Mobile Edge Computing-Enabled Internet of Vehicles: Toward Energy-Efficient Scheduling

Ning, Zhaolong; Huang, Jun; Wang, Xiaojie; Rodrigues, Joel J. P. C.; Guo, Lei

doi:10.1109/mnet.2019.1800309

Cited by 231 publications

(139 citation statements)

References 15 publications

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“…Ning et al [15], investigated a joint computation offloading, power allocation, and channel assignment (COPACA) scheme for 5Genabled traffic management systems, with the purpose of maximizing the achievable sum rate. In the work of Ning et al [16], in order to satisfy heterogeneous requirements of communication, computation and storage in IoVs, they constructed an energy-efficient scheduling framework for MEC-enabled IoVs to minimize the energy consumption of road side units (RSUs) under task latency constraints. Ning et al [17] , proposed a deep learning based data transmission scheme by exploring trirelationships among vehicles at the edge of networks (i.e., edge of vehicles) by jointly considering the social and physical characteristics.…”

Section: Related Workmentioning

confidence: 99%

A new load balancing strategy by task allocation in edge computing based on intermediary nodes

Yao

et al. 2020

J Wireless Com Network

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The latency of cloud computing is high for the reason that it is far from terminal users. Edge computing can transfer computing from the center to the network edge. However, the problem of load balancing among different edge nodes still needs to be solved. In this paper, we propose a load balancing strategy by task allocation in edge computing based on intermediary nodes. The intermediary node is used to monitor the global information to obtain the real-time attributes of the edge nodes and complete the classification evaluation. First, edge nodes can be classified to three categories (light-load, normal-load, and heavy-load), according to their inherent attributes and realtime attributes. Then, we propose a task assignment model and allocate new tasks to the relatively lightest load node. Experiments show that our method can balance load among edge nodes and reduce the completion time of tasks.

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Section: Related Workmentioning

confidence: 99%

A new load balancing strategy by task allocation in edge computing based on intermediary nodes

Yao

et al. 2020

J Wireless Com Network

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“…Obtaining RSSI or CSI information don't require any additional operations or data on transmitters, which means that transmitters don't consume extra energy for localization algorithms. This feature is also applied to other fields [11,12]. In addition, there is a channel state information (CSI) feature that can be used for indoor localization technology.…”

Section: Rss Feature In Harsh Environmentsmentioning

confidence: 99%

“…By repeating the first step, we can obtain multiple sets of training data and stride length. Using these data, we can perform a linear regression of Equation (12) to acquire the parameters.…”

Section: Strategy For Selecting Location Datamentioning

confidence: 99%

“…The RSS can be easily interfered with by many environmental factors, such as multipath, NLoS propagation, etc., which causes the relationship between distance and RSS to become extremely unreliable [3].Obtaining RSSI or CSI information don't require any additional operations or data on transmitters, which means that transmitters don't consume extra energy for localization algorithms. This feature is also applied to other fields [11,12]. In addition, there is a channel state information (CSI) feature that can be used for indoor localization technology.…”

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confidence: 99%

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A Robust PDR/UWB Integrated Indoor Localization Approach for Pedestrians in Harsh Environments

Tong

Ning

et al. 2019

Sensors

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Wireless sensor networks (WSNs) and the Internet of Things (IoT) have been widely used in industrial, construction, and other fields. In recent years, demands for pedestrian localization have been increasing rapidly. In most cases, these applications work in harsh indoor environments, which have posed many challenges in achieving high-precision localization. Ultra-wide band (UWB)-based localization systems and pedestrian dead reckoning (PDR) algorithms are popular. However, both have their own advantages and disadvantages, and both exhibit a poor performance in harsh environments. UWB-based localization algorithms can be seriously interfered by non-line-of-sight (NLoS) propagation, and PDR algorithms display a cumulative error. For ensuring the accuracy of indoor localization in harsh environments, a hybrid localization approach is proposed in this paper. Firstly, UWB signals cannot penetrate obstacles in most cases, and traditional algorithms for improving the accuracy by NLoS identification and mitigation cannot work in this situation. Therefore, in this study, we focus on integrating a PDR and UWB-based localization algorithm according to the UWB communication status. Secondly, we propose an adaptive PDR algorithm. UWB technology can provide high-precision location results in line-of-sight (LoS) propagation. Based on these, we can train the parameters of the PDR algorithm for every pedestrian, to improve the accuracy. Finally, we implement this hybrid localization approach in a hardware platform and experiment with it in an environment similar to industry or construction. The experimental results show a better accuracy than traditional UWB and PDR approaches in harsh environments.Sensors 2020, 20, 193 2 of 20 most important feature of harsh environments. Indoor localization technology is still very challenging under NLoS propagation in harsh environments.The current mainstream indoor localization systems are radio frequency (RF)-based. This kind of system basically requires beacon nodes, or beacon nodes (BNs), with known coordinates in the localization system. Each target to be located has an electronic tag, or tag node (TN). Specific features generated from the communication between the TN and BN are used for localization. These features mainly include the radio signal strength (RSS), time, signal angle, etc. The challenge of indoor localization systems in harsh environments is that these features can be severely disturbed [1] by NLoS propagation or other interference. As a result, the localization accuracy will be greatly reduced.In harsh environments, interference of an RF-based localization system can be mainly divided into three types. Firstly, multipath refraction is a common type of interference in harsh environments. It means that an RF signal will be transmitted to the receiver through multiple paths, which causes the transmission time to be unreliable and unpredictable RSS superposition. Additionally, it apparently has a greater impact on the signal angle. Secondly, non-line-of-sight (NLoS) propagati...

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“…The existing invehicle equipment cannot meet these conditions. This brings huge challenges to IoV in terms of computing and communication capabilities [6]. Cloud computing can make up for the lack of computing resources of on-board equipment.…”

Section: Introductionmentioning

confidence: 99%

Task Offloading Strategy Based on Reinforcement Learning Computing in Edge Computing Architecture of Internet of Vehicles

Wang

Li³

et al. 2020

IEEE Access

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With the rapid increase of vehicles, the explosive growth of data flow and the increasing shortage of spectrum resources, the performance of existing task offloading scheme is poor, and the onboard terminal can't achieve efficient computing. Therefore, this paper proposes a task offload strategy based on reinforcement learning computing in edge computing architecture of Internet of vehicles. Firstly, the system architecture of Internet of vehicles is designed. The Road Side Unit receives the vehicle data in community and transmits it to Mobile Edge Computing server for data analysis, while the control center collects all vehicle information. Then, the calculation model, communication model, interference model and privacy issues are constructed to ensure the rationality of task offloading in Internet of vehicles. Finally, the user cost function is minimized as objective function, and double-layer deep Q-network in deep reinforcement learning algorithm is used to solve the problem for real-time change of network state caused by user movement. The results show that the proposed offloading strategy can achieve fast convergence. Besides, the impact of user number, vehicle speed and MEC computing power on user cost is the least compared with other offloading schemes. The task offloading rate of our proposed strategy is the highest with better performance, which is more suitable for the scenario of Internet of vehicles.

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Mobile Edge Computing-Enabled Internet of Vehicles: Toward Energy-Efficient Scheduling

Cited by 231 publications

References 15 publications

A new load balancing strategy by task allocation in edge computing based on intermediary nodes

A new load balancing strategy by task allocation in edge computing based on intermediary nodes

A Robust PDR/UWB Integrated Indoor Localization Approach for Pedestrians in Harsh Environments

Task Offloading Strategy Based on Reinforcement Learning Computing in Edge Computing Architecture of Internet of Vehicles

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