EA-RDSP: Energy Aware Rapidly Deployable Wireless Ad hoc System for Post Disaster Management
Source abstract
Abstract
In post disaster scenarios such as war zones floods and earthquakes, the cellular communication infrastructure can be lost or severely damaged. In such emergency situations, remaining in contact with other rescue response teams in order to provide inputs for both headquarters and disaster survivors becomes very necessary. Therefore, in this research work, a design, implementation and evaluation of energy aware rapidly deployable system named EA-RDSP is proposed. The proposed research work assists the early rescue workers and victims to transmit their location information towards the remotely located servers. In EA-RDSP, two algorithms are proposed i.e., Hop count Assignment (HCA) algorithm and Maximum Neighbor Selection (MNS) algorithm. The EA-RDSP contains three types of nodes; the client node sends information about casualty in the disaster area to the server, the relay nodes transmit this information from client node to server nodes via multi-hop transmission, the server node receives messages sent by client node to alert rescue teams. The EAM-RDSP contains three types of nodes; the client node sends information about casualty in the disaster area to the server, the relay nodes transmit this information from client node to server nodes via multi-hop transmission, the server node receives messages sent by client node to alert rescue teams. The proposed EA-RDSP scheme is simulated using NS-2 simulator and its performance is compared with existing scheme in terms of end-to-end delay, message delivery ratio, network overhead and energy consumption.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Mobile Ad Hoc Networks · Opportunistic and Delay-Tolerant Networks · Wireless Networks and Protocols
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (42)
Show source-provided references
1. Measuring the information society report
2. Effects of hurricane odile on the infrastructure of baja california Sur, Mexico10.1007/s11069-017-3165-z ↗
3. K. Mase, “How to deliver your message from/to a disaster area,” IEEE Communications Magazine, vol. 49, no. 1, pp. 52–57, 2011.10.1109/MCOM.2011.5681015 ↗
4. K. Miranda, A. Molinaro and T. Razafindralambo, “A survey on rapidly deployable solutions for post-disaster networks,” IEEE Communications Magazine, vol. 54, no. 4, pp. 117–123, 2016.10.1109/MCOM.2016.7452275 ↗
5. A low-energy, multi-copy inter-contact routing protocol for disaster response networks
6. Mobility model and relay management for disaster area wireless networks
7. Routing protocols and architecture for disaster area network: A survey10.1016/j.adhoc.2018.08.005 ↗
8. A survey on multihop ad hoc networks for disaster response scenarios
9. Design and deployment of infrastructure-independent D2D networks without centralized coordination
10. Rescue base station
11. Unmanned ground vehicle radio relay deployment system for non-line-of-sight operations
12. Using mobile robots to establish mobile wireless mesh networks and increase network throughput10.1155/2012/614532 ↗
13. ECO-Udc: An energy efficient data collection method for disaster area networks
14. M. Li, H. Nishiyama, N. Kato, Y. Owada and K. Hamaguchi, “On the energy-efficient of throughput-based scheme using renewable energy for wireless mesh networks in disaster area,” IEEE Transactions on Emerging Topics in Computing, vol. 3, no. 3, pp. 420–431, 2015.10.1109/TETC.2014.2386135 ↗
15. An ad hoc network in the sky, skymesh, for large-scale disaster recovery
16. T. Sakano, S. Kotabe, T. Komukai, T. Kumagai. Y. Shimizu et al., “Bringing movable and deployable networks to disaster areas: Development and field test of MDRU,” IEEE Network, vol. 30, no. 1, pp. 86–91, 2016.10.1109/MNET.2016.7389836 ↗
17. T. Ngo, H. Nishiyama, N. Kato, T. Sakano and A. Takahara, “A spectrum- and energy-efficient scheme for improving the utilization of mdru-based disaster resilient networks,” IEEE Transactions on Vehicular Technology, vol. 63, no. 5, pp. 2027–2037, 2014.10.1109/TVT.2014.2314462 ↗
18. Supporting urban search and rescue with digital assessments of structures and requests of response resources10.1016/j.aei.2012.06.004 ↗
19. Detecting, locating, and characterizing voids in disaster rubble for search and rescue10.1016/j.aei.2019.100974 ↗
20. Mobile ad hoc network-enabled collaboration framework supporting civil engineering emergency response operations10.1061/(ASCE)CP.1943-5487.0000033 ↗
21. A collaborative GIS framework to support equipment distribution for civil engineering disaster response operations10.1016/j.autcon.2010.12.007 ↗
22. Crawford · Advances in Civil Engineering · 2018 · 2018 · 725815610.1155/2018/7258156 ↗
23. Menon · Mobile Information Systems · 2016 · 2016 · 914132910.1155/2016/9141329 ↗
24. Natural disaster and environmental monitoring system for smart cities: Design and installation insights
25. An introduction of real-time embedded optimisation programming for UAV systems under disaster communication
26. Real-time deployment of multi hop relays for range extension
27. Performance analysis of highly available ad hoc surveillance networks based on dropped units
28. Rapid deployment of wireless Ad Hoc backbone networks for public safety incident management
29. H. Liu, Z. Xie, J. Li, S. Lin, D. J. Siu et al., “An automatic, robust, and efficient multi-user breadcrumb system for emergency response applications,” IEEE Transactions on Mobile Computing, vol. 13, no. 4, pp. 723–736, 2014.10.1109/TMC.2013.63 ↗
30. Self-managing energy efficient multicast support in MANETs under end-to-end reliability constraints10.1016/j.comnet.2008.11.013 ↗
31. Lifetime-aware leisure degree adaptive routing protocol for mobile ad hoc networks
32. N. Iqbal, “Energy efficient architecture for wireless geophone networks,” SEG Technical Program Expanded Abstracts, vol. 2019, pp. 107–111, 2019.10.1190/segam2019-3215665.1 ↗
33. Multi-objective cluster head selection using fitness averaged rider optimization algorithm for IoT networks in smart cities10.1016/j.seta.2020.100973 ↗
34. A heuristic angular clustering framework for secured statistical data aggregation in sensor networks,”10.3390/s20174937 ↗
35. Green communication in IoT networks using a hybrid optimization algorithm10.1016/j.comcom.2020.05.020 ↗
36. M. Tang, M. Alazab and Y. Luo, “Big data for cybersecurity: Vulnerability disclosure trends and dependencies,” IEEE Transactions on Big Data, vol. 5, no. 3, pp. 317–329, 2019.10.1109/TBDATA.2017.2723570 ↗
37. A. Khan, F. Ullah, Z. Kaleem, S. Ur Rahman, H. Anwar et al., “EVP-Stc: Emergency vehicle priority and self-organising traffic control at intersections using internet-of-things platform,” IEEE Access, vol. 6, pp. 68242–68254, 2018.10.1109/ACCESS.2018.2879644 ↗
38. RDSP: Rapidly deployable wireless ad hoc system for post-disaster management10.3390/s20020548 ↗
39. IEEE 802.11X WLAN standards10.1002/9780470167960.ch9 ↗
40. N. Iqbal, S. I. Al-Dharrab, A. H. Muqaibel, W. Mesbah and G. L. Stuber, “Cross-layer design and analysis of wireless geophone networks utilizing TV white wpace,” IEEE Access, vol. 8, pp. 118542–118558, 2020.10.1109/ACCESS.2020.3005237 ↗
41. Analysis of wireless seismic data acquisition networks using markov chain models
42. OFDMA-TDMA based seismic data transmission over TV white space