Vehicular ad hoc network (VANET) is an emerging trend where vehicles communicate with each other and possibly with a roadside unit. Collaboration among vehicles is significant in VANET. Resource constraint is one of the great challenges of VANETs. Owing to the absence of centralised management, there is pitfall in optimal resource allocation that leads ineffective routing. Effective reliable routing is quite essential to achieve intelligent transportation. Stochastic dynamic programming (SDP) is currently employed as a tool to analyse and solve network resource constraint and allocation issues of resources in VANET. The authors have considered this work as a geographic angular zone-based two-phase dynamic resource allocation problem with homogeneous and heterogeneous resource class. This work uses relaxed approximation-based SDP algorithm to generate optimal resource allocation strategies over time in response to past task completion status history. The second-phase resource allocation uses the observed outcome of the first-phase task completion to provide optimal viability decisions. They have also suggested an alternative solution called model predictive control algorithm (MPCA) that used approximation as a part to allocate resource over time in response to information on data transmission completion status. Simulation results show that the proposed schemes works significantly well for homogeneous resources.

References

1. 1)
  - 13. Chhetri, A.S., Morrell, D., Papandreou-Suppappola, A.: ‘On the use of binary programming for sensor scheduling’, IEEE Trans. Signal Process., 2006, 55, (6), pp. 2826–2839.
2. 2)
  - 23. Vakilinia, S., Qiu, D., Ali, M.M.: ‘Optimal multi-dimensional dynamic resource allocation in mobile cloud computing’, in ‘EURASIP J. Wireless Communications and Networking’, doi: 10.1186/1687-1499-2014-201.
3. 3)
  - 7. Zhu, L., Li, C., Li, B., et al: ‘Geographic routing in multilevel scenarios of vehicular ad hoc networks’, IEEE Trans. Veh. Technol., 2016, 65, (9), pp. 7740–7753.
4. 4)
  - 19. Lim, J.M.-Y., Chang, Y.C., Alias, M.Y., et al: ‘Joint optimization and threshold structure dynamic programming with enhanced priority scheme for adaptive VANET MAC’, Wirel. Netw, 2014, 22, (3), pp. 897–913, doi: 10.1007/s11276-015-1009-x.
5. 5)
  - 21. Dhurandher, S.K.., Misra, S., Obaidat, M.S., et al: ‘An efficient angular routing protocol for inter-vehicular communication in vehicular ad hoc networks’, IET Commun., 2010, 4, (7), pp. 826–836.
6. 6)
  - 20. Cheung, M.H., Hou, F., Wong, V.W.S., et al: ‘DORA: dynamic optimal random access for vehicle-to-roadside communications’, IEEE J. Commun., 2012, 30, (4), pp. 792–803.
7. 7)
  - 18. Williams, J.L., Fisher, J.W., Willsky, A.S.: ‘An approximate dynamic programming approach to a communication constrained sensor management problem’. Int. Conf. Information Fusion, Philadelphia, PA, USA, July 2005, p. 8.
8. 8)
  - 11. Araniti, G., Campolo, C., Condoluci, M., et al: ‘LTE for vehicular networking: a survey’, Commun. Mag., 2013, 51, (5), pp. 148–157.
9. 9)
  - 10. Cao, Y., Sun, Z.: ‘Geographic based spray and relay (GSAR): an efficient routing scheme for DTNs’, IEEE Trans. Veh. Technol., 2015, 64, (4), pp. 1548–1564.
10. 10)
  - 2. Puterman, M.L.: ‘Markov decision processes: discrete stochastic dynamic programming’ (John Wiley and Sons, New York, 2005).
11. 11)
  - 14. Miller, M., Vaidya, N.: ‘A MAC protocol to reduce a sensor network energy consumption using a wakeup radio’, IEEE Trans. Mob. Comput., 2005, 4, (3), pp. 228–242.
12. 12)
  - 12. Krishnamurthy, V.: ‘Algorithms for optimal scheduling of hidden Markov model sensors’, IEEE Trans. Signal Process., 2002, 50, (6), pp. 1382–1397.
13. 13)
  - 3. Fricker, J.D., Whitford, R.K.: ‘Fundamentals of transportation engineering: amultimodal systems approach’ (Prentice Hall, 2004).
14. 14)
  - 1. Castañón, D.A., Wohletz, J.M..: ‘Model predictive control for stochastic resource allocation’, IEEE Trans. Autom. Control, 2009, 54, (8), pp. 1739–1750.
15. 15)
  - 5. Bejaoui, T.: ‘QoS-oriented high dynamic resource allocation in vehicular communication networks’. The Scientific World Journal, 2014: 718698..
16. 16)
  - 6. Xing, W., Wang, N., Wang, C., et al: ‘Resource allocation schemes for D2D communication used in VANETs’. IEEE 80th Vehicular Technology Conf., Vancouver, 2014, pp. 1–6, doi: 10.1109/VTCFall.2014.6966159.
17. 17)
  - 15. Chhetri, A.S., Morrell, D., Papandreou-Suppappola, A.: ‘Sensor resource allocation for tracking using outer approximation’, IEEE Trans. Signal Process. Lett., 2007, 14, (3), pp. 213–216.
18. 18)
  - 17. Jarupan, B., Ekici, E.: ‘Location and delay aware cross layer communication in V2I multi hop vehicular networks’, IEEE Commun. Mag., 2009, 47, (11), pp. 112–118.
19. 19)
  - 22. Paramasivan, B., Bhuvaneswari, M., Pitchai, K.M.: ‘Augmented DTN based energy efficient routing protocol for vehicular ad hoc networks’. IEEE Sensors, Busan, 2015, pp. 1–4, doi: 10.1109/ICSENS.2015.7370257.
20. 20)
  - 4. Rawat, D.B., Song, M., Shetty, S.: ‘Resource allocation for cognitive radio enabled vehicular network users’, in ‘Dynamic spectrum access for wireless networks’ (Springer International Publishing, Cham, 2015), pp. 57–65, ISBN: 978-3-319-15299-8.
21. 21)
  - 8. Salkuyeh, M.A., Abolhassani, B.: ‘An adaptive multipath geographic routing for video transmission in urban VANETs’, IEEE Trans. Intell. Transp. Syst., 2016, 17, (10), pp. 1–10.
22. 22)
  - 16. Kaufman, B., Lilleberg, J., Aazhang, B.: ‘Spectrum sharing scheme between cellular users and ad-hoc device-to-device users’, IEEE Trans. Wirel. Commun., 2013, 12, (3), pp. 1038–1049.
23. 23)
  - 9. Zhu, L., et al: ‘On stochastic analysis of greedy routing in vehicular networks’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (6), pp. 3353–3366.

Geographical angular zone-based optimal resource allocation and efficient routing protocols for vehicular ad hoc networks

References

Related content