MWAHCA: A Multimedia Wireless Ad Hoc Cluster Architecture

Wireless Ad hoc networks provide a flexible and adaptable infrastructure to transport data over a great variety of environments. Recently, real-time audio and video data transmission has been increased due to the appearance of many multimedia applications. One of the major challenges is to ensure the quality of multimedia streams when they have passed through a wireless ad hoc network. It requires adapting the network architecture to the multimedia QoS requirements. In this paper we propose a new architecture to organize and manage cluster-based ad hoc networks in order to provide multimedia streams. Proposed architecture adapts the network wireless topology in order to improve the quality of audio and video transmissions. In order to achieve this goal, the architecture uses some information such as each node's capacity and the QoS parameters (bandwidth, delay, jitter, and packet loss). The architecture splits the network into clusters which are specialized in specific multimedia traffic. The real system performance study provided at the end of the paper will demonstrate the feasibility of the proposal.Díaz Santos, JR.; Lloret, J.; Jimenez, JM.; Sendra, S. (2014). MWAHCA: A Multimedia Wireless Ad Hoc Cluster Architecture. Scientific World Journal. 2014. doi:10.1155/2014/913046S2014Lacuesta, R., Lloret, J., Garcia, M., & Peñalver, L. (2010). A Spontaneous Ad Hoc Network to Share WWW Access. EURASIP Journal on Wireless Communications and Networking, 2010(1). doi:10.1155/2010/232083Lloret, 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/s11390-008-9147-6Yu, J. Y., & Chong, P. H. J. (2005). A survey of clustering schemes for mobile ad hoc networks. IEEE Communications Surveys & Tutorials, 7(1), 32-48. doi:10.1109/comst.2005.1423333Lloret, 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.v2i2.325Zhou, C., & Maxemchuk, N. (2011). Distributed Bottleneck Flow Control in Mobile Ad Hoc Networks. Network Protocols and Algorithms, 3(1). doi:10.5296/npa.v3i1.576Zhang, R., Cai, L., Pan, J., & Shen, X. (Sherman). (2011). Resource management for video streaming in ad hoc networks. Ad Hoc Networks, 9(4), 623-634. doi:10.1016/j.adhoc.2010.08.012Tarique, M. (2010). ISSUES OF LONG-HOP AND SHORT-HOP ROUTING IN MOBILE AD HOC NETWORKS: A COMPREHENSIVE STUDY. Network Protocols and Algorithms, 2(2). doi:10.5296/npa.v2i2.430Abdrabou, A., & Zhuang, W. (2009). Statistical QoS routing for IEEE 802.11 multihop ad hoc networks. IEEE Transactions on Wireless Communications, 8(3), 1542-1552. doi:10.1109/twc.2008.080573Kandris, D., Tsagkaropoulos, M., Politis, I., Tzes, A., & Kotsopoulos, S. (2011). Energy efficient and perceived QoS aware video routing over Wireless Multimedia Sensor Networks. Ad Hoc Networks, 9(4), 591-607. doi:10.1016/j.adhoc.2010.09.00

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

Smart PIN: utility-based replication and delivery of multimedia content to mobile users in wireless networks

Next generation wireless networks rely on heterogeneous connectivity technologies to support various rich media services such as personal information storage, file sharing and multimedia streaming. Due to users’ mobility and dynamic characteristics of wireless networks, data availability in collaborating devices is a critical issue. In this context Smart PIN was proposed as a personal information network which focuses on performance of delivery and cost efficiency. Smart PIN uses a novel data replication scheme based on individual and overall system utility to best balance the requirements for static data and multimedia content delivery with variable device availability due to user mobility. Simulations show improved results in comparison with other general purpose data replication schemes in terms of data availability