Energy-Efficient Device-to-Device Communications for Green Smart Cities

Kai, Caihong; Li, Hui; Xu, Lei; Li, Yuzhou; Jiang, Tao

doi:10.1109/tii.2017.2789304

Cited by 91 publications

(61 citation statements)

References 22 publications

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“…Some concrete future research directions and novel technologies, which can open new vistas for the future, have been added in our study. Our research results would be integrated with some advanced research directions including cybersecurity, (39) green communication, (40) cognitive radio, (41) and optical mobile communications technologies (42) to further improve energy efficiency and autonomy in SHCM.…”

Section: Discussionmentioning

confidence: 99%

A Machine-learning-enabled Context-driven Control Mechanism for Software-defined Smart Home Networks

Ru¹,

Chu²,

Zhang³

et al. 2019

Sensors and Materials

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Section: Discussionmentioning

confidence: 99%

A Machine-learning-enabled Context-driven Control Mechanism for Software-defined Smart Home Networks

Ru¹,

Chu²,

Zhang³

et al. 2019

Sensors and Materials

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“…Communication between SG and consumers is also an important aspect. In [41], authors minimized the power consumption and improved the data rate of device-to-device (D2D) communication named as green communication in smart city development. Implementation of green communication is a very critical issue.…”

Section: Guo-feng Fan Et Al Proposed a Novel Electricity Load Forecamentioning

confidence: 99%

Electricity Price and Load Forecasting using Enhanced Convolutional Neural Network and Enhanced Support Vector Regression in Smart Grids

et al. 2019

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Short-Term Electricity Load Forecasting (STELF) through Data Analytics (DA) is an emerging and active research area. Forecasting about electricity load and price provides future trends and patterns of consumption. There is a loss in generation and use of electricity. So, multiple strategies are used to solve the aforementioned problems. Day-ahead electricity price and load forecasting are beneficial for both suppliers and consumers. In this paper, Deep Learning (DL) and data mining techniques are used for electricity load and price forecasting. XG-Boost (XGB), Decision Tree (DT), Recursive Feature Elimination (RFE) and Random Forest (RF) are used for feature selection and feature extraction. Enhanced Convolutional Neural Network (ECNN) and Enhanced Support Vector Regression (ESVR) are used as classifiers. Grid Search (GS) is used for tuning of the parameters of classifiers to increase their performance. The risk of over-fitting is mitigated by adding multiple layers in ECNN. Finally, the proposed models are compared with different benchmark schemes for stability analysis. The performance metrics MSE, RMSE, MAE, and MAPE are used to evaluate the performance of the proposed models. The experimental results show that the proposed models outperformed other benchmark schemes. ECNN performed well with threshold 0.08 for load forecasting. While ESVR performed better with threshold value 0.15 for price forecasting. ECNN achieved almost 2% better accuracy than CNN. Furthermore, ESVR achieved almost 1% better accuracy than the existing scheme (SVR).

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“…In order to solve this key issue, some resource allocation schemes have been proposed in D2D communications, which are mainly divided into two major categories. One is the uplink resource allocation for D2D communications [12][13][14][15][16][17][18][19][20]. Specifically, in order to achieve the maximum total data transmission rate, different types of methods are proposed, such as range division [12], mode selection [13][14][15], and power control [14,15].…”

Section: Introductionmentioning

confidence: 99%

“…An optimization problem is formulated to maximize the effectiveness of the D2D user devices, and a novel algorithm [19] is presented based on the many-to-one matching game with companion effects. In reference [20], the maximization method of the weighted system data rate is developed, which can guarantee the minimum individual CU's data rate and proportional fairness among D2D. The other category is the downlink resource allocation for D2D communications [21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

“…However, these methods in references [12][13][14][15][16][17][18][19][20][21] reuse the uplink channel, and in references [21][22][23][24][25][26][27][28] D2D users can share the downlink channel, therefore the total radio spectrum has not been fully utilized. To account for this problem, jointly uplink and downlink channel scheme [29,30] are proposed in cellular networks.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Joint Uplink and Downlink Resource Allocation for D2D Communications System

Song

Han

et al. 2019

Future Internet

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In cellular networks, device-to-device communications can increase the spectrum efficiency, but some conventional schemes only consider uplink or downlink resource allocation. In this paper, we propose the joint uplink and downlink resource allocation scheme which maximizes the system capacity and guarantees the signal-to-noise-and-interference ratio of both cellular users and device-to-device pairs. The optimization problem is formulated as a mixed integer nonlinear problem that is usually NP hard. To achieve the reasonable resource allocation, the optimization problem is divided into two sub-problems including power allocation and channel assignment. It is proved that the objective function of power control is a convex function, in which the optimal transmission power can be obtained. The Hungarian algorithm is developed to achieve joint uplink and downlink channel assignment. The proposed scheme can improve the system capacity performance and increase the spectrum efficiency. Numerical results reveal that the performance of the proposed scheme of jointly uplink and downlink is better than that of the schemes for independent allocation.

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Energy-Efficient Device-to-Device Communications for Green Smart Cities

Cited by 91 publications

References 22 publications

A Machine-learning-enabled Context-driven Control Mechanism for Software-defined Smart Home Networks

A Machine-learning-enabled Context-driven Control Mechanism for Software-defined Smart Home Networks

Electricity Price and Load Forecasting using Enhanced Convolutional Neural Network and Enhanced Support Vector Regression in Smart Grids

Joint Uplink and Downlink Resource Allocation for D2D Communications System

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