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An Efficient Approach for Merging Multidimensional Blockchains in Mobile IoT 19

An Efficient Approach for Merging Multidimensional Blockchai...

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19th Annual international conference on distributed computing in Smart systems and the Internet of Things, dcoss-IoT 2023

作者： Zangoti, Hussein Pissinou, Niki Kamhoua, Charles A School of Computing and Information Sciences Florida International University MiamiFL United States Jazan University Faculty of Computer Science and Information Technology Jazan Saudi Arabia Network Security Branch Us Army Research Laboratory AdelphiMD United States

ISBN: (纸本)9798350346497

Graph-based or multidimensional blockchains have been proposed to improve the scalability and efficiency of existing blockchain applications. However, when implemented in mobile Internet of Things (mIoT) networks, these blockchain systems can frequently split and merge and cause the merging algorithm to process a large number of similar blocks (a block is similar or identical when it exists once in multiple blockchains). The presence of similar blocks hinders the merging process, as it consumes time and computational resources to scan, validate, and potentially merge similar blocks. This paper presents an efficient approach for merging graph-based or multidimensional blockchains in mIoT networks by avoiding similar blocks and effectively detecting and merging new blocks that were created after the split. Our proposed merging algorithm employs depth-first search and Merkle tree techniques to minimize the time and computational resources spent on identical blocks. Finally, we evaluate the performance of our method in highly mobile networks and demonstrate that it can execute the merge with a more than 72% reduction in time in comparison to merging algorithms without block similarity handling. © 2023 ieee.

关键词： Blockchain

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On Reconstructing the Patient Zero from sensor Measurements 43

On Reconstructing the Patient Zero from Sensor Measurements

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43rd ieee international conference on distributed computing systems (ICDCS)

作者： Hahn-Klimroth, Max Kaaser, Dominik TU Dortmund Univ Dortmund Germany TU Hamburg Hamburg Germany

ISBN: (纸本)9798350339864

Epidemic spreading processes have been widely studied over the last years, with an additional boost due to the ongoing COVID-19 pandemic. However, epidemic spreading is not limited to infectious diseases;it forms the basis of understanding opinion formation processes in social networks, or models the spread of computer viruses in network security. In all of these application domains, the forward processes are typically well understood, both from a theoretical and a practical point of view. Interestingly, much less is known about the converse direction: suppose we are given "sensors" that report on the infection status, can we recover the source of the epidemic? This problem is known under the name of patient zero, rumor source detection, or finding the point of entrance in the context of intrusion detection systems. In this work we assume that the epidemic process spreads according to the classical Independent Cascade Model, and we are given sensors on edges of the communication network. We rigorously analyze under which sensor placement one can recover the source of the epidemic process. Our main contribution is an impossibility result: we formally prove a lower bound on the number of sensors required to recover the source of the epidemic process. Furthermore, we introduce a monitoring strategy that succeeds in recovering the patient zero with the minimum number of sensors possible for acyclic networks. Finally, we discuss unreliable sensor measurements and provide extensive simulations of according heuristics on realistic communication networks.

关键词： Epidemic Spreading Patient Zero Rumor Source Detection Independent Cascade Model SIR Inference

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Scaling Machine Learning at the Edge-Cloud: A distributed computing Perspective 19

Scaling Machine Learning at the Edge-Cloud: A Distributed Co...

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19th Annual international conference on distributed computing in Smart systems and the Internet of Things, dcoss-IoT 2023

作者： Marozzo, Fabrizio Orsino, Alessio Talia, Domenico Trunfio, Paolo Dimes University of Calabria Rende Italy

ISBN: (纸本)9798350346497

The widespread diffusion of Internet of Things (IoT) devices has led to an exponential growth in the volume of data generated at the edge of the network. With the rapid spread of machine learning (ML)-based applications, performing compute and resource-intensive learning tasks at the edge has become a critical issue, resulting in the need for scalable and efficient solutions that can overcome the resource constraints of edge devices. This paper analyzes the problem of scaling ML applications and algorithms at the edge-cloud continuum from a distributed computing perspective. In particular, we first highlight the limitations of traditional distributed architectures (e.g., clusters, clouds, and HPC systems) when running ML applications that use data generated at the edge. Next, we discuss how to enable traditional ML algorithms combining the benefits of edge computing, such as low-latency processing and privacy preservation of personal user data, with those of cloud computing, such as virtually unlimited computational and storage capabilities. Our analysis provides insights into how properly separated parts of a ML application can be deployed across edge-cloud architectures in order to optimize its execution. More-over, examples of ML applications and algorithms appropriately adapted for the edge-cloud continuum are shown. © 2023 ieee.

关键词： Internet of things

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Collaborative User Mobility Prediction in distributed Edge computing Framework 21

Collaborative User Mobility Prediction in Distributed Edge C...

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21st ieee international conference on Mobile Ad-Hoc and Smart systems (MASS)

作者： Ali, Sardar Jaffar Raza, Syed M. Choo, Hyunseung Sungkyunkwan Univ Dept AI Syst Engn Suwon South Korea Univ Glasgow James Watt Sch Engn Glasgow Lanark Scotland Sungkyunkwan Univ Dept Elect & Comp Engn Suwon South Korea

ISBN: (纸本)9798350363999;9798350364002

User attachment forecasting with Deep Learning (DL) is an effective tool for proactive mobility management in dense Beyond 5G (B5G)/6G deployments with reduced cell sizes. However, collecting user data in a central cloud to facilitate DL model learning causes extensive overhead and privacy concerns. distributed edge cloud-based federated learning solves these issues, but it faces challenges in handling out-of-distribution data from decentralized edges at the network periphery, and the model biases due to data heterogeneity. This paper addresses these limitations by proposing a fully distributed Collaborative User Mobility Prediction (CUMP) framework that mitigates the out-of-distribution data issue through collaboration among initial layers of DL models in edges that are selected using inter-edge mobility rates. The remaining part of each model only trains on local data, preserving biases towards their respective edges. This enhances the generalization, robustness, and predictive performance of the DL models. Results show that CUMP outperforms conventional global learning and state-of-the-art distributed personalized federated learning and cyclic incremental institutional learning by 63%, 12%, and 10% in predicting the next Point of Attachment (PoA) of a user and by 70%, 22%, and 28% in predicting user dwell time in current PoA, respectively. Thus, CUMP improves prediction performance while reducing network and storage overheads while preserving privacy.

关键词： distributed edge architecture multi-access edge computing collaborative learning proactive mobility B5G/6G

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LiPI: Lightweight Privacy-Preserving Data Aggregation in IoT 22

LiPI: Lightweight Privacy-Preserving Data Aggregation in IoT

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ieee 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Goyal, Himanshu Kodali, Krishna Saha, Sudipta Indian Inst Technol Bhubaneswar Sch Elect Sci Bhubaneswar Odisha India

ISBN: (纸本)9798350381993;9798350382006

The ability of IoT devices to sense and share various physical parameters plays a key role in a smart system. Along side benefits, it also bears the potential to cause a breach of privacy for the users of the smart-systems. Existing solutions for privacy-preserving computation for distributed systems either extensively use highly complex cryptographic techniques or exploit an extremely high degree of message passing among the devices through secure channels. However, for the resource-constrained IoT devices, which compose a significant fraction of the smart-systems, the existing solutions for privacy-preserving computation strategies does not fulfill the current requirements. To address this issue, in this work, we propose a novel real-time lightweight strategy LiPI for Privacy-Preserving Data Aggregation in low-power IoT systems. LiPI uses lightweight distributed and collaborative data obfuscation, which substantially minimizes the computation requirements. In addition, it exploits the recent advances in Synchronous-Transmission (ST)-based protocols to efficiently fulfill the communication requirements too, making it efficient to work in real-time. Furthermore, LiPI also avoids dependency on any trusted third party. Extensive evaluation based on comprehensive experiments in both simulation platforms and publicly available WSN/IoT testbeds demonstrates that our strategy achieves the goal at least 51.7% faster and consumes 50.5% lesser energy compared to the existing state-of-the-art strategies.

关键词： Privacy-Preserving Data Aggregation Collaborative Obfuscation Synchronous Communication Concurrent-Transmissions Internet of Things (IoT) Wireless sensor Networks

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distributed Service Discovery over Heterogeneous Robotic systems-of-systems 19

Distributed Service Discovery over Heterogeneous Robotic Sys...

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19th Annual international conference on distributed computing in Smart systems and the Internet of Things, dcoss-IoT 2023

作者： Montecchiari, Leonardo Trotta, Angelo Gigli, Lorenzo Bellone, Lorenzo Di Felice, Marco Natalizio, Enrico University of Bologna Department of Computer Science and Engineering Italy University of Bologna Advanced Research Center on Electronic Systems Ercole de Castro Italy United Arab Emirates

ISBN: (纸本)9798350346497

In robotic swarms, nodes must cooperate in order to accomplish complex tasks. Robot interactions may occur at run-time based on the dynamic network topology and on mission-specific goals. For this reason, service discovery mechanisms are fundamental in order to inform each robot about the functionalities offered by the other peers. Although several network discovery protocols have been proposed so far, none of them fits the unique characteristics of robotic swarms in terms of dynamic topology, communication heterogeneity, and latency requirements. In this paper, we fill such gap by designing, implementing and evaluating a novel service discovery mechanisms for generic Robotic systems-of-systems (RSoS). The study proposes algorithmic and practical contributions. Regarding the first, we describe a novel distributed service discovery algorithm for RSoS which adapts to highly mobile robotic environments while limiting the network overhead and latency. Regarding the latter, we implemented the proposed algorithm within the Uhura swarm robotics framework;as a result, our solution is able to support multi-radio scenarios where robots are provided with heterogeneous Machine-to-Machine (M2M) communication technologies. In addition, we validate our solution through large-scale simulations and a test-bed in which ground robots are able to discover a Federated Learning (FL) task and join it at run-time to improve the accuracy. © 2023 ieee.

关键词： Topology

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Routing Drones Being Aware of Wind Conditions: a Case Study 17

Routing Drones Being Aware of Wind Conditions: a Case Study

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17th Annual ieee international conference on distributed computing in sensor systems (ieee dcoss)

作者： Palazzetti, Lorenzo Univ Florence Dept Comp Sci Florence Italy

ISBN: (纸本)9781665439299

In this paper, we investigate the manner in which energy consumption in drone deliveries is affected by windy environmental conditions. We know in fact that the energy consumption of the drone will depend on the static and dynamic parameters of the scenario where it moves and according to these it decides, during the mission, to detour from the originally planned path to take advantage of the wind changes. In order to validate this, we simulate possible deliveries among fixed destinations relying on a real data-set of recorded winds obtained from different weather stations in Corsica, France. For our analysis, we will mainly concentrate on the evaluation of the delivery scheme proposed in the literature, where completing a delivery means finding a cycle for the drone that is feasible, i.e., that can be completed with the available energy autonomy of the drone.

关键词： Wind Energy consumption Routing sensor systems Batteries distributed computing Drones

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Towards On-demand Gas Sensing 17

Towards On-demand Gas Sensing

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17th Annual ieee international conference on distributed computing in sensor systems (ieee dcoss)

作者： Gherman, Markus-Philipp Cheng, Yun Gomez, Andres Saukh, Olga Graz Univ Technol Inst Tech Informat Graz Austria Swiss Fed Inst Technol Comp Engn & Networks Lab Zurich Switzerland Univ St Gallen Interact & Commun Based Syst St Gallen Switzerland Complex Sci Hub Vienna Vienna Austria

ISBN: (纸本)9781665439299

Low-power operation of power-hungry MOX sensors is usually achieved by duty cycling them periodically. However, a difficulty arises if a sensor is operated at irregular time intervals due to intermittent energy availability in energy-neutral or batteryless applications. In this work, we propose a compensation method which re-maps on-demand measurements to virtually duty-cycled readings in the value domain by taking the duration of the last off-time into account. We evaluate our compensation algorithm based on Sensirion's SGP30 sensor and achieve up to 79% accuracy improvement compared to uncompensated measurements.

关键词： sensor systems sensors distributed computing

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Evaluation of AI-Supported Input Methods in Augmented Reality Environment 19

Evaluation of AI-Supported Input Methods in Augmented Realit...

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19th Annual international conference on distributed computing in Smart systems and the Internet of Things, dcoss-IoT 2023

作者： Nagy, Akos Lagkas, Thomas Sarigiannidis, Panagiotis Argyriou, Vasileios School of Computer Science and Maths Ece Kingston University Department of Networks and Digital Media Kingston upon Thames United Kingdom International Hellenic University Kavala Campus Greece and South-East European Research Centre Department of Computer Science Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani Greece

ISBN: (纸本)9798350346497

Augmented Reality (AR) solutions are providing tools that could improve applications in the medical and industrial fields. Augmentation can provide additional information in training, visualization, and work scenarios, to increase efficiency, reliability, and safety, while improving communication with other devices and systems on the network. Unfortunately, tasks in these fields often require both hands to execute, reducing the variety of input methods suitable to control AR applications. People with certain physical disabilities, where they are not able to use their hands, are also negatively impacted when using these devices. The goal of this work is to provide novel hand-free interfacing methods, using AR technology, in association with AI support approaches to produce an improved Human-Computer interaction solution. © 2023 ieee.

关键词： Augmented reality

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A Vision-based Virtual sensor to Enhance Privacy and Energy Efficiency on Edge computing

A Vision-based Virtual Sensor to Enhance Privacy and Energy ...

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ieee international conference on Pervasive computing and Communications (PerCom)

作者： Kania, Nicholas Bogliolo, Alessandro Calisti, Lorenzo Contoli, Chiara Di Fabrizio, Giacomo Romanelli, Luca Lattanzi, Emanuele Univ Urbino Dept Pure & Appl Sci Urbino Italy

ISBN: (纸本)9798350304367;9798350304374

Integrating vision-based technologies into distributed sensor domains offers unprecedented potential for data collection. However, it raises privacy concerns over the incredible amount of extra information inadvertently carried by the video stream. On the other hand, the advent of tiny machine learning models running on edge devices with embedded GPUs/TPUs is revolutionizing computer vision and real-time tracking systems, enabling the local execution of computationally demanding tasks traditionally performed in the cloud. This study focuses on developing and characterizing vision-based virtual sensors capable of processing data from a local camera source to provide real-time measures of relevant metrics without storing or transmitting any video stream. The main advantages of vision-based virtual sensors running on the edge are data protection, reduced communication cost, and reduced detection latency. In addition, we propose a dynamic inference power manager (DIPM), based on adaptive frame rate, that allows us to explore the trade-off between power consumption and accuracy. Experimental results conducted on a real hardware platform show that the proposed virtual sensor, equipped with DIPM, can save up to 40% of the processing energy with a reduction of tracking accuracy lower than 10%, while retaining the privacy preservation benefits of virtual sensors.

关键词： edge computing virtual sensor energy efficiency privacy preservation

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