SCIENCE CHINA Information Sciences, Volume 59, Issue 12: 122311(2016) https://doi.org/10.1007/s11432-016-0320-1

Energy efficiency and area spectral efficiency tradeoff for coexisting wireless body sensor networks

More info
  • ReceivedJun 13, 2016
  • AcceptedAug 16, 2016
  • PublishedNov 2, 2016


The coexistence of wireless body sensor networks (WBSNs) is a very challenging problem, due to strong interference, which seriously affects energy consumption and spectral reuse. The energy efficiency and spectral efficiency are two key performance evaluation metrics for wireless communication networks. In this paper, the fundamental tradeoff between energy efficiency and area spectral efficiency of WBSNs is first investigated under the Poisson point process (PPP) model and Matern hard-core point process (HCPP) model using stochastic geometry. The circuit power consumption is taken into consideration in energy efficiency calculation. The tradeoff judgement coefficient is developed and is shown to serve as a promising complementary measure. In addition, this paper proposes a new nearest neighbour distance power control strategy to improve energy efficiency. We show that there exists an optimal transmit power highly dependant on the density of WBSNs and the nearest neighbour distance. Some important properties are also addressed in the analysis of coexisting WBSNs based on the IEEE 802.15.4 standard, such as the impact of intensity nodes distribution, optimal guard zone, and outage probability. Simulation results show that the proposed power control design can reduce the outage probability and enhance energy efficiency. Energy efficiency and area spectral efficiency of the HCPP model are better than those of the PPP model. In addition, the optimal density of WBSNs coexistence is obtained.



This work was supported by EPSRC TOUCAN Project (Grant No. EP/L020009/1), EU FP7 QUICK Project (Grant No. PIRSES-GA-2013-612652), EU H2020 ITN 5G Wireless Project (Grant No. 641985), National Natural Science Foundation of China (Grant Nos. 61210002, 61401256), MOST 863 Project in 5G (Grant No. 2014AA01A701), and International S&T Cooperation Program of China (Grant No. 2014DFA11640).


[1] Yang G Z. Body Sensor Networks. London: Springer, 2006. 1--397. Google Scholar

[2] Pantelopoulos A, Bourbakis N G. A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans on Syst, 2010, 18: 1-12 CrossRef Google Scholar

[3] Martelli F, Verdone R. Coexistence issues for wireless body area networks at 2.45 GHz. In: Proceedings of European Wireless, Poznan, 2012. 18--20. Google Scholar

[4] Deylami M, Jovanov E. Performance analysis of coexisting IEEE 802.15.4-based health monitoring WBSNs. In: Proceedings of 34th Annual International Conference of the IEEE EMBS, San Diego, 2012. 2464--2467. Google Scholar

[5] Wang L S, Goursaud C, Nikaein N, et al. Cooperative scheduling for coexisting Body Area Networks. IEEE Trans Wirel Commun, 2012, 12: 123-133 Google Scholar

[6] ElSawy H, Hossain E, Haenggi M. Stochastic geometry for modeling, analysis, and design of multi-tier and cognitive cellular wireless networks: a survey. IEEE Commun Surv Tut, 2013, 15: 996-1019 CrossRef Google Scholar

[7] Peng J L, Tang H, Hong P L, et al. Stochastic geometry analysis of energy efficiency in heterogeneous network with sleep control. IEEE Wirel Commun Lett, 2013, 2: 615-618 CrossRef Google Scholar

[8] Cavallari R, Martelli F, Rosini R. A survey on wireless body area networks: technologies and design challenges. IEEE Commun Surv Tut, 2014, 16: 1635-1657 CrossRef Google Scholar

[9] Williams B, Allen B, True H, et al. A real-time, mobile timed up and go system. In: Proceedings of IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, Cambridge, 2015. 9--12. Google Scholar

[10] Hata Y, Kobashi S, Kuramoto K, et al. Home care system for aging people confined to bed by detached sensor netork. In: Proceedings of IEEE Workshop on Robotic Intelligence In Informationally Structured Space (RiiSS), Paris, 2011. 1--6. Google Scholar

[11] Mitchell E, Ahmadi A, Richter C, et al. Automatically detecting asymmetric running using time and frequency domain features. In: Proceedings of IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, Cambridge, 2015. 1--6. Google Scholar

[12] IEEE Computer Society. IEEE 802.15.4 Standard, Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs). IEEE Std 802.15.4-2006. 2006. Google Scholar

[13] IEEE Computer Society. IEEE Standard for Local and Metropolitan Area Networks Part 15.6: Wireless Body Area Networks. IEEE Std 802.15.6-2012. 2012. Google Scholar

[14] Bluetooth SIG. Specification of the Bluetooth System. Version 4.0. 2010. Google Scholar

[15] Park P, Marco D P, Soldati P, et al. In: Proceeding of 6th Interference Conference on Mobile Adhoc and Sensor Systems, Macau, 2009. 130--139. Google Scholar

[16] Hesham E, Ekram H, Sergio C. Spectrum-efficient multi-channel design for coexisting IEEE 802. 15.4 networks:a stochastic geometry approach. IEEE J Sel Area Commun, 2014, 13: 1611-1624 Google Scholar

[17] Zhang C Q, Wang Y L, Liang Y Q, et al. An energy-efficient MAC protocol for medical emergency monitoring body sensor networks. Sensors, 2016, 16: 1-19 CrossRef Google Scholar

[18] IEEE Computer Society. IEEE 802.15.4, Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs). Revision of IEEE Std 802.15.4-2003. 2006. Google Scholar

[19] Otal B, Alonso L, Verikoukis C. Highly reliable energy-saving MAC for wireless body sensor networks in healthcare systems. IEEE J Sel Area Commun, 2009, 27: 553-565 CrossRef Google Scholar

[20] Su H, Zhang X. Battery-dynamics driven TDMA MAC protocols for wireless body-area monitoring networks in healthcare applications. IEEE J Sel Area Commun, 2009, 27: 424-434 CrossRef Google Scholar

[21] Kim T H, Ha J Y, Choi S. Improving spectral and temporal efficiency of collocated IEEE 802. 15.4 LR-WPANs. IEEE Trans Mobile Comput, 2009, 8: 1596-1609 CrossRef Google Scholar

[22] Hong X M, Jie Y, Wang C X, et al. Energy-spectral efficiency trade-off in virtual MIMO cellular systems. IEEE J Sel Area Commun, 2013, 31: 2128-2140 CrossRef Google Scholar

[23] Ku I, Wang C X, Thompson J. Spectral-energy efficiency tradeoff in relay-aided cellular networks. IEEE Trans Wirel Commun, 2013, 12: 4970-4982 CrossRef Google Scholar

[24] Ngo H Q, Larsson E G, Marzetta T L. Energy and spectral efficiency of very large multiuser MIMO Systems. IEEE Trans Commun, 2013, 61: 1436-1449 CrossRef Google Scholar

[25] Yao Y W, Cai X D, Giannakis G B. On energy efficiency and optimum resource allocation of relay transmissions in the low-power regime. IEEE Trans Wirel Commun, 2005, 4: 2917-2927 CrossRef Google Scholar

[26] Martelli F, Buratti C, Verdone R. Modeling query-based wireless CSMA networks through stochastic geometry. IEEE Trans Veh Technol, 2014, 63: 2876-2885 CrossRef Google Scholar

[27] Shah-Mansouri H, Pakravan M R, Khalaj B H. Analytical modeling and performance analysis of flooding in CSMA-based wireless networks. IEEE Trans Veh Technol, 2011, 60: 664-679 CrossRef Google Scholar

[28] Kim T S, Kim S L. Random power control in wireless Ad Hoc networks. IEEE Commun Lett, 2005, 9: 1046-1048 CrossRef Google Scholar

[29] Zhang X C, Haenggi M. Random power control in Poisson networks. IEEE Trans Commun, 2012, 60: 2602-2611 CrossRef Google Scholar

[30] Pei Y Y, Liang Y C, eh K C, et al. Energy-efficient design of sequential channel sensing in cognitive radio networks: optimal sensing strategy, power allocation. IEEE J Sel Area Commun, 2011, 29: 1648-1659 CrossRef Google Scholar

[31] Tang S S, Zhang Y, Zhang L Q, et al. Spectrum-efficient wireless sensor networks. Int J Distrib Sens N, 2015, 11: 1-2 Google Scholar

[32] Alouini M S, Goldsmith A J. Area spectral efficiency of cellular mobile radio systems. IEEE Trans Veh Technol, 1999, 48: 1047-1066 CrossRef Google Scholar

[33] Zhang L, Yang H C, Hasna M O. Generalized area spectral efficiency: an effective performance metric for green wireless communications. IEEE Trans Commun, 2014, 62: 5367-5380 Google Scholar

[34] Akhtman J, Hanzo L. Power versus bandwidth efficiency in wireless communications: the economic perspective. In: Proceedings of IEEE 70th hicular Technology Conference-Fall, Alaska, 2009. 1--5. Google Scholar

[35] Guo W, O'Farrell T. Capacity-energy-cost tradeoff in small cell networks. In: Proceedings of IEEE 75th hicular Technology Conference-Spring, Yokohama, 2012. 1--5. Google Scholar

[36] Ha J Y, Kim T H, Park H S, et al. An enhanced CSMA-CA algorithm for IEEE 802. 15.4 LR-WPANs. IEEE Commun Lett, 2007, 11: 461-463 Google Scholar

[37] Baccelli F, Blaszczyszyn B. Stochastic Geometry and Wireless Networks, Volume II: Applications. Paris: Now Press, 2005. 68--88. Google Scholar

[38] Sousa E S, Silvester J. Optimum transmission ranges in a direct- sequence spread-spectrum multihop packet radio network. IEEE J Sel Area Commun, 1990, 8: 762-771 CrossRef Google Scholar

[39] Hasan A, Andrews J G. The guard zone in wireless Ad hoc networks. IEEE Trans Wirel Commun, 2007, 6: 897-906 CrossRef Google Scholar

[40] Haenggi M, On distances in uniformly random networks. IEEE Trans Inf Theory, 2005, 51: 3584--3586. Google Scholar

[41] Chiu S N, Stoyan D, Kendall W S, et al. Stochastic Geometry and Its Applications. 3rd ed. UK: Wiley Press, 2013. 35--55. Google Scholar

[42] Busson A, Chelius G, Gorce J. Interference Modeling in CSMA Multi-Hop Wireless Networks. Research Report--6624, INRIA. 2009. Google Scholar

Copyright 2020 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1       京公网安备11010102003388号