Other affiliations: Pondicherry Engineering College
Bio: A. Rajesh is an academic researcher from VIT University. The author has contributed to research in topic(s): Wireless sensor network & Node (networking). The author has an hindex of 7, co-authored 40 publication(s) receiving 336 citation(s). Previous affiliations of A. Rajesh include Pondicherry Engineering College.
TL;DR: The proposed hybrid HSA–PSO algorithm shows an improvement in residual energy and throughput by 83.89% and 29.00%, respectively, than the PSO algorithm and exhibits high search efficiency of HSA and dynamic capability of PSO that improves the lifetime of sensor nodes.
Abstract: Energy efficiency is a major concern in wireless sensor networks as the sensor nodes are battery-operated devices. For energy efficient data transmission, clustering based techniques are implemented through data aggregation so as to balance the energy consumption among the sensor nodes of the network. The existing clustering techniques make use of distinct Low-Energy Adaptive Clustering Hierarchy (LEACH), Harmony Search Algorithm (HSA) and Particle Swarm Optimization (PSO) algorithms. However, individually, these algorithms have exploration-exploitation tradeoff (PSO) and local search (HSA) constraint. In order to obtain a global search with faster convergence, a hybrid of HSA and PSO algorithm is proposed for energy efficient cluster head selection. The proposed algorithm exhibits high search efficiency of HSA and dynamic capability of PSO that improves the lifetime of sensor nodes. The performance of the hybrid algorithm is evaluated using the number of alive nodes, number of dead nodes, throughput and residual energy. The proposed hybrid HSA–PSO algorithm shows an improvement in residual energy and throughput by 83.89% and 29.00%, respectively, than the PSO algorithm.
TL;DR: This study is applying Naive Bayes data mining classifier technique which produces an optimal prediction model using minimum training set which predicts attributes such as age, sex, blood pressure and blood sugar and the chances of a diabetic patient getting heart disease.
Abstract: objective of our paper is to predict the chances of diabetic patient getting heart disease. In this study, we are applying Naive Bayes data mining classifier technique which produces an optimal prediction model using minimum training set. Data mining is the analysis step of the Knowledge Discovery in Databases process (KDD). Data mining involves use of techniques to find underlying structures and relationships in a large database. Diabetes is a set of related diseases in which body cannot regulate the amount of sugar specifically glucose (hyperglycemia) in the blood. The diagnosis of diseases is a vital role in medical field. Using diabetic"s diagnosis, the proposed system predicts attributes such as age, sex, blood pressure and blood sugar and the chances of a diabetic patient getting a heart disease.
TL;DR: The proposed DESA reduces the number of dead nodes than Low Energy Adaptive Clustering Hierarchy (LEACH) by 70%, Harmony Search Algorithm (HSA), modified HSA by 40% and differential evolution by 60%.
Abstract: The major concerns in Wireless Sensor Networks (WSN) are energy efficiency as they utilize small sized batteries, which can neither be replaced nor be recharged Hence, the energy must be optimally utilized in such battery operated networks One of the traditional approaches to improve the energy efficiency is through clustering In this paper, a hybrid differential evolution and simulated annealing (DESA) algorithm for clustering and choice of cluster heads is proposed As cluster heads are usually overloaded with high number of sensor nodes, it tends to rapid death of nodes due to improper election of cluster heads Hence, this paper aimed at prolonging the network lifetime of the network by preventing earlier death of cluster heads The proposed DESA reduces the number of dead nodes than Low Energy Adaptive Clustering Hierarchy (LEACH) by 70%, Harmony Search Algorithm (HSA) by 50%, modified HSA by 40% and differential evolution by 60%
TL;DR: In this paper, a dual-polarized ultra-wideband (UWB) MIMO antenna is proposed, which consists of an F-shaped monopole which band rejects the IEEE 802.11ac frequency band from 5.1 to 5.95 GHz with microstrip line feeding.
Abstract: We propose a novel, compact, dual-polarized ultra-wideband (UWB)–multiple-input multiple-output (MIMO) antenna, which consists of an F-shaped monopole which band rejects the IEEE 802.11ac frequency band from 5.1 to 5.95 GHz with microstrip line feeding. The suppression of the inevitable mutual coupling is achieved by using techniques such as orthogonal polarization, defected ground structure, and metamaterials. A split-ring resonator is placed between the antenna elements to reduce the coupling. The antenna has wideband impedance matching with S11 < −10 dB in the UWB frequency range from 3.1 to 10.6 GHz and has a low mutual coupling with |S21| < −20 dB. The antenna has very low envelope correlation coefficient with values equal to zero and low capacity loss value of 0.358, which proves that the MIMO antenna shows good diversity performance. The antenna has a bandwidth of 8.6 GHz and a fractional bandwidth of 33% in the lower band and 56% in the higher band.
01 Sep 2019-Evolutionary Intelligence
TL;DR: A self-adaptive mutation factor cross-over probability based differential evolution (SA-MCDE) algorithm is proposed for LSL problem to improve convergence speed and improve localization accuracy with high convergence speed.
Abstract: Node localization or positioning is essential for many position aware protocols in a wireless sensor network. The classical global poisoning system used for node localization is limited because of its high cost and its unavailability in the indoor environments. So, several localization algorithms have been proposed in the recent past to improve localization accuracy and to reduce implementation cost. One of the popular approaches of localization is to define localization as a least square localization (LSL) problem. During optimization of LSL problem, the performance of the classical Gauss–Newton method is limited because it can be trapped by local minima. By contrast, differential evolution (DE) algorithm has high localization accuracy because it has an ability to determine global optimal solution to the LSL problem. However, the convergence speed of the conventional DE algorithm is low as it uses fixed values of mutation factor and cross-over probability. Thus, in this paper, a self-adaptive mutation factor cross-over probability based differential evolution (SA-MCDE) algorithm is proposed for LSL problem to improve convergence speed. The SA-MCDE algorithm adaptively adjusts the mutation factor and cross-over probability in each generation to better explore and exploit the global optimal solution. Thus, improved localization accuracy with high convergence speed is expected from the SA-MCDE algorithm. The rigorous simulation results conducted for several localization algorithms declare that the propose SA-MCDE based localization has about (40–90) % more localization accuracy over the classical techniques.
TL;DR: An improved version of the energy aware distributed unequal clustering protocol (EADUC), by electing cluster heads considering number of nodes in the neighborhood in addition to the above two parameters, outperforms the existing protocols in terms of network lifetime in all the scenarios.
Abstract: In this paper, an improved version of the energy aware distributed unequal clustering protocol (EADUC) is projected. The EADUC protocol is commonly used for solving energy hole problem in multi-hop wireless sensor networks. In the EADUC, location of base station and residual energy are given importance as clustering parameters. Based on these parameters, different competition radii are assigned to nodes. Herein, a new approach has been proposed to improve the working of EADUC, by electing cluster heads considering number of nodes in the neighborhood in addition to the above two parameters. The inclusion of the neighborhood information for computation of the competition radii provides better balancing of energy in comparison with the existing approach. Furthermore, for the selection of next hop node, the relay metric is defined directly in terms of energy expense instead of only the distance information used in the EADUC and the data transmission phase has been extended in every round by performing the data collection number of times through use of major slots and mini-slots. The methodology used is of retaining the same clusters for a few rounds and is effective in reducing the clustering overhead. The performance of the proposed protocol has been evaluated under three different scenarios and compared with existing protocols through simulations. The results show that the proposed scheme outperforms the existing protocols in terms of network lifetime in all the scenarios.
01 Jan 2012
TL;DR: The data mining methods and techniques will be explored to identify the suitable methods and Techniques for efficient classification of Diabetes dataset and in mining useful patterns.
Abstract: Medical professionals need a reliable prediction methodology to diagnose Diabetes. Data mining is the process of analysing data from different perspectives and summarizing it into useful information. The main goal of data mining is to discover new patterns for the users and to interpret the data patterns to provide meaningful and useful information for the users. Data mining is applied to find useful patterns to help in the important tasks of medical diagnosis and treatment. This project aims for mining the relationship in Diabetes data for efficient classification. The data mining methods and techniques will be explored to identify the suitable methods and techniques for efficient classification of Diabetes dataset and in mining useful patterns.
TL;DR: Comparison studies of tracking accuracy and speed of the Hybrid SCA-PSO based tracking framework and other trackers, viz., Particle filter, Mean-shift, Particle swarm optimization, Bat algorithm, Sine Cosine Algorithm (SCA) and Hybrid Gravitational Search Al algorithm (HGSA) is presented.
Abstract: Due to its simplicity and efficiency, a recently proposed optimization algorithm, Sine Cosine Algorithm (SCA), has gained the interest of researchers from various fields for solving optimization problems. However, it is prone to premature convergence at local minima as it lacks internal memory. To overcome this drawback, a novel Hybrid SCA-PSO algorithm for solving optimization problems and object tracking is proposed. The P b e s t and G b e s t components of PSO (Particle Swarm Optimization) is added to traditional SCA to guide the search process for potential candidate solutions and PSO is then initialized with P b e s t of SCA to exploit the search space further. The proposed algorithm combines the exploitation capability of PSO and exploration capability of SCA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 23 classical, CEC 2005 and CEC 2014 benchmark functions. Statistical parameters are employed to observe the efficiency of the Hybrid SCA-PSO qualitatively and results prove that the proposed algorithm is very competitive compared to the state-of-the-art metaheuristic algorithms. The Hybrid SCA-PSO algorithm is applied for object tracking as a real thought-provoking case study. Experimental results show that the Hybrid SCA-PSO-based tracker can robustly track an arbitrary target in various challenging conditions. To reveal the capability of the proposed algorithm, comparative studies of tracking accuracy and speed of the Hybrid SCA-PSO based tracking framework and other trackers, viz., Particle filter, Mean-shift, Particle swarm optimization, Bat algorithm, Sine Cosine Algorithm (SCA) and Hybrid Gravitational Search Algorithm (HGSA) is presented.
TL;DR: Experimental investigation on abrasive water jet machining (AWJM) process for the machining of material AA631-T6 using the Taguchi methodology shows that both the optimization techniques and Taguchi method are the effective tools in optimizing the process parameters of the AWJM process.
Abstract: In the last decade, numerous new materials are rapidly emerging and developed; it creates considerable interest in the researcher to search out the optimum combination of machining parameters during machining of these materials using advanced machining processes (AMP). In this work, an experimental investigation is carried out on abrasive water jet machining (AWJM) process for the machining of material AA631-T6 using the Taguchi methodology. Parameters such as transverse speed, standoff distance and mass flow rate are considered to obtain the influence of these parameters on kerf top width and taper angle . Regression models have been developed to correlate the data generated using experimental results. Seven advanced optimization techniques, i.e., particle swarm optimization, firefly algorithm, artificial bee colony, simulated annealing, black hole, biogeography based and non-dominated sorting genetic algorithm are attempted for the considered AWJM process. The effectiveness of these algorithms is compared and found that bio-geography algorithm is performing better compared to other algorithms. Furthermore, a non-dominated set of solution is obtained to have diversity in the solutions for the AWJM process. The result obtained using the Taguchi method and optimization techniques are confirmed using confirmation experiments. Confirmatory experiments show that both the optimization techniques and Taguchi method are the effective tools in optimizing the process parameters of the AWJM process.
TL;DR: The paper analyzes in detail the node architecture, focusing on the energy saving technologies and policies, with the purpose of extending the batteries lifetime by reducing power consumption, through hardware and software optimization.
Abstract: This paper focuses on the realization of an Internet of Things (IoT) architecture to optimize waste management in the context of Smart Cities. In particular, a novel typology of sensor node based on the use of low cost and low power components is described. This node is provided with a single-chip microcontroller, a sensor able to measure the filling level of trash bins using ultrasounds and a data transmission module based on the LoRa LPWAN (Low Power Wide Area Network) technology. Together with the node, a minimal network architecture was designed, based on a LoRa gateway, with the purpose of testing the IoT node performances. Especially, the paper analyzes in detail the node architecture, focusing on the energy saving technologies and policies, with the purpose of extending the batteries lifetime by reducing power consumption, through hardware and software optimization. Tests on sensor and radio module effectiveness are also presented.