Emerging Research Directions

7.1 Task and Resource Scheduling

https://ieeexplore.ieee.org/document/9519636 Q. Luo, S. Hu, C. Li, G. Li and W. Shi, "Resource Scheduling in Edge Computing: A Survey," in IEEE Communications Surveys & Tutorials, vol. 23, no. 4, pp. 2131-2165, Fourthquarter 2021, doi: 10.1109/COMST.2021.3106401. keywords: {Edge computing;Processor scheduling;Task analysis;Resource management;Cloud computing;Job shop scheduling;Internet of Things;Internet of things;edge computing;resource allocation;computation offloading;resource provisioning},

https://www.sciencedirect.com/science/article/abs/pii/S014036641930831X Congfeng Jiang, Tiantian Fan, Honghao Gao, Weisong Shi, Liangkai Liu, Christophe Cérin, Jian Wan, Energy aware edge computing: A survey, Computer Communications, Volume 151, 2020, Pages 556-580, ISSN 0140-3664, https://doi.org/10.1016/j.comcom.2020.01.004. (https://www.sciencedirect.com/science/article/pii/S014036641930831X) Abstract: Edge computing is an emerging paradigm for the increasing computing and networking demands from end devices to smart things. Edge computing allows the computation to be offloaded from the cloud data centers to the network edge and edge nodes for lower latency, security and privacy preservation. Although energy efficiency in cloud data centers has been broadly investigated, energy efficiency in edge computing is largely left uninvestigated due to the complicated interactions between edge devices, edge servers, and cloud data centers. In order to achieve energy efficiency in edge computing, a systematic review on energy efficiency of edge devices, edge servers, and cloud data centers is required. In this paper, we survey the state-of-the-art research work on energy-aware edge computing, and identify related research challenges and directions, including architecture, operating system, middleware, applications services, and computation offloading. Keywords: Edge computing; Energy efficiency; Computing offloading; Benchmarking; Computation partitioning

https://onlinelibrary.wiley.com/doi/10.1002/spe.3340 https://www.sciencedirect.com/science/article/abs/pii/S0167739X18319903 Wazir Zada Khan, Ejaz Ahmed, Saqib Hakak, Ibrar Yaqoob, Arif Ahmed, Edge computing: A survey, Future Generation Computer Systems, Volume 97, 2019, Pages 219-235, ISSN 0167-739X, https://doi.org/10.1016/j.future.2019.02.050. (https://www.sciencedirect.com/science/article/pii/S0167739X18319903) Abstract: In recent years, the Edge computing paradigm has gained considerable popularity in academic and industrial circles. It serves as a key enabler for many future technologies like 5G, Internet of Things (IoT), augmented reality and vehicle-to-vehicle communications by connecting cloud computing facilities and services to the end users. The Edge computing paradigm provides low latency, mobility, and location awareness support to delay-sensitive applications. Significant research has been carried out in the area of Edge computing, which is reviewed in terms of latest developments such as Mobile Edge Computing, Cloudlet, and Fog computing, resulting in providing researchers with more insight into the existing solutions and future applications. This article is meant to serve as a comprehensive survey of recent advancements in Edge computing highlighting the core applications. It also discusses the importance of Edge computing in real life scenarios where response time constitutes the fundamental requirement for many applications. The article concludes with identifying the requirements and discuss open research challenges in Edge computing. Keywords: Mobile edge computing; Edge computing; Cloudlets; Fog computing; Micro clouds; Cloud computing

https://www.sciencedirect.com/science/article/abs/pii/S1383762121001570 Akhirul Islam, Arindam Debnath, Manojit Ghose, Suchetana Chakraborty, A Survey on Task Offloading in Multi-access Edge Computing, Journal of Systems Architecture, Volume 118, 2021, 102225, ISSN 1383-7621, https://doi.org/10.1016/j.sysarc.2021.102225. (https://www.sciencedirect.com/science/article/pii/S1383762121001570) Abstract: With the advent of new technologies in both hardware and software, we are in the need of a new type of application that requires huge computation power and minimal delay. Applications such as face recognition, augmented reality, virtual reality, automated vehicles, industrial IoT, etc. belong to this category. Cloud computing technology is one of the candidates to satisfy the computation requirement of resource-intensive applications running in UEs (User Equipment) as it has ample computational capacity, but the latency requirement for these applications cannot be satisfied by the cloud due to the propagation delay between UEs and the cloud. To solve the latency issues for the delay-sensitive applications a new network paradigm has emerged recently known as Multi-Access Edge Computing (MEC) (also known as mobile edge computing) in which computation can be done at the network edge of UE devices. To execute the resource-intensive tasks of UEs in the MEC servers hosted in the network edge, a UE device has to offload some of the tasks to MEC servers. Few survey papers talk about task offloading in MEC, but most of them do not have in-depth analysis and classification exclusive to MEC task offloading. In this paper, we are providing a comprehensive survey on the task offloading scheme for MEC proposed by many researchers. We will also discuss issues, challenges, and future research direction in the area of task offloading to MEC servers. Keywords: Multi-access edge computing; Task offloading; Mobile edge computing; Survey

7.3 Vehicle Computing

Introduction

The rapid advancement in intelligent transportation systems (ITS), autonomous driving, and vehicle-to-everything (V2X) communication has made edge computing an indispensable enabler of innovation. As vehicles become increasingly autonomous, the need for fast, reliable, and low-latency processing grows. Edge computing, by decentralizing data processing and moving it closer to the data source, is revolutionizing how vehicles interact with their environment. This chapter explores the growing field of vehicle edge computing (VEC), analyzing its current role, challenges, and emerging use cases. We synthesize insights from industry presentations, academic research, and real-world implementations to provide a cohesive narrative of the transformative impact of edge computing on the transportation ecosystem.

Overview of Edge Computing

Edge computing refers to the deployment of computing services closer to data sources such as sensors, actuators, or connected devices. Unlike traditional cloud computing, which relies on remote data centers, edge computing reduces latency, saves bandwidth, and enhances real-time decision-making capabilities.

Key Benefits for Automotive Applications:

• Ultra-low latency: Enables real-time decision-making for safety-critical functions.
• Bandwidth optimization: Reduces data transmission by processing information locally.
• Improved privacy and security: Minimizes data exposure by localizing computation.
• Increased reliability: Maintains operation in areas with intermittent connectivity.

2. The Need for Edge in Vehicles

2.1. Data Explosion in Connected Vehicles Modern vehicles are equipped with over 300 sensors and generate terabytes of data daily. As noted by Shi et al. [1], a connected vehicle can produce up to 35 TB of data per day. Centralized cloud architectures are insufficient to handle such high-throughput data streams efficiently.

2.2. Latency and Safety Latency is critical in autonomous systems. In traditional architectures, a vehicle may need to send sensor data to the cloud, await processing, and receive instructions—a delay that could be fatal in scenarios like pedestrian detection. "By the time the information has traveled to and from the server, the accident has already occurred." Edge computing enables immediate action, reducing the delay to milliseconds.

2.3. Software-Defined Vehicles The vehicle industry is moving towards software-defined vehicles (SDVs), which require OTA (Over-The-Air) updates, real-time diagnostics, and modular software services. Edge computing facilitates dynamic service delivery, AI inference, and adaptive updates without overwhelming the cloud infrastructure.

3. Architectures for Vehicle Edge Computing

3.1. Multi-Tiered Architecture Vehicle edge computing generally involves a four-layer architecture:

• On-board Vehicle Computing (VEC): Direct AI inference and sensor processing.
• Edge Servers (e.g., RSUs, roadside units): Shared compute resources at intersections or base stations.
• IoT Layer: Includes environmental sensors and auxiliary connected devices.
• Cloud Tier: For large-scale analytics, simulations, and HD map updates.

3.2. Real-World Example: Autonomous Vehicle Stack

Autonomous driving systems like those developed by Waymo and Uber use a distributed computing model:

• Local x86 nodes with GPUs and FPGAs on the vehicle.
• Sensors (LIDAR, radar, cameras) feed into real-time AI models.
• Local control software performs path planning and object avoidance.

Figure 1: [Vehicle Stack from Uber AV System – Custom Compute Nodes, Sensor Interfaces, Telematics Modules. Source: Seminar Presentation.]

4. Emerging Use Cases in Vehicle Edge Computing

4.1. Autonomous Driving Autonomous vehicles (AVs) rely on edge computing to interpret sensor data in real-time. They use:

• LIDAR and radar for object detection.
• Local AI models for path planning.
• Embedded GPUs/ASICs for deep learning inference.

Edge computing enables AVs to operate safely without full cloud reliance. Simulation systems allow for massive scenario-based testing, further validating algorithms [Seminar Presentation].

4.2. Predictive Maintenance In the user's final project [2], edge computing was leveraged for preventive maintenance warnings using AI models deployed on vehicle microcontrollers. These systems: Monitor signals from OBD-II interfaces. Detect early anomalies in vibration, RPM, or temperature. Alert drivers before mechanical failures occur. This showcases how edge AI can extend vehicle life and improve safety.

4.3. Smart Infrastructure Monitoring Connected vehicles can serve as mobile sensors for cities. Shi [1] highlights how they: Detect potholes or damaged infrastructure. Share data with city edge servers for repair scheduling. Enable crowdsourced infrastructure diagnostics. This creates a synergistic ecosystem between vehicles and smart cities.

4.4. Safety and Law Enforcement Edge-powered applications include: Real-time passenger behavior monitoring for ride-sharing. Situational awareness for police vehicles using AI and camera feeds. Edge-assisted bodycam processing for faster incident reporting.

4.5. Enhanced V2X Communication with Beamforming Using dynamic radar-assisted beamforming, vehicle-to-infrastructure (V2I) communications can adaptively focus beams toward edge units based on detected obstacles [3]. This ensures: Reliable data transmission under high mobility. Better handling of occlusions and multi-path scenarios. Figure 2: [Illustration of Beamforming System in V2I from Paper [3]]

5. Technical Challenges and Research Directions

5.1. Heterogeneity and Resource Constraints Vehicles use different CPUs, GPUs, and network modules. Developing uniform SDKs and runtime environments (like the VPI mentioned by Shi) is crucial.

5.2. Real-Time OS and Scheduling Due to safety requirements, OS-level guarantees are needed for tasks like braking, detection, or communication. Time-sensitive networking and real-time scheduling are active research areas.

5.3. Data Offloading and Bandwidth Optimization Not all data can be offloaded to the cloud. Techniques like compressed AI inference, model partitioning, and log prioritization are necessary to reduce bandwidth costs.

5.4. Security and Privacy With increased vehicle connectivity comes increased risk. Secure boot, sandboxing, trusted execution environments, and decentralized authentication are vital in VEC systems.

5.5. Standardization and APIs There is a need for standardized vehicle-to-cloud and vehicle-to-infrastructure APIs to enable third-party service integration.

6. Future Vision and Business Implications

6.1. Vehicles as Edge Platforms Just like smartphones, vehicles will host apps that: Provide ride-sharing capabilities. Monitor infrastructure. Run entertainment or diagnostic services. OEMs could open SDKs for 3rd-party app development, creating a new vehicle app economy.

6.2. Distributed AI at the Edge Future systems will involve collaborative intelligence, where vehicles communicate with other vehicles and infrastructure to jointly solve navigation, perception, and safety problems.

6.3. Greener Cities and Sustainable Transport Edge-driven predictive maintenance, optimized routing, and dynamic parking can reduce fuel usage and improve traffic flow, supporting sustainability goals. Figure 3: [Four-Tier Architecture of Vehicle Computing – Source: Shi et al. [1]]

Conclusion

Edge computing is set to redefine the automotive landscape. By enabling real-time, decentralized intelligence, it supports safer, smarter, and more efficient transportation. From predictive maintenance and autonomous navigation to infrastructure monitoring and V2X communication, edge computing is the foundation of tomorrow’s intelligent mobility ecosystem. As research and development continue, addressing challenges such as heterogeneity, security, and scalability will be essential. The vehicle edge computing paradigm not only presents technical innovation but also opens doors for new business models, societal benefits, and academic exploration.

References

1. Weisong Shi et al., "Vehicle Computing: Vision and Challenges," 2023.
2. Mohamed Aboulsaad, "Preventive Maintenance Warning in Vehicles by Using AI," Final Project, 2024.
3. Dynamic Beamforming for Vehicle-to-Infrastructure MIMO Communication Assisted by Sensing: Extended Target Case.

7.4 Energy-Efficient Edge Architectures

The exponential growth of the Internet of Things (IoT) devices, coupled with the emergence of artificial intelligence (AI) and high-speed communication networks (5G/6G), has led to the proliferation of edge computing. In an edge computing paradigm, data processing is distributed away from centralized cloud data centers and relocated closer to the data source or end-users. This architectural shift offers benefits such as reduced network latency, efficient bandwidth usage, and real-time analytics. However, the distribution of processing to a multiplicity of geographically dispersed devices has profound implications for energy consumption. Although large-scale data centers have been the subject of extensive research concerning energy efficiency, smaller edge nodes—including micro data centers, IoT gateways, and embedded systems—also generate significant carbon emissions [1].

High-Level Edge-Fog-Cloud Architecture

Modern IoT and AI systems often rely on an edge-fog-cloud architecture. Data collected by IoT sensors typically undergoes initial processing at edge nodes or micro data centers. This local processing minimizes the data volume that must be sent to the cloud, thereby reducing network congestion and latency. Intermediate fog nodes can then aggregate data from multiple edge devices for further analysis or buffering, while centralized cloud data centers handle large-scale storage and intensive computational tasks [2].

Simplified edge–fog–cloud architecture. IoT devices collect data at the edge, which is processed locally by edge nodes (micro data centers). Fog nodes handle intermediate processing, while cloud data centers provide large-scale analytics and storage.

Lifecycle of an Edge Device

Evaluating the carbon footprint of edge devices requires considering their entire lifecycle. The manufacturing phase often involves substantial energy consumption and the use of raw materials. During deployment, the energy efficiency of operation including effective cooling, is critical. Ongoing maintenance and updates can extend a device's lifespan, while end-of-life disposal or recycling presents further environmental challenges. Each stage of the lifecycle offers opportunities for reducing carbon emissions through measures such as modular upgrades, the use of recycled materials, and environmentally responsible disposal practices [3].

Lifecycle stages of an edge device. Each phase, from material extraction and manufacturing to final disposal, impacts the overall carbon footprint. Interventions such as using recycled materials, adopting modular components, and extending product lifespans can substantially reduce environmental impact.

Hardware-Level Approaches

Research on hardware-focused strategies for reducing the carbon footprint at the edge has been extensive. Xu et al. examined system-on-chips (SoCs) designed specifically for energy efficiency, integrating ultra-low-power states and selective core activation [4]. Mendez and Ha evaluated heterogeneous multicore processors for embedded systems, highlighting the benefits of activating only the cores necessary to meet real-time performance requirements [5]. Similarly, the introduction of custom AI accelerators has been shown to yield significant power savings for neural network inference tasks [6].

Bae et al. emphasized that sustainable manufacturing practices and the use of recycled materials can reduce the overall lifecycle emissions of edge devices [7]. Kim et al. explored biologically inspired materials to enhance heat dissipation at the package level, while Liu and Zhang demonstrated that compact liquid-cooling solutions are viable even for micro data centers near the edge [8][9].

Representative Hardware-Level Studies in Edge Computing

Study	Key Focus	Contributions	Findings
[4] Xu et al.	Ultra-low-power SoC design	Introduced SoC with power gating and selective core activation	Significant reduction in idle power consumption
[5] Mendez and Ha	Heterogeneous multicore processors	Evaluated activating only necessary cores for real-time tasks	Improved balance of performance and energy usage
[6] Ramesh et al.	Custom AI accelerators	Developed specialized hardware for on-device inference	Reported substantial energy savings in neural network operations
[7] Bae et al.	Sustainable manufacturing	Employed lifecycle assessments and recycled materials	Achieved measurable decrease in manufacturing emissions
[8] Kim et al.	Biologically inspired packaging	Integrated biomimetic materials for enhanced heat dissipation	Reduced cooling energy overhead and improved thermal performance
[9] Liu and Zhang	Liquid cooling solutions	Demonstrated compact liquid-cooling viability in micro data centers	Quantitative improvement in cooling efficiency

Software-Level Optimizations

Energy-aware software design is integral to achieving sustainability in edge computing. Wan et al. initiated the discussion on applying dynamic voltage and frequency scaling (DVFS) within edge-based real-time systems [10]. Martinez et al. refined DVFS strategies by incorporating reinforcement learning methods that adaptively tune voltage and frequency according to workload fluctuations, illustrating substantial improvements in power efficiency [11]. On the task scheduling front, Li et al. proposed multi-objective algorithms to distribute computing workloads among heterogeneous IoT gateways, balancing performance, latency, and energy considerations [12].

Partial offloading techniques have also gained traction, particularly in AI inference. Zhang et al. presented a partitioning mechanism whereby only computationally heavy layers of a neural network are offloaded to specialized infrastructure, while simpler layers run on the edge device [13]. Hassan et al. and Moreno et al. examined lightweight containerization at the edge, demonstrating that resource overhead can be minimized through optimized container runtimes such as Docker, containerd, and CRI-O [14][15].

Representative System-Level and Policy-Focused Studies

Study	Contribution	Findings	Implications
[16] Chiang et al.	Proposed an edge–fog–cloud migration framework	Demonstrated dynamic workload relocation based on resource availability	Highlighted potential for reduced overall carbon footprint
[18] Qiu et al.	Developed sleep-mode protocols for 5G base stations	Showed drastic energy reduction during off-peak usage	Enabled significant cost savings and lowered emissions
[17] Yang et al.	Introduced carbon-intensity-aware scheduling	Aligned workload placement with regional grid data	Improved sustainability in multi-tier edge–fog–cloud environments
[22] White et al.	Proposed standardized carbon footprint metrics	Offered a uniform reporting structure for edge infrastructures	Facilitated consistent policy and regulatory compliance
[24] Johnson et al.	Analyzed economic incentives for green edge computing	Demonstrated effectiveness of tax benefits and carbon credits	Encouraged broader adoption of low-power deployments

System-Level Coordination and Policy Frameworks

A holistic perspective that spans hardware, network, and orchestration layers has been pivotal in advancing carbon footprint reduction. Chiang et al. and Yang and Li introduced integrated edge–fog–cloud architectures, showing how workload migration across geographically distributed nodes can leverage variations in carbon intensity [16][17]. Qiu et al. and Nguyen et al. developed adaptive networking protocols to reduce base-station energy consumption, such as utilizing sleep modes during off-peak periods or coordinating workload consolidation across neighboring edge gateways [18][19]. These system-wide efforts are increasingly driven by AI-based methods, where machine learning algorithms predict resource utilization or carbon intensity to trigger proactive power management [20][21].

Policy and regulation also play a crucial role. White et al. underscored the need for standardized carbon footprint metrics in edge infrastructures [22], while Gao et al. examined regional regulations enforcing minimum energy efficiency levels for gateways and micro data centers [23]. Johnson et al. explored how carbon credits or tax benefits can incentivize low-power chipset adoption, and Schaefer et al. investigated the impact of green certifications on consumer purchasing behaviors [24][25]. Devic et al. integrated eco-design principles, such as modular battery packs and real-time energy monitoring, to extend hardware life and reduce e-waste [26].

Key Strategies for Reducing Carbon Emissions

Recent publications demonstrate that strategies to mitigate carbon emissions in edge computing frequently span multiple system layers. Hardware-centric measures include deploying ultra-low-power SoCs, optimizing chip layouts, and adopting novel packaging materials to improve heat dissipation. Complementary software-based techniques revolve around power-aware scheduling, partial offloading, and containerized orchestration with minimal resource overhead. AI-driven coordination has also gained traction in predicting workload spikes, carbon intensity variations, and thermal thresholds, thus enabling proactive resource scaling.

Integrating localized renewable energy sources such as solar or wind power at edge sites can enhance sustainability, although practical deployment remains challenging in certain regions. Government policies and industry standards further encourage the adoption of green practices, including energy efficiency mandates and carbon credits. Eco-design principles, which consider recyclability and modular maintenance, help to reduce e-waste and extend device lifespans.

Integrated Measures for Carbon Footprint Reduction in Edge Computing

Dimension	Techniques / References	Contributions	Findings
Hardware	Low-power SoCs ([4] Xu et al.) and AI accelerators ([6] Ramesh et al.)	Minimized idle power and specialized hardware for inference	Notable reductions in power usage across diverse workloads
Software	DVFS with reinforcement learning ([11] Martinez et al.) and partial offloading ([13] Zhang et al.)	Dynamically adjusted CPU frequency and partitioned compute tasks	Demonstrated adaptive energy savings under varying load conditions
System Orchestration	Edge–fog–cloud migration ([16] Chiang et al.) and container optimization ([14] Hassan et al.)	Relocated tasks across network layers using lightweight virtualization	Improved resource utilization and reduced operational overhead
Policy/Regulation	Carbon credits ([24] Johnson et al.) and standardized metrics ([22] White et al.)	Encouraged greener practices through financial and reporting mechanisms	Facilitated consistent adoption of sustainability measures across stakeholders

Open Challenges

Despite clear progress, several open challenges persist. One concern is the wide heterogeneity of edge devices, complicating unified energy-saving approaches. Energy monitoring and carbon-intensity data are not consistently available worldwide, impeding real-time or dynamic optimizations [17]. Trade-offs between reliability and energy efficiency are particularly evident in mission-critical scenarios such as autonomous vehicles or healthcare, where service disruptions or latency spikes may be unacceptable [27]. Current policy frameworks differ across regions, creating fragmented regulations and disjointed compliance requirements for global operators [23]. Furthermore, security and privacy concerns arise when implementing AI-driven power management and data collection, as such systems may become attack vectors or inadvertently compromise sensitive user information [21].

Future Directions

Federated learning for energy management represents a promising avenue, allowing distributed edge nodes to collaborate on model training without consolidating sensitive data [21]. Cross-layer co-design, integrating hardware, operating system functionality, and application-level optimizations, could offer more substantial efficiency gains than focusing on single layers. The development of dynamic carbon-aware energy markets, where edge nodes can schedule tasks based on real-time prices and carbon intensity, also presents a compelling framework for sustainable resource allocation [17]. Standardized metrics and benchmarking tools for energy usage and emissions, analogous to data center metrics like Power Usage Effectiveness (PUE), would further facilitate solution comparisons across device types and vendors, while life-cycle assessments (LCAs) need to be embedded into procurement processes for edge hardware [28].

References

1. Shi, Weisong (2020). "Edge computing: Vision and challenges". IEEE Internet of Things Journal, 7(5): 4238–4260.
2. Satyanarayanan, Mahadev (2019). "The Emergence of Edge Computing". Computer, 52(8): 30–39.
3. Abdollahi, Ali (2019). "Environmental implications of micro data centers: A case study". Sustainability, 11(10): 2728.
4. Xu, Lili (2019). "Energy-efficient SoC design for IoT edge devices". IEEE Transactions on Circuits and Systems I: Regular Papers, 66(8): 2952–2963.
5. Mendez, Carlos (2020). "Heterogeneous multicore edge processors for power-aware embedded systems". ACM Transactions on Design Automation of Electronic Systems, 25(6): Article 40.
6. Ramesh, K. (2022). "Low-power AI accelerators for on-device computer vision". IEEE Transactions on Circuits and Systems for Video Technology, 32(2): 360–372.
7. Bae, Seunghyun (2021). "Sustainable manufacturing of edge devices: A holistic analysis". Journal of Manufacturing Systems, 60: 426–439.
8. Kim, Hyunsu (2023). "Biologically inspired materials for enhanced cooling in edge devices". Nature Electronics, 6(2): 153–164.
9. Liu, Qian (2019). "Liquid cooling in micro data centers at the edge: A quantitative study". IEEE Transactions on Industrial Informatics, 15(10): 5551–5561.
10. Wan, Jiafu (2018). "Dynamic voltage and frequency scaling for real-time systems in edge computing". Journal of Parallel and Distributed Computing, 119: 29–40.
11. Martinez, Daniel (2021). "Reinforcement learning-based DVFS control for energy-efficient edge nodes". IEEE Transactions on Sustainable Computing, 6(3): 403–414.
12. Li, Guanyu (2019). "Multi-objective task scheduling for heterogeneous IoT gateways in edge computing". Future Generation Computer Systems, 100: 223–238.
13. Zhang, Fan (2022). "Partial offloading of convolutional neural networks in edge computing environments". IEEE Transactions on Industrial Electronics, 69(9): 8957–8967.
14. Hassan, Ahmed (2021). "Performance and energy analysis of containerization in edge micro data centers". IEEE Access, 9: 79028–79039.
15. Moreno, Juan (2023). "Kubernetes-based energy-aware orchestration for edge computing". ACM Transactions on Internet Technology, 23(1): Article 5.
16. Chiang, Mung (2018). "Fog and IoT: An overview of research opportunities". IEEE Internet of Things Journal, 5(4): 2451–2461.
17. Yang, Yifan (2023). "Carbon-intensity-aware workload placement in hybrid edge-fog-cloud environments". IEEE Transactions on Cloud Computing, 11(2): 548–561.
18. Qiu, Xiaobo (2020). "Adaptive sleep-mode protocols for 5G base stations in edge scenarios". IEEE Transactions on Green Communications and Networking, 4(3): 670–683.
19. Nguyen, Tuan (2021). "Collaborative workload consolidation for green edge computing". IEEE Transactions on Sustainable Computing, 6(4): 998–1009.
20. Tang, Wei (2019). "AI-driven power management in IoT edge devices using deep Q-networks". Sensors, 19(18): 4043.
21. He, Sheng (2022). "Federated learning for energy efficiency in edge networks: A novel predictive approach". Computer Networks, 202: 108614.
22. White, Robert (2020). "Standardizing carbon footprint metrics in edge computing infrastructures". IEEE Communications Standards Magazine, 4(3): 12–19.
23. Gao, Yuan (2023). "Regional regulations and compliance for edge energy efficiency: Framework and insights". IEEE Transactions on Sustainable Computing, 8(2): 546–560.
24. Johnson, Tyler (2021). "Incentivizing low-power edge deployments through carbon credits and tax reductions". IEEE Transactions on Industrial Informatics, 17(9): 6402–6413.
25. Schaefer, Vanessa (2022). "Green labels for IoT devices: Consumer awareness and adoption". Electronic Markets, 32(3): 1041–1056.
26. Devic, Aleksandar (2024). "Eco-design in edge hardware: Modular battery packs and real-time energy monitoring". IEEE Transactions on Components, Packaging and Manufacturing Technology, 14(3): 312–324.
27. Sakr, Mahmoud (2020). "A game-theoretic approach to minimizing energy consumption via offloading in edge-cloud environments". IEEE Transactions on Mobile Computing, 19(11): 2624–2638.
28. Du, Shengnan (2019). "Life cycle analysis of IoT devices: Energy, emissions, and disposal". Journal of Cleaner Production, 231: 341–350.

7.5 Data Persistence

7.5.1 Introduction

Data persistence plays a crucial role in ensuring that data generated is reliably stored and managed, even in volatile environments. Persistence at the edge is especially important, as edge applications are often deployed in highly non-deterministic conditions. By bringing data closer to the edge, it becomes essential to ensure its reliability. Exploring persistent storage allows us to safeguard data integrity by capturing information locally until it can be properly synchronized.

This section introduces the topic of persistence at the edge highlighting prominent challenges in the space of data integrity in distributed edge systems. This section explores three main challenges of data persistence at the edge:

1. Byzantine Faults
2. Data Consistency
3. Limited Storage Capacity

References

1. G. Jing, Y. Zou, D. Yu, C. Luo and X. Cheng, "Efficient Fault-Tolerant Consensus for Collaborative Services in Edge Computing," in IEEE Transactions on Computers, vol. 72, no. 8, pp. 2139-2150, 1 Aug. 2023, doi: 10.1109/TC.2023.3238138.
2. Y. Tao et al., "Byzantine-Resilient Federated Learning at Edge," in IEEE Transactions on Computers, vol. 72, no. 9, pp. 2600-2614, 1 Sept. 2023, doi: 10.1109/TC.2023.3257510.
3. G. Cui et al., "Efficient Verification of Edge Data Integrity in Edge Computing Environment," in IEEE Transactions on Services Computing, vol. 15, no. 6, pp. 3233-3244, 1 Nov.-Dec. 2022, doi: 10.1109/TSC.2021.3090173.
4. Y. Lin, B. Kemme, M. Patino-Martinez and R. Jimenez-Peris, "Enhancing Edge Computing with Database Replication," 2007 26th IEEE International Symposium on Reliable Distributed Systems (SRDS 2007), Beijing, China, 2007, pp. 45-54, doi: 10.1109/SRDS.2007.10.
5. E. Chandra and A. I. Kistijantoro, "Database development supporting offline update using CRDT: (Conflict-free replicated data types)," 2017 International Conference on Advanced Informatics, Concepts, Theory, and Applications (ICAICTA), Denpasar, Indonesia, 2017, pp. 1-6, doi: 10.1109/ICAICTA.2017.8090961.