- Open Access
Load balancing ad hoc on-demand multipath distance vector (LBAOMDV) routing protocol
© Alghamdi. 2015
- Received: 16 May 2015
- Accepted: 16 September 2015
- Published: 6 November 2015
Researchers working in the area of a mobile ad hoc network (MANET) strive to conserve the battery energy of individual nodes to reduce the frequency of a node breakdown. The model of multiple-path on demand data routing protocols has been an effective scheme for the majority of MANET application scenarios in recent times. The availability of multiple paths for data transfer can both prove to be effective as well as dismal in certain cases. The selection of the most suitable path is always tricky, if not associated with exact metrics of concern. The contribution of this work is the introduction of the load balancing ad hoc on-demand multipath distance vector (LBAOMDV) protocol, an adaptation of AOMDV, an ad hoc on-demand multipath distance vector protocol. The adaption is done in order to enhance the reliability of the given network by considering the parameter of path weight (energy) of all the available multiple paths. The LBAOMDV regulates the fair usage of both node energy and available bandwidth by exploiting the availability of multiple paths for data transfer. The uniform distribution of data across multiple paths enhances the quality of service of the given network by ensuring fair usage of both network bandwidth and node energy. The LBAOMDV protocol ensures reduced node breakdowns, thus enhancing the reliability of the given MANET.
- Load balancing
- Fault tolerance
The rapid node addition or departure frequency of wireless nodes adds to the complexity of routing in MANETs. The inherent issue of rapid mobility complicates the issue further, by making it difficult to address a particular topology for a long [9–11]. The fundamental concern related to the complexity of routing has been addressed, but still some inherent bottlenecks are unresolved. The issue related to delay in route selection has been discussed, and many viable solutions have been proposed in the past [12, 13]. The issues related to congestion and its impact on data delivery have also been addressed . In the past, the basic principle of routing protocols generally was limited to identifying a single path between source and destination node, with a constraint of minimum hop counts. This genre of routing protocols had limitations of reliability and bandwidth usage owing to path breakdown. Nevertheless, in a reasonably well-connected network, there exists several paths between a source node and a destination node. Multipath routing is one of the most effective trends in the area of wireless routing . The concept of multipath routing involves discovering all available paths from the source node to the destination by taking advantage of path redundancy of the underlying network. These multiple available paths may be used alternately or in certain scenarios concurrently for data transfer [16, 17].
The process of routing both in general computer networks, as well as in wireless networks, can be reactive or proactive. The category of reactive routing protocols has been the most effective for wireless scenarios, and the introduction of reactive multipath on demand protocols has steered to a new generation of MANET routing. The most rigorously discussed reactive wireless (on-demand) routing protocols to date have been Dynamic Source Routing (DSR), Ad hoc On-Demand Distance Vector Routing (AODV) and Temporally Ordered Routing Algorithm (TORA) [18–20]. All these reactive routing protocols flood a network with route request packets (RREQ) to discover and maintain routes, only after receiving a request for data transfer. Proactive routing protocols such as Destination Sequenced Distance Vector Routing (DSDV) or other DSDV variants tend to discover and maintain routes between all pairs of nodes in advance to any data requests . The application of proactive protocols seems unrealistic for mobile wireless scenarios, as the frequent topology changes, that may require frequent updating of routing tables which will incur huge routing overhead .
The transfer of data over a single path among the various paths has been the principle of the most on-demand multiple path routing schemes. If the route fails again, a new route is evaluated from source to destination, consuming time and resources. So it is obvious that distributing data packets among the discovered multipaths is the key solution. The research in the recent past has resulted in some advanced techniques for routing in wireless communication networks . Advanced areas of data communication in wireless sensor networks have been taken both by the academia and industry and have yielded significant results [24, 25]. Techniques addressing the issues related to sensor networks for data processing and delivery have also been part of recent research developments in wireless technology [26–28]. The concept of information-centric networks (ICN) has also come into light during the last half decade. These information-centric networks pose quite a different set of challenges . Advances in making IoT a successful technology in future requires greater integration between existing network platforms and the internet, thus enabling their ability to provide services across the globe .
The work through this paper mainly concentrates on effectively balancing the data load among the discovered multipaths in order to maintain nodes resources and reduce the traffic to avoid creating traffic load or bottlenecks.
This paper is organized as follows. Section 2 covers related work in the area of on-demand multipath routing protocols. Section 3 proposes the LBAOMDV load-balanced ad hoc on-demand multipath distance vector routing protocol. Section 4 provides the simulation details of the environment set for the NS2 network simulator. Section 5 of this paper discusses in detail the results generated from simulation of the proposed LBAOMDV protocol.
Many conventional routing protocols developed in the past have least emphasized the optimum utilization of network bandwidth and node energy . But lately, several multipath routing protocols have targeted the energy consumption issues while devising routing schemes for wireless mobile networks . A detailed discussion of the advances in mobile ad hoc routing is provided in this section.
Effective load balancing of data across multiple available paths has been utilized for fair bandwidth usage in mobile ad hoc networks . Pearlman et al. proposed an alternate path routing (APR) that provided load balancing by distributing traffic among a set of diverse paths. APR is an ideal candidate for mobile ad hoc networks with limited channel bandwidth . Yin et al. introduced a load balancing technique called multipath adaptive load balancing (MALB). MALB dynamically distributes the traffic among multiple paths, based on path statistics measurement. MALB is an inclusive framework and can collaborate with any kind of multipath source routing protocols . Mérindol et al. had proposed a scheme that offers the possibility to use temporarily alternative routes in order to reduce packet loss and degraded throughput . Nagarjun et al. proposed a Packet Count-Based Routing Mechanism (PCRM) protocol. PCRM is based on Dynamic Source Routing (DSR) protocol. In contrary of all other regular load balancing techniques, PCRM selects the least used path for sending data packets rather than the mostly used one that regularly includes the minimum hop count . Sharma et al. proposed a similar solution to utilize available bandwidth of the channel multiple disjoint paths. The approximation of bandwidth of a given path is done by sending detector packets across a network. The source node chooses the path with maximum bandwidth as the primary route for forwarding data . Recently, Qi et al. proposed a multipath routing protocol (EM-AODV), based on AODV. EM-AODV evaluates the paths using the parameter values of path energy and hop counts . The advancements in AODV have resulted in more efficient protocols like ad hoc on-demand multipath distance vector (AOMDV) that is more suitable for MANET scenarios . Advancements in the routing techniques in wireless communication have also been based on various intelligent techniques. Athanasios et al. introduced an intelligent approach to improve quality of service (QoS) routing in ATM networks . Thrasyvoulos et al. in their work provided insights into design of routing algorithms for vehicular ad hoc networks . Moustafa et al. described the routing metrics for routing in cognitive radio networks in detail . The designing of vehicular networks is also quite challenging owing to rapid mobility. Few techniques to enhance the functionality of vehicular networks are being floated for discussion in the industry and academia [44, 45]. The latest advancements in improving the quality of routing has been studied by researchers, and their findings have uncovered new challenges . The most interesting solution for these kinds of challenges in dynamic routing has been the efficient multipath routing schemes . Some recent work carried out in the direction of QoS, the quality of service constrained efficient path selection has been noteworthy [48–50]. The applications of basic network concepts have been realized in other spheres of science and technology as well. These realizations have also been incorporated into wireless networks to achieve significant gains in efficient routing [51, 52]. The current work is in continuation of the previous established attempts and has shown significant improvement in the same direction. The current work has utilized the popular NS2 network simulator for deriving and showcasing the results . In the next section, we propose the LBAOMDV protocol that takes QoS to a higher level by enhancing both performance and reliability of MANETs.
The value of the weight WE ij of a given edge may be zero, in case a direct link is not available between a node pair V ij . A given path “P ij ” from a source node V i to a destination node V j can be comprised of n nodes and n−1 multiple hops where 1 ≤ n ≤ V.
3.1 Dynamic load balancing
The abrupt increase in the non-uniform requests from individual network nodes for data has lead to a burst mode of data transfer across network channels. This burst mode of network data transfer has increased the importance of reliability assessment at peak times. The challenges of increasing the reliability of a given network has been associated to the two significant factors, namely node energy and channel bandwidth. By transferring data across multiple paths, the LBAOMDV protocol addresses both these factors of bandwidth and node energy.
Where K is the number of discovered paths, after the route discovery session gets completed. The selection of paths on the condition given in Equation (7) allows balanced energy utilization of nodes in a given network. After the selection of paths, the qualified paths are arranged based on the average unused bandwidth (PUB) of a particular path P ij . This arrangement ensures that paths are used in the descending order of value for PUB ij .
The next phase of the LBAOMDV protocol ensures actual data transfer over the qualified multiple channels. A complete message is divided into fixed-sized chunks which are transferred over the multiple qualified paths. A successful data transfer of a complete message will result in the end of the LBAOMDV protocol process. An unsuccessful data transfer due to path breakdown will require that the failed data chunk be retransferred. This retransfer of data will be treated as a new message and will be distributed across all the K−1 available channels.
3.2 LBAOMDV algorithm
The implementation of LBAOMDV protocol is divided into three phases.
3.3 Reliability assessment of the LBAOMDV Protocol
In a load-balanced multipath routing environment, a message is decomposed into equal-sized data chunks and each such data chunk is transferred over a single path to its intended destination. Here, we assume that “K” number of available multiple paths are both disjoint (node and link) and non-cyclic for the current discussion. An intermediate node “n” which is part of ith disjoint path “K i ” may fail due to any of the reasons like exhausting energy below the given energy threshold, malfunctioning of any node component, physical damage, or any another correlated reasons. Here, we define three node failure types based on the consequences a node breakdown may have on an ongoing data transmission.
Type 1 failure: A breakdown of an intermediate network node which will halt current ongoing data transfer and initiates a retransmission of the same data chuck over the rest of the alternate available paths, as proposed in the LBAOMDV algorithm.
Type 2 failure: A breakdown of an intermediate network node which does not belong to any of the multiple possible paths for the current data transmission; thus, it will not affect the ongoing data transfer over the network.
Type 3 failure: A breakdown of source/destination node or both or a breakdown of multiple network nodes at the same time, thus affecting the overall functionality of the whole network.
Here, in this work, we will be limited to only type 1 failures, and the mention of the term failure in the subsequent discussion will be taken as type 1 failure.
The results for the reliability and related improvements in energy and delay are discussed in following section of this paper.
4.1 Performance metrics
End-to-end delay: the average delay of data packets from a source to a destination
Average energy consumption: the average energy consumed by all nodes in the network
Number of dead nodes: the number of node breakdowns during complete data transfer simulations
4.2 Simulation environment
LBAOMDV, AOMDV, PCRM, and AntHocNet
Various simulation time spaces (s)
50, 100, 200, 400, 1000
1000 m * 1000 m
Various network sizes (node number)
Transmission range (m)
Random way point
Data packet size
Traffic source CBR
Traffic source CBR
Initial energy of participating network nodes
4.3 End-to-end delay
4.4 Average energy consumption
4.5 Number of dead nodes
The increasing number of dead nodes causes a double jeopardy to the MANET as it causes data loss and network partitioning. One of the objectives of LBAOMDV is the drastic decrease in the frequency of node breakdowns. Figure 6 shows a less number of dead nodes while simulating LBAOMDV as compared to AOMDV, PCRM, and AntHocNet. A 900 s of simulation showed that LBAOMDV resulted in a drastic decrease for the number of dead nodes (35 out of 200 nodes) which nearly equals half of the number of dead nodes resulted while using AOMDV (64 out of 200 nodes).
4.6 Reliability analysis
A multipath routing protocol needs to be governed by a load balancing scheme in order to escape from devouring the energy of participating nodes. Unbalanced battery energy consumption may lead to node breakdown consequently affecting the reliability of the given network in general. The objective of this research work was to develop a bandwidth and energy-aware multipath on-demand routing protocol for MANETs. The proposed LBAOMDV protocol achieves the objective by load balancing of data across multiple available paths. The LBAOMDV protocol further ensures balanced retransmission of the data, in case of any path or node breakdown during a data transmission. The results generated from the simulation for node breakdown and average energy consumption parameters showed the improvements achieved by LBAOMDV in comparison to AOMDV, PCRM, and AntHocNet. These results conclude to the fact that the LBAOMDV protocol enhances the reliability of a given MANET and ensures better network performance at peak data transfer times. This work requires further enhancements in future so as to improve the computational efficiency of load balancing.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- S. Corson, J. Macker, Mobile ad hoc networking (MANET): routing protocol performance issues and evaluation considerations, RFC2501, Naval Research Laboratory, University of Maryland, (1999).Google Scholar
- C Hongju, Xiong, AV Vasilakos, Y Laurence Tianruo, C Guolong, Z Xiaofang, Nodes organization for channel assignment with topology preservation in multiradio wireless mesh networks. Ad Hoc Networks 10(5), 60773 (2012).Google Scholar
- L Mo, L Zhenjiang, AV Vasilakos, A survey on topology control in wireless sensor networks: taxonomy, comparative study, and open issues. Proc. IEEE 101(12), 25382557 (2013).Google Scholar
- M. Reza. Rahimi, Nalini, Venkatasubramania, MAPCloud: mobile applications on an elastic and scalable 2tier cloud architecture, IEEE/ACM UCC (2012).Google Scholar
- Yanjun Yao; Qing Cao; Vasilakos, A.V, EDAL: An energy efficient delay aware, and lifetime balancing data collection protocol for wireless sensor networks. MASS (2013), IEEE International Conference, 182-190.Google Scholar
- S Yuning, L Liang, M Huadong, AV Vasilakos, A biology based algorithm to minimal exposure problem of wireless sensor networks. IEEE Trans. Netw. Serv. Manag. 11(3), 417430 (2014).Google Scholar
- S Sengupta, S Das, M Nasir, AV Vasilakos, W Pedrycz, An evolutionary multi objective sleep scheduling scheme for differentiated coverage in wireless sensor networks. IEEE Trans Syst Man Cybern Part C 42(6), 10931102 (2012).View ArticleGoogle Scholar
- W Xiaofei, AV Vasilakos, C Min, L Yunhao, K Ted Taekyoung, A survey of green mobile networks: opportunities and challenges. MONET 17(1), 4–20 (2012).Google Scholar
- Niranjan Potnis, Atulya Mahajan, Mobility models for vehicular ad hoc network simulations, Proceedings of the 44th annual Southeast regional conference, ACM New York, Melbourne, Florida, (2006).Google Scholar
- E. Royer and C-K. Toh, A review of current routing protocols for ad hoc mobile wireless networks, IEEE Journal Personal Communications, 6(2), 46-55, (1999).Google Scholar
- S Marwaha, D Srinivasan, CK Tham, A Vasilakos, Evolutionary fuzzy multi objective routing for wireless mobile ad hoc networks, Evolutionary Computation, in Proceedings of the 2004 Congress on Evolutionary Computation (CEC '04), 2 1964–1971, (2004).Google Scholar
- D Der-Rong, J Jhong-Yan, Delay-constrained survivable multicast routing problem in WDM networks. Comput Commun 35(10), 1172–1184 (2012).View ArticleGoogle Scholar
- Z Xin Ming, Z Yue, Y Fan, AV Vasilakos, Interference-based topology control algorithm for delay-constrained mobile ad hoc networks, in mobile computing. IEEE Trans 14(4), 742–754 (2015).Google Scholar
- C Busch, R Kannan, AV Vasilakos, Approximating congestion + dilation in networks via “quality of routing” games. IEEE Trans. Computers 61(9), 12701283 (2012).MathSciNetView ArticleGoogle Scholar
- S Mueller, RP Tsang, D Ghosal, Multipath routing in mobile ad hoc networks: issues and challenges, in Performance tools and applications to networked systems, vol. 2965 of lecture notes in computer science (Springer, Berlin, Germany, 2004), 209–234.Google Scholar
- Peng Li; Song Guo; Shui Yu; Vasilakos, A.V., CodePipe: an opportunistic feeding and routing protocol for reliable multicast with pipelined network coding, in INFOCOM, 2012 Proceedings IEEE, 100-108, 25-30 (2012).Google Scholar
- W Lou, W Liu, Y Zhang, Performance optimization using multipath routing in mobile ad hoc and wireless sensor networks. Combinator. Optim. Commun. Netw. 2, 117–146 (2006).MathSciNetGoogle Scholar
- L Peng, G Song, Y Shui, AV Vasilakos, Reliable multicast with pipelined network coding using opportunistic feeding and routing. IEEE Trans Parallel Distrib Syst 25(12), 32643273 (2014).Google Scholar
- R. N. Noorani, Comparative analysis of reactive MANET routing protocols under the traffic of TCP VEGAS with mobility considerations, International Conference on Emerging Technologies, 457-461, 19-20 Oct. (2009).Google Scholar
- D. Johnson and D. Maltz., Dynamic source routing in ad hoc wireless networks, In Mobile Computing book, The Kluwer International Series in Engineering and Computer Science, Springer USA, 353 153-181, (1996).Google Scholar
- C. E. Perkins and E. M. Royer, Ad hoc on-demand distance-vector routing, In IEEE Workshop on Mobile Computing Systems and Applications, Louisiana, 90-100, (1999).Google Scholar
- V. D. Park & M. S. Corson, A highly adaptive distributed routing algorithm for mobile wireless networks, In Proceedings of the INFOCOM 97, IEEE, Japan, 3 1405-1413, (1997).Google Scholar
- C. E. Perkins & P. Bhagwat, Highly dynamic destination-sequenced distance vector routing (DSDV) for mobile computers. In ACM SIG-COMM, 234-244, (1994).Google Scholar
- Y-S Yen, H-C Chao, R-S Chang, A Vasilakos, Flooding-limited and multi-constrained QoS multicast routing based on the genetic algorithm for MANETs. Math Comput Model Int J 53(11-12), 2238–2250 (2011).View ArticleGoogle Scholar
- B Berger, M Brady, D Brown, T Leighton, Nearly optimal algorithms and bounds for multilayer channel routing. J ACM 42(2), 500–542 (1995).MATHMathSciNetView ArticleGoogle Scholar
- Vasilakos, W MinYou, CDC: compressive data collection for wireless sensor networks. IEEE Trans Parallel Distrib Syst 26(8), 21882197 (2015).Google Scholar
- Xi Xu, Rashid Ansari, Ashfaq Khokhar, Athanasios V. Vasilakos, Hierarchical Data Aggregation Using Compressive Sensing (HDACS) in WSNs. ACM Transactions on Sensor Networks (TOSN), 11(3) March (2015).Google Scholar
- Liu Xiang, Jun Luo, Athanasios V. Vasilakos: Compressed data aggregation for energy efficient wireless sensor networks. SECON, IEEE, USA: 46-54, (2011).Google Scholar
- AV Vasilakos, L Zhe, S Gwendal, Y Wei, Information centric network: research challenges and opportunities. J Netw Comput Appl 52, 110 (2015).View ArticleGoogle Scholar
- S Zhengguo, Y Shusen, Y Yifan, A Vasilakos, J McCann, L Kin, A survey on the ietf protocol suite for the internet of things: standards, challenges, and opportunities. Wirel Commun IEEE 20(6), 91–98 (2013).View ArticleGoogle Scholar
- X Yang, P Miao, J Gibson, GG Xie, D Ding-Zhu, AV Vasilakos, Tight performance bounds of multihop fair access for mac protocols in wireless sensor networks and underwater sensor networks. Mob Comput IEEE Trans 11(10), 1538–1554 (2012).View ArticleGoogle Scholar
- N Chilamkurti, S Zeadally, A Vasilakos, V Sharma, Cross-layer support for energy efficient routing in wireless sensor networks. Journal of Sensors, Hindawi Publishing Corporation 2009, 9 (2009).Google Scholar
- Pham, P.P.; Perreau, S. Performance analysis of reactive shortest path and multipath routing mechanism with load balance, INFOCOM, San Francisco, CA, USA, 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies, 1 251–259, (2003).Google Scholar
- M.R. Pearlman, Z.J. Haas, P. Sholander and S.S. Tabrizi,On the impact of alternate path routing for load balancing in mobile ad hoc networks, Proceedings of the ACM MobiHoc, ACM, Boston, 3-10, (2000).Google Scholar
- S Yin, X Lin, MALB: MANET adaptive load balancing. IEEE Vehicular Technol Conf 4, 2843–2847 (2004).Google Scholar
- P Merindol, J-J Pansiot, S Cateloin, Improving load balancing with multipath routing, Proceedings of 17th International Conference on Computer Communications and Networks, 2008 (ICCCN’08, Seattle, Washington, USA, 27–38, (2008).Google Scholar
- B Nagarjun, L Sathish, S Chaitanya, Md. Ansari, S Tapaswi, Packet count based routing mechanism–a load balancing approach in MANETS. Networked Digital Technologies, Communications in Computer and Information Science 88, 669–675 (2010).View ArticleGoogle Scholar
- B. Sharma, S. Chugh, V. Jain, Energy efficient load balancing approach to improve AOMDV routing in MANET, In 2014 Fourth International Conference on Communication Systems and Network Technologies, IEEE, India, (2014).Google Scholar
- X Qi, Q Wang, F Jiang, Multi-path routing improved protocol in AODV based on nodes energy. Int J Futur Gener Commun Netw 8(1), 207–214 (2015).Google Scholar
- S. Ijlal Ali Shah, M. Ilyas & H.T. Mouftah, Pervasive communications handbook, In CRC Press Taylor & Francis Group, LLC, ISBN-10: 1420051091, ISBN-13: 978-1420051094, (2011).Google Scholar
- A Vasilakos, MP Saltouros, AF Atlassis, W Pedrycz, Optimizing QoS routing in hierarchical ATM networks using computational intelligence techniques, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions (2003).Google Scholar
- S Thrasyvoulos, R Rao Naveed, T Thierry, O Katia, V Athanasios, Routing for disruption tolerant networks: taxonomy and design. Wirel Netw 16(8), 2349–2370 (2010).View ArticleGoogle Scholar
- Moustafa Youssef, Magdy Abd El-Azim, Mohamed El-Derini, Channel assignment with closeness multipath routing in cognitive networks, Alexandria Engineering Journal, 52(4) 665-670, (2013).Google Scholar
- Z Yuanyuan, X Kai, L Deshi, AV Vasilakos, Directional routing and scheduling for green vehicular delay tolerant networks. Wirel Netw 19(2), 161173 (2013).Google Scholar
- Y Liu, N Xiong, Y Zhao, AV Vasilakos, J Gao, Y Jia, Multi-layer clustering routing algorithm for wireless vehicular sensor networks. Commun IET 4(7), 810-816 (2010).Google Scholar
- W. Pattara-Atikom, P. Krishnamurthy, Quality of service support in IEEE 802.11 Wireless Lan, IEEE Wireless Communications, 10(3) 26-34, (2003).Google Scholar
- Mahesh K. Marina and Samir R. Das, Ad hoc on-demand multipath distance vector routing, in wireless communications and mobile computing, Wireless Comm, 6 92-96, (2006).Google Scholar
- H Kai, L Jun, L Yang, AV Vasilakos, Algorithm design for data communications in duty-cycled wireless sensor networks: a survey. Commun Mag IEEE 51(7), 107–113 (2011).Google Scholar
- Meng, T.; Wu, F.; Yang, Z.; Chen, G.; Vasilakos, A., Spatial reusability-aware routing in multi-hop wireless networks, in Computers, IEEE Transactions on, PP(99) 1-13, (2015).Google Scholar
- Y Yanjun, C Qing, AV Vasilakos, EDAL: an energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. Netw IEEE/ACM Trans 23(3), 810–823 (2015).View ArticleGoogle Scholar
- Zhang, I., Dhurandher, S.K., Anpalagan, A., Vasilakos, A.V., Routing in opportunistic networks. Springer book, New York, (2013).Google Scholar
- L Liang, S Yuning, Z Haiyang, M Huadong, AV Vasilakos, Physarum optimization: a biology-inspired algorithm for the Steiner Tree Problem in networks. Comput IEEE Trans 64(3), 818–831 (2015).View ArticleGoogle Scholar
- The Network Simulator - ns-2 available on: http://www.isi.edu/nsnam/ns/. September 2015.