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MMSMAC: A Multi-mode Medium Access Control Protocol for Wireless Sensor Networks with Latency and Energy-Awareness

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Abstract

In this paper, we propose a new medium access control (MAC) protocol for wireless sensor networks called MMSMAC (multi-mode sensor MAC protocol), which operates according to the application requirements and traffic load, in three main modes: synchronous, asynchronous, and hybrid. In the synchronous mode, MMSMAC organizes the sensor nodes under even and odd clusters. Each sensor node has its own active/sleep and send/receive periods according to its cluster identifier, which ensures better load balancing among nodes. In the asynchronous mode, sensor nodes communicate freely without the utilization of even and odd clusters. We propose a mechanism to wake up the destination node and minimize the overhead. In this mode, we propose another mechanism to circumvent the problem of hidden host. In the hybrid mode, the features of asynchronous and asynchronous modes are combined. Our simulation results and analysis show that each of the MMSMAC modes shows convincing performance gains and outperforms B-MAC and Hybrid CSMA/TDMA protocols.

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Notes

  1. A preliminary version of the paper has been appeared in [34].

References

  1. Ahn, G. S., Hong, S. G., Miluzzo, E., Campbell, A. T., & Cuomo, F. (2006). Funneling-mac: A localized, sink-oriented mac for boosting fidelity in sensor networks. In Proceedings of the 4th international conference on embedded networked sensor systems (pp. 293–306). ACM.

  2. Alvi, A. N., Bouk, S. H., Ahmed, S. H., Yaqub, M. A., Javaid, N., & Kim, D. (2015). Enhanced tdma based mac protocol for adaptive data control in wireless sensor networks. Journal of Communications and Networks, 17(3), 247–255.

    Article  Google Scholar 

  3. Bachir, A., Dohler, M., Watteyne, T., & Leung, K. K. (2010). Mac essentials for wireless sensor networks. IEEE Communications Surveys & Tutorials, 12(2), 222–248.

    Article  Google Scholar 

  4. Brownfield, M., Mehrjoo, K., Fayez, A., & Iv, N. J. D. (2006). Wireless sensor network energy-adaptive mac protocol. In 3rd IEEE consumer communications and networking conference (CCNC 2006) (Vol. 2, pp. 778–782).

  5. Buettner, M., Yee, G. V., Anderson, E., & Han, R. (2006). X-mac: A short preamble mac protocol for duty-cycled wireless sensor networks. In Proceedings of the 4th international conference on embedded networked sensor systems (pp. 307–320). ACM.

  6. Bulusu, N., Heidemann, J., Estrin, D., & Tran, T. (2004). Self-configuring localization systems: Design and experimental evaluation. ACM Transactions on Embedded Computing Systems (TECS), 3(1), 24–60.

    Article  Google Scholar 

  7. Buratti, C., & Verdone, R. (2015). L-csma: A mac protocol for multi-hop linear wireless (sensor) networks. IEEE Transactions on Vehicular Technology.

  8. Carrano, R. C., Passos, D., Magalhaes, L., & Albuquerque, C. V. (2014). Survey and taxonomy of duty cycling mechanisms in wireless sensor networks. IEEE Communications Surveys & Tutorials, 16(1), 181–194.

    Article  Google Scholar 

  9. Casari, P., Marcucci, A., Nati, M., Petrioli, C., & Zorzi, M. (2005). A detailed simulation study of geographic random forwarding (geraf) in wireless sensor networks. In IEEE military communications conference (MILCOM 2005) (pp. 59–68). IEEE.

  10. Chiasserini, C., & Garetto, M. (2006). An analytical model for wireless sensor networks with sleeping nodes. IEEE Transactions on Mobile Computing, 5(12), 1706–1718.

    Article  Google Scholar 

  11. Cristescu, R., Beferull-Lozano, B., & Vetterli, M. (2004). On network correlated data gathering. In Proceedings of 23rd annual joint conference of the IEEE computer and communications societies (INFOCOM 2004) (Vol. 4, pp. 2571–2582). IEEE.

  12. De Vito, S., Massera, E., Burrasca, G., Di Girolamo, A., Miglietta, M., Di Francia, G., et al. (2008). Tinynose: Developing a wireless e-nose platform for distributed air quality monitoring applications. In IEEE sensors (pp. 701–704). IEEE

  13. Dong, Q., & Dargie, W. (2013). A survey on mobility and mobility-aware mac protocols in wireless sensor networks. IEEE Communications Surveys & Tutorials, 15(1), 88–100.

    Article  Google Scholar 

  14. Doudou, M., Djenouri, D., & Badache, N. (2013). Survey on latency issues of asynchronous mac protocols in delay-sensitive wireless sensor networks. IEEE Communications Surveys & Tutorials, 15(2), 528–550.

    Article  Google Scholar 

  15. Doudou, M., Djenouri, D., Badache, N., & Bouabdallah, A. (2014). Synchronous contention-based mac protocols for delay-sensitive wireless sensor networks: A review and taxonomy. Journal of Network and Computer Applications, 38, 172–184.

    Article  Google Scholar 

  16. El-Hoiydi, A., & Decotignie, J. D. (2004). Wisemac: An ultra low power mac protocol for multi-hop wireless sensor networks. In Algorithmic aspects of wireless sensor networks (pp. 18–31). Springer.

  17. Enz, C. C., El-Hoiydi, A., Decotignie, J. D., & Peiris, V. (2004). Wisenet: An ultralow-power wireless sensor network solution. IEEE Computer, 37(8), 62–70.

    Article  Google Scholar 

  18. Gilani, M. H. S., Sarrafi, I., & Abbaspour, M. (2013). An adaptive csma/tdma hybrid mac for energy and throughput improvement of wireless sensor networks. Ad Hoc Networks, 11(4), 1297–1304.

    Article  Google Scholar 

  19. Guerroumi, M., Pathan, A. S. K., Badache, N., & Moussaoui, S. (2014). On the medium access control protocols suitable for wireless sensor networks—A survey. International Journal of Communication Networks and Information Security (IJCNIS), 6(2), 89.

    Google Scholar 

  20. Halkes, G. P., van Dam, T., & Langendoen, K. (2005). Comparing energy-saving mac protocols for wireless sensor networks. Mobile Networks and Applications, 10(5), 783–791.

    Article  Google Scholar 

  21. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences (p. 10). IEEE.

  22. Huang, P., Xiao, L., Soltani, S., Mutka, M. W., & Xi, N. (2013). The evolution of mac protocols in wireless sensor networks: A survey. IEEE Communications Surveys & Tutorials, 15(1), 101–120.

    Article  Google Scholar 

  23. Kim, J., Lin, X., & Shroff, N. B. (2011). Optimal anycast technique for delay-sensitive energy-constrained asynchronous sensor networks. IEEE/ACM Transactions on Networking (TON), 19(2), 484–497.

    Article  Google Scholar 

  24. Kim, J., Lin, X., Shroff, N. B., & Sinha, P. (2008). On maximizing the lifetime of delay-sensitive wireless sensor networks with anycast. In The 27th conference on computer communications (INFOCOM 2008). IEEE.

  25. Kim, S., Pakzad, S., Culler, D., Demmel, J., Fenves, G., Glaser, S., et al. (2006). Wireless sensor networks for structural health monitoring. In Proceedings of the 4th international conference on Embedded networked sensor systems (pp. 427–428). ACM.

  26. Kredo, K, I. I., & Mohapatra, P. (2007). Medium access control in wireless sensor networks. Computer Networks, 51(4), 961–994.

    Article  MATH  Google Scholar 

  27. Krontiris, I., Benenson, Z., Giannetsos, T., Freiling, F. C., & Dimitriou, T. (2009). Cooperative intrusion detection in wireless sensor networks. In Wireless sensor networks (pp. 263–278). Springer.

  28. Lee, S. H., Lee, S., Song, H., & Lee, H. S. (2009). Wireless sensor network design for tactical military applications: Remote large-scale environments. In IEEE military communications conference (MILCOM 2009) (pp. 1–7). IEEE.

  29. Levis, P., Madden, S., Polastre, J., Szewczyk, R., Whitehouse, K., Woo, A., et al. (2005). Tinyos: An operating system for sensor networks. In Ambient intelligence (pp. 115–148). Springer.

  30. Liu, Q., Chang, Y., & Jia, X. (2013). A hybrid method of csma/ca and tdma for real-time data aggregation in wireless sensor networks. Computer Communications, 36(3), 269–278.

    Article  Google Scholar 

  31. Mehta, S., & Kwak, K. S. (2007). H-mac: A hybrid mac protocol for wireless sensor networks. In ITC-CSCC: International technical conference on circuits systems, computers and communications (pp. 755–756).

  32. Milenković, A., Otto, C., & Jovanov, E. (2006). Wireless sensor networks for personal health monitoring: Issues and an implementation. Computer communications, 29(13), 2521–2533.

    Article  Google Scholar 

  33. Miller, M. J., & Vaidya, N. (2005). A mac protocol to reduce sensor network energy consumption using a wakeup radio. IEEE Transactions on Mobile Computing, 4(3), 228–242.

    Article  Google Scholar 

  34. Mohamed, G., Derhab, A., Pathan, A. S. K., Badache, N., & Moussaoui, S. (2016). Mmsmac: A multi-mode medium access control protocol for wireless sensor networks. In IEEE wireless communications and networking conference (WCNC 2016).

  35. Nguyen, C. K., & Kumar, A. (2006). Medium access control with adjustable sleeps for wireless sensor networks. In Proceedings of 11th IEEE symposium on computers and communications (ISCC’06) (pp. 270–276). IEEE.

  36. Polastre, J., Hill, J., & Culler, D. (2004). Versatile low power media access for wireless sensor networks. In Proceedings of the 2nd international conference on embedded networked sensor systems (pp. 95–107). ACM.

  37. Rhee, I., Warrier, A., Aia, M., Min, J., & Sichitiu, M. L. (2008). Z-mac: A hybrid mac for wireless sensor networks. IEEE/ACM Transactions on Networking (TON), 16(3), 511–524.

    Article  Google Scholar 

  38. Ritsema, C. J., Kuipers, H., Kleiboer, L., Van Den Elsen, E., Oostindie, K., Wesseling, J. G., et al. (2009). A new wireless underground network system for continuous monitoring of soil water contents. Water Resources Research, 45(4), 2009.

    Article  Google Scholar 

  39. Salajegheh, M., Soroush, H., & Kalis, A. (2007). Hymac: Hybrid tdma/fdma medium access control protocol for wireless sensor networks. In 18th International symposium on personal, indoor and mobile radio communications (PIMRC 2007) (pp. 1–5). IEEE

  40. Shah, R. C., & Rabaey, J. M. (2002). Energy aware routing for low energy ad hoc sensor networks. In Wireless communications and networking conference (WCNC 2002) (Vol. 1, pp. 350–355). IEEE

  41. Shi, X., & Stromberg, G. (2007). Syncwuf: An ultra low-power mac protocol for wireless sensor networks. IEEE Transactions on Mobile Computing, 6(1), 115–125.

    Article  Google Scholar 

  42. Sitanayah, L., Sreenan, C. J., & Brown, K. N. (2010). Er-mac: A hybrid mac protocol for emergency response wireless sensor networks. In Fourth international conference on sensor technologies and applications (SENSORCOMM) (pp. 244–249). IEEE.

  43. Sun, Y., Du, S., Gurewitz, O., & Johnson, D. B. (2008). Dw-mac: A low latency, energy efficient demand-wakeup mac protocol for wireless sensor networks. In Proceedings of the 9th ACM international symposium on mobile ad hoc networking and computing (pp. 53–62). ACM.

  44. Van Dam, T., & Langendoen, K. (2003). An adaptive energy-efficient mac protocol for wireless sensor networks. In Proceedings of the 1st international conference on embedded networked sensor systems (pp. 171–180). ACM.

  45. Wong, K. J., & Arvind, D. (2006). Speckmac: Low-power decentralized mac protocol low data rate transmissions in specknets. In 2nd International workshop on multi-hop ad hoc networks: From theory to reality (REALMAN’06) (pp. 71–78). ACM SIGMOBILE, Italy.

  46. Woo, A., & Culler, D. E. (2001) A transmission control scheme for media access in sensor networks. In Proceedings of the 7th annual international conference on mobile computing and networking (pp. 221–235). ACM.

  47. Ye, W., Heidemann, J., & Estrin, D. (2002). An energy-efficient mac protocol for wireless sensor networks. In Proceedings of 21st annual joint conference of the IEEE computer and communications societies (INFOCOM 2002) (Vol. 3, pp. 1567–1576). IEEE.

  48. Ye, W., Heidemann, J., & Estrin, D. (2004). Medium access control with coordinated adaptive sleeping for wireless sensor networks. IEEE/ACM Transactions on Networking, 12(3), 493–506.

    Article  Google Scholar 

  49. Yessad, S., Nait-Abdesselam, F., Taleb, T., & Bensaou, B. (2007). R-mac: Reservation medium access control protocol for wireless sensor networks. In 32nd IEEE conference on local computer networks (LCN 2007) (pp. 719–724). IEEE.

  50. Zeng, X., Bagrodia, R., & Gerla, M. (1998). Glomosim: A library for parallel simulation of large-scale wireless networks. In Proceedings of the 12th workshop on parallel and distributed simulation (PADS’98) (pp. 154–161). IEEE.

  51. Zhao, Y., Ma, M., Miao, C., & Nguyen, T. (2010). An energy-efficient and low-latency mac protocol with adaptive scheduling for multi-hop wireless sensor networks. Computer Communications, 33(12), 1452–1461.

    Article  Google Scholar 

  52. Zhao, Y. Z., Miao, C., Ma, M., Zhang, J. B., & Leung, C. (2012). A survey and projection on medium access control protocols for wireless sensor networks. ACM Computing Surveys (CSUR), 45(1), 7.

    Article  MATH  Google Scholar 

  53. Zhuo, S., Wang, Z., Song, Y., Wang, Z., & Almeida, L. (2016). A traffic adaptive multi-channel mac protocol with dynamic slot allocation for wsns. IEEE Transactions on Mobile Computing, 15(7), 1600–1613.

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Appendix

Appendix

1.1 Calculation of Tradeoff Between Two Metrics

For tradeoff between two metrics, we consider six cases: (energy, delay), (delay, throughput), (throughput, collision), (energy, collision), (delay, collision), and (energy, throughput). We calculate for each protocol from Table 2, its ranking vector with respect to the two metrics. We define the optimal ranking vector as ORV (1,1) and we calculate \({\textit{RV}}_{protocol}\) and \({\textit{RTI}}_{protocol}\) of each protocol and for each case.

(a) Tradeoff between energy and delay

$$\begin{aligned} {\textit{RV}}_{b} &= (5,3) \\ {\textit{RV}}_{c} &= (4,2) \\ {\textit{RV}}_{s} &= (1,5) \\ {\textit{RV}}_{a} &= (3,1) \\ {\textit{RV}}_{h} &= (2,4) \\ \end{aligned}$$

we calculate \({\textit{RTI}}_{protocol}\) of each protocol

$$\begin{aligned} {\textit{RTI}}_{b} &= \sqrt{(5-1)^2+(3-1)^2}=2\sqrt{5} \\ {\textit{RTI}}_{c} &= \sqrt{(4-1)^2+(2-1)^2}=\sqrt{10} \\ {\textit{RTI}}_{s} &= \sqrt{(1-1)^2+(5-1)^2}=4 \\ {\textit{RTI}}_{a} &= \sqrt{(3-1)^2+(1-1)^2}=2 \\ {\textit{RTI}}_{h} &= \sqrt{(2-1)^2+(4-1)^2}=\sqrt{10} \end{aligned}$$

Thus, the best tradeoff between energy and delay is ensured by MMSMAC-Asyn.

(b) Tradeoff between delay and throughput

$$\begin{aligned} {\textit{RV}}_{b} &= (3,3) \\ {\textit{RV}}_{c} &= (2,2) \\ {\textit{RV}}_{s} &= (5,5) \\ {\textit{RV}}_{a} &= (1,1) \\ {\textit{RV}}_{h} &= (4,4) \\ {\textit{RTI}}_{b} &= \sqrt{32-1)^2+(3-1)^2}=2\sqrt{2} \\ {\textit{RTI}}_{c} &= \sqrt{(2-1)^2+(2-1)^2}=\sqrt{2} \\ {\textit{RTI}}_{s} &= \sqrt{(5-1)^2+(5-1)^2}=4\sqrt{2} \\ {\textit{RTI}}_{a} &= \sqrt{(1-1)^2+(1-1)^2}=0 \\ {\textit{RTI}}_{h} &= \sqrt{(4-1)^2+(4-1)^2}=3\sqrt{2} \end{aligned}$$

The best choice in this case is obvious, MMSMAC-Asyn is ranked first with respect of the two metrics (\({\textit{RV}}_{a}=(1,1)\) ), and the distance between ranking vector of MMSMAC-Asyn and the optimal ranking vector is 0.

(c) Tradeoff between throughput and collision

$$\begin{aligned} {\textit{RV}}_{b} &= (3,5) \\ {\textit{RV}}_{c} &= (2,3) \\ {\textit{RV}}_{s} &= (5,1) \\ {\textit{RV}}_{a} &= (1,4) \\ {\textit{RV}}_{h} &= (4,2)\\ {\textit{RTI}}_{b} &= \sqrt{(3-1)^2+(5-1)^2}=2\sqrt{5} \\ {\textit{RTI}}_{c} &= \sqrt{(2-1)^2+(3-1)^2}=\sqrt{5} \\ {\textit{RTI}}_{s} &= \sqrt{(5-1)^2+(1-1)^2}=4 \\ {\textit{RTI}}_{a} &= \sqrt{(1-1)^2+(4-1)^2}=3 \\ {\textit{RTI}}_{h} &= \sqrt{(4-1)^2+(2-1)^2}=\sqrt{10} \end{aligned}$$

Thus, the best tradeoff between throughput and collision is ensured by Hyb-CSMA/TDMA.

(d) Tradeoff between energy and collision

$$\begin{aligned} {\textit{RV}}_{b} &= (5,5) \\ {\textit{RV}}_{c} &= (4,3) \\ {\textit{RV}}_{s} &= (1,1) \\ {\textit{RV}}_{a} &= (3,4) \\ {\textit{RV}}_{h} &= (2,2) \\ \end{aligned}$$

The choice in this case is also obvious, MMSMAC-Syn is ranking first with respect to the two metrics (\({\textit{RV}}_{s}=(1,1)\)), as the distance between \({\textit{RV}}_{s}\) and the optimal ranking vector (ORV) is 0.

(e) Tradeoff between delay and collision

$$\begin{aligned} {\textit{RV}}_{b} &= (3,5) \\ {\textit{RV}}_{c} &= (2,3) \\ {\textit{RV}}_{s} &= (5,1) \\ {\textit{RV}}_{a} &= (1,4) \\ {\textit{RV}}_{h} &= (4,2)\\ {\textit{RTI}}_{b} &= \sqrt{(3-1)^2+(5-1)^2}=2\sqrt{5} \\ {\textit{RTI}}_{c} &= \sqrt{(2-1)^2+(3-1)^2}=\sqrt{5} \\ {\textit{RTI}}_{s} &= \sqrt{(5-1)^2+(1-1)^2}=4 \\ {\textit{RTI}}_{a} &= \sqrt{(1-1)^2+(4-1)^2}=3 \\ {\textit{RTI}}_{h} &= \sqrt{(4-1)^2+(2-1)^2}=\sqrt{10} \end{aligned}$$

Thus, the best tradeoff between delay and collision is ensured by Hyb-CSMA/TDMA.

(f) Tradeoff between energy and throughput

$$\begin{aligned} {\textit{RV}}_{b} &= (5,3) \\ {\textit{RV}}_{c} &= (4,2) \\ {\textit{RV}}_{s} &= (1,5) \\ {\textit{RV}}_{a} &= (3,1) \\ {\textit{RV}}_{h} &= (2,4) \\ {\textit{RTI}}_{b} &= \sqrt{(5-1)^2+(3-1)^2}=2\sqrt{5} \\ {\textit{RTI}}_{c} &= \sqrt{(4-1)^2+(2-1)^2}=\sqrt{10} \\ {\textit{RTI}}_{s} &= \sqrt{(1-1)^2+(5-1)^2}=4 \\ {\textit{RTI}}_{a} &= \sqrt{(3-1)^2+(1-1)^2}=2 \\ {\textit{RTI}}_{h} &= \sqrt{(2-1)^2+(4-1)^2}=\sqrt{10} \\ \end{aligned}$$

Thus, the best tradeoff between energy and throughput, is ensured by MMSMAC-Asyn.

1.2 Calculation of Tradeoff Between Three Metrics

If we want to compute the tradeoff between three metrics, we consider four cases: (energy, delay, throughput), (delay, throughput, collision), (throughput, collision, energy), and (collision, energy, delay). We calculate for each protocol from Table 2, its ranking vector with respect to the three metrics. Also, we define the optimal ranking vector as ORV (1,1,1) and calculate \({\textit{RV}}_{protocol}\) and \({\textit{RTI}}_{protocol}\) of each protocol for all the above four cases.

(a) Tradeoff between energy, delay and throughput

$$\begin{aligned} {\textit{RV}}_{b} &= (5,3,3) \\ {\textit{RV}}_{c} &= (4,2,2) \\ {\textit{RV}}_{s} &= (1,5,5) \\ {\textit{RV}}_{a} &= (3,1,1) \\ {\textit{RV}}_{h} &= (2,4,4) \\ {\textit{RTI}}_{b} &= \sqrt{(5-1)^2+(3-1)^2+(3-1)^2}=2\sqrt{6} \\ {\textit{RTI}}_{c} &= \sqrt{(4-1)^2+(2-1)^2+(3-1)^2}=\sqrt{11} \\ {\textit{RTI}}_{s} &= \sqrt{(1-1)^2+(5-1)^2+(5-1)^2}=4\sqrt{2} \\ {\textit{RTI}}_{a} &= \sqrt{(3-1)^2+(1-1)^2+(1-1)^2}=2 \\ {\textit{RTI}}_{h} &= \sqrt{(2-1)^2+(4-1)^2+(4-1)^2}=\sqrt{19} \end{aligned}$$

Thus, the best tradeoff between energy, delay and throughput is ensured by MMSMAC-Asyn.

(b) Tradeoff between delay, throughput and collision

$$\begin{aligned} {\textit{RV}}_{b} &= (3,3,5) \\ {\textit{RV}}_{c} &= (2,2,3) \\ {\textit{RV}}_{s} &= (5,5,1) \\ {\textit{RV}}_{a} &= (1,1,4) \\ {\textit{RV}}_{h} &= (4,4,2) \\ {\textit{RTI}}_{b} &= \sqrt{(3-1)^2+(3-1)^2+(5-1)^2}=2\sqrt{6} \\ {\textit{RTI}}_{c} &= \sqrt{(2-1)^2+(2-1)^2+(3-1)^2}=\sqrt{6} \\ {\textit{RTI}}_{s} &= \sqrt{(5-1)^2+(5-1)^2+(1-1)^2}=4\sqrt{2} \\ {\textit{RTI}}_{a} &= \sqrt{(1-1)^2+(1-1)^2+(4-1)^2}=3 \\ {\textit{RTI}}_{h} &= \sqrt{(4-1)^2+(4-1)^2+(2-1)^2}=\sqrt{19} \\ \end{aligned}$$

Thus, the best tradeoff between delay, throughput and collision can be ensured by Hyb-CSMA/TDMA.

(c) Tradeoff between throughput, collision and energy

$$\begin{aligned} {\textit{RV}}_{b} &= (3,5,5) \\ {\textit{RV}}_{c} &= (2,3,4) \\ {\textit{RV}}_{s} &= (5,1,1) \\ {\textit{RV}}_{a} &= (1,4,3) \\ {\textit{RV}}_{h} &= (4,2,2) \\ {\textit{RTI}}_{b} &= \sqrt{(3-1)^2+(5-1)^2+(5-1)^2}=6 \\ {\textit{RTI}}_{c} &= \sqrt{(2-1)^2+(3-1)^2+(4-1)^2}=\sqrt{13} \\ {\textit{RTI}}_{s} &= \sqrt{(5-1)^2+(1-1)^2+(1-1)^2}=4 \\ {\textit{RTI}}_{a} &= \sqrt{(1-1)^2+(4-1)^2+(3-1)^2}=\sqrt{13} \\ {\textit{RTI}}_{h} &= \sqrt{(4-1)^2+(2-1)^2+(2-1)^2}=\sqrt{11} \\ \end{aligned}$$

Thus, the best choice in this case is MMSMAC-Hyb.

(d) Tradeoff between collision, energy and delay

$$\begin{aligned} {\textit{RV}}_{b} &= (5,5,3) \\ {\textit{RV}}_{c} &= (3,4,2) \\ {\textit{RV}}_{s} &= (1,1,5) \\ {\textit{RV}}_{a} &= (4,3,1) \\ {\textit{RV}}_{h} &= (2,2,4) \\ {\textit{RTI}}_{b} &= \sqrt{(5-1)^2+(5-1)^2+(3-1)^2}=6 \\ {\textit{RTI}}_{c} &= \sqrt{(3-1)^2+(4-1)^2+(2-1)^2}=\sqrt{14} \\ {\textit{RTI}}_{s} &= \sqrt{(1-1)^2+(1-1)^2+(5-1)^2}=4 \\ {\textit{RTI}}_{a} &= \sqrt{(4-1)^2+(3-1)^2+(1-1)^2}=\sqrt{13} \\ {\textit{RTI}}_{h} &= \sqrt{(2-1)^2+(2-1)^2+(4-1)^2}=\sqrt{11} \\ \end{aligned}$$

The best choice in this case is MMSMAC-Hyb.

1.3 Calculation of Tradeoff Between Four Metrics

To compute the tradeoff between all the four metrics (energy, delay, throughput and collision), we have only one case. We define the optimal ranking vector as \({\textit{ORV}}=(1,1,1,1)\) and calculate using Table 2 \({\textit{RV}}_{protocol}\) and \({\textit{RTI}}_{protocol}\) of each protocol.

$$\begin{aligned} {\textit{RV}}_{b} &= (5,3,3,5) \\ {\textit{RV}}_{c} &= (4,2,2,3) \\ {\textit{RV}}_{s} &= (1,5,5,1) \\ {\textit{RV}}_{a} &= (3,1,1,4) \\ {\textit{RV}}_{h} &= (2,4,4,2) \\ {\textit{RTI}}_{b} &= \sqrt{(5-1)^2+(3-1)^2+(3-1)^2+(5-1)^2}=\sqrt{40} \\ {\textit{RTI}}_{c} &= \sqrt{(4-1)^2+(2-1)^2+(2-1)^2+(3-1)^2}=\sqrt{15} \\ {\textit{RTI}}_{s} &= \sqrt{(1-1)^2+(5-1)^2+(5-1)^2+(1-1)^2}=4\sqrt{2} \\ {\textit{RTI}}_{a} &= \sqrt{(3-1)^2+(1-1)^2+(1-1)^2+(4-1)^2}=\sqrt{13} \\ {\textit{RTI}}_{h} &= \sqrt{(2-1)^2+(4-1)^2+(4-1)^2+(2-1)^2}=2\sqrt{5} \\ \end{aligned}$$

Thus, the best tradeoff between energy, delay, throughput and collision is ensured by MMSMAC-Asyn.

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Guerroumi, M., Pathan, AS.K., Derhab, A. et al. MMSMAC: A Multi-mode Medium Access Control Protocol for Wireless Sensor Networks with Latency and Energy-Awareness. Wireless Pers Commun 96, 4973–5010 (2017). https://doi.org/10.1007/s11277-016-3726-6

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