Towards a Dynamic Future with Adaptable Computing and Network Convergence (ACNC)

In the context of advancing 6G, a substantial paradigm shift is anticipated, highlighting comprehensive everything-to-everything interactions characterized by numerous connections and stringent adherence to Quality of Service/Experience (QoS/E) prerequisites. The imminent challenge stems from resource scarcity, prompting a deliberate transition to Computing-Network Convergence (CNC) as an auspicious approach for joint resource orchestration. While CNC-based mechanisms have garnered attention, their effectiveness in realizing future services, particularly in use cases like the Metaverse, may encounter limitations due to the continually changing nature of users, services, and resources. Hence, this paper presents the concept of Adaptable CNC (ACNC) as an autonomous Machine Learning (ML)-aided mechanism crafted for the joint orchestration of computing and network resources, catering to dynamic and voluminous user requests with stringent requirements. ACNC encompasses two primary functionalities: state recognition and context detection. Given the intricate nature of the user-service-computing-network space, the paper employs dimension reduction to generate live, holistic, abstract system states in a hierarchical structure. To address the challenges posed by dynamic changes, Continual Learning (CL) is employed, classifying the system state into contexts controlled by dedicated ML agents, enabling them to operate efficiently. These two functionalities are intricately linked within a closed loop overseen by the End-to-End (E2E) orchestrator to allocate resources. The paper introduces the components of ACNC, proposes a Metaverse scenario to exemplify ACNC's role in resource provisioning with Segment Routing v6 (SRv6), outlines ACNC's workflow, details a numerical analysis for efficiency assessment, and concludes with discussions on relevant challenges and potential avenues for future research

An Open Wireless Mesh Testbed Architecture with Data Collection and Software Distribution Platform

Abstract—A Wireless Mesh Network (WMN) is a fast growing network, which is now a popular technology for providing wireless internet connection to industry as well as community. A WMN is a collection of nodes (usually a computer with one or more wireless Network Interface Cards (NICs)) that are connected to one another with single or multiple hop ad hoc links forming a mesh backbone network. Ad hoc links are popular in mesh connectivity as they are self-configuring and self-healing. In this paper, we discuss WMN design and deployment issues with reference to our WiSEMesh testbed. WiSEMesh has 56 nodes deployed in the campus area providing internet connection for over 1000 users. Each node consists of a small form factor computer with three wireless NICs. We developed the WiSEMesh node software stack that contains unix based operating system, wireless NIC drivers, tools such as DHCP server, NAT etc

Localized Laser-Based Photohydrothermal Synthesis of Functionalized Metal-Oxides

We discuss the rapid in situ hydrothermal synthesis of metal oxide materials based on the photothermal superheating of light-absorbing metal layers for simple and facile on-demand placement of semiconductor materials with micrometer-scale lateral resolution. Localized heating from pulsed and focused laser illumination enables ultrafast growth of metal oxide materials with high spatiotemporal precision in aqueous precursor solution. Among many possible electronic and optoelectronic applications, the proposed method can be used for laser-based in situ real-time soldering of separated metal structures and electrodes with functionalized semiconductor materials. Resistive electrical interconnections of metal strip lines as well as sensitive UV detection using photohydrothermally grown metal oxide bumps are experimentally demonstrated

DeepVehicleSense: An Energy-efficient Transportation Mode Recognition Leveraging Staged Deep Learning over Sound Samples

IEEEIn this paper, we present a new transportation mode recognition system for smartphones called DeepVehicleSense, which is widely applicable to mobile context-aware services. DeepVehicleSense aims at achieving three performance objectives: high accuracy, low latency, and low power consumption at once by exploiting sound characteristics captured from the built-in microphone while being on candidate transportations. To attain high energy efficiency, DeepVehicleSense adopts hierarchical accelerometer-based triggers that minimize the activation of the microphone of smartphones. Further, to achieve high accuracy and low latency, DeepVehicleSense makes use of non-linear filters that can best extract the transportation sound samples. For recognition of five different transportation modes, we design a deep learning based sound classifier using a novel deep neural network architecture with multiple branches. Our staged inference technique can significantly reduce runtime and energy consumption while maintaining high accuracy for the majority of samples. Through 263-hour datasets collected by seven different Android phone models, we demonstrate that DeepVehicleSense achieves the recognition accuracy of 97.44\% with only sound samples of 2 seconds at the power consumption of 35.08 mW on average for all-day monitoring.N

VehicleSense: A Reliable Sound-based Transportation Mode Recognition System for Smartphones

A new transportation mode recognition system for smartphones, VehicleSense that is widely applicable to mobile context-aware services is proposed. VehicleSense aims at achieving three performance objectives: high accuracy, low latency, and low power consumption at once by exploiting sound characteristics captured from the built-in microphone while being on candidate transportations. To attain high energy efficiency, VehicleSense adopts hierarchical accelerometer-based triggers that minimize the activation of the microphone of smartphones. Further, to attain high accuracy and low latency, VehicleSense makes use of non-linear filters that can best extract the transportation sound samples. Our 186-hour log of sound and accelerometer data collected by seven different Android smartphone models confirms that VehicleSense achieves the recognition accuracy of 98.2% with only 0.5 seconds of sound sampling at the power consumption of 26.1 mW on average for all day monitoring

CarrierMix: How Much Can User-side Carrier Mixing Help?

Energy consumption for cellular communication is increasingly gaining importance in smartphone battery lifetime as the bandwidth of wireless communication and the demand for mobile traffic increase. For energy-efficient cellular communication, we tackle two energy characteristics of cellular networks: (1) transmission energy highly varies upon channel condition, and (2) transmission of a packet accompanies unnecessary tail energy waste. Under the objective of transmitting packets when the best channel is provided as well as a number of packets are accumulated, we propose a new mobile collaboration framework ???CarrierMix??? that aggregates smart devices across multiple heterogeneous cellular carriers. Compared to the standalone operation, even without a buffering delay, CarrierMix allows better channel and reduces more tail energy in a statistical point of view. To maximize the energy benefit while maintaining the fairness among the nodes in collaboration, we further develop a dynamic programming framework providing the optimal algorithm of CarrierMix and its approximated heuristic. Trace-driven simulations on our experimental HSPA/EVDO/LTE network traces show that CarrierMix of 5 devices achieves up to 42% of energy reduction.clos

SyncCoding: A Compression Technique Exploiting References for Data Synchronization Services

In this work, we raise a question on why the abundant information previously shared between a server and its client is not effectively utilized in the exchange of a new data which may be highly correlated with the shared data. We formulate this question as an encoding problem that is applicable to general data synchronization services including a wide range of Internet services such as cloud data synchronization, web browsing, messaging, and even data streaming. To this problem, we propose a new encoding technique, SyncCoding that maximally replaces subsets of the data to be transmitted with the coordinates pointing to the matching subsets included in the set of relevant shared data, called references. SyncCoding can be easily integrated into a transport layer protocol such as HTTP and enables significant reduction of network traffic. Our experimental evaluations of SyncCoding implemented in Linux shows that it outperforms existing popular encoding techniques, Brotli, LZMA, Deflate, and Deduplication in two practical use networking applications: cloud data sharing and web browsing. The gains of SyncCoding over Brotli, LZMA, Deflate, and Deduplication in the encoded size to be transmitted are shown to be about 12.4%, 20.1%, 29.9%, and 61.2% in the cloud data sharing and about 78.3%, 79.6%, 86.1%, and 92.9% in the web browsing, respectively. The gains of SyncCoding over Brotli, LZMA, and Deflate when Deduplication is applied in advance are about 7.4%, 10.6%, and 17.4% in the cloud data sharing and about 79.4%, 82.0%, and 83.2% in the web browsing, respectively

QuickTalk: An Association-Free Communication Method for IoT Devices in Proximity

IoT devices are in general considered to be straightforward to use. However, we find that there are a number of situations where the usability becomes poor. The situations include but not limited to the followings: 1) when initializing an IoT device, 2) when trying to control an IoT device which is initialized by another person, and 3) when trying to control an IoT device out of many of the same type. We tackle these situations by proposing a new association-free communication method, QuickTalk. QuickTalk lets a user device such as a smartphone pinpoint and activate an IoT device with the help of an IR transmitter and communicate with the pinpointed IoT device through the broadcast channel of WiFi without a conventional association process. This nature, QuickTalk allows a user device to immediately give a command to a specific IoT device in proximity even when the IoT device is uninitialized, unassociated with the control interface of the user, or associated but visually indistinguishable from others of the same kind. Our experiments of QuickTalk implemented on Raspberry Pi 2 devices show that QuickTalk does its job quickly and intuitively. The end-to-end delay of QuickTalk for transmitting an IoT command is on average about 0.74 seconds, and is upper bounded by 2.5 seconds. We further confirm that even when an IoT device has ongoing data sessions with other devices, which disturb the broadcast channel, QuickTalk can still reliably communicate with the IoT device at the cost of minor throughput degradation