Edge-Facilitated Mobile Computing and Communication

The proliferation of IoT devices and rapidly developing wireless techniques boost the data volume and service demand at the edge of the Internet. Meanwhile, increased requirement for low latency feedback has become a must for most popular mobile applications, e.g., Augmented Reality (AR), Virtual Reality (VR) and Connected Vehicles. To address these challenges, edge computing has emerged as an extensional solution for cloud computing. This thesis studies edge computing-facilitated mobile computing and communication systems. We first propose solutions to improve edge resource utilization regarding general edge systems. We present a mechanism to cluster user requests based on similarity for better Content Delivery Net- work (CDN) performance. This mechanism works directly on current CDN architecture and can be deployed incrementally. Then we extend the mechanism by adding cache resource grouping algorithm, so that the system directs similar requests to same servers and group those servers which receive similar requests. This iterative mechanism optimizes the edge utilization by concentrating the resource on similar requests to achieve higher cache hit ratio and computation efficiency. Thereafter, we present solutions for mobile edge systems specifically for three most promising use cases, i.e., Connected Vehicles, Mobile AR (MAR) and Smart city (traffic control). We explore the potential of edge computing in connected vehicular AR applications with real data sets. We design a lightweight edge system and data flow fit for general connected vehicular AR applications and implement a prototype. With an indoor test and real data set analysis, we find out that our system can improve the performance of vehicular AR applications with reasonable cost. To optimize the system, we formulate the problem of edge server allocation and task scheduling as a mutant multiprocessor scheduling problem and develop a two-stage edge-cloud decentralized algorithm as well as a centralized algorithm to schedule the offloading tasks on the fly. We conduct a raw road test and an extensive evaluation based on the road test results and large data sets from real world. The results show that our system improve at least twice the application performance comparing with cloud solutions. For MAR, we consider to offload tasks to multiple edge servers via multiple paths simultaneously to further improve the MAR performance. We develop a fast scheduling algorithm to split the workloads among the avail- able edge servers and show promising results with real implementations. At last, we explore the potential of combining edge computing and ma- chine learning techniques to realize intelligent traffic control by letting edge servers co-located with traffic lights learn the waiting traffic and adapt the light periods with reinforcement learning.Esineiden Internetin leviäminen ja nopeasti kehittyvät langattomat tekniikat lisäävät datan määrää ja palvelutarvetta Internetin reunalla. Samanaikaisesti lisääntyneestä alhaisen viiveen palautteen vaatimuksesta on tullut välttämätön suosituimpiin mobiilisovelluksiin, esim. lisättyyn todellisuuteen (AR), virtuaalitodellisuuteen (VR) ja yhdistettyihin ajoneuvoihin. Reunalaskenta on noussut pilvilaskennan rinnalle näihin haasteisiin vastaavaksi ratkaisuksi. Tässä väitöskirjassa tutkitaan laskennallisesti laajennettuja mobiililaskenta- ja viestintäjärjestelmiä. Ehdotamme ensin ratkaisuja reunaresurssien käytön parantamiseksi yleisten reunajärjestelmien suhteen. Esitämme mekanismin käyttäjien pyyntöjen klusterointiin perustuen samankaltaisuuteen sisällönjakeluverkon (CDN) suorituskyvyn parantamiseksi. Tämä mekanismi toimii suoraan nykyisessä CDN-arkkitehtuureissa ja voidaan ottaa käyttöön asteittain. Sitten laajennamme mekanismia lisäämällä välimuistiresurssien ryhmittelyalgoritmin siten, että järjestelmä ohjaa samankaltaiset pyynnöt samoille palvelimille ja ryhmittelee palvelimet pyyntöjen mukaan. Tämä iteratiivinen mekanismi optimoi reunakäytön keskittämällä resurssit samanlaisiin pyyntöihin suuremman välimuistin osumissuhteen ja laskentatehokkuuden saavuttamiseksi. Sen jälkeen esittelemme ratkaisuja liikkuviin reunajärjestelmiin erityisesti kolmeen lupaavimpaan käyttötapaukseen, ts. yhdistetyt ajoneuvot, laajennettu mobiilitodellisuus (MAR) ja älykäs kaupunki (erityisesti liikenteenohjaus). Tutkimme reunalaskennan mahdollisuuksia yhdistettyjen ajoneuvojen AR-sovelluksissa. Suunnittelemme kevyen reunajärjestelmän ja tiedonkulun, joka sopii yleisesti yhdistettyjen ajoneuvojen AR-sovelluksiin ja toteutamme prototyypin. Sisätilojen testin ja reaalimaailman datan avulla saamme selville, että järjestelmämme voi parantaa ajoneuvojen AR-sovellusten suorituskykyä kohtuullisin kustannuksin. Järjestelmän optimoimiseksi formuloimme reunapalvelimien allokoinnin ja tehtävien ajoituksen ongelman muuttuvana moniprosessorien skedulointiongelmana ja kehitämme kaksivaiheisen reunapilviin soveltuvan hajautetun algoritmin sekä keskitetyn algoritmin kuormansiirtotehtävien ajonaikaiseen ajoittamiseen. Suoritamme kokeellisen testin oikeassa ajossa ja datapohjaisen arvioinnin, joka perustuu tietestien tuloksiin ja todellisen maailman suuriin tietojoukkoihin. Tulokset osoittavat, että järjestelmämme parantaa merkittävästi sovelluksen suorituskykyä verrattuna pilviratkaisuihin. MAR:n osalta käsittelemme tehtävien lataamista useille reunapalvelimille useiden reittien kautta samanaikaisesti MAR:n suorituskyvyn parantamiseksi. Kehitämme nopean aikataulutusalgoritmin työkuormien jakamiseen käytettävissä olevien reunapalvelimien. Lopuksi tutkimme mahdollisuuksia yhdistää reunalaskenta ja koneoppimistekniikat älykkään liikennevalo-ohjauksen toteuttamiseksi liikennevaloihin sijoitetuilla reunapalvelimilla

Multipath Computation Offloading for Mobile Augmented Reality

Mobile Augmented Reality (MAR) applications employ computationally demanding vision algorithms on resource-limited devices. In parallel, communication networks are becoming more ubiquitous. Offloading to distant servers can thus overcome the device limitations at the cost of network delays. Multipath networking has been proposed to overcome network limitations but it is not easily adaptable to edge computing due to the server proximity and networking differences. In this article, we extend the current mobile edge offloading models and present a model for multi-server device-to-device, edge, and cloud offloading. We then introduce a new task allocation algorithm exploiting this model for MAR offloading. Finally, we evaluate the allocation algorithm against naive multipath scheduling and single path models through both a real-life experiment and extensive simulations. In case of sub-optimal network conditions, our model allows reducing the latency compared to single-path offloading, and significantly decreases packet loss compared to random task allocation. We also display the impact of the variation of WiFi parameters on task completion. We finally demonstrate the robustness of our system in case of network instability. With only 70% WiFi availability, our system keeps the excess latency below 9 ms. We finally evaluate the capabilities of the upcoming 5G and 802.11ax.Peer reviewe

NLPBench: Evaluating Large Language Models on Solving NLP Problems

Recent developments in large language models (LLMs) have shown promise in enhancing the capabilities of natural language processing (NLP). Despite these successes, there remains a dearth of research dedicated to the NLP problem-solving abilities of LLMs. To fill the gap in this area, we present a unique benchmarking dataset, NLPBench, comprising 378 college-level NLP questions spanning various NLP topics sourced from Yale University's prior final exams. NLPBench includes questions with context, in which multiple sub-questions share the same public information, and diverse question types, including multiple choice, short answer, and math. Our evaluation, centered on LLMs such as GPT-3.5/4, PaLM-2, and LLAMA-2, incorporates advanced prompting strategies like the chain-of-thought (CoT) and tree-of-thought (ToT). Our study reveals that the effectiveness of the advanced prompting strategies can be inconsistent, occasionally damaging LLM performance, especially in smaller models like the LLAMA-2 (13b). Furthermore, our manual assessment illuminated specific shortcomings in LLMs' scientific problem-solving skills, with weaknesses in logical decomposition and reasoning notably affecting results

What if we have Meta GPT? From Content Singularity to Human-Metaverse Interaction in AIGC Era

The global metaverse development is facing a "cooldown moment", while the academia and industry attention moves drastically from the Metaverse to AI Generated Content (AIGC) in 2023. Nonetheless, the current discussion rarely considers the connection between AIGCs and the Metaverse. We can imagine the Metaverse, i.e., immersive cyberspace, is the black void of space, and AIGCs can simultaneously offer content and facilitate diverse user needs. As such, this article argues that AIGCs can be a vital technological enabler for the Metaverse. The article first provides a retrospect of the major pitfall of the metaverse applications in 2022. Second, we discuss from a user-centric perspective how the metaverse development will accelerate with AIGCs. Next, the article conjectures future scenarios concatenating the Metaverse and AIGCs. Accordingly, we advocate for an AI-Generated Metaverse (AIGM) framework for energizing the creation of metaverse content in the AIGC era.Comment: 11 pages, 4 figure

Theoretical and experimental investigation of an absorption refrigeration and pre-desalination system for marine engine exhaust gas heat recovery

Absorption-refrigeration-cycle-based exhaust gas heat recovery technology is effective in improving the thermal efficiency and fuel economy of marine diesel engines. However, the absorption refrigeration system is inflexible in the start–stop operation, and this cannot fulfil the fluctuating demand of refrigeration. This paper presents both the theoretical and experimental investigations of an absorption refrigeration and freezing pre-desalination-based marine engine exhaust gas heat recovery system. The energy storage subcycle is introduced to overcome the energy underutilisation and balance the excessive refrigerating output of the absorption refrigeration cycle. Seawater is utilised as the phase-change material and it is pre-desalinated in the energy storage subcycle. A mathematical model of the system is established and experimental investigation is conducted. Furthermore, the theoretical and experimental performances are compared, and an economic analysis of seawater desalination is performed to evaluate its economy. The results show that the total refrigeration output of the system ranges from 6.1 kW to 9.9 kW, and the system COP (Coefficient of Performance) can reach 16% under the experimental operating conditions. Additionally, the salinity of pre-desalinated seawater can be reduced to below 10 ppt. Moreover, the cost of RO (Reverse Osmosis) seawater desalination can be reduced by 26% through the pre-desalination process of seawater

Managing Data in Computational Edge Clouds

Peer reviewe

Towards Robust Real-Time Scene Text Detection: From Semantic to Instance Representation Learning

Due to the flexible representation of arbitrary-shaped scene text and simple pipeline, bottom-up segmentation-based methods begin to be mainstream in real-time scene text detection. Despite great progress, these methods show deficiencies in robustness and still suffer from false positives and instance adhesion. Different from existing methods which integrate multiple-granularity features or multiple outputs, we resort to the perspective of representation learning in which auxiliary tasks are utilized to enable the encoder to jointly learn robust features with the main task of per-pixel classification during optimization. For semantic representation learning, we propose global-dense semantic contrast (GDSC), in which a vector is extracted for global semantic representation, then used to perform element-wise contrast with the dense grid features. To learn instance-aware representation, we propose to combine top-down modeling (TDM) with the bottom-up framework to provide implicit instance-level clues for the encoder. With the proposed GDSC and TDM, the encoder network learns stronger representation without introducing any parameters and computations during inference. Equipped with a very light decoder, the detector can achieve more robust real-time scene text detection. Experimental results on four public datasets show that the proposed method can outperform or be comparable to the state-of-the-art on both accuracy and speed. Specifically, the proposed method achieves 87.2% F-measure with 48.2 FPS on Total-Text and 89.6% F-measure with 36.9 FPS on MSRA-TD500 on a single GeForce RTX 2080 Ti GPU.Comment: Accepted by ACM MM 202