Secure and efficient data delivery for fog-assisted wireless body area networks

  • Thaier HayajnehEmail author
  • Kristen Griggs
  • Muhammad Imran
  • Bassam J. Mohd
Part of the following topical collections:
  1. Special issue on Fog Computing for Healthcare


The growth of remote patient monitoring technology introduces new opportunities for improving patient outcomes, and Wireless Body Area Networks (WBANs) are a key piece in building a successful system. However, due to the limited power and computational resources of WBAN sensor nodes, combined with user mobility and large network coverage areas, integrating WBANs with cloud and fog computing presents one of the most viable options for successful remote monitoring. In order to help maintain the real-time operations of a fog-assisted WBAN, we propose a secure and efficient data delivery protocol that will reduce delay and protect against malicious attacks on the wireless signal. The protocol is composed of three custom algorithms that address channel assignment, gateway association, and introduce a new delay- and energy-aware routing metric. The channel assignment algorithm is designed to minimize and avoid interference, including jamming nodes. The fog gateway association algorithm helps to improve the efficiency and security of the connection between the WBAN and the remote resources. Similarly, the proposed routing metric is used to construct routes that both minimize delay and conserve power at the nodes along the path for improved efficiency and lifespan of the network. The system was simulated and tested under a variety of conditions to evaluate its performance in regards to mutual interference, human mobility, fog density, and attacks by jamming nodes. The results showed clear improvements in the efficiency and resiliency of the fog-assisted WBAN system when utilizing our protocol.


E-Health Body area networks routing Fog computing Cloud computing Efficient routing Channel assignment 



Imran’s work is supported by the Deanship of Scientific Research, King Saud University, through Research Group No. RG-1435-051.


  1. 1.
  2. 2.
  3. 3.
  4. 4.
    The Network Simulator - ns-2.
  5. 5.
  6. 6.
    Ahmed E, Ahmed A, Yaqoob I, Shuja J, Gani A, Imran M, Shoaib M (2017) Bringing computation closer toward the user network: is edge computing the solution? IEEE Commun Mag 55(11):138–144CrossRefGoogle Scholar
  7. 7.
    Ahnn JH, Potkonjak M (2013) mhealthmon: Toward energy-efficient and distributed mobile health monitoring using parallel offloading. J Med Syst 37(5):9957CrossRefGoogle Scholar
  8. 8.
    Al-Turjman F, Radwan A (2017) Data delivery in wireless multimedia sensor networks: Challenging and defying in the iot era. IEEE Wirel Commun 24(5):126–131CrossRefGoogle Scholar
  9. 9.
    Almashaqbeh G, Hayajneh T, Vasilakos AV (2014) A cloud-based interference-aware remote health monitoring system for non-hospitalized patients. In: Global communications conference (GLOBECOM), 2014 IEEE, IEEE, pp 2436–2441Google Scholar
  10. 10.
    Almashaqbeh G, Hayajneh T, Vasilakos AV, Mohd BJ (2014) Qos-aware health monitoring system using cloud-based wbans. J Med Syst 38(10):121CrossRefGoogle Scholar
  11. 11.
    Appelboom G, Camacho E, Abraham ME, Bruce SS, Dumont EL, Zacharia BE, D’Amico R, Slomian J, Reginster JY, Bruyère O et al (2014) Smart wearable body sensors for patient self-assessment and monitoring. Archives of Public Health 72(1):28CrossRefGoogle Scholar
  12. 12.
    Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Proceedings of the first edition of the MCC workshop on Mobile cloud computing, ACM, pp 13–16Google Scholar
  13. 13.
    Brogi A, Forti S (2017) Qos-aware deployment of iot applications through the fog. IEEE Internet Things J 4(5):1185–1192CrossRefGoogle Scholar
  14. 14.
    Calvo RA, Campo JP (2007) Adding multiple interface support in ns-2. University of CantabriaGoogle Scholar
  15. 15.
    Camp T, Boleng J, Davies V (2002) A survey of mobility models for ad hoc network research. Wirel Commun Mob Comput 2(5):483–502CrossRefGoogle Scholar
  16. 16.
    Chen M, Gonzalez S, Vasilakos A, Cao H, Leung VC (2011) Body area networks: a survey. Mobile Networks and Applications 16(2):171–193CrossRefGoogle Scholar
  17. 17.
    Davenport DM, Deb B, Ross FJ (2009) Wireless propagation and coexistence of medical body sensor networks for ambulatory patient monitoring. In: 2009. BSN 2009. Sixth international workshop on wearable and implantable body sensor networks, IEEE, pp 41–45Google Scholar
  18. 18.
    Dinh HT, Lee C, Niyato D, Wang P (2013) A survey of mobile cloud computing: architecture, applications, and approaches. Wirel Commun Mob Comput 13(18):1587–1611CrossRefGoogle Scholar
  19. 19.
    Domenicali D, De Nardis L, Di Benedetto MG (2009) Uwb body area network coexistence by interference mitigation. In: 2009. ICUWB 2009. IEEE international conference on Ultra-wideband, IEEE, pp 713–717Google Scholar
  20. 20.
    Dubey H, Yang J, Constant N, Amiri AM, Yang Q, Makodiya K (2015) Fog data: Enhancing telehealth big data through fog computing. In: Proceedings of the ASE BigData & SocialInformatics 2015, ACM, p 14Google Scholar
  21. 21.
    Folea S, Ghercioiu M (2008) Ultra-low power wi-fi tag for wireless sensing. In: 2008. AQTR 2008. IEEE international conference on Automation, quality and testing, robotics, vol 3. IEEE, pp 247–252Google Scholar
  22. 22.
    Ghanavati S, Abawajy JH, Izadi D, Alelaiwi AA (2017) Cloud-assisted iot-based health status monitoring framework. Clust Comput 20(2):1843–1853CrossRefGoogle Scholar
  23. 23.
    Gia TN, Jiang M, Rahmani AM, Westerlund T, Liljeberg P, Tenhunen H (2015) Fog computing in healthcare internet of things: a case study on ecg feature extraction. In: 2015 IEEE international conference on computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing (CIT/IUCC/DASC/PICOM), IEEE, pp 356–363Google Scholar
  24. 24.
    Hamidian A, Korner U, Nilsson A (2003) A study of internet connectivity for mobile ad hoc networks in ns 2. Department of Communication Systems, Lund Institute of Technology, Lund UniversityGoogle Scholar
  25. 25.
    Hansen T (2015) Rfc 7677: Scram-sha-256 and scram-sha-256-plus simple authentication and security layer (sasl) mechanisms.
  26. 26.
    Hayajneh T, Almashaqbeh G, Ullah S, Vasilakos AV (2014) A survey of wireless technologies coexistence in wban: analysis and open research issues. Wirel Netw 20(8):2165–2199CrossRefGoogle Scholar
  27. 27.
    Hu JX, Chen CL, Fan CL, Wang Kh (2017) An intelligent and secure health monitoring scheme using iot sensor based on cloud computing. Journal of Sensors 2017:11. Google Scholar
  28. 28.
    Jacob NA, Pillai V, Nair S, Harrell DT, Delhommer R, Chen B, Sanchez I, Almstrum V, Gopalan S (2011) Low-cost remote patient monitoring system based on reduced platform computer technology. Telemedicine and e-Health 17(7):536–545CrossRefGoogle Scholar
  29. 29.
    Johnson DB, Maltz DA, Broch J et al (2001) Dsr: The dynamic source routing protocol for multi-hop wireless ad hoc networks. Ad Hoc Netw 5:139–172Google Scholar
  30. 30.
    Kraemer FA, Braten AE, Tamkittikhun N, Palma D (2017) Fog computing in healthcare—a review and discussion. IEEE Access 5:9206–9222CrossRefGoogle Scholar
  31. 31.
    Lai X, Liu Q, Wei X, Wang W, Zhou G, Han G (2013) A survey of body sensor networks. Sensors 13(5):5406–5447CrossRefGoogle Scholar
  32. 32.
    Mahmud R, Kotagiri R, Buyya R (2018) Fog computing: a taxonomy, survey and future directions. In: Internet of everything, Springer, pp 103–130Google Scholar
  33. 33.
    Mollah MB, Azad MAK, Vasilakos A (2017) Security and privacy challenges in mobile cloud computing: Survey and way ahead. J Netw Comput Appl 84:38–54CrossRefGoogle Scholar
  34. 34.
    Monteiro A, Dubey H, Mahler L, Yang Q, Mankodiya K (2016) Fit a fog computing device for speech teletreatments. arXiv:1605.06236
  35. 35.
    Nepal S, Pudasani A, Shin S (2017) A fast channel assignment scheme for emergency handling in wireless body area networks. Sensors 17(3):477CrossRefGoogle Scholar
  36. 36.
    Perkins C, Belding-Royer E, Das S (2003) Ad hoc on-demand distance vector (aodv) routing. Tech repGoogle Scholar
  37. 37.
    Rahmani AM, Gia TN, Negash B, Anzanpour A, Azimi I, Jiang M, Liljeberg P (2018) Exploiting smart e-health gateways at the edge of healthcare internet-of-things: a fog computing approach. Futur Gener Comput Syst 78:641–658CrossRefGoogle Scholar
  38. 38.
    Rasheed MB, Javaid N, Imran M, Khan ZA, Qasim U, Vasilakos A (2017) Delay and energy consumption analysis of priority guaranteed mac protocol for wireless body area networks. Wirel Netw 23 (4):1249–1266CrossRefGoogle Scholar
  39. 39.
    Roy A, Roy C, Misra S, Rahulamathavan Y, Rajarajan M (2018) Care: criticality-aware data transmission in cps-based healthcare systems. In: 2018 IEEE International conference on communications workshops (ICC workshops), IEEE, pp 1–6Google Scholar
  40. 40.
    Roy M, Chowdhury C, Aslam N (2017) Designing an energy efficient wban routing protocol. In: 2017 9th international conference on communication systems and networks (COMSNETS), IEEE, pp 298–305Google Scholar
  41. 41.
    Salameh HB, Almajali S, Ayyash M, Elgala H (2017) Security-aware channel assignment in iot-based cognitive radio networks for time-critical applications. In: 2017 fourth international conference on software defined systems (SDS), IEEE, pp 43–47Google Scholar
  42. 42.
    Salayma M, Al-Dubai A, Romdhani I, Nasser Y (2017) Wireless body area network (wban): a surveyon reliability, fault tolerance, and technologies coexistence. ACM Comput Surv (CSUR) 50(1):3CrossRefGoogle Scholar
  43. 43.
    Sandhu MM, Javaid N, Imran M, Guizani M, Khan ZA, Qasim U (2015) Bec: a novel routing protocol for balanced energy consumption in wireless body area networks. In: IWCMC, pp 653–658Google Scholar
  44. 44.
    Sun G, Qiao G, Xu B (2012) Link characteristics measuring in 2.4 ghz body area sensor networks. Int J Distrib Sens Netw 8(10):519792CrossRefGoogle Scholar
  45. 45.
    Touati F, Tabish R (2013) U-healthcare system: State-of-the-art review and challenges. J Med Syst 37 (3):9949CrossRefGoogle Scholar
  46. 46.
    Tozlu S, Senel M, Mao W, Keshavarzian A (2012) Wi-fi enabled sensors for internet of things: a practical approach IEEE Communications Magazine 50(6):134–143CrossRefGoogle Scholar
  47. 47.
    Ullah S, Higgins H, Braem B, Latre B, Blondia C, Moerman I, Saleem S, Rahman Z, Kwak KS (2012) A comprehensive survey of wireless body area networks. J Med Syst 36(3):1065–1094CrossRefGoogle Scholar
  48. 48.
    Vilaplana J, Solsona F, Abella F, Filgueira R, Rius J (2013) The cloud paradigm applied to e-health. BMC Med Inform Decis Mak 13(1):35CrossRefGoogle Scholar
  49. 49.
    Wan J, Zou C, Ullah S, Lai CF, Zhou M, Wang X (2013) Cloud-enabled wireless body area networks for pervasive healthcare. IEEE Netw 27(5):56–61CrossRefGoogle Scholar
  50. 50.
    Wang Y, Wang Q, Zeng Z, Zheng G, Zheng R (2011) Wicop: engineering wifi temporal white-spaces for safe operations of wireless body area networks in medical applications. In: Real-time systems symposium (RTSS), 2011 IEEE 32nd, IEEE, pp 170–179Google Scholar
  51. 51.
    Yang D, Xu Y, Gidlund M (2011) Wireless coexistence between ieee 802.11-and ieee 802.15. 4-based networks: A survey. Int J Distrib Sens Netw 7(1):912152CrossRefGoogle Scholar
  52. 52.
    Yannuzzi M, Milito R, Serral-Gracià R, Montero D, Nemirovsky M (2014) Key ingredients in an iot recipe: Fog computing, cloud computing, and more fog computing. In: 2014 IEEE 19th international workshop on computer aided modeling and design of communication links and networks (CAMAD), IEEE, pp 325–329Google Scholar
  53. 53.
    Yousaf S, Javaid N, Khan ZA, Qasim U, Imran M, Iftikhar M (2015) Incremental relay based cooperative communication in wireless body area networks. Procedia Computer Science 52:552–559CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Fordham Center for CybersecurityFordham UniversityNew YorkUSA
  2. 2.College of Applied Computer ScienceKing Saud UniversityAlMuzahmiahSaudi Arabia
  3. 3.Computer Engineering DepartmentHashemite UniversityZarqaJordan

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