures that chemical releases are maintained below allowable levels. Thus, the experimental results show the improved recycling water quality level is raised to 97.98%, enhancing secure communication and less moisture content when compared to other methods.

1. Introduction to IoT for sewage wastewater treatment systems

The rapidly increasing population produces enormous wastewater, contaminating the streams, ponds, and reservoirs and putting freshwater under demand as a limited and valuable supply. Climatic changes, depletion of ecosystems, inappropriate use of renewable capital, and ecological stresses inseparably connected with reduced streams due to low groundwater rates. Further, it deteriorates reservoirs that have a detrimental effect on water supply and availability (Mortazavi-Naeini et al., 2019). The declining water ecosystem has become a crucial sub-ject that limits urban development, accelerates water scarcity, and af-fects public well-being (Wei et al., 2020).

The Internet of Things (IoT) is built to enhance the protection and quality of wastewater treatment (Sundarasekar et al., 2019). IoT is based on the physical environment’s connection to the Internet, monitored for wastewater management (Drenoyanis et al., 2019). The IoT technologies are implemented with sensors to track sewage water across connected environments, evaluating water and environmental management (AlZu’bi et al., 2019). The control and distribution of water using IoT can overcome major obstacles for efficient water management (Sam-brekar and Rajpurohit, 2019). It provides valuable information about effective water management methods via real-time information and

intervention, making better planning strategic judgments via its detecting, interaction technologies (Manogaran et al., 2019).

The benefits of high financial capacity, high technique, and good management expertise are the extensive wastewater treatment systems in major cities (Galiautdinov, 2021). As the size increases, energy con-sumption is reduced and running cost is reduced. Water quality man-agement can be defined as a water consumption tool for sampling and analyzing (Abbasnia et al., 2019). Water quality provides the framework for water resource protection and ecological resources that receive agricultural wastewater supplies (Spahr et al., 2020).

Correspondingly, in wastewater treatment facilities, the core tech-niques eliminate waste before releasing it into the groundwater (Al-Qerem et al., 2020). For other purposes, such as agriculture and residential wastewater, the IoT platform sensor (Martínez et al., 2020). Wastewater, commonly identified as groundwater, comprises over 99% water and is distinguished by flow volumes, fluid viscosity, physical status, chemical substances, and microbiological species contained within (Jha et al., 2021). New techniques for water flow, energy pre-diction, and the relation between water quantities and ground use may be built and linked to the grid (Hinge et al., 2021).

Traditional monitoring techniques can satisfy water use, efficiency, quantitative sensing, and processing (Qi et al., 2019). Therefore, the

* Corresponding author. E-mail addresses: mkpriyan@khu.ac.kr (P.M. Kumar), cshong@khu.ac.kr (C.S. Hong).

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Environmental Research

journal homepage: www.elsevier.com/locate/envres

https://doi.org/10.1016/j.envres.2021.111899 Received 19 April 2021; Received in revised form 15 July 2021; Accepted 13 August 2021

Environmental Research 203 (2022) 111899

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storage and underlying circumstances indicators can be established in sewage and wastewater treatment plants (Hu et al., 2019). Further, it facilitates efficient control through wireless connectivity and remote monitoring of water strains, contaminants, water data, land use, re-sources, and drains (Gupta and Quamara, 2020). Water management in IoT has a huge ability to provide effective treatment options in massive environmental, structural, and ecological attributes of various water media forms (Vardhan et al., 2019). IoT is important for identifying huge water contaminants and offers important ideas on the contaminant’s effects (Esposito et al., 2021a). Sensitivity analyses to set pollutant levels can therefore be established by the various devices in IoT. Furthermore, the entities would give health warnings based on water management’s actual detecting capability utilizing IoT.

The management of water in IoT is indeed a catalyst of creativity for incorporating the main decision of sensing technologies, including sensors, remote sensing, and others. IoT would have the advantage of controlling the water performance comment by developing new sensing technologies to protect human intake. The possibilities of wireless un-derwater innovation and sensor implementations were investigated in various urban transforming IoT technologies. IoT is a secure networking system for transmitting data via a wireless network from a physical object through a computer with smart analysis software. The wastewater treatment system is often used to remove and process pollutants into effluents, which can be applied immediately to the water source with limited environmental impacts. A safe treatment system for wastewater is built to tackle changes directly. The main problem is the monitoring of waste disposal in the treatment facilities. This study has thus been rec-ommended to monitor treating wastewater and increasing water quality using SSWMS with IoT. Here, the smart water sensor analyses the water quality, water pressure, and water temperature and quantifies the dy-namic water to map water flow through the whole treatment plant. The suggested technique analyses the efficacy of the wastewater treatment plant and makes sure that chemical discharges are kept within permis-sible limits. To overcome all the above challenges, SSWMS is proposed, and the main contribution is described below:

SSWM is proposed to control wastewater treatment and improve the consistency of the water quality with IoT. The IoT-enabled smart water sensor measures water quality, water pressure, and water temperature and quantifies water flow dynamics by a water supply in the whole treatment plant. The experimental results indicate improved water quality for recy-cling, improving safe contact, and less humidity than other approaches.

2. Background study on sewage wastewater treatment systems

This section discusses several works that various researchers have carried out; Xiaoyan Song et al. (2020) developed Management Mode Construction (MMC). MMC focuses on smart data gathering monitoring structures enabled by a smart administration framework to set up essential elements. MMC is promised to inspect the entire process development output through database resources. Several systems have been built to incorporate features such as data acquisition and process analysis, knowledge value evaluation, and structured process control that allow operators to master the operations of data in timely terms to facilitate decision-making.

Morteza Hadipour et al. (2020) discussed Multi-Intelligent Control System (MICS). MICS introduced a creative, functional design, instal-lation, and implementation of technology products in a water pump and a pumping station. MICS is implemented with three controllers: an electric pump operator, a reservoirs water rate, and an alarm control unit. The whole device is controlled by IoT technology which can be handled from anywhere via SMS. In addition to the increased water management model, the device performance and profitability lead to 60% of water being preserved through IoT via MICS.

Chunbo Xiu et al. (Xiu and Dong, 2019) proposed Sewage Treatment Monitoring System (STMS). An IoT-based STMS was implemented to increase the commercial wastewater treatment process’s productivity and the incomplete operational management experiences. The device consists of a sensing element and an implementation framework. The detection layer gathers plant sewage variables via a range of sensors and specifications of appliances. STMS offers an all-around approach and is very reliable in practice for the activity and maintenance of treatment facilities.

Jung-Jeng Su et al. (2020) introduced Smart Farm-Scale Piggery Wastewater Treatment System (SFS-PWT). The traditional wastewater management process for piggery is primarily a manual process that skilled workers can handle well. In SFS-PWT, IoT technologies on a 1000-pig farm are implemented to create an intelligent wastewater treatment system updated by a fully automated self-designed waste-water treatment system. Focused on sensor data before and since water performance measurement data were found to be the extraction output of Biochemical Oxygen Demand (BOD), chemical oxygen demand (COD), and Suspended Solids (SS) for the piggery wastewater, respectively.

Rizqi Putri Nourma Budiarti et al. (2019) discussed Automated Water Quality Monitoring (AWQM) system. AWQM measured the quality of water supplies such as rivers, lakes, and ponds based on water use. Of course, due to various water contamination, water becomes the majority of environmental assets. AWQM ensured the water’s sustain-ability functions as a natural resource, and it is important to control water quality. AWQM with an integrated solution focused on the IoT to measure water levels through sensor-based environmental management systems.

Nitin Asthana et al. (Asthana and Bahl, 2019) proposed Sewage Gas Monitoring And Alert System (SG-AS). To build the SG-AS framework, dynamically positioned IoT surveillance devices and the IoT platform are incorporated. SG-AS includes gas sensor adjustment for industrial applications and the appropriate threshold values determined by sewage plants and installations. SG-AS has been suggested for recording and drawing sample values for sensors on the analytical technique. The recorded values were up to 2.3, 60 ppm, which infringed the show, and the GSM module was used for transmitting alerts to contact numbers fed into the code. This warning was not supported.

Zhang et al. (2021) discussed sensing and control equipment on the Internet of Things (IoT) to dynamically monitor the sewage treatment process’s characteristics to service the online distribution and moni-toring of pharmaceutical equipment. This research presented an appli-cation scenario based on IoT sensor control and maintenance assistance. The paper offers the IoT technique of introducing the MBR system of industrial wastewater treatment, capable of constantly and correctly following system nodes, via the actual application of this technology to an environmental protection treatment company in Wuxi.

Liang, Z., et al. (Liang et al., 2021) explored Excessive greenhouse gas (GHG) emissions that are largely considered to have an essential role in global warming. To tackle climate change challenges, several gov-ernments were created appropriate GHG emission reduction programs. The wastewater treatment systems produce a major source of GHG emissions. However, there has not been a complete quantitative GHG emission assessment methodology for sewage sludge treatment systems due to diverse emission sources, complicated processes, and varying standards.

Abdulqadir, H. R et al. (Abdulqadir et al., 2021) presented a two-tier data management system (DMS) in IoT communication for user traffic smoothing (TS). This approach used a traffic-conscious queuing system and minimal time scheduling methods for regulating the message flow of a request. The decision-making method was based on time-based pro-cessing so that the delay of queuing and request planning may be minimized. The DMS considered the cloud and device properties for classifying request messages to avoid failure in resource mapping.

Esposito, C., et al. (Esposito et al., 2021b) uses a multi-locator

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deployment approach imposing several critical security barriers to reduce initial investment costs to create large-scale solutions for smart cities. Smart cities provide essential applications that require data and functionality to be protected from dangerous and unauthorized usage. It is necessary to equip the supporting platforms with adequate access control mechanisms. However, they are common to use the main method in which a single server keeps identifying and authorization attributes.

Zhao et al. (2021) discuss more and more high-capacity network services needed to increase global network traffic. In terms of service scheduling and system administration, traditional Wavelength Multi-plexing (WDM) networks are unsuitable, so optical transport networks (OTN) are recommended. OTN provides the option to swap electric layers for greater granularity and efficiency.

Based on the survey, SSWMS is to monitor wastewater treatment and improve water quality with IoT. The IoT-enabled smart water sensor measures water quality, water pressure, and water temperature and quantifies water flow dynamics by a water supply in the whole treat-ment plant.

3. Surveillance-based Sewage Wastewater Monitoring System (SSWMS)

Apart from typical wastewater management through timely recy-cling, this research proposes a Surveillance-based Sewage Wastewater Monitoring System (SSWMS) with IoT, which monitors water quality, water pressure, and water temperature. It further quantifies water dy-namics to map water flow across the whole treatment plant. The sug-gested technique evaluates the efficacy of a wastewater treatment facility and guarantees that chemical emissions are kept to a minimum. Deposition of waste into the atmosphere usually decreases the waste content, consequently enhancing the environment’s protection. In re-turn, the government eliminates health damage caused by the envi-ronment’s poisoning and reduces water loss caused by water waste. IoT is a protected networking technology that enables data to be transmitted from a physical entity to a device with smart analysis tools through a Wi- Fi channel. In extracting and turning pollutants into effluents, the wastewater treatment method is added or regenerated to a water supply with minimal environmental consequences. The development of a secure wastewater treatment facility is to address the changes with an immediate challenge. As a result, it has been used to track wastewater treatment and boost the water quality with a lower frequency signal, leading to temperature modulation and increasing signal power. The data encoding schemes allow end-users to transmit on several gates without additional overhead signal expenses for both network sessions and migration from the base. Generally, data/bit rates and transmission range are to be balanced by the propagation factors.

Fig. 1 explores the Basic Operation of sewage wastewater collection.

The sensor node includes the power supplies, memory, transceiver, and battery that supplies and depends on the sensor node’s application. The design of wireless network drainage control systems is examined to di-agnose conditions. The sensors are mounted on the drainage handle and transmitted about drainage status to the cloud (base station) and municipal mobile data. The water levels control conditions of the ventilation, such as humidity and manhole temperature. It even checks whether the drainage manhole if an exposed manhole is open or closed to prevent accidents. The surrounding parameter is collected by a sensor node and energy-efficient equipment based on a surveillance system. When an IoT device is opened, the sensor drop, and the threshold value passes such that it transfers the data to the controller. The base station’s basic goal is to collect sensed data in a memory analyzed in real-time from all sensor nodes. If a sensor reaches the threshold value, it sends a request to the cloud and the designated person to deliver the order to the GSM modem. A drainage zone is a whole region, upstream of the river stage, from the rain or the water that is not absorbed into the ground, draining into the rivers.

IoT’s smart water sensor analyses the water quality, water pressure, and water temperature and quantifies the dynamic water to map the flow of water through the whole treatment plant. The suggested tech-nique analyses the efficacy of the wastewater treatment plant and makes sure that chemical discharges are kept within permissible limits.

Fig. 2 demonstrated the Proposed SSWMS. A dispersed condition is assumed in the field of wastewater treatment in many regions. SSWMS is a dynamic IoT situation where a huge range of sensors is being managed. The sensors are placed on the instruments designed to capture, process, and distribute asset-related data (nitrate and nitrite concentrations). The proposed scenario requires a solution that enables many IoT devices and users to be interconnected, controlled, and scale. The monitoring system has been used to meet these standards in a wastewater treatment plant situation. Integration of the system in the information management middleware for the IoT platform is accomplished. Specifically, data obtained by the platform instruments are used to operate the water quality. The function helps the request be customized and offers the HTTP parser, a web service for the parsing and preprocessing of a special URL. The equipment is used in each WWTP influential and effluent. The monitoring system communicates with the cloud through mobile network using the general radio packet service (GPRS).

Garbage deposits in the atmosphere typically lower the level of waste, and hence the proposed model helps protect the environment. In exchange, the government removes the damage to health resulting from the contamination of the environment and lowers the loss of water due to trash is another advantage. IoT is a secure networking technology that allows data to be exchanged over a Wi-Fi channel from a natural entity to a system with intelligent analytical tools has been considered as a significant limitation.

The implementation of the SSWMS involves five distinct processes: device configuration, information capture and storage, data modeling, information management, data analytics, and visualization. The administrator sends a request from the platform user interface (things manager) to the message broker to develop a product. In the object registry, the message broker establishes the product entity. This entity is used to consolidate assessment tools and objects. Each computer sends to the message broker a thing activate request. This creates and trans-mits a special identification (thingsToken).

The message broker creates an object entity in the object repository for every request received. The administrative officer submits a request for IoT program development to the message broker from the platform user interface. In the object registry, the message broker generates a program entity. The administrator will give as many requests as users log in to the device to the message broker. As a consequence, the object registry contains a variety of user entities. The administrator of the item enables the activity to create anything with a user interface request. As IoT information is published in the message broker each event reports to the user an HTTP parser. Fig. 1. Basic Operation of Sewage Wastewater collection.

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The HTTP parser decodes the message broker’s details to extract the measurement alarms. The maps are rendered in the object registry, where the relationships between system entities such as resource thing products are registered. The requirements are built as services and stored by the object, such as HTTP parser alarms in the device time- series database. Triggers are issued if incidents happen after the HTTP parser’s write operations. The use of stimuli enables alerts to be regis-tered on Twitter, email, short messages, or audio. Finally, the data’s visualization and control are accomplished by a user interface consisting of a web service that utilizes the information contained in the device. The data from IoT devices and the alarms generated by the HTTP Parser are immediately released while the key indicators are determined for each work-frequency analysis procedure. This gives the interface users useful water quality information for decision-making purposes.

The sensor drops, and the threshold level is passed to the controller when the IoT device is opened. The main objective of the base station is to gather sensed data from all sensor nodes in a memory analyzed in real-time. If the threshold value is reached, a sensor transmits an order for the GSM modem to the cloud. The intended individual is one of the effective safety measures to tackle changes immediately. A drainage area is an entire area upstream of the river stage that drains into the rivers from the precipitation or waters that are not absorbed into the earth.

The sensor information collected is decoded, pre-processed, and modeled for collection, interpretation, and extraction of information. This expertise helps decision-making by the environmental and waste-water plant operators’ governments. IoT services are organized to enable searchable, accessible, and used sources associated with devices

Fig. 2. Proposed SSWMS

Fig. 3. Sewage waste and water treatment planning.

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and maximize their contact with the user interface. Fig. 3 explores the Sewage Waste and Water Treatment Planning.

The aim is to produce clean sewages or wastewater and solid waste, such as biosolids and sludge for reuse disposal. Water treatment planning covers physical, biological, and sometimes chemical methods for pollutant removal. Essentially an air-circulating sewage treatment plant promotes bacterial development to damage the drainage system. The proposed method aims to deliver even more clean, eco-friendly waste-water. It is similar to a standard septic tank that has a variety of sig-nificant variations. Depending on their size, wastewater plants can treat agricultural waste from many household dwellings. The liquid is sent into a second chamber during the isolation process, where wastewater treatment systems are different from septic tanks. The sewage treatment process room is equipped with an air pump that flows air across the room to facilitate aerobic bacteria development. These bacteria allow the toxins in the water to be broken down and efficiently purified. The last phase in a water treatment system is a settling tank. This last tank allows the last solids to fall to the tank’s bottom until the effluent is drained into a river. The filtering process has been finished, and the sewage water should be dumped into the atmosphere as thoroughly as possible.

Pump sewer stations are used for moving wastewater upwards to facilitate transportation by gravity. Wastes are fed into a wet well in a sealed underground field. Disposal of wastewater from residential property that does not access the principal wastewater infrastructure is responsible for private pumping centers. A manhole is a wide loop on the road or trail sealed with a removed metal plate. When they try to inspect and scrub the sinks, employees move down into manholes.

The workers climb into manholes as they want to check to clean sinks. A shallow manhole is provided at the start of the wastewater or location, not heavy traffic. It came with a light cover and was considered a chamber of examination. The regular manhole has a dense top cover at 150 cm and is usually quadratic in the sewer line. Deep manhole, covered thickly about 150 cm deeply. The size is gradually increased, and the descent is made simple. A gravity mains sewer is an energy- saving pipe that eliminates unnecessary water using an altitude differ-ential. The term sewer means that waste is not used instead of water, and the term gravity excludes the flow of water by force handles vacuum sewage. Rising mains transport waste from a pumping plant to the point of discharge under pressure, such as a gravitational sewer or sewage treatment works. The rising mains are typically made of plastic or ferrous materials, although some older properties have been built of asbestos.

Fig. 4 explored the Sewage Waste and Water Treatment Process helps

to monitor Water Quality. Chemical wastewater comes from industrial or commercial production methods, including agriculture, and is usually harder to handle than domestic waste. Water treatment is any method that changes the water content and makes it suitable for a particular use. End-use can include drinking, industrial water supplies, irrigation, river- flow storage, water use. In water treatment plants, residues build up sludge. Chemical precipitation, sedimentation, and other primary pro-cesses are responsible for primary sludge, while secondary sludges are permitted as waste biomass with biological treatment. The prices for sludge disposal and wastewater sludge removal are nearly the same. The method of reducing volume known as dewatering usually uses a polymer chemical.

The goal of a sand collector is to remove heavy and rapid sinking of inorganic matter from wastewater to avoid mechanical plant harm or cause problems in wastewater purification. An arenophile gathers sand samples in a range of textures, colors, mineral places. Structures of generators and pumping equipment are pumping stations from one po-sition to the other.

The pump house has an electric motor driving a rotor or a centrifugal pump. The impeller pushes water from the well, known as drive water, through a narrow aperture or jet placed within the box before the impeller. The user’s grid lists containing a loT can be managed in terms of content and filtering the displayed material.

A water filter is a system that eliminates impurities from the water through physical, chemical, and biological processes. Distillation means boiling impure water and the collection and condensation of vapor into a new bottle, leaving much of the solid contaminants. Bio-waste treatment has been used as a secondary treatment to remove residual materials, including solved flotation, after primary treatment by processes. Sedi-ments and contaminants, such as asphalt, are extracted from wastewater during the main water treatment process.

Conditions for diagnosis are investigated for the construction of wireless network drainage systems. The sensors are put on the drainage handle and relayed to the cloud and municipal mobile data on drainage status. Water levels regulate ventilation parameters such as humidity and maintenance hole temperature. It even monitors if the drainage manhole is open or closed to prevent mishaps. A sensor node and energy- saving equipment are used to collect the surrounding parameter using a monitoring system. The sensor drops, and the threshold level is passed to the controller when the IoT device is opened.

Sludge digestion is a chemical mechanism in which organic solids become stable. The sludge then reaches a second tank that transforms the dissolved matter into biogasses and a blend of carbon dioxide and methane with other bacteria. A broad array of water handling processes

Fig. 4. Sewage waste water treatment process and water quality monitoring.

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isolate and concentrate, rather than remove, the toxins found in the water. Both physical and biological therapies produce solid waste or sludge, in which the amounts of their solids are normally between 0.5% and 5%.

Sludge thickness increases solid quality, decreases the consistent water volume, and reduces downstream costs for digesting and dew-atering operations. Gravity thickening, dissolved air flotation, and rotational drum thickening are the most common processes for thick-ening. Destroyed or impaired micro-organisms bring about the end of growth and replication. If microorganisms are not eliminated with drinking water, humans get infectious.

Tertiary treatment is the final washing process, which increases the wastewater’s efficiency before re-using it for environmental discharge. The procedure extracts residual inorganic compounds, nitrogen and phosphorus substances. The Langmuir equation as shown in equation (1):

P=aPnD

1 + aD(1)

The isotherm of Langmuir explains the balance between the two phases, the gas and the adsorbed phases. A Langmuir isotherm is the classical association between solid and liquid concentrations, explaining no change in the sorption phase.

Fig. 5 shows the Langmuir equation and Freundlich equation output. As shown in equation (1) Langmuir equation has been evaluated. The Freundlich equation is an analytical expression of the isothermal water or gases adsorption on a stable surface, obtained from Freundlich’s empirical relationship. The Freundlich equation as described in equation (2):

P=LD1/m (2)

As described in equation (2) Freundlich equation has been explored. In those areas where P is a volume of adsorbed in the balance (mg/ g), Pn

Reflect the optimum potential for the monolayer (mg/g), a is the adsorption energy constant of Langmuir (L/mg), D is the balance (mg/L), L is an approximately adsorption capacity indicator, and 1/ m is the adore pressure. The size of the exponent 1/m, usually provides an example of adsorption favourability and linear fitting of experimental data is proceeded according to equation (3)

DP=

1PnD

+DPn

LhP= Lhl +1m

LhD (3)

As express in equation (3), absorption favourability has been delib-erated. D is a balance, L is an absorption capacity indicator, 1/ m is a adore pressure. The linear fitting of experimental data is proceeding in the above equation.

Qo =Qs + ho + hs − Ket (4)

The transmitter-intake receiver (TR) transmits the pathway O from the transmission antenna can be expressed using the logarithmic scale as the transmission power obtained in air medium (OTA) in equation (4).

Fig. 6 describes the Transmission Power Path at Free Space with Attenuation Loss. As explored in equation (4) transmitting power pathway has been discussed. Where the transmitter’s transmission power is Qs, the transmitting and receiving antennas gains in the an-tenna are expressed as ho and hs, and Ket are seen in the free space medium (explores in dB) over the airway. The complete propagation path is denoted as c, and the contact device operating frequency is expressed in the MHz unit as F (e.g. the interval between the trans-mission of the antenna and the receiver expressed in metres). The log-arithm scale has been used to obtain the transmission power pathway in equation (4).

The effects of the various layer engaged in communications shall be considered for dissemination across the layered medium. The power of the signal obtained by the end-user can therefore be reworked in equation (5):

Qo = − Kn + ho + Qs + hs (5)

A receiver-side transmitting power obtained in equation (5) Where Kn = Ket + Kkand Kk The additional attenuation of the EM wave prop-agation signal propagation through the layered medium is determined by considering space free and current variations in the layered medium’s EM wave distribution. Absorption of receiver side transmitting power has been collected in equation (5). The additional extension wave loss Kk

is the accumulative loss in a complete number of wireless communica-tion layers in the stratified medium.

Kk =∑M− 1

m=0km (6)

The transmission extension loss is calculated in equation (6) where kn

is the attenuation loss in the mth layer for each of the M layers The loss of transmission seen in the basic layer called kn which is primarily dependent on the dielectric permittivity, and in that particular layer on the wavenumber of the medium, which can be expressed as iγ + σ =

βgiven as:

σ =ϕ

ω∈′

2

⎡

⎣

1 +

(∈ ˝

∈′

)2

− 1

√ ⎤

⎦

√√√√√

Fig. 5. Langmuir equation and Freundlich equation output. Fig. 6. Transmission power path at free space with attenuation loss.

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γ =ϕ

ω∈′

2

⎡

⎣

1 +

(∈ ˝

∈′

)2

+ 1

√ ⎤

⎦

√√√√√ (7)

The basic layer of transmission loss characteristic’s has been derived in equation (7). Dielectric permittivity σ and γ wavenumber of the me-dium Where the ϕ, analogous to the 2πe, indicates the angulated fre-quency range magnetic permeability as represented by the ω, the true and imaginary component of the material permittivity is designated as ∈˝ and ∈′ the propagation loss, kn In a given layer in the stratified medium. The propagation loss can be seen in the thickness of the medium layer D, and operating frequency e other medium properties as the electromag-netic wave propagation dynamic dielectric permittivity rely. First, it is essential to scatter the next layer in the sewage overflow rate regulation.

The material’s capacity to holdout, the applied electric charge in various sub-surface layers define its permissiveness. The allowability depends on the material’s property for electromagnet absorption. The charge fluctuates and results in two load components of the current with the electric field oscillation. The loss of heat reflects the energy being dissipated into thermal excitement. In the subsurface layers, the polar-isation of soils and asphalt material is combined with the dielectric characteristics and classified into dipolar, atomical and electrical groups. The frequency often depends on the dielectric movement and polarisation reaction of various carriers. It addresses the dispersion of materials in the subsurface layers hereafter. There are expressions of the content permittivity forecast in equation (8):

∈t = i∈˝t + ∈

′

t (8)

The solar medium dielectric permittivity has been founded in equation (8). The results of an ambitious soil permittivity empirical campaign and medium permittivity spectrum can be defined between 300 and 1300 MHz as seen in the following in equation (9):

∈′

t = 1.15[1 +

ρaρt

(εσ′

t

)+ nγ′

u ∈ ′σ′

z − nu

]1/σ′

(9)

As deliberated in equation (9), soil permittivity and frequency me-dium permittivity spectrum has been obtained. Where ∈t denotes the relative dielectric permittivity of a solar medium, the volumetric water content of the medium is indicated by nu, the water content of the me-dium volume is measured by ρa which represents the compaction of soil material with unit g/cm3, used by ρt, solid soil particles which are 2.65 g/cm3. The σ -component value is 0.65. The value of other terrestrial- based constants γ′′current and γ′ present as experimentally established. The soil components have been expressed in equation (10):

∈′′t =

[nγ′′

u ∈ ′′σ′

ez

]1/σ′

(10)

As obtained in equation (10), soil components have been obtained. When the sand particles in the soil are indicated by t, and the clay particles contained in the soil are indicated by D in their contents. The relative dielectric permittivity of free water is expressed by ∈′

ez and ∈′′

ez

(real and imaginary components). The communications media’s multi-layer configuration in the

sewage overflow control application asphalt layer’s dielectric value must be determined. The formula is given in equation (11)

∈′

=3

4π∈0 − 1∈0 + 2

(11)

Equation (11) shows the asphalt layer’s dielectric value. The values of the electrical conductivity of asphalt often increase as the frequency is increased with security. Dipolar polarisation induces the effect of dielectric consistency on the wavelength. There are already aromatic and asphalt compounds found in the asphalt material. It depends, however, on the electric field applied as well.

The gravel base aggregates consist of materials such as bricks, rocks,

sand and air vacuums, which are less structured. The dispersion in such layers is dependent on the wavelength particle and size due to this semi- random organisation. The effective permissiveness of gravel (consistent with a layer in which mainstay particles, caves and soil particles air voids are organized jointly with different distribution properties) is calculated in equation (12):

i∈0 − 1∈0 + 2∈′ (12)

As calculated in equation (12), the effective permittivity of the gravel aggregate layer volume has been determined where i is the volume solid percentage content. The proposed SSWMS has been proposed to achieve a high-security level, enhance water quality detection, high flow rate, low signal to noise ratio, route failure rate, temperature modulation, and signal strength.

4. Results and discussion

SSWMS has been validated based on the security level, water quality level and the data distribution factors between the devices. The re-quirements of SSWMS is based on the distribution of various supported distributed factors. SSWMS allows simultaneous interaction among several separate devices on various channels and distributed variables. The data encoding strategies enable end-users to transmit on several gates and move from the base without additional signal overhead costs for both Network Session. The propagation factors provide a balance of data/bit rates and the scope of transmissions. The performance matrices of SSWMS is shown in Table .1.

The route failure rate is one of the performance metrics parameters in residential and underground IoT mobile telecommunications for wastewater tracking. The possibilities of wireless underwater technol-ogies and detector applications were explored in various urban disrup-tive IoT applications. Assessments of route failure models are carried out in various platforms on multiple levels. Such results may profit from the construction of sewage and wastewater discharge control schemes. The route failure rate of SSWMS is shown in Table .2.

The main objective of modern research is to ensure that effective SSWMS are accessible and required to track and regulate them in actual environments. Flow rate can be evaluated by SSWMS and possible by various means, particularly in calculating the pipe flow rate and valve efficiency and introducing reliable measurement instruments. The SSWMS is used to calculate the distribution rate and includes recording shift in the wastewater level in an IoT based monitoring system during the sewage operations. WMS is performed across an initial few seconds in a cycle and used to measure the piston outflow implicitly. The flow rate of SSWMS is shown in Table 3.

If a maximum of data packages is sent through the Gateway of IoT devices, there must be a successful data flow. Further, the separation of statistical features into growing areas from the portal shows the effects of range, ground terrain, and signal output barriers. The increase of data flow leads to more strength of the signal with a lower noise ratio. The mean signal-to-noise ratio (SNR) of the received signal intensity could be less. The signal to noise ratio of SSWMS is shown in Fig. 7.

Packages are authenticated end-to-end with particular specifications using the wide range of network data packet and the database client unit. The encoded message is sent across the atmosphere, and encrypted packets are sent to the database server with the related device node’s

Table 1 The performance matrices of SSWMS.

Parameters (%) SSWMS

Security level 97.67 Water Quality Detection 96.89 Flow rate 94.77 Signal to noise ratio 7.1

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indirect access points. SSWMS prevents packet detectors from reading sensitive information over the air and reducing the cyber-attack area channels for bad attackers. The security level of SSWMS is shown in Table 4.

Sewage treatment is a natural, mechanical or biochemical manage-ment process such that the sewage can fulfil the requisite water quality until discharging or recycling is specified into the aquatic environment. The principle of sewage treatment processing applies to wastewater treatment facilities that could be a unique manufacturing process. The process is indeed applicable to wastewater treatment plants as the principle of cleaner development. Saving fuel, heat, and different chemicals in production processes reduce emissions of environmental gases such as carbon dioxide and nitric oxides, reduce sludge output, and improve waste treatment systems’ falseness. The water quality of the

sewage treatment plant is therefore greatly enhanced. The water quality of SSWMS is shown in Fig. 8.

The effect of the temperature modulation on the loss of surface spreading leads to reduced quality of water. The route loss rises to certain decibels when the water’s intensity varies with a certain amount of shift indications. Consequently, in city infrastructure security testing, IoT and the wireless transmission network must be built in various sit-uations by considering climate changes on water quality media. The temperature modulation is shown in Table 5.

The trajectory loss with a distance transition is used to measure the signal strength of water. For distances of up to a few kilometres, the route failure is less than the particular strength. The length measure-ment of received signal strength with water quality is illustrated in Fig. 8. The signal strength and the water quality declines by the range that is observed. The signal strength of SSWMS is shown in Table 6.

The proposed method achieves the highest water quality level when compared to other existing Multi-Intelligent Control System (MICS), Smart Farm-Scale Piggery Wastewater Treatment System (SFS-PWT), Sewage Treatment Monitoring System (STMS).

The trial findings therefore show that the quality level of water recycled for safe contact and humidity improved to 97.98% compared to previous ways. With signal strength of 81.3%, the suggested approach reaches the greatest degree of water quality at 94.2%. In addition, 90.6% of sewage treatment with safety ratio.

5. Conclusion

This paper presents SSWMS to track wastewater treatment and improve water quality with IoT. It is a secure networking technology for

Table 2 The route failure rate of SSWMS.

Number of Devices MICS STMS SFS-PWT SSWMS

10 51.3 72.3 70.1 32.1 20 52.6 75.2 70.2 30.2 30 53.8 71.3 70.9 35.1 40 54.7 69.2 78.2 33.6 50 55.6 66.2 74.1 34.1 60 52.3 62.11 75.6 39.2 70 51.4 90.3 72.3 33.5 80 50.2 62.1 74.1 35.1 90 50.8 60.1 72.3 33.9 100 51.3 72.3 70.1 32.1

Table 3 The flow rate of SSWMS.

Number of Devices MICS STMS SFS-PWT SSWMS

10 65.2 80.2 84.2 90.2 20 66.3 80.8 84.3 91.3 30 67.1 80.9 84.4 94.5 40 68.3 81.4 86.3 93.3 50 69.2 82.3 81.2 93.6 60 70.3 83.5 86.3 94.5 70 72.3 81.2 84.1 98.2 80 76.8 83.2 85.5 92.1 90 77.9 81.4 86.6 96.5 100 79.2 83.2 87.7 94.2

Fig. 7. The SNR of SSWMS

Table 4 The security level of SSWMS.

Number of Devices MICS STMS SFS-PWT SSWMS

10 65.2 51.3 65.2 90.2 20 66.3 52.6 66.2 98.3 30 67.1 53.8 67.1 97.5 40 68.3 54.7 68.2 96.4 50 69.2 55.6 65.3 94.2 60 70.3 52.3 64.2 92.9 70 72.3 51.4 64.3 91.2 80 76.8 50.2 65.2 95.7 90 77.9 50.8 62.3 93.1 100 79.2 55.2 63.6 90.6

Fig. 8. Water Quality obtained by SSWMS.

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transmitting data via a wireless network from a physical object through a computer with smart analysis software. The wastewater treatment system is used to remove and process pollutants into effluents that can either be applied to or recuperated directly to the water source with minimal environmental effects. A safe treatment system for wastewater is built to tackle changes immediately. The IoT-enabled smart water sensor measures water quality, water pressure and water temperature and quantifies water flow dynamics by a water supply in the whole treatment plant. The proposed process measures the wastewater treat-ment plant’s efficiency and guarantees that chemical releases remain below permissible concentrations. Therefore, the experimental results indicate that the recycling water quality level has increased to 97.98% for enhancing safe contact and less humidity than other approaches.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program(IITP-2021-0-00742) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation).

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Table 5 The temperature modulation of SSWMS.

Number of Devices SSWMS STMS SFS-PWT MICS

10 56.3 65.2 80.2 90.2 20 54.1 66.3 80.8 91.3 30 51.2 67.1 80.9 94.5 40 58.2 68.3 81.4 93.3 50 59.8 69.2 82.3 93.6 60 62.3 70.3 83.5 94.5 70 62.1 72.3 81.2 98.2 80 61.8 76.8 83.2 92.1 90 63.2 77.9 81.4 96.5 100 62.1 79.2 83.2 94.2

Table 6 The signal strength of SSWMS.

Number of Devices SSWMS STMS SFS-PWT MICS

10 52.3 62.3 75.2 80.2 20 54.2 64.3 76.4 83.2 30 56.1 63.9 74.1 84.6 40 53.2 65.8 73.2 86.2 50 52.3 66.7 76.5 81.3 60 54.7 62.3 72.5 88.2 70 51.4 66.6 77.1 84.3 80 58.2 64.1 74.2 86.4 90 55.4 62.5 71.9 89.7 100 56.3 61.8 76.2 81.3

P.M. Kumar and C.S. Hong