iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.3390/sym13101889
Smart Architectural Framework for Symmetrical Data Offloading in IoT
Next Article in Journal
An Assessment of the Hopping Strategy and Inter-Limb Asymmetry during the Triple Hop Test: A Test–Retest Pilot Study
Next Article in Special Issue
Smart Architecture Energy Management through Dynamic Bin-Packing Algorithms on Cloud
Previous Article in Journal
Dichotomy of Baryons as Quantum Hall Droplets and Skyrmions: Topological Structure of Dense Matter
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Symmetry
Volume 13
Issue 10
10.3390/sym13101889
symmetry-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Smart Architectural Framework for Symmetrical Data Offloading in IoT

by
Malvinder Singh Bali
1,*,
Kamali Gupta
1,
Deepika Koundal
2,*,
Atef Zaguia
3,
Shubham Mahajan
4 and
Amit Kant Pandit
4
1
Chitkara University Institute of Engineering & Technology, Chitkara University, Rajpura 141410, India
2
School of Computer Science, University of Petroleum & Energy Studies, Dehradun 248001, India
3
Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
4
School of Electronics & Communication, Shri Mata Vaishno Devi University, Katra 182320, India
*
Authors to whom correspondence should be addressed.
Symmetry 2021, 13(10), 1889; https://doi.org/10.3390/sym13101889
Submission received: 28 August 2021 / Revised: 29 September 2021 / Accepted: 2 October 2021 / Published: 7 October 2021
(This article belongs to the Special Issue Symmetry in IoT and Cloud Infrastructure Development)
Browse Figures
Versions Notes

Abstract

:
With new technologies coming to the market, the Internet of Things (IoT) is one of the technologies that has gained exponential rise by facilitating Machine to Machine (M2M) communication and bringing smart devices closer to end users. By 2025, it is expected that IoT will bring together 78.4 billion of devices, thus improving the quality of life beyond our imagination; however, there are multiple potential challenges, such as the exploitation of energy consumption and the huge data traffic being generated by smart devices causing congestion and utilizing more bandwidth. Various researchers have provided an alternative to this problem by performing offloading of data, the task and computational requirements of an application at edge and fog nodes of IoT, thus helping to overcome latency issues for critical applications. Despite the importance of an offloading approach in IoT, there is need for a systematic, symmetric, comprehensive, and detailed survey in this field. This paper provides a systematic literature review (SLR) on data offloading approaches in IoT network at edge and fog nodes in the form of a classical taxonomy in order to recognize the state-of-the art mechanism(s) associated with this important topic and provide open consideration of issues as well. All of the research on classified offloading approaches done by researchers is compared with each other according to important factors such as performance metrics, utilized techniques, and evaluation tools, and their advantages and disadvantages are discussed. Finally, an efficient smart architecture-based framework is proposed to handle the symmetric data offloading issues.

1. Introduction

The Internet of Things (IoT) provides the interconnection of various heterogeneous devices that can transfer data via the internet. Today, we have various people connected with the internet via several type of communication devices. In the past two decades, the internet has gained huge popularity in terms of making various devices work smartly. The term ‘IoT’ was coined by Kevin Ashton [1] in 1999 to provide optimization support to his company’s supply chain via RFID. He used RFID along with the internet to track and count the supply of goods without human intervention. CompTIA, a vendor-neutral IT certification company, has predicted that internet-connected IoT devices will reach 50 billion by 2020 [2] as shown in Figure 1. This will entirely change the computing and network paradigm, affecting scalability of the network [3]. Additionally, heterogeneity of devices will bring interoperability issues [4], and overcoming network congestion in IoT will also be a big challenge [5].
IoT [6] is a computer paradigm that predicts a future in which any low-power device connects to the Internet, such as a smartphone, wearables, sensors, and so on. The devices’ heterogeneity, particularly in terms of computational power and battery life, presents substantial issues in terms of how to efficiently utilize and execute apps on them. Offloading is one of the most important approaches for increasing the efficiency of low-power devices’ resource utilization (e.g., CPU, battery, and storage). Offloading is the process of a device delegating the processing of a task to a more powerful unit. To evaluate whether offloading is advantageous, the device must measure the difficulty of running a program locally against the possible benefits of offloading it during runtime. Multiple system parameters [7], such as network latency, data exchange size, distant server abilities, and so on, are taken into account when calculating the cost of outsourcing the activity. The technique’s potential rests in its ability to assist low-power devices with processing [8]. Although, an offloading approach in IoT has huge importance in terms of Quality of Service and Quality of Experience, there has been no thorough analysis or survey on the offloading challenges associated with the IoT edge computing that may assist researchers with their investigations in the field. As a result, the goal of this work is to completely and methodically review and analyze the available symmetric offloading methodologies in edge computing. Offloading methodologies are divided into three categories in this paper: data offloading, computation offloading, and task offloading. The main contributions of this review are as follows:
  • the major concerns and challenges associated with IoT edge computing symmetric offloading techniques are outlined;
  • a thorough examination of existing data offloading techniques employed in IoT is conducted;
  • finally, a proposed smart architecture is presented for symmetric data offloading that addresses issues like data traffic, bandwidth utilization, and offloading issues.
This paper is organized as follows: Section 2 elucidates the concept of offloading, issues in data offloading, and various approaches of data offloading, and provides a summary of the findings. Section 3 illustrates the research methodology used to select papers from various databases and the process adopted to select the best quality papers. Section 4 outlines important aspects that address various considerations for data offloading in IoT. Section 5 proposes a smart architecture for data offloading and proposes a data offloading workflow for industrial IoT. Subsequently, in Section 6, conclusions and recommendations for future research are presented.

2. Literature Review

This section introduces basic definitions, fundamental concepts relevant to the area of data offloading in IoT networks, and related work.

2.1. A Brief Overview of Offloading in IoT Architecture

This sub-section presents a generic offloading done at a three-tier level architecture of IoT (see Figure 2). Offloading is a technique that allows a low-power device, such as a smartphone or a wearable, to delegate the processing of a task, such as a code, service, or job, to a device with more skills and resources [6,7,8]. When a device can reach the server with low latency rates of transfer in the presence of network connectivity, it is opportunistically outsourced. Offloading all the time is not very effective or practical when the processing needs are very low in comparison with the expenses incurred during communication (particularly latency), so the devices must not unload if the advantage of offloading does not outweigh the cost of doing the operation at the device. The technique’s ultimate goal is to minimize the device’s overall processing load in order to extend battery life.
One of the notable studies of offloading was performed by Kumar et al. [9], who conducted a survey to provide readers with explanations of the architecture of the offloading system, areas of research, technologies used to offload, and motivations for carrying out the computation offloading with the goal of allowing readers to become more familiar and adaptive to the use of these technologies in mobile systems. In [10,11,12,13,14], the authors conducted their analysis by adopting computation offloading to eliminate resource constraints in mobile cloud computing, and performed some research on challenges, such as collective optimization efforts among devices, rating offloading services that had not been previously defined and studied. In [15,16,17,18], the authors reviewed the primary approaches used in compute offloading techniques, including communication with the client and server, the virtualization process, and the use of mobile agents, as well as the benefits and drawbacks. Wang et al. [19] provided a survey overview on compute offloading efforts related to the following research areas like systems pertaining to single users’ multi-user systems, offloading on edge servers with different users, offloading to some other edge devices, and mobility awareness.
Briefly, the previous review papers suffer from some weaknesses, as follows:
  • The papers do not contain a systematic heuristic technique on data offloading in IoT, especially between the years 2017 and 2020.
  • Some papers [10,11,12,13] did not present any reasonable categorization of offloading approaches in IoT.
  • Many papers [14] did not study the entire scope of data offloading in IoT.
  • Some papers [16,17,18,20,21,22] did not provide future directions for offloading approaches in IoT.
  • The existing works did not have a systematic format for selecting papers.
  • The aforementioned reasons motivated us to prepare a survey paper on offloading approaches in IoT to overcome all of these existing deficiencies.

2.2. Data Offloading Issues

The bandwidth usage from 4G’s rich data services is not expected to decrease. As a result, the expense of maintaining the expected exponential growth in traffic created by mobile data services using current 4G network topologies is unsustainable. As a result of the expected surge in data traffic on 4G networks, carriers must immediately consider alternate solutions in order to ensure that both voice and data services run properly. In [20,21,22], the unanticipated data explosion is also due to the rapid uptake of smartphones by mobile phone subscribers, as well as the significantly greater usage profile of smartphones compared to basic handsets. Therefore, with the exponential rise in data traffic due to smart devices, offloading data every time at fog and edge is not the solution, as it will create a scalability issue in the near future and we will have to come up with some other new technique(s). In consideration of the above, an efficient framework should be readily prepared to tackle this increasing data in order to handle this scalability issue.

2.3. Overview of Data Offloading Approaches

In [23], the authors devised a novel offloading approach for edge computing by optimizing the efficiency in order to improve the performance of deep learning in IoT-based applications. Furthermore, in [24], the authors developed a fog computing-based system to handle mobile data in real time. In the edge context, the authors in [25] used in-memory storage and processing to reduce long-term energy use while maintaining acceptable latency. In [26], the authors presented one of the first efforts in opportunistic-based mobile data offloading. The authors addressed the subject of target set selection and proposed three techniques for selecting a set of targets: greedy, heuristic, and random. In [27], the authors suggested a method for offloading by direct connectivity or intermediate nodes, with data from mobile devices to infrastructure. The review looked into the relationship between data amount and the probability of a failed delivery for a particular path and time limitation.
In [28], the authors proposed an incentive-based strategy for incentivizing users who give data offloading space for requesters. In [29], the authors developed an offloading technique based on Wi-Fi. Before the deadline, every user of the new system was required to get some particular data using a Wi-Fi access point. The authors in [30] investigated a dataset of video requests obtained from real-world cellular traffic over a one-month period in a big metropolitan region to come up with a viable offloading approach.
The authors in [31] developed an operator-assisted offloading system in which a central controller determines which nodes the material must be transported across the cellular network dynamically over time, based on the current state of dissemination. The proposed approach is based on centralized control rather than being entirely opportunistic. In [32], the authors suggested a cooperative data offloading based on Wi-Fi architecture to ensure that dependable data is stored using smart phones.
In [33], the authors investigated the effects of self-contention on data storage of mobile devices and offered a strategy for maximizing offloading throughput’s upper limits. At the edge, multi-device collaborative data offloading is more appropriate. As a result, some research has attempted to address the challenge of collaborative data dumping. The authors in [34] introduced optimization models and presented energy-aware collaborative data offloading, as shown in Figure 3. In [35], the random forests approach and Apache Hadoop were used to build a machine learning method incorporated within a big data analytics platform to estimate highway travel time based on data collected from highway electronic toll collection in Taiwan.

2.4. Summary

The majority of the offloading strategies that were reviewed utilized techniques that are model-based, such as queueing theory, game theory, and Lyapunov optimization, although heuristic-based techniques were used to a lesser extent. In terms of case studies, it was discovered that the majority of researchers evaluated offloading solutions for compute-intensive mobile apps, including video surveillance, face detection, and AR/VR, but less work was done on traffic intensive applications. From the standpoint of performance metrics, it was observed that practically all research looked into metrics related to energy consumption because the offloading of duties represents an energy-intensive operation. In terms of assessment tools, it was discovered that the majority of strategies related to offloading were tested with the help of iFogSim, despite the fact that benchmarks should be used to verify the efficiency with which mobile applications work.

3. Research Methodology

In this section, we present a systematic literature review (SLR) method for categorizing the offloading approaches in IoT used in recent research [26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41]. The following studied string words were used to determine important synonyms and keywords of the approaches:
  • (“Off” OR “Data Offloading” OR “Allocation” OR “Task Offloading” OR “Offloading” OR “Edge computing”). We created some technical questions (TQs) based on the scope of the data offloading technique in IoT network using the SLR method:
  • TQ1: What are the primary considerations for data offloading in IoT?
  • TQ2: What evaluation tools are used to assess data offloading strategies?
  • TQ3: What are the most common criteria used to assess data offloading approaches?
  • TQ4: Which techniques are used for data offloading approaches?
Various heuristic-based techniques proposed by different researchers are highlighted in Table 1. The table contains a literature survey related to techniques utilized, parameters used, case studies adopted, and simulators used.
In order to refine the key research, addition and removal techniques were employed for the screening of the final paper selection. Due to the tremendous potential of Web of Science journals, research articles that index in the Web of Science and ISI proceedings with the peer-reviewed process were considered for offloading challenges in IoT. Some of the papers’ drawbacks were as follows: (1) non-English research articles are not considered in the SLR method; and (2) low-quality conferences with less than four pages are not considered in the SLR method. Finally, we chose 100 papers to respond to our technical queries. Scientific publishers such as Google Scholar, IEEE, Elsevier, Springer, ACM, and Wiley publish a wide range of research studies each year, as seen in Figure 4. Figure 5 depicts the addition and rejection flowchart for the final selection of papers.

4. Discussion and Comparison

This section shows an analytical examination and discussion of existing data offloading studies in IoT. The analytical examination and reports are based on the existing TQs in Section 3:
  • TQ1: What are the primary considerations for data offloading in IoT?
Figure 6 represents a statistical comparison of various issues related with data offloading. In this figure, we consider four different issues, namely traffic congestion, allocation of resources, load balancing, and scheduling approach. Traffic congestion is featured most prominently in the selected papers due to the scalability of the devices followed by scheduling (in 27% of papers), load balancing (in 24% of papers), and allocation (in 17% of papers).
  • TQ2: What evaluation tools are used to assess data offloading strategies?
As per Figure 7, 36% of the research papers implemented iFogSim in their proposed approach. In addition to this, MATLAB tool was used in 26% of research papers, followed by CloudSim (in 22% of papers). A few of the research papers did not specify a simulation tool for evaluating their methods.
  • TQ3: What are the most common criteria used to assess data offloading approaches?
In Figure 8, the analytical report on QoS depicts that response time parameter was used in 29% of the papers on data offloading, followed by energy (20% of papers), bandwidth utilization (19% of papers), and latency (18% of papers).
  • TQ4: Which techniques are used for data offloading approaches?
In Figure 9, the analytical report depicts that most of the papers utilized heuristic-based techniques, followed by meta-heuristics-based approaches. Model-based approaches, such game theory and queueing theory were used less.

5. Proposed Smart Architectures for Symmetric Data Offloading

The proposed IoT-based system for symmetric data offloading (the architecture is shown in Figure 10) consists of ESP32 with integrated Wi-Fi and Bluetooth, GSM module, a sensor device, and a cloud data repository (for storing offloaded data).The sensor device senses the signal (such as temperature, smog percentage, humidity) and then sends the data to the ESP32 module, which acts as a gateway through an NRF module embedded in sensor devices. Low data rate transmission is preferred using one of the IoT protocols such as Zigbee, LoRa WAN, Bluetooth, etc., depending on the use case. The gateway in the system ESP32 acting as an edge node allows data to come from sensor devices and then offloads data to cloud either via GSM module or via local Wi-Fi using a heuristic-based offloading algorithm. Offloaded data requiring a quick response will be sent to the user via GSM for quick action, and attached actuators will respond as per the response of the user. With limited storage facility at the edge node, data resides here for a certain period of time and is then replaced by new data. A cloud server equipped with offloaded data can be utilized for data analytics in order to make effective decisions using pattern-matching techniques of machine learning.

Proposed Workflow for IIoT

The proposed workflow in Figure 11 is specifically designed for the above architecture, where data sensed by the sensors is sent to the edge node, which will filter out the data based on the threshold value. If the data is above the threshold value, the flag value will be set to one and thread one will be activated, which will send the data further to the cloud end. Thread 2 will allocate the server to the offloaded data based on the response received from Thread 1. This workflow curtails the bogus data that is sent every time to the cloud, which causes congestion due to data traffic, and will minimize the load on the server in comparison to existing techniques; thus, it is anticipated to help improve issues related to network congestion, reduced bandwidth, and green computing. Allocation of task will depend upon the server with the smaller ping value and will be AI-based. This proposed workflow for the above architecture will be implemented in traffic-generating applications like industrial IoT.
The novelty of the proposed approach is in its curtailing of data outreaching to cloud environments; additionally, algorithms will be implemented at edge node as per changing scenarios.

6. Conclusions and Future Research

In this paper, a systematic literature review on data offloading approaches used in IoT networks was conducted, and some important issues were highlighted. This paper provides a brief overview of primary considerations for data offloading, evaluation tools, and common criteria used to assess various data offloading strategies. It also highlights various parameters used to evaluate data offloading algorithms. As per the literature review analysis, we observed that traffic congestion has been identified as the main issue even after offloading data at edge and fog nodes. Additionally, we observed that mobile applications have been used most (with 15 studies), followed by vehicular networks (with 8 case studies). Regarding the tools used for implementation, 36% of the papers used iFogSim, followed by 26% of the papers using MATLAB. Pertaining to the QoS parameters used to evaluate the performance of the proposed model, response time was the parameter that was used the most in the selected data offloading papers. From an analysis of the utilized techniques, it was observed that heuristic techniques were used in 35 studies. Finally, based on the statistical analysis conducted, a smart architecture framework for data offloading in IoT networks was presented along with a proposed workflow for an industrial IoT-specific application that will be used to implement traffic-intensive applications in the future. The authors hope that this paper will help researchers to gain a better understanding of current data offloading approaches and will help support future research in this field.

Author Contributions

Study conception and design, M.S.B. and K.G.; data collection, A.Z. and S.M.; analysis and interpretation of results, M.S.B. and D.K.; draft manuscript preparation, K.G., M.S.B. and A.K.P. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Taif University Researchers Supporting Project Number (TURSP-2020/114), Taif University, Taif, Saudi Arabia.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

This work was supported by Taif University Researchers Supporting Project Number (TURSP-2020/114), Taif University, Taif, Saudi Arabia.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ashton, K. That ‘internet of things’ thing. RFID J. 2009, 22, 97–114. [Google Scholar]
  2. Tanweer, A. A reliable communication framework and its use in inteernet of things (IoT). IJSRCSEIT 2018, 3, 2456–3307. [Google Scholar]
  3. Gomes, M.; Rodrigo, R.; Cristiano Costa, C. Internet of things scalability: Analyzing the bottlenecks and proposing alternatives. In Proceedings of the International Congress on Ultra-Modern Telecommunications and Control Systems, St. Petersburg, Russia, 6–8 October 2014; pp. 269–276. [Google Scholar]
  4. Ishaq, I.; Girum, D.C.; Demeester, P. IETF standardization in the field of the internet of things: A Survey. Sens. Actuatuor Netw. 2013, 2, 235–287. [Google Scholar] [CrossRef] [Green Version]
  5. Betzler, A.; Gomez, C.; Demirkol, I.; Paradells, J. CoAP congestion control for the internet of things. IEEE Commun. Mag. 2016, 54, 154–160. [Google Scholar] [CrossRef] [Green Version]
  6. Al-Fuqaha, A.; Guizani, M.; Mohammadi, M.; Aledhari, M.; Ayyash, M. Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 2015, 17, 2347–2376. [Google Scholar] [CrossRef]
  7. Flores, H.; Srirama, S. Mobile code offloading: Should it be a local decision or global inference? In Proceedings of the International Conference on Mobile Systems, Applications, and Services, Taipei, Taiwan, 25–28 June 2013; pp. 539–540. [Google Scholar]
  8. Flores, H.; Hui, P.; Tarkoma, S.; Li, Y.; Srirama, S.; Buyya, R. Mobile code offloading: From concept to practice and beyond. IEEE Commun. Mag. 2015, 53, 80–88. [Google Scholar] [CrossRef]
  9. Ghosh, A.; Khalid, O.; Rais, R.N.; Rehman, A.; Malik, S.U.; Khan, I.A. Data offloading in IoT environments: Modeling, analysis, and verification. EURASIP J. Wirel. Commun. Netw. 2019, 1, 53. [Google Scholar] [CrossRef]
  10. Aazam, M.; Zeadally, S.; Harras, K.A. Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities. Future Gener. Comput. Syst. 2018, 87, 278–289. [Google Scholar] [CrossRef]
  11. Kumar, K.; Liu, J.; Lu, Y.H.; Bhargava, B. A survey of computation offloading for mobile systems. Mob. Netw. Appl. 2013, 18, 129–140. [Google Scholar] [CrossRef]
  12. Dinh, H.T.; Lee, C.; Niyato, D.; Wang, P. A survey of mobile cloud computing: Architecture, applications, and approaches. Wirel. Commun. Mob. Comput. 2011, 13, 1587–1611. [Google Scholar] [CrossRef]
  13. Fernando, N.; Loke, S.W.; Rahayu, W. Mobile cloud computing: A survey. Future Gener. Comput. Syst. 2013, 29, 84–106. [Google Scholar] [CrossRef]
  14. Rahimi, M.R.; Ren, J.; Liu, C.H.; Vasilakos, A.V.; Venkatasubramanian, N. Mobile cloud computing: A survey, state of art and future directions. Mob. Netw. Appl. 2014, 19, 133–143. [Google Scholar] [CrossRef]
  15. Wang, Y.; Chen, I.R.; Wang, D.C. A survey of mobile cloud computing applications: Perspectives and challenges. Wirel. Person. Commun. 2015, 80, 1607–1623. [Google Scholar] [CrossRef]
  16. Ahmed, A.; Ahmed, E. A Survey on Mobile Edge Computing; ISCO: Coimbatore, India, 2016; pp. 1–8. [Google Scholar]
  17. Pang, Z.; Sun, L.; Wang, Z.; Tian, E.; Yang, S. A survey of cloudlet based mobile computing. In Proceedings of the International Conference on Cloud Computing and Big Data, Macau, China, 16–18 November 2016; pp. 268–275. [Google Scholar]
  18. Mach, P.; Becvar, Z. Mobile edge computing: A survey on architecture and computation offloading. IEEE Commun. Surv. Tutor. 2017, 19, 1628–1656. [Google Scholar] [CrossRef] [Green Version]
  19. Wang, X.; Han, Y.; Wang, C.; Zhao, Q.; Chen, X.; Chen, M. In-edge AI: Intelligent zing mobile edge computing, caching and communication by federated learning. IEEE Netw. 2019, 33, 156–165. [Google Scholar] [CrossRef] [Green Version]
  20. Tang, W.; Li, S.; Rafique, W.; Dou, W.; Li, S. An Offloading approach in Fog Computing Environmnet. In Proceedings of the 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Guangzhou, China, 8–12 October 2018; pp. 857–864. [Google Scholar]
  21. Fei, X.; Shah, N.; Verba, N.; Chao, K.M.; Sanchez-Anguix, V.; Lewandowski, J.; James, A.; Usman, Z. CPS data streams analytics based on machine learning for cloud and fog computing: A survey. Future Gener. Comput. Syst. 2019, 90, 435–450. [Google Scholar] [CrossRef] [Green Version]
  22. Zhou, Z.; Chen, X.; Li, E.; Zeng, L.; Zhang, J. Edge intelligence: Paving the last mile of artificial Intelligence with edge computing. Proc. IEEE 2019, 107, 1738–1762. [Google Scholar] [CrossRef] [Green Version]
  23. Li, H.; Ota, K.; Dong, M. Learning IoT in edge: Deep learning for the internet of things with edge computing. IEEE Netw. 2018, 32, 96–101. [Google Scholar] [CrossRef] [Green Version]
  24. Li, L.; Ota, K.; Dong, M. Deep learning for smart industry: Efficient manufacture inspection system with fog computing. IEEE Trans. Ind. Inform. 2018, 14, 4665–4673. [Google Scholar] [CrossRef] [Green Version]
  25. Xu, J.; Ota, K.; Dong, M. Saving energy on the edge: In-memory caching for multi-tier heterogeneous networks. IEEE Commun. Mag. 2018, 56, 102–107. [Google Scholar] [CrossRef] [Green Version]
  26. Han, B.; Hui, P.; Kumar, V.A.; Marathe, M.V.; Shao, J.; Srinivasan, A. Mobile data offloading through opportunistic communications and Social participation. IEEE Trans. Mob. Comput. 2012, 11, 821–834. [Google Scholar] [CrossRef]
  27. Lu, Z.; Sun, X.; Porta, T.L. Cooperative data offload in opportunistic networks: From mobile devices to infrastructure. IEEE ACM Trans. Netw. 2017, 25, 3382–3395. [Google Scholar] [CrossRef] [Green Version]
  28. Bao, X.; Zhang, Y.; Ding, J.; Song, M. Offloading Cellular Traffic through Opportunistic Networks: A Stackelberg-Game Perspective; ICCSE: Nagoya, Japan, 2016; pp. 682–688. [Google Scholar]
  29. Deng, H.; Hou, I.-H. Online scheduling for delayed mobile offloading. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM), Hong Kong, China, 26 April–1 May 2015; pp. 1867–1875. [Google Scholar]
  30. Valerio, L.; Abdesslemy, F.B.; Lindgreny, A.; Passarella, A.; Luoto, M. Offloading cellular traffic with opportunisticnetworks: A feasibility study. In Proceedings of the 14th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOCNET), Vilamoura, Portugal, 17–18 June 2015; pp. 1–8. [Google Scholar]
  31. Valerio, L.; Bruno, R.; Passarella, A. Cellular traffic offloading via opportunistic networking with reinforcement learning. Comput. Commun. 2015, 71, 129–141. [Google Scholar] [CrossRef]
  32. Ding, A.Y.; Hui, P.; Kojo, M.; Tarkoma, S. Enabling energy-aware mobile data offloading for smartphones through vertical collaboration. In Proceedings of the ACM Conference on CONEXT Student Workshop, Nice, France, 10 December 2012; pp. 27–28. [Google Scholar]
  33. Yang, W.; Li, H.; Wu, J. ACK offloading for reliable multipath transfer over self-contention wireless network. In Proceedings of the International Conference on Communications and Mobile Computing, Qingdao, China, 18–20 April 2011; pp. 165–169. [Google Scholar]
  34. Tran, D.H.; Tran, N.H.; Pham, C.; Kazmi, S.M.; Huh, E.N.; Hong, C.S. OaaS: Offload as a service in fog networks. Computing 2017, 99, 1081–1104. [Google Scholar] [CrossRef]
  35. Fan, S.K.S.; Su, C.J.; Nien, H.T.; Tsai, P.F.; Cheng, C.Y. Using machine learning and big data approaches to predict travel time based on historical and real-time data from Taiwan electronic toll collection. Soft Comput. 2018, 22, 5707–5718. [Google Scholar] [CrossRef]
  36. Liu, L.; Chang, Z.; Guo, X.; Mao, S.; Ristaniemi, T. Multiobjective optimization for computation offloading in fog computing. IEEE Internet Things J. 2018, 5, 283–294. [Google Scholar] [CrossRef]
  37. Wang, X.; Ning, Z.; Wang, L. Offloading in internet of vehicles: A fog-enabled real-time traffic Management system. IEEE Trans. Ind. Inform. 2018, 14, 4568–4578. [Google Scholar] [CrossRef]
  38. Liu, L.; Chang, Z.; Guo, X. Socially-aware dynamic computation offloading scheme for fog computing system with energy harvesting devices. IEEE Internet Things J. 2018, 5, 2327–4662. [Google Scholar] [CrossRef] [Green Version]
  39. Zhao, X.; Zhao, L.; Liang, K. An energy consumption oriented offloading algorithm for fog computing. In Proceedings of the International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, Seoul, Korea, 7–8 July 2016; pp. 293–301. [Google Scholar]
  40. Bozorgchenani, A.; Tarchi, D.; Corazza, G.E. An energy and delay-efficient partial offloading technique for fog computing architectures. In Proceedings of the IEEE Global Communications Conference GLOBECOM, Singapore, 4–8 December 2017; pp. 1–6. [Google Scholar]
  41. Liang, K.; Zhao, L.; Wang, X.; Ou, S. Joint resource allocation and coordinated computation offloading for fog radioaccess networks. China Commun. 2016, 13, 131–139. [Google Scholar] [CrossRef]
  42. Zhang, C.; Gu, B.; Liu, Z.; Yamori, K.; Tanaka, Y. Cost and energy-aware multi-flow mobile data offloading using Markov decision process. In Proceedings of the 13th International Conference on Network & Service Management, Tokyo, Japan, 26–30 November 2017; pp. 26–30. [Google Scholar]
  43. Kim, Y.; Lee, J.; Joeseong, J.; Chong, S. Multi-flow management for mobile data offloading. ICT Express 2016, 3, 33–37. [Google Scholar] [CrossRef] [Green Version]
  44. Huan, W.; Wen, X.; Lu, Z.; Pan, Q. Mobile data offloading under attractor selection in heterogeneous networks. In Proceedings of the International Symposium on Wireless Communication Systems (ISWCS), Bologna, Italy, 28–31 August 2017; pp. 28–31. [Google Scholar]
  45. Yang, X.; Wang, X.; Wu, Y.; Qian, L.P.; Lu, W.; Zhou, H. Small-cell assisted secure traffic offloading for narrowband internet of thing (NB-IoT) systems. IEEE Internet Things J. 2017, 5, 1516–1526. [Google Scholar] [CrossRef]
  46. Skarlat, O.; Nardelli, M.; Schulte, S.; Borkowski, M.; Leitner, P. Optimized IoT service placement in the fog. Serv. Oriented Comput. Appl. 2017, 11, 427–443. [Google Scholar] [CrossRef]
  47. Duan, Z.; Yan, M.; Han, Q.; Li, L.; Li, Y. IoT-based cost saving offloading system for cellular networks. Tsinghua Sci. Technol. 2017, 22, 379–388. [Google Scholar] [CrossRef]
  48. Nhu-Ngoc, D.; Duc-Nghia, V.; Woongsoo, N.; Joongheon, K.; Sungrae, C. SGCO: Stabilized Green Crosshaul Orchestration for dense IoT offloading services. IEEE J. Sel. Areas Commun. 2018, 36, 2538–2548. [Google Scholar]
  49. Shan, F.; Luo, J.; Jin, J.; Wu, W. Offloading delay constrained transparent computing tasks with energy efficient transmission power scheduling in wireless IoT environment. IEEE Internet Things J. 2019, 6, 4411–4422. [Google Scholar] [CrossRef]
  50. Liu, J.; Gao, W.; Li, D.; Huang, S.; Liu, H. An incentive mechanism combined with anchoring effect and loss aversion to stimulate data offloading in IoT. IEEE Internet Things J. 2019, 6, 4491–4551. [Google Scholar] [CrossRef]
  51. Gao, Z.; Meng, J.; Wang, Q.; Yang, Y. Data offloading for deadline-varying tasks in mobile edge computing. In Proceedings of the IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted and Smart City Innovation, Guangzhou, China, 7–11 October 2018; pp. 1479–1484. [Google Scholar]
  52. Yan, H.; Zhang, X.; Chen, H.; Zhou, Y.; Bao, W.; Yang, L.T. DEED: Dynamic Energy-Efficient Data offloading for IoT applications under unstable channel conditions. Future Gener. Comput. Syst. 2019, 96, 425–437. [Google Scholar] [CrossRef]
  53. Roy, D.G.; Mahato, B.; Ghosh, A.; De, D. Service aware resource management into cloudlets for data offloading towards IoT. Microsyst. Technol. 2019, 1–15. [Google Scholar] [CrossRef]
  54. Pranvera, K.; Liang, Z.; Joe, W.C.; Francesco, M.; Chiang, M. Fog-based data offloading in urban IoT scenarios. In Proceedings of the IEEE INFOCOM, Paris, France, 29 April–2 May 2019; pp. 784–792. [Google Scholar]
  55. Xu, X.; Li, D.; Dai, Z.; Li, S.; Chen, X. A heuristic offloading method for deep learning edge services in 5G networks. IEEE Access 2019, 7, 67734–67744. [Google Scholar] [CrossRef]
  56. Shahhosseini, S.; Anzanpour, A.; Azimi, I.; Labbaf, S.; Seo, D.; Lim, S.S.; Liljeberg, P.; Dutt, N.; Rahmani, A.M. Exploring Computational Offloading in IoT Systems. Inf. Syst. 2021, 101860. [Google Scholar] [CrossRef]
  57. Huang, J.; Qian, Y.; Hu, R.Q. A vehicle-assisted data offloading in mobile edge computing enabled vehicular networks. In Proceedings of the IEEE Global Communications Conference (GLOBECOM), Waikoloa Village, HI, USA, 9–13 December 2019; pp. 1–6. [Google Scholar]
  58. Xu, Z.; Liu, X.; Jiang, G.; Tang, B. A time-efficient data offloading method with privacy preservation for intelligent sensors in edge computing. EURASIP J. Wirel. Commun. Netw. 2019, 12, 236. [Google Scholar] [CrossRef]
  59. Xu, X.; Tang, B.; Jiang, G.; Liu, X.; Xue, Y.; Yuan, Y. Privacy-aware data offloading for mobile devices in edge computing. In Proceedings of the International Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom), Atlanta, GA, USA, 14–17 July 2019. [Google Scholar]
  60. Georgios, F.; Nicholas, K.; Tsiropoulou, E.; Symeon, P. Artificial intelligence empowered UAVs data offloading in mobile edge computing. In Proceedings of the IEEE International Conference on Communications (ICC), Dublin, Ireland, 7–11 June 2020; pp. 7–11. [Google Scholar]
  61. Wang, P.; Yu, R.; Gao, N.; Lin, C.; Liu, Y. Task-driven data offloading for fog-enabled urban IoT services. IEEE Internet Things J. 2021, 8, 7562–7574. [Google Scholar] [CrossRef]
  62. Sony, G.; Elhadi, S.; Yasar, A. IoT mobile device data offloading by small-base station using intelligent software defined network. Proced. Comput. Sci. 2020, 177, 234–244. [Google Scholar]
  63. Romano, F.; Benedetta, P. Performance analysis of a delay constrained data offloading scheme in an integrated cloud-fog-edge computing system. IEEE Trans. Veh. Technol. 2020, 69, 12004–12014. [Google Scholar]
  64. Zhang, X.; Shen, Y.; Yang, B.; Zang, W.; Wang, S. DRL based data offloading for intelligent reflecting surface aided Mobile edge computing. In Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC), Nanjing, China, 29 March–1 April 2021; pp. 1–7. [Google Scholar]
  65. Apostolopoulos, P.A.; Fragko, G.; Tsiropoulou, E.E.; Papavassiliou, S. Data offloading in UAV-assisted multi-access edge computing systems under resource uncertainty. IEEE Trans. Mob. Comput. 2021, 20, 1. [Google Scholar] [CrossRef]
  66. Bajaj, K.; Sharma, B.; Singh, R. Implementation analysis of IoT-based offloading Frameworks on cloud/edge computing for sensor generated big data. Complex Intell. Syst. 2021, 1–18. [Google Scholar] [CrossRef]
  67. Shan, Y.; Wang, H.; Cao, Z.; Yury, K. Data offloading in heterogenious dynamic fog computing network: A contextual bandit approach. In Proceedings of the IEEE International conference on Computation computer and the Internet (ICCCI), Nagoya, Japan, 25–27 June 2021; pp. 73–77. [Google Scholar]
  68. Alkatheiri, M.S. PCOS—Privacy Controlled Offloading Scheme for secure service data offloading in edge-internet of things-cloud scenario. Arab. J. Sci. Eng. 2021, 1–14. [Google Scholar] [CrossRef]
  69. Melo, S.; Silva, C.; Aquino, G. Classification aspects of the data offloading process applied to fog computing. In Proceedings of the Conference on Computational Science and Its Applications—ICCSA, Cagliari, Italy, 13–16 September 2021; pp. 340–353. [Google Scholar]
  70. Nguyen, D.C.; Pathirana, P.N.; Ding, M.; Seneviratne, A. A cooperative architecture of data offloading and sharing for smart healthcare with blockchain. In Proceedings of the IEEE International Conference on Blockchain and Cryptocurrency (IEEE ICBC), Sydney, Australia, 3–6 May 2021. [Google Scholar]
Symmetry 13 01889 g001 550
Figure 1. Exponential growth of the Internet of Things (IoT).
Figure 1. Exponential growth of the Internet of Things (IoT).
Symmetry 13 01889 g001
Symmetry 13 01889 g002 550
Figure 2. Offloading in IoT [2].
Figure 2. Offloading in IoT [2].
Symmetry 13 01889 g002
Symmetry 13 01889 g003 550
Figure 3. Data offloading in IoT environments.
Figure 3. Data offloading in IoT environments.
Symmetry 13 01889 g003
Symmetry 13 01889 g004 550
Figure 4. Research paper publications by publishers and year.
Figure 4. Research paper publications by publishers and year.
Symmetry 13 01889 g004
Symmetry 13 01889 g005 550
Figure 5. Paper selection procedure.
Figure 5. Paper selection procedure.
Symmetry 13 01889 g005
Symmetry 13 01889 g006 550
Figure 6. Percentage of presented issues in data offloading.
Figure 6. Percentage of presented issues in data offloading.
Symmetry 13 01889 g006
Symmetry 13 01889 g007 550
Figure 7. Evaluation tools used in the papers of the literature study.
Figure 7. Evaluation tools used in the papers of the literature study.
Symmetry 13 01889 g007
Symmetry 13 01889 g008 550
Figure 8. Percentage of papers employing various parameters for evaluating data offloading.
Figure 8. Percentage of papers employing various parameters for evaluating data offloading.
Symmetry 13 01889 g008
Symmetry 13 01889 g009 550
Figure 9. Utilized techniques for data offloading.
Figure 9. Utilized techniques for data offloading.
Symmetry 13 01889 g009
Symmetry 13 01889 g010 550
Figure 10. Proposed architecture for data offloading.
Figure 10. Proposed architecture for data offloading.
Symmetry 13 01889 g010
Symmetry 13 01889 g011 550
Figure 11. Data offloading Workflow for IIoT.
Figure 11. Data offloading Workflow for IIoT.
Symmetry 13 01889 g011
Table
Table 1. Comparison of heuristic-based techniques in data offloading.
Table 1. Comparison of heuristic-based techniques in data offloading.
Ref.Utilized TechniqueParameterEvaluation ToolAdvantageWeakness
[42]Low time complexity heuristic offloading algorithmThroughput, energy consumption, and average LatencySimulation developed in Python 2.7Simulation study shows that algorithm has comparable performanceHA is theoretically not proven to be optimal
[43]Threshold-based rate control algorithm and dynamic-based rate control algorithmData rates and traffic loadTrace-driven simulationAlgorithm is useful to reduce the computationCommunication overhead is not addressed
[44]Attractor selection algorithmThroughputSimulation (N/A)Proposed system provides better throughput and scalability compared with Wi-Fi and on spot offloadingScalability issue not addressed
[45]TPM offloading algorithm for single smart deviceAverage channel gainSimulation (N/A)Algorithm is useful to minimize the total power consumption of SDSAlgorithm only applicable for NB-IoT system
[46]Genetic algorithm (greedy first fit heuristic)Response time & service execution delaysiFogSimFog service placement problem (FPSS) is solved using GAProposed algorithm has not been evaluated in a real-world scenario
[47]Greedy algorithm and two-step algorithm (TSA)Downloading ratio and average delaySimulation (N/A)Algorithm is effective in reducing the bandwidth and decreasing the cost of the cellular networkNot able to address scalability issue overhead
[48]SGCO (stabilized green cross haul orchestration) algorithmAverage CPU utilizationSimulation (N/A)Program algorithm provides energy efficient workload executionScalability issue not addressed
[49]AELAO (anchoring effect and loss aversion on offloading)Amount of data offloading, actual reward of APsRepastAlgorithm can increase the amount of data offloading while improving participation rateProposed approach not evaluated in real-world scenario
[50]Offline heuristic algorithm and online data offloading algorithmData size, average deadline, cost, and offloadingDieselNetProposed algorithm outperforms another compared algorithmOverhead of the proposed approach has not been investigated
[51]HIF algorithm (highest water level interval first policy)Energy consumption, average delaySimulation (N/A)Emphasis on energy consumptionLack of an appropriate simulation
[52]DEED (dynamic energy efficient data offloading scheduling algorithm)Task completion ratio, task acceptance ratio, ratio of runtime over host timeSimulation (N/A)Reduced energy consumption while ensuring the task reliabilityLack of appropriate simulation
[53]Prediction offloading algorithmNumber of requests, running time, and operational costSimulation (N/A)Proposed algorithm is efficient in terms of delay reduction; cost and execution time is also reducedAccuracy of the proposed solution has not been investigated
[54]Collaborative data offloading protocolData drop rate, time, and number of sensorsCustom Python simulatorSignificantly reduces the data drop off rates in IoTEnergy consumption has not been evaluated
[55]HOM (heuristic offloading method)Running time, number of tasks, and data volumeSimulation (N/A)Reduces transmission delay of deep learning tasksThere is no guarantee of components
[56]FAR, HSM, UBS, and prediction-based offloading schemeDelivery ratio, latency, and overheadOne SimulatorThe three proposed schemes show significant improvements in performanceHigh computational complexity
[57]Graph theory and heuristic methodData transmission rate, maximum time constraints Simulation (N/A)Offloading strategy can greatly reduce the vehicular cellular trafficConfined only to one application
[58]TEO (time efficient offloading method)Transmission time, calculation time, and time consumptionSimulation (N/A)Proposed method is reliable, time consumption is minimized, and privacy is maximizedScalability issue
[59]PDO (privacy aware data offloading) and SPEA2Time of data transmission and privacy entropySimulation (N/A)Evaluations verify the reliability of the privacy entropy and transmission efficiencyOverheads have not been investigated
[60]BRD (best response dynamics)AVG offload data, number of UAVs, and average utilitySimulation (N/A)Overall framework achieves efficiency and effectiveness under different scenariosEnergy consumption has not been evaluated
[61]TDO (task-driven data offloading)
GTDO (greedy TDO)
RG-TDO (Reorganize Task)		Successful ratio, average task cost, average task completion ratioSimulation (N/A)The performance of the proposed algorithms is evaluated using real-world datasetsThe accuracy of the proposed solution has not been investigated
[62]Smart ranking-based task offloading for SBSResidual energyOMNET++Proposed algorithm helps to balance the load between SBS and improves the data communication delayLack of weighting of different parameters
[63]Heuristic algorithmNumber of active processors, energy capacity, completion failure probabilitySimulation (N/A)Paper investigated the behavior of an integrated clou-fog-edge infrastructurePractical approach of the proposed algorithm is not presented
[64]DRL-based offloading algorithmEnergy efficiency, time latency, and priceSimulation (N/A)Proposed algorithm can achieve better system performanceEnergy consumption and delay not evaluated
[65]DCP algorithmSatisfaction utility, total offloaded data, and energy consumptionSimulation (N/A)A novel approach to determine user optimal data offloading strategyCertain factors such as coverage area and overall energy availability UAVs are not considered
[66]CoSMOSTime sensitivity and energy efficiencySimulation (N/A)Existing frameworks are well discussed and analyzedComparison is based on a theoretical analysis
[67]LCBODAverage offload latency and data offload success ratioiFogSimResults confirm the effectiveness of the algorithmPractical approach of the proposed algorithm is not presented
[68]PCOSService loss %, false alarm, and trusted device %Contiki Cooja SimulatorProposed scheme achieves less service loss ratio and false alarmsOverheads have not been investigated
[69]Heuristic policiesEnergy savings and network usageSimulation (N/A)Paper presents an approach to help govern data offloading policies Not evaluated practically
[70]EHRSReduced time latency and energy consumptionLambda edge service with Amazon EC2 ServiceProposed scheme is better in terms of time latency and energy consumptionOverhead issues not addressed
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Bali, M.S.; Gupta, K.; Koundal, D.; Zaguia, A.; Mahajan, S.; Pandit, A.K. Smart Architectural Framework for Symmetrical Data Offloading in IoT. Symmetry 2021, 13, 1889. https://doi.org/10.3390/sym13101889

AMA Style

Bali MS, Gupta K, Koundal D, Zaguia A, Mahajan S, Pandit AK. Smart Architectural Framework for Symmetrical Data Offloading in IoT. Symmetry. 2021; 13(10):1889. https://doi.org/10.3390/sym13101889

Chicago/Turabian Style

Bali, Malvinder Singh, Kamali Gupta, Deepika Koundal, Atef Zaguia, Shubham Mahajan, and Amit Kant Pandit. 2021. "Smart Architectural Framework for Symmetrical Data Offloading in IoT" Symmetry 13, no. 10: 1889. https://doi.org/10.3390/sym13101889

APA Style

Bali, M. S., Gupta, K., Koundal, D., Zaguia, A., Mahajan, S., & Pandit, A. K. (2021). Smart Architectural Framework for Symmetrical Data Offloading in IoT. Symmetry, 13(10), 1889. https://doi.org/10.3390/sym13101889

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop