10 research outputs found

    Secured Aggregation for Privacy and Efficiency in Energy in WSN

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    The proposed system in WSN’s have many applications in critical secured areas, mostly in military applications, since it hides data using many nodes from third parties. The existing techniques uses hop by hop based protocols which does not provide efficiency in energy, due to which it may reveals large amount of data to the adversaries. There by loses its confidentiality of data. The proposed technique is best suited to overcome the constraints of the existing system. This uses end to end encryption which aggregates the encrypted data and sends to the base station, which provide a complete security, data freshness, confidentiality. Because of the aggregation of the encrypted data it reduces the energy consumption

    Comparison between Routing Technologies of Wireless Sensor Networks

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    WSNs have crafted new prospects across the field of human activities, embracing monitoring and control of environmental systems, animal tracking, forest fire tracking, medical care, battlefield surveillance, calamity management. These different applications involves data collection from different millions of sensors and propagating to base stations via sink nodes. WSN makes this communication possible by forwarding data directly to base station that exhaust energy reserves. Use of multi-hop data transmission reduces loss of energy and increase lifetime of network. This paper discusses various routing techniques used in multi-hop WSN to select best path

    Optimized Data Aggregation Method for Time, Privacy and Effort Reduction in Wireless Sensor Network

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    Wireless sensor networks (WSNs) have gained wide application in recent years, such as in intelligent transportation system, medical care, disaster rescue, structure health monitoring and so on. In these applications, since WSNs are multi-hop networks, and the sink nodes of WSNs require to gather every sensor node’s data, data aggregation is emerging as a critical function for WSNs. Reducing the latency of data aggregation attracts much research because many applications are event urgent. Data aggregation is ubiquitous in wireless sensor networks (WSNs). Much work investigates how to reduce the data aggregation latency. This paper considers the data aggregation method based on optimization of required time, maintain privacy while keeping lesser efforts by data aggregation in a wireless sensor network (WSN) and propose a method for the solution of the problem

    The importance of energy efficient in wireless sensor networks

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    Mobile Node-based routing is an efficient routing technique compared to traditional approaches. Due to this FERP majorly data isolation is provided for sensor nodes, and the network is more energy efficient. The Mobile data collector collects data from only Family heads and forwards to the cluster head. The Node level energy saving scheme is proposed in this work. The performance of this routing protocol is assessed based on Energy consumption, Throughput, Lifetime, Packet Delivery Ratio, Energy efficiency. Most of the Energy is saved due to the introducing of mobile nodes for data collection. Apart from this, we are reducing the load for mobile data collectors also. In general, mobile data collectors have high energy resources. But it is not possible in all terrains. This FERP gives better results in military and plateaus, and irregular terrains where multihop communication is complex. This work is further enhanced by Trust node based routing to improve the lifetime of the network

    Optimization of Energy Efficient Advance Leach Protocol

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    In WSNs, the only source to save life for the node is the battery consumption. During communication with other area nodes or sensing activities consumes a lot of power energy in processing the data and transmitting the collected/selected data to the sink. In wireless sensor networks, energy conservation is directly to the network lifetime and energy plays an important role in the cluster head selection. A new threshold has been formulated for cluster head selection, which is based on remaining energy of the sensor node and the distance from the base station. Proposed approach selects the cluster head nearer to base station having maximum remaining energy than any other sensor node in multi-hop communication. The multi hop approach minimizing the inter cluster communication without effecting the data reliability

    Denial of service mitigation approach for IPv6-enabled smart object networks

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    Denial of service (DoS) attacks can be defined as any third-party action aiming to reduce or eliminate a network's capability to perform its expected functions. Although there are several standard techniques in traditional computing that mitigate the impact of some of the most common DoS attacks, this still remains a very important open problem to the network security community. DoS attacks are even more troublesome in smart object networks because of two main reasons. First, these devices cannot support the computational overhead required to implement many of the typical counterattack strategies. Second, low traffic rates are enough to drain sensors' battery energy making the network inoperable in short times. To realize the Internet of Things vision, it is necessary to integrate the smart objects into the Internet. This integration is considered an exceptional opportunity for Internet growth but, also, a security threat, because more attacks, including DoS, can be conducted. For these reasons, the prevention of DoS attacks is considered a hot topic in the wireless sensor networks scientific community. In this paper, an approach based on 6LowPAN neighbor discovery protocol is proposed to mitigate DoS attacks initiated from the Internet, without adding additional overhead on the 6LoWPAN sensor devices.This work has been partially supported by the Instituto de Telecomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, and by National Funding from the FCT - Fundacao para a Ciencia e Tecnologia through the Pest-OE/EEI/LA0008/2011.Oliveira, LML.; Rodrigues, JJPC.; De Sousa, AF.; Lloret, J. (2013). Denial of service mitigation approach for IPv6-enabled smart object networks. Concurrency and Computation: Practice and Experience. 25(1):129-142. doi:10.1002/cpe.2850S129142251Gershenfeld, N., Krikorian, R., & Cohen, D. (2004). The Internet of Things. Scientific American, 291(4), 76-81. doi:10.1038/scientificamerican1004-76Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393-422. doi:10.1016/s1389-1286(01)00302-4Karl, H., & Willig, A. (2005). Protocols and Architectures for Wireless Sensor Networks. doi:10.1002/0470095121IEEE Std 802.15.4-2006 Part 15.4: wireless medium access control (MAC) and physical layer (PHY) specificationsfor low-rate wireless personal area networks (LR-WPANs) 2006ZigBee Alliance ZigBee Specification 2007WirelessHARThomepage 2012 http://www.hartcomm.org/Hui, J. W., & Culler, D. E. (2008). Extending IP to Low-Power, Wireless Personal Area Networks. IEEE Internet Computing, 12(4), 37-45. doi:10.1109/mic.2008.79Kushalnagar N Montenegro G Schumacher C IPv6 over Low-Power Wireless Personal Area Networks (6LoWPANs): Overview, Assumptions, Problem Statement, and Goals 2007Montenegro G Kushalnagar N Hui J Culler D Transmission of IPv6 Packets over IEEE 802.15.4 Networks 2007Shelby Z Thubert P Hui J Chakrabarti S Bormann C Nordmark E 6LoWPAN Neighbor Discovery 2011Zhou, L., Chao, H.-C., & Vasilakos, A. V. (2011). Joint Forensics-Scheduling Strategy for Delay-Sensitive Multimedia Applications over Heterogeneous Networks. IEEE Journal on Selected Areas in Communications, 29(7), 1358-1367. doi:10.1109/jsac.2011.110803Roman, R., & Lopez, J. (2009). Integrating wireless sensor networks and the internet: a security analysis. Internet Research, 19(2), 246-259. doi:10.1108/10662240910952373Wang, Y., Attebury, G., & Ramamurthy, B. (2006). A survey of security issues in wireless sensor networks. IEEE Communications Surveys & Tutorials, 8(2), 2-23. doi:10.1109/comst.2006.315852Xiaojiang Du, & Hsiao-Hwa Chen. (2008). Security in wireless sensor networks. IEEE Wireless Communications, 15(4), 60-66. doi:10.1109/mwc.2008.4599222Pelechrinis, K., Iliofotou, M., & Krishnamurthy, S. V. (2011). Denial of Service Attacks in Wireless Networks: The Case of Jammers. IEEE Communications Surveys & Tutorials, 13(2), 245-257. doi:10.1109/surv.2011.041110.00022Zhou, L., Wang, X., Tu, W., Muntean, G., & Geller, B. (2010). Distributed scheduling scheme for video streaming over multi-channel multi-radio multi-hop wireless networks. IEEE Journal on Selected Areas in Communications, 28(3), 409-419. doi:10.1109/jsac.2010.100412Lin, K., Lai, C.-F., Liu, X., & Guan, X. (2010). Energy Efficiency Routing with Node Compromised Resistance in Wireless Sensor Networks. Mobile Networks and Applications, 17(1), 75-89. doi:10.1007/s11036-010-0287-xLi, H., Lin, K., & Li, K. (2011). Energy-efficient and high-accuracy secure data aggregation in wireless sensor networks. Computer Communications, 34(4), 591-597. doi:10.1016/j.comcom.2010.02.026Oliveira, L. M. L., de Sousa, A. F., & Rodrigues, J. J. P. C. (2011). Routing and mobility approaches in IPv6 over LoWPAN mesh networks. International Journal of Communication Systems, 24(11), 1445-1466. doi:10.1002/dac.1228Narten T Nordmark E Simpson W Soliman H Neighbor Discovery for IP version 6 (IPv6) 2007Singh H Beebee W Nordmark E IPv6 Subnet Model: The Relationship between Links and Subnet Prefixes 2010Roman, R., Lopez, J., & Gritzalis, S. (2008). Situation awareness mechanisms for wireless sensor networks. IEEE Communications Magazine, 46(4), 102-107. doi:10.1109/mcom.2008.4481348Sakarindr, P., & Ansari, N. (2007). Security services in group communications over wireless infrastructure, mobile ad hoc, and wireless sensor networks. IEEE Wireless Communications, 14(5), 8-20. doi:10.1109/mwc.2007.4396938Tsao T Alexander R Dohler M Daza V Lozano A A Security Framework for Routing over Low Power and Lossy Networks 2009Karlof C Wagner D Secure Routing in Wireless Sensor Networks: Attacks and Countermeasures First IEEE International Workshop on Sensor Network Protocols and Applications 2003 113 127 10.1109/SNPA.2003.1203362Hui J Thubert P Compression Format for IPv6 Datagrams in 6LoWPAN Networks 2009Elaine Shi, & Perrig, A. (2004). Designing Secure Sensor Networks. IEEE Wireless Communications, 11(6), 38-43. doi:10.1109/mwc.2004.1368895Akkaya, K., & Younis, M. (2005). A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 3(3), 325-349. doi:10.1016/j.adhoc.2003.09.01

    A Survey on Privacy Preserving Data Aggregation Protocols forWireless Sensor Networks

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    The data aggregation is a widely used mechanism in Wireless Sensor Networks (WSNs) to increase lifetime of a sensor node, send robust information by avoiding redundant data transmission to the base station. The privacy preserving data aggregation is a challenge in wireless communication medium as it could be eavesdropped; however it enhances the security without compromising energy efficiency. Thus the privacy protecting data aggregation protocols aims to prevent the disclosure of individual data though an adversary intercept a link or compromise a node’s data. We present a study of different privacy preserving data aggregation techniques used in WSNs to enhance energy and security based on the types of nodes in the network, topology and encryptions used for data aggregation.</p

    Distributed estimation of stochastic multiagent systems for cooperative control with a virtual network

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    This article proposes a distributed estimation algorithm that uses local information about the neighbors through sensing or communication to design an estimation-based cooperative control of the stochastic multiagent system (MAS). The proposed distributed estimation algorithm solely relies on local sensing information rather than exchanging estimated state information from other agents, as is commonly required in conventional distributed estimation methods, reducing communication overhead. Furthermore, the proposed method allows interactions between all agents, including non-neighboring agents, by establishing a virtual fully connected network with the MAS state information independently estimated by each agent. The stability of the proposed distributed estimation algorithm is theoretically verified. Numerical simulations demonstrate the enhanced performance of the estimation-based linear and nonlinear control. In particular, using the virtual fully connected network concept in the MAS with the sensing/communication range, the flock configuration can be tightly controlled within the desired boundary, which cannot be achieved through the conventional flocking methods
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