A Review of TV White Space Technology and its Deployments in Africa

The emergence of bandwidth-driven applications in the current wireless communication environment is driving a paradigm shift from the conventional fixed spectrum assignment policy to intelligent and dynamic spectrum access. Practical demands for efficient spectrum utilization have continued to drive the development of TV white space technology to provide affordable and reliable wireless connectivity. It is envisaged that transition from analogue transmission to Digital Terrestrial Television (DTT) creates more spectrum opportunity for TV white space access and regulatory agencies of many countries had begun to explore this opportunity to address spectrum scarcity. To convey the evolutionary trends in the development of TV white space technology, this paper presents a comprehensive review on the contemporary approaches to TV white space technology and practical deployments of pilot projects in Africa. The paper outlines the activities in TV white space technology, which include regulations and standardization, commercial trials, research challenges, open issues and future research directions. Furthermore, it also provides an overview of the current industrial trends in TV white space technology which demonstrates that cognitive radio as an enabling technology for TV white space technology

Chronology of the development of Active Queue Management algorithms of RED family. Part 1: from 1993 up to 2005

This work is the first part of a large bibliographic review of active queue management algorithms of the Random Early Detection (RED) family, presented in the scientific press from 1993 to 2023. The first part will provide data on algorithms published from 1993 to 2005

Analysis and Simulation of LTE Downlink and Uplink Transceiver

LTE (Long Term Evolution) is a next generation standard by 3rd Generation Partnership Project (3GPPP) consortium. In this paper, the physical layer (PHY) of LTE transceiver is analyzed in downlink and uplink transmissions. Simulations of the physical layer of LTE transceiver are obtained with the use of LTE System Toolbox by Mathworks. Simulation results are presented to show the performance of LTE transceivers in Physical Downlink Shared Channel (PDSCH) and Physical Uplink Shared Channel (PUSCH). Measurements of throughput and Bit Error Rate (BER) are obtained for different simulation configurations

Novel Anti Co-Channel Interference Scheme for Sensor Networks

With improvement of the automation level, wireless sensors are widely used, but various kinds of interference lead to problems in the application. In order to deal with co-channel interference, a throughput efficient scheme based on Extended Binary Phase Shift Keying (EBPSK) modulation is introduced in physical layer. On this basis, the corresponding transmission scheme and the important impacting filter are presented. Effects of co-channel interference on the EBPSK waveform in different cases are analyzed. Simulation results illustrate the excellent anti-interference performance of the EBPSK system itself, when the initial phase of the co-channel interference is small

Mesmerizer: A Effective Tool for a Complete Peer-to-Peer Software Development Life-cycle

In this paper we present what are, in our experience, the best practices in Peer-To-Peer(P2P) application development and how we combined them in a middleware platform called Mesmerizer. We explain how simulation is an integral part of the development process and not just an assessment tool. We then present our component-based event-driven framework for P2P application development, which can be used to execute multiple instances of the same application in a strictly controlled manner over an emulated network layer for simulation/testing, or a single application in a concurrent environment for deployment purpose. We highlight modeling aspects that are of critical importance for designing and testing P2P applications, e.g. the emulation of Network Address Translation and bandwidth dynamics. We show how our simulator scales when emulating low-level bandwidth characteristics of thousands of concurrent peers while preserving a good degree of accuracy compared to a packet-level simulator

A Study on Security Mechanism of Civil Air Defense and Disaster Warning Control System based on CDMA Wireless Access

Due to the use of wireless transmission and open networks, mobile communications are faced with enormous security threats. This study focuses on security mechanisms of the civil air defense and disaster warning control system based on CDMA wireless access. The working principle and process of authentication and data encryption are presented in detail. Further we propose and develop a novel hybrid cryptosystem combining AES and ECC for this control system in order to achieve the convenience of a public-key cryptosystem and the efficiency of a symmetric-key cryptosystem. Providing high security and encryption efficiency as well as simple management of keys, the proposed cryptographic approach can meet the requirements for security and real-time-ness of data transmission in the wireless access control system

MANET performance optimization using network-based criteria and unmanned aerial vehicles

In this contribution we consider the problem of optimal drone positioning for improving the 1 operation of a mobile ad hoc network. We build upon our previous results devoted to the application 2 of game-theoretic methods for computing optimal strategies. One specific problem that arises in this 3 context is that the optimal solution cannot be uniquely determined. In this case, one has to use some 4 other criteria to choose the best (in some sense) of all optimal solutions. It is argued that centrality 5 measures as well as node ranking can provide a good criterion for the selection of a unique solution. 6 We showed that for two specific networks most criteria yielded the same solution thus demonstrating 7 good coherence in their predictions.

Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility

[EN] Air pollution monitoring has recently become an issue of utmost importance in our society. Despite the fact that crowdsensing approaches could be an adequate solution for urban areas, they cannot be implemented in rural environments. Instead, deploying a fleet of UAVs could be considered an acceptable alternative. Embracing this approach, this paper proposes the use of UAVs equipped with off-the-shelf sensors to perform air pollution monitoring tasks. These UAVs are guided by our proposed Pollution-driven UAV Control (PdUC) algorithm, which is based on a chemotaxis metaheuristic and a local particle swarm optimization strategy. Together, they allow automatically performing the monitoring of a specified area using UAVs. Experimental results show that, when using PdUC, an implicit priority guides the construction of pollution maps by focusing on areas where the pollutants' concentration is higher. This way, accurate maps can be constructed in a faster manner when compared to other strategies. The PdUC scheme is compared against various standard mobility models through simulation, showing that it achieves better performance. In particular, it is able to find the most polluted areas with more accuracy and provides a higher coverage within the time bounds defined by the UAV flight time.This work has been partially carried out in the framework of the DIVINA Challenge Team, which is funded under the Labex MS2T program. Labex MS2T is supported by the French Government, through the program "Investments for the Future" managed by the National Agency for Research (Reference: ANR-11-IDEX-0004-02). This work was also supported by the "Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a Retos de la Sociedad, Proyecto I+D+I TEC2014-52690-R," the "Programa de Becas SENESCYT de la Republica del Ecuador," and the Research Direction of University of Cuenca.Alvear-Alvear, Ó.; Zema, NR.; Natalizio, E.; Tavares De Araujo Cesariny Calafate, CM. (2017). Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility. Journal of Advanced Transportation. 2017:1-14. https://doi.org/10.1155/2017/8204353S1142017Seaton, A., Godden, D., MacNee, W., & Donaldson, K. (1995). Particulate air pollution and acute health effects. The Lancet, 345(8943), 176-178. doi:10.1016/s0140-6736(95)90173-6McFrederick, Q. S., Kathilankal, J. C., & Fuentes, J. D. (2008). Air pollution modifies floral scent trails. Atmospheric Environment, 42(10), 2336-2348. doi:10.1016/j.atmosenv.2007.12.033Mage, D., Ozolins, G., Peterson, P., Webster, A., Orthofer, R., Vandeweerd, V., & Gwynne, M. (1996). Urban air pollution in megacities of the world. Atmospheric Environment, 30(5), 681-686. doi:10.1016/1352-2310(95)00219-7Mayer, H. (1999). Air pollution in cities. Atmospheric Environment, 33(24-25), 4029-4037. doi:10.1016/s1352-2310(99)00144-2Kanaroglou, P. S., Jerrett, M., Morrison, J., Beckerman, B., Arain, M. A., Gilbert, N. L., & Brook, J. R. (2005). Establishing an air pollution monitoring network for intra-urban population exposure assessment: A location-allocation approach. Atmospheric Environment, 39(13), 2399-2409. doi:10.1016/j.atmosenv.2004.06.049Alvear, O., Zamora, W., Calafate, C., Cano, J.-C., & Manzoni, P. (2016). An Architecture Offering Mobile Pollution Sensing with High Spatial Resolution. Journal of Sensors, 2016, 1-13. doi:10.1155/2016/1458147Adam-Poupart, A., Brand, A., Fournier, M., Jerrett, M., & Smargiassi, A. (2014). Spatiotemporal Modeling of Ozone Levels in Quebec (Canada): A Comparison of Kriging, Land-Use Regression (LUR), and Combined Bayesian Maximum Entropy–LUR Approaches. Environmental Health Perspectives, 122(9), 970-976. doi:10.1289/ehp.1306566Pujadas, M., Plaza, J., Terés, J., Artı́ñano, B., & Millán, M. (2000). Passive remote sensing of nitrogen dioxide as a tool for tracking air pollution in urban areas: the Madrid urban plume, a case of study. Atmospheric Environment, 34(19), 3041-3056. doi:10.1016/s1352-2310(99)00509-9Eisenman, S. B., Miluzzo, E., Lane, N. D., Peterson, R. A., Ahn, G.-S., & Campbell, A. T. (2009). BikeNet. ACM Transactions on Sensor Networks, 6(1), 1-39. doi:10.1145/1653760.1653766André, M. (2004). The ARTEMIS European driving cycles for measuring car pollutant emissions. Science of The Total Environment, 334-335, 73-84. doi:10.1016/j.scitotenv.2004.04.070Hu, S.-C., Wang, Y.-C., Huang, C.-Y., & Tseng, Y.-C. (2011). Measuring air quality in city areas by vehicular wireless sensor networks. Journal of Systems and Software, 84(11), 2005-2012. doi:10.1016/j.jss.2011.06.043Dunbabin, M., & Marques, L. (2012). Robots for Environmental Monitoring: Significant Advancements and Applications. IEEE Robotics & Automation Magazine, 19(1), 24-39. doi:10.1109/mra.2011.2181683Hugenholtz, C. H., Moorman, B. J., Riddell, K., & Whitehead, K. (2012). Small unmanned aircraft systems for remote sensing and Earth science research. Eos, Transactions American Geophysical Union, 93(25), 236-236. doi:10.1029/2012eo250005Pajares, G. (2015). Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs). Photogrammetric Engineering & Remote Sensing, 81(4), 281-330. doi:10.14358/pers.81.4.281Colomina, I., & Molina, P. (2014). Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92, 79-97. doi:10.1016/j.isprsjprs.2014.02.013Anderson, K., & Gaston, K. J. (2013). Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment, 11(3), 138-146. doi:10.1890/120150Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 13(6), 693-712. doi:10.1007/s11119-012-9274-5Bellvert, J., Zarco-Tejada, P. J., Girona, J., & Fereres, E. (2013). Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle. Precision Agriculture, 15(4), 361-376. doi:10.1007/s11119-013-9334-5Erman, A., Hoesel, L., Havinga, P., & Wu, J. (2008). Enabling mobility in heterogeneous wireless sensor networks cooperating with UAVs for mission-critical management. IEEE Wireless Communications, 15(6), 38-46. doi:10.1109/mwc.2008.4749746Khan, A., Schaefer, D., Tao, L., Miller, D. J., Sun, K., Zondlo, M. A., … Lary, D. J. (2012). Low Power Greenhouse Gas Sensors for Unmanned Aerial Vehicles. Remote Sensing, 4(5), 1355-1368. doi:10.3390/rs4051355Illingworth, S., Allen, G., Percival, C., Hollingsworth, P., Gallagher, M., Ricketts, H., … Roberts, G. (2014). Measurement of boundary layer ozone concentrations on-board a Skywalker unmanned aerial vehicle. Atmospheric Science Letters, n/a-n/a. doi:10.1002/asl2.496Wang, W., Guan, X., Wang, B., & Wang, Y. (2010). A novel mobility model based on semi-random circular movement in mobile ad hoc networks. Information Sciences, 180(3), 399-413. doi:10.1016/j.ins.2009.10.001Wan, Y., Namuduri, K., Zhou, Y., & Fu, S. (2013). A Smooth-Turn Mobility Model for Airborne Networks. IEEE Transactions on Vehicular Technology, 62(7), 3359-3370. doi:10.1109/tvt.2013.2251686Briante, O., Loscri, V., Pace, P., Ruggeri, G., & Zema, N. R. (2015). COMVIVOR: An Evolutionary Communication Framework Based on Survivors’ Devices Reuse. Wireless Personal Communications, 85(4), 2021-2040. doi:10.1007/s11277-015-2888-yMeier, L., Tanskanen, P., Heng, L., Lee, G. H., Fraundorfer, F., & Pollefeys, M. (2012). PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision. Autonomous Robots, 33(1-2), 21-39. doi:10.1007/s10514-012-9281-4Boussaïd, I., Lepagnot, J., & Siarry, P. (2013). A survey on optimization metaheuristics. Information Sciences, 237, 82-117. doi:10.1016/j.ins.2013.02.041Stein, M. L. (1999). Interpolation of Spatial Data. Springer Series in Statistics. doi:10.1007/978-1-4612-1494-