SBGC             Final Year Projects           SOFTWAREPROJECTS        JAVA | DOTNET | NS-2 |       Matlab | Power Electro...
SBGC Provides IEEE 2011 Projects For all Final Year Students. We do assist the students with TechnicalGuidance for both th...
MANAGEMENT, PROJECT MANAGEMENT,HOSPITAL MANAGEMENT, SCHOOL MANAGEMENT, MARKETING MANAGEMENT,SAFETY MANAGEMENT)DIPLOMA (CE,...
sensor-actor or actor-actor communications. In this paper, the issue of choosing a set of working actorsfor coordinating d...
intended destinations using a fully-distributed MAC protocol. A packet transmission is consideredsuccessful if the receive...
deployed to provide k-coverage for real-time applications, and we study both single- and multi-hopnetwork topologies. We v...
Fast Data Collection in Tree-Based Wireless Sensor NetworksWe investigate the following fundamental question - how fast ca...
bounds of both time and forwarding costs, and it well resists to the wireless loss with good scalability onthe network siz...
A Privacy-Preserving Location Monitoring System for Wireless Sensor NetworksMonitoring personal locations with a potential...
HEAD OFFICESBGC4th FLOOR SURYA COMPLEX,SINGARATHOPE BUS STOP,OLD MADURAI ROAD,TRICHY- 620002Phone No: 0431-4012303Mobile:+...
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Ieee projects 2011 ns 2 SBGC ( Trichy, Madurai, Chennai, Dindigul, Natham, Pudukkottai )

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IEEE Projects 2011 NS-2 Chennai, IEEE Projects 2011 NS-2 Trichy, IEEE Projects 2011 NS-2 Thanjavur, IEEE Projects 2011 NS-2 Pudukkottai, IEEE Projects 2011 NS-2 Perambalur, IEEE Projects 2011 NS-2 Karur, IEEE Projects 2011 NS-2 Namakkal, IEEE Projects 2011 NS-2 Bangalore, IEEE Projects 2011 NS-2 Hyderabad, IEEE Projects 2011 NS-2 Cochin, IEEE Projects 2011 NS-2 Ernakulam, IEEE Projects 2011 NS-2 Thiruvananthapuram, IEEE Projects 2011 NS-2 Vellore, IEEE Projects 2011 NS-2 Madurai, IEEE Projects 2011 NS-2 Salem, IEEE Projects 2011 NS-2 Ariyalur, IEEE Projects 2011 NS-2 Erode, IEEE Projects 2011 NS-2 Coimbatore, IEEE Projects 2011 NS-2 Dindigul, IEEE Projects 2011 NS-2 Manapparai, IEEE Projects 2011 NS-2 Kakinada, IEEE Projects 2011 NS-2 Nellore, IEEE Projects 2011 NS-2 Chittoor, IEEE Projects 2011 NS-2 Tirupati, IEEE Projects 2011 NS-2 Tirunelveli, IEEE Projects 2011 NS-2 Mumbai, IEEE Projects 2011 NS-2 Pune, IEEE Projects 2011 NS-2 Nagpur

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Ieee projects 2011 ns 2 SBGC ( Trichy, Madurai, Chennai, Dindigul, Natham, Pudukkottai )

  1. 1. SBGC Final Year Projects SOFTWAREPROJECTS JAVA | DOTNET | NS-2 | Matlab | Power Electronics www.ieee2011projects.sbgc.in www.ieeeproject.in contact@sbgc.in, sathish@sbgc.inSBGC 4th FLOOR SURYA24/83, O Block, MMDA COMPLEX,COLONY SINGARATHOPE BUSARUMBAKKAM STOP,CHENNAI-106 OLD MADURAI ROAD,09944361169 TRICHY- 620002 0431-4012303 09003012150
  2. 2. SBGC Provides IEEE 2011 Projects For all Final Year Students. We do assist the students with TechnicalGuidance for both the categories.Category 1 : Students with new project ideas.Category 2 : Students selecting from our list.When you register for a project we ensure that the project is implemented to yourfullest satisfactionand you have a thorough understanding of every aspect of the project.SBGC PROVIDES YOU THE LATEST IEEE 2011 PROJECTS/ IEEE 2012 PROJECTS FOR FOLLOWINGDEPARTMENT STUDENTSB.E, B.TECH, M.TECH, M.E, DIPLOMA, MS, BSC, MSC, BCA, MCA, MBA, BBA, PHD,NS-2, GLOMOSIM, MATLAB, JAVA, .NET,B.E (ECE, EEE, E&I, ICE, MECH, PROD, CSE, IT, THERMAL, AUTOMOBILE,MECATRONICS, ROBOTICS)B.TECH(ECE, MECATRONICS, E&I, EEE, MECH , CSE, IT, ROBOTICS)M.TECH(EMBEDDED SYSTEMS, COMMUNICATION SYSTEMS, POWER ELECTRONICS,COMPUTER SCIENCE,SOFTWARE ENGINEERING, APPLIED ELECTRONICS, VLSI Design)M.E(EMBEDDED SYSTEMS, COMMUNICATION SYSTEMS, POWER ELECTRONICS,COMPUTER SCIENCE, SOFTWAREENGINEERING, APPLIED ELECTRONICS, VLSI Design)MBA(HR, FINANCE, MANAGEMENT, HOTEL MANAGEMENT, SYSTEM
  3. 3. MANAGEMENT, PROJECT MANAGEMENT,HOSPITAL MANAGEMENT, SCHOOL MANAGEMENT, MARKETING MANAGEMENT,SAFETY MANAGEMENT)DIPLOMA (CE, EEE, E&I, ICE, MECH,PROD, CSE, IT)We also have training and project, R & D division to serve the students and make them job orientedprofessionalsIEEE 2011 NS-2 PROJECTSLoss Performance Modeling for Hierarchical Heterogeneous Wireless Networks With Speed-SensitiveCall Admission ControlA hierarchical overlay structure is an alternative solution that integrates existing and futureheterogeneous wireless networks to provide subscribers with better mobile broadband services. Trafficloss performance in such integrated heterogeneous networks is necessary for an operators networkdimensioning and planning. This paper investigates the computationally efficient loss performancemodeling for multiservice in hierarchical heterogeneous wireless networks. A speed-sensitive calladmission control (CAC) scheme is considered in our model to assign overflowed calls to appropriatetiers. This approach avoids unnecessary and frequent handoff between cells and reduces signalingoverheads. An approximation model with guaranteed accuracy and low computational complexity ispresented for the loss performance of multiservice traffic. The accuracy of numerical results is validatedby comparing the results from the approximation with simulationsCommunication Cost Minimization in Wireless Sensor and Actor Networks for Road Surveillancewireless sensor and actor networks (WSANs) have been extensively deployed to monitor physicalenvironment and facilitate decision making based on data collected. Emerging applications such as roadsurveillance highlight some interesting research issues in WSANs, including coordination problems in
  4. 4. sensor-actor or actor-actor communications. In this paper, the issue of choosing a set of working actorsfor coordinating data transmission in a road sensor and actor network with minimum communicationcost is studied. A theoretical model is introduced to analyze the communication cost of datatransmission in WSANs, and the sensor-actor coordination problem is formulated as an optimizationproblem. It is demonstrated that the problem can be divided into subproblems, and optimal solutionscan be obtained by using a dynamic programming algorithm. A novel graph-based algorithm is alsoproposed with a communication-cost graph used to depict the cost of data transmission and a modifiedDijkstra’s algorithm to find optimal solutions in reduced time complexity. The efficiency of the proposedalgorithms is confirmed using extensive simulations.Distributed Sensing in Multi-band Cognitive NetworksWe consider a short range cognitive network searching for spectrum holes from very wide bandwidth. Inpractice, one cognitive user can sense only a small portion of spectrum. Unfortunately, in fadingenvironment a reliable detection scheme requires measurements collected by multiple users. Becauseof that, it is unreasonable to expect a small-sized network to sense the complete candidate bandwidth.In this paper we propose an algorithm for optimal sensing of multiple spectrum bands by multiplecognitive users. The user allocation is optimized so that the expected opportunistic throughput ismaximized and the total power spent for spectrum measurements is controlled. As a constraint we usethe detection performance requirements imposed by the primary systems. For a small number ofspectrum bands the optimal solution can be found by exhaustive search. For a large number of spectrumbands we view the spectrum sensing as a multiple choice knapsack problem. By using algorithms for thisclass of problems we propose two heuristics that are suitable for optimizing spectrum sensing inmultiband cognitive networks. These algorithms provide quick, near optimal solutions and are thereforesuitable for practical spectrum sensing systems.Improving the Performance of Wireless Ad Hoc Networks Through MAC Layer Designthe performance of the ALOHA and CSMA MAC protocols are analyzed in spatially distributed wirelessnetworks. The main system objective is correct reception of packets, and thus the analysis is performedin terms of outage probability. In our network model, packets belonging to specific transmitters arriverandomly in space and time according to a 3-D Poisson point process, and are then transmitted to their
  5. 5. intended destinations using a fully-distributed MAC protocol. A packet transmission is consideredsuccessful if the received SINR is above a predefined threshold for the duration of the packet. Accuratebounds on the outage probabilities are derived as a function of the transmitter density, the number ofbackoffs and retransmissions, and in the case of CSMA, also the sensing threshold. The analyticalexpressions are validated with simulation results. For continuous-time transmissions, CSMA withreceiver sensing (which involves adding a feedback channel to the conventional CSMA protocol) isshown to yield the best performance. Moreover, the sensing threshold of CSMA is optimized. It is shownthat introducing sensing for lower densities (i.e., in sparse networks) is not beneficial, while for higherdensities (i.e., in dense networks), using an optimized sensing threshold provides significant gainOptimal Selective Forwarding for Energy Saving in Wireless Sensor NetworksScenarios where nodes have limited energy and forward messages of different importances (priorities)are frequent in the context of wireless sensor networks. Tailored to those scenarios, this paper relies onstochastic tools to develop selective message forwarding schemes. The schemes will depend onparameters such as the available battery at the node, the energy cost of retransmitting a message, orthe importance of messages. The forwarding schemes are designed for three different cases: 1) whensensors maximize the importance of their own transmitted messages; 2) when sensors maximize theimportance of messages that have been successfully retransmitted by at least one of its neighbors; and3) when sensors maximize the importance of messages that successfully arrive to the sink. Moresophisticated schemes will achieve better importance performance, but will also require informationfrom other sensors. The results contribute to identify the variables that, when made available to othernodes, have a greater impact on the overall network performance. Suboptimal schemes that rely onlocal estimation algorithms and entail reduced computational cost are also designed.Transient Analysis of IEEE 802.15.4 Sensor NetworksWe study the delay performance of a sensor network, whose nodes access the medium by using theunslotted MAC protocol specified by the IEEE 802.15.4 standard. Unlike previous works, which focus onthe average throughput and delay analysis, we develop a detailed model that allows us to obtain thedelivery delay distribution of messages sent by concurrently contending sensors toward a centralcontroller. We carry out a transient analysis that is of particular interest when sensor networks are
  6. 6. deployed to provide k-coverage for real-time applications, and we study both single- and multi-hopnetwork topologies. We validate our analytical results against simulation results obtained through ns2.Fast Detection of Mobile Replica Node Attacks in Wireless Sensor Networks Using SequentialHypothesis TestingDue to the unattended nature of wireless sensor networks, an adversary can capture and compromisesensor nodes, generate their replicas, and thus mount a variety of attacks with these replicas. Suchattacks are dangerous because they allow the attacker to leverage the compromise of a few nodes toexert control over much of the network. Several replica node detection schemes have been proposed inthe literature to defend against such attacks in static sensor networks. However, these schemes rely onfixed sensor locations and hence do not work in mobile sensor networks, where sensors are expected tomove. In this work, we propose a fast and effective mobile replica node detection scheme using theSequential Probability Ratio Test. To the best of our knowledge, this is the first work to tackle theproblem of replica node attacks in mobile sensor networks. We show analytically and throughsimulation experiments that our scheme provides effective and robust replica detection capability withreasonable overheads.Fault Localization Using Passive End-to-End Measurements and Sequential Testing for Wireless SensorNetworksFaulty components in a network need to be localized and repaired to sustain the health of the network.In this paper, we propose a novel approach that carefully combines active and passive measurements tolocalize faults in wireless sensor networks. More specifically, we formulate a problem of optimalsequential testing guided by end-to-end data. This problem determines an optimal testing sequence ofnetwork components based on end-to-end data in sensor networks to minimize testing cost. We provethat this problem is NP-hard and propose a greedy algorithm to solve it. Extensive simulation shows thatin most settings our algorithm only requires testing a very small set of network components to localizeand repair all faults in the network. Our approach is superior to using active and passive measurementsin isolation. It also outperforms the state-of-theart approaches that localize and repair all faults in anetwork.
  7. 7. Fast Data Collection in Tree-Based Wireless Sensor NetworksWe investigate the following fundamental question - how fast can information be collected from awireless sensor network organized as tree? To address this, we explore and evaluate a number oftechniques using realistic simulation models under the many-to-one communication paradigm known asconvergecast. We first consider time scheduling on a single frequency channel with the aim ofminimizing the number of time slots required (schedule length) to complete a convergecast. Next, wecombine scheduling with transmission power control to mitigate the effects of interference, and showthat while power control helps in reducing the schedule length, scheduling transmissions using multiplefrequencies is more efficient. We give lower bounds on the schedule length when interference iscompletely eliminated, and propose algorithms that achieve these bounds. We also evaluate theperformance of various channel assignment methods and find empirically that for moderate sizenetworks of about 100 nodes, multi-frequency scheduling can suffice to eliminate most of theinterference. Then, the data collection rate no longer remains limited by interference but by thetopology of the routing tree. To this end, we construct degree-constrained spanning trees andcapacitated minimal spanning trees, and show significant improvement in scheduling performance overdifferent deployment densitiesOn Reliable Broadcast in Low Duty-Cycle Wireless Sensor NetworksBroadcast is one of the most fundamental services in wireless sensor networks, where a distinct featureis that sensor nodes may alternate between active and dormant states, so as to conserve energy andextend the network lifetime. Unfortunately, the impact of such cycles has been largely ignored inexisting broadcast implementations that adopt the common assumption of all nodes being active allover the time. In this paper, we revisit the broadcast problem with active/dormant cycles. We showstrong evidence that conventional broadcast approaches will suffer from severe performancedegradation, and, under low duty-cycles, they could easily fail to cover the whole network in anacceptable timeframe. We remodel the broadcast problem in this new context, seeking a balancebetween efficiency and latency with coverage guarantees. We demonstrate that this problem can betranslated into a graph equivalence, and develop a centralized optimal solution. We then extend it to anefficient and scalable distributed implementation. The performance of our solution is evaluated underdiverse network configurations. The results suggest that our distributed solution is close to the lower
  8. 8. bounds of both time and forwarding costs, and it well resists to the wireless loss with good scalability onthe network size and density.Efficient Data Collection in Wireless Sensor Networks with Path-Constrained Mobile SinksRecent work shows that sink mobility along a constrained path can improve the energy efficiency inwireless sensor networks. However, due to the path constraint, a mobile sink with constant speed haslimited communication time to collect data from the sensor nodes deployed randomly. This posessignificant challenges in simultaneously improving the amount of data collected and reduction in energyconsumption. To address this issue, we propose a novel data collection scheme, called the maximumamount shortest path (MASP), that increases network throughput as well as conserves energy tooptimize the assignment of sensor nodes. MASP is formulated as an integer linear programmingproblem and then solved with the help of a genetic algorithm. A two-phase communication protocol isdesigned to implement the MASP scheme. Simulations experiments using OMNET++ show that MASPoutperforms the shortest path tree (SPT) and static sink methods in terms of system throughput andenergy efficiency.Computing Localized Power-Efficient Data Aggregation Trees for Sensor NetworksWe propose localized, self organizing, robust, and energy-efficient data aggregation tree approaches forsensor networks,which we call Localized Power-Efficient Data Aggregation Protocols (L-PEDAPs). Theyare based on topologies, such as LMST and RNG,that can approximate minimum spanning tree and canbe efficiently computed using only position or distance information of one-hopneighbors. The actualrouting tree is constructed over these topologies. We also consider different parent selection strategieswhileconstructing a routing tree. We compare each topology and parent selection strategy and concludethat the best among them is theshortest path strategy over LMSTstructure. Our solution also involvesroute maintenance procedures that will be executed when a sensor node fails or a new node is added tothe network. The proposed solution is also adapted to consider the remaining power levels of nodesinorder to increase the network lifetime. Our simulation results show that by using our power-awarelocalized approach, we can almost have the same performance of a centralized solution in terms ofnetwork lifetime, and close to 90 percent of an upper bound derived here.
  9. 9. A Privacy-Preserving Location Monitoring System for Wireless Sensor NetworksMonitoring personal locations with a potentially untrusted server poses privacy threats to themonitored individuals. To this end, we propose a privacy-preserving location monitoring system forwireless sensor networks. In our system, we design two in-network location anonymization algorithms,namely, resource and quality-aware algorithms, that aim to enable the system to provide high-qualitylocation monitoring services for system users, while preserving personal location privacy. Bothalgorithms rely on the well-established k-anonymity privacy concept, that is, a person isindistinguishable among k persons, to enable trusted sensor nodes to provide the aggregate locationinformation of monitored persons for our system. Each aggregate location is in a form of a monitoredarea A along with the number of monitored persons residing in A, where A contains at least k persons.The resource-aware algorithm aims to minimize communication and computational cost, while thequality-aware algorithm aims to maximize the accuracy of the aggregate locations by minimizing theirmonitored areas. To utilize the aggregate location information to provide location monitoring services,we use a spatial histogram approach that estimates the distribution of the monitored persons based onthe gathered aggregate location information. Then, the estimated distribution is used to providelocation monitoring services through answering range queries. We evaluate our system throughsimulated experiments. The results show that our system provides high-quality location monitoringservices for system users and guarantees the location privacy of the monitored persons.
  10. 10. HEAD OFFICESBGC4th FLOOR SURYA COMPLEX,SINGARATHOPE BUS STOP,OLD MADURAI ROAD,TRICHY- 620002Phone No: 0431-4012303Mobile:+919003012150.BRANCH OFFICESBGC24/83 , "O" Block,MMDA Colony, Arumbakkam,Chennai - 600 106.Land Mark : Near By MMDA MarketMail Id: contact@sbgc.inMobile:+919944361169BRANCH OFFICESBGC ( Near To Dindigul , Near To Madurai )AVT COMPLEX NATHAM09003012150sathish@sbgc.in, contact@sbgc.in

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