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38th International Conference on Information Networking (ICOIN)

作者： Vyas, Mayank Anand, Garv Nayak, Sanjeet Kumar Yadav, Ramanarayan IITRAM Ahmadabad Gujarat India IIITDM Kancheepuram Chennai Tamil Nadu India

ISBN: (纸本)9798350330946;9798350330953

Nowadays, Internet of Things (IoT) technologies are very useful to transform traditional farming practices into smart farming across the globe. Since most of the agricultural land is located in remote places, there is a need for new technologies for making them smart. For smart farming, low-cost, and resource-constraint sensors are placed across the agricultural field. To provide continuous connectivity for data communication, there must be a suitable and reliable transmission over long distances and, at the same time, should consume less energy. In particular, Long Range (LoRa), a relatively new communication technology, uses long waves to work over long distances. This is extremely useful in agriculture, where the communicating areas are broad fields of crops and Internet connectivity is low and/or intermittent. So, to reduce data transmission between layers (sensing, fog and cloud), a machine learning based data forwarding architecture is proposed for the three tier architecture, namely, Regressive Prediction Data Forwarding Model (RPDM). A suitable and lightweight machine learning model is deployed on each layer which will predict the next coming sensing data. So, the upcoming data will not be forwarded from the sensing layer to the fog layer if they match with the predicted one. Also, in the case of Internet failure, the sensing layer made capable of performing the actuation based on current data value. We validated the performance of RPDM on a real testbed in terms of the amount of energy consumed and data transmitted.

关键词： Smart agriculture LoRa Data reduction Machine learning IoT fog/edge computing

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Geofence-Based Service Discovery in the computing Continuum 15

Geofence-Based Service Discovery in the Computing Continuum

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IEEE/ACM 15th International Conference on Utility and Cloud computing (UCC) / 9th International Conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Horvath, Kurt Kimovski, Dragi Uran, Christoph Prodan, Radu Woellik, Helmut Univ Klagenfurt Inst Informat Technol Klagenfurt Austria Carinthian Univ Appl Sci Fac Engn & IT Klagenfurt Austria

ISBN: (纸本)9781665460873

Service discovery is a vital process that enables low latency provisioning of Internet of Things applications across the computing continuum. Unfortunately, due to the high heterogeneity of the computing continuum, it becomes increasingly difficult to identify a proper service within strict time constraints. To address these issues, in the paper, we introduce a mobile edge service discovery approach named MESDD. The MESDD service discovery algorithm utilizes the notion of intermediate code to identify the best suitable instance of a service across the computing continuum. The core function of this approach is the selection of the most siutable location descriptor based on the naming scheme used to identify the users' location. To aid this process, the MESDD approach utilizes geofences, which enable fine-grained resource discovery.

关键词： fog/edge computing Service Discovery Contextual Location Awareness DNS geocoding

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Communication-efficient distributed AI strategies for the IoT edge

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 131卷 292-308页

作者： Mwase, Christine Jin, Yi Westerlund, Tomi Tenhunen, Hannu Zou, Zhuo Fudan Univ Sch Informat Sci & Technol State Key Lab ASIC & Syst Shanghai Peoples R China Univ Turku Fac Technol Turku Finland

The impact that artificial intelligence (AI) has made across several industries in today's society is clearly seen in applications ranging from medical diagnosis to customer service chatbots, to financial trading. It is also evident that AI has a huge role to play in emerging and future applications and will be increasingly used in mission-critical and time-sensitive applications such as remote surgeries, cybersecurity and self-driving cars. To satisfy the latency, security and privacy requirements that such applications require, AI which has gained its merit by utilising resource-heavy cloud infrastructure, needs to perform well in resource-constrained environments at the network edge. To address this need, this paper characterises the cloud-to-thing continuum and provides an architecture for enabling AI in fully edge-based scenarios. In addition, the paper provides strategies to tackle the communication inefficiencies that arise from the distributed nature of fully edge-based scenarios. Performance improvements exhibited by these strategies in state-of-the art research is presented, as well as directions where further advancements can be made. The material is presented in a simple manner to catalyse the understanding and hence the participation of multidisciplinary researchers in addressing this challenge.(C) 2022 Elsevier B.V. All rights reserved.

关键词： Distributed AI/ML Communication efficient AI/ML fog/edge computing edge ML IIoT

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Combination of Task Allocation and Approximate computing for fog-Architecture-Based IoT

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IEEE INTERNET OF THINGS JOURNAL 2021年第9期8卷 7638-7648页

作者： Yu, Wanli Najafi, Ardalan Huang, Yanqiu Garcia-Ortiz, Alberto Univ Bremen Inst Electrodynam & Microelect D-28359 Bremen Germany Univ Twente Fac Elect Engn Math & Comp Sci NL-7522 NH Enschede Netherlands

Achieving energy efficiency is always a primary concern for fog-architecture-based Internet of Things (IoT) applications. As the IoT devices are typically of small sizes and powered by battery energy, it is essential to address the energy consumption at all levels from the circuit to the system. Two of the promising solutions at circuit and system levels are approximate computing and energy-aware task allocation, respectively. However, the existing task allocation approaches are designed without considering the aspect of approximate computing. In this work, we fill this gap and aim to maximize the network lifetime subject to the accuracy requirements of the applications. By considering both the approximate computing and task allocation simultaneously, a nonlinear problem is obtained to allocate the tasks for the devices (fog nodes and IoT end devices) and to select the corresponding execution modes (tasks in approximate or exact modes). To efficiently solve this problem, a centralized algorithm is first proposed by transferring the above nonlinear problem as a linear programming (LP) problem. As executing the centralized algorithm is a challenge for the resource-limited IoT devices, this work further proposes an optimal distributed algorithm based on Dantzig-Wolfe decomposition to solve the problem of tasks distribution and execution modes selection. The centralized large-scaled LP problem is decomposed into small-scaled subproblems, which can be efficiently solved by each IoT device. The proposed algorithms are tested by extensive simulations. The results demonstrate that the distributed algorithm achieves the same results as the centralized algorithm, and both of them significantly outperform the previous approaches.

关键词： Approximate computing fog/edge computing Internet of Things (IoT) network lifetime task allocation

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DILoCC: An approach for Distributed Incremental Learning across the computing Continuum 7

DILoCC: An approach for Distributed Incremental Learning acr...

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7th IEEE International Conference on Smart computing (SMARTCOMP)

作者： Cicceri, Giovanni Tricomi, Giuseppe Benomar, Zakaria Longo, Francesco Puliafito, Antonio Merlino, Giovanni Univ Messina Engn Dept Messina Italy CINI Natl Interuniv Consortium Informat Rome Italy

ISBN: (纸本)9781665412520

The Internet of Medical Things (IoMT), combined with interconnected wearable devices and medical-grade sensors, can play an essential role in healthcare evolution. By exploiting the data generated by the plethora of interconnected devices (vital parameters, location-based info, patients activity and more), advanced ICT systems can be put in place with predicting capabilities. This way potentially critical situations, that may evolve in serious complications to patients' well-being, can be promptly recognized and successfully addressed, first of all, to save lives and secondarily to limit economical damages. To support continuous patient monitoring in public and private healthcare, this paper proposes "DILoCC", an architecture to manage wearable devices, sensors and applications, that uses a Distributed Incremental Learning (DIL) approach to exploit cooperation among the sensing devices and increase the overall system efficiency through the mitigation of "Catastrophic Forgetting" consequences.

关键词： Incremental Learning fog/edge computing e-Health Catastrophic Forgetting IoMT computing Continuum

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Re-configuration from the edge: alternative Software Models for time-sensitive IoT Applications 27

Re-configuration from the Edge: alternative Software Models ...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Tselikis, C. Douligeris, C. Mitropoulos, S. Maglaras, Leandros Univ Piraeus Dept Informat Piraeus 18534 Greece Ionian Univ Dept Reg Dev Lefkada 31100 Greece De Montfort Univ Cyber Technol Inst Leicester Leics England

ISBN: (纸本)9781665497923

In this paper we examine traditional and novel software models for critical IoT applications. We propose a hybrid cellular IoT model that offers optimizations with respect to automated network management, re-configuration and optimized performance. This hybrid model utilizes the fog/edge computing model and can satisfy better the requirements of time-sensitive IoT applications.

关键词： industrial IoT fog/edge computing automation

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WSNs Information Life Cycle in the Context of Smart Cities 3rd

WSNs Information Life Cycle in the Context of Smart Cities

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3rd International Workshop on Distributed computing for Emerging Smart Networks (DiCES-N)

作者： Ksouri, Chahrazed Belghith, Abdelfettah Lab DAVID 45 Ave Etats Unis F-78000 Versailles France Uni Bordeaux CNRS Bordeaux INP LaBRIUMR 5800 F-33400 Talence France Natl Sch Engineers Sfax Sfax Tunisia King Saud Univ Coll Comp & Informat Sci Riyadh Saudi Arabia

ISBN: (纸本)9783030990046;9783030990039

Smart City concept is an emerging vision of cities with wide connectivity, systems inter-operability, sustainable solutions and prevalent security. Wireless Sensor Networks (WSNs) have shown themselves to be very useful in the process of the realization of this paradigm. Data collection is the first step on which the whole system is based. The new services and innovative applications offered by the Smart City are based on the extracted information from the data collected by sensors which are widely deployed and integrated in many smart devices such as smart phones and connected cars. In this paper, we introduce a WSNs information life cycle in Smart Cities encompassing six phases, namely;deployment, management, collect and transfer, use and share, storage, and analyses and plan. We specify for each phase the technologies involved. Then, we discuss the challenges of integration.

关键词： Smart Cities Wireless Sensor Networks IoT ICT Big Data Cloud computing SDN fog/edge computing UAV

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Privacy-preserving security of IoT networks: A comparative analysis of methods and applications

Cyber Security and Applications

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Cyber Security and Applications 2025年 3卷

作者： Wakili, Abubakar Bakkali, Sara Euromed University of Fez Morocco

The Internet of Things (IoT) connects devices to enhance efficiency, productivity, and quality of life. However, deploying IoT networks introduces critical privacy and security challenges, including resource constraints, scalability issues, interoperability gaps, and risks to data privacy. Addressing these challenges is vital to ensure the reliability and trustworthiness of IoT applications. This study provides a comprehensive analysis of privacy-preserving security methods, evaluating cryptography, blockchain, machine learning, and fog/edge computing against performance indicators such as scalability, efficiency, robustness, and usability. Through a structured literature review and thorough data analysis, the study reveals that while cryptography offers high security, it faces scalability challenges;blockchain excels in decentralization but struggles with efficiency;machine learning provides adaptive intelligence but raises privacy concerns;and fog/edge computing delivers low-latency processing yet encounters operational complexities. The findings highlight the importance of adopting a hybrid approach that combines the strengths of various methods to overcome their limitations. This study serves as a valuable resource for academia, industry professionals, and policymakers, providing guidance to strengthen IoT infrastructures and influence the direction of future research. © 2025

关键词： Blockchain Cryptography fog/edge computing Internet of Things (IoT) networks Machine learning Privacy-preserving security

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A Cognitive IoT Smart Surveillance Framework for Crowd Behavior Analysis

A Cognitive IoT Smart Surveillance Framework for Crowd Behav...

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International Conference on COMmunication Systems and NETworkS (COMSNETS)

作者： Varghese, Elizabeth B. Thampi, Sabu M. Indian Inst Informat Technol & Management Kerala Trivandrum Kerala India Cochin Univ Sci & Technol Cochin Kerala India

ISBN: (纸本)9781728191270

Smart and proactive surveillance using IoT framework has recently attracted a lot of attention due to the impracticality of traditional monitoring to handle large amounts of data from distributed cameras. However, in the case of IoT platforms for video processing, centralized cloud servers may fail due to high latency and high bandwidth usage, leading to the need for distributed processing with fog/edge computing. Moreover, tools and techniques are also necessary to intelligently analyze incoming data with minimum human intervention. This paper proposes a fog/edge computing-based IoT smart surveillance framework that adds additional cognitive knowledge in the form of informative frame selection and attention maps. The experimental analysis was conducted in the context of crowd behavior analysis, and the simulation results proved that the main concerns of latency and bandwidth are addressed efficiently by the proposed framework.

关键词： Internet of Things (IoT) Cognitive computing Smart Surveillance Crowd Behavior fog/edge computing

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Labeling Privacy Protection SVM Using Privileged Information for COVID-19 Diagnosis

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ACM TRANSACTIONS ON INTERNET TECHNOLOGY 2022年第3期22卷 65-65页

作者： Ni, Tongguang Zhu, Jiaqun Qu, Jia Xue, Jing Changzhou Univ Sch Comp Sci & Artificial Intelligence 21 Rehu Rd Changzhou 213164 Peoples R China Nanjing Med Univ Affiliated Wuxi Peoples Hosp Dept Nephrol 299 Qingyang Rd Wuxi 214023 Jiangsu Peoples R China

edge/fog computing works at the local area network level or devices connected to the sensor or the gateway close to the sensor. These nodes are located in different degrees of proximity to the user, while the data processing and storage are distributed among multiple nodes. In healthcare applications in the Internet of things, when data is transmitted through insecure channels, its privacy and security are the main issues. In recent years, learning from label proportion methods, represented by inverse calibration (InvCal) method, have tried to predict the class label based on class label proportions in certain groups. For privacy protection, the class label of the sample is often sensitive and invisible. As a compromise, only the proportion of class labels in certain groups can be used in these methods. However, due to their weak labeling scheme, their classification performance is often unsatisfactory. In this article, a labeling privacy protection support vector machine using privileged information, called LPP-SVM-PI, is proposed to promote the accuracy of the classifier in infectious disease diagnosis. Based on the framework of the InvCal method, besides using the proportion information of the class label, the idea of learning using privileged information is also introduced to capture the additional information of groups. The slack variables in LPP-SVM-PI are represented as correcting function and projected into the correcting space so that the hidden information of training samples in groups is captured by relaxing the constraints of the classification model. The solution of LPP-SVM-PI can be transformed into a classic quadratic programming problem. The experimental dataset is collected from the Coronavirus disease 2019 (COVID-19) transcription polymerase chain reaction at Hospital Israelita Albert Einstein in Brazil. In the experiment, LPP-SVM-PI is efficiently applied for COVID-19 diagnosis.

关键词： fog/edge computing labeling privacy protection supervised classifier learning using privileged information COVID-19

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