2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)最新文献

{"title":"Reviewers: TwinNets 2022","authors":"","doi":"10.1109/wowmom54355.2022.00013","DOIUrl":"https://doi.org/10.1109/wowmom54355.2022.00013","url":null,"abstract":"","PeriodicalId":275324,"journal":{"name":"2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125849068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WIP: Local Heuristics for Very Likely Connected and Intersection Free Wireless Network Topologies under Log-Normal Shadowing","authors":"Steffen Böhmer, Lucas Böltz, Hannes Frey","doi":"10.1109/WoWMoM54355.2022.00049","DOIUrl":"https://doi.org/10.1109/WoWMoM54355.2022.00049","url":null,"abstract":"Local algorithms and other wireless network protocols require the underlying network graph to have specific structural properties to guarantee correctness. Two of these properties are connectivity and absence of intersecting links. Assuring only one of these properties is very often possible, either by considering a dense graph, which is very likely connected, but contains many intersections or a sparse graph which contains only few intersections, but is split up into many components. The task is therefore to choose the edges in a given graph in such a way that the intersections are removed while connectivity is preserved. Based on a Poisson point process and the log-normal shadowing model, we analyse the frequency of connected graphs without intersecting links. To further support such graph structure, we also restrict the maximal length of the edges in the network graph. By simulation we observe conditions how the maximal length of the edges in a graph should be chosen to assure the existence of a large component with few intersections.","PeriodicalId":275324,"journal":{"name":"2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127334479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SIC-EDGE: Semantic Iterative ECG Compression for Edge-Assisted Wearable Systems","authors":"Delaram Amiri, Janne Takalo-Mattila, L. Bedogni, M. Levorato, N. Dutt","doi":"10.1109/WoWMoM54355.2022.00036","DOIUrl":"https://doi.org/10.1109/WoWMoM54355.2022.00036","url":null,"abstract":"Wearable sensors and Internet of Things technologies are enabling automated health monitoring applications, where signals captured by sensors are analyzed in real-time by algorithms detecting health issues and conditions. However, continuous clinical-level monitoring of patients in everyday settings often requires computation, storage and connectivity capabilities beyond those possessed by wearable sensors. While edge computing partially resolves this issue by connecting the sensors to compute-capable devices positioned at the network edge, the wireless links connecting the sensors to the edge servers may not have sufficient capacity to transfer the information-rich data that characterize these applications. A possible solution is to compress the signal to be transferred, accepting the tradeoff between compression gain and detection accuracy. In this paper, we propose SIC-EDGE: a \"semantic compression\" framework whose goal is to dynamically optimize the resolution of an electrocardiogram (ECG) signal transferred from a wearable sensor to an edge server to perform real-time detection of heart diseases. The core idea is to establish a collaborative control loop between the sensor and the edge server to iteratively build a semantic representation that is: (i) ECG-cycle specific; (ii) personalized, and (iii) targeted to support the classification task rather than signal reconstruction. The core of SIC-EDGE is a Sequential Hypothesis Testing (SHT) algorithm that analyzes partial representations along the iterations to determine which and how many representation layers (wavelet coefficients in our implementation) are requested. Our results on established datasets demonstrates the need for adaptive \"semantic\" compression, and illustrate the dynamic compression strategy realized by SIC-EDGE. We show that SIC-EDGE leads to an increase in terms of recall and F1 score of up to 35% and 26% respectively compared to an optimized but static wavelet compression for a given maximum channel usage.","PeriodicalId":275324,"journal":{"name":"2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115457945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OROS: Orchestrating ROS-driven Collaborative Connected Robots in Mission-Critical Operations","authors":"Carmen Delgado, Lanfranco Zanzi, Xi Li, X. Costa","doi":"10.48550/arXiv.2205.03256","DOIUrl":"https://doi.org/10.48550/arXiv.2205.03256","url":null,"abstract":"Battery life for collaborative robotics scenarios is a key challenge limiting operational uses and deployment in real life. Mission-Critical tasks are among the most relevant and challenging scenarios. As multiple and heterogeneous on-board sensors are required to explore unknown environments in simultaneous localization and mapping (SLAM) tasks, battery life problems are further exacerbated. Given the time-sensitivity of mission-critical operations, the successful completion of specific tasks in the minimum amount of time is of paramount importance. In this paper, we analyze the benefits of 5G-enabled collaborative robots by enhancing the Robot Operating System (ROS) capabilities with network orchestration features for energy-saving purposes. We propose OROS, a novel orchestration approach that minimizes mission-critical task completion times of 5G-connected robots by jointly optimizing robotic navigation and sensing together with infrastructure resources. Our results show that OROS significantly outperforms state-of-the-art solutions in exploration tasks completion times by exploiting 5G orchestration features for battery life extension.","PeriodicalId":275324,"journal":{"name":"2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116284035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Link Budget Analysis for Free-Space Optical Satellite Networks","authors":"Jintao Liang, Aizaz U. Chaudhry, Eylem Erdogan, H. Yanikomeroglu","doi":"10.1109/WoWMoM54355.2022.00073","DOIUrl":"https://doi.org/10.1109/WoWMoM54355.2022.00073","url":null,"abstract":"Free-space optical satellite networks (FSOSNs) will employ free-space optical links between satellites and between satellites and ground stations, and the link budget for optical inter-satellite links and optical uplink/downlink is analyzed in this paper. The satellites in these FSOSNs will have limited energy and thereby limited power, and we investigate the effect of link distance and link margin on optical inter-satellite link transmission power, and the effect of slant distance, elevation angle, and link margin on optical uplink/downlink transmission power. We model these optical links and compute the results for various parameters. We observe that the transmission power increases when the link distance increases for inter-satellite and uplink/downlink communications, while the transmission power decreases when the elevation angle increases for uplink/downlink transmission. We also observe an inverse relationship between link margin and link distance. Furthermore, we highlight some practical insights and design guidelines gained from this analysis.","PeriodicalId":275324,"journal":{"name":"2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"359 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125646133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WIP: Achieving Self-Interference-Free Operation on SDR Platform with Critical TDD Turnaround Time","authors":"Thijs Havinga, Xianjun Jiao, Muhammad Aslam, Wei Liu, I. Moerman","doi":"10.1109/WoWMoM54355.2022.00051","DOIUrl":"https://doi.org/10.1109/WoWMoM54355.2022.00051","url":null,"abstract":"Software Defined Radio (SDR) platforms are valuable for research and development activities or high-end systems that demand real-time adaptable wireless protocols. While low latency can be achieved using the dedicated digital processing unit of a state-of-the-art SDR platform, its Radio Frequency (RF) front-end often poses a limitation in terms of turnaround time (TT), the time needed for switching from the receiving to the transmitting mode (or vice versa). Zero Intermediate Frequency (ZIF) transceivers are favorable for SDR, but suffer from self-interference even if the device is not currently transmitting. The strict MAC-layer requirements of Time Division Duplex (TDD) protocols like Wi-Fi cannot be achieved using configurable ZIF transceivers without having to compromise receiver sensitivity. Using a novel approach, we show that the TT using the AD9361 RF front-end can be as low as 640 ns, while the self-interference is at the same level as achieved by the conventional TDD mode, which has a TT of at least 55 μs. As compared to Frequency Division Duplex (FDD) mode, a decrease of receiver noise floor by about 13 dB in the 2.4 GHz band and by about 4.5 dB in the 5 GHz band is achieved.","PeriodicalId":275324,"journal":{"name":"2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"290 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130701165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal Characterization of XR Traffic with Application to Predictive Network Slicing","authors":"Mattia Lecci, Federico Chiariotti, Matteo Drago, A. Zanella, M. Zorzi","doi":"10.1109/WoWMoM54355.2022.00060","DOIUrl":"https://doi.org/10.1109/WoWMoM54355.2022.00060","url":null,"abstract":"Over the past few years, eXtended Reality (XR) has attracted increasing interest thanks to its extensive industrial and commercial applications, and its popularity is expected to rise exponentially over the next decade. However, the stringent Quality of Service (QoS) constraints imposed by XR’s interactive nature require Network Slicing (NS) solutions to support its use over wireless connections: in this context, quasi-Constant Bit Rate (CBR) encoding is a promising solution, as it can increase the predictability of the stream, making the network resource allocation easier. However, traffic characterization of XR streams is still a largely unexplored subject, particularly with this encoding. In this work, we characterize XR streams from more than 4 hours of traces captured in a real setup, analyzing their temporal correlation and proposing two prediction models for future frame size. Our results show that even the state-of-the-art H.264 CBR mode can have significant frame size fluctuations, which can impact the NS optimization. Our proposed prediction models can be applied to different traces, and even to different contents, achieving very similar performance. We also show the trade-off between network resource efficiency and XR QoS in a simple NS use case.","PeriodicalId":275324,"journal":{"name":"2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131251335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SmartDet: Context-Aware Dynamic Control of Edge Task Offloading for Mobile Object Detection","authors":"Davide Callegaro, Francesco Restuccia, M. Levorato","doi":"10.1109/WoWMoM54355.2022.00034","DOIUrl":"https://doi.org/10.1109/WoWMoM54355.2022.00034","url":null,"abstract":"Mobile devices such as drones and autonomous vehicles increasingly rely on object detection (OD) through deep neural networks (DNNs) to perform critical tasks such as navigation, target-tracking and surveillance, just to name a few. Due to their high complexity, the execution of these DNNs requires excessive time and energy. Low-complexity object tracking (OT) is thus used along with OD, where the latter is periodically applied to generate \"fresh\" references for tracking. However, the frames processed with OD incur large delays, which does not comply with real-time applications requirements. Offloading OD to edge servers can mitigate this issue, but existing work focuses on the optimization of the offloading process in systems where the wireless channel has a very large capacity. Herein, we consider systems with constrained and erratic channel capacity, and establish parallel OT (at the mobile device) and OD (at the edge server) processes that are resilient to large OD latency. We propose Katch-Up, a novel tracking mechanism that improves the system resilience to excessive OD delay. We show that this technique greatly improves the quality of the reference available to tracking, and boosts performance up to 33%. However, while Katch-Up significantly improves performance, it also increases the computing load of the mobile device. Hence, we design SmartDet, a low-complexity controller based on deep reinforcement learning (DRL) that learns to achieve the right trade-off between resource utilization and OD performance. SmartDet takes as input highly-heterogeneous context-related information related to the current video content and the current network conditions to optimize frequency and type of OD offloading, as well as Katch-Up utilization. We extensively evaluate SmartDet on a real-world testbed composed by a JetSon Nano as mobile device and a GTX 980 Ti as edge server, connected through a Wi-Fi link, to collect several network-related traces, as well as energy measurements. We consider a state-of-the-art video dataset (ILSVRC 2015 - VID) and state-of-the-art OD models (EfficientDet 0, 2 and 4). Experimental results show that SmartDet achieves an optimal balance between tracking performance – mean Average Recall (mAR) and resource usage. With respect to a baseline with full Katch-Up usage and maximum channel usage, we still increase mAR by 4% while using 50% less of the channel and 30% power resources associated with Katch-Up. With respect to a fixed strategy using minimal resources, we increase mAR by 20% while using Katch-Up on 1/3 of the frames.","PeriodicalId":275324,"journal":{"name":"2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132421741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing","authors":"Yoshitomo Matsubara, Davide Callegaro, Sameer Singh, M. Levorato, Francesco Restuccia","doi":"10.1109/WoWMoM54355.2022.00032","DOIUrl":"https://doi.org/10.1109/WoWMoM54355.2022.00032","url":null,"abstract":"Although mission-critical applications require the use of deep neural networks (DNNs), their continuous execution at mobile devices results in a significant increase in energy consumption. While edge offloading can decrease energy consumption, erratic patterns in channel quality, network and edge server load can lead to severe disruption of the system’s key operations. An alternative approach, called split computing, generates compressed representations within the model (called \"bottlenecks\"), to reduce bandwidth usage and energy consumption. Prior work has proposed approaches that introduce additional layers, to the detriment of energy consumption and latency. For this reason, we propose a new framework called BottleFit, which, in addition to targeted DNN architecture modifications, includes a novel training strategy to achieve high accuracy even with strong compression rates. We apply BottleFit on cutting-edge DNN models in image classification, and show that BottleFit achieves 77.1% data compression with up to 0.6% accuracy loss on ImageNet dataset, while state of the art such as SPINN loses up to 6% in accuracy. We experimentally measure the power consumption and latency of an image classification application running on an NVIDIA Jetson Nano board (GPU-based) and a Raspberry PI board (GPU-less). We show that BottleFit decreases power consumption and latency respectively by up to 49% and 89% with respect to (w.r.t.) local computing and by 37% and 55% w.r.t. edge offloading. We also compare BottleFit with state-of-the-art autoencoders-based approaches, and show that (i) BottleFit reduces power consumption and execution time respectively by up to 54% and 44% on the Jetson and 40% and 62% on Raspberry PI; (ii) the size of the head model executed on the mobile device is 83 times smaller. We publish the code repository for reproducibility of the results in this study.","PeriodicalId":275324,"journal":{"name":"2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"583 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134515623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ReWiS: Reliable Wi-Fi Sensing Through Few-Shot Multi-Antenna Multi-Receiver CSI Learning","authors":"Niloofar Bahadori, J. Ashdown, Francesco Restuccia","doi":"10.1109/WoWMoM54355.2022.00027","DOIUrl":"https://doi.org/10.1109/WoWMoM54355.2022.00027","url":null,"abstract":"Thanks to the ubiquitousness of Wi-Fi access points and devices, Wi-Fi sensing enables transformative applications in remote health care, home/office security, and surveillance, just to name a few. Existing work has explored the usage of machine learning on channel state information (CSI) computed from Wi-Fi packets to classify events of interest. However, most of these algorithms require a significant amount of data collection, as well as extensive computational power for additional CSI feature extraction. Moreover, the majority of these models suffer from poor accuracy when tested in a new/untrained environment. In this paper, we propose ReWiS, a novel framework for robust and environment-independent Wi-Fi sensing. The key innovation of ReWiS is to leverage few-shot learning (FSL) as the inference engine, which (i) reduces the need for extensive data collection and application-specific feature extraction; (ii) can rapidly generalize to new environments by leveraging only a few new samples. Moreover, ReWiS leverages multi-antenna, multi-receiver diversity, as well as fine-grained frequency resolution, to improve the overall robustness of the algorithms. Finally, we propose a technique based on singular value decomposition (SVD) to make the FSL input constant irrespective of the number of receive antennas. We prototype the ReWiS using off-the-shelf Wi-Fi equipment and showcase its performance by considering a compelling use case of human activity recognition. Thus, we perform an extensive data collection campaign in three different propagation environments with two human subjects. We evaluate the impact of each diversity component on the performance and compare ReWiS with an existing convolutional neural network (CNN)-based approach. Experimental results show that ReWiS improves the performance by about 40% with respect to existing single-antenna low-resolution approaches. Moreover, when compared to a CNN-based approach, ReWiS shows a 35% more accuracy and less than 10% drop in accuracy when tested in different environments, while the CNN drops by more than 45%. To allow reproducibility of our results and to address the current dearth of Wi-Fi sensing datasets, we pledge to release our 60 GB dataset and the entire code repository to the community.","PeriodicalId":275324,"journal":{"name":"2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128880997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}