P. Ganesh Kumar

An Effective Clustering Approach with Data Aggregation Using Multiple Mobile Sinks for Heterogeneous WSN

Wireless Sensor Networks (WSNs) mostly uses static sink to collect data from the sensor nodes randomly deployed in the sensor region. In the static sink based approach, the data packets are flooded across the network to reach the mobile base station in multi-hop communication. Due to this, the static sink is inefficient in energy utilization. Recently, mobile sink are used for data gathering, has less energy utilization which in turn increases the network lifetime. Thus, the sink mobility has difficulties in finding the routing path for the data packets. This paper proposes an effective clustering approach with data aggregation using multiple mobile sinks for heterogeneous WSN. The proposed algorithm achieves network lifetime increases with limited energy utilization.Wireless Sensor Networks (WSNs) mostly uses static sink to collect data from the sensor nodes randomly deployed in the sensor region. In the static sink based approach, the data packets are flooded across the network to reach the mobile base station in multi-hop communication. Due to this, the static sink is inefficient in energy utilization. Recently, mobile sink are used for data gathering, has less energy utilization which in turn increases the network lifetime. Thus, the sink mobility has difficulties in finding the routing path for the data packets. This paper proposes an effective clustering approach with data aggregation using multiple mobile sinks for heterogeneous WSN. The proposed algorithm achieves network lifetime increases with limited energy utilization.

Energy Efficient Cluster Based Scheduling Scheme for Wireless Sensor Networks.

The energy utilization of sensor nodes in large scale wireless sensor network points out the crucial need for scalable and energy efficient clustering protocols. Since sensor nodes usually operate on batteries, the maximum utility of network is greatly dependent on ideal usage of energy leftover in these sensor nodes. In this paper, we propose an Energy Efficient Cluster Based Scheduling Scheme for wireless sensor networks that balances the sensor network lifetime and energy efficiency. In the first phase of our proposed scheme, cluster topology is discovered and cluster head is chosen based on remaining energy level. The cluster head monitors the network energy threshold value to identify the energy drain rate of all its cluster members. In the second phase, scheduling algorithm is presented to allocate time slots to cluster member data packets. Here congestion occurrence is totally avoided. In the third phase, energy consumption model is proposed to maintain maximum residual energy level across the network. Moreover, we also propose a new packet format which is given to all cluster member nodes. The simulation results prove that the proposed scheme greatly contributes to maximum network lifetime, high energy, reduced overhead, and maximum delivery ratio.The energy utilization of sensor nodes in large scale wireless sensor network points out the crucial need for scalable and energy efficient clustering protocols. Since sensor nodes usually operate on batteries, the maximum utility of network is greatly dependent on ideal usage of energy leftover in these sensor nodes. In this paper, we propose an Energy Efficient Cluster Based Scheduling Scheme for wireless sensor networks that balances the sensor network lifetime and energy efficiency. In the first phase of our proposed scheme, cluster topology is discovered and cluster head is chosen based on remaining energy level. The cluster head monitors the network energy threshold value to identify the energy drain rate of all its cluster members. In the second phase, scheduling algorithm is presented to allocate time slots to cluster member data packets. Here congestion occurrence is totally avoided. In the third phase, energy consumption model is proposed to maintain maximum residual energy level across the network. Moreover, we also propose a new packet format which is given to all cluster member nodes. The simulation results prove that the proposed scheme greatly contributes to maximum network lifetime, high energy, reduced overhead, and maximum delivery ratio.

Computer aided brain tumor detection system using watershed segmentation techniques

Magnetic Resonance Imaging (MRI) is an advanced medical imaging technique that has proven to be an effective tool in the study of the human brain. In this article, the brain tumor is detected using the following stages: enhancement stage, anisotropic filtering, feature extraction, and classification. Histogram equalization is used in enhancement stage, gray level co‐occurrence matrix and wavelets are used as features and these extracted features are trained and classified using Support Vector Machine (SVM) classifier. The tumor region is detected using morphological operations. The performance of the proposed algorithm is analyzed in terms of sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). The proposed system achieved 0.95% of sensitivity rate, 0.96% of specificity rate, 0.94% of accuracy rate, 0.78% of PPV, and 0.87% of NPV, respectively.

Data aggregation in wireless sensor network using SVM-based failure detection and loss recovery

Abstract In wireless sensor network, data aggregation can cause increased transmission overhead, failures, data loss and security-related issues. Earlier works did not concentrate on both fault management and loss recovery issues. In order to overcome these drawbacks, in this paper, a reliable data aggregation scheme is proposed that uses support vector machine (SVM) for performing failure detection and loss recovery. Initially, a group head, selected based on node connectivity, splits the nodes into clusters based on their location information. In each cluster, the cluster member with maximum node connectivity is chosen as the cluster head. When the aggregator receives data from the source, it identifies node failures in the received data by classifying the faulty data using SVM. Furthermore, a reserve node-based fault recovery mechanism is developed to prevent data loss. Through simulations, we show that the proposed technique minimises the transmission overhead and increases reliability.

Dynamic Detection and Prevention of Clone Attack in Wireless Sensor Networks

Wireless sensor networks are often deployed in adverse environments where an attackers can physically capture some of the nodes, first can reconstruct the programme, and then, can replicate them in large number of clones, easily takeover the control of network. Wireless Sensor Networks highly indispensable for securing network protection. Various kinds of major attacks have been documented in wireless sensor network, till now by many researchers. The Clone attack is a massive harmful attack against the sensor network where large number of genuine replicas are used for illegal entry into a network. Discerning the Clone attack, Sybil attack, sinkhole, and wormhole attack while multicasting is a excellent job in the wireless sensor network. The existing method Randomised, Efficient, and Distributed (RED) has only a scheme of self-healing mechanism, which just verifies the node identities by analyzing the neighbours. A survey was done on a Clone attack on the objective of dissolving this problem. The overview of survey has proposed a combined PVM (position verification method) with MVP (Message Verification and Passing) for detecting, eliminating, and eventually preventing the entry of Clone nodes within the network.Wireless sensor networks are often deployed in adverse environments where an attackers can physically capture some of the nodes, first can reconstruct the programme, and then, can replicate them in large number of clones, easily takeover the control of network. Wireless Sensor Networks highly indispensable for securing network protection. Various kinds of major attacks have been documented in wireless sensor network, till now by many researchers. The Clone attack is a massive harmful attack against the sensor network where large number of genuine replicas are used for illegal entry into a network. Discerning the Clone attack, Sybil attack, sinkhole, and wormhole attack while multicasting is a excellent job in the wireless sensor network. The existing method Randomised, Efficient, and Distributed (RED) has only a scheme of self-healing mechanism, which just verifies the node identities by analyzing the neighbours. A survey was done on a Clone attack on the objective of dissolving this problem. The overview of survey has proposed a combined PVM (position verification method) with MVP (Message Verification and Passing) for detecting, eliminating, and eventually preventing the entry of Clone nodes within the network.

Ergonomic Study on Drudgery Reduction Using Three Tyne Wheel Hoe For Weeding in Tomato

Agriculture has been established as one of the drudgery prone occupation of unorganized sector due to lack of access to improved agricultural technologies. Weeding is a main drudgery prone activity mostly performed by farm women and to resolve this problem Krishi Vigyan Kendra, Kalikiri conducted front line demonstrations on use of three tyne wheel hoe to prove the eficacy of improved weeder in reducing drudgery among women engaged in weeding activity in tomato. Twenty farm women were selected randomly for the study. The main focus was to change the attitude, skill and knowledge towards recommended practices in the work. The women traditionally carried out weeding operation by using tools like hand hoe in squatting and bending position which decrease the work eficiency as time progresses. In the recommended weeding practice i.e. with three tyne wheel hoe, the same amount of work could be done in almost half of the time and work eficiency was increased by 93.8 per cent than normal weeding. Farm women adopted the improved technique as it increased the eficiency to work, reduced the drudgery and helped in avoiding bending or squatting posture. It lessened the exertion and fatigue to make the farm women comfortable.

Predicting shortest path for goods delivery to fair price shops in India

Designing a smart system for delivering goods to various fairprice shops effectively is one of the major goals in the mission of smart city development. In this paper, the smart city environment is treated as a distributed environment for carrying goods across different parts of the city. Hence the widely used Dijkstras algorithm is implemented as MapReduce model using Hadoop environment to compute the sh ortest path for speedy delivery of goods. A front end is designed to carry out interstate and intrastate transportation of items. Distance matrix comprising of 50,100,150…500 cities is considered for the simulation. From the experiment, it is observed that the Dijkstras algorithm implemented in map reduce model meets the objective of delivering the materials to the right destination in minimum time. The performance of the proposed MapReduce Dijkstras algorithm is compared with other popular algorithms like Bellman ford algorithm, Throup algorithm and Gobow algorithm. It is observed that the proposed MapReduce Dijkstras algorithm gives out a reliable shortest path between any source and destination city with less CPU time than the other algorithms.

A high speed proficient power reduction method using clustering based flip flop merging

In todays electronic scenario, low power has grasped attention. The ought for low power has been a main epitome whereby the power dissipation is one of the constraint as with performance and area. In the past, the major apprehension of the VLSI designer are its area, performance, cost, reliability but now, the power obsessive is mainly due to clocking for the circuit that incorporate deeply scaled CMOS technology. Eliminating redundant inverter in merging out one bit flip flops into multi bit causes optimization in wirelength which would probably result in reduction of power. Its been a successful power saving methodology whose dynamic power of clock and total flip flop area is effectively encapsulated. In this paper, an agglomerative clustering algorithm is utilized to obtain nearest clustering for merging flip flops. The multi bit technique is introduced in fir circuit to lessen power as well as area. This satisfies with the above given constraints. According to the experimental results, our algorithm considerably reduces clock power by 21.98% and it is found that total gate count is reduced from 264 to 148 which increases the speed thus reducing the delay.

Comparative Analysis of Polynomial FIR Multirate DSP Applications

Multirate DSP systems in which different parts at different sampling rates. The icrease in the sampling interval results in more time availble for processing. Decimation generally icludes first low-pass filtering the signal and then discarding some of the samples. Zero stuffing is performed in the process of interpolating a discrete-time signal, and then low-pass filtering the resulting signal.The proposed decimator is implemented using MATLAB as standard FIR, Half Band FIR and Nyquist FIR by using the multistage design techniques. The performance of different decimator designs is compared in terms of error and hardware requirements. The results show that the performance of all designs is almost identical but their implementation cost varies greatly in terms of hardware requirements. The hardware saving of 49% to 84% can be achieved by using multistage Nyquist decimator design. Reduced computational work load,lower filter order, lower coefficient sensitivity and noise and less stringent memory requirements.