scholarly journals Energy-aware mobile edge computing for low-latency visual data processing

2017 ◽  
Author(s):  
◽  
Huy Trinh
Keyword(s):  
Decision Making ◽  
Energy Consumption ◽  
Data Processing ◽  
Real Time ◽  
Energy Efficient ◽  
User Preferences ◽  
Low Latency ◽  
Mobile Edge Computing ◽  
Incident Response ◽  
Visual Data

New paradigms such as Mobile Edge Computing (MEC) are becoming feasible for use in e.g., real-time decision-making during disaster incident response to handle the data deluge occurring in the network edge. However, MEC deployments today lack flexible IoT device data handling such as e.g., handling user preferences for real-time versus energy-efficient processing. Moreover, MEC can also benefit from a policy based edge routing to handle sustained performance levels with efficient energy consumption. In this thesis, we study the potential of MEC to address application issues related to energy management on constrained IoT devices with limited power sources, while also providing low-latency processing of visual data being generated at high resolutions. Using a facial recognition application that is important in disaster incident response scenarios, we propose a novel 'offload decision-making' algorithm that analyzes the tradeoffs in computing policies to offload visual data processing (i.e., to an edge cloud or a core cloud) at low-to-high workloads. This algorithm also analyzes the impact on energy consumption in the decision-making under different visual data consumption requirements (i.e., users with thick clients or thin clients). To address the processing-throughput versus energy-efficiency tradeoffs, we propose a ‘Sustainable Policy-based Intelligence-Driven Edge Routing' (SPIDER) algorithm that uses machine learning within Mobile Ad hoc Networks (MANETs). This algorithm improves the geographic routing baseline performance (i.e., minimizes impact of local minima) for performance sustainability, and enables easy/flexible policy specification. We evaluate our proposed algorithms by conducting experiments on a realistic edge and core cloud testbed, and recreate disaster scenes of tornado damages (occurred in Joplin, MO in 2011) within simulations. From our empirical results obtained from experiments with a facial recognition application in the GENI Cloud testbed, we show how MEC can provide flexibility to users who desire energy conservation over low-latency or vice versa in the visual data processing. Our NS-3 based simulation results show that our routing approach is more sustainable in terms of throughput, more energy-efficient and flexible than existing solutions to handle diverse user preferences under high node mobility and severe node failure conditions.

scholarly journals Energy-Aware Mobile Edge Computing and Routing for Low-Latency Visual Data Processing

2018 ◽  
Vol 20 (10) ◽  
pp. 2562-2577 ◽  
Author(s):  
Huy Trinh ◽  
Prasad Calyam ◽  
Dmitrii Chemodanov ◽  
Shizeng Yao ◽  
Qing Lei ◽  
...  
Keyword(s):  
Data Processing ◽  
Edge Computing ◽  
Low Latency ◽  
Mobile Edge Computing ◽  
Visual Data ◽  
Energy Aware

scholarly journals An NB-IoT-Based Edge-of-Things Framework for Energy-Efficient Image Transfer

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5929
Author(s):  
Sikandar Zulqarnain Khan ◽  
Yannick Le Moullec ◽  
Muhammad Mahtab Alam
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Low Power ◽  
Data Transmission ◽  
Energy Efficient ◽  
Transmission Time ◽  
Processing Unit ◽  
Image Transfer ◽  
Visual Data ◽  
Smart Gateway

Machine Learning (ML) techniques can play a pivotal role in energy efficient IoT networks by reducing the unnecessary data from transmission. With such an aim, this work combines a low-power, yet computationally capable processing unit, with an NB-IoT radio into a smart gateway that can run ML algorithms to smart transmit visual data over the NB-IoT network. The proposed smart gateway utilizes supervised and unsupervised ML algorithms to optimize the visual data in terms of their size and quality before being transmitted over the air. This relaxes the channel occupancy from an individual NB-IoT radio, reduces its energy consumption and also minimizes the transmission time of data. Our on-field results indicate up to 93% reductions in the number of NB-IoT radio transmissions, up to 90.5% reductions in the NB-IoT radio energy consumption and up to 90% reductions in the data transmission time.

scholarly journals Energy-Efficient Computation Offloading and Transmit Power Allocation Scheme for Mobile Edge Computing

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaohui Gu ◽  
Li Jin ◽  
Nan Zhao ◽  
Guoan Zhang
Keyword(s):  
Energy Consumption ◽  
Power Allocation ◽  
Energy Efficient ◽  
Completion Time ◽  
Computation Offloading ◽  
Battery Life ◽  
Transmission Power ◽  
Mobile Edge Computing ◽  
Efficient Computation ◽  
Allocation Scheme

Mobile edge computing (MEC) is considered a promising technique that prolongs battery life and enhances the computation capacity of mobile devices (MDs) by offloading computation-intensive tasks to the resource-rich cloud located at the edges of mobile networks. In this study, the problem of energy-efficient computation offloading with guaranteed performance in multiuser MEC systems was investigated. Given that MDs typically seek lower energy consumption and improve the performance of computing tasks, we provide an energy-efficient computation offloading and transmit power allocation scheme that reduces energy consumption and completion time. We formulate the energy efficiency cost minimization problem, which satisfies the completion time deadline constraint of MDs in an MEC system. In addition, the corresponding Karush–Kuhn–Tucker conditions are applied to solve the optimization problem, and a new algorithm comprising the computation offloading policy and transmission power allocation is presented. Numerical results demonstrate that our proposed scheme, with the optimal computation offloading policy and adapted transmission power for MDs, outperforms local computing and full offloading methods in terms of energy consumption and completion delay. Consequently, our proposed system could help overcome the restrictions on computation resources and battery life of mobile devices to meet the requirements of new applications.

Collaborative Task Offloading with Computation Result Reusing for Mobile Edge Computing

2019 ◽  
Vol 62 (10) ◽  
pp. 1450-1462 ◽  
Author(s):  
Zikai Zhang ◽  
Jigang Wu ◽  
Long Chen ◽  
Guiyuan Jiang ◽  
Siew-Kei Lam
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Online Algorithm ◽  
Computation Time ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Delay Constraint ◽  
Programming Technique ◽  
System Environment ◽  
Task Offloading

AbstractThe task offloading problem, which aims to balance the energy consumption and latency for Mobile Edge Computing (MEC), is still a challenging problem due to the dynamic changing system environment. To reduce energy while guaranteeing delay constraint for mobile applications, we propose an access control management architecture for 5G heterogeneous network by making full use of Base Station’s storage capability and reusing repetitive computational resource for tasks. For applications that rely on real-time information, we propose two algorithms to offload tasks with consideration of both energy efficiency and computation time constraint. For the first scenario, i.e. the rarely changing system environment, an optimal static algorithm is proposed based on dynamic programming technique to get the exact solution. For the second scenario, i.e. the frequently changing system environment, a two-stage online algorithm is proposed to adaptively obtain the current optimal solution in real time. Simulation results demonstrate that the exact algorithm in the first scenario runs 4 times faster than the enumeration method. In the second scenario, the proposed online algorithm can reduce the energy consumption and computation time violation rate by 16.3% and 25% in comparison with existing methods.

Multi-Criteria Decision-Making for Heterogeneous Multiprocessor Scheduling

2018 ◽  
Vol 17 (05) ◽  
pp. 1399-1427 ◽  
Author(s):  
S. Saroja ◽  
T. Revathi ◽  
Nitin Auluck
Keyword(s):  
Decision Making ◽  
Energy Consumption ◽  
Energy Efficient ◽  
High Performance ◽  
Scheduling Algorithm ◽  
Multiprocessor Scheduling ◽  
Multi Criteria Decision Making ◽  
Link Failures ◽  
Order Preference ◽  
Simulation Results

This paper proposes a new tri-objective scheduling algorithm called Heterogeneous Reliability-Driven Energy-Efficient Duplication-based (HRDEED) algorithm for heterogeneous multiprocessors. The goal of the algorithm is to minimize the makespan (schedule length) and energy consumption, while maximizing the reliability of the generated schedule. Duplication has been employed in order to minimize the makespan. There is a strong interest among researchers to obtain high-performance schedules that consume less energy. To address this issue, the proposed algorithm incorporates energy consumption as an objective. Moreover, in order to deal with processor and link failures, a system reliability model is proposed. The three objectives, i.e., minimizing the makespan and energy, while maximizing the reliability, have been met by employing a method called Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). TOPSIS is a popular Multi-Criteria Decision-Making (MCDM) technique that has been employed to rank the generated Pareto optimal schedules. Simulation results demonstrate the capability of the proposed algorithm in generating short, energy-efficient and reliable schedules. Based on simulation results, we observe that HRDEED algorithm demonstrates an improvement in both the energy consumption and reliability, with a reduced makespan. Specifically, it has been shown that the energy consumption can be reduced by 5–47%, and reliability can be improved by 1–5% with a 1–3% increase in makespan.

scholarly journals A LSTM Approach for Secure Energy Efficient Computational Offloading in Mobile Edge Computing

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 856-874
Author(s):  
S. Anoop ◽  
Dr.J. Amar Pratap Singh
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Energy Efficient ◽  
Routing Algorithm ◽  
Computing Methods ◽  
Edge Computing ◽  
Mobile Technologies ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Computational Offloading

Mobile technologies is evolving so rapidly in every aspect, utilizing every single resource in the form of applications which creates advancement in day to day life. This technological advancements overcomes the traditional computing methods which increases communication delay, energy consumption for mobile devices. In today’s world, Mobile Edge Computing is evolving as a scenario for improving in these limitations so as to provide better output to end users. This paper proposed a secure and energy-efficient computational offloading scheme using LSTM. The prediction of the computational tasks done using the LSTM algorithm. A strategy for computation offloading based on the prediction of tasks, and the migration of tasks for the scheme of edge cloud scheduling based on a reinforcement learning routing algorithm help to optimize the edge computing offloading model. Experimental results show that our proposed algorithm Intelligent Energy Efficient Offloading Algorithm (IEEOA), can efficiently decrease total task delay and energy consumption, and bring much security to the devices due to the firewall nature of LSTM.

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