In emergency, it is very important to make a real-time and precise positioning of emergency address. As a large amount of emergencies take place in mountainous region, river valley and high building, original node location based on plane does not apply any more, thus, in order to improve precision of early warning and location of disaster, we have proposed a 3D-DVHop localization algorithm, and compare it with the typical 3-D location algorithm APIS. Through the simulation experiment, the localization error of the improved 3D-DVHop algorithm is far less than that of APIS algorithm, which improves the localization accuracy greatly under unexpected circumstances and laying a solid foundation for the following decision-making and rescuing work.
RawatP., SinghK.D., ChaouchiH. and BonninJ.M., Wireless sensor networks: A survey on recent developments and potential synergies, The Journal of Supercomputing68(1) (2014), 1–48.
2.
RawatP., SinghK.D., ChaouchiH. and BonninJ.M., Wireless sensor networks: A survey on recent developments and potential synergies, The Journal of Supercomputing68(1) (2014), 1–48.
3.
ChandraS.R., SurajS., DeepakP. and Zomaya AlbertY., Location of things (LoT): A review and taxonomy of sensors localization in IoT infrastructure, IEEE Communications Surveys and Tutorials20(3) (2018), 2028–2061.
4.
KaurA., GuptaG.P. and KumarP., A survey of recent developments in DV-Hop localization techniques for wireless sensor network, Journal of Telecommunication, Electronic and Computer Engineering9(2) (2017), 61–71.
5.
SubirH. and AmritaG., A survey on mobile anchor assisted localization techniques in wireless sensor networks, Wireless Networks22(7) (2016), 2317–2336.
6.
HanG., XuH., DuongT.Q., JiangJ. and HaraT., Localization algorithms of wireless sensor networks: A survey, Telecommunication Systems52(4) (2013), 2419–2436.
7.
JunboW., ZixueC., TaishiY., YinghuiZ. and LeiJ., A 3D localisation method for searching survivors/corpses based on WSN and Kalman filter, International Journal of Sensor Networks19(3-4) (2015), 181–193.
8.
AhmadT., LiX.J. and SeetB.C., Parametric loop division for 3D localization in wireless sensor networks, Sensors17(7) (2017), 1697.
9.
0uC.H. and SsuK.F., Sensor position determination with flying anchors in three-dimensional wireless sensor networks, IEEE Transactions on Mobile Computing7(9) (2008), 1084–1097.
10.
AhmadT., LiX.J. and SeetB.C., Parametric loop division for 3D localization in wireless sensor networks, Sensors17(7) (2017), 1697.
11.
TomicS. and MezeiI., Improvements of DV-Hop localization algorithm for wireless sensor networks, Telecommunication Systems61(1) (2016), 93–106.
12.
KashiS.S., A load balanced location service for location information management of multi-sink Wireless Sensor Networks, Computing100(2) (2018), 93–117.
13.
LuoQ., PengY., LiJ. and PengX., RSSI-based localization through uncertain data mapping for wireless sensor networks, IEEE Sensors Journal16(9) (2016), 3155–3162.
14.
SinghM. and KhilarP.M., An analytical geometric range free localization scheme based on mobile beacon points in wireless sensor network, Wireless Networks22(8) (2016), 2537–2550.
15.
MuneshS. and MohanK.P., A range free geometric technique for localization of wireless sensor network (WSN) based on controlled communication range, Wireless Personal Communications94(3) (2017), 1359–1385.
16.
WuS., A traffic motion object extraction algorithm, International Journal of Bifurcation and Chaos25(14) (2015), Article Number 1540039.
17.
WuS., WangM. and ZouY., Research on internet information mining based on agent algorithm, Future Generation Computer Systems86 (2018), 598–602.
18.
WuS., WangM. and YuntaoZ., Bidirectional cognitive computing method supported by cloud technology, Cognitive Systems Research52 (2018), 615–621.
19.
SongG. and TamD., Two novel DV-Hop localization algorithms for randomly deployed wireless sensor networks, International Journal of Distributed Sensor Networks11(7) (2015), 187670.
20.
KumarS. and LobiyalD.K., Novel DV-Hop localization algorithm for wireless sensor networks, Telecommunication Systems64(3) (2017), 509–524.
21.
PengB. and LiL., An improved localization algorithm based on genetic algorithm in wireless sensor networks, Cognitive Neurodynamics9(2) (2015), 249–256.
22.
DuttaA.K., ElhosenyM., DahiyaV. and ShankarK., An efficient hierarchical clustering protocol for multihop Internet of vehicles communication, Transactions on Emerging Telecommunications Technologies (2019). In Press. 10.1002/ett.3690.
23.
LakshmanaprabuS.K., ElhosenyM. and ShankarK., Optimal tuning of decentralized fractional order PID controllers for TITO process using equivalent transfer function, Cognitive Systems Research (2019). In Press. 10.1016/j.cogsys.2019.07.005.
24.
ElhosenyM. and ShankarK., Reliable data transmission model for mobile ad hoc network using signcryption technique, IEEE Transactions on Reliability (2019). 1–10. In Press 10.1109/TR.2019.2915800.
25.
KrishnarajN., ElhosenyM., ThenmozhiM., SelimM.M. and ShankarK., Deep learning model for real-time image compression in Internet of Underwater Things (IoUT), Journal of Real-Time Image Processing (2019). In Press. 10.1007/s11554-019-00879-6.
26.
ElhosenyM. and ShankarK., Optimal bilateral filter and convolutional neural network based denoising method of medical image measurements, Measurement143 (2019), 125–135.
27.
Senthil RagavanV.K., ElhosenyM. and ShankarK., An enhanced whale optimization algorithm for vehicular communication networks, International Journal of Communication Systems (2019). 10.1002/dac.3953.
28.
ElhosenyM. and ShankarK., Energy efficient optimal routing for communication in VANETs via clustering model, emerging technologies for connected internet of vehicles and intelligent transportation system networks, Studies in Systems, Decision and Control242 (2019), 1–14, ISBN: 978-3-030-22772-2.
29.
ShankarK., ElhosenyM., PerumalE., IlayarajaM. and Sathesh KumarK., An efficient image encryption scheme based on signcryption technique with adaptive elephant herding optimization, cybersecurity and secure information systems: Challenges and solutions in smart environments, Advanced Sciences and Technologies for Security Applications (2019), 31–42, ISBN: 978-3-030-16837-7.
