Enhancing mobile crowdsensing in Fog-based Internet of Things utilizing Harris hawks optimization

Abstract

Processing and analysis of the expedited volume of data are considered significant challenges for the Internet of Things (IoT) systems in which devices are constantly generating data. The Fog architecture will allow delay-sensitive applications to run everywhere. In IoT, users with smart devices can sense tasks and contribute to their performance. Mobile Crowdsensing (MCS) utilizes users’ crowds as leverage to collect information from the surroundings through their mobile sensors in Fog Computing networks. An efficient reward mechanism to attract the optimal users’ participation in different regions that take into account the sensing cost and, on the other hand, the quality of data collected is at an acceptable level, and the need to balance these two factors are significant for MCS. This research considers the Mobile Crowdsensing in Fog-Based IoT (FITMCS) to allocate optimal user rewards. For this purpose, improving the Coverage Factor (CF) and rewarding system (sensing cost) in MCS is regarded as an optimization problem. The Harris hawks optimization (HHO) tries to provide an optimal solution to solve it. FITMCS was simulated in a MATLAB environment, and two different scenarios, CF and sensing cost metrics, were used to measure its efficiency. The first scenario considers the number of users (50, 100, and 150 users), and the second scenario assumes the number of tasks (20, 25, and 30 sensing tasks) in the sensing environment as a variable. The results revealed that FITMCS improved the sensing cost by an average of 11.59% and CF by 25.1% compared to the previous scheme.

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References

  1. Aazam M (2014) Huh E–N Fog computing and smart gateway based communication for cloud of things. In: 2014 International Conference on Future Internet of Things and Cloud. IEEE, pp 464–470. https://doi.org/10.1109/FiCloud.2014.83

  2. Agarwal N, Chauhan S, Kar AK, Goyal S (2017) Role of human behaviour attributes in mobile crowd sensing: a systematic literature review Digital Policy. Regul Gov. https://doi.org/10.1108/DPRG-05-2016-0023

    Article  Google Scholar 

  3. Ashton K (2009) That ‘internet of things’ thing. RFID J 22:97–114

    Google Scholar 

  4. Atlam HF, Walters RJ, Wills GB (2018) Fog computing and the Internet of Things: a review. Big Data Cogn Comput 2:10. https://doi.org/10.3390/bdcc2020010

    Article  Google Scholar 

  5. Bala MI, Chishti MA (2019) Survey of applications, challenges and opportunities in Fog computing. Int J Pervas Comput Commun. https://doi.org/10.1108/IJPCC-06-2019-059

    Article  Google Scholar 

  6. Boubiche DE, Imran M, Maqsood A, Shoaib M (2019) Mobile crowd sensing–taxonomy, applications, challenges, and solutions. Comput Hum Behav 101:352–370. https://doi.org/10.1016/j.chb.2018.10.028

    Article  Google Scholar 

  7. Capponi A, Fiandrino C, Kantarci B, Foschini L, Kliazovich D, Bouvry P (2019) A survey on mobile crowdsensing systems: challenges, solutions, and opportunities. IEEE Commun Surv Tutor 21:2419–2465. https://doi.org/10.1109/COMST.2019.2914030

    Article  Google Scholar 

  8. Chen ET (2017) The Internet of Things: opportunities, issues, and challenges. The Internet of Things in the modern business environment. IGI Global, pp 167–187. https://doi.org/10.4018/978-1-5225-2104-4.ch009

    Book  Google Scholar 

  9. Chettri L, Bera R (2019) A comprehensive survey on Internet of Things (IoT) toward 5G wireless systems. IEEE Internet Things J 7:16–32. https://doi.org/10.1109/JIOT.2019.2948888

    Article  Google Scholar 

  10. Darwish A, Hassanien AE, Elhoseny M, Sangaiah AK, Muhammad K (2019) The impact of the hybrid platform of Internet of Things and cloud computing on healthcare systems: opportunities, challenges, and open problems. J Ambient Intell Hum Comput 10:4151–4166. https://doi.org/10.1007/s12652-017-0659-1

    Article  Google Scholar 

  11. Elhoseny M, Abdelaziz A, Salama AS, Riad AM, Muhammad K, Sangaiah AK (2018) A hybrid model of Internet of Things and cloud computing to manage big data in health services applications. Fut Gen Comput Syst 86:1383–1394. https://doi.org/10.1016/j.future.2018.03.005

    Article  Google Scholar 

  12. Ghaffari A (2015) Congestion control mechanisms in wireless sensor networks: a survey. J Netw Comput Appl 52:101–115. https://doi.org/10.1016/j.jnca.2015.03.002

    Article  Google Scholar 

  13. Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of Things (IoT): a vision, architectural elements, and future directions. Fut Gen Comput Syst 29:1645–1660. https://doi.org/10.1016/j.future.2013.01.010

    Article  Google Scholar 

  14. Guo W, Zhu W, Yu Z, Wang J, Guo B (2019) A survey of task allocation: contrastive perspectives from wireless sensor networks and mobile crowdsensing. IEEE Access 7:78406–78420. https://doi.org/10.1109/ACCESS.2019.2896226

    Article  Google Scholar 

  15. He S, Chen J, Li X, Shen X, Sun Y (2013) Mobility and intruder prior information improving the barrier coverage of sparse sensor networks. IEEE Trans Mob Comput 13:1268–1282. https://doi.org/10.1109/TMC.2013.129

    Article  Google Scholar 

  16. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Fut Gen Comput Syst 97:849–872. https://doi.org/10.1016/j.future.2019.02.028

    Article  Google Scholar 

  17. Jazebi SJ, Ghaffari A (2020) RISA: routing scheme for Internet of Things using shuffled frog leaping optimization algorithm. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-020-01708-6

    Article  Google Scholar 

  18. Kucuk K, Bayilmis C, Sonmez AF, Kacar S (2019) Crowd sensing aware disaster framework design with IoT technologies. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-019-01384-1

    Article  Google Scholar 

  19. Li T, Liu Y, Gao L, Liu A (2017a) A cooperative-based model for smart-sensing tasks in Fog computing. IEEE Access 5:21296–21311. https://doi.org/10.1109/ACCESS.2017.2756826

    Article  Google Scholar 

  20. Li T, Zhao M, Liu A, Huang C (2017b) On selecting vehicles as recommenders for vehicular social networks. IEEE Access 5:5539–5555. https://doi.org/10.1109/ACCESS.2017.2678512

    Article  Google Scholar 

  21. Li S, Da Xu L, Zhao S (2018) 5G Internet of Things: a survey. J Ind Inform Integrat 10:1–9. https://doi.org/10.1016/j.jii.2018.01.005

    Article  Google Scholar 

  22. Li G, Wu J, Li J, Wang K, Ye T (2018a) Service popularity-based smart resources partitioning for Fog computing-enabled industrial Internet of Things. IEEE Trans Ind Inf 14:4702–4711. https://doi.org/10.1109/TII.2018.2845844

    Article  Google Scholar 

  23. Liu Y, Liu A, Li Y, Li Z, Choi Y-J, Sekiya H, Li J (2017) APMD: A fast data transmission protocol with reliability guarantee for pervasive sensing data communication. Pervas Mob Comput 41:413–435. https://doi.org/10.1016/j.pmcj.2017.03.012

    Article  Google Scholar 

  24. Liu CH, Dai Z, Zhao Y, Crowcroft J, Wu DO, Leung K (2019) Distributed and energy-efficient mobile crowdsensing with charging stations by deep reinforcement learning. IEEE Trans Mob Comput. https://doi.org/10.1109/TMC.2019.2938509

    Article  Google Scholar 

  25. Lu R, Heung K, Lashkari AH, Ghorbani AA (2017) A lightweight privacy-preserving data aggregation scheme for Fog computing-enhanced IoT. IEEE Access 5:3302–3312. https://doi.org/10.1109/ACCESS.2017.2677520

    Article  Google Scholar 

  26. Luceri L et al (2018) VIVO: a secure, privacy-preserving, and real-time crowd-sensing framework for the Internet of Things. Pervas Mob Comput 49:126–138. https://doi.org/10.1016/j.pmcj.2018.07.003

    Article  Google Scholar 

  27. Marjanović M, Antonić A, Žarko IP (2018) Edge computing architecture for mobile crowdsensing. IEEE Access 6:10662–10674. https://doi.org/10.1109/ACCESS.2018.2799707

    Article  Google Scholar 

  28. Mousavi SK, Ghaffari A, Besharat S, Afshari H (2020) Improving the security of Internet of Things using cryptographic algorithms: a case of smart irrigation systems. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-020-02303-5

    Article  Google Scholar 

  29. Musolesi M, Piraccini M, Fodor K, Corradi A (2010) Campbell AT supporting energy-efficient uploading strategies for continuous sensing applications on mobile phones. In: International Conference on Pervasive Computing. Springer, pp 355–372. https://doi.org/10.1007/978-3-642-12654-3_21

  30. Peng S, Yu S, Yang A (2013) Smartphone malware and its propagation modeling: a survey. IEEE Commun Surv Tutor 16:925–941. https://doi.org/10.1109/SURV.2013.070813.00214

    Article  Google Scholar 

  31. Perera C, Qin Y, Estrella JC, Reiff-Marganiec S, Vasilakos AV (2017) Fog computing for sustainable smart cities: a survey. ACM Comput Surv (CSUR) 50:1–43. https://doi.org/10.1145/3057266

    Article  Google Scholar 

  32. Pourghebleh B, Navimipour NJ (2017) Data aggregation mechanisms in the Internet of Things: a systematic review of the literature and recommendations for future research. J Netw Comput Appl 97:23–34. https://doi.org/10.1016/j.jnca.2017.08.006

    Article  Google Scholar 

  33. Rahman H, Ahmed N, Hussain I (2016) Comparison of data aggregation techniques in internet of things (IoT). In: 2016 international conference on wireless communications, signal processing and networking (WiSPNET). IEEE, pp 1296–1300. https://doi.org/10.1109/WiSPNET.2016.7566346

  34. Ray A, Chowdhury C, Mallick S, Mondal S, Paul S, Roy S (2020) Designing energy efficient strategies using markov decision process for crowd-sensing applications. Mob Netw Appl. https://doi.org/10.1007/s11036-020-01522-6

    Article  Google Scholar 

  35. Roy S, Ghosh N, Ghosh P, Das SK bioMCS: A bio-inspired collaborative data transfer framework over Fog computing platforms in mobile crowdsensing. In: Proceedings of the 21st International Conference on Distributed Computing and Networking, 2020. pp 1–10. https://doi.org/10.1145/3369740.3369788

  36. Salman O, Elhajj I, Chehab A, Kayssi A (2018) IoT survey: An SDN and Fog computing perspective. Comput Netw 143:221–246. https://doi.org/10.1016/j.comnet.2018.07.020

    Article  Google Scholar 

  37. Sethi P, Sarangi SR (2017) Internet of Things: architectures, protocols, and applications. J Elect Comput Eng. https://doi.org/10.1155/2017/9324035

    Article  Google Scholar 

  38. Seyfollahi A, Ghaffari A (2020) Reliable data dissemination for the Internet of Things using harris hawks optimization. Peer-to-Peer Netw Appl. https://doi.org/10.1007/s12083-020-00933-2

    Article  Google Scholar 

  39. Seyfollahi A, Ghaffari A (2020a) A lightweight load balancing and route minimizing solution for routing protocol for low-power and lossy networks. Comput Netw 179:107368. https://doi.org/10.1016/j.comnet.2020.107368

    Article  Google Scholar 

  40. Shahraki A, Taherkordi A, Haugen Ø, Eliassen F (2020) Clustering objectives in wireless sensor networks: a survey and research direction analysis. Comput Netw 180:107376. https://doi.org/10.1016/j.comnet.2020.107376

    Article  Google Scholar 

  41. Singh P, Kaur A, Kumar N (2020) A reliable and cost-efficient code dissemination scheme for smart sensing devices with mobile vehicles in smart cities. Sustain Urban Areas 62:102374. https://doi.org/10.1016/j.scs.2020.102374

    Article  Google Scholar 

  42. Sudha L, Thangaraj P (2019) Improving energy utilization using multi hop data aggregation with node switching in wireless sensor network. Clust Comput 22:12749–12757. https://doi.org/10.1007/s10586-018-1754-6

    Article  Google Scholar 

  43. Tange K, De Donno M, Fafoutis X, Dragoni N (2020) A systematic survey of industrial Internet of Things security: requirements and Fog computing opportunities. IEEE Commun Surv Tutor. https://doi.org/10.1109/COMST.2020.3011208

    Article  Google Scholar 

  44. Wang XV, Wang L (2017) A cloud-based production system for information and service integration: an Internet of Things case study on waste electronics. Enterp Inform Syst 11:952–968. https://doi.org/10.1080/17517575.2016.1215539

    Article  Google Scholar 

  45. Wang J, Hu C, Liu A (2017) Comprehensive optimization of energy consumption and delay performance for green communication in Internet of Things. Mob Inform Syst. https://doi.org/10.1155/2017/3206160

    Article  Google Scholar 

  46. Wang X, Ning Z, Hu X, Ngai EC-H, Wang L, Hu B, Kwok RY (2018) A city-wide real-time traffic management system: enabling crowdsensing in social Internet of vehicles. IEEE Commun Mag 56:19–25. https://doi.org/10.1109/MCOM.2018.1701065

    Article  Google Scholar 

  47. Winter T et al (2012) RFC 6550: RPL: IPv6 routing protocol for low-power and lossy networks. Enterp Inform Syst. https://doi.org/10.17487/RFC6550

    Article  Google Scholar 

  48. Xiong H, Zhang D, Chen G, Wang L, Gauthier V (2015) Crowdtasker: maximizing coverage quality in piggyback crowdsensing under budget constraint. In: 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom). IEEE, pp 55–62. https://doi.org/10.1109/PERCOM.2015.7146509

  49. Yaghmazadeh O, Cicoira F, Bernards DA, Yang SY, Bonnassieux Y, Malliaras GG (2011) Optimization of organic electrochemical transistors for sensor applications. J Polym Sci Part B Polym Phys 49:34–39. https://doi.org/10.1002/polb.22129

    Article  Google Scholar 

  50. Yang X-S (2010) Nature-inspired metaheuristic algorithms. Luniver press

    Google Scholar 

  51. Zhong M, Yang Y, Yao H, Fu X, Dobre OA, Postolache O (2019) 5G and IoT: towards a new era of communications and measurements. IEEE Instrum Measure Mag 22:18–26. https://doi.org/10.1109/MIM.2019.8917899

    Article  Google Scholar 

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Correspondence to Ali Ghaffari.

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Seyfollahi, A., Abeshloo, H. & Ghaffari, A. Enhancing mobile crowdsensing in Fog-based Internet of Things utilizing Harris hawks optimization. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-03344-0

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Keywords

  • IoT
  • Fog computing
  • Mobile crowdsensing
  • Coverage factor
  • Sensing cost