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Han, Z.; Liu, L.; Guo, Z.; Su, Z.; Suo, L.; Cai, S.; Han, H. Static Configuration in Urban Rail Transit. Encyclopedia. Available online: https://encyclopedia.pub/entry/48842 (accessed on 07 May 2024).
Han Z, Liu L, Guo Z, Su Z, Suo L, Cai S, et al. Static Configuration in Urban Rail Transit. Encyclopedia. Available at: https://encyclopedia.pub/entry/48842. Accessed May 07, 2024.
Han, Zijie, Liu Liu, Zhibin Guo, Zhaoyang Su, Lei Suo, Shiyuan Cai, Haitao Han. "Static Configuration in Urban Rail Transit" Encyclopedia, https://encyclopedia.pub/entry/48842 (accessed May 07, 2024).
Han, Z., Liu, L., Guo, Z., Su, Z., Suo, L., Cai, S., & Han, H. (2023, September 05). Static Configuration in Urban Rail Transit. In Encyclopedia. https://encyclopedia.pub/entry/48842
Han, Zijie, et al. "Static Configuration in Urban Rail Transit." Encyclopedia. Web. 05 September, 2023.
Static Configuration in Urban Rail Transit
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This entry is adapted from the peer-reviewed paper 10.3390/electronics12173571

With the rapid development of urban rail transit, the traditional urban rail wireless network based on fixed infrastructure is not in a position to meet the increasingly complex communication demand. At the same time, Ad Hoc network, as a special wireless mobile network, is developing rapidly. Applying this self-organized networking architecture to the urban rail vehicle–ground communication network can overcome the problems existing in the traditional urban rail communication system. The routing protocols that can achieve low delay and highly reliable data transmission are important in the urban rail transit scenario.

urban rail train–ground communication self-organizing network routing protocol

1. Introduction

As the urbanization process has accelerated, the urban transportation industry has achieved rapid development. At the same time, the development of urban rail transit plays an important role because its advantages of environmental friendliness, safety and reliability, high speed, large traffic volume, and other advantages can help solve the relatively serious urban traffic congestion problem in China [1]. Along with the application of various new technologies and equipment, as well as the increased demand of passengers for travel convenience and personalization, new information technologies such as cloud computing, big data, the Internet of Things, artificial intelligence, 5G, satellite communication, and blockchain will be deeply integrated with urban rail transit and provide more services for the future urban rail transit systems. Their application will also cover the shortages of exiting communication technologies in terms of capacity and reliability [2] and achieve large-scale, comprehensive, and efficient operation control and management. To promote the interconnection of urban rail transit systems to the Internet, collaborative and intelligent development has become a trend.
Urban rail transmissions transfer business information like train control systems, passenger information systems (including train access video monitoring and train access video playback) through built train–ground wireless communication networks [3]. The traditional urban rail transit vehicle–ground communication network is based on a fixed infrastructure, which transmits information through central nodes. Central nodes are interconnected through cables or optical fibers, and each terminal directly communicates with the central node. Typical applications are wireless local area networks, wireless cellular networks, etc. This traditional network structure relies on base stations or wireless access points for communication. When the fixed infrastructure is interfered with or damaged, it will affect the communication of the network and will be unable to meet the needs of future development.
In recent years, wireless Ad Hoc network technology has developed rapidly as a new type of network communication technology. Reference [4] discusses the most important results from 2000 to 2022 from various authors. It is widely used in satellite communications, vehicle networking, and other fields. Using a self-organized network architecture with decentralized and distributed control advantages in urban rail transit can overcome the problems existing in traditional urban rail communication systems. Therefore, the rail self-organization scheme for urban rail transit with high real-time and high bandwidth wireless communication has been proposed in recent years. Rail Ad Hoc networks are a new generation of digital infrastructure in the field of rail transit. They provide safe, efficient, and convenient standardized services through environmental awareness, data fusion computing, and decision control, including communication, positioning, timing, clearance detection, trackside equipment control, and other functions. Trackside equipment with wireless communication capabilities is installed along the rail line, and communication equipment is installed on the train.
In the communication technology of urban rail Ad Hoc networks, the network layer is an important layer that can achieve the conversion of network addresses to physical addresses and find suitable paths for communication. In urban rail scenarios, communication between vehicles generally exceeds the single-hop range, and suitable routing protocols need to be used to perform multi-hop forwarding through trackside equipment to complete data transmission functions. To fully utilize the performance of urban rail Ad Hoc networks, efficient routing protocols are essential. However, there is little research work directly aimed at urban rail Ad Hoc network routing technology at home and abroad. Therefore, actively researching key routing technologies for urban rail transit Ad Hoc networks based on low-latency and high-reliability communication have important scientific significance and application value.

2. A Dynamic Addressing Hybrid Routing Mechanism Based on Static Configuration in Urban Rail Transit Ad Hoc Network

In recent years, researchers at home and abroad have fully studied and optimized routing protocols that meet the communication environment of wireless Ad Hoc networks based on their characteristics. The earliest innovative research on routing technology was mainly concentrated in the mid-1990s. The MANET research team composed of IETF mainly studied the design and standardization of routing protocols for wireless Ad Hoc networks [5]. These protocols can be categorized into reactive, proactive, and hybrid routing protocols. Representative routing protocols include Dynamic Source Routing (DSR) [6], Optimized Link State Routing (OLSR) [7], Destination Sequence Distance Vector (DSDV) [8], and Ad Hoc On-Demand Distance Vector (AODV) [9].
In the DSDV routing protocol, mobile nodes periodically broadcast their routing information to their neighbors. Each node is required to maintain their routing table. The AODV protocol finds routes using the route request packet, and the route is discovered when needed. Research has shown that AODV performs better than DSDV in packet delivery ratio, throughput, and routing overhead. The delay of AODV is greater than DSDV [10]. For real-time applications, AODV performs better than the DSDV routing protocol [11]. In addition, in urban rail transit scenarios, the trackside nodes are fixed, and trains run along the track. Compared to traditional wireless Ad Hoc network nodes, the communication between them is relatively fixed, and it is not necessary to use active routing protocols to obtain the routing tables of all nodes. Therefore, active routing protocols will incur a significant amount of unnecessary overhead in this scenario.
To continuously improve the performance of routing protocols and better adapt to the communication environment, a large number of studies have optimized routing protocols from different perspectives. Compared with the traditional AODV, the proposed improved protocol has advantages in terms of the packet delivery fraction, overhead, and route setup time.
Many researchers have made improvements to classic protocols. Sheng Liu et al. present an optimized protocol, called B-AODV, based on the shortage of routing finding and routing repair of AODV. In B-AODV, first, through a reverse request by sending BRREQ to replace RREP, it reduces the time of routing finding. Second, two hops IP recorded in control messages and the routing table can improve the rate of routing repair and reduce the time of routing finding [12]. Abdalla M. Hanashi et al. propose a new probabilistic approach that dynamically fine-tunes the rebroadcasting probability of a node for routing request packets (RREQs) according to the number of neighbor nodes. Their proposed approach demonstrates better performance than blind flooding, fixed probabilistic, and adjusted flooding approaches [13]. Reference [14] presents a new fitness function (FFn) used in the Genetic Algorithm (GA) to obtain the optimized route from those routes offered by the Ad Hoc On-demand Multipath Distance Vector (AOMDV) routing protocol. These protocols provide an optimization process to select the efficient routes that have the highest fitness values implementing the shortest route and less data traffic. Ref. [15] proposes a new dynamic relationship zone routing protocol (DRZRP) for Ad Hoc networks. In this protocol, each node in the network establishes a neighboring zone with a radius of ρ hops and activates a relationship zone according to the service request frequency and service hotspot condition. DRZRP establishes proactive routing for the neighboring zone and relationship zone of the node, and the relationship zone of the node can be dynamically maintained, including initialization, relationship zone activation, and relationship zone inactivation. It matches the service relationship among nodes in the network and has comprehensive performance advantages in communication overhead and routing request delay, which improves the quality of the network service. Ref. [16] proposes an improved routing protocol, called L-AODV, which collects the link quality information during the broadcasting progress in network. According to the link quality information included in the arrived RREQ packets, the destination nodes choose the path after evaluating the performance by computing path cost. Experimental results show that the L-AODV routing protocol is improved in packet delivery rate. Ref. [17] presents an AODV with end-to-end reliability (AODV-EER). The main idea of the proposed modification in AODV is to find the route with the lowest drop rate from source to destination. It also proposes a backward route entry mechanism in order to initiate repair action after primary route breaks. The experiments clearly indicate that the proposed protocol performs better than the traditional AODV protocol. Ref. [18] proposes a new modified AODV routing protocol, called EGBB-AODV, where the RREQ mechanism uses a grid-based broadcast (EGBB), which considerably reduces the number of rebroadcasted RREQ packets, and hence improves the performance of the routing protocol. A simulation study shows that EGBB-AODV outperforms AODV in terms of end-to-end delay, delivery ratio, and consumption power.
Ref. [19] proposes a new link lifetime prediction method for greedy and contention-based routing. The evaluation of the proposed method is conducted via the use of a stability-based greedy routing algorithm, which selects the next hop node having the highest link lifetime. Ref. [20] proposes a link-quality- and topology-quality-based routing protocol that uses the residual link lifetime of neighbors as the metric of link quality and uses the relationship of the distance between the intermediate node and the tie line of the source-destination as the metric of topology quality. By combining the two metrics to set the forwarding probability, the proposed protocol can not only avoid frequent route disconnect, but also restricts the propagation range of RREQ. The proposed protocol can significantly reduce routing overhead, as well as increase the packet delivery ratio and decrease end-to-end delay, thus improving routing performance. Many of the reactive protocols use a route searching mechanism where a route request is flooded in the network. Ref. [21] investigates this search procedure and tries to reduce it to a limited region with the aid of location information. Different search regions, applied both in a static and an adaptive way, are investigated, greatly reducing routing overhead. Ref. [22] proposes a new probabilistic protocol called Dichotomic AODV. It aims to reduce the number of messages in RREQ using a new probabilistic and dichotomic protocol for the discovery of the destination. This protocol significantly reduces the number of RREQ packets transmitted during route-discovery operations. Ref. [23] presents a scheme called dynamic hybrid routing (DHR). The basic idea is to configure several routing policies in advance and then dynamically rebalance traffic by applying different preconfigured routing policies to react to traffic fluctuations.
Most of the above studies are based on mobile Ad Hoc networks, but there are few studies on practical application scenarios. Ref. [24] utilizes network simulator 3 to apply AODV, DSDV, and OLSR routing on V2V nodes. The evaluation criteria for the comparison of these routing protocols include the use of QoS (Quality of Service) parameters such as PLR, packet overhead, and throughput. The results of the simulation demonstrate that AODV is the most optimal method among AODV, OLSR, and DSDV for the model. AOMDV performs well, but it does not have intelligent decision capabilities to select immediate routes by their source without discovering new routes or selecting alternate routes via intermediate nodes during failure, which diminishes performance and enhances end-to-end delay. To conquer this dilemma, Ref. [25] proposes an AOMDV intelligent decision protocol (AOMDV-ID). AOMDV-ID acts as hybrid protocol (proactive and reactive). Ref. [26] proposes an improved AODV routing protocol based on restricted broadcasting by communication zones for transmitting data in a largescale VANET. Following the maximum grey correlation degree principle, a series of key communication zones is selected to construct a restricted broadcasting area for route discovery in AODV. Simulation results show that, compared with the traditional AODV, the proposed improved protocol has advantages in packet delivery fraction, overhead, and route setup time.

References

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  2. Zhao, J.; Liu, J.; Yang, L.; Ai, B.; Ni, S. Future 5G-oriented system for urban rail transit: Opportunities and challenges. China Commun. 2021, 18, 1–12.
  3. Wu, H.; Li, F.; Du, C.; Li, G.; Meng, Y. City urban rail transit train-ground wireless communication network research based on LTE technology. In Proceedings of the 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China, 6–8 November 2020; IEEE: Piscataway, NJ, USA, 2020; Volume 1.
  4. Agrawal, R.; Faujdar, N.; Romero, C.A.T.; Sharma, O.; Abdulsahib, G.M.; Khalaf, O.I.; Mansoor, R.F.; Ghoneim, O.A. Classification and comparison of ad hoc networks: A review. Egypt. Inform. J. 2022, 24, 1–25.
  5. Bruno, R.; Conti, M.; Gregori, E. Mesh networks: Commodity multihop ad hoc networks. IEEE Commun. Mag. 2005, 43, 123–131.
  6. Johnson, D.B.; Maltz, D.A. Dynamic source routing in ad hoc wireless networks. In Mobile Computing; Springer: Berlin/Heidelberg, Germany, 1996; pp. 153–181.
  7. Jacquet, P.; Muhlethaler, P.; Clausen, T.; Laouiti, A.; Qayyum, A.; Viennot, L. Optimized link state routing protocol for ad hoc networks. In Proceedings of the IEEE International Multi Topic Conference, 2001, IEEE INMIC 2001, Technology for the 21st Century, Lahore, Pakistan, 30 December 2001; IEEE: Piscataway, NJ, USA, 2001; pp. 62–68.
  8. Manjunath, M.; Manjaiah, D.H. Spatial DSDV (S-DSDV) routing algorithm for mobile ad hoc network. In Proceedings of the 2014 International Conference on Contemporary Computing and Informatics (IC3I), Mysore, India, 27–29 November 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 625–629.
  9. Shibao, L.I.; Wei, J. AODV route protocol research based on improved ERS algorithm. In Proceedings of the 2010 IEEE International Conference on Wireless Communications, Networking, and Information Security, Beijing, China, 25–27 June 2010; IEEE: Piscataway, NJ, USA, 2010; pp. 102–106.
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  13. Hanashi, A.M.; Siddique, A.; Awan, I.; Woodward, M. Performance evaluation of dynamic probabilistic broadcasting for flooding in mobile ad hoc networks. Simul. Model. Pract. Theory 2009, 17, 364–375.
  14. Bhardwaj, A.; El-Ocla, H. Multipath routing protocol using genetic algorithm in mobile ad hoc networks. IEEE Access 2020, 8, 177534–177548.
  15. HHu, Y.-Z.; Zhang, F.-B.; Tian, T. Dynamic relationship-zone routing protocol for Ad Hoc networks. Wirel. Pers. Commun. 2020, 114.3, 2461–2476.
  16. Wang, W.; Li, C.; Wang, Y. An Improved AODV Routing Protocol Based on WSN. Comput. Mod. 2012, 1, 182–185.
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  22. Boudhir, A.; Bouhorma, M.; Ahmed, M.B.; Elbrak, S. New routing protocol “Dicho-AODV” for energy optimization in MANETS. In Proceedings of the 2012 International Conference on Multimedia Computing and Systems, Tangiers, Morocco, 10–12 May 2012; IEEE: Piscataway, NJ, USA, 2012; pp. 444–447.
  23. Zhang, J.; Xi, K.; Luo, M.; Chao, H.J. Dynamic hybrid routing: Achieve load balancing for changing traffic demands. In Proceedings of the 2014 IEEE 22nd International Symposium of Quality of Service (IWQoS), Hong Kong, China, 26–27 May 2014; IEEE: Piscataway, NJ, USA, 2014.
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  25. Kushwaha, U.S.; Dixit, M.K.; Singh, A.K. AOMDV Intelligent Decision (AOMDV-ID) to Minimize Routing Delay for Diverse VANETs. In Proceedings of the 2022 International Conference for Advancement in Technology (ICONAT), Goa, India, 21–22 January 2022; IEEE: Piscataway, NJ, USA, 2022.
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Subjects: Transportation; Telecommunications
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Zijie Han
,
Liu Liu
,
Zhibin Guo
,
Zhaoyang Su
,
Lei Suo
,
Shiyuan Cai
,
Haitao Han
View Times: 179
Revisions: 3 times (View History)
Update Date: 07 Sep 2023
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