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检索条件"任意字段=18th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2022"

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Drone-based Optimal and Heuristic Orienteering Algorithms Towards Bug Detection in Orchards

Drone-based Optimal and Heuristic Orienteering Algorithms To...

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international conference on distributed computing in sensor systems (dcoss)

作者： Francesco Betti Sorbelli Federico Corò Sajal K. Das Lorenzo Palazzetti Cristina M. Pinotti Department of Computer Science and Mathematics University of Perugia Italy Department of Computer Science Missouri Science and Technology University USA Department of Computer Science and Mathematics University of Florence Italy

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

In this paper, we consider the problem of using a drone to collect information within orchards in order to detect bugs. An orchard can be modeled as an aisle-graph, which is a regular data structure formed by consecutive aisles where trees are arranged in a straight line. For monitoring the presence of bugs, a drone flies close to the trees and takes videos and/or pictures that will be analyzed offline. As the drone’s energy is limited, only a subset of locations in the orchard can be visited with a fully charged battery. those places that are most likely to be infested should be selected to promptly detect the parasite. We study the budgeted constrained position selection problem in the orchard from an algorithmic point of view. We present the Single-drone Orienteering Aisle-graph Problem (SOAP), a variant of the well-known orienteering problem where the finite resource is the drone’s battery. We first show that SOAP can be optimally solved for aisle-graphs in polynomial time. However, the optimal solution is not efficient for large orchards. then, we propose two efficient heuristics that work even for large (orchard) instances. After a thorough analysis of the proposed solutions, we evaluate their performance by simulation experiments on both synthetic and real data sets.

关键词： Heuristic algorithms Computer bugs Data models sensor systems Batteries Simple object access protocol distributed computing

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Digital Twin Driven Blockchain Based Reliable and Efficient 6G Edge Network

Digital Twin Driven Blockchain Based Reliable and Efficient ...

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international conference on distributed computing in sensor systems (dcoss)

作者： Mehmet Ozgen Ozdogan Levent Carkacioglu Berk Canberk Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Artificial Intelligence and Data Engineering Istanbul Technical University Istanbul Turkey Aselsan Inc. Ankara Turkey

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

With the rapid development of intelligent devices in wireless communication, the fifth generation (5G) mobile networks have limited high data rates, low latency, high avail-ability demands. the sixth-generation (6G) mobile network can use Digital-twin (DT) techniques to meet these demands. DT is the virtual representation of physical aspects such as 6G edge nodes. DT optimize the 6G edge nodes parameters using artificial intelligence (AI) and especially machine learning (ML) algorithms. However, AI and ML bring along privacy and security concerns. therefore, user data must be protected from unauthorized persons during the 6G edge network recovery and expansion phases. In this paper, we proposed a new reliable Digital Twin-based 6G edge network recovery framework using Blockchain technology. We applied the Transfer Learning (TL) technique to improve our proposed framework’s performance. We ensured data privacy and security using TL and Blockchain.

关键词： 6G mobile communication Wireless communication Privacy Machine learning algorithms Transfer learning sensor systems Blockchains

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A Middleware for Digital Twin-Enabled Flying Network Simulations Using UBSim and UB-ANC

A Middleware for Digital Twin-Enabled Flying Network Simulat...

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international conference on distributed computing in sensor systems (dcoss)

作者： Sabarish Krishna Moorthy Ankush Harindranath Maxwell McManus Zhangyu Guan Nicholas Mastronarde Elizabeth Serena Bentley Michael Medley Department of Electrical Engineering University at Buffalo Buffalo NY USA Air Force Research Laboratory (AFRL) Rome NY USA

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

Data-driven control based on AI/ML techniques has a great potential to enable zero-touch automated modeling, optimization and control of complex wireless systems. However, it is challenging to collect network traces in the real world because of high time and labor cost, weather limitations as well as safety concerns. In this work we attempt to tackle this challenge by designing a multi-fidelity simulator taking wireless Unmanned Aerial Vehicle (UAV) networks into consideration. We design the simulator by interfacing two Unmanned Aerial System (UAS) simulators we have developed in prior years: UBSim and UB-ANC. the former focuses on UAV network optimization and policy training by considering explicitly the network environments such as blockage dynamics, while the latter focuses more on high-fidelity UAV flight control. We first develop a coordination interface referred to as SimSocket for signaling exchanges between UBSim and UB-ANC in simulations, and then showcase coordinated simulations based on UBSim and UB-ANC. the new research that can be enabled by the integrated simulator is also discussed for digital twin-based UAS systems.

关键词： Wireless communication Training Wireless sensor networks Autonomous aerial vehicles Ad hoc networks sensor systems Safety

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eAFH: Informed Exploration for Adaptive Frequency Hopping in Bluetooth Low Energy

eAFH: Informed Exploration for Adaptive Frequency Hopping in...

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international conference on distributed computing in sensor systems (dcoss)

作者： Valentin Poirot Olaf Landsiedel Kiel University Germany Chalmers University of Technology Sweden

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

With more than 4 billion devices produced in 2020, Bluetooth and Bluetooth Low Energy (BLE) have become the dominant solutions for short-range wireless communication in IoT. BLE mitigates interference via Adaptive Frequency Hopping (AFH), spreading communication over the entire spectrum. However, the ever-growing number of BLE devices and WiFi traffic in the already crowded 2.4 GHz band lead to situations where the quality of BLE connections dynamically changes with nearby wireless traffic, location, and time of day. these dynamic environments demand new approaches for channel management in AFH, by both dynamically excluding frequencies suffering from localized interference and adaptively re-including channels, thus providing sufficient channel diversity to survive the rise of new *** introduce eAFH, a new channel-management approach in BLE with a strong focus on efficient channel re-inclusion. eAFH introduces informed exploration as a driver for inclusion: using only past measurements, eAFH assesses which frequencies we are most likely to benefit from re-inclusion into the hopping sequence. As a result, eAFH adapts in dynamic scenarios where interference varies over time. We show that eAFH achieves 98-99.5% link-layer reliability in the presence of dynamic WiFi interference with 1% control overhead and 40% higher channel diversity than state-of-the-art approaches.

关键词： Wireless communication Bluetooth Interference Spread spectrum communication Frequency diversity sensor systems Frequency measurement

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AI Powered COVID-19 Detection System using Non-Contact Sensing Technology and Deep Learning Techniques

AI Powered COVID-19 Detection System using Non-Contact Sensi...

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international conference on distributed computing in sensor systems (dcoss)

作者： S. V. Kogilavani Sathishkumar V E Malliga Subramanian Department of Computer Science and Engineering Kongu Engineering College Erode Tamilnadu India Department of Industrial Engineering Hanyang University Seoul Reublic of Korea

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

the SARS-CoV-2 virus causes coronary artery disease (COVID-19). the majority of persons who are infected with the virus will have mild to severe respiratory illness and recover without the need for therapy. Some, on the other hand, will become critically unwell and require medical assistance. People over the age of 65, as well as those with underlying medical diseases such as cardiovascular disease, diabetes, chronic respiratory disease, or cancer, are at a higher risk of developing serious illness. Being thoroughly informed on the disease and how it spreads is the best strategy to avoid and slow down transmission. Stay at least 1 metre apart from other people to avoid infection. In this research work, we focus on how non-contact sensing technology and deep learning technique are being used to detect COVID-19 and assist healthcare workers in caring for COVID-19 patients. the proposed system captures images from the patient using non-contact sensing technologies and feeds the data into deep learning convolutional neural network architectures such as VGG16, VGG19, ResNet101, NASNet, DenseNet121, MobileNet, Xception, EfficientNet, and InceptionV3. In comparison to other architectures, the VGG16 architecture delivers superior accuracy.

关键词： COVID-19 Deep learning Wireless communication Wireless sensor networks Pulmonary diseases Computer architecture sensor systems

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Work in Progress paper: Experiment Planning for Heterogeneous Programmable Networks

Work in Progress paper: Experiment Planning for Heterogeneou...

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international conference on distributed computing in sensor systems (dcoss)

作者： Nik Sultana Dept. of Computer Science Illinois Tech Chicago Illinois

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

Private and publicly-funded cloud infrastructure and testbeds increasingly feature programmable network hardware. Programmable network cards and switches support the execution of increasingly-complex in-network programs that can operate independently of end-hosts to improve the network’s performance, resilience and utilisation. Reasoning about in-network programs, their placement, and workloads is needed to plan jobs on programmable networks. On programmable testbed networks, this reasoning feeds into resource allocation, fairness and reproducible research. But this reasoning is made challenging by the performance and resource diversity of hardware and by the failure modes that can arise in a distributed *** is currently the most comprehensive reasoning system for distributed and heterogeneous in-network programs but it uses a custom formalism and tool implementation, making it difficult to understand, extend, and *** paper describes Lightplanner, a generalisation of Flight-planner’s reasoning system that has been implemented on Prolog. It provides an executable formalisation in a well-understood logic. By relying on Prolog’s proof search, Lightplanner is 10 smaller than Flightplanner’s implementation in C++, making×it better suited for others to understand, extend, and scale. A benchmark of publicly-available in-network programs is used to evaluate Lightplanner against Flightplanner. though the time overhead is slightly larger, Lightplanner can find better allocations than the original, more complex C++ *** is being incubated to plan experiments in a local programmable network testbed at Illinois Tech, and as a future step it will be extended to work across federated networks such as FABRIC.

关键词： C++ languages Cognition Hardware sensor systems Fabrics Planning Resource management

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Efficient Localness Transformer for Smart sensor-Based Energy Disaggregation

Efficient Localness Transformer for Smart Sensor-Based Energ...

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international conference on distributed computing in sensor systems (dcoss)

作者： Zhenrui Yue Huimin Zeng Ziyi Kou Lanyu Shang Dong Wang School of Information Sciences University of Illinois Urbana-Champaign Champaign IL USA

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

Modern smart sensor-based energy management systems leverage non-intrusive load monitoring (NILM) to predict and optimize appliance load distribution in real-time. NILM, or energy disaggregation, refers to the decomposition of electricity usage conditioned on the aggregated power signals (i.e., smart sensor on the main channel). Based on real-time appliance power prediction using sensory technology, energy disaggregation has great potential to increase electricity efficiency and reduce energy expenditure. With the introduction of transformer models, NILM has achieved significant improvements in predicting device power readings. Nevertheless, transformers are less efficient due to O(l 2 ) complexity w.r.t. sequence length l. Moreover, transformers can fail to capture local signal patterns in sequence-to-point settings due to the lack of inductive bias in local context. In this work, we propose an efficient localness transformer for non-intrusive load monitoring (ELTransformer). Specifically, we leverage normalization functions and switch the order of matrix multiplication to approximate self-attention and reduce computational complexity. Additionally, we introduce localness modeling with sparse local attention heads and relative position encodings to enhance the model capacity in extracting short-term local patterns. To the best of our knowledge, ELTransformer is the first NILM model that addresses computational complexity and localness modeling in NILM. With extensive experiments and quantitative analyses, we demonstrate the efficiency and effectiveness of the the proposed ELTransformer with considerable improvements compared to state-of-the-art baselines.

关键词： Load monitoring Statistical analysis Computational modeling Switches Transformers Real-time systems Encoding

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Smart-Hop: Low-Latency Multi-hop Networking for LoRa

Smart-Hop: Low-Latency Multi-hop Networking for LoRa

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international conference on distributed computing in sensor systems (dcoss)

作者： Absar-Ul-Haque Ahmar Wouter Joosen Danny Hughes imec-DistriNet Computer Science KU Leuven Belgium

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

Low Power Wide Area Networks (LPWANs) provide the Internet of things (IoT) with energy-efficient, low cost, and long-range networking. LoRaWAN is one of the most widely deployed and studied LPWAN technologies due to its license-free operation and open standard, which has enabled 3rd parties to deploy millions of networks worldwide. LoRa allows transmission range and speed to be traded off by configuring the Spreading Factor (SF) setting. By tuning this parameter, the performance envelope of the network can be configured to favor a higher speed or longer range. While the SF of LoRA provides a simple and elegant means to tailor performance, at the highest spreading factor, maximum throughput drops to a few bits per second, dramatically increasing data extraction times. In this paper, we address this problem by proposing Smart-Hop, a novel multi-hop MAC layer protocol for LoRa which implements low-overhead routing. In contrast to prior work, Smart-Hop exploits the variable range afforded by different SFs to eliminate much of the complexity and overhead of traditional multi-hop mesh networking. Smart-Hop reduces the number of end-devices operating at high SFs while implementing low overhead routing in order to reduce data extraction times. these improvements are achieved while preserving the maximum range offered by LoRa in its highest SF configuration.

关键词： Wireless sensor networks Spread spectrum communication Routing throughput sensor systems Internet of things Data mining

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Semi-supervised Multi-source Domain Adaptation in Wearable Activity Recognition

Semi-supervised Multi-source Domain Adaptation in Wearable A...

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international conference on distributed computing in sensor systems (dcoss)

作者： Avijoy Chakma Abu Zaher Md Faridee Raghuveer Rao Nirmalya Roy Information Systems University of Maryland Baltimore County Army Research Lab United States

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

the scarcity of labeled data has traditionally been the primary hindrance in building scalable supervised deep learning models that can retain adequate performance in the presence of various heterogeneities in sample distributions. Domain adaptation tries to address this issue by adapting features learned from a smaller set of labeled samples to that of the incoming unlabeled samples. the traditional domain adaptation approaches normally consider only a single source of labeled samples, but in real world use cases, labeled samples can originate from multiple-sources – providing motivation for multi-source domain adaptation (MSDA). Several MSDA approaches have been investigated for wearable sensor-based human activity recognition (HAR) in recent times, but their performance improvement compared to single source counterpart remained marginal. To remedy this performance gap that, we explore multiple avenues to align the conditional distributions in addition to the usual alignment of marginal ones. In our investigation, we extend an existing multi-source domain adaptation approach under semi-supervised settings. We assume the availability of partially labeled target domain data and further explore the pseudo labeling usage with a goal to achieve a performance similar to the former. In our experiments on three publicly available datasets, we find that a limited labeled target domain data and pseudo label data boost the performance over the unsupervised approach by 10-35% and 2-6%, respectively, in various domain adaptation scenarios.

关键词： Deep learning Adaptation models Buildings distributed databases Performance gain Data models Labeling

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Data-driven soft sensing towards quality monitoring of industrial pasteurization processes

Data-driven soft sensing towards quality monitoring of indus...

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international conference on distributed computing in sensor systems (dcoss)

作者： Gabriel Filios Andreas Kyriakopoulos Stavros Livanios Fotis Manolopoulos Sotiris Nikoletseas Stefanos H. Panagiotou Paul Spirakis Computer Engineering and Informatics Department University of Patras Greece Computer Technology Institute and Press “Diophantus” (CTI) Greece Heineken Patras Plant Greece Department of Computer Science University of Liverpool UK

ISBN: (数字)9781665495127

ISBN: (纸本)9781665495134

In the food and beverage industry many foods, beers and soft drinks usually need to get pasteurized, a process that holds a significant role in the quality and taste of the final product but is difficult to monitor due to the process nature. Soft sensing techniques, also called virtual sensing or surrogate sensing, can be leveraged to monitor the product quality, by using information available from other measurements and process parameters to calculate an estimation of the quantity of interest. In this paper, we develop a soft sensing methodology that is based on machine learning algorithms for continuous, end-to-end estimation of the temperature of products during the pasteurization process, with the vision to serve as an intermediate step towards monitoring live the final quality of the pasteurized products. this work studies a real beer pasteurization process in collaboration with Heineken’s plant in Patras, Greece and the results demonstrate notable performance in temperature prediction accuracy, with average root mean square error (RMSE) of 1.85°C in the test sets. thus, we claim that it is possible to obtain measurements quite similar to the ones by the respective physical sensors with sufficient accuracy, and our methodology can be considered as a virtual low-cost solution for monitoring product quality in legacy pasteurizer operation.

关键词： Temperature measurement Temperature sensors Machine learning algorithms Soft sensors Estimation Product design sensor systems

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