Multi-access edge computing (MEC) has recently been proposed to aid mobile end devices in providing compute- and data-intensive services with low latency. Growing service demands by the may overwhelm MEC installations, while cost constraints limit increases of installed data storage capacities. At same time, ever increasing computation capabilities capacities are valuable resources that can be utilized enhance MEC. This article comprehensively surveys topic area device-enhanced MEC, i.e.,...

Cooperative edge offloading to nearby end devices via Device-to-Device (D2D) links in networks with sliced computing resources has mainly been studied for (helper nodes) that are stationary (or follow predetermined mobility paths) and independent computation tasks. However, often mobile, a given application request commonly requires set of dependent We formulate novel model the cooperative tasks mobile helper nodes. task dependencies general dependency graph. Our employs state-of-the-art...

Traffic light control falls into two main categories: Agnostic systems that do not exploit knowledge of the current traffic state, e.g., positions and velocities vehicles approaching intersections, holistic state. Emerging fifth generation (5G) wireless networks enable Vehicle-to-Infrastructure (V2I) communication to reliably quickly collect However, best our knowledge, optimized management without with state information has been compared in detail. This study fills this gap literature by...

Computation offloading is one of the main use-cases Multi-access Edge Computing (MEC) paradigm which can help to save battery life resource-poor mobile devices by transferring computation-intensive tasks resource-rich edge cloud servers. However, ever-increasing internet traffic negate this benefit due possible failures MEC On other hand, growth computation capabilities end as result recent developments Central Processing Units (CPUs), enhance systems performance and broaden concept...

Forward Error Correction (FEC) has become an integral part of communication technology to address expected losses during transfer data. Among various layer three block codes, Random Linear Network Coding (RLNC) emerged as adaptable, powerful approach. However, its most straightforward implementation, Full Vector (FVC), introduces too much delay for widespread adoption. Handcrafted schemes were introduced optimise coding delay, while keeping resilience reasonably high. These works have...

Enabling computation intensive tasks with low latency requirements on mobile devices considering their battery life time is one of the challenges in 5G networks. an era massive device connectivities, therefore, a realistic offloading scenario should consider dense and dynamic users' movements. Considering powerful Central Processing Units (CPUs) current Device-to-Device (D2D) communications, available resources adjacent can be exploited to fulfil recent applications. However, order prevent...

Next-generation networks are envisioned to be empowered by artificial intelligence with predictive capabilities. Predicting handovers in high mobility scenarios enables and applications adapt ahead of time improve the Quality Service (QoS). In this paper, we present a two-step machine learning (ML) method, consisting classifier regressor, that can predict remaining until handover occurs. Our approach is validated on dataset was captured real cellular network. The results show upcoming...

Cloud Computing has been widely adopted in industry, administrations, as well research. At the same time, development and testing of new technologies is troubled by inconsistent environments. Exchanging modules a system during research becomes major obstacle. As result, baselines for comparisons are lacking, evaluating developments may lead to heavy time cost. Big industrial players typically circumvent this problem relying on scale their datacenters, experiments, problems, which makes them...

The rise of chiplets in personal and high performance computing is mirrored System on Chip (SOC) mobile devices. Both paradigms allow vendors designers to integrate dedicated circuitry for accelerating computation. Implementations like cryptographic or vector engines are well known, nowadays Machine Learning (ML) blocks often included accelerate Deep Neural Network (DNN) inference. shift toward diverse device architectures, as exemplified by RISC-V, poised gain momentum. widespread...

Present-day and future network protocols that include implement Forward Error Correction are configurable by internal parameters, typically incorporating expert knowledge to set up.We introduce a framework systematically, objectively efficiently determine parameters for Random Linear Network Codes (RLNC). Our approach uses an unbiased, consistent simulator in optimization loop utilizes customizable, powerful extendable parametric loss function. This allows tailor existing various use cases,...

Wireless Sensor Networks (WSNs) applications have attracted attention in Internet of Things (IoT) as a novel networking paradigm consisting billions small sensor nodes. These sensors collect environmental information and communicate with each other to provide solutions for real time IoT applications' requirements. Since the majority require location-based services, it is necessary improve accuracy localization algorithms. DV-Hop one most attractive range-free algorithms wireless networks...

Machine learning (ML) equips next-generation networks with anticipatory capabilities. End-to-end predictive Quality of Service (pQoS) leverages ML models to estimate QoS indicators. In this paper, we present several that can the maximum achievable instantaneous throughput (link capacity) cellular networks. The do not only most likely value, but also quantify uncertainty their own by providing estimated quantile values as bounds. These estimates bounds enable network functions and user...