Geometric deep learning: going beyond Euclidean data

Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions), and are natural targets for machine learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure, and in cases where the invariances of these structures are built into networks used to model them. Geometric deep learning is an umbrella term for emerging techniques attempting to generalize (structured) deep neural models to non-Euclidean domains such as graphs and manifolds. The purpose of this paper is to overview different examples of geometric deep learning problems and present available solutions, key difficulties, applications, and future research directions in this nascent field

Recent advances on filtering and control for nonlinear stochastic complex systems with incomplete information: A survey

This Article is provided by the Brunel Open Access Publishing Fund - Copyright @ 2012 Hindawi PublishingSome recent advances on the filtering and control problems for nonlinear stochastic complex systems with incomplete information are surveyed. The incomplete information under consideration mainly includes missing measurements, randomly varying sensor delays, signal quantization, sensor saturations, and signal sampling. With such incomplete information, the developments on various filtering and control issues are reviewed in great detail. In particular, the addressed nonlinear stochastic complex systems are so comprehensive that they include conventional nonlinear stochastic systems, different kinds of complex networks, and a large class of sensor networks. The corresponding filtering and control technologies for such nonlinear stochastic complex systems are then discussed. Subsequently, some latest results on the filtering and control problems for the complex systems with incomplete information are given. Finally, conclusions are drawn and several possible future research directions are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61104125, 61028008, 61174136, 60974030, and 61074129, the Qing Lan Project of Jiangsu Province of China, the Project sponsored by SRF for ROCS of SEM of China, the Engineering and Physical Sciences Research Council EPSRC of the UK under Grant GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications

The advent of Wireless Sensor Networks (WSNs) has inspired various sciences and telecommunication with its applications, there is a growing demand for robust methodologies that can ensure extended lifetime. Sensor nodes are small equipment which may hold less electrical energy and preserve it until they reach the destination of the network. The main concern is supposed to carry out sensor routing process along with transferring information. Choosing the best route for transmission in a sensor node is necessary to reach the destination and conserve energy. Clustering in the network is considered to be an effective method for gathering of data and routing through the nodes in wireless sensor networks. The primary requirement is to extend network lifetime by minimizing the consumption of energy. Further integrating cognitive radio technique into sensor networks, that can make smart choices based on knowledge acquisition, reasoning, and information sharing may support the network's complete purposes amid the presence of several limitations and optimal targets. This examination focuses on routing and clustering using metaheuristic techniques and machine learning because these characteristics have a detrimental impact on cognitive radio wireless sensor node lifetime

H-infinity filtering with randomly occurring sensor saturations and missing measurements

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 ElsevierIn this paper, the H∞ filtering problem is investigated for a class of nonlinear systems with randomly occurring incomplete information. The considered incomplete information includes both the sensor saturations and the missing measurements. A new phenomenon of sensor saturation, namely, randomly occurring sensor saturation (ROSS), is put forward in order to better reflect the reality in a networked environment such as sensor networks. A novel sensor model is then established to account for both the ROSS and missing measurement in a unified representation by using two sets of Bernoulli distributed white sequences with known conditional probabilities. Based on this sensor model, a regional H∞ filter with a certain ellipsoid constraint is designed such that the filtering error dynamics is locally mean-square asymptotically stable and the H∞-norm requirement is satisfied. Note that the regional l2 gain filtering feature is specifically developed for the random saturation nonlinearity. The characterization of the desired filter gains is derived in terms of the solution to a convex optimization problem that can be easily solved by using the semi-definite program method. Finally, a simulation example is employed to show the effectiveness of the filtering scheme proposed in this paper.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61028008 and 60974030, the National 973 Program of China under Grant 2009CB320600, and the Alexander von Humboldt Foundation of Germany

Energy-Efficient Multi-Level and Distance-Aware Clustering Mechanism for WSNs

[EN] Most sensor networks are deployed at hostile environments to sense and gather specific information. As sensor nodes have battery constraints, therefore, the research community is trying to propose energyefficient solutions for wireless sensor networks (WSNs) to prolong the lifetime of the network. In this paper, we propose an energy-efficient multi-level and distance-aware clustering (EEMDC) mechanism for WSNs. In this mechanism, the area of the network is divided into three logical layers, which depends upon the hop-count-based distance from the base station. The simulation outcomes show that EEMDC is more energy efficient than other existing conventional approaches.This work has been partially supported by the 'Ministerio de Ciencia e Innovacion', through the 'Plan Nacional de I+D+i 2008-2011' in the 'Subprograma de Proyectos de Investigacion Fundamental', project TEC2011-27516, and by the Polytechnic University of Valencia, through the PAID-15-11 multidisciplinary projectsMehmood, A.; Khan, S.; Shams, B.; Lloret, J. (2015). Energy-Efficient Multi-Level and Distance-Aware Clustering Mechanism for WSNs. International Journal of Communication Systems. 28(5):972-989. https://doi.org/10.1002/dac.2720S972989285Sendra, S., Lloret, J., Garcia, M., & Toledo, J. F. (2011). Power Saving and Energy Optimization Techniques for Wireless Sensor Neworks (Invited Paper). Journal of Communications, 6(6). doi:10.4304/jcm.6.6.439-459Bri D Garcia M Lloret J Dini P Real deployments of wireless sensor networks Third International Conference on Sensor Technologies and Applications (SENSORCOMM 2009) 2009 8 23GUI, L., VAL, T., & WEI, A. (2011). A Novel Two-Class Localization Algorithm in Wireless Sensor Networks. Network Protocols and Algorithms, 3(3). doi:10.5296/npa.v3i3.863Rajeswari, A., & P.T, K. (2011). A Novel Energy Efficient Routing Protocols for Wireless Sensor Networks Using Spatial Correlation Based Collaborative Medium Access Control Combined with Hybrid MAC. Network Protocols and Algorithms, 3(4). doi:10.5296/npa.v3i4.1296Lloret, J., Garcia, M., Tomás, J., & Boronat, F. (2008). GBP-WAHSN: A Group-Based Protocol for Large Wireless Ad Hoc and Sensor Networks. Journal of Computer Science and Technology, 23(3), 461-480. doi:10.1007/s11390-008-9147-6Lloret, J., Garcia, M., Bri, D., & Diaz, J. (2009). A Cluster-Based Architecture to Structure the Topology of Parallel Wireless Sensor Networks. Sensors, 9(12), 10513-10544. doi:10.3390/s91210513LEHSAINI, M., GUYENNET, H., & FEHAM, M. (2010). Cluster-based Energy-efficient k-Coverage for Wireless Sensor Networks. Network Protocols and Algorithms, 2(2). doi:10.5296/npa.v2i2.325Liu, G., Xu, B., & Chen, H. (2011). Decentralized estimation over noisy channels in cluster-based wireless sensor networks. International Journal of Communication Systems, 25(10), 1313-1329. doi:10.1002/dac.1308Cheng, L., Chen, C., Ma, J., & Shu, L. (2011). Contention-based geographic forwarding in asynchronous duty-cycled wireless sensor networks. International Journal of Communication Systems, 25(12), 1585-1602. doi:10.1002/dac.1325Wang, X., & Qian, H. (2011). Hierarchical and low-power IPv6 address configuration for wireless sensor networks. International Journal of Communication Systems, 25(12), 1513-1529. doi:10.1002/dac.1318Zhang, D., Yang, Z., Raychoudhury, V., Chen, Z., & Lloret, J. (2013). An Energy-Efficient Routing Protocol Using Movement Trends in Vehicular Ad hoc Networks. The Computer Journal, 56(8), 938-946. doi:10.1093/comjnl/bxt028Chen, J.-S., Hong, Z.-W., Wang, N.-C., & Jhuang, S.-H. (2010). Efficient Cluster Head Selection Methods for Wireless Sensor Networks. Journal of Networks, 5(8). doi:10.4304/jnw.5.8.964-970Peiravi, A., Mashhadi, H. R., & Hamed Javadi, S. (2011). An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithm. International Journal of Communication Systems, 26(1), 114-126. doi:10.1002/dac.1336Zeynali, M., Mollanejad, A., & Khanli, L. M. (2011). Novel hierarchical routing protocol in wireless sensor network. Procedia Computer Science, 3, 292-300. doi:10.1016/j.procs.2010.12.050Heinzelman W Chandrakasan A Balakrishnan H Energy-efficient communication protocol for wireless microsensor networks 33rd Hawaii International Conference on System Sciences (HICSS) 2000 3005 3014Wang, A., Yang, D., & Sun, D. (2012). A clustering algorithm based on energy information and cluster heads expectation for wireless sensor networks. Computers & Electrical Engineering, 38(3), 662-671. doi:10.1016/j.compeleceng.2011.11.017Gou H Yoo Y An energy balancing LEACH algorithm for wireless sensor networks Proceedings of the 7th International Conference on Information Technology: New Generations (ITNG) 2010Ding, P., Holliday, J., & Celik, A. (2005). Distributed Energy-Efficient Hierarchical Clustering for Wireless Sensor Networks. Lecture Notes in Computer Science, 322-339. doi:10.1007/11502593_25Bandyopadhyay S Coyle E An energy-efficient hierarchical clustering algorithm for wireless sensor networks The 32nd IEEE International Conference on Computer Communication (INFOCOM 2003) 2003Jarry, A., Leone, P., Nikoletseas, S., & Rolim, J. (2011). Optimal data gathering paths and energy-balance mechanisms in wireless networks. Ad Hoc Networks, 9(6), 1036-1048. doi:10.1016/j.adhoc.2010.11.003Zhu, Y., Wu, W., Pan, J., & Tang, Y. (2010). An energy-efficient data gathering algorithm to prolong lifetime of wireless sensor networks. Computer Communications, 33(5), 639-647. doi:10.1016/j.comcom.2009.11.008Khamfroush H Saadat R Khademzadeh A Khamfroush K Lifetime increase for wireless sensor networks using cluster-based routing International Association of Computer Science and Information Technology-Spring Conference (IACSIT-SC 2009) 2009Li, H., Liu, Y., Chen, W., Jia, W., Li, B., & Xiong, J. (2013). COCA: Constructing optimal clustering architecture to maximize sensor network lifetime. Computer Communications, 36(3), 256-268. doi:10.1016/j.comcom.2012.10.006Aslam N Phillips W Robertson W Sivakumar S A multi-criterion optimization technique for energy efficient cluster formation in wireless sensor networks 4th IEEE Consumer Communications and Networking Conference, (CCNC 2007) 2007 650 654Yi, S., Heo, J., Cho, Y., & Hong, J. (2007). PEACH: Power-efficient and adaptive clustering hierarchy protocol for wireless sensor networks. Computer Communications, 30(14-15), 2842-2852. doi:10.1016/j.comcom.2007.05.034Yong, Z., & Pei, Q. (2012). A Energy-Efficient Clustering Routing Algorithm Based on Distance and Residual Energy for Wireless Sensor Networks. Procedia Engineering, 29, 1882-1888. doi:10.1016/j.proeng.2012.01.231Chuan-Chi W A minimum transmission energy consumption routing protocol for user-centric wireless networks 2011 1143 1148Kumar, D., Aseri, T. C., & Patel, R. B. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications, 32(4), 662-667. doi:10.1016/j.comcom.2008.11.025Kim KT Moon SS Tree-Based Clustering (TBC) for energy efficient wireless sensor networks IEEE 24th International Conference on Advanced Information Networking and Applications Workshops (WAINA) 2010 680 685Yu, J., Qi, Y., Wang, G., & Gu, X. (2012). A cluster-based routing protocol for wireless sensor networks with nonuniform node distribution. AEU - International Journal of Electronics and Communications, 66(1), 54-61. doi:10.1016/j.aeue.2011.05.002Ye M Li C Wu J EECS: an Energy Efficient Clustering Scheme in wireless sensor networks 24th IEEE International Performance on Computing, and Communications Conference 2005 535 540Gautama N Lee W Pyun J Dynamic clustering and distance aware routing protocol for wireless sensor networks PE-WASUN'09 2009Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660-670. doi:10.1109/twc.2002.804190Lai, W. K., Fan, C. S., & Lin, L. Y. (2012). Arranging cluster sizes and transmission ranges for wireless sensor networks. Information Sciences, 183(1), 117-131. doi:10.1016/j.ins.2011.08.029Pantazis, N. A., Vergados, D. J., Vergados, D. D., & Douligeris, C. (2009). Energy efficiency in wireless sensor networks using sleep mode TDMA scheduling. Ad Hoc Networks, 7(2), 322-343. doi:10.1016/j.adhoc.2008.03.006OMNeT++ Community Documentation and Tutorials of omnet++ http://www.omnetpp.org/Castallia Documentation and Tutorials of Castalia Simulator for WSN and BAN http://castalia.research.nicta.com.au/index.php/en/Research Group on Computer Networks and Multimedia Communication UFPA - Brazil Download-Leach-v2-for-Castalia http://www.gercom.ufpa.br/index.php?option=com_filecabinet&view=files&id=1&Itemid=31&lang=p

H-infinity state estimation for discrete-time complex networks with randomly occurring sensor saturations and randomly varying sensor delays

This is the post-print of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, the state estimation problem is investigated for a class of discrete time-delay nonlinear complex networks with randomly occurring phenomena from sensor measurements. The randomly occurring phenomena include randomly occurring sensor saturations (ROSSs) and randomly varying sensor delays (RVSDs) that result typically from networked environments. A novel sensor model is proposed to describe the ROSSs and the RVSDs within a unified framework via two sets of Bernoulli-distributed white sequences with known conditional probabilities. Rather than employing the commonly used Lipschitz-type function, a more general sector-like nonlinear function is used to describe the nonlinearities existing in the network. The purpose of the addressed problem is to design a state estimator to estimate the network states through available output measurements such that, for all probabilistic sensor saturations and sensor delays, the dynamics of the estimation error is guaranteed to be exponentially mean-square stable and the effect from the exogenous disturbances to the estimation accuracy is attenuated at a given level by means of an $H_{infty}$-norm. In terms of a novel Lyapunov–Krasovskii functional and the Kronecker product, sufficient conditions are established under which the addressed state estimation problem is recast as solving a convex optimization problem via the semidefinite programming method. A simulation example is provided to show the usefulness of the proposed state estimation conditions.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., the National Natural Science Foundation of China under Grants 61028008, 61134009, 61104125 and 60974030, the Natural Science Foundation of Universities in Anhui Province of China under Grant KJ2011B030, and the Alexander von Humboldt Foundation of Germany

A Cooperative Emergency Navigation Framework using Mobile Cloud Computing

The use of wireless sensor networks (WSNs) for emergency navigation systems suffer disadvantages such as limited computing capacity, restricted battery power and high likelihood of malfunction due to the harsh physical environment. By making use of the powerful sensing ability of smart phones, this paper presents a cloud-enabled emergency navigation framework to guide evacuees in a coordinated manner and improve the reliability and resilience in both communication and localization. By using social potential fields (SPF), evacuees form clusters during an evacuation process and are directed to egresses with the aid of a Cognitive Packet Networks (CPN) based algorithm. Rather than just rely on the conventional telecommunications infrastructures, we suggest an Ad hoc Cognitive Packet Network (AHCPN) based protocol to prolong the life time of smart phones, that adaptively searches optimal communication routes between portable devices and the egress node that provides access to a cloud server with respect to the remaining battery power of smart phones and the time latency.Comment: This document contains 8 pages and 3 figures and has been accepted by ISCIS 2014 (29th International Symposium on Computer and Information Sciences