International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

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 Volume 12, Issue 9 (September 2025), Pages: 21-35

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 Review Paper

A review of parallel processing in resource-constrained Internet of Things (IoT) devices

 Author(s): 

 Nasser S. Albalawi 1, *, Abdulaziz Ghabash Alanazi 2, Sami Alshammari 3, Fahd Alhamazani 1, Amnah A. Alshammari 2

 Affiliation(s):

  1Department of Computer Science, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia
  2Department of Information Systems, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia
  3Department of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia

 Full text

    Full Text - PDF

 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0002-5948-4260

 Digital Object Identifier (DOI)

  https://doi.org/10.21833/ijaas.2025.09.003

 Abstract

The Internet of Things (IoT) has transformed the connection between physical and digital systems by enabling continuous data exchange and communication. However, the rapid increase in IoT devices brings significant challenges due to limited memory, processing power, and low-energy communication standards. Addressing these resource constraints is essential for improving system performance. This review explores existing parallel processing techniques specifically developed for resource-limited IoT devices, including hardware and software approaches that aim to enhance efficiency and speed. A comprehensive analysis of the literature highlights the importance of parallel processing in overcoming these limitations. The paper also discusses key challenges, potential benefits, and future directions, aiming to guide further research toward more efficient use of computational resources in IoT environments.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Internet of Things, Parallel processing, Resource constraints, IoT efficiency, Embedded systems

 Article history

 Received 10 February 2025, Received in revised form 24 June 2025, Accepted 5 August 2025

 Acknowledgment

The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA, for funding this research work through the project number "NBU-FFR-2025-1260-03." 

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Albalawi NS, Alanazi AG, Alshammari S, Alhamazani F, and Alshammari AA (2025). A review of parallel processing in resource-constrained Internet of Things (IoT) devices. International Journal of Advanced and Applied Sciences, 12(9): 21-35

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 Figures

  Fig. 1   Fig. 2   Fig. 3   Fig. 4   Fig. 5   Fig. 6   Fig. 7   Fig. 8   Fig. 9  

 Tables

  Table 1  Table 2  Table 3  Table 4  Table 5 

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 References (59)

  1. Ahmad T and Zhang D (2021). Using the Internet of Things in smart energy systems and networks. Sustainable Cities and Society, 68: 102783.  https://doi.org/10.1016/j.scs.2021.102783    [Google Scholar]
  2. Al-Kashoash HA, Kharrufa H, Al-Nidawi Y, and Kemp AH (2019). Congestion control in wireless sensor and 6LoWPAN networks: Toward the Internet of Things. Wireless Networks, 25(8): 4493-4522.  https://doi.org/10.1007/s11276-018-1743-y    [Google Scholar]
  3. Allioui H and Mourdi Y (2023). Exploring the full potentials of IoT for better financial growth and stability: A comprehensive survey. Sensors, 23(19): 8015.  https://doi.org/10.3390/s23198015    [Google Scholar] PMid:37836845 PMCid:PMC10574902
  4. Amadeo M, Campolo C, Quevedo J, Corujo D, Molinaro A, Iera A, Aguiar RL, and Vasilakos AV (2016). Information-centric networking for the Internet of Things: Challenges and opportunities. IEEE Network, 30(2): 92-100.  https://doi.org/10.1109/MNET.2016.7437030    [Google Scholar]
  5. Amrahov SE, Ar Y, Tugrul B, Akay BE, and Kartli N (2024). A new approach to Mergesort algorithm: Divide smart and conquer. Future Generation Computer Systems, 157: 330-343.  https://doi.org/10.1016/j.future.2024.03.049    [Google Scholar]
  6. Ashton K (2009). That ‘Internet of Things’ thing. RFID Journal, 22(7): 97-114.    [Google Scholar]
  7. Atzori L, Iera A, and Morabito G (2017). Understanding the internet of things: definition, potentials, and societal role of a fast evolving paradigm. Ad Hoc Networks, 56: 122-140.  https://doi.org/10.1016/j.adhoc.2016.12.004    [Google Scholar]
  8. Bibri SE (2018). The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability. Sustainable Cities and Society, 38: 230-253.  https://doi.org/10.1007/978-3-319-73981-6    [Google Scholar]
  9. Calore E, Gabbana A, Schifano SF, and Tripiccione R (2017). Evaluation of DVFS techniques on modern HPC processors and accelerators for energy‐aware applications. Concurrency and Computation: Practice and Experience, 29(12): e4143.  https://doi.org/10.1002/cpe.4143    [Google Scholar]
  10. Chander B and Kumaravelan G (2019). Internet of Things: Foundation. In: Peng SL, Pal S, and Huang L (Eds.), Principles of internet of things (IoT) ecosystem: Insight paradigm: 3-33. Springer International Publishing, Cham, Switzerland.  https://doi.org/10.1007/978-3-030-33596-0_1    [Google Scholar]
  11. Deb PK, Mukherjee A, Singh D, and Misra S (2022). Loop-the-loops: Fragmented learning over networks for constrained IoT devices. IEEE Transactions on Parallel and Distributed Systems, 34(1): 316-327.  https://doi.org/10.1109/TPDS.2022.3220221    [Google Scholar]
  12. Deep S, Zheng X, Jolfaei A, Yu D, Ostovari P, and Kashif Bashir A (2022). A survey of security and privacy issues in the Internet of Things from the layered context. Transactions on Emerging Telecommunications Technologies, 33(6): e3935.  https://doi.org/10.1002/ett.3935    [Google Scholar]
  13. Devasena CL (2016). IPv6 low power wireless personal area network (6LoWPAN) for networking Internet of Things (IoT)–Analyzing its suitability for IoT. Indian Journal of Science and Technology, 9(30): 1-6.  https://doi.org/10.17485/ijst/2016/v9i30/98730    [Google Scholar]
  14. Escamilla-Ambrosio PJ, Rodríguez-Mota A, Aguirre-Anaya E, Acosta-Bermejo R, and Salinas-Rosales M (2018). Distributing computing in the Internet of Things: Cloud, fog and edge computing overview. In the NEO 2016: Results of the Numerical and Evolutionary Optimization Workshop NEO 2016 and the NEO Cities 2016 Workshop, Springer International Publishing, Tlalnepantla, Mexico: 87-115.  https://doi.org/10.1007/978-3-319-64063-1_4    [Google Scholar]
  15. Farhan MN, Habib MA, and Ali MA (2018). A study and performance comparison of MapReduce and Apache Spark on Twitter data on Hadoop cluster. International Journal of Information Technology and Computer Science (IJITCS), 10(7): 61-70.  https://doi.org/10.5815/ijitcs.2018.07.07    [Google Scholar]
  16. Fossati T and Tschofenig H (2016). Transport layer security (TLS)/datagram transport layer security (DTLS) profiles for the Internet of Things. IETF RFC 7925.  https://doi.org/10.17487/RFC7925    [Google Scholar]
  17. Fuentes-Samaniego RA, La VH, Cavalli AR, Nolazco-Flores JA, and Ramirez-Velarde RV (2019). A monitoring-based approach for WSN security using IEEE-802.15. 4/6LowPAN and DTLS communication. International Journal of Autonomous and Adaptive Communications Systems, 12(3): 218-243.  https://doi.org/10.1504/IJAACS.2019.10022471    [Google Scholar]
  18. Hong CH and Varghese B (2019). Resource management in fog/edge computing: A survey on architectures, infrastructure, and algorithms. ACM Computing Surveys (CSUR), 52(5): 1-37.  https://doi.org/10.1145/3326066    [Google Scholar]
  19. Ijemaru GK, Ang KLM, and Seng JK (2022). Wireless power transfer and energy harvesting in distributed sensor networks: Survey, opportunities, and challenges. International Journal of Distributed Sensor Networks, 18(3).  https://doi.org/10.1177/15501477211067740    [Google Scholar]
  20. Jeyaraj R, Balasubramaniam A, MA AK, Guizani N, and Paul A (2023). Resource management in cloud and cloud-influenced technologies for Internet of Things applications. ACM Computing Surveys, 55(12): 1-37.  https://doi.org/10.1145/3571729    [Google Scholar]
  21. Kahira AN, Nguyen TT, Gomez LB, Takano R, Badia RM, and Wahib M (2021). An oracle for guiding large-scale model/hybrid parallel training of convolutional neural networks. In the Proceedings of the 30th International Symposium on High-Performance Parallel and Distributed Computing, ACM, Virtual Event, Sweden: 161-173.  https://doi.org/10.1145/3431379.3460644    [Google Scholar]
  22. Kan C, Yang H, and Kumara S (2018). Parallel computing and network analytics for fast industrial Internet-of-Things (IIoT) machine information processing and condition monitoring. Journal of Manufacturing Systems, 46: 282-293.  https://doi.org/10.1016/j.jmsy.2018.01.010    [Google Scholar]
  23. Kasarapu S, Shukla S, and Dinakarrao SMP (2024). Enhancing IoT malware detection through adaptive model parallelism and resource optimization. Arxiv Preprint Arxiv:2404.08808.  https://doi.org/10.48550/arXiv.2404.08808    [Google Scholar]
  24. Kennedy RK, Khoshgoftaar TM, Villanustre F, and Humphrey T (2019). A parallel and distributed stochastic gradient descent implementation using commodity clusters. Journal of Big Data, 6: 16.  https://doi.org/10.1186/s40537-019-0179-2    [Google Scholar]
  25. Ketu S, Mishra PK, and Agarwal S (2020). Performance analysis of distributed computing frameworks for big data analytics: Hadoop vs Spark. Computación y Sistemas, 24(2): 669-686.  https://doi.org/10.13053/cys-24-2-3401    [Google Scholar]
  26. Khalil K, Elgazzar K, Seliem M, and Bayoumi M (2020). Resource discovery techniques in the Internet of Things: A review. Internet of Things, 12: 100293.  https://doi.org/10.1016/j.iot.2020.100293    [Google Scholar]
  27. Khattak SBA, Nasralla MM, Farman H, and Choudhury N (2023). Performance evaluation of an IEEE 802.15. 4-based thread network for efficient Internet of Things communications in smart cities. Applied Sciences, 13(13): 7745.  https://doi.org/10.3390/app13137745    [Google Scholar]
  28. Kim MJ and Yu YS (2015). Development of real-time big data analysis system and a case study on the application of information in a medical institution. International Journal of Software Engineering and Its Applications, 9(7): 93-102.  https://doi.org/10.14257/ijseia.2015.9.7.10    [Google Scholar]
  29. Kotenko IV, Saenko I, and Kushnerevich A (2017). Parallel big data processing system for security monitoring in Internet of Things networks. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 8(4): 60-74.  https://doi.org/10.15622/sp.59.1    [Google Scholar]
  30. Krishnamoorthy S, Dua A, and Gupta S (2023). Role of emerging technologies in future IoT-driven Healthcare 4.0 technologies: A survey, current challenges and future directions. Journal of Ambient Intelligence and Humanized Computing, 14(1): 361-407.  https://doi.org/10.1007/s12652-021-03302-w    [Google Scholar]
  31. Kushwaha D, Redhu S, Brinton CG, and Hegde RM (2023). Optimal device selection in federated learning for resource-constrained edge networks. IEEE Internet of Things Journal, 10(12): 10845-10856.  https://doi.org/10.1109/JIOT.2023.3243082    [Google Scholar]
  32. Lee I and Lee K (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4): 431-440. https://doi.org/10.1016/j.bushor.2015.03.008    [Google Scholar]
  33. Li Y, Ge X, Lei B, Zhang X, and Wang W (2023). Joint task partitioning and parallel scheduling in device-assisted mobile edge networks. IEEE Internet of Things Journal, 11(8): 14058-14075.  https://doi.org/10.1109/JIOT.2023.3341062    [Google Scholar]
  34. Liu H and Huang HH (2015). Enterprise: Breadth-first graph traversal on GPUs. In the Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, ACM, Austin, USA: 1-12.  https://doi.org/10.1145/2807591.2807594    [Google Scholar]
  35. Liyanage NH (2017). Comprehensive comparison of parallel sorting techniques, architectures and behaviors which support for distributed environments. In the International Conference on Big Data Analytics and Computational Intelligence, IEEE, Chirala, Andhra Pradesh, India: 412-417.  https://doi.org/10.1109/ICBDACI.2017.8070874    [Google Scholar]
  36. Makhdoom I, Abolhasan M, Abbas H, and Ni W (2019). Blockchain's adoption in IoT: The challenges, and a way forward. Journal of Network and Computer Applications, 125: 251-279.  https://doi.org/10.1016/j.jnca.2018.10.019    [Google Scholar]
  37. Malik PK, Sharma R, Singh R, Gehlot A, Satapathy SC, Alnumay WS, Pelusi D, Ghosh U, and Nayak J (2021). Industrial internet of things and its applications in Industry 4.0: State of the art. Computer Communications, 166: 125-139.  https://doi.org/10.1016/j.comcom.2020.11.016    [Google Scholar]
  38. Mohammadabadi SMS, Zawad S, Yan F, and Yang L (2024). Speed up federated learning in heterogeneous environments: A dynamic tiering approach. IEEE Internet of Things Journal, 12(5): 5026–5035.  https://doi.org/10.1109/JIOT.2024.3487473    [Google Scholar]
  39. Molanes RF, Amarasinghe K, Rodriguez-Andina J, and Manic M (2018). Deep learning and reconfigurable platforms in the Internet of Things: Challenges and opportunities in algorithms and hardware. IEEE Industrial Electronics Magazine, 12(2): 36-49.  https://doi.org/10.1109/MIE.2018.2824843    [Google Scholar]
  40. Mourtzis D, Vlachou E, Milas N, and Xanthopoulos N (2016). A cloud-based approach for maintenance of machine tools and equipment based on shop-floor monitoring. Procedia CIRP, 41: 655-660.  https://doi.org/10.1016/j.procir.2015.12.069    [Google Scholar]
  41. Nižetić S, Šolić P, Gonzalez-De DLDI, and Patrono L (2020). Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future. Journal of Cleaner Production, 274: 122877.  https://doi.org/10.1016/j.jclepro.2020.122877    [Google Scholar] PMid:32834567 PMCid:PMC7368922
  42. Raghavan NS and Waghmare T (2002). DPAC: An object-oriented distributed and parallel computing framework for manufacturing applications. IEEE Transactions on Robotics and Automation, 18(4): 431-443.  https://doi.org/10.1109/TRA.2002.802236    [Google Scholar]
  43. Raj M, Gupta S, Chamola V, Elhence A, Garg T, Atiquzzaman M, and Niyato D (2021). A survey on the role of Internet of Things for adopting and promoting Agriculture 4.0. Journal of Network and Computer Applications, 187: 103107.  https://doi.org/10.1016/j.jnca.2021.103107    [Google Scholar]
  44. Ray PP, Dash D, and De D (2019). Edge computing for Internet of Things: A survey, e-healthcare case study and future direction. Journal of Network and Computer Applications, 140: 1-22.  https://doi.org/10.1016/j.jnca.2019.05.005    [Google Scholar]
  45. Razzaque MA, Milojevic-Jevric M, Palade A, and Clarke S (2015). Middleware for Internet of Things: A survey. IEEE Internet of Things Journal, 3(1): 70-95.  https://doi.org/10.1109/JIOT.2015.2498900    [Google Scholar]
  46. Sanislav T, Mois GD, Zeadally S, and Folea SC (2021). Energy harvesting techniques for Internet of Things (IoT). IEEE Access, 9: 39530-39549.  https://doi.org/10.1109/ACCESS.2021.3064066    [Google Scholar]
  47. Sathish Kumar L, Ahmad S, Routray S, Prabu AV, Alharbi A, Alouffi B, and Rajasoundaran S (2022). Modern energy optimization approach for efficient data communication in IoT‐based wireless sensor networks. Wireless Communications and Mobile Computing, 2022: 7901587.  https://doi.org/10.1155/2022/7901587    [Google Scholar]
  48. Sehgal A, Perelman V, Kuryla S, and Schonwalder J (2012). Management of resource constrained devices in the Internet of Things. IEEE Communications Magazine, 50(12): 144-149.  https://doi.org/10.1109/MCOM.2012.6384464    [Google Scholar]
  49. Shuvo MMH, Islam SK, Cheng J, and Morshed BI (2022). Efficient acceleration of deep learning inference on resource-constrained edge devices: A review. Proceedings of the IEEE, 111(1): 42-91.  https://doi.org/10.1109/JPROC.2022.3226481    [Google Scholar]
  50. Tao F, LaiLi Y, Xu L, and Zhang L (2012). FC-PACO-RM: A parallel method for service composition optimal-selection in cloud manufacturing system. IEEE Transactions on Industrial Informatics, 9(4): 2023-2033.  https://doi.org/10.1109/TII.2012.2232936    [Google Scholar]
  51. Tao F, Wang Y, Zuo Y, Yang H, and Zhang M (2016). Internet of Things in product life-cycle energy management. Journal of Industrial Information Integration, 1: 26-39.  https://doi.org/10.1016/j.jii.2016.03.001    [Google Scholar]
  52. Teodoro G, Sachetto R, Sertel O, Gurcan MN, Meira W, Catalyurek U, and Ferreira R (2009). Coordinating the use of GPU and CPU for improving performance of compute intensive applications. In the IEEE International Conference on Cluster Computing and Workshops, IEEE. New Orleans, USA: 1-10.  https://doi.org/10.1109/CLUSTR.2009.5289193    [Google Scholar]
  53. Yang D and Luo Z (2023). A parallel processing CNN accelerator on embedded devices based on optimized MobileNet. IEEE Internet of Things Journal, 10(21): 18844-18852.  https://doi.org/10.1109/JIOT.2023.3277869    [Google Scholar]
  54. Yang HC, Dasdan A, Hsiao RL, and Parker DS (2007). Map-reduce-merge: simplified relational data processing on large clusters. In the Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, ACM, Beijing, China: 1029-1040.  https://doi.org/10.1145/1247480.1247602    [Google Scholar]
  55. Yuan MM, Baker JW, and Meilander WC (2013). Comparisons of air traffic control implementations on an associative processor with a MIMD and consequences for parallel computing. Journal of Parallel and Distributed Computing, 73(2): 256-272.  https://doi.org/10.1016/j.jpdc.2012.05.009    [Google Scholar]
  56. Zhang H, Diao Y, and Immerman N (2014). On complexity and optimization of expensive queries in complex event processing. In the SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, Snowbird, USA: 217-228.  https://doi.org/10.1145/2588555.2593671    [Google Scholar]
  57. Zhang Y, Tsai PA, and Tseng HW (2022). SIMD2: A generalized matrix instruction set for accelerating tensor computation beyond GEMM. In the Proceedings of the 49th Annual International Symposium on Computer Architecture, ACM, New York, USA: 552-566.  https://doi.org/10.1145/3470496.3527411    [Google Scholar]
  58. Ziegler S, Kirstein P, Ladid L, Skarmeta A, and Jara A (2015). The case for IPv6 as an enabler of the Internet of Things. IEEE Internet of Things. Available online at:  http://iot.ieee.org/newsletter/july-2015/the-case-for-ipv6-as-an-enabler-of-the-internet-of-things.html    [Google Scholar]
  59. Zygouras N, Zacheilas N, Kalogeraki V, Kinane D, and Gunopulos D (2015). Insights on a scalable and dynamic traffic management system. In the 18th International Conference on Extending Database Technology (EDBT), Brussels, Belgium: 653-664.  https://doi.org/10.5441/002/edbt.2015.65    [Google Scholar]