SGCO: Stabilized Green Crosshaul Orchestration for Dense IoT Offloading Services

Dao, Nhu‐Ngoc; Vu, Duc‐Nghia; Na, Woongsoo; Kim, Joongheon; Cho, Sungrae

doi:10.1109/jsac.2018.2874124

Cited by 41 publications

(18 citation statements)

References 26 publications

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“…The computing energy consumption of the UAV in n th slot changes along with the computing frequency in n th slot

. Hence, the computing energy consumption for user k at the UAV in n th slot is derived as

where

is the effective switched capacitance of the CPU [ 38 , 40 , 49 , 50 ]. The computing energy consumption of the UAV during the period of T is expressed as

…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

An Energy Efficient Design of Computation Offloading Enabled by UAV

Wen

et al. 2020

Sensors

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The data volume is exploding due to various newly-developing applications that call for stringent communication requirements towards 5th generation wireless systems. Fortunately, mobile edge computing makes it possible to relieve the heavy computation pressure of ground users and decrease the latency and energy consumption. What is more, the unmanned aerial vehicle has the advantages of agility and easy deployment, which gives the unmanned aerial vehicle enabled mobile edge computing system opportunities to fly towards areas with communication demand, such as hotspot areas. However, the limited endurance time of unmanned aerial vehicle affects the performance of mobile edge computing services, which results in the incomplete mobile edge computing services under the time limit. Consequently, this paper concerns the energy-efficient scheme design of the unmanned aerial vehicle while providing high-quality offloading services for ground users, particularly in the regions where the ground communication infrastructures are overloaded or damaged after natural disasters. Firstly, the model of energy-efficient design of the unmanned aerial vehicle is set up taking the constraints of the energy limitation of the unmanned aerial vehicle, the data causality, and the speed of the unmanned aerial vehicle into account. Subsequently, aiming at maximizing the energy efficiency of the unmanned aerial vehicle in the unmanned aerial vehicle enabled mobile edge computing system, the bits allocation in each time slot and the trajectory of the unmanned aerial vehicle are jointly optimized. Secondly, a successive convex approximation based alternating algorithm is brought forward to deal with the non-convex energy efficiency maximization problem. Finally, it is proved that the proposed energy efficient scheme design of the unmanned aerial vehicle is superior to other benchmark schemes by the simulation results. Besides, how the performance of proposed scheme design change under different parameters is discussed.

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“…The computing energy consumption of the UAV in n th slot changes along with the computing frequency in n th slot

. Hence, the computing energy consumption for user k at the UAV in n th slot is derived as

where

is the effective switched capacitance of the CPU [ 38 , 40 , 49 , 50 ]. The computing energy consumption of the UAV during the period of T is expressed as

…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

An Energy Efficient Design of Computation Offloading Enabled by UAV

Wen

et al. 2020

Sensors

View full text Add to dashboard Cite

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“…1 for the simulation model and suppose that there is an arbitrary number of IoT devices N in the range of [50, 100] associated with an MEC server via the corresponding eRRH during each unit timeslot t. The timeslot duration is set to 1 ms. Uplink data rates of IoT devices r i vary in the range of [128, 1000] kbps, representing different services such as sensor reading, motion detection, online games, augmented reality, and video surveillance. For each uploading data stream from the IoT device, we assume that a complexity set of {10, 50, 100, 500, 1000} cycle/bit is given through intensive analysis and classification of experimental executions, as done in [25], [26], [28], and the workload computation complexity c i is mapped to one in this set. Following these To follow the assumption in Section II-A that the MEC server is designed with sufficient resources to maintain its computational stability and buffer stability, to account for the maximum arrival rate with some redundancy, we selected the maximum CPU frequency of MEC server f max to be equal to (100 + 30)% of the average workload arrival rate resulting in 2.6 GHz.…”

Section: A Simulation Settingsmentioning

confidence: 99%

Self-Calibrated Edge Computation for Unmodeled Time-Sensitive IoT Offloading Traffic

et al. 2020

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With the characterizing benefits of ultra-low latency, contextual computing, and mobile scalability, mobile edge computing (MEC) is considered as a key enabler for realizing a tremendous boom in heterogeneously time-sensitive Internet-of-Things (IoT) services in the fifth-generation (5G) ecosystems. However, achieving low-latency comes at a cost of energy-efficiency reduction. To address and balance this tradeoff, this paper proposes a joint optimization of energy consumption and latency satisfaction in MEC servers, called latency-aware green (LAG) computing algorithm. To fully consider the heterogeneity of IoT services offloaded to the MEC servers, offloading traffic at the MEC servers is assumed to be unmodeled and unpredictable. Using the proposed LAG algorithm, each MEC server autonomously and dynamically calibrates its own computing frequency based on the current status of the workload buffer size and computational workload arrival rate. This dynamic calibration provides minimum energy consumption for the workload computation while maintaining the computational latency stabilized under a desired threshold. Evaluation results show that the proposed algorithm maintains stable MEC servers in an energyefficient manner. INDEX TERMS edge computing, Internet of Things, mobile offloading, unmodeled traffic VOLUME 8, 2020 This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

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“…Projeções apontam que as aplicações IoT utilizando LoRaWAN serão densas [Dao et al 2018], gerando a incapacidade de um GW decodificar corretamente os sinais simultâneos enviados por dispositivos que utilizam o mesmo SF no mesmo CF, tornandose necessário considerar um mecanismo de alocação de recursos eficiente para ajustar os parâmetros de rádio para mitigar os efeitos da densificação de uma LoRaWAN. Desta forma,é possível fornecer um tradeoff entre o aumento da cobertura ao mesmo tempo que reduz o atraso, energia e interferência.…”

Section: Visão Geral Do Cenáriounclassified

Mecanismo de Alocação de Recursos para LoRaWAN Ciente da Prioridade das Aplicações de IoT

Lima¹,

Matni²,

Moraes³

et al. 2020

Anais Do Simpósio Brasileiro De Computação Ubíqua E Pervasiva (SBCUP 2020)

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O aumento no numero de dispositivos LoRaWAN conectados, tem aumentado as colisões e interferências na rede. Neste sentido, surge a necessidade de um mecanismo para gerenciar o fator de propagação usado pelos dispositivos durante as transmissões. No entanto, os mecanismos existentes não tratam a prioridade das aplicações, ocasionando em dispositivos com funções importantes tenham o mesmo tratamento que dispositivos com funções menos importantes ,trazendo menos segurança do ponto de vista da aplicação. Este artigo propõe o PRA, um mecanismo de alocação de fator de propagação de acordo com o nível de importância da aplicação, priorizando aplicações com alta e media prioridade. Simulações mostram que o PRA reduz em até 85% e 80% o tempo no ar e o consumo de energia para grupos de dispositivos de alta e media prioridade, respectivamente, se comparado a mecanismos do estado-da-arte.

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SGCO: Stabilized Green Crosshaul Orchestration for Dense IoT Offloading Services

Cited by 41 publications

References 26 publications

An Energy Efficient Design of Computation Offloading Enabled by UAV

An Energy Efficient Design of Computation Offloading Enabled by UAV

Self-Calibrated Edge Computation for Unmodeled Time-Sensitive IoT Offloading Traffic

Mecanismo de Alocação de Recursos para LoRaWAN Ciente da Prioridade das Aplicações de IoT

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