我的台灣, 看見心靈的故鄉 =2009林磐聳藝術與設計展

Driven by a wide range of real-world applications, significant efforts have recently been made to explore device-free human activity recognition techniques that utilize the information collected by various wireless infrastructures to infer human activities without the need for the monitored subject to carry a dedicated device. Existing device free human activity recognition approaches and systems, though yielding reasonably good performance in certain cases, are faced with a major challenge. The wireless signals arriving at the receiving devices usually carry substantial information that is specific to the environment where the activities are recorded and the human subject who conducts the activities. Due to this reason, an activity recognition model that is trained on a specific subject in a specific environment typically does not work well when being applied to predict another subject's activities that are recorded in a different environment. To address this challenge, in this paper, we propose EI, a deep-learning based device free activity recognition framework that can remove the environment and subject specific information contained in the activity data and extract environment/subject-independent features shared by the data collected on different subjects under different environments. We conduct extensive experiments on four different device free activity recognition testbeds: WiFi, ultrasound, 60 GHz mmWave, and visible light. The experimental results demonstrate the superior effectiveness and generalizability of the proposed EI framework.

https://dl.acm.org/doi/pdf/10.1145/3241539.3241548

Towards Environment Independent Device Free Human Activity Recognition

Due to its potential for multi-gigabit and low latency wireless links, millimeter wave (mmWave) technology is expected to play a central role in 5th generation (5G) cellular systems. While there has been considerable progress in understanding the mmWave physical layer, innovations will be required at all layers of the protocol stack, in both the access and the core network. Discrete-event network simulation is essential for end-to-end, cross-layer research and development. This paper provides a tutorial on a recently developed full-stack mmWave module integrated into the widely used open-source ns–3 simulator. The module includes a number of detailed statistical channel models as well as the ability to incorporate real measurements or ray-tracing data. The physical and medium access control layers are modular and highly customizable, making it easy to integrate algorithms or compare orthogonal frequency division multiplexing numerologies, for example. The module is interfaced with the core network of the ns–3 Long Term Evolution (LTE) module for full-stack simulations of end-to-end connectivity, and advanced architectural features, such as dual-connectivity, are also available. To facilitate the understanding of the module, and verify its correct functioning, we provide several examples that show the performance of the custom mmWave stack as well as custom congestion control algorithms designed specifically for efficient utilization of the mmWave channel.

End-to-End Simulation of 5G mmWave Networks

Due to its potential for multi-gigabit and low latency wireless links, millimeter wave (mmWave) technology is expected to play a central role in 5th generation cellular systems. While there has been considerable progress in understanding the mmWave physical layer, innovations will be required at all layers of the protocol stack, in both the access and the core network. Discrete-event network simulation is essential for end-to-end, cross-layer research and development. This paper provides a tutorial on a recently developed full-stack mmWave module integrated into the widely used open-source ns--3 simulator. The module includes a number of detailed statistical channel models as well as the ability to incorporate real measurements or ray-tracing data. The Physical (PHY) and Medium Access Control (MAC) layers are modular and highly customizable, making it easy to integrate algorithms or compare Orthogonal Frequency Division Multiplexing (OFDM) numerologies, for example. The module is interfaced with the core network of the ns--3 Long Term Evolution (LTE) module for full-stack simulations of end-to-end connectivity, and advanced architectural features, such as dual-connectivity, are also available. To facilitate the understanding of the module, and verify its correct functioning, we provide several examples that show the performance of the custom mmWave stack as well as custom congestion control algorithms designed specifically for efficient utilization of the mmWave channel.

As an emerging service architecture, microservice enables decomposition of a monolithic web service into a set of independent lightweight services which can be executed independently. With mobile edge computing, microservices can be further deployed in edge clouds dynamically, launched quickly, and migrated across edge clouds easily, providing better services for users in proximity. However, the user mobility can result in frequent switch of nearby edge clouds, which increases the service delay when users move away from their serving edge clouds. To address this issue, this article investigates microservice coordination among edge clouds to enable seamless and real-time responses to service requests from mobile users. The objective of this work is to devise the optimal microservice coordination scheme which can reduce the overall service delay with low costs. To this end, we first propose a dynamic programming-based offline microservice coordination algorithm, that can achieve the globally optimal performance. However, the offline algorithm heavily relies on the availability of the prior information such as computation request arrivals, time-varying channel conditions and edge cloud's computation capabilities required, which is hard to be obtained. Therefore, we reformulate the microservice coordination problem using Markov decision process framework and then propose a reinforcement learning-based online microservice coordination algorithm to learn the optimal strategy. Theoretical analysis proves that the offline algorithm can find the optimal solution while the online algorithm can achieve near-optimal performance. Furthermore, based on two real-world datasets, i.e., the Telecom's base station dataset and Taxi Track dataset from Shanghai, experiments are conducted. The experimental results demonstrate that the proposed online algorithm outperforms existing algorithms in terms of service delay and migration costs, and the achieved performance is close to the optimal performance obtained by the offline algorithm.

Delay-Aware Microservice Coordination in Mobile Edge Computing: A Reinforcement Learning Approach

The IEEE 802.11ad amendment to the 802.11 standard for multi-gigabit communication at 60 GHz was published several years ago, but to date, no precise simulation model for networking in this band is available. In this paper, we present a model for IEEE 802.11ad implemented in the network simulator ns-3. We model new techniques that are essential for IEEE 802.11ad operation such as beamforming training and steering, relay support, and fast session transfer. We then evaluate by simulation the performance of IEEE 802.11ad as well as the gains obtained through the aforementioned techniques. The code for our simulation model is publicly available.

Implementation and Evaluation of a WLAN IEEE 802.11ad Model in ns-3

Wireless networks operating in the 60 GHz band have the potential to provide very high throughput but face a number of challenges (e.g., high attenuation, beam training, and coping with mobility) which are widely accepted but often not well understood in practice. Understanding these challenges, and especially their actual impact on consumer-grade hardware is fundamental to fully exploit the high physical layer rates in the 60 GHz band. To this end, we perform an extensive measurement campaign using two commercial off-the-shelf 60 GHz routers in practical real-world environments. Our study is centered around two fundamental adaptation mechanisms in 60 GHz networks-beam training and rate control- whose interactions are key for performance. Understanding these interactions allows us to revisit a range of issues and provide much deeper insights into the reasons for specific performance compared to prior work on performance characterization. Further, our study goes beyond basic link characterization and explores for the first time practical considerations such as coverage and access point deployment. While some of our observations are expected, we also obtain highly surprising insights that challenge the prevailing wisdom in the community.

Fast and Infuriating: Performance and Pitfalls of 60 GHz WLANs Based on Consumer-Grade Hardware

The use of directional antennas in millimeter-wave communication promises high spatial reuse at multi-gigabit-per-second data rates in dense wireless networks. Existing work studies such networks using commercial hardware but is limited to individual links. Moreover, such hardware typically allows for little or no control of the lower layers of the protocol stack. In this paper, We study the performance of dense millimeterwave deployments featuring up to eight stations. To this end, we use a practical IEEE 802.11ad millimeter-wave testbed that allows access to the lower layer parameters of each station. This enables us to analyze the impact of these parameters on upper layer performance. We study, for first time to our best knowledge, issues such as the impact of channel contention on the buffer size at the transport layer, the effect of frame aggregation, and the efficiency of spatial sharing. Our results show that using large buffer sizes with TCP is harmful due to channel contention despite the multi-gigabit-per-second data rates. Further, frame aggregation is only beneficial up to a certain level due to higher error rates for large frames. Finally, we also study delay, showing that the regular beacon transmission time can degrade performance.

Medium Access and Transport Protocol Aspects in Practical 802.11 ad Networks

Millimeter-wave technology is one of the main pillars of the future wireless networks. The main reason lies in the quantum leap of capacity it provides with respect to wireless networks operating in the sub 6-GHz band. Nevertheless, efficient and reliable communication in this band demands novel techniques to tackle all the associated barriers related to wireless propagation in those bands. In this paper, we present the extension of our ns-3 IEEE 802.11ad model and provide design and implementation details of the new techniques, including dynamic and static channel access schemes, decentralized clustering, beamformed link maintenance, spatial sharing, and half-duplex relay operation as defined in the IEEE 802.11ad amendment. We show how these techniques can boost and enhance wireless networking operation in the 60 GHz band. Our work is the first to implement these techniques in a networking simulator and make the implementation publicly available.

Extending the IEEE 802.11ad Model: Scheduled Access, Spatial Reuse, Clustering, and Relaying

Millimeter-wave devices must use highly directional antennas to achieve GBit/s data rates over reasonable distances due to the high path loss. As a consequence, it is important to precisely align the antenna beams between sender and receiver. Even minor movement or rotation of a device can result in beam misalignment and thus a strong performance degradation. Existing work as well as standards such as IEEE 802.11ad tackle this issue by means of antenna sector probing. This comes at the expense of a significant overhead, which may significantly reduce the performance of millimeter-wave communication, particularly in mobile scenarios. In this paper, we present a mechanism that can track both movement and rotation of 60 GHz mobile devices with zero overhead. To this end, we transmit part of the preamble of each packet using a multi-lobe beampattern. Our approach does not require any additional control messages and is backward compatible with 802.11ad. We implement our scheme on a 60 GHz testbed using phased antenna arrays, and show that we reduce the angle error to less than 5° in most cases. We also perform simulations to validate our approach in a wide range of scenarios, achieving up to 2x throughput gain.

/pdf/zero-overhead-device-tracking-in-60-ghz-wireless-networks-w49l4gsp8g.pdf

Hany Assasa

Papers

Implementation and Evaluation of a WLAN IEEE 802.11ad Model in ns-3

Fast and Infuriating: Performance and Pitfalls of 60 GHz WLANs Based on Consumer-Grade Hardware

Medium Access and Transport Protocol Aspects in Practical 802.11 ad Networks

Extending the IEEE 802.11ad Model: Scheduled Access, Spatial Reuse, Clustering, and Relaying

Zero Overhead Device Tracking in 60 GHz Wireless Networks using Multi-Lobe Beam Patterns