A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
Abstract
:1. Introduction
- Unlike centrally managed clouds, massively heterogeneous systems in the continuum (including IoT devices, edge devices, and cloud infrastructures) are significantly more complex to manage. Furthermore, distributed data management raises an additional level of complexity by classifying data infrastructures, collecting vast and diverse data volumes, providing transparent data access methods, optimizing the internal data flow, and effectively preserving data collections [13].
- Because of the heterogeneity of the involved devices and associated technologies, hardware and technology-agnostic protocols are important, not only to manipulate a large number of interconnected entities but also to enable scalability which is a key concept in the IEC continuum.
- The continuum needs to be effectively managed to optimally meet the application demands during service execution, taking into account multiple constraints, such as the location of the involved nodes (edge or IoT), their transmission and processing capabilities, as well as their energy footprint. Optimum resource allocation in multi-node heterogeneous environments might lead to highly non-convex problems. In this context, machine learning (ML) algorithms have emerged as a promising approach that can solve various optimization problems providing near-optimal solutions [14,15]. In traditional centralized ML approaches, all collected data are sent to a high-performance computing node for proper model training and inference. However, on one hand, the collection of heterogeneous data from all involved nodes of the continuum might increase the pre-processing load, and on the other hand, centralized ML training might jeopardize latency requirements in critical applications. Therefore, as will also be described in Section 2, the support of distributed and decentralized ML approaches is a key concept in IEC systems [16,17,18].
- Due to the distributed and dynamic nature of the continuum, with plenty of devices from different owners and provenance, the application of reliability and trustiness becomes fundamentally challenging. Secure mechanisms for accessing the distributed nodes, preserving data privacy, and providing open and transparent operation are fundamental to enhancing trustworthiness [19,20].
- As it was previously mentioned, the continuum puts together a broad and diverse space with multiple heterogeneous devices and protocols. Although there are several standards, open-source projects, and foundations that focus on global communication and management protocols, the envisioned continuum must also consider that some constrained devices will not support any specific tool. Therefore, contributing to an open ecosystem favors interoperability with existing and emerging frameworks, which is a key challenge for next-generation broadband wireless networks [21].
1.1. Related Works
1.2. Contributions
- Recent works in the IEC continuum are presented, with emphasis on the challenges they deal with, as well as on the supporting technologies.
- Basic limitations are identified, and open research directions are analyzed as well. This discussion on open issues takes into consideration the coexistence of IEC systems with next-generation broadband wireless networks.
- Indicative use cases are presented, where the synergy among IoT, edge, and cloud nodes is highlighted for optimum service deployment and user experience.
2. Supporting Technologies in the IoT-Edge-Cloud Continuum
2.1. Distributed and Decentralized Machine Learning
2.2. Serverless Computing
2.3. Blockchain Technology
2.4. Subnetworks
2.5. Device to Device Communications
3. Indicative Use Cases
3.1. IoT in Agriculture
3.2. Energy Management and Decision Support System
3.3. Smart Manufacturing
3.4. Smart Cities
3.5. Maritime Applications
4. Recent Works in IoT-Edge-Cloud Continuum Architectures
5. Discussion—Open Issues
- In the cases of ML model training, in the majority of the studied works, such as in [79,84,106,111], DRL approaches have been considered, as they can adapt more effectively to various network deployments and reconfigurations. The alternative approaches of supervised or unsupervised learning would involve data collection from scratch and retraining that can increase the computational load and deteriorate the system’s latency.
- The full deployment of highly demanding latency applications, such as autonomous driving or zero touch smart manufacturing, could be leveraged by private IEC infrastructures to avoid an imbalance in the computational load of edge or cloud servers from other applications and public networks’ latency. In this case, private 5G infrastructures can be deployed within the premises of a manufacturing unit along with dedicated edge servers that process data and train ML models. In cases of dispersed units, these data can be then sent to private cloud domains for further analysis and macroscopic KPI optimization.
- There is a fundamental tradeoff between the enforcement of strict security policies in every data transfer procedure between IoT nodes and edge-cloud/servers and computational load. In these cases, the concept of trusted nodes can be applied: IoT nodes enter the IEC only after their robustness against specific security attacks has been verified. Even so, this does not rule out the possibility of future attacks. Therefore, anomaly detection algorithms in the edge or cloud domain can detect unusual data patterns.
- Serverless computing along with blockchain technology can be quite effective towards the concept of PaaS in IEC systems, due to the elimination of third-party involvement. By decomposing service computations as a set of microservices, service providers could make the most out of cloud and edge computing by exploiting their elasticity. During its lifetime, the service could scale-in or scale-down to adjust the number of instances of each microservice to the workload, thus reducing the operational expenditures of the service.
- Resource optimization is important in the IEC continuum, due to the magnitude of involved infrastructures and applications. Therefore, the employed ML models should be in a position to collect and aggregate a vast amount of heterogeneous data from diverse sources. Moreover, the developed ML models should be applicable to multiple scenarios in the IEC framework.
- Although hardware and computational complexity along with latency reduction during task execution and efficient task offloading are the main KPIs under evaluation in an IEC ecosystem, the design and development of «green» networks are extremely important in the new era of 6G communications. In this context, the mass number of interconnected devices may have a severe impact on the energy footprint, which should be taken into account during IEC design as well. In addition, ML algorithms consume more energy compared to regular algorithms due to the increasing complexity of running and training models. Therefore, the design and implementation of less complex algorithms, while maintaining the accuracy rate, is also a key challenge towards green-based architectures.
- While some recent ML research works address intelligent orchestration and control of different edge-cloud systems, there is still a lack of integrated AI solutions to optimize the edge-cloud continuum. Therefore, it is essential to integrate ML techniques at different layers of the architecture while dynamically optimizing the edge-cloud continuum software, data, and resource orchestration as a whole. A range of techniques such as federated learning for privacy, transfer learning for model reuse at the device, and different deep reinforcement learning architectures can be applicable to various optimization goals.
- The vast majority of related works consider performance evaluation of the proposed approach per case in limited orientations (e.g., laboratory evaluation or a moderate number of participating nodes). Therefore, large-scale evaluation is important not only to examine scalability issues but also to identify potential limitations when full deployment takes place.
- The full deployment of the IEC computing systems is inextricably connected with an integration of diverse hardware elements and infrastructures, thus leading not only to a highly heterogeneous environment but also to functions and features that cannot be anticipated at the time of design. The distributed, dynamic, and programmable nature of the entire IEC continuum along with the fragmentation of data as well as the need for supporting cross-platform interoperability, makes the application of security and trust fundamentally challenging. Since such systems unavoidably lead to a corresponding increase in the number and types of potential attacks, such attacks need to be predicted and anticipated with the help of AI systems.
- The IEC framework should be able to provide autonomous reconfigurability according to network conditions and user and application requirements, as well as according to other design goals such as minimization of the energy footprint, which was previously mentioned. Indeed, the requirement for increased autonomous reconfigurability is emerging due to the ever more demanding services and the impact these services have on daily human activities (e.g., e-health applications).
- Although there are many open-source frameworks for various functionalities involved in the IEC continuum, such as Apache Kafka for telemetry and data streaming [113], TensorFlow for machine learning [114], and Flower for federating learning deployment [115], there is not until now a unified open-source framework that can support scalability over large IEC infrastructures. This diversity in programming frameworks and models hinders the efficient development of the continuum solutions.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
2D/3D | Two/Three Dimensional |
5G | Fifth Generation |
6G | Sixth-Generation |
AI | Artificial Intelligence |
AP | Access Point |
CCM | Container Cluster Manager |
CPU | Central Processing Unit |
D2D | Device to Device |
DL | Deep Learning |
DRL | Deep Reinforcement Learning |
DNS | Domain Name Server |
DNSNA | DNS Name Autoconfiguration |
DQL | Deep Q-Learning |
E2E | End-to-End |
EC | Edge Computing |
ECN | Edge Computing Node |
EV | Electrical Vehicle |
FL | Federated Learning |
IEC | IoT-Edge-Cloud |
IioT | Industrial Internet of Things |
IoE | Internet of Energy |
IoT | Internet of Things |
IoTEF | Internet of Things Edge-Cloud Federation |
IoV | Internet of Vehicles |
ECDriven IoT | Edge Computing-Driven IoT |
KPI | Key Performance Indicator |
LAN | Local Area Network |
MEC | Multi-access Edge Computing |
ML | Machine Learning |
NAN | Neighborhood Area Network |
NB | NarrowBand |
NN | Neural Network |
NFV | Network Function Virtualization |
OS | Operating System |
P2P | Peer to Peer |
PaaS | Platform-as-a-Service |
PA | Precision Agriculture |
PDS | Post-Decision State |
QoS | Quality of Service |
RSSI | Received Signal Strength Indicator |
SAR | Search and Rescue |
SDN | Software Defined Networking |
UAV | Unmanned Aerial Vehicle |
USV/UUV | Unmanned Surface/Underwater Vehicle |
V2V | Vehicle to Vehicle |
VNE | Virtual Network Embedding |
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Survey Paper | Year | Contributions |
---|---|---|
[25] | 2022 | Edge computing-driven IoT under various technological aspects |
[26] | 2022 | Security considerations in edge-based IoT |
[27] | 2021 | Security and privacy considerations in IEC continuum systems and classification of attacks |
[28] | 2023 | Edge AI |
[29] | 2022 | Analysis of different node categories in an edge-cloud-IoT environment |
[30] | 2022 | Frameworks and simulation tools for the support of distributed intelligence in the edge to cloud environments |
[31] | 2022 | Integration of edge computing with novel technologies |
[32] | 2023 | Management and orchestration in edge computing IoT systems |
[33] | 2022 | Recent advances in edge computing |
This work | - | Analysis of recent works in IEC continuum systems in the context of various challenges and key enabling technologies |
Survey Paper | Year | Contributions | Methodology | Limitations—Open Issues |
---|---|---|---|---|
[75] | 2019 | Decentralized hybrid cloud MEC architecture | AI, security and privacy, serverless computing | Evaluation in real world scenarios |
[76] | 2023 | Localization of IoT devices | DNS naming, IPv6 | Secure communication and multi-user management |
[77] | 2022 | Resources integration via virtualization | ML for resource optimization, agnostic application development | Security mechanisms, efficient task offloading |
[78] | 2023 | Parallel data ingestion | ML for resource optimization, ensemble learning | Security mechanisms |
[79] | 2021 | Data reduction prior to cloud processing | Feature learning | Extension of the proposed framework in additional applications |
[80] | 2022 | Multiple edge/cloud providers | Resource allocation and sharing optimization model | Evaluation in real world scenarios |
[81] | 2021 | Node utilization in IoT environments | Security via blockchain technology | Evaluation in real world scenarios |
[82] | 2022 | Task failure minimization | Task offloading according to network, security, and latency requirements | Optimum task offloading for complex applications |
[83] | 2021 | Efficient task offloading via security mechanisms | Advanced encryption standard cryptographic technique Low complexity load balancing and computation offloading | ML for efficient computational offloading |
[84] | 2021 | IoT microservice deployment problem | Deep reinforcement learning | Load balancing in multi-cloud environment deployment strategies Decentralized training approaches for privacy enhancement |
[86] | 2023 | Delay, job failure, computational overhead, energy consumption improvement | Deep Post-Decision State (PDS) for learning efficiency Blockchain technology during task offloading | ML in large-scale MEC orientations |
[87] | 2023 | Cloud-to-Edge-to-IoT Continuum for SAR applications | NFV, virtual objects at the edge | Advanced AI approaches and computer vision for additional post-disaster scenarios |
[89] | 2023 | Integration of multiple use case scenarios in a common architectural approach | Reference architecture based on end-edge, net-edge, cloud-edge | Construction of a multi-tier EC-IoT architecture with one central cloud and multiple edge-clouds with unified regulation and standards |
[90] | 2021 | Task offloading for latency minimization | Application characteristics and heterogeneity of the infrastructure | Evaluation in real world scenarios |
[91] | 2021 | AI in edge-cloud-IoT environments | Sophon edge framework for ML model training and inference | Distributed ML for latency reduction and privacy enhancement |
[92] | 2023 | Interaction between Cloud-IoT environments | Cognitive management of services | Evaluation in real world scenarios and security mechanisms |
[93] | 2016 | Software in edge-cloud-IoT environments | Model-based approach to automatically assigning multiple software deployment plans to hundreds of edge gateways | Performance evaluation in additional scenarios apart from e-Health |
[94] | 2021 | IoT application modules placement | Particle swarm optimization to acquire the best application module placement strategy | Extension in multiple IoT application services |
[96] | 2020 | Multi-cluster IoT applications | Four-layered architecture | Extension in additional use case scenarios |
[97] | 2021 | Optimum cluster usage | Task scheduling problem at the edge | Performance evaluation in real world scenarios |
[98] | 2022 | Model fleet deployment | Μodel-based techniques | Evaluation in real worlds scenarios |
[99] | 2019 | IoT platform based on edge and cloud computing for smart agriculture | Three-tier open-source software platform at local, edge and cloud planes. | Performance evaluation in additional farming scenarios |
[100] | 2021 | IoT-Edge-Fog-Cloud architecture for agricultural applications | Optimization of energy consumption, CO2 emission, and network traffic | Machine learning approaches |
[101] | 2018 | IoT-based manufacturing | Interlayer coordination | Performance evaluation in large-scale orientations |
[102,103] | 2019, 2023 | Smart manufacturing based on a three-layered architecture | Edge-fog-cloud cooperation Efficient task offloading | Performance evaluation in large-scale orientations |
[104] | 2020 | IoT-based manufacturing | Edge computing with blockchain Task assignment based on particle swarm optimization | Performance evaluation in large-scale orientations |
[105] | 2020 | IEC collaboration in smart cities | Distributed deep learning task offloading | Blockchain-based decentralized offloading scheme Metalearning in offloading decisions |
[106] | 2019 | IEC in e-health applications | Lightweight security mechanism based on trusted nodes | Scalability |
[107] | 2023 | IEC in e-health applications | FL for data transfer reduction D2D communications | Single cell wireless sensor network scenario with one MEC server |
[108] | 2023 | IEC in e-health applications | Blockchain and smart contacts | Efficient consensus algorithms |
[109] | 2023 | Energy efficiency in IEC systems | Problem formulation, iterative optimization and comparison to baseline approaches | Energy prediction algorithms |
[110] | 2023 | Anomaly detection in IEC systems | Interpolation and marching squares algorithm | Extension in additional real world scenarios |
[111] | 2019 | Energy-efficient task offloading | Deep reinforcement learning in an IEC smart city scenario | Extension in additional real world scenarios |
[112] | 2021 | Load forecasting in microgrids | Edge servers in microgrids, data collection, ML for load forecasting | Federated learning, transfer learning, insertion of historical data via deep learning |
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Gkonis, P.; Giannopoulos, A.; Trakadas, P.; Masip-Bruin, X.; D’Andria, F. A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues. Future Internet 2023, 15, 383. https://doi.org/10.3390/fi15120383
Gkonis P, Giannopoulos A, Trakadas P, Masip-Bruin X, D’Andria F. A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues. Future Internet. 2023; 15(12):383. https://doi.org/10.3390/fi15120383
Chicago/Turabian StyleGkonis, Panagiotis, Anastasios Giannopoulos, Panagiotis Trakadas, Xavi Masip-Bruin, and Francesco D’Andria. 2023. "A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues" Future Internet 15, no. 12: 383. https://doi.org/10.3390/fi15120383