RGIM: An Integrated Approach to Improve QoS in AODV, DSR and DSDV Routing Protocols for FANETS Using the Chain Mobility Model

Flying ad hoc networks (FANETs) are a collection of unmanned aerial vehicles that communicate without any predefined infrastructure. FANET, being one of the most researched topics nowadays, finds its scope in many complex applications like drones used for military applications, border surveillance systems and other systems like civil applications in traffic monitoring and disaster management. Quality of service (QoS) performance parameters for routing e.g. delay, packet delivery ratio, jitter and throughput in FANETs are quite difficult to improve. Mobility models play an important role in evaluating the performance of the routing protocols. In this paper, the integration of two selected mobility models, i.e. random waypoint and Gauss–Markov model, is implemented. As a result, the random Gauss integrated model is proposed for evaluating the performance of AODV (ad hoc on-demand distance vector), DSR (dynamic source routing) and DSDV (destination-Sequenced distance vector) routing protocols. The simulation is done with an NS2 simulator for various scenarios by varying the number of nodes and taking low- and high-node speeds of 50 and 500, respectively. The experimental results show that the proposed model improves the QoS performance parameters of AODV, DSR and DSDV protocol

Topographical Automation of MANET using Reactive Routing Protocols

Wireless mobile ad-hoc networks (MANET) are characterized as infrastructure less networks. Topologies are formed with movement of regular nodes which has multi radio links and these regular nodes under demand behaves as backbone node (router) to forward packets across the network. These networks suffer frequent topology changes due to the dynamic stochastic process behavior of incoming nodes. Mobile ad-hoc networks lack load balancing that causes unnecessary packet loss and route break up in real-time data transmission. Area of operation, interference, and communication link range and path loss are the factors to affect the throughput of MANET. In this paper we evaluated the performance of AODV and DSR routing protocols which are enhanced by an Automation Topography, In our proposed Topographical Automation the location of incoming nodes are completely random and those will be confined themselves within a certain communication range such that the throughput is enhanced to meet better QoS level. As location of the nodes are system defined and quite automatic, nodes before being forwarded with the full assurance of successful session flows. It is often advantageous to position stable and capable relay nodes, including unmanned ground vehicles (UGVs) or unmanned aerial vehicles (UAVs), and unmanned under sea vehicles (UUVs) used by Defense to save cost as well as life

Unmanned Aerial ad Hoc Networks: Simulation-Based Evaluation of Entity Mobility Models’ Impact on Routing Performance

An unmanned aerial ad hoc network (UAANET) is a special type of mobile ad hoc network (MANET). For these networks, researchers rely mostly on simulations to evaluate their proposed networking protocols. Hence, it is of great importance that the simulation environment of a UAANET replicates as much as possible the reality of UAVs. One major component of that environment is the movement pattern of the UAVs. This means that the mobility model used in simulations has to be thoroughly understood in terms of its impact on the performance of the network. In this paper, we investigate how mobility models affect the performance of UAANET in simulations in order to come up with conclusions/recommendations that provide a benchmark for future UAANET simulations. To that end, we first propose a few metrics to evaluate the mobility models. Then, we present five random entity mobility models that allow nodes to move almost freely and independently from one another and evaluate four carefully-chosen MANET/UAANET routing protocols: ad hoc on-demand distance vector (AODV), optimized link state routing (OLSR), reactive-geographic hybrid routing (RGR) and geographic routing protocol (GRP). In addition, flooding is also evaluated. The results show a wide variation of the protocol performance over different mobility models. These performance differences can be explained by the mobility model characteristics, and we discuss these effects. The results of our analysis show that: (i) the enhanced Gauss–Markov (EGM) mobility model is best suited for UAANET; (ii) OLSR, a table-driven proactive routing protocol, and GRP, a position-based geographic protocol, are the protocols most sensitive to the change of mobility models; (iii) RGR, a reactive-geographic hybrid routing protocol, is best suited for UAANET

A survey on network simulators in three-dimensional wireless ad hoc and sensor networks

© 2016 The Author(s). As steady research in wireless ad hoc and sensor networks is going on, performance evaluation through relevant network simulator becomes indispensable procedure to demonstrate superiority to comparative schemes and suitability in most literatures. Thus, it is very important to establish credibility of simulation results by investigating merits and limitations of each simulator prior to selection. Based on this motivation, in this article, we present a comprehensive survey on current network simulators for new emerging research area, three-dimensional wireless ad hoc and sensor networks which is represented by airborne ad hoc networks and underwater sensor networks by reviewing major existing simulators as well as presenting their main features in several aspects. In addition, we address the outstanding mobility models which are main components in simulation study for self-organizing ad hoc networks. Finally, open research issues and research challenges are discussed and presented

A Discretized Approach to Air Pollution Monitoring Using UAV-based Sensing

[EN] Recently, Unmanned Aerial Vehicles (UAVs) have become a cheap alternative to sense pollution values in a certain area due to their flexibility and ability to carry small sensing units. In a previous work, we proposed a solution, called Pollution-driven UAV Control (PdUC), to allow UAVs to autonomously trace pollutant sources, and monitor air quality in the surrounding area. However, despite operational, we found that the proposed solution consumed excessive time, especially when considering the battery lifetime of current multi-rotor UAVs. In this paper, we have improved our previously proposed solution by adopting a space discretization technique. Discretization is one of the most efficient mathematical approaches to optimize a system by transforming a continuous domain into its discrete counterpart. The improvement proposed in this paper, called PdUC-Discretized (PdUC-D), consists of an optimization whereby UAVs only move between the central tile positions of a discretized space, avoiding monitoring locations separated by small distances, and whose actual differences in terms of air quality are barely noticeable. We also analyze the impact of varying the tile size on the overall process, showing that smaller tile sizes offer high accuracy at the cost of an increased flight time. Taking into account the obtained results, we consider that a tile size of 100 x 100 meters offers an adequate trade-off between flight time and monitoring accuracy. Experimental results show that PdUC-D drastically reduces the convergence time compared to the original PdUC proposal without loss of accuracy, and it also increases the performance gap with standard mobility patterns such as Spiral and Billiard.This work was partially supported by the "Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a Retos de la Sociedad, Proyecto I+D+I TEC2014-52690-R", 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), the "Programa de becas SENESCYT de la Republica del Ecuador", and the Research Direction of the University of Cuenca.Alvear-Alvear, Ó.; Tavares De Araujo Cesariny Calafate, CM.; Zema, N.; Natalizio, E.; Hernández-Orallo, E.; Cano, J.; Manzoni, P. (2018). A Discretized Approach to Air Pollution Monitoring Using UAV-based Sensing. Mobile Networks and Applications. 23(6):1693-1702. https://doi.org/10.1007/s11036-018-1065-4S16931702236Adam-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. Environ Health Perspect 970(2013):1–19. https://doi.org/10.1289/ehp.1306566Agency U.S.E.P. (2015) Air Quality Index Available: http://cfpub.epa.gov/airnow/index.cfm?action=aqibasics.aqiAlvear O, Calafate CT, Hernández-Orallo E, Cano JC, Manzoni P (2015) Mobile Pollution Data Sensing Using UAVs The 13th International Conference on Advances in Mobile Computing and MultimediaAlvear O, Zamora W, Calafate C, Cano JC, Manzoni P (2016) An architecture offering mobile pollution sensing with high spatial resolution. J Sens:2016Alvear O, Zema NR, Natalizio E, Calafate CT (2017) Using uav-based systems to monitor air pollution in areas with poor accessibility. J Adv Transp:2017Alvear OA, Zema NR, Natalizio E, Calafate CT (2017) A chemotactic pollution-homing uav guidance system. In: 2017 13th international Wireless communications and mobile computing conference (IWCMC). IEEE, pp 2115–2120André M (2004) The artemis european driving cycles for measuring car pollutant emissions. Sci Total Environ 334:73–84Basu P, Redi J, Shurbanov V (2004) Coordinated flocking of uavs for improved connectivity of mobile ground nodes. In: 2004 IEEE Military communications conference, MILCOM, vol 3. IEEE, pp 1628–1634Biomo JDMM, Kunz T, St-Hilaire M (2014) An enhanced gauss-markov mobility model for simulations of unmanned aerial ad hoc networks. In: 2014 7th IFIP Wireless and mobile networking conference (WMNC). IEEE, pp 1–8Bouachir O, Abrassart A, Garcia F, Larrieu N (2014) A mobility model for uav ad hoc network. In: 2014 international conference on Unmanned aircraft systems (ICUAS). IEEE, pp 383–388Cox TH, Nagy CJ, Skoog MA, Somers IA, Warner R Civil uav capability assessmentEisenman SB, Miluzzo E, Lane ND, Peterson RA, Ahn GS, Campbell AT (2009) Bikenet: a mobile sensing system for cyclist experience mapping. ACM Transactions on Sensor Networks (TOSN) 6(1):6Erman AT, van Hoesel L, Havinga P, Wu J (2008) Enabling mobility in heterogeneous wireless sensor networks cooperating with uavs for mission-critical management. IEEE Wirel Commun 15(6):38–46Fayyad U, Irani K (1993) Multi-interval discretization of continuous-valued attributes for classification learningHugenholtz CH, Moorman BJ, Riddell K, Whitehead K (2012) Small unmanned aircraft systems for remote sensing and earth science research. Eos, Trans Amer Geophysical Union 93(25):236–236Illingworth S, Allen G, Percival C, Hollingsworth P, Gallagher M, Ricketts H, Hayes H, adosz H, Crawley PD, Roberts G (2014) Measurement of boundary layer ozone concentrations on-board a Skywalker unmanned aerial vehicle. Atmos Sci Lett 15(4):252–258Kennedy J (2011) Particle swarm optimization. In: Encyclopedia of machine learning. Springer, pp 760–766Khan A, Schaefer D, Tao L, Miller DJ, Sun K, Zondlo MA, Harrison WA, Roscoe B, Lary DJ (2012) Low power greenhouse gas sensors for unmanned aerial vehicles. Remote Sens 4(5):1355–1368Kuiper E, Nadjm-Tehrani S (2006) Mobility models for uav group reconnaissance applications. In: 2006 International conference on wireless and mobile communications (ICWMC’06). IEEE, pp 33–33McFrederick Q, Kathilankal J, Fuentes J (2008) Air pollution modifies floral scent trails. Atmos Environ 42(10):2336–2348MQ131 Ozone Sensor (2017) Datasheet: http://www.sensorsportal.com/downloads/mq131.pdfOrfanus D, de Freitas EP (2014) Comparison of uav-based reconnaissance systems performance using realistic mobility models. In: 2014 6Th international congress on ultra modern telecommunications and control systems and workshops (ICUMT). IEEE, pp 248–253Pajares G (2015) Overview and current status of remote sensing applications based on unmanned aerial vehicles (uavs). Photogram Eng Remote Sens 81(4):281–329Pujadas M, Plaza J, Teres Jx, Artıñano B, Millan 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. Atmos Environ 34(19):3041–3056R Core Team: R (2016) A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/Seaton A, Godden D, MacNee W, Donaldson K (1995) Particulate air pollution and acute health effects. The lancet 345(8943):176–178Stein ML (1999) Statistical interpolation of spatial data: some theory for kriging. Springer, New YorkTeh SK, Mejias L, Corke P, Hu W (2008) Experiments in integrating autonomous uninhabited aerial vehicles(uavs) and wireless sensor networks. In: 2008 Australasian Conference on Robotics and Automation (ACRA 08). The Australian Robotics and Automation Association Inc., Canberra. https://eprints.qut.edu.au/15536/Wan Y, Namuduri K, Zhou Y, Fu S (2013) A smooth-turn mobility model for airborne networks. IEEE Trans Veh Technol 62(7):3359–3370Wang W, Guan X, Wang B, Wang Y (2010) A novel mobility model based on semi-random circular movement in mobile ad hoc networks. Inf Sci 180(3):399–413Zhou B, Xu K, Gerla M (2004) Group and swarm mobility models for ad hoc network scenarios using virtual tracks. In: 2004 IEEE Military communications conference, MILCOM 2004, vol 1. IEEE, pp 289–29

IMPLEMENTATION AND OPTIMIZATION OF RWP MOBILITY MODEL IN WSNS UNDER TOSSIM SIMULATOR

Mobility has always represented a complicated phenomenon in the network routing process. This complexity is mainly facilitated in the way that ensures reliable connections for efficient orientation of data. Many years ago, different studies were initiated basing on routing protocols dedicated to static environments in order to adapt them to the mobile environment. In the present work, we have a different vision of mobility that has many advantages due to its 'mobile' principle. Indeed, instead of searching to prevent mobility and testing for example to immobilize momentarily a mobile environment to provide routing task, we will exploit this mobility to improve routing. Based on that, we carried out a set of works to achieve this objective. For our first contribution, we found that the best way to make use of this mobility is to follow a mobility model. Many models have been proposed in the literature and employed as a data source in most studies. After a careful study, we focused on the Random Waypoint mobility model (RWP) in order to ensure routing in wireless networks. Our contribution involves a Random Waypoint model (in its basic version) that was achieved on the TOSSIM simulator, and it was considered as a platform for our second (and main) contribution, in which we suggested an approach based RWP where network nodes can collaborate and work together basing on our recommended algorithm. Such an approach offers many advantages to ensure routing in a dynamic environment. Finally, our contributions comprise innovative ideas for suggesting other solutions that will improve them

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-

A Survey of Security in UAVs and FANETs: Issues, Threats, Analysis of Attacks, and Solutions

Thanks to the rapidly developing technology, unmanned aerial vehicles (UAVs) are able to complete a number of tasks in cooperation with each other without need for human intervention. In recent years, UAVs, which are widely utilized in military missions, have begun to be deployed in civilian applications and mostly for commercial purposes. With their growing numbers and range of applications, UAVs are becoming more and more popular; on the other hand, they are also the target of various threats which can exploit various vulnerabilities of UAV systems in order to cause destructive effects. It is therefore critical that security is ensured for UAVs and the networks that provide communication between UAVs. In this survey, we aimed to present a comprehensive detailed approach to security by classifying possible attacks against UAVs and flying ad hoc networks (FANETs). We classified the security threats into four major categories that make up the basic structure of UAVs; hardware attacks, software attacks, sensor attacks, and communication attacks. In addition, countermeasures against these attacks are presented in separate groups as prevention and detection. In particular, we focus on the security of FANETs, which face significant security challenges due to their characteristics and are also vulnerable to insider attacks. Therefore, this survey presents a review of the security fundamentals for FANETs, and also four different routing attacks against FANETs are simulated with realistic parameters and then analyzed. Finally, limitations and open issues are also discussed to direct future wor

Combining LoRaWAN and a New 3D Motion Model for Remote UAV Tracking

Over the last few years, the many uses of Unmanned Aerial Vehicles (UAVs) have captured the interest of both the scientific and the industrial communities. A typical scenario consists in the use of UAVs for surveillance or target-search missions over a wide geographical area. In this case, it is fundamental for the command center to accurately estimate and track the trajectories of the UAVs by exploiting their periodic state reports. In this work, we design an ad hoc tracking system that exploits the Long Range Wide Area Network (LoRaWAN) standard for communication and an extended version of the Constant Turn Rate and Acceleration (CTRA) motion model to predict drone movements in a 3D environment. Simulation results on a publicly available dataset show that our system can reliably estimate the position and trajectory of a UAV, significantly outperforming baseline tracking approaches.Comment: 6 pages, 6 figures, in review for IEEE WISARN 2020 (INFOCOM WORKSHOP) 2020 : IEEE WiSARN 2020 (INFOCOM WORKSHOP) 2020: 13th International Workshop on Wireless Sensor, Robot and UAV Network