Accurate Anomaly Detection With Energy Efficiency in IoT–Edge–Cloud Collaborative Networks

Li, Yi; Zhou, Zhangbing; Xue, Xiao; Deng, Zhidong; Hung, Patrick C. K.

doi:10.1109/jiot.2023.3273542

Cited by 6 publications

(1 citation statement)

References 37 publications

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“…To this end, the proposed approach outperforms other baseline technologies according to the resource allocation policies that have been considered, such as an equal computational load to all involved servers, a popularity-based and workload-aware assignment, as well as a communicationbased assignment. In [110], the authors deal with anomaly detection in the IEC continuum. In particular, the inverse distance weighted algorithm and marching squares algorithm are adopted to generate the boundary of an anomaly in terms of isopleths.…”

Section: Recent Work In Iot-edge-cloud Continuum Architecturesmentioning

confidence: 99%

A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues

Gkonis,

Giannopoulos,

Trakadas

et al. 2023

Future Internet

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The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things—IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to the limited battery size and computational capabilities of IoT nodes, data processing usually takes place on external devices. Since latency minimization is a key concept in modern-era networks, edge servers that are in close proximity to IoT nodes gather and process related data, while in some cases data offloading in the cloud might have to take place. The interconnection of a vast number of heterogeneous IoT devices with the edge servers and the cloud, where the IoT, edge, and cloud converge to form a computing continuum, is also known as the IoT-edge-cloud (IEC) continuum. Several key challenges are associated with this new computing systems’ architectural approach, including (i) the design of connection and programming protocols aimed at properly manipulating a huge number of heterogeneous devices over diverse infrastructures; (ii) the design of efficient task offloading algorithms aimed at optimizing services execution; (iii) the support for security and privacy enhancements during data transfer to deal with the existent and even unforeseen attacks and threats landscape; (iv) scalability, flexibility, and reliability guarantees to face the expected mobility for IoT systems; and (v) the design of optimal resource allocation mechanisms to make the most out of the available resources. These challenges will become even more significant towards the new era of sixth-generation (6G) networks, which will be based on the integration of various cutting-edge heterogeneous technologies. Therefore, the goal of this survey paper is to present all recent developments in the field of IEC continuum systems, with respect to the aforementioned deployment challenges. In the same context, potential limitations and future challenges are highlighted as well. Finally, indicative use cases are also presented from an IEC continuum perspective.

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Section: Recent Work In Iot-edge-cloud Continuum Architecturesmentioning

confidence: 99%

A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues

Gkonis,

Giannopoulos,

Trakadas

et al. 2023

Future Internet

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Enhancing Sustainable Edge Computing Offloading via Renewable Prediction for Energy Harvesting

Alhartomi,

Salh,

Audah

et al. 2024

IEEE Access

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The integration of Edge Computing (EC) and Energy Harvesting (EH) technologies has facilitated the growth of the Internet of Things (IoT), allowing for the interconnectivity of a wide range of devices. The integration of this technology has not only enhanced energy sustainability but also significantly extended the battery life of these devices. Adopting Renewable Energy (RE) sources has become more widespread in energy systems as a strategy to reduce carbon emissions. Low energy consumption and constrained battery capacity for IoT devices are concerns related to offloading. The unpredictability of RE quality makes it difficult for edge servers to maintain high quality of service in EH-EC systems, which impedes effective energy conservation for IoT. To solve an optimization problem, RE Predictions with a Deep Reinforcement Learning algorithm named (REP-DRL) are proposed. Accurately, REP-DRL used the actor-critic technique to identify the best approach for predicting RE and optimal offloading decisions. The approach improves IoT device processing and expands the system state to offload experiences per time slot. To store excess energy during periods of abundance and use it during times of higher demand, the service offloading process is modeled based on the predicted amount of RE, to find the best service offloading technique and improve energy sustainability for IoT. By determining the most efficient service offloading approach using the predicted RE amount, this solution increases the energy sustainability of the IoT ecosystem. Finally, the simulation results show that the REP-DRL system utilizes local computing to conserve power when both the battery level and projected EH are low, showcasing its capacity to adapt to varying operating conditions and optimize the utilization of resources.INDEX TERMS Edge computing, energy harvesting, IoT, RE, DRL.

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A Pragmatical Approach to Anomaly Detection Evaluation in Edge Cloud Systems

Skaperas,

Koukis,

Kapetanidou

et al. 2024

IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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Accurate Anomaly Detection With Energy Efficiency in IoT–Edge–Cloud Collaborative Networks

Cited by 6 publications

References 37 publications

A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues

A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues

Enhancing Sustainable Edge Computing Offloading via Renewable Prediction for Energy Harvesting

A Pragmatical Approach to Anomaly Detection Evaluation in Edge Cloud Systems

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