International Journal of Smart Electrical Engineering
International Journal of Smart Electrical Engineering (IJSEE) is an international journal founded at the middle of 2012. This journal is published by the IAU, Central Tehran Branch, and it appears four times a year. All papers published by IJSEE are rigorously peer reviewed, free of charge and open access. IJSEE follows the uniform requirements for manuscripts submitted, issued by the Committee on Publication Ethics (COPE) code of conduct for editors. All submitted manuscripts are checked for similarity through a trustworthy software named iThenticate to be assured about its originality and then rigorously peer-reviewed by the international reviewers. Journal Abbreviation: Int. J. Smart Electr. Eng. Journal Title Acronym: IJSEE Journal Owner: Islamic Azad University Publisher: Islamic Azad University, Co-publishing, OICC Press, UK Director-in-Charge: Shahram Javadi 0000-0001-8423-7272 Editor-in-chief: Mahmoodreza Haghifam, PhD
A Brief Review on Classification of Patients with High Blood Pressure Using Machine Learning Algorithms
Blood pressure is the force and pressure that the blood exerts on the walls of the vessels when it flows through the vessels, and it is not a problem on its own. One of the important diseases is high blood pressure, which is caused by various factors. Many patients with high blood pressure (or hypertension) do not control their disease. As a person ages, blood pressure naturally increases. Blood pressure is proportional to dietary and behavioral habits, excitement, and stress, and even changes during the hours of the day and night. Today, the use of machine learning algorithms is widely increasing to classify patients with high blood pressure. This paper conducts a succinct investigation into the application of machine learning algorithms for the classification of individuals with high blood pressure, drawing on a comprehensive analysis of existing research in the field. The machine learning algorithms considered are categorized into three distinct groups: unsupervised learning, supervised learning, and reinforcement learning. While the majority of studies have traditionally concentrated on the analysis of at least one performance criterion, a limited number have ventured into the exploration of multiple criteria.
Copyright (c) 2026 International Journal of Smart Electrical Engineering
Enhancing DOA Estimation: MUSIC Algorithm and Comparative Analysis of TOA, TDOA, and AOA Techniques
DOA estimation plays an important role in array signal processing, and has a wide range of application. In this thesis, I will describe what DOA (Direction of arrival) estimation is, and give a mathematical model of DOA estimation. Then estimate DOA based on the MUSIC algorithm, and give some simulations with MATLAB to simulate what factors can affect the accuracy and resolution of DOA estimation when using the MUSIC algorithm. An angle of arrival (AOA) estimator is presented. Many applications require accurate AOA estimates such as wireless positioning and signal enhancement using space-processing techniques. This paper examines methods of Direction finding TOA, TDOA, and AOA. Experimental results confirm the methods success. Signal routing is very important in telecommunications and we have explained some methods in this article and specifically we have explained an aoa method. We described the results and simulations in the result section. We compared our method with other methods and our method shows a better result.
Copyright (c) 2025 International Journal of Smart Electrical Engineering
Optimal Placement of Substations Based on Economic and Technical Risk Management
Design and expansion of distribution systems seems inevitable in view of the need to satisfy the rise in energy consumption in a technical and economical way. Optimal location, sizing and determining the service area of substations is one of the principle problems in expansion of distribution systems. Also uncertainty is one of the important factors that increase risk of exact decision makings. This paper presents a fuzzy multi-objective model for HV/MV substations planning so that uncertainties are modeled using fuzzy numbers (trapezoidal form). The proposed fuzzy model is based on the risk of economic and technical objectives as well as fuzzy values of investment, operation and loss cost of the substations and primary feeders. This model determines the optimal time, location and size of substations using a multi-objective genetic algorithm (NSGA-II). The proposed model is applied on a typical distribution system to assess the efficiency of the approach.
Copyright (c) 2024 International Journal of Smart Electrical Engineering
Hybrid Fuzzy-PID Application in Boilers to Obtain Optimum Efficiency
Many real time processes have complex, uncertain and nonlinear dynamics. Boilers are nonlinear, time varying, multi-input multi-output (MIMO) systems, whose states generally vary with operating conditions. The major problem in controlling that system is that its drum water pressure and steam flow dynamics include an integrator that results a critically stable behavior. Conventional controller previously used have a set of limitations, e.g. empirical tuning of their parameters when the operating conditions of the controlled process are changed. The application of fuzzy control scheme which is compounded with classic controller (PID) may provide more effective and flexible control of boilers in power stations. This research employing fuzzy logic systems due to their transparency and nonlinear features for controlling dynamics, uncertain and highly nonlinear boiler systems and PID controller due to its fast response
A New Real-Time Pricing Scheme Considering Smart Building Energy Management System
Real-time pricing schemes make the customers to feel the energy price volatility and improve their load profiles. However, these schemes have no significant effect on demand-side uncertainty reduction. In this paper, considering smart grid infrastructures and smart building Energy Management System (EMS), a new real-time pricing scheme is presented to reduce the uncertainty of demand-side. In the proposed method, EMS announces its electric demand during each period of next day to the retailer. The price of energy for the pre-specified amounts is day-ahead price, but any deviation from this amount is settled through spot market price will be determined several minutes before the corresponding period by retailer. Numerical results of an illustrative example are implemented to demonstrate how this scheme makes motivation in customers to reduce their demand uncertainties
Congestion Management in Power Systems Via Intelligent Method
In the deregulated power systems, transmission congestion is one of the significant and main problems of the electrical networks which can cause incremental cost in the energy. This problem has resulted to new challenging issues in different parts of power systems which there was not in the traditional systems or at least had very little importance. Transmission congestion happens when the maximum available power transmission capacity is lower than the consumption side. As congestion happens, the system power losses are increased which can cause problem in the voltage constraints. Therefore, this paper proposes a new method to handle the optimal management and control of congestion problem by the use of distributed generations (DGs). In this regard, the optimal size and location of DGs are investigated using the powerful bacteria foraging algorithm (BFA) as a new intelligence-based optimization technique to solve the congestion problem on the three IEEE 14-bus, 30-bus and 57-bus test systems. The simulation results show the high speed, fast convergence and accurate performance of the proposed algorithm to solve the congestion problem in the system
Robust Model for Networked Control System with Packet Loss
The Networked Control System in modern control widely uses to decrease the implementation cost and increasing the performance. NCS in addition to its advantages is inevitable. Nevertheless they suffer of some limitations and deficiencies. Packet loss is one of the main limitations which affect the control system in different conditions and finally may lead to system instability. For this reason, it is important to model the system properly. In this paper, a new model has been proposed that is very simple and independent from the main system. This model based on Robust Theory structure. Robust theory is a branch of control theory that explicitly deals with uncertainty in its approach to controller design. Robust control methods are designed to function properly so long as uncertain parameters or disturbances are within some (typically compact) set.
A Novel Method for VANET Improvement using Cloud Computing
In this paper, we present a novel algorithm for VANET using cloud computing. We accomplish processing, routing and traffic control in a centralized and parallel way by adding one or more server to the network. Each car or node is considered a Client, in such a manner that routing, traffic control, getting information from client and data processing and storing are performed by one or more server in different bases. The procedure is about each client that receives its situation by GPS system and sends it online to the concerned server via GPRS networks. In order to perform routing and displaying data, each client sends a request to server. All processes and operations will be executable by the software which is inside the server. Finally, we propose an algorithm to work in server and make decision on route selection on the basis of three priorities of traffic, safety and shortest route. This algorithm is represented for the first time. The results indicate that by using this method VANET network is improved. By using this algorithm, we can communicate generally, not regionally, and improvement of processing and accurate statistics is achieved. In fact, the processing in the server is Fuzzy. It means that these priorities are Fuzzy.
Flexibility Enhancement of Energy Delivery Systems through Smart Operation of Micro Energy Hub
In this paper smart operation of micro energy hub is presented. Energy hub system consists of certain energy hubs and interconnectors that are coordinated by energy hub system operator. An energy hub contains several converters and storages to serve demanded services in most efficient manner from available energy carriers. In this paper, the flexibility of energy delivery point is enhanced using micro energy hub concept. Smart micro energy hub is operated in minimum cost considering the price of input energy carriers, the amount of forecasted output energy carriers and the available facilities included in the hub. Regarding this matter, two micro energy hubs with different characteristics are modelled which are equipped with CHP unit, warmer, boiler and heat storage. The effectiveness of the proposed system is validated by running numerical study on a test system.
New Ant Colony Algorithm Method based on Mutation for FPGA Placement Problem
Many real world problems can be modelled as an optimization problem. Evolutionary algorithms are used to solve these problems. Ant colony algorithm is a class of evolutionary algorithms that have been inspired of some specific ants looking for food in the nature. These ants leave trail pheromone on the ground to mark good ways that can be followed by other members of the group. Ant colony optimization uses a similar mechanism to solve the optimization problem. Usually the main difficulties of evolutionary algorithm for solving the optimization problem are: early convergence, loss of population diversity, and placing in a local minimum .Therefore, it needs the way that preserves the variation and tries to avoid trapping in local minimum. In this paper by combining ant colony algorithm and mutation hybrid algorithms that leads to the better solution for optimization of FPGA (Field Programmable Gate Array) placement problem is made. They are different types of swarm intelligence algorithm. After designing the algorithm, its parameters tuning have been done by solving several problems, and then the proposed methods have been compared with the other approaches. The results show that in most problems, the proposed hybrid method is able to obtain better solutions and makes fewer errors.
TC-FLSFCL Provision for Improvement of Distribution System Reliability by TOPSIS based NSGA-II Method
An approach for assignment of the optimal location and tap changer adjustment related to flux-lock type superconducting fault current limiters with tap changer (TC-FLSFCL) is used in this paper by debating the reduction of fault current flowing from each device and enhancement of reliability varying with customer type in a distribution network connected with distribution generation (DG). TC-FLSFCL is a flexible SFCL that it has some preference than previous SFCLs. In this type of SFCL the current limiting characteristics are improved and the fault current limiting level during a fault period can be adjusted by controlling the current in third winding, which also made the magnetic field apply to the high-Tc superconducting (HTSC) element. Three objective functions based on reliability index, reduction of fault current and number of installed TC-FLSFCL is systematized and non-dominated sorting genetic algorithm-II (NSGA-II) style is then formed in searching for best location and tuning of tap changer of TC-FLSFCL to meet the fitness requirements. A decision-making procedure based on technique for order preference by similarity to ideal solution (TOPSIS) is used for finding best compromise solution from the set of Pareto-solutions obtained through NSGA-II. In a distribution network as Bus 4 of Roy Billinton test system (RBTS), comparative analysis of the results obtained from application of the resistive SFCL (RSFCL) and TC-FLSFCL is presented. The results show that optimal placement of TC-FLSFCL than RSFCL can improve reliability index and fault current reduction index with less number
Image Stitching of the Computed Radiology images Using a Pixel-Based Approach
In this paper, a method for automatic stitching of radiology images based on pixel features has been presented. In this method, according to the smooth texture of radiological images and in order to increase the number of the extracted features after quality enhancement of initial radiology images, 45 degree isotropic mask is applied to each radiology image to observe the image details. After this process, we used statistical and heuristic image noise extraction method (SHINE) to acceptably reduce the noise resulting from radiation of alternating X-rays on detector. Pixel point’s features are obtained by selecting maximum or minimum value of the brightness of pixels in certain neighborhood of the resulting radiology images. This algorithm transmutes point’s features to 128 dimensional vector features. In order to identify the segments overlapping in basic radiology images, we specify equivalent vector features of each radiology image using the mathematical properties of the vectors and find the fit geometry transform between pairs features matched by the random sample consensus (RANSAC) algorithm. Finally, resulted motion model is applied to the initial radiology images and we stitch them together in a common surface
Reliability Assessment of Power Generation Systems in Presence of Wind Farms Using Fuzzy Logic Method
A wind farm is a collection of wind turbines built in an area to provide electricity. Wind power is a renewable energy resource and an alternative to non-renewable fossil fuels. In this paper impact of wind farms in power system reliability is investigate and a new procedure for reliability assessment of wind farms in HL1 level is proposed. In proposed procedure, application of Fuzzy – Markov for wind speed modelling and calculating reliability indices by probabilities of generation units by calculating state transition matrix is proposed. Fuzzy logic for this method will be possible to calculate the reliability in accordance with the existing uncertainties. Fuzzy-Markov approach is appropriate for wind farms that have insufficient data of wind speed. In this theory, by state transition probability matrix solution can be obtained probabilities of states of fuzzy- Markov model, by solving state- space differential equation. Finally, the authenticity of approach is shown with some simulation
Decreasing Starting Current for Separate Excited DC Motor using ANFIS Controller
Today, DC motors is still being used globally due to their easy speed controllability. In this article, an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller is designed for DC motors. The main purpose of performing such task is to reduce the DC motor starting current and deleting the ripple current during starting time in considering control parameters such as: rise time, settling time, maximum overshoot and system steady state error. The results have been simulated in MATLAB and a comparison is made between ANFIS controller and PID controller
Optimization of the Microgrid Scheduling with Considering Contingencies in an Uncertainty Environment
In this paper, a stochastic two-stage model is offered for optimization of the day-ahead scheduling of the microgrid. System uncertainties including dispatchable distributed generation and energy storage contingencies are considered in the stochastic model. For handling uncertainties, Monte Carlo simulation is employed for generation several scenarios and then a reduction method is used to decrease the number of scenarios. The scenarios are used in second stage of the stochastic model to check the system security. The amount of spinning reserve and energy are optimized in the first stage by minimizing the total cost of operation. A sample microgrid is used to compare the offered stochastic model with the deterministic one
An Approach for Accurate Edging using Dynamic Membership Functions
In this paper, by means of fuzzy approaches, an accurate method is introduced for edging of color photographs. The difference between our method with other similar methods is the use of a morphological operation to think or thick the obtained edges. In this proposed method, a 3×3 window is dragged on the photo. For each block, 12 point sets will be defined, each including two non-overlapping point sets. Then, a fuzzy membership function will be designed for each point sets according to data of contrast. At last, the range of membership contrast degree for the points of second point sets will be assessed. A comparison of membership degree of second point sets with a pre-defined threshold indicates whether central point of window is bordered or non-bordered. The method was performed on some reference images and the results were compared with common edging methods. The results show that proposed method has a high capability to edge photographs
Effective Feature Selection for Pre-Cancerous Cervix Lesions Using Artificial Neural Networks
Since most common form of cervical cancer starts with pre-cancerous changes, a flawless detection of these changes becomes an important issue to prevent and treat the cervix cancer. There are 2 ways to stop this disease from developing. One way is to find and treat pre-cancers before they become true cancers, and the other is to prevent the pre-cancers in the first place. The presented approach uses precancerous images which are taken from a digital colposcope, and a set of texture and color features is extracted which includes low and high grade SIL (Squamous Interepithelial Lesion ) .After extracting, features are fed to a classifier, which could be KNN,RBF,MLP and Neuro-Fuzzy network and after training effective features are selected using UTA algorithm for each classifier individually. Finally, results come in a comparison table, show that the landa fourteenth, theta-x and together with Neuro-fuzzy classifier have the best overall performance. This approach has an acceptable and simple early diagnosis of cervix cancer and may have found clinical application
Designing Decision Maker in a Smart Home for Energy Consumption Optimization Using Fuzzy Modeling
existed electricity grids deliver produced power to the consumer passing through transmission and distribution grids. According to high losses of these grids in transmission level and inexistence of bilateral interaction for simultaneous information exchange, a concept of smart grids were made by capabilities such as consciously participation of consumers in the smart electricity grids, an amount of energy saving, monitoring and automation of electricity grids. These grids give necessary information about the cost of power consumption to the users and it is possible that subscribers change their consumption power amount based on a balance between requested power and electricity grid. Since smart home in a form of environment where all control systems were designed in a pre-defined logical framework, so it can be introduced as a solution to reduce energy consumption through controlling equipment based on function schedule charts. In this paper, an approach has been presented to optimize energy consumption which decision maker model is designed in order to reduce cost and to increase user utility. Indeed designed home energy management system select optimum duration about how to use the equipment by using fuzzy modelling
Optimal Design of Regulated Capacitor Banks Based on Load Curve Changes Using Intelligent Algorithms
The use of controlled parallel capacitor banks is one of the most common methods to reduce losses and compensate reactive power in industrial distribution networks. Compensating reactive power can achieve goals such as reducing energy losses, peak power reduction, releasing equipment capacity, and improving voltage profile. The main challenge in selecting variable capacitor banks (controlled by regulators) is determining the appropriate number and value of each capacitor and the number of regulator steps. The optimal number and capacity of capacitors in each capacitor bank should be determined in harmony with the load curve of the network. In choosing a capacitor bank, the number and capacity of capacitors should be selected in a way that, while controlling the power factor and voltage range within the permissible range and proportional to the load curve, minimizes costs and the potential drawbacks of capacitor switching. Therefore, after evaluating the capacitor bank's capacity, this article aims to use an intelligent algorithm to select the number and value of capacitors (regulator steps) in a way that optimizes the cost of equipment procurement and the number of capacitor switching operations for the specified load curve and problem constraints. The optimization results using intelligent algorithms are simulated in DigSilent software to evaluate the algorithm's performance and accuracy. This article utilizes DigSilent for load flow and power network analysis and MATLAB for implementing optimization algorithms, and the results of the studies demonstrate good accuracy and reliability.
A New Highly Controllable and Accurate Algorithm for Defuzzifier Circuit Implementation
Defuzzifier circuit is one of the most important parts of fuzzy logic controllers that determine the output accuracy. The Center Of Gravity method (COG) is one of the most accurate methods that so far been presented for defuzzification. In this paper, a simple algorithm is presented to generate triangular output membership functions in the Mamdani method using the multiplier/divider circuit and mirror current CMOS technology. As well as the method simplicity, the method is highly accuracy and controllable in the height, slope of output membership functions, number of rules and number of input/output membership functions cause to use mixed voltages. This method covers each type of the input fuzzifier. The proposed Circuits in this paper are implemented by a standard 0.35um CMOS technology in Hspice software
Economic Evaluation of Optimal Capacitor Placement in Reconfiguration Distribution System Using Genetic Algorithm
Optimal capacitor placement, considering power system loss reduction, voltage profile improvement, line reactive power decrease and power factor correction, is of particular importance in power system planning and control. The distribution system operator calculates the optimal place, number and capacity of capacitors based on two major purposes: active power loss reduction and return on investment maximization. In this paper, the optimization problem of various values of economic amount of reactive power is formulated; then after evaluation of objective function and implementation of optimization algorithm for each value, the optimum capacity of capacitors and their arrangement in load nodes of power system are extracted. Also, using the proposed objective function, the threshold price of reactive power selling can be calculated; thus the investment of capacitor installation will be beneficial for distribution system operator
In this paper, echo cancellation is done using genetic algorithm (GA). The genetic algorithm is implemented by two kinds of crossovers; heuristic and microbial. A new procedure is proposed to estimate the coefficients of adaptive filters used in echo cancellation with combination of the GA with Least-Mean-Square (LMS) method. The results are compared for various values of LMS step size and different types of crossovers which are all satisfactory. Reverse SNR is used as the fitness function. It can estimate an echo path with definite length of impulse response with an adaptive filter with desired length. Results show that the proposed combined GA-LMS method operates more satisfactory than simple GA in terms of the number of generations needed to achieve a particular amount of echo cancellation. Different tests show that GAs running with heuristic crossover converge faster than GAs with microbial crossover. Results are also compared with LMS algorithm. Although LMS is faster, but its solutions are less precise and it diverges in some cases. But our proposed method always converges.
Evaluation of Optimal Fuzzy Membership Function for Wind Speed Forecasting
In this paper, a new approach is proposed in order to select an optimal membership function for inputs of wind speed prediction system. Then using a fuzzy method and the stochastic characteristics of wind speed in the previous year, the wind speed modeling is performed and the wind speed for the future year will be predicted. In this proposed method, the average and the standard deviation of inputs data are calculated. The membership function shape and the domain intervals are evaluated using the variance of system. This technique prevents from trial and error method for defining the shape and domain intervals of optimal membership function and helps to achieve the desired prediction in a quick way. The wind speed is estimated in the fuzzy inference system and simulated with the fuzzy logic. The sensitivity analyses are performed by changing the input parameters and membership functions shape and the results are compared. The results demonstrate that this new prediction method is a fast and applicable method compared to the other methods since the calculated error will be more than the error of this method if the shape and domain interval of membership function are changed
Optimal Path Diagnosis by Genetic Algorithm for NoCs
Nowadays Network-on-Chips is used instead of System-on-Chips for better performance. This paper presents a new algorithm to find a shorter path, and shows that genetic algorithm is a potential technique for solving routing problem for mesh topology in on-chip-network.
Optimal DG Placement for Power Loss Reduction and Improvement Voltage Profile Using Smart Methods
Distributed Generations (DGs) are utilized to supply the active and reactive power in the transmission and distribution systems. These types of power sources have many benefits such as power quality enhancement, voltage deviation reduction, power loss reduction, load shedding reduction, reliability improvement, etc. In order to reach the above benefits, the optimal placement and sizing of DG is significant. In this regard, this paper gets use of the Bacteria Foraging Algorithm (BFA) and Binary Genetic Algorithm (BGA) to investigate the DG placement with the purpose of power loss and voltage deviation reduction. The proposed method is applied on the 33-bus and 69-bus IEEE test systems and the optimal place and size of DGs from the power losses and voltage deviation minimization are assessed. Also, the performance of the above two algorithms are compared with each other.
Congestion Management in Electricity Markets Using Demand Response Programs and Series FACTS Devices
In today’s restructured environment, congestion management plays an essential role in power system operation. Different methods are presented and discussed in this respect for congestion management in short-term and long-term intervals. It is attempted in the present paper to investigate the impact mechanism of FACTS devices and demand response programs together with generation re-dispatch as some facilities from transmission, consumption and generation sides on short-term congestion management of electricity market. For this purpose, Thyristor controlled Series Capacitor (TCSC) representing series FACTS devices and Direct Load Control (DLC) program representing demand response programs in day-ahead power pool market are mathematically modeled and results will be numerically studied and analyzed on the 14-bus IEEE test system.
Optimal Current Meter Placement for Accurate Fault Location Purpose using Dynamic Time Warping
This paper presents a fault location technique for transmission lines with minimum current measurement. This algorithm investigates proper current ratios for fault location problem based on thevenin theory in faulty power networks and calculation of short circuit currents in each branch. These current ratios are extracted regarding lowest sensitivity on thevenin impedance variations of the network structure. Proposed algorithm compares current ratios from offline calculations with corresponding values achieved from measurements with a look-up table system. Best solution based on Dynamic Time Warping (DTW) algorithm is introduced as an output (location of the fault) which includes the line and the distance. Among many current ratios to form look-up table system, the minimum number of them will be extracted by a multi-objective optimization technique using Bees Algorithm (BA). This extraction is based on lowest possible number of buses for instruments installation and required current measurements, estimation accuracy and sensitivity degree from thevenin impedances changes. Accuracy of proposed algorithm is evaluated in a widely used multi-machine network of Western Systems Coordinating Council (WSCC).
Price Spikes Reduction with EDRP Program
With the development of deregulated power systems and increase of prices in some hours of day and increase fuel price, demand response programs were noticed more by customers. demand response consists of a series of activities that governments or utilities design to change the amount or time of electric energy consumption, to achieve better social welfare or some times for maximizing the benefits of utilities or consumers. In this paper we try to evaluate the effect of DR programs especially EDRP on Nodal Marginal Pricing spikes reduction of Restructured Power Systems while occurs events. In order to reach to this target, EDRP program (Emergency Demand Response Program), as common demand response program, is considered. Effects of EDRP program on Nodal Marginal Pricing spikes and operation cost reduction of Restructured Power Systems are investigated using EDRP and economic load model, AC-OPF Formulation and nodal marginal price evaluation techniques. The IEEE 9 bus Test System is used to implement comparisons of impacts with and without EDRP activity on nodal marginal pricing spikes and operation cost reduction. According to obtained results, EDRP using lead to volatility decrease in local marginal price (LMP). It can be said that solving problems such as congestion in transmission lines, power system reliability decrease and volatility decrease in local marginal price at load network peak hours, is impossible without customer interfering in power market. In other hand Consumer participation, makes the power markets more competition and enhance its performance.
Predicting the Next State of Traffic by Data Mining Classification Techniques
Traffic prediction systems can play an essential role in intelligent transportation systems (ITS). Prediction and patterns comprehensibility of traffic characteristic parameters such as average speed, flow, and travel time could be beneficiary both in advanced traveler information systems (ATIS) and in ITS traffic control systems. However, due to their complex nonlinear patterns, these systems are burdensome. In this paper, we have applied some supervised data mining techniques (i.e. Classification Tree, Random Forest, Naïve Bayesian and CN2) to predict the next state of Traffic by a categorical traffic variable (level of service (LOS)) in different short-time intervals and also produce simple and easy handling if-then rules to reveal road facility characteristic. The analytical results show prediction accuracy of 80% on average by using methods
Energy-Saving in Wireless Sensor Networks Based on Optimization Sink Movement Control
A sensor network is made up of a large number of sensors with limited energy. Sensors collect environmental data then send them to the sink. Energy efficiency and thereby increasing the lifetime of sensor networks is important. Direct transfer of the data from each node to the central station will increase energy consumption. Previous research has shown that the organization of nodes in clusters and selection the appropriate cluster head increases the network lifetime. In this study, clustering, determine to cluster heads and the sink movement on the predefined paths has been done with fuzzy method. There are two inputs for the fuzzy model; residual energy of the node and distance from the sink. The output is priority of cluster heads. Sink moves base on the highest priorities on the predefined paths. Then by using genetic algorithm, the number of clusters, shape type and area is optimized. Fitness function is based on network lifetime.