Optimal User Association Strategy for Large-Scale IoT Sensor Networks with Mobility on Cloud RANs

Kim, Taewoon; Chun, Chanjun; Choi, Wooyeol

doi:10.3390/s19204415

Cited by 4 publications

(6 citation statements)

References 30 publications

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“…Further, some studies were conducted to optimize the handover using a centralized controller [17], [18]. The controller observes the user and necessary measurements, and then selects an optimal base station or time of handover occurrence, similar to that in C-RAN.…”

Section: B User Association For Reducing Handovermentioning

confidence: 99%

“…The controller observes the user and necessary measurements, and then selects an optimal base station or time of handover occurrence, similar to that in C-RAN. An optimal user association scheme for uplink data transmission in C-RAN was proposed to reduce handover while balancing the load between base stations [17]. The association solution is reusable, which implies the same association can be repeated if no changes occur in the network topology.…”

Section: B User Association For Reducing Handovermentioning

confidence: 99%

“…To this end, we implemented two schemes: FLRL and RL-AC. Additionally, we have compared the performance in terms of the number of handovers with another existing handover scheme, namely UAM [17]. The UAM performs user association by optimizing load balancing while reducing handover in C-RAN.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Fuzzy Logic and Accelerated Reinforcement Learning-Based User Association for Dense C-RANs

Rodoshi

Kim

Choi³

2021

IEEE Access

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Cloud radio access network (C-RAN) is a potential mobile network architecture providing seamless connectivity to users with high data rates by integrating it with the small-cell technology of 5G mobile communication systems. In C-RAN, base station functionality is divided into a baseband unit (BBU) and remote radio head (RRH); the BBUs from multiple sites are centralized and virtualized using cloud computing and virtualization techniques. Frequent handovers occur in the network, which results in control message flooding and repeated service outages because of the dense deployment of short-range RRHs and user mobility. It is necessary to optimize handover control parameters before the handover and re-associate the user with an RRH to minimize unnecessary handovers in the network. Traditional handover schemes rely on signal strengths of RRHs, which cause a large number of unnecessary handovers when a user moves within the coverage of multiple-overlapped RRHs. This study investigates the handover in C-RAN by carefully optimizing the handover control parameter and selecting the target RRH for handover. Our main goal is associating the user with an RRH such that association after the handover remains possible for as long as possible while maintaining the quality of service (QoS) requirements of the users. We have used fuzzy logic to optimize the handover control parameter and a reinforcement learning-based algorithm to select the target RRH. A key component of the proposed RL-based scheme is using an acceleration technique for the faster convergence of the algorithm. Numerical results show that the proposed scheme can significantly reduce the number of handovers while ensuring QoS requirements.

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Section: B User Association For Reducing Handovermentioning

confidence: 99%

Section: B User Association For Reducing Handovermentioning

confidence: 99%

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Fuzzy Logic and Accelerated Reinforcement Learning-Based User Association for Dense C-RANs

Rodoshi

Kim

Choi³

2021

IEEE Access

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“…Other approaches involve a meta-heuristic approach for a robust association that also takes into account the uncertainty of data rate transmissions [ 25 ]. Application to sensors can be found in [ 26 ], where an optimal user association strategy for large-scale Internet-of-Things (IoT) sensor networks is illustrated.…”

Section: Introductionmentioning

confidence: 99%

User-Cell Association for Security and Energy Efficiency in Ultra-Dense Heterogeneous Networks

Marabissi

Mucchi

Morosi

2021

Sensors

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The last decades have been characterized by an exponential increase in digital services. The demand is foreseen to further increase in the next years, and mobile networks will have to mandatorily supply connections to enable digital services with very different requirements, from ultra high speed to ultra low latency. The deployment and the coexistence of cells of different size, from femto to macro, will be one of the key elements for providing such pervasive wireless connection: the ultra dense networks (UDN) paradigm. How to associate users and base stations is one of the most investigated research topics. Many criteria can be drawn, from minimization of power consumption to optimization of throughput. In this paper we propose a new utility to optimize two of the most important features of future mobile connection: security and energy consumption. By using our utility it is possible to jointly select the base station to be activated in a UDN, and associate users to the base stations with the aim of maximizing the secure throughput by spending the minimum energy. Moreover, we propose a heuristic that allows to achieve performance very close to the optimal one with reduced complexity. Effectiveness of the proposed approach is proved by means of comparison with benchmark approaches.

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“…Furthermore, if the largest probability

was not so much different from the second-largest

, the model might be, what we call, uncertain about its prediction or decision. We propose to measure such decision uncertainty in a single quantity by using the well-known Jain’s fairness index [ 24 ], which has been widely used in the computer network domain [ 25 ]. In this paper, the computed fairness score of the softmax output is referred to as the uncertainty score , and is used to measure the level of uncertainty as to the model’s prediction.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Uncertainties in Softmax Decision-Making Models for Low-Power IoT Devices

Cho

Choi

Kim

2020

Sensors

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Internet of Things (IoT) devices bring us rich sensor data, such as images capturing the environment. One prominent approach to understanding and utilizing such data is image classification which can be effectively solved by deep learning (DL). Combined with cross-entropy loss, softmax has been widely used for classification problems, despite its limitations. Many efforts have been made to enhance the performance of softmax decision-making models. However, they require complex computations and/or re-training the model, which is computationally prohibited on low-power IoT devices. In this paper, we propose a light-weight framework to enhance the performance of softmax decision-making models for DL. The proposed framework operates with a pre-trained DL model using softmax, without requiring any modification to the model. First, it computes the level of uncertainty as to the model’s prediction, with which misclassified samples are detected. Then, it makes a probabilistic control decision to enhance the decision performance of the given model. We validated the proposed framework by conducting an experiment for IoT car control. The proposed model successfully reduced the control decision errors by up to 96.77% compared to the given DL model, and that suggests the feasibility of building DL-based IoT applications with high accuracy and low complexity.

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Optimal User Association Strategy for Large-Scale IoT Sensor Networks with Mobility on Cloud RANs

Cited by 4 publications

References 30 publications

Fuzzy Logic and Accelerated Reinforcement Learning-Based User Association for Dense C-RANs

Fuzzy Logic and Accelerated Reinforcement Learning-Based User Association for Dense C-RANs

User-Cell Association for Security and Energy Efficiency in Ultra-Dense Heterogeneous Networks

Leveraging Uncertainties in Softmax Decision-Making Models for Low-Power IoT Devices

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