iosr-JCE   Volume-17 ~ Issue-3 ~ May-June 2015

Abstract: This project presents an alert system for Women safety detection using common commercially available electronic devices to both detect the problem and alert authorities. We use an Android based smart phone with an integrated features that alert and provide location based information to alert authorities. Data from the application is evaluated with several threshold based AES algorithms and position data to determine a problem.

[1]. Michael Burton ,Dohn Ferker (2012). Android Application For Dummies. 2nd ed. 111,river street,Hoboker: jhon wiely and sons,inc. p12-90.
[2]. Cook book,reipeng liu (2013). Android Application devlopment kit. Mumbai: packt bimingam. p1-50,p67-90..
[3]. Peter Erickson, Andrew Weinert, and Dr. Paul Breimyer,Matt Samperi, Jason Huff, Carlos Parra, and Dr. Scarlett Miller. (2013). Designing Public Safety Mobile Applications for Disconnected, Interrupted, and Low Bandwidth Communication Environments. IEEE. 10 (978-1-4799-1535-4/13/$31.00), p790-796

Abstract: The energy supply of nodes will be limited strictly in the wireless sensor networks (WSN). Many algorithms propose to increase the efficiency of Sensor Networks. Many Clustering protocols have been proposed to improve system throughput and system delay, and increase energy saving. This work proposes to improve the life of a heterogeneous sensor network. Earlier work in this field e.g. LEACH (Low Energy Adaptive Clustering Hierarchy) protocol and SEP (A Stable Election Protocol) use probabilistic algorithm. This paper uses a deterministic approach. It takes into consideration of many factors such as current energy of sensor node, percentage of nodes that not have been selected as Cluster Heads (CHs) in each round due to location reason and number of consecutive rounds in which a node has not been cluster-head. The proposed protocol is simulated and the results show a significant reduction in network energy consumption compared to heterogeneous network setup with LEACH and SEP protocol.

[1]. Akyildiz, Ian F., et al. "Wireless sensor networks: a survey." Computer networks 38.4 (2002): 393-422.

[2]. Smaragdakis, Georgios, Ibrahim Matta, and Azer Bestavros. SEP: A stable election protocol for clustered heterogeneous wireless sensor networks. Boston University Computer Science Department, 2004.

[3]. Aderohunmu, Femi A., and Jeremiah D. Deng. "An enhanced stable election protocol (SEP) for clustered heterogeneous WSN." Department of Information Science, University of Otago, New Zealand (2010).

[4]. Patel, R., Pariyani, S., & Ukani, V. (2011). Energy and throughput analysis of hierarchical routing protocol (LEACH) for wireless sensor network. International Journal of Computer Applications, 20(4).

[5]. Handy, M. J., Haase, M., & Timmermann, D. (2002). Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In Mobile and Wireless Communications Network, 2002. 4th International Workshop on (pp. 368-372). IEEE

Abstract: Cloud computing revolutionize IT and business by offering computing as a utility over the internet. The evolution from internet to a cloud computing platform, the emerging development paradigm and technology and how these will change the way enterprise applications should be architected for cloud deployment plays an important role but these enterprise technologies are critical to cloud computing. New cloud analytics and business intelligence (BI) services can help businesses (organizations) better manage big data and cloud applications.Analysing and gathering business intelligence (BI) has never been easy, but today BI is complicated further by overwhelming amounts of data loads and the number of data entry and access points. New cloud analytics advancements may offer BI relief and even profit-increasing predictability for enterprises. These new cloud analytics applications can deliver functional capabilities that can be easily, quickly and economically deployed, producing tangible and measurable benefits far more rapidly than in the past. Many organizations that recognized, effectively analysing their business needs and providing the data they require to make the right business decisions depends on a combination of internally generated data and externally available data.

Keywords: Enterprise clouds, Business Intelligence, Analytics, business models.

[1]. George Mironescu, Nick Mayes, ―Managing Enterprise Applications Workloads in the cloud‖, Analyst IT Services and cloud, IBM, UK, White Paper, June 2014.
[2]. IBM Center for Applied Insights, ―Raising the game: The IBM Business Tech Trends Study,‖ August, 2014.
[3]. Gartner, ―Predicts 2014: Big Data,‖ Gartner #G00258154, Nick Heudecker, Mark A. Beyer, Douglas Laney, Michele Cantara, Andrew White, RoxaneEdjlali, Andrew Lerner, Angela McIntyre, November 20, 2013.
[4]. QuinStreet, ―2014 Big Data Outlook: Big Data is Transformative – Where is Your Company?‖ 2014.
[5]. Cisco, ―Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2013–2018,‖ February 5, 2014.

Abstract: This paper goes to provide an overview of various techniques used for change detection of landcover with conventional and object based techniques. Now days, progression in remote sensing technologies has become a great encouragement for the researchers to analyze various changes in landcover to maintain the environmental balance on the earth surface but due to some addressable issues, it is relatively difficult for researchers to choose one most effective technique.Change detection techniques are having various limitations of extent of defining changes, correctness of classification maps to point out the variations and so on. Therefore several methods have been modeled to prove their effectiveness and accurateness in context of change detection. This paper starts with illustration of prime issues in change detection procedure; review the elementary techniques of change detection followed by the recent object oriented methods for change analysis. Study highlighted the technical soundness of the demonstrated approaches.

Keywords: Change Detection, Forest monitoring, Object-oriented,Remote sensing data.

[1] D. Lu, P.Mausel, E. Brondizioand E. Moran, "Change detection techniques", International Journal of Remote Sensing, 25, pp. 2365–2407, 2004.
[2] A.Singh, "Digital change detection techniques using remotely-sensed data", International Journal of Remote Sensing, 10, pp. 898–1003, 1989.
[3] P.R. Coppin, M.E. Bauer, "Digital change detection in forest ecosystems with remote sensing imagery", Remote Sensing Reviews, 13, pp. 207–234, 1996.
[4] T.Blaschke, "Towards a framework for change detection based on image objects",GöttingerGeographischeAbhandlungen, 113, pp. 1–9, 2005.

Abstract: In this research, we have developed an algorithm that maintains the shape and integrity of the Bio-Signal over Body- Sensor Network. A new approach has been experimented successfully using set theory operators (union, intersection, complement, Cartesian product) instead of using traditional interpolation method for maintenance of original properties of Bio-signal and synchronization. A secure key management scheme is used to enhance the security of wireless body area network (WBAN) and energy analysis is also done. The operational resources of biosensor nodes in BSNs are very restricted, and traditional security technologies are not directly applicable to BSNs. Due to characteristics of biosensors, time synchronization and low-energy communication are two challenging problems for BSNs. Various cryptography techniques are employed in order to provide security as the information exchanged during health monitoring are highly sensitive and require strict protection from unauthorized access.

Keywords: Body Sensor Network, Energy Consumption.

[1]. Khan, Tareq Hasan, and Khan A. Wahid. "An advanced physiological data logger for medical imaging applications." EURASIP Journal on Embedded Systems 2012, no. 1 (2012): 1-14.
[2]. Chen, Min, Chin-Feng Lai, and Honggang Wang. "Mobile multimedia sensor networks: architecture and routing." EURASIP Journal on Wireless Communications and Networking 2011, no. 1 (2011): 1-9.
[3]. Quwaider, Muhannad, Mahmoud Taghizadeh, and Subir Biswas. "Modeling on-body dtn packet routing delay in the presence of postural disconnections." EURASIP journal on wireless communications and networking 2011 (2011): 3.
[4]. Begonya, Otal, Alonso Luis, and Verikoukis Christos. "Design and analysis of an energy-saving distributed mac mechanism for wireless body sensor networks." EURASIP Journal on Wireless Communications and Networking 2010 (2010).
[5]. Le, T.Q.; Changqing Cheng; Sangasoongsong, A.; Wongdhamma, W.; Bukkapatnam, S.T.S., "Wireless Wearable Multisensory Suite and Real-Time Prediction of Obstructive Sleep Apnea Episodes," Translational Engineering in Health and Medicine, IEEE Journal of , vol.1, no., pp.2700109,2700109, 2013

Abstract: Vehicular AdhocNETworks (VANETs) relies on repetitive data exchange among vehicles and vehicles to RoadSide Units (RSU) to facilitate road safety, route planning, etc. This may often lead to congestion in the network. To deal with this dilemma, various congestion control schemes have been considered so far. The basic objective of congestion control is to best utilize the available network resources while preventing persistent overloads of network nodes and links. Suitable congestion control mechanisms are essential to maintain the competent operation of a network. This paper highlights attributes of some of the proposed congestion control algorithms and compares their qualities and limits.

Keywords: Congestion Control, Control Channel, DSRC, IEEE 802.11p, VANETs, WAVE.

[1]. Jae-Han Lim, Wooseong Kim, Katsuhiro Naito, Mario Gerla, "Interplay Between TVWS and DSRC: Optimal Strategy for QoS of Safety Message Dissemination in VANET", International Conference on Computing, Networking and Communications (ICNC), 2013, pp. 1156-1161.
[2]. M. A. Benatia1, L. Khoukhi, M. Esseghir, L. MerghemBoulahia, "A Markov Chain Based Model for Congestion Control in VANETs", 27th International Conference on Advanced Information Networking and Applications Workshops, 2013, pp. 1021-1026.
[3]. Florian Knorr, Daniel Baselt, Michael Schreckenberg, and Martin Mauve, "Reducing Traffic Jams via VANETS", In Proceeding of IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 61, NO. 8, OCTOBER 2012, pp 3490-3498.
[4]. SoufieneDjahel and YacineGhamri-Doudane, "A Robust Congestion Control Scheme for Fast and Reliable Dissemination of Safety Messages in VANETs", In Proceeding of IEEE Wireless Communications and Networking Conference: Mobile and Wireless Networks, 2012.

Abstract: This paper attempts to provide an overview of various protocols used in wireless sensor network which include data dissemination and data gathering protocols .Wireless network is any type of computer network which uses wireless communication between nodes. Wireless network of autonomous sensors is used to monitor physical and environmental conditions like pressure, temperature, air pollution, water quality etc. and pass them through the network to the desired location. Each sensor network has several parts – a radio transceiver with antenna, a microcontroller, an electronic circuit for interfacing with sensors and energy source which is generally a battery or an embedded form of energy harvesting. Wireless sensor network are having limitation of storage space, computing power and energy. Therefore various protocols are used to design energy efficient wireless network. This paper reviews all hierarchical routing protocols .Energy consumption and network lifespan are primary issues considered in this paper.

Keywords: Wireless Sensor network, battery, routing protocols,energy efficient, Energy consumption, network life time.

[1]. I.F.Akyildiz,, W.Su, Y.Sankarasubramaniam, &E.Cayirci 2002. "Wireless sensor networks: a survey."Computer networks, 38(4), 393-422.
[2]. N.M. Elshakankiri, N. M. Moustafa and Y. H.Dakroury, 2008 "Energy Efficient Routing Protocol for Wireless Sensor Network "in IEEE International Conference on pp. 393–398.
[3]. Monica R Mundada, SavanKiran , Shivanand Khobanna1, Raja NahushaVarsha and Seira Ann George, 2012"A study on energy efficient routing protocols in wireless sensor network", International Journal of Distributed and Parallel Systems (IJDPS) Vol.3, No.3.
[4]. Vikash Kumar, Anshu Jain and P N Barwal,2014"Wireless Sensor Networks: Security Issues, Challenges and Solutions",International Journal of Information & Computation Technology.ISSN 0974-2239 Volume 4, Number 8 pp. 859-868.

Abstract: The aim of this paper is to present a detailed step by step method for classification of high resolution satellite imagery into defined classes such as high dense vegetation, water, wet land, Agricultural land etc., using fuzzy rule set. In this area, object based analysis is used for image classification. An Objectoriented approach isbased on the segmentation that is being followed for information extraction of variety of thematic information from high-resolution satelliteimagery. In this method, fuzzy rule set is defined for every class based on the class hierarchy; every defined class is assigned associated segments with high similarity degree, using eCognition Software. Object-oriented classification rule set provide a complete accurate and efficient method of information extraction. In compared the object-oriented with traditional pixel-based classification and proved that object-oriented method has provided high accuracy classification. Nearest Neighbor (NN) classification method was used to discriminate the land cover classes. The overall accuracy is obtained 99.99% and kappa coefficient obtained 99% for the satellite image of Landsat-8.

Keyword: Classification, eCognition, Fuzzy rule based, High resolution satellite image, Segmentation.

[1]. Xie, "Object Oriented Classification, Remote Sensing Image Process and Analysis", 2005
[2]. eCognition User Guider 4, Defines Imaging, 2003
[3]. Dare, P. M., and Fraser, C. S., "Mapping informal settlements using high resolution satellite imagery", International Journal of Remote Sensing, 2001.
[4]. Jensen, J.R., "Introduction to Digital Image Processing: A Remote Sensing Perspective", Practice Hall, 1996.
[5]. L. Bruzzone and S., B. Serpico,"An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images",IEEE Trans. Geosci. Remote Sensing,vol. 35, pp. 858–867, July 1997.
[6]. Pacifici, F., Chini, M., Emery, W.J., "A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification", RemoteSensing. Environment, 2009.
[7]. Lizarazo, I. Barros, J., "Fuzzy image segmentation for urban land-cover classification". Photogramm. Eng. Remote Sens., 76, 151–162, 2010

Abstract: Among the various challenges to industry, the major challenge is to offer products with high level of quality andfunctionality at inexpensive value and short time and energy to market. Component-based software development(CBSD) approach have become quite popular from the point of view of quality assurance. The most captivating reason for embracing CBSD approach is the conjecture of reuse. The goal of this study is to comprehend categorize and inspect prevailing research in CBSD field from quality point of view. The main focus is given onproposals that are accustomed to assess the quality of component- based software system (CBSS). This paper represents the methodology through which the quality of component-based software system can be assured. Quality assurance is taken as a vital research term. In this paper, two main approaches for assuring quality are taken under consideration: encapsulation and composition rules.

[1]. Abdellatief, M., Sultan, A. B. M., Ghani, A. A. A., & Jabar, M. A. (2013). A mapping study to investigate component-based software system metrics.Journal of systems and software, 86(3), 587-603.

[2]. Alvaro, A., Almeida, E. S., & Meira, S. L. (2005). Quality attributes for a component quality model. 10th WCOP/19th ECCOP, Glasgow, Scotland.

[3]. Carlson, J. (2012, June). Timing analysis of component-based embedded systems. In Proceedings of the 15th ACM SIGSOFT symposium on Component Based Software Engineering (pp. 151-156). ACM.

[4]. Crnkovic, I., Sentilles, S., Vulgarakis, A., & Chaudron, M. R. (2011). A classification framework for software component models. Software Engineering, IEEE Transactions on, 37(5), 593-615.

[5]. Dias, M. S., & Vieira, M. E. (2000). Software architecture analysis based on statechart semantics. In Software Specification and Design, 2000. Tenth International Workshop on (pp. 133-137). IEEE.

Abstract: Wireless Sensor Networks is one of the most important area of research. Over the time, WSN is expanding its reach from scientific research and monitoring to industrial as well as military operations. With its increasing applications in various areas of use it is becoming important to consider the location of deployment form where the sensors can generate the desired results The proposed algorithm for WSN localization is designed to connect the WSN nodes during the network initialization phase.

[1]. Chen, Manju, Xiangqian Ding, Xiaodong Wang, and XiaoweiXu. "A novel three-dimensional localization algorithm based on DV-HOP." In Signal Processing, Communications and Computing (ICSPCC), 2014 IEEE International Conference on, pp. 70-73. IEEE, 2014.Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report.University of Maryland at College Park.
[2]. Gayan, Samiru, and Dileeka Dias. "Improved DV-Hop algorithm through anchor position re-estimation."In Wirelessand Mobile, 2014 IEEE Asia Pacific Conference on, pp. 126-131.IEEE, 2014.Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
[3]. Lazos, Loukas, and RadhaPoovendran. "HiRLoc: high-resolution robust localization for wireless sensor networks." Selected Areas in Communications, IEEE Journal on 24, no. 2 (2006): 233-246.Forman, G. 2003.An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.
[4]. Wu, Jiawei, Jinming Yu, AijunOu, Yiming Wu, and WujunXu. "RCDV-Hop Localization Algorithm for WSN." In Wireless Communications, Networking and Mobile Computing (WiCOM), 2012 8th International Conference on, pp. 1-4. IEEE, 2012.Y.T. Yu, M.F. Lau, "A comparison of MC/DC, MUMCUT and several other coverage criteria for logical decisions", Journal of Systems and Software, 2005, in press.

Abstract: Wireless Sensor Networks is the result of latest advancements in the field of wireless technology. The recent upgrades to the wireless network provides the opportunity to wireless sensor networks to fully utilize all the possible resources to achieve results in the desired area of deployment, further leading to increased energy requirements within the network. There are plenty of routing algorithms applicable inside WSNs which claim to maintain the desired energy levels within the network by controlling certain parameters such as packet delay, load, etc. In this paper we have proposed a tree based routing approach that makes the use of aomdv routing to choose the path for transmission by taking each sensor node's distance and residual energy as a parameter and performs effective load balancing to increase the network lifetime. The Proposed model is tested under various performance parameters like network load, throughput, data loss and delay and the results shows an increased network lifetime incomparison to the existing model. The proposed work has been implemented in the Ns-2 simulator. The experimental results have shown the significant improvement in the results obtained from the proposed algorithm implementation.

[1]. Delaney, D., Russell Higgs, and G. O'Hare. "A stable routing framework for tree-based routing structures in wsns." (2014, IEEE)
[2]. Ghadimi, Euhanna, Olaf Landsiedel, Pablo Soldati, Simon Duquennoy, and Mikael Johansson. "Opportunistic Routing in Low Duty-Cycled Wireless Sensor Networks."ACM Transactions on Sensor Networks 10, no. 4 (2014).
[3]. Sahin, Dilan, VehbiCagriGungor, TaskinKocak, and Gurkan Tuna. "Quality-of-service differentiation in single-path and multi-path routing for wireless sensor network-based smart grid applications." Ad Hoc Networks (2014).
[4]. Singh, Dharmendra, Shubhanjali Sharma, Vinesh Jain, and JyotiGajrani. "Energy efficient source based tree routing with time stamp in WSN." In Signal Propagation and Computer Technology (ICSPCT), 2014 International Conference on, pp. 120-124. IEEE, 2014.Liu, Yong, Yu Hen Hu, and Quan Pan. "Distributed, robust acoustic source localization in a wireless sensor network." Signal Processing, IEEE Transactions on 60, no. 8 (2012): 4350-4359.
[5]. Kwon, Kiwoong, Minkeun Ha, Taehong Kim, SeongHoon Kim, and Daeyoung Kim. "The stateless point to point routing protocol based on shortcut tree routing algorithm for IP-WSN." In Internet of Things (IOT), 2012 3rd International Conference on the, pp. 167-174.
[6]. Tunca, Can, SinanIsik, M. Donmez, and CemErsoy. "Distributed Mobile Sink Routing for Wireless Sensor Networks: A Survey." (2014): 1-21.