Edge AI: A survey

In the paper

“However, this technology faces key issues: security, speed of services and slow connections, which are often combined as low bandwidth/high latency and jitter as mobile devices offload computational and processing capacity to cloud computing services [3,4].”

What the sources say

• Partly supported

  The Introduction (p. 1) says remote-cloud offloading can cause long network latency that harms interactive response, and explicitly mentions delay and jitter. It also says wireless interference can reduce offloading data rates. The cited paper does not identify security as an issue, so it supports only part of the attributed list. (LLM, conf=0.88)

  cites [3] Chen, 2016, Efficient multi-user computation offloading for mobile-edge cloud computing, IEEE/ACM Trans. Netw. sourceExperimental — accuracy not yet measured
• ?Not checked — no full text of the sourcecites [4] Singh, 2022, To offload or not? an analysis of big data offloading strategies from edge to cloud source

In the paper

“The concept of MEC is simply to provide mobile and cloud computing services within close proximity of the mobile user, i.e. the provision of computing power in a delocalized manner close to mobile users (smartphones, tablets, etc.), aiming to decrease latency, achieve as high throughput as possible and provide direct access to real-time network information [16].”

What the sources say

• Partly supported

  The System Architecture section describes smartphones and tablets as gateway devices and says fog resources offer lower latency than geographically distant cloud resources. This supports the proximity and latency aspects of the claim, but our copy does not define MEC or state that it aims for high throughput or direct access to real-time network information (searched for MEC and real-time network information). (LLM, conf=0.83)

  cites [16] Iftikhar, 2023, Hunterplus: ai based energy-efficient task scheduling for cloud–fog computing environments, Internet Things sourceExperimental — accuracy not yet measured

In the paper

“Most of the survey articles on Edge AI have been published before 2021 except [23], but this [23] work is just an overview on Edge AI or a visionary work which offers only future directions in many relevant areas but not in-depth.”

What the sources say

• Partly supported

  The Introduction and Section 5 describe the paper as an envisioned Edge AI roadmap spanning 5G, future cloud, and AI/ML, supporting its characterization as a broad, forward-looking work. But the claim that it offers only future directions and no in-depth discussion goes beyond the source: Section 3 says it will examine technological challenges in detail. The paper also does not establish when most other Edge AI surveys were published. (LLM, conf=0.85)

  cites [23] Ding, 2022 sourceExperimental — accuracy not yet measured

In the paper

“With emerging technologies, such as software-defined networking (SDN) and Network Function Virtualization (NFV), this enables reliability and robustness of the network and improves integration with existing IoT environments [59].”

What the sources say

• ?Could not verify

  The Future Research Directions section discusses IoT and attributes system reliability to a blockchain-based framework, while section 2.3 discusses IoT–edge–cloud integration through FogBus. Searches for SDN, NFV, and the claimed reliability and integration benefits found no passage connecting those benefits to SDN or NFV. (LLM, conf=0.76)

  cites [59] Nabavi, 2022, Tractor: traffic-aware and power-efficient virtual machine placement in edge-cloud data centers using artificial bee colony optimization, Int. J. Commun. Syst. sourceExperimental — accuracy not yet measured

In the paper

“while cloudlets are dedicated either (as originally envisioned) to small-enterprise scenarios or can be expanded to publicly funded schemes with free access and open source software [48].”

What the sources say

• ?Could not verify

  The Introduction discusses cloud and serverless computing, while the Conclusions describe AIBLOCK; neither addresses cloudlets or their intended users. Searches for cloudlets, small-enterprise use, publicly funded access, and open-source schemes found no supporting passage in our copy. (LLM, conf=0.78)

  cites [48] Golec, 2022, Aiblock: blockchain based lightweight framework for serverless computing using ai sourceExperimental — accuracy not yet measured

In the paper

“In 2017, Mobile Edge Computing was retitled Multi Access Edge Computing to recognize the large IoT component in proof-of-concept studies and to harmonize Edge Computing systems and applications in highly heterogeneous wireless networks [49].”

What the sources say

• ?Could not verify

  The Introduction discusses mobile cloud computing and wireless offloading, while the Abstract mentions IoT devices. Neither section addresses a 2017 renaming of Mobile Edge Computing, proof-of-concept studies, or the stated reasons for a name change. Searches for the new name and those reasons found no supporting passage in our copy. (LLM, conf=0.78)

  cites [49] Malik, 2021, Effort: energy efficient framework for offload communication in mobile cloud computing, Software Pract. Ex. sourceExperimental — accuracy not yet measured

In the paper

“Fog Computing as the optimal solution for IoT applications to minimize the time required for time-critical processing and Big Data analytics;”

What the sources say

• ?Not checked — no full text of the sourcecites [47] Nabavi, 2023, Seagull optimization algorithm based multi-objective vm placement in edge-cloud data centers, Internet Things and Cyber-Phys. Syst. source
• ?Could not verify

  The Introduction (page unavailable) discusses processing large amounts of IoT data using cloud and serverless computing, not fog computing. Searches for fog computing, time-critical processing, and Big Data analytics found no passage calling fog computing optimal or saying it minimizes processing time for those tasks. (LLM, conf=0.77)

  cites [48] Golec, 2022, Aiblock: blockchain based lightweight framework for serverless computing using ai sourceExperimental — accuracy not yet measured

In the paper

“Nearly every single industry today has tasks that might be better served by using edge AI [22].”

What the sources say

• Partly supported

  The Introduction says AI and machine learning are being deployed in various sectors, including healthcare, education and safety, and identifies tasks requiring processing at the edge. The Discussion also describes demanding edge-AI tasks. These passages support applications across multiple fields, but not the claim that nearly every industry has tasks better served by edge AI. (LLM, conf=0.83)

  cites [22] Kalapothas, 2022, Efficient edge-ai application deployment for fpgas, Information sourceExperimental — accuracy not yet measured

In the paper

“Micro Data Centres as the optimum for industrial applications in specific locations or at temporary sites”

What the sources say

• ?Not checked — no full text of the sourcecites [47] Nabavi, 2023, Seagull optimization algorithm based multi-objective vm placement in edge-cloud data centers, Internet Things and Cyber-Phys. Syst. source
• ?Could not verify

  The Introduction discusses cloud and serverless computing for IoT, while the Hardware Instruments & Software Components section describes a serverless datacenter used for COVID-19 detection. Searches for micro data centres and temporary industrial sites found no passage assessing whether micro data centres are optimal for those applications. (LLM, conf=0.76)

  cites [48] Golec, 2022, Aiblock: blockchain based lightweight framework for serverless computing using ai sourceExperimental — accuracy not yet measured

In the paper

“Mobile Edge Computing as centred on applications intended for large numbers of mobile devices,”

What the sources say

• ?Not checked — no full text of the sourcecites [47] Nabavi, 2023, Seagull optimization algorithm based multi-objective vm placement in edge-cloud data centers, Internet Things and Cyber-Phys. Syst. source
• ?Could not verify

  The Introduction (page unavailable) discusses the growing number of IoT devices and serverless cloud computing, but does not characterize Mobile Edge Computing. Searches for Mobile Edge Computing and applications for large numbers of mobile devices found no passage addressing that claim. (LLM, conf=0.76)

  cites [48] Golec, 2022, Aiblock: blockchain based lightweight framework for serverless computing using ai sourceExperimental — accuracy not yet measured

In the paper

“However, due to hardware limitations of wireless devices' computation, offloading must be performed to increase functionality, but overloaded offloads can degrade performance [85].”

What the sources say

• ?Could not verify

  The available indexed passages list the paper’s title and keywords, including computation offloading, mobile devices, and task completion time (Abstract; page unavailable). Searches for device hardware limitations and performance degradation from overloaded offloading found no passage addressing either assertion. (LLM, conf=0.72)

  cites [85] Singh, 2019, The advantage of computation offloading in multi-access edge computing sourceExperimental — accuracy not yet measured

In the paper

“In reality, edge applications are leading the charge for the future generation of AI computing, which will have positive effects on our everyday lives in many contexts, including at home, in the workplace, in the classroom, and when travelling [23].”

What the sources say

• Partly supported

  The Introduction describes Edge AI as a fast-evolving field already entering mainstream services, including connected vehicles and smart factories. Section 2.4 anticipates benefits for everyday life but says they still need evaluation. The paper does not establish that edge applications are leading the next generation of AI computing or support the full list of settings, particularly homes and classrooms. (LLM, conf=0.84)

  cites [23] Ding, 2022 sourceExperimental — accuracy not yet measured

In the paper

“The key objective of Edge Computing is to put resources within close proximity to the users and sources of data and information to help overcome cloud computing's recognized weaknesses of high latency, jitter and narrow bandwidth [11,52].”

What the sources say

• ?Not checked — no full text of the sourcecites [11] Satyanarayanan, 2009, The case for vm-based cloudlets in mobile computing, IEEE pervasive Comput. source
• Partly supported

  The Introduction (p. 2) describes placing fog resources near mobile users to replace a long, thin cloud connection, and “Why Fog?” (p. 4) reports reduced latency and higher data rates. The paper treats fog as an edge-computing platform (Computing, p. 7), but these passages focus on mobile users rather than data sources and do not identify jitter as a cloud weakness. (LLM, conf=0.86)

  cites [52] T.H. Luan, L. Gao, Z. Li, Y. Xiang, G. Wei, L. Sun, Fog computing: focusing on mobile users at the edge, arXiv preprint arXiv:1502.01815 (2015), 1-11. sourceExperimental — accuracy not yet measured

In the paper

“International Data Corporation (IDC) predicts that by 2025, there will be 150 billion intelligent edge devices in use [72].”

What the sources say

• ?Could not verify

  The Edge AI section discusses industry interest and applications, but gives no IDC forecast. Searches for “150 billion,” “2025,” and “International Data Corporation/IDC” found no passage in our copy stating the claimed prediction; no page numbers were available for the retrieved passages. (LLM, conf=0.74)

  cites [72] McEnroe, 2022 sourceExperimental — accuracy not yet measured

In the paper

“This was unfortunate because confusion with Fog Computing was made possible and MEC and Fog Computing have subsequently been completely “mixed and matched” by various authors [52,112,135,136].”

What the sources say

• Partly supported

  The Introduction (p. 2) describes mobile edge computing as a system similar to fog computing and says fog computing lacks a standardized definition. It does not say that various authors subsequently used the two terms interchangeably or “mixed and matched” them completely. (LLM, conf=0.83)

  cites [52] T.H. Luan, L. Gao, Z. Li, Y. Xiang, G. Wei, L. Sun, Fog computing: focusing on mobile users at the edge, arXiv preprint arXiv:1502.01815 (2015), 1-11. sourceExperimental — accuracy not yet measured
• Not checked yetcites [112] Natraj, 2016, Fog computing” focusing on users at the edge of internet of things, Int. J. Eng. Res. Special
• ?Not checked — no full text of the sourcecites [135] Orsini, 2015, Computing at the mobile edge: designing elastic android applications for computation offloading source
• ?Not checked — no full text of the sourcecites [136] Vallati, 2016, Mobile-edge computing come home connecting things in future smart homes using lte device-to-device communications, IEEE Consumer Electron. Magaz. source

In the paper

“This explicit linkage to fixed devices with connectivity and intelligent data analysing and data processing capabilities clearly distinguished FC from cloudlets [7].”

What the sources say

• Partly supported

  Section 2.2 (page unavailable) describes fog computing as edge computing in which IoT devices can gain computational capabilities. Section 3.0.1 (page unavailable) also describes edge nodes with communication and computation capabilities. Neither passage says fog computing is explicitly linked to fixed devices or distinguishes it from cloudlets; searches for “cloudlet” and the claimed distinction found no such comparison. (LLM, conf=0.78)

  cites [7] Aslanpour, 2020 sourceExperimental — accuracy not yet measured

In the paper

“The great value of this approach was noted as the dense geographical distribution with a local focus can analyse big data faster [105].”

What the sources say

• Supported

  The “Keeping the Data close to the user” section says fog computing’s geographically distributed edge infrastructure helps handle and analyze big data faster. It also describes keeping data close to users, supporting the claim’s local-focus paraphrase. (LLM, conf=0.94)

  cites [105] Saharan, 2015, Fog in comparison to cloud: a survey, Int. J. Comput. Appl. source

In the paper

“In 2016, Open Edge Computing discussed gaps in the current raft of technologies and concluded that the aim should be the development of an open platform for Edge Computing aligned with the IT and telecommunications industries [55].”

What the sources say

• ?Could not verify

  The Introduction (page unavailable) discusses extending cloud computing to the network edge, while Related Work (page unavailable) addresses fog-computing resource management. Neither those passages nor searches for Open Edge Computing, an open platform, and telecommunications-industry alignment address the attributed 2016 conclusion. (LLM, conf=0.75)

  cites [55] Gill, 2019, Router: fog enabled cloud based intelligent resource management approach for smart home iot devices, J. Syst. Software sourceExperimental — accuracy not yet measured

In the paper

“These authors also asserted that trust in the security of personal and socially sensitive data would be increased if the management of such sensitive data could be ensured at the edge rather than being centred in distant data centres [114].”

What the sources say

• ?Could not verify

  The Introduction and BioSec System sections discuss protecting personal biometric data through authentication and encryption, including storage in a server database. Searches for trust and for edge-based management versus distant data centers found no passage asserting that managing sensitive data at the edge would increase trust. (LLM, conf=0.74)

  cites [114] Golec, 2020, Biosec: a biometric authentication framework for secure and private communication among edge devices in iot and industry 4.0, IEEE Consumer Electron. Magaz. sourceExperimental — accuracy not yet measured

In the paper

“One example of a MEC application from Nokia is video surveillance which enables security personnel the ability to analyse unusual activities from anywhere at any time [131].”

What the sources say

• ?Could not verify

  The Introduction (p. 1) describes IoT security and post-quantum cryptography, not Nokia’s MEC applications. Searches for Nokia, video surveillance, and remote analysis of unusual activities found no passage addressing the claim in our copy. (LLM, conf=0.76)

  cites [131] Kumar, 2022, Securing the future internet of things with post-quantum cryptography, Secur. Priv. sourceExperimental — accuracy not yet measured

In the paper

“A variant term -“mobile edge cloud computing” -has also been used, but this is conceptually indistinguishable from MEC: “mobile-edge cloud computing can provide cloud-computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users” [126].”

What the sources say

• Supported

  The Introduction (p. 1) contains the cited quotation verbatim: it says mobile-edge cloud computing provides cloud-computing capabilities at the edge of radio access networks near mobile users. The statement that this makes it conceptually indistinguishable from MEC is the citing authors’ framing, not part of the quotation attributed to this source. (LLM, conf=0.92)

  cites [126] Chen, 2016, Efficient multi-user computation offloading for mobile-edge cloud computing, IEEE/ACM Trans. Netw. source

In the paper

“Analysing data close to the device that collected the data can make the difference between averting disaster and a cascading system failure”

What the sources say

• Partly supported

  Section 2.2 says computing closer to data sources helps meet low-latency and real-time requirements, and §4.5 says mission-critical IoT applications may fail to react promptly without rapid processing. Section 5.10 warns that unhandled function or edge-node failures can cause a disaster, but it does not say that analyzing data near its collecting device averts a cascading system failure. Page numbers were not available in our copy. (LLM, conf=0.84)

  cites [6] Aslanpour, 2021, Serverless Edge Computing: Vision and Challenges sourceExperimental — accuracy not yet measured

In the paper

“In contrast to other edge computing models, mDCs form more of a hardware solution for Edge Computing than a novel IT scenario [16].”

What the sources say

• ?Could not verify

  The Introduction discusses cloud–fog computing and hardware costs, while §3.1 describes a fog datacenter with edge and cloud layers. Neither passage characterizes micro data centers as a hardware solution rather than a novel IT scenario; searches for that distinction and for mDCs found no passage addressing it. Page numbers were not available in our copy. (LLM, conf=0.75)

  cites [16] Iftikhar, 2023, Hunterplus: ai based energy-efficient task scheduling for cloud–fog computing environments, Internet Things sourceExperimental — accuracy not yet measured

In the paper

“This application considers a “Vehicle Edge Computing” where a gateway to the intra-vehicle network as the edge device and hence placing the edge functionality on a mobile node.”

What the sources say

• ?Could not verify

  The Introduction and II. Motivating Scenario describe mobile users connecting to edge servers near base stations, not a gateway to an intra-vehicle network acting as an edge device. Searches for “Vehicle Edge Computing” and vehicle gateways found no passage addressing that arrangement in our copy. (LLM, conf=0.72)

  cites [35] Ghafouri, 2022, Mobile-kube: mobility-aware and energy-efficient service orchestration on kubernetes edge servers sourceExperimental — accuracy not yet measured

In the paper

“MEC can shift mobile networks into real-time location-based gaming, bringing cloud computing capabilities inside a radio access network and this allows delivery of computationally demanding AR applications running close to the end user by avoiding high latency, high real-time responses and backhaul network bottlenecks [61].”

What the sources say

• ?Could not verify

  Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.

  cites [61] Golec, 2021, Ifaasbus: a security-and privacy-based lightweight framework for serverless computing using iot and machine learning, IEEE Trans. Ind. Inf. sourceExperimental — accuracy not yet measured

In the paper

“Edge AI has a wide range of applications in various industries [54].”

What the sources say

• Partly supported

  The Abstract describes an AI-based cardiac diagnosis system using fog computing, which supports healthcare as one application. Its reference to IoT adoption in various domains does not establish that Edge AI has applications across various industries; the Introduction likewise focuses on healthcare. The indexed passages provide no page numbers. (LLM, conf=0.84)

  cites [54] Iftikhar, 2022, Fogdlearner: a deep learning-based cardiac health diagnosis framework using fog computing, Austral. Comput. Sci. Week sourceExperimental — accuracy not yet measured

In the paper

“For example, in smart parking scenarios where data is coming from sensors and transferred to edge devices efficiently and reliably [66].”

What the sources say

• Partly supported

  Section 5.1 (page unavailable) describes sensor data being forwarded to gateway devices such as tablets, laptops and mobile phones; the Introduction identifies mobile phones and personal computers as potential edge devices. The copy does not establish this transfer in smart parking or say it is reliable, and section 7.1 treats reliability as a parameter for future work. (LLM, conf=0.84)

  cites [66] Singh, 2022, Machine learning for cloud, fog, edge and serverless computing environments: comparisons, performance evaluation benchmark and future directions, Int. J. Grid Util. Comput. sourceExperimental — accuracy not yet measured

In the paper

“Smart Home: Edge AI can be used to power smart home devices such as voice assistants, thermostats, and security cameras [161].”

What the sources say

• Partly supported

  The paper’s abstract (p. 1) describes AI processing video locally on IoT devices for parking surveillance, supporting the general idea of edge AI in a camera-based system. It does not address smart homes, voice assistants, thermostats, or home security cameras; its tested setting is a parking garage. (LLM, conf=0.84)

  cites [161] Ke, 2020, A smart, efficient, and reliable parking surveillance system with edge artificial intelligence on iot devices, IEEE Trans. Intell. Transport. Syst. sourceExperimental — accuracy not yet measured

In the paper

“This means that data is processed and analysed locally, in real-time, at the edge of the network, rather than being transmitted to a remote server for processing [185].”

What the sources say

• Partly supported

  Section II (p. 2) describes efficient, real-time data processing at the edge, and the Key Challenges section (p. 1) says Edge AI systems are expected to provide real-time responses. The copy does not explicitly say that data is processed locally instead of being transmitted to a remote server, so that contrast is not fully supported. (LLM, conf=0.82)

  cites [185] Shafique, 2021, Towards energy-efficient and secure edge ai: a cross-layer framework iccad special session paper sourceExperimental — accuracy not yet measured

In the paper

“Currently, various companies are working on a solution to provide a service to car drivers requiring parking spaces in city centres by providing an up-to-date “heat map” of availability [153]”

What the sources say

• Partly supported

  The Introduction (p. 1) describes real-time parking information and an envisaged city road map showing where drivers should search. The Conclusions and Future Work (p. 6) say visualising city parking occupancy remains a future feature. Neither passage establishes that various companies are currently developing an up-to-date availability heat map. (LLM, conf=0.84)

  cites [153] Carnelli, 2017, Parkus: a novel vehicle parking detection system sourceExperimental — accuracy not yet measured

In the paper

“Central to resource management are dynamic load balancing [166] and developing resource allocation platforms [16].”

What the sources say

• ?Not checked — no full text of the sourcecites [166] Ningning, 2016, Fog computing dynamic load balancing mechanism based on graph repartitioning, China Commun. source
• Supported

  Citation [16] attaches to developing resource allocation platforms, not to dynamic load balancing. The paper’s “Resource Manager” section describes the HUNTERPlus scheduler as a system that selects resource allocations for cloud–fog tasks, making it an example of such a platform. (LLM, conf=0.87)

  cites [16] Iftikhar, 2023, Hunterplus: ai based energy-efficient task scheduling for cloud–fog computing environments, Internet Things source

In the paper

“Edge AI has the potential to revolutionize industries by enabling intelligent and autonomous devices that can make real-time decisions based on sensor data [164].”

What the sources say

• Supported

  The Introduction describes processing IoT data at the edge to produce immediate, actionable decisions. Section 2 gives examples across healthcare, manufacturing, and security in which AI-equipped devices analyze sensor or camera data locally and act with little or no latency; it also describes greater autonomy for next-generation IoT devices. These passages support the claim's broad statement of potential across industries. (LLM, conf=0.90)

  cites [164] Surianarayanan, 2023, A survey on optimization techniques for edge artificial intelligence (ai), Sensors source

In the paper

“There are two main types of simulation packages available: continuous simulation and discrete event simulation [55].”

What the sources say

• ?Could not verify

  Section 4 (Performance Evaluation) describes using CloudSim’s event-simulation functions, but does not classify simulation packages into continuous and discrete-event types. Searches for the claimed two-type classification and its terms found no passage in our copy that addresses it; no page number is available for the passage. (LLM, conf=0.74)

  cites [55] Gill, 2019, Router: fog enabled cloud based intelligent resource management approach for smart home iot devices, J. Syst. Software sourceExperimental — accuracy not yet measured

In the paper

“Better Privacy/Security: Edge computing can assist in increasing confidentiality and security by lowering the quantity of information that has to be transferred over the Internet and by offering more command over the processes and storage of confidential documents [164].”

What the sources say

• Partly supported

  The Introduction (page unavailable) says edge computing moves processing and storage away from centralized servers, can prevent data transfer to centralized infrastructure, and can keep financial data at the edge for security and privacy. It does not specifically discuss greater control over the processing and storage of confidential documents. (LLM, conf=0.82)

  cites [164] Surianarayanan, 2023, A survey on optimization techniques for edge artificial intelligence (ai), Sensors sourceExperimental — accuracy not yet measured

In the paper

“Client Edge Computing, including CCTV cameras, mobile phones and smart sensors, can perform some processing rather than sending all data to the edge network; therefore, this model can be helpful to service providers [49].”

What the sources say

• ?Could not verify

  The Introduction discusses smartphones and decisions about what data to offload, while the Discussion describes offloading communication from mobile devices to the cloud. Neither passage addresses Client Edge Computing, local processing by CCTV cameras or smart sensors instead of sending all data to an edge network, or benefits to service providers. Searches for those terms and for local processing returned no passage establishing the attributed claim. (LLM, conf=0.73)

  cites [49] Malik, 2021, Effort: energy efficient framework for offload communication in mobile cloud computing, Software Pract. Ex. sourceExperimental — accuracy not yet measured

In the paper

“Although cloudlets initiated these developments in edge computing, they have proved marginal in commercial practice but could find implementations in municipality-centred schemes based on free access and open-source software, in contrast to the subscription or fee-based services in other edge computing paradigms [66].”

What the sources say

• ?Could not verify

  Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.

  cites [66] Singh, 2022, Machine learning for cloud, fog, edge and serverless computing environments: comparisons, performance evaluation benchmark and future directions, Int. J. Grid Util. Comput. sourceExperimental — accuracy not yet measured

In the paper

“In contrast, mDCs have an already defined segment in the Industrial IoT but could readily be deployed to support FC, MEC and even Cloudlets (although CL would require some form of public funding if open-access platforms are to be implemented) [35].”

What the sources say

• ?Could not verify

  The Introduction (page unavailable) discusses MEC, fog computing and cloudlets, but does not address mobile data centers (mDCs) or their use in Industrial IoT. Searches for mDC deployment and public funding for open-access cloudlets found no passage supporting those claims. (LLM, conf=0.77)

  cites [35] Ghafouri, 2022, Mobile-kube: mobility-aware and energy-efficient service orchestration on kubernetes edge servers sourceExperimental — accuracy not yet measured

In the paper

“With the right combination of hardware and software, Edge AI technology can enable AI algorithms to run on edge devices, bringing intelligence to the network's edge and enabling new applications and use cases [32].”

What the sources say

• Supported

  The paper’s “B. Platforms” section describes software and hardware platforms for deploying edge AI models and algorithms. Its Abstract says AI models can execute at the network edge, and “C. Applications” discusses edge-AI-enabled application scenarios. Page numbers are unavailable in our copy. (LLM, conf=0.91)

  cites [32] Letaief, 2021, Edge artificial intelligence for 6g: vision, enabling technologies, and applications, IEEE J. Sel. Area. Commun. source

In the paper

“Data”

What the sources say

• ?Could not verify

  Section 5.9 (p. 11) discusses processing user data in fog and edge computing, and Section 5.13 (p. 12) discusses software-defined networking. Neither addresses ICN-defined data chunks, semantic interpretation of content, or avoiding deep packet inspection and the application layer. Searches for ICN, information-centric networking, and deep packet inspection found no passage supporting the claim. (LLM, conf=0.74)

  cites [88] Gill, 2021, A manifesto for modern fog and edge computing: vision, new paradigms, opportunities, and future directions sourceExperimental — accuracy not yet measured

In the paper

“The evolving discussion on Edge Computing has identified four distinct paradigms differentiated on the basis of intended markets and business models [59].”

What the sources say

• ?Could not verify

  The Introduction discusses cloud-computing services, and the Performance Evaluation discusses edge-cloud data-center topologies, but neither identifies four edge-computing paradigms. Searches for the claimed paradigms and their differentiation by intended markets or business models found no supporting passage in our copy; page numbers were not available. (LLM, conf=0.76)

  cites [59] Nabavi, 2022, Tractor: traffic-aware and power-efficient virtual machine placement in edge-cloud data centers using artificial bee colony optimization, Int. J. Commun. Syst. sourceExperimental — accuracy not yet measured

In the paper

“Sectors: Moving resources closer to the”

What the sources say

• Partly supported

  Section A. Context says fog computing places storage, computing, and networking functions near connected devices, supporting the claim’s central point. The paper also discusses video surveillance (Development) and fog-based traffic-signal control (Platforms), but the passages do not establish the claimed ‘fixed edge’ category or identify smart meters as an example. (LLM, conf=0.83)

  cites [13] Singh, 2021, Fog computing: a taxonomy, systematic review, current trends and research challenges, J. Parallel Distr. Comput. sourceExperimental — accuracy not yet measured

In the paper

“Instead of a data centre, edge computing processes data at the network's edge.This provides real-time processing and lower latency, but AI implementation is complicated by constrained CPU cores and network access [63].”

What the sources say

• Partly supported

  The Introduction (page unavailable) describes processing near data-producing devices to avoid cloud-network latency and support immediate AI calculations. It also identifies hardware limits and limited communication resources as challenges for Edge AI. The paper does not specifically identify constrained CPU cores as the cause of those implementation difficulties. (LLM, conf=0.83)

  cites [63] Sipola, 2022 sourceExperimental — accuracy not yet measured

In the paper

“The technology is rapidly evolving, with new techniques and frameworks being developed to improve the performance, efficiency, and security of Edge AI solutions [62].”

What the sources say

• Partly supported

  The ‘AI on the Edge and Related Work’ section (p. 2) describes hardware innovations aimed at improving Edge AI performance and power efficiency. The ‘Methodology and Architecture Framework’ section (p. 4) describes successive, faster and more accurate detection models suitable for edge devices. These passages support the claim about evolving techniques and efficiency, but our copy does not address improvements to Edge AI security. (LLM, conf=0.86)

  cites [62] Mazzia, 2020, Real-time apple detection system using embedded systems with hardware accelerators: an edge ai application, IEEE Access sourceExperimental — accuracy not yet measured

In the paper

“Vehicular Edge Computing introduces a computation offloading mechanism with embedded devices in cars offloading data to MEC servers [46].”

What the sources say

• ?Could not verify

  The Introduction and simulation-results sections discuss smart-home devices sending data to a home router or cloud facility. Searches for vehicular edge computing, cars, and MEC servers found no passage addressing the claimed vehicle-to-MEC offloading mechanism; the smart-home example does not establish that specific claim. (LLM, conf=0.75)

  cites [46] Iftikhar, 2022, Fog computing based router-distributor application for sustainable smart home sourceExperimental — accuracy not yet measured

In the paper

“Mobile MEC users form a transient population with varying requirements [96].”

What the sources say

• ?Could not verify

  The “Cloud, Mobile & IoT” and “Fog and Edge Computing” sections discuss mobile devices and edge infrastructure, but do not describe MEC users as a transient population or say their requirements vary. Searches for those user characteristics found no passage addressing them in our copy. (LLM, conf=0.72)

  cites [96] Lindsay, 2021, The evolution of distributed computing systems: from fundamental to new frontiers, Computing sourceExperimental — accuracy not yet measured

In the paper

“Machine learning frameworks: ML frameworks, such as TensorFlow Lite and PyTorch, are used to develop and run machine learning models on edge devices [202].”

What the sources say

• Partly supported

  The Quantization section (page unavailable) names TensorFlow Lite among tools supporting efficient execution of quantized models for resource-constrained edge systems. It also identifies PyTorch as a framework supporting post-training quantization, but does not say PyTorch is used to develop or run models on edge devices. The cited passages therefore support the edge-use claim for TensorFlow Lite, but not its full application to both examples. (LLM, conf=0.84)

  cites [202] Liu, 2022, Bringing ai to edge: from deep learning’s perspective, Neurocomputing sourceExperimental — accuracy not yet measured

In the paper

“Central to MEC were strategies for avoiding overloading and “disaster management” procedures for recovering non-functional base stations [96].”

What the sources say

• ?Could not verify

  The ‘Fog and Edge Computing (2010-present)’ section discusses edge infrastructure and decentralized workflows, but not Multi-access Edge Computing strategies for preventing overload or recovering base stations. Searches for disaster management, non-functional base stations, and MEC overload found no passage addressing those procedures. Page numbers were unavailable in our copy. (LLM, conf=0.76)

  cites [96] Lindsay, 2021, The evolution of distributed computing systems: from fundamental to new frontiers, Computing sourceExperimental — accuracy not yet measured

In the paper

“Nascent commercial opportunities have been identified for mDCS and in proof of concept studies for MEC [49].”

What the sources say

• ?Could not verify

  The Introduction discusses projected business use and revenue for mobile cloud computing, while the Conclusions describe prospects for cloud computing generally. Neither section identifies commercial opportunities for mDCs or proof-of-concept studies for MEC; searches for those specific terms and ideas found no supporting passage in our copy. (LLM, conf=0.76)

  cites [49] Malik, 2021, Effort: energy efficient framework for offload communication in mobile cloud computing, Software Pract. Ex. sourceExperimental — accuracy not yet measured

In the paper

“Finding and accessing the appropriate nodes in an Edge Computing network and discovery and trust are features that must be considered together, especially for mobile devices [14].”

What the sources say

• Supported

  Section 4.1 (page unavailable) says edge-centric platforms may need to provide communication, discovery, and trust together. Section 3.2 calls for rendezvous and communication services for edge nodes, while section 4.3 highlights security and trust challenges as mobile users move between providers and contexts. Together, these passages support the claim’s emphasis on finding and accessing nodes, discovery, and trust for mobile devices. (LLM, conf=0.84)

  cites [14] Garcia Lopez, 2015, Edge-centric computing: vision and challenges, Comput. Commun. Rev. source

In the paper

“The upshot of this is improved uptime and dependability for AI applications that are essential to business operations [164].”

What the sources say

• Partly supported

  The Conclusions (page unavailable) say edge AI can deliver reliable functions with low latency, which supports the dependability aspect. The Introduction discusses data security and privacy, but the paper does not say that improved security increases uptime or dependability for business-essential AI applications. (LLM, conf=0.82)

  cites [164] Surianarayanan, 2023, A survey on optimization techniques for edge artificial intelligence (ai), Sensors sourceExperimental — accuracy not yet measured

In the paper

“By keeping such data on-premises, Edge AI additionally protects users' confidentiality, sending just the results of their analyses and observations to the cloud [185].”

What the sources say

• ?Could not verify

  The security discussion (Section III, pp. 3–5) addresses vulnerabilities and protection against attacks, but does not describe keeping users’ data on-premises or sending only analysis results to the cloud. Searches for local processing, confidentiality, and cloud transmission found no passage establishing that data-sharing arrangement. (LLM, conf=0.72)

  cites [185] Shafique, 2021, Towards energy-efficient and secure edge ai: a cross-layer framework iccad special session paper sourceExperimental — accuracy not yet measured

In the paper

“Industry players: Edge computing has already demonstrated the active participation of different industrial interests, namely hardware and IT (mDCs and FC) and telecommunications (MEC) [31].”

What the sources say

• ?Could not verify

  The Introduction (page unavailable) describes fog computing as an intermediary layer at the network edge, but does not discuss participation by hardware, IT, or telecommunications companies. Searches for industrial interests, mDCs, FC, and MEC found no passage in our copy supporting that claim. (LLM, conf=0.75)

  cites [31] Moqurrab, 2022, A deep learning-based privacy-preserving model for smart healthcare in internet of medical things using fog computing, Wireless Pers. Commun. sourceExperimental — accuracy not yet measured

In the paper

“In addition, bodies such as ETSI have extended the use of the word “mobile” to include devices such as surveillance cameras which are immobile (but rotatable) [13].”

What the sources say

• ?Could not verify

  The Expectation/Interest section discusses mobility in fog computing, and the Development section mentions cameras on vehicles, but neither defines stationary, rotatable cameras as “mobile.” Searches for ETSI, surveillance cameras, and immobile or rotatable devices found no passage addressing the attributed definition. The retrieved passages have no page numbers. (LLM, conf=0.76)

  cites [13] Singh, 2021, Fog computing: a taxonomy, systematic review, current trends and research challenges, J. Parallel Distr. Comput. sourceExperimental — accuracy not yet measured

In the paper

“As the number of relay devices increases and as their rate of data transfer increases, data throughput and the number of disconnected mobile users capable of being serviced increase greater the number of relay devices per disconnected user, the faster the recovery system works [16].”

What the sources say

• ?Could not verify

  The Introduction discusses migrating jobs between cloud-fog servers, and §4.7 analyzes host overload and task allocation; neither addresses relay devices or disconnected mobile users. Searches for MEC overload recovery, relay counts, data-transfer rates, and throughput found no passage in our copy supporting the attributed relationship. Page numbers were not available for these passages. (LLM, conf=0.76)

  cites [16] Iftikhar, 2023, Hunterplus: ai based energy-efficient task scheduling for cloud–fog computing environments, Internet Things sourceExperimental — accuracy not yet measured

In the paper

“This unlocks new possibilities for real-time making decisions as well as enhanced confidentiality and safety [81].”

What the sources say

• Partly supported

  The Introduction and Discussion (page not available) say LFNL enhances privacy and reduces training latency; the Results also report resistance to model-poisoning attacks. Those findings support the confidentiality aspect, but the paper does not establish real-time decision-making or improved safety. (LLM, conf=0.84)

  cites [81] Yang, 2022, Lead federated neuromorphic learning for wireless edge artificial intelligence, Nat. Commun. sourceExperimental — accuracy not yet measured

In the paper

“In summary, implementing AI systems in situations with limited resources, such as edge computing and other resource-constrained settings, brings a mix of benefits and constraints [76].”

What the sources say

• Partly supported

  The Introduction describes advantages of edge and fog computing, including energy efficiency, while noting connectivity and data-rate constraints in remote farming. The Edge layer section describes a CNN-based approach that reduces data sent over LoRa. These passages support a mix of benefits and constraints for this edge-AI application, but do not establish the claim for other resource-constrained settings generally. (LLM, conf=0.82)

  cites [76] Gia, 2019, Edge ai in smart farming iot: cnns at the edge and fog computing with lora sourceExperimental — accuracy not yet measured

In the paper

“In particular, the implementation of Multi Access Edge Computing has a clear business case for service providers to develop services and products for “vertical” markets, i.e.where an entire sector such as healthcare is considered as a whole; hardware enterprises must also be flexible on precise mechanisms for implementing Multi-Access Edge Computing because a wide range of applications will be rec”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [223] Rayal
• ?Could not verify

  The Introduction discusses computational offloading in MEC, and the Conclusions describe a flexible offloading method that accommodates users’ choices. Neither section addresses hardware enterprises, implementation mechanisms, or applications with different market sizes. Searches for those terms and for vertical markets found no supporting passage in our copy; page numbers were not provided. (LLM, conf=0.74)

  cites [224] Singh, 2021, Towards multi-criteria heuristic optimization for computational offloading in multi-access edge computing sourceExperimental — accuracy not yet measured

In the paper

“These grey areas have allowed overlapping views that over-claim for particular paradigms; for example [221], defines Edge Computing too narrowly: “connected embedded computing for the Industrial Internet of Things (IIoT)” while [52] brings all mobile users into FC.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [221] Steve Jennis, 2017
• Supported

  Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  cites [52] T.H. Luan, L. Gao, Z. Li, Y. Xiang, G. Wei, L. Sun, Fog computing: focusing on mobile users at the edge, arXiv preprint arXiv:1502.01815 (2015), 1-11. source

In the paper

“The financial investments required are key items in decision making but, equally, opportunities are major factors in implementation routes for Edge Computing (Table 4).”

In the paper

“In a separate practical test of cloudlet versus cloud, reductions in power usage by 50%, a nearly 10-fold reduction in delay time and an increased throughput of 10-fold (Table 3) were achieved using a single-device cloudlet with one mobile user [141].”

What the sources say

• ?Not checked — no full text of the sourcecites [141] Jararweh, 2013, Resource efficient mobile computing using cloudlet infrastructure source

In the paper

“Table 2 shows the ETSI-sponsored proofs of concept (PoCs) that showed the viability of the MEC concept.”

In the paper

“The principal differentiators for applications are mobile devices versus fixed devices, means of access, application domains and drivers (Table 5).”

In the paper

“The renaming of MEC as Multi-access Edge Computing reflects aims for applications development in 2017 for non-mobile devices; this is a crucial change of direction and its full implications will be discussed later in this survey.1”

In the paper

“With the latest innovations in Artificial Intelligence (AI) efficiency, the proliferation of IoT devices, and the rise of edge computing, the potential of edge AI has now been unlocked [19].”

What the sources say

• ?Not checked — no full text of the sourcecites [19] Shi, 2020, Communication-efficient edge ai: algorithms and systems, IEEE Commun. Surv. Tutor. source

In the paper

“Overlap between the four concepts as they have been discussed in the literature has, however, resulted in significant degradation of the boundaries between the different approaches.”

In the paper

“The proliferation of IoT-connected devices is largely responsible for the current era's unprecedented growth in data volume [34].”

What the sources say

• ?Not checked — no full text of the sourcecites [34] Raychaudhuri, 2022, Green internet of things using mobile cloud computing: architecture, applications, and future directions source

In the paper

“Furthermore, Edge AI could be provided with the computational intelligence to develop intelligent systems that can perform tasks that typically require human-level intelligence, such as decision-making, problem-solving, pattern recognition, and learning [79].”

What the sources say

• ?Not checked — no full text of the sourcecites [79] Singh, 2017, The journey of qos-aware autonomic cloud computing, It Profession. source

In the paper

“This suggests that AI computation on such devices will grow at an exponential rate, allowing consumers to engage with compiled code at the source in a safe and optimal manner [75].”

What the sources say

• ?Not checked — no full text of the sourcecites [75] Lv, 2022, An edge-ai based forecasting approach for improving smart microgrid efficiency, IEEE Trans. Ind. Inf. source

In the paper

“However, computational offloading still faces many challenges, in particular, how a wireless device will decide which tasks need to perform locally or which to hand over to the edge server.Another critical issue is on the edge server side: what happens if the edge server starts to overload due to the high demand from the wireless devices?”

In the paper

“Smart sensors make decisions and this improves the performance of the overall network and sends only useful data to the cloud [56].”

What the sources say

• ?Not checked — no full text of the sourcecites [56] Kaur, 2018, The future of cloud computing: opportunities, challenges and research trends source

In the paper

“This case study overlaps FC, as discussed in (Sec.II-F) and (with images uploaded from mobile devices) MEC (Sec II-G).”

In the paper

“Between 2009 and 2014, four paradigms for Edge Computing Cloudlets, Fog Computing, Mobile Edge Computing and Micro Data Centres emerged as practical contenders for implementation of the Edge Computing concept [44].”

What the sources say

• ?Not checked — no full text of the sourcecites [44] Dhillon, 2022, Iotpulse: machine learning-based enterprise health information system to predict alcohol addiction in Punjab (India) using iot and fog computing, Enterprise Inf. Syst. source

In the paper

“In other words, the original cloudlet in a fixed location has become one of many components in an intervening layer between the mobile user and the cloud but offering better performance for the mobile user, whether or not the user is connected to a geographically closer server (i.e. the “logical proximity” effect).”

In the paper

“Edge computing proposes moving data processing capability away from distant consolidated data centres in the cloud to servers usually (but not exclusively) physically closer to the end user in order to support high QoE applications for heterogeneous mobile device users and fixed Internet-connected streaming devices via wireless networks [43].”

What the sources say

• ?Not checked — no full text of the sourcecites [43] Carvalho, 2021, Edge computing: current trends, research challenges and future directions, Computing source

In the paper

“A more rapid solution is to bring computation capability close and Edge Computing can provide limited computation capability to make quick (lower latency) decisions.”

In the paper

“IoT is also benefiting from the improved speed, reliability, and security that 5G/6G is bringing to the battlefield [36].”

What the sources say

• ?Not checked — no full text of the sourcecites [36] Adhikari, 2021, A roadmap of next-generation wireless technology for 6g-enabled vehicular networks, IEEE Internet Things Magaz. source

In the paper

“Cloudlets as a business concept have failed to gain traction and the default offering from small enterprises has become free Wi-Fi for mobile devices (including laptops and notebooks) [91][92][93].”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [91][92][93]

In the paper

“Existing survey articles [39][40][41] introduced detailed surveys on edge computing by focusing on only IoT applications but [41] has also discussed about resource management in edge computing.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [39][40][41]

In the paper

“When AI models are put on the Edge instead of in the cloud, we allow Edge AI devices to analyse data more quickly, make decisions more quickly, increase the security of data processing, and improve the user experience [71].”

What the sources say

• ?Not checked — no full text of the sourcecites [71] Lin, 2019, Making knowledge tradable in edge-ai enabled iot: a consortium blockchain-based efficient and incentive approach, IEEE Trans. Ind. Inf. source

In the paper

“The implication of these findings was that an unspecified number of smaller cloudlet servers would be positioned to provide diffuse coverage to ensure a high QoE [5].”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [5] Iftikhar, 2022

In the paper

“Numerous experts and companies are discussing and adopting edge computing, which has its roots in the 1990s with the advent of content delivery networks that employ edge servers located near customers to provide web and gameplay videos [21].”

What the sources say

• ?Not checked — no full text of the sourcecites [21] Wang, 2020 source

In the paper

“Currently, an intelligent transportation environment uses cloud computing, where all the processing is done at remote data centres, but cloud computing lacks key safety features in smart transportation [56].”

What the sources say

• ?Not checked — no full text of the sourcecites [56] Kaur, 2018, The future of cloud computing: opportunities, challenges and research trends source

In the paper

“Smart cities and smart factories would not have been possible without edge computing, which has emerged as an essential part of the Industrial Internet [50].”

What the sources say

• ?Not checked — no full text of the sourcecites [50] Lovén, 2019, Edgeai: a vision for distributed, edge-native artificial intelligence in future 6g networks, 1st 6G wireless summit.

In the paper

“The solution is an edge server: a wireless device can transfer a series of tasks to the edge server for offloading.Therefore, this substantially reduces the latency and improves the bandwidth between the wireless users and the edge server.”

In the paper

“This allows for faster and more efficient data processing, as well as improved privacy and security [78].”

What the sources say

• ?Not checked — no full text of the sourcecites [78] Doyle, 2022, Blockchainbus: a lightweight framework for secure virtual machine migration in cloud federations using blockchain, Secur. Priv. source

In the paper

“In order to analyse, update, and integrate the existing research and explore prospective trends and futuristic views in the field of edge computing, especially Edge AI, there is a requirement for a new innovative study as this area of edge computing is continuing to expand towards Edge AI.”

In the paper

“This has resulted in hitherto inconceivable applications for edge AI, such as assisting radiologists in diagnosing diseases, driving automobiles on the highway, and even fertilizing plants [20].”

What the sources say

• ?Not checked — no full text of the sourcecites [20] Kamruzzaman, 2021, New opportunities, challenges, and applications of edge-ai for connected healthcare in smart cities source

In the paper

“As of 2019, the worldwide edge computing industry is estimated to be worth $3.5 billion.By 2027, that figure might have increased to $43.4 billion [74].”

What the sources say

• ?Not checked — no full text of the sourcecites [74] Hu, 2021, Rim: offloading inference to the edge source

In the paper

“Machine-to-machine (M2M) approach could be considered when direct communication is required between devices or machines, without the need for human intervention [83].”

What the sources say

• ?Not checked — no full text of the sourcecites [83] Agarwal, 2021, Edge ai driven technology advancements paving way towards new capabilities, Int. J. Innovat. Technol. Manag. source

In the paper

“By 2025, Gartner expects 75% of enterprise-generated data will be created and processed outside of the conventional data centre or cloud, thanks to the expansion of the IoT [73].”

What the sources say

• ?Not checked — no full text of the sourcecites [73] Foukalas, 2021, Edge artificial intelligence for industrial internet of things applications: an industrial edge intelligence solution, IEEE Indu. Electron. Magaz. source

In the paper

“The use case study was, however, entirely within the definition of Multi-Access Edge Computing, which aims to converge telecommunication and IT services in multiple settings [146].”

What the sources say

• ?Not checked — no full text of the sourcecites [146] Taleb, 2017, On multi-access edge computing: a survey of the emerging 5g network edge architecture orchestration, IEEE Commun. Surv. Tutor. source

In the paper

“This approach optimized bandwidth and reduced uplink congestion as well as decreasing power requirements for uploading the content, thus making smartphones more energy efficient and with extended battery lifetimes.8”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [8] Church, 2008

In the paper

“Once the concept of a network of fixed or moving sensors streaming data to an FC server is lost, the paradigm is radically altered, which is discussed next [9].”

What the sources say

• ?Not checked — no full text of the sourcecites [9] Gill, 2019 source

In the paper

“If, for example, cloudlets are essentially free at the point of use, this eliminates many applications using fixed sensors [150].”

What the sources say

• ?Not checked — no full text of the sourcecites [150] Shaukat, 2016, Cloudlet deployment in local wireless networks: motivation, architectures, applications, and open challenges, J. Netw. Comput. Appl. source

In the paper

“It can also help reduce costs, simplify deployment, and improve overall efficiency [30].”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [30] Xu, 2022

In the paper

“As its core concept, MEC reconfigures the devices already deployed at the mobile edge as mobile access points, i.e. base stations forward traffic but also adds computing and storage capabilities to act as MEC servers [127].”

What the sources say

• ?Not checked — no full text of the sourcecites [127] Beck, 2014, Mobile edge computing: a taxonomy

In the paper

“While augmented reality can be viewed as being more in the domain of MEC than of FC, urgent computing in disaster and emergencies is an FC application that can significantly speed up response times and optimize responses [115].”

What the sources say

• ?Not checked — no full text of the sourcecites [115] Tran, 2016, A systematic literature review of fog computing, Norsk konferanse for organisasjoners bruk av IT

In the paper

“this clearly differentiates MEC from Cloudlets (the business model based on free access) and FC (the focus on mobile devices in the IoT) while again aiming to bring cloud computing capabilities closer to the user base similar to all other Edge Computing paradigms [15].”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [15] Patel

In the paper

“This scenario (in a sports stadium) does not convincingly address mobile device users because they were in a precisely defined location and were not “mobile” in the sense of freely moving in, for example, a city centre or a large airport.”

In the paper

“Mobile-edge Computing provides IT and cloud-computing capabilities within the Radio Access Network (RAN) in close proximity to mobile subscribers”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [15] Patel

In the paper

“While these authors did not explicitly compare MEC and Cloud performance parameters, the results demonstrated advantages in an optimized “cloudlet network” in terms of traffic volume and reduction of latency [144].”

What the sources say

• ?Not checked — no full text of the sourcecites [144] Ceselli, 2015, Cloudlet network design optimization source

In the paper

“The challenge for edge computing is to provide standardized means of access for users and novel service providers as well as an efficient interface to the cloud as shown in Fig. 6.”

In the paper

“The authors concluded from a detailed mathematical analysis that the client service faced a major problem in scheduling computational events and tasks in a transient architecture of mobile processing capacity but the design architecture enables a useable configuration and users can be given incentives to participate if the concept were to be implemented [102].”

What the sources say

• ?Not checked — no full text of the sourcecites [102] Habak, 2015, Femto clouds: leveraging mobile devices to provide cloud service at the edge source

In the paper

“Edge Computing projects and proposals are distributed across academic, industry and standardization activities and standardization initiatives have been active since 2009 [34].”

What the sources say

• ?Not checked — no full text of the sourcecites [34] Raychaudhuri, 2022, Green internet of things using mobile cloud computing: architecture, applications, and future directions source

In the paper

“The authors concluded from a detailed mathematical analysis that the client service faced a major problem in scheduling computational events and tasks in a transient architecture of mobile processing capacity but the design architecture enables a useable configuration and users can be given incentives to participate if the concept were to be implemented [102].”

What the sources say

• ?Not checked — no full text of the sourcecites [102] Habak, 2015, Femto clouds: leveraging mobile devices to provide cloud service at the edge source

In the paper

“This approach distributes the work overload between mobile users and reduces energy consumption, and increases battery longevity [140].”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [140] S. Singh et al., “An Iiot Based Secure and Sustainable Smart Supply Chain System Using Sensor Networks,” Transactions on Emerging Telecommunications Technologies, p. e4681.

In the paper

“Thus, the solution to this problem is a multi-tiered architecture (with at least 3 tiers) whereby an IoT application is deployed as follows: a part on the “thing” (e.g. a car), a 2nd part on the fog platform (e.g. a roadside cabinet or a router in a wireless access network or an LTE base station), and in the case of three tiers a final 3rd part in a datacentre of the main cloud (e.g.Amazon EC2)”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [109] Klas, 2015

In the paper

“Standardization for micro-data centres is important to ensure consistency, reliability, and interoperability between different components and systems.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [30] Xu, 2022

In the paper

“The above analysis emphasizes the diversity of applications being investigated for Edge Computing but demonstrates the different conceptual landscapes being considered [17].”

What the sources say

• ?Not checked — no full text of the sourcecites [17] Chakraborty, 2023, Journey from cloud of things to fog of things: survey, new trends, and research directions, Software Pract. Ex. source

In the paper

“The dilemma lies in extending the definition of “thing” to personal mobile devices, particularly smartphones and tablets.”

In the paper

“The authors concluded from a detailed mathematical analysis that the client service faced a major problem in scheduling computational events and tasks in a transient architecture of mobile processing capacity but the design architecture enables a useable configuration and users can be given incentives to participate if the concept were to be implemented [102].”

What the sources say

• ?Not checked — no full text of the sourcecites [102] Habak, 2015, Femto clouds: leveraging mobile devices to provide cloud service at the edge source

In the paper

“Nevertheless, different drivers have been emphasized when these two edge computing paradigms are compared, and a critical difference between FC and MEC is that wireless IoT networks can be viewed as the principal driver for FC, whilst low latency and resource efficiency in cellular networks are dominant considerations for MEC [137].”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [137] Schuster

In the paper

“As such, mDCs are compatible with FC, MEC and cloudlets but are highly suitable for a completely different market sector, IIoT [138].”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [138] S. S. Gill, “Quantum and Blockchain Based Serverless Edge Computing: A Vision, Model, New Trends and Future Directions,” Internet Technology Letters, p. e275.

In the paper

“At present, there are many different offered definitions for the term FC [123,124].”

What the sources say

• ?Not checked — no full text of the sourcecites [123] Yi, 2015, Fog computing: platform and applications source
• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [124] Borcoc

In the paper

“Edge AI is the newest development in the field of AI and Computing [83].”

What the sources say

• ?Not checked — no full text of the sourcecites [83] Agarwal, 2021, Edge ai driven technology advancements paving way towards new capabilities, Int. J. Innovat. Technol. Manag. source

In the paper

“proposed the offloads framework, which automatically offloads the computation among the peers within the local network.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [140] S. Singh et al., “An Iiot Based Secure and Sustainable Smart Supply Chain System Using Sensor Networks,” Transactions on Emerging Telecommunications Technologies, p. e4681.

In the paper

“This highlights the role of telecommunications in MEC, which can be viewed as uniting the telecommunications industry with IT at the mobile network edge [120,125].”

What the sources say

• ?Not checked — no full text of the sourcecites [120] Ahmad, 2016, Health fog: a novel framework for health and wellness applications, J. Supercomput. source
• ?Not checked — no full text of the sourcecites [125] Li, 2016, Mobile edge computing: progress and challenges source

In the paper

“With mobile application demands growing at high rates globally, mobile devices are increasingly constrained by limited resources and poor battery longevity [10].”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [10] Bahl

In the paper

“On-demand resources, variable workloads and integrating data streams from a number of heterogeneous devices across a geographical area are important considerations and but trade-offs between computing power and speed of communication may be inevitable [167].”

What the sources say

• ?Not checked — no full text of the sourcecites [167] Hong, 2013, Mobile fog: a programming model for large-scale applications on the internet of things source

In the paper

“An alternative argument is to improve the resilience by moving the functionality closer to the end devices by relying less on any less reliable links (wireless in particular) [89].”

What the sources say

• ?Not checked — no full text of the sourcecites [89] Sri Raghavendra, 2021, Deedsp: deadline-aware and energy-efficient dynamic service placement in integrated internet of things and fog computing environments, Trans. Emerg. Telecommun. Technol. source

In the paper

“An Approach inspired by Edge Computing principles can be used by an algorithm to do processing on the user device and make decisions on whether or not to send out information to the server in the cloud.”

In the paper

“There are three main drivers for Edge Computing development: the application-driven view, the functionality-driven view and the technology-driven view.”

In the paper

“Healthcare: Edge AI can be used to analyse medical data from wearable devices and sensors to monitor patients in real-time, detect abnormalities, and provide early warnings for potential health issues [162]”

What the sources say

• ?Not checked — no full text of the sourcecites [162] Marculescu, 2020, Edge ai: systems design and ml for iot data analytics source

In the paper

“At the edge network smart sensors can offload the data and a computation offloading algorithm can filter the Fig. 8. Taxonomy of edge computing functionality.meaningful data for further processing and sending to the remote cloud data centres [182].”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [182] Frank, 2013

In the paper

“Business Marketing: Edge AI can be used to analyse customer data, including purchase history and behaviour, to provide personalized recommendations and improve customer experiences [163].”

What the sources say

• ?Not checked — no full text of the sourcecites [163] Sachdev, 2020, Towards security and privacy for edge ai in iot/ioe based digital marketing environments source

In the paper

“These are not mutually exclusive but focus attention on how novel service offerings can be initiated at the conceptual level.”

In the paper

“A centralized controller allows for optimal decision-making for each unit within the system, and a dedicated control channel allows translation of high-level policies to low-level configuration instructions to give the system fine-grained control [197].”

What the sources say

• ?Not checked — no full text of the sourcecites [197] Jararweh, 2016, Sdmec: software defined system for mobile edge computing source

In the paper

“These new technologies provide novel tools that increase flexibility in designing networks [103].”

What the sources say

• ?Not checked — no full text of the sourcecites [103] Tuli, 2020, ithermofog: iot-fog based automatic thermal profile creation for cloud data centers using artificial intelligence techniques, Internet Technol. Lett. source

In the paper

“FC has been claimed to be well suited for this purpose [174,175].”

What the sources say

• ?Not checked — no full text of the sourcecites [174] Abdullahi, 2015, Ubiquitous shift with information centric network caching using fog computing source
• ?Not checked — no full text of the sourcecites [175] Jingtao, 2015, Steiner tree based optimal resource caching scheme in fog computing, China Commun. source

In the paper

“With various types of cloud applications and services, the performance of data centre networks becomes highly important [60].”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [60] Gill, 2022

In the paper

“According to one survey, delays should not exceed 20 ms, otherwise virtual scenes become unworkable, and this demotivates AR users [45].”

What the sources say

• ?Not checked — no full text of the sourcecites [45] Shao, 2022, Iot-pi: a machine learning-based lightweight framework for cost-effective distributed computing using iot, Internet Technol. Lett. source

In the paper

“The functionality of Edge AI is to enable AI algorithms to run on edge devices, such as smartphones, IoT devices, and robots, without relying on cloud computing [184].”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [184] Shi, 2021

In the paper

“Heterogeneous data flow has been increasing continuously the energy efficiency of end devices declining; therefore, edge networks require intelligent data reduction methods.”

In the paper

“Therefore, SDN is an evolving approach to maintaining and managing network topology and controlling congestion efficiently [9].”

What the sources say

• ?Not checked — no full text of the sourcecites [9] Gill, 2019 source

In the paper

“Simulation software tools provide insight results with a set of mathematical formulas through simulation without having to implement and deploy a real system which is often complex and expensive [189].”

What the sources say

• ?Not checked — no full text of the sourcecites [189] Ghobaei-Arani, 2020, Resource management approaches in fog computing: a comprehensive review, J. Grid Comput. source

In the paper

“This solution tries to implement a delay-tolerant and cost-effective solution leveraged on opportunistic wireless access.”

In the paper

“Using SDN to manage the interaction between cloud and edge resources, a network can remain dynamic, agile and efficient while providing a better experience for the end user [151].”

What the sources say

• ?Not checked — no full text of the sourcecites [151] Gill, 2019, Fog-based smart healthcare as a big data and cloud service for heart patients using iot source

In the paper

“SDN offers higher programmability, automation, and network control; highly scalable and flexible networks with fast adaptation to changing business needs [124].”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [124] Borcoc

In the paper

“Edge Computing applications have the potential for different industries, but social uses, specifically the field of healthcare has attracted attention [116].”

What the sources say

• ?Not checked — no full text of the sourcecites [116] Desai, 2022, Healthcloud: a system for monitoring health status of heart patients using machine learning and cloud computing, Internet Things source

In the paper

“Virtualization is part of any new emerging technology in the IT industry, whether running multiple operating systems or several applications on the single server [128].”

What the sources say

• ?Not checked — no full text of the sourcecites [128] Bansal, 2020, Deepbus: machine learning based real time pothole detection system for smart transportation using iot, Internet Technol. Lett. source

In the paper

“With the growth of IoT, mobile devices and sensors place more pressure on remote data centres [187,188], so there is a chance to transfer the applications and intelligence from the cloud to the edge network.”

What the sources say

• ?Not checked — no full text of the sourcecites [187] Morabito, 2016, Enabling data processing at the network edge through lightweight virtualization technologies source
• ?Not checked — no full text of the sourcecites [188] Ramalho, 2016, Virtualization at the network edge: a performance comparison source

In the paper

“However, the application inevitably collects large amounts of data from the various sensors causing a serious problem on the cloud side of data processing.”

In the paper

“From time to time, conveying large amounts of data to a data centre is costly and unrealistic and to solve this problem data mining algorithms have been developed [183].”

What the sources say

• ?Not checked — no full text of the sourcecites [183] Zeng, 2012, Distributed data mining: a survey, Inf. Technol. Manag. source

In the paper

“Respectively, In this scenario, processing can either be fully completed in the car with an embedded device or data is sent to the central server for processing, the choice is being made on the basis of bandwidth and latency factors in the communications available to the vehicle [44].”

What the sources say

• ?Not checked — no full text of the sourcecites [44] Dhillon, 2022, Iotpulse: machine learning-based enterprise health information system to predict alcohol addiction in Punjab (India) using iot and fog computing, Enterprise Inf. Syst. source

In the paper

“In 2001, the first smart parking experiment was initiated at the Baltimore/Washington International Airport [152]; car parking was terminated only at 99% occupancy.”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [152]

In the paper

“In all likelihood, these architectures will emerge logically from multiple successful applications emerging from demonstration projects and defining their own characteristics and functional requirements [44].”

What the sources say

• ?Not checked — no full text of the sourcecites [44] Dhillon, 2022, Iotpulse: machine learning-based enterprise health information system to predict alcohol addiction in Punjab (India) using iot and fog computing, Enterprise Inf. Syst. source

In the paper

“Associated long-term data storage can therefore be allocated to cloud computing resources [30].”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [30] Xu, 2022

In the paper

“NFV aims at improved capital efficiencies compared to dedicated hardware-based implementation solutions by deploying commercial offthe-shelf computing hardware to provide virtualized networks, sharing hardware and reducing the number of different hardware architectures [124].”

What the sources say

• ?Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  cites [124] Borcoc

In the paper

“Federated learning: It is a technique that allows machine learning models to be trained on distributed data sources without sharing raw data, improving privacy and reducing network traffic [25].”

What the sources say

• ?Not checked — no full text of the sourcecites [25] Rausch, 2019

In the paper

“MEC and ICN are complementary concepts but independent: both are solutions with, for example, 5G mobile networks, which could utilize ICN for content distribution, transparent mobility among multiple technologies, or to retransmit lost packets over an unreliable radio link, but without a commitment to developing a full MEC scenario [90].”

What the sources say

• ?Not checked — no full text of the sourcecites [90] Sengupta, 2021 source

In the paper

“This virtualization proposes better scalability and improved resilience and accelerated service innovation through software-based service deployment [129].”

What the sources say

• ?Not checked — no full text of the sourcecites [129] Tuli, 2020, Next generation technologies for smart healthcare: challenges, vision, model, trends and future directions, Internet Technol. Lett. source

In the paper

“The aim is to provide a network infrastructure service for contemporary uses of and demands placed on the Internet focusing on user mobility and content distribution [198].”

What the sources say

• ?Not checked — no full text of the sourcecites [198] Ahlgren, 2012, A survey of information-centric networking, IEEE Commun. Mag. source

In the paper

“Edge computing and other resource-constrained locations provide problems and possibilities for AI deployment [50].”

What the sources say

• ?Not checked — no full text of the sourcecites [50] Lovén, 2019, Edgeai: a vision for distributed, edge-native artificial intelligence in future 6g networks, 1st 6G wireless summit.

In the paper

“Edge devices: These devices are hardware components that are equipped with processing power, sensors, and storage, which enable them to process data and run AI algorithms locally [201].”

What the sources say

• ?Not checked — no full text of the sourcecites [201] Yang, 2019, An efficient edge artificial intelligence multipedestrian tracking method with rank constraint, IEEE Trans. Ind. Inf. source