Introduction
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Data management is becoming a highly difficult task in the smart environment due to the tremendous development of massive data. One of the considerable issues in the smart environment is energy theft; it should be focused on reducing by implementing a security system.
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Lacking better optimization in the presence of constraints
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In the existing technology, the optimization technique utilized for analyzing the presence of constraints is not effective; therefore, there is a need for implementing better optimization.
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The performance of the existing technology is not providing better results. So, the need for enhancing performance and efficiency is highly demanded.
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To propose a mechanism to detect the energy theft in the smart cities.
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To propose a machine learning algorithm to make the decision faster and more accurate.
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To analyze the network parameters and their effect on energy theft.
Background of study
Materials and methods
Proposed system for energy theft prevention
Data collection
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Steps 1: multi-Objective prediction framework: The prediction model gauge followed 24 hours through utilizing multi-objective prediction framework. The data can be measured, and it utilizes the expectations, correlation and determining the theft energy burglary circumstance.
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Step 2: Algorithms and Multi-Model Forecasting Systems: Multi-objective diagnosing method utilize distinctive AI strategies and uses the most exact system with condition expectation method for dynamic condition \(sp(n)\). This predictive method Grey Wolf Optimization (GWO), Deep Recurrent Conventional Neural Network (DRCNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). These methods are utilized in steps and deep explanations are given below.
GREY WOLF optimization
Leadership hierarchy
Encircling behavior
Hunting behavior
In a grey wolf package, it is believed that large wolves are capable of hunting prey. Therefore, these lines can be used simultaneously to approach the position of the victim. The hunting strategy of a mathematical representation is given below,
Exploration and exploitation in GWO
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GRU –It has three LSTMs and two gates.
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GRU –It haven't internal memory and output gate.
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GRU – It sequences the faster movement according to lesser parameters.
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Level 1: Procedures: The following steps are taken for this level:This is initiated by Pre-processing the data to accumulative data, and then the next one is done by utilizing the prediction model to predict the data. This is followed by the usage of Mean Absolute Percentage Error (MAPE), and it provides the best model prediction continued by utilizing the MAPE for the updated system and comparing the Absolute Percentage error (APE) for every hour. Then finally, in level 1, if \({sp}_{\left(n\right)}\) = 1. It went to the next stage, and the remaining data goes to the next iteration.
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Level 2: Primary Decision-Making Model: The Simple Moving Average (SMA) can be determined for energy theft detection by the usage of the following levels,a)Level 2.1: Algorithms: The subsequent formulas can be used to determine theft prediction for further stages:Level 2.1 starts up by the Simple Moving Average (SMA) as below given equation.$${SMA}_{(n)}=\frac{1}{n}\sum\nolimits_{i=1}^{n}{l}_{i}$$(29)where, \(n\) represents the hours level in SMA and \(l\) represents the different quantities in the hour list. For the next step is the Maximum SMA difference algorithm given by$${SMA}_{(md)}=\begin{array}{c}max\\ i\in n\end{array}\left|{SMA}_{(i)}-{SMA}_{(i-1)}\right|, where n\ne 0$$(30)where, \({SMA}_{(md)}\) represents the determined difference level in between before and after process of SMA and the level of hour state is given by$${sh}_{\left(n\right)}=\left\{\begin{array}{c}0, if \left({SMA}_{\left(i\right)}-{SMA}_{\left(i-1\right)}\right)\le \frac{3}{4}{SMA}_{\left(md\right)}\\ 1, otherwise \end{array}\right.$$(31)where, \({sh}_{\left(n\right)}\) represents the algorithm for state of hours to take the decision-making condition.b)Level 2.2: Procedures: The ensuing steps are taken at this stage:
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Level 3: Continuous Hour Model:In this, first step determines the Simple Moving Average (SMA) to utilize the whole day and to determine the difference between the SMA calculation for the current hour and the last hour after the measured data for 24 h. The next step is used to determine the extreme difference in algorithm SMA and provide the algorithm in the state of hours. In \({sh}_{\left(n\right)}\)= 1, to start the hour model in the day and else the data went to the next iteration.
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Level 4: Same Day and Hour Model:Here the first process is rearranging data according to the day and hour and then to determine SMA utilized for 4 h continuously when the data transfer from the day and the data differs to the hour. The next process is to develop the alteration between the SMA calculation for the current point and the last point after the measured data in point 5. Next, the Maximum SMA algorithm can be used in difference clarification and proceed to the level of hours algorithm. Then finally, \({sh}_{\left(n\right)}\) = 1 is went to the next stage, else the data goes to the next iteration and next iteration.
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Level 5: Secondary Decision-Making model: In this level, we used to find the history of user's with maximum occasional usages of power.
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Level 5.1: Algorithms: The below mathematical models are utilized in that stage:• The Maximum wattage:$${O}_{(md)}=\begin{array}{c}max\\ i\in n\end{array}f\left|{O}_{(i)}\right|$$(32)where P (md) represents the maximum power. It is taken from the measurement list.• energy theft detection state:$${sets}_{\left(n\right)}=\left\{\begin{array}{c}0, if \frac{3}{4}\left({O}_{\left(md\right)}\le {O}_{n}\le {O}_{(md)}\right)\\ 1, otherwise \end{array}\right.$$(33)where,\({sets}_{\left(n\right)}\) represents the energy theft state in the algorithm in the decision-making condition.
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Level 5.2: Procedures: In this stage utilize several steps taken by the algorithm:
In the initial step to determine the level of energy theft detection from the algorithm and maximum watt and then the next is if, \({sets}_{\left(n\right)}\)= 1 is the possible energy theft, else unexpected usage of consumer for high power consumption. Then finally proceed the data in the next iteration.Once all the preceding phases have been completed, it will move to the next period and repeat the process from stage 1. However, ETPS requires at least 5 weeks of non-malicious data collection at every hour in order for the system to learn from the historical data. This learning will be continually updated for real-time monitoring, and it has the potential to increase its accuracy as additional data is brought in. -
Results and discussion
Parameter | Value |
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Simulator | NS 2 |
Channel | Warless |
Propagation model | TwoRayGround |
Antenna Model | Omni |
Mac Type | 802.11 |
Interface queue type | DropTail/PriQueue |
Dimension | 1000mx1000m |
Queue Length | 2500 bytes |
Routing protocol | AOMDV |
Simulation time | 500 s |
Transmission range | 500 m |
Packet size | 512 bytes |
Packet rate | 20 packets/s |
Nodes | Energy Conservation (Joule) | Network life(ms) | Throughput (bit/second) | Delay(ms) | ||||
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ETPS | Non-ETPS | ETPS | Non-ETPS | ETPS | Non-ETPS | ETPS | Non-ETPS | |
30 | 88 | 108 | 456 | 304 | 76,023 | 70,023 | 0.102563 | 0.112563 |
50 | 177 | 197 | 273 | 182 | 45,614 | 40,614 | 0.061538 | 0.081538 |
70 | 177 | 207 | 195 | 130 | 32,581 | 30,581 | 0.043956 | 0.063956 |
90 | 266 | 296 | 152 | 101 | 25,341 | 20,341 | 0.034188 | 0.054188 |
110 | 355 | 385 | 124 | 82 | 20,733 | 10,733 | 0.027972 | 0.047972 |