A Survey on the Edge Computing for the Internet of Things

By Wei Yu, Fan Liang, Xiaofei He, William Grant Hatcher, Chao Lu, Jie Lin, Xinyu Yang. IEEE Access, 2018

1,262 citations recorded by the source corpus.

The Internet of Things (IoT) now permeates our daily lives, providing important measurement and collection tools to inform our every decision. Millions of sensors and devices are continuously producing data and exchanging important messages via complex networks supporting machine-to-machine communications and monitoring and controlling critical smart-world infrastructures. As a strategy to mitigate the escalation in resource congestion, edge computing has emerged as a new paradigm to solve IoT and localized computing needs. Compared with the well-known cloud computing, edge computing will migrate data computation or storage to the network “edge,” near the end users. Thus, a number of computation nodes distributed across the network can offload the computational stress away from the centralized data center, and can significantly reduce the latency in message exchange. In addition, the distributed structure can balance network traffic and avoid the traffic peaks in IoT networks, reducing the transmission latency between edge/cloudlet servers and end users, as well as reducing response times for real-time IoT applications in comparison with traditional cloud services. Furthermore, by transferring computation and communication overhead from nodes with limited battery supply to nodes with significant power resources, the system can extend the lifetime of the individual nodes. In this paper, we conduct a comprehensive survey, analyzing how edge computing improves the performance of IoT networks. We categorize edge computing into different groups based on architecture, and study their performance by comparing network latency, bandwidth occupation, energy consumption, and overhead. In addition, we consider security issues in edge computing, evaluating the availability, integrity, and the confidentiality of security strategies of each group, and propose a framework for security evaluation of IoT networks with edge computing. Finally, we compare the performance of various IoT applications (smart city, smart grid, smart transportation, and so on) in edge computing and traditional cloud computing architectures.

Subfields: Computer Networks and Communications, Health Information Management, Information Systems

View Paper: https://www.semanticscholar.org/paper/f863f0aaea94656807d21b66ba786137f5e28e1f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. F. Yu
2. Zehui Xiong
3. Jiawen Kang
4. Sheng Zhou
5. Pengbo Si
6. Min Chen
7. Wei Yu
8. Ruizhe Yang

In Health Information Management

1. Min Chen
2. Qiang Yang

In Information Systems

1. Dusist Niyato
2. Zehui Xiong
3. Jiawen Kang
4. Pengbo Si
5. Min Chen
6. Wei Yu
7. Ruizhe Yang
8. Minrui Xu

Citations in the corpus

• Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems (2019, background): “manage radio and computing resources to execute tasks, which has been widely investigated recently, see surveys [4]–[6] and technical papers [7]–[9].”
• A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective (2018, background): “Examples include programming for edge computing, edge device naming in networks, and others [85], [88].”
• AI for Next Generation Computing: Emerging Trends and Future Directions (2022, neutral): “Instant data that is created y the user, and only for the user, requires compute and storage on the edge, but big data always requires cloud-based storage [104].”
• A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures (2019, background): “In [8] the authors have compared edge computing with traditional cloud systems to secure IoT systems.”
• A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends (2018, background): “Securing the data, operation, and mechanisms of deep learning are all the more relevant in considering edge computing, which can be a viable computing infrastructure to provision deep learning schemes [155], supporting a variety of smart-world systems (smart cities, smart manufacturing, smart grid, ”
• Edge-computing-driven Internet of Things: A Survey (2022, neutral): “[194] ✓ ✓ ✓ ✓ ✓ ✓ Elazhary [49] ✓ ✓ ✓ ✓ Y.”
• A Survey on Security and Privacy Issues in Edge-Computing-Assisted Internet of Things (2020, background): “, spoofing or modification of information) as well as black hole attacks [1], [11], [35], [40].”
• Smart Anomaly Detection in Sensor Systems (2020, methodology): “This trend has also been documented in [8], whereby the focuswas on comparing IoT deployments on different architectural models, based on Cloud and Edge computing, respectively.”
• A Full Dive Into Realizing the Edge-Enabled Metaverse: Visions, Enabling Technologies, and Challenges (2022, background): “Surveys on the convergence of the Internet of Things (IoT) and edge computing [35], [36], [37] also study challenges and”
• Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues and Challenges (2019, background): “A set of issues, challenges, and future research directions for edge computing are discussed in [5] and [29], for instance, the adaption to the dynamic environments and the large-scale optimization for the collaboration of multiple edge servers.”
• Edge-Computing-Enabled Smart Cities: A Comprehensive Survey (2019, neutral): “However, [24], [36]–[43], and [46] reviewed either edge computing alone or edge computing with IoT.”
• A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, neutral): “Several works in [21]–[23] revealed the role of MEC for IoT applications and realization.”