Vinod Kumar Jain

Correlation feature selection based improved-Binary Particle Swarm Optimization for gene selection and cancer classification

Abstract DNA microarray technology has emerged as a prospective tool for diagnosis of cancer and its classification. It provides better insights of many genetic mutations occurring within a cell associated with cancer. However, thousands of gene expressions measured for each biological sample using microarray pose a great challenge. Many statistical and machine learning methods have been applied to get most relevant genes prior to cancer classification. A two phase hybrid model for cancer classification is being proposed, integrating Correlation-based Feature Selection (CFS) with improved-Binary Particle Swarm Optimization (iBPSO). This model selects a low dimensional set of prognostic genes to classify biological samples of binary and multi class cancers using Naive–Bayes classifier with stratified 10-fold cross-validation. The proposed iBPSO also controls the problem of early convergence to the local optimum of traditional BPSO. The proposed model has been evaluated on 11 benchmark microarray datasets of different cancer types. Experimental results are compared with seven other well known methods, and our model exhibited better results in terms of classification accuracy and the number of selected genes in most cases. In particular, it achieved up to 100% classification accuracy for seven out of eleven datasets with a very small sized prognostic gene subset (up to

Location Estimation Based on Semi-Supervised Locally Linear Embedding (SSLLE) Approach for Indoor Wireless Networks

Location aware computing is popularized and location information use has important due to huge application of mobile computing devices and local area wireless networks. In this paper, we have proposed a method based on Semi-supervised Locally Linear Embedding for indoor wireless networks. Previous methods for location estimation in indoor wireless networks require a large amount of labeled data for learning the radio map. However, labeled instances are often difficult, expensive, or time consuming to obtain, as they require great efforts, meanwhile unlabeled data may be relatively easy to collect. So, the use of semi-supervised learning is more feasible. In the experiment 101 access points (APs) have been deployed so, the RSS vector received by the mobile station has large dimensions (i.e. 101). At first, we use Locally Linear Embedding to reduce the dimensions of data, and then we use semi-supervised learning algorithm to learn the radio map. The algorithm performs nonlinear mapping between the received signal strengths from nearby access points and the user’s location. It is shown that the proposed scheme has the advantage of robustness and scalability, and is easy in training and implementation. In addition, the scheme exhibits superior performance in the nonline-of-sight (NLOS) situation. Experimental results are presented to demonstrate the feasibility of the proposed SSLLE algorithm.

Performance Analysis of Received Signal Strength Fingerprinting Based Distributed Location Estimation System for Indoor WLAN

Location Estimation has become important for many applications of indoor wireless networks. Received Signal Strength (RSS) fingerprinting methods have been widely used for location estimation. Most of the location estimation system suffers with the problem of scalability and unavailability of all the access points at all the location for large site. The accuracy and response time of estimation are critical issues in location estimation system for large sites. In this paper, we have proposed a distributed location estimation method, which divide the location estimation system into subsystems. Our method partitions the input signal space and output location space into clusters on the basis of visibility of access points at various locations of the site area. Each cluster of input signal space together with output location subspace is used to learn the association between RSS fingerprint and their respective location in a subsystem. We have performed experimentation on two RSS dataset, which are gathered on different testbeds, and compared our results with benchmark RADAR method. Experimental results show that our method provide better results in terms of accuracy and response time in comparison to centralized systems, in which a single system is used for large site.

Recurrent Grid Based Voting Approach for Location Estimation in Wireless Sensor Networks

With the advent of location aware sensor applications, precise location discovery has become an important technology in Wireless Sensor Networks. Inter Peer communication in the sensor network has been modeled as the graph with constraints defined in terms of proximity. Recurrent Grid Based Voting Approach (RGBV) has been introduced to estimate the location of unknown nodes in the network. Voting Scheme is adopted on an iterative basis for the nodes. For each node, region of interest (ROI) with the maximum votes is figured out as the collection of two-dimensional points after recursive voting. Convex hull is generated from this set of points to frame the actual ROI. Additionally, minimum bounding rectangle algorithm has been applied to figure out the centroid of the region. The centroid thus estimated is the required location of unknown node. Our methodology is shown to have fast convergence with low estimation error, even for complex networks. The simulation results demonstrate that the proposed method is promising for the current generation of sensor networks.

Fuzzy based energy efficient sensor network protocol for precision agriculture

Abstract Today’s, wireless sensor network have become a more emerging technology in precision agriculture. This paper proposes a novel approach based on sensor network technology to control the irrigation in agricultural field automatically. All sensor nodes deployed in the field, continuously sense soil temperature, soil moisture and air humidity of the agricultural field and transmit this information to base station only when the user defined periodic timer or sensed attributes values exceed desired threshold. In the proposed routing protocol, region-based static clustering approach is used to provide efficient coverage over entire agricultural area and threshold sensitive hybrid routing is used for transmitting sensed data to base station. The proposed protocol uses fuzzy logic technique to select the best cluster head among other sensor nodes in a particular round which minimizes the energy consumption of nodes in every data transmission period. The proposed energy-efficient protocol is compared with existing benchmark EEHC, DEEC, DDEEC and RBHR routing protocols. The analysis and experimental results show a significant decrement in data transmission rate due to user-defined transmission thresholds. The balanced use of fuzzy logic technique, static clustering and hybrid routing approach efficiently reduce energy consumption of sensor nodes in every data transmission round and prolongs the overall network lifetime by 173.16%, 149.22%, 99.49% and 47.39% as compared to EEHC, DEEC, DDEEC and RBHR protocol respectively.

Distributed location estimation system using WLAN received signal strength fingerprints

Location Estimation has become important for many applications of indoor wireless networks. Received Signal Strength (RSS) fingerprinting methods have been widely used for location estimation. The accuracy and response time of estimation are critical issue in location estimation system. Most of the location estimation system suffers with the problem of scalability and unavailability of all the access points at all the location for large site. In this paper, we have proposed a distributed location estimation method, which divide the location estimation system into subsystems. Our method partition the input signal space and output location space into clusters on the basis of visibility of access points at various locations of the site area. Each cluster of input signal space together with output location subspace is used to learn the association between RSS fingerprint and their respective location in a subsystem. We have compared our results with benchmark RADAR method. Experimental results show that our method provide better results in terms of accuracy and response time in comparison to centralized systems, in which a single system is used for large site.

Distributed Growing Radial Basis Function Neural Networks for Location Estimation in Indoor Wireless Networks

Location aware computing is popularized and use of location information has become important due to huge application of mobile computing devices and local area wireless networks. To serve us well, mobile computing applications need to know the physical location of things so that they can record them and report them to us. Therefore in the future ubiquitous services, location estimation will be a key technology. This paper present distributed growing radial basis function neural networks (DGRBFNN) for location estimation of mobile device in wireless networks. DGRBFNN partitions the location space into clusters on the basis of availability of signals from various access points, and employ separate neural network architecture for each cluster to estimate the location of mobile device in indoor wireless networks. It provides better location estimation results than other approaches and systematically caters for unavailable signals at estimation time.

RSS Fingerprints Based Distributed Semi-Supervised Locally Linear Embedding (DSSLLE) Location Estimation System for Indoor WLAN

An important requirement for many novel location based services, is to determine the locations of people, equipment, animals, etc. The accuracy and response time of estimation are critical issues in location estimation system. Most of the location estimation system suffers with the problem of scalability and unavailability of all the access points at all the location for large site. In this paper, we have proposed a distributed semi-supervised location estimation method, which divide the location estimation system into subsystems. Our method partition the input signal space and output location space into clusters on the basis of visibility of access points at various locations of the site area. Each cluster of input signal space together with output location subspace is used to learn the association between Received Signal Strength fingerprint and their respective location in a subsystem. Previous methods for location estimation in indoor wireless networks require a large amount of labeled data for learning the radio map. However, labeled instances are often difficult, expensive, or time consuming to obtain, as they require great efforts, meanwhile unlabeled data may be relatively easy to collect. So, the use of semi-supervised learning is more feasible. On each subsystem at first, we use Locally Linear Embedding to reduce the dimensions of data, and then we use semi-supervised learning algorithm to learn the radio map. The algorithm performs nonlinear mapping between the received signal strengths from nearby access points and the user’s location. It is shown that the proposed Distributed Semi-Supervised Locally Linear Embedding scheme has the advantage of robustness, scalability, useful in large site application and is easy in training and implementation. We have compared our results with Distributed Subtract on Negative Add on Positive (DSNAP) and benchmark method RADAR. Experimental results show that our method provide better results in terms of accuracy and response time in comparison to centralized systems, in which a single system is used for large site as well as with DSNAP and benchmark method RADAR.

Energy-Efficient Network Protocol for Precision Agriculture: Using threshold sensitive sensors for optimal performance.

Wireless sensor networks (WSNs) have become an emerging technology for precision agriculture. This article details a periodic threshold sensitive hybrid routing protocol for precision agriculture. The proposed protocol uses region-based static clustering approaches for deploying sensor nodes, which provide efficient coverage to the entire agricultural area. Deploying heterogeneous types of nodes in different regions as per their performing task and energy level provides a better solution for the coverage-hole problem. The sensor nodes deployed in the network sense the environment parameter continuously, but the sensed data acquired about the environmental factor of the agricultural area is transmitted to the base station (BS) only when the user-defined periodic timer (PT) or sensed attributes value exceeds the desired threshold.

Threshold Sensitive Region-Based Hybrid Routing protocol for precision agriculture

Todays precision agriculture needs advance methods and technology to reduce cost and improve productivity. This paper explores the potential use of wireless sensor network in precision agriculture. An energy efficient network layer routing protocol is required to maximize the lifetime of sensor network. The proposed Threshold Sensitive Region-Based Hybrid Routing (TS-RBHR) protocol uses region-based static clustering approach to provide efficient coverage of agricultural area. The fuzzy based hybrid routing approach is used for transmitting sensed data to base station which minimizes the energy consumption of nodes. Sensor nodes continuously sense temperature and soil moisture content of agricultural field and if sensed value exceeds the desired threshold, a data packet is sent to the base station which reduces the continuous transmission rate. The experimental results show that proposed protocol has a significant increase in network lifetime due to reduction in frequent data transmission.