Content sharing algorithm for device to device cache communication with minimum inner-cluster energy consumption

doi:10.11772/j.issn.1001-9081.2017123015

Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (6): 1703-1708.DOI: 10.11772/j.issn.1001-9081.2017123015

Previous Articles Next Articles

Content sharing algorithm for device to device cache communication with minimum inner-cluster energy consumption

TONG Piao^1,2, LONG Long¹, HAN Xue², QIU Dawei², HU Qian²

1. Laboratory of Broadband Wireless Mobile Internet, Chongqing University of Posts and Communications, Chongqing 400065, China;
2. Beijing Key laboratory of Mobile Computing and Pervasive Device(Institute of Computing Technology, Chinese Academy of Sciences), Beijing 100190, China

Received:2017-12-22 Revised:2018-01-31 Online:2018-06-13 Published:2018-06-10
Supported by:
This work is partially supported by the Program for Changjiang Scholars and Innovative Research Team in Universities (IRT1299), the Special Fund for Key Laboratories of Chongqing Municipal Science and Technology Commission (cstc2013yykfA40010).

基于簇内能耗最优的设备到设备缓存通信内容共享算法

佟飘^1,2, 龙隆¹, 韩雪², 邱大伟², 胡茜²

1. 重庆邮电大学宽带无线移动互联网络实验室, 重庆 400065;
2. 移动计算与新型终端北京市重点实验室(中国科学院计算技术研究所), 北京 100190)

通讯作者: 龙隆
作者简介:佟飘(1993-),女,安徽亳州人,硕士研究生,主要研究方向:设备到设备缓存通信;龙隆(1988-),男,山西长治人,博士研究生,主要研究方向:设备到设备缓存通信;韩雪(1981-),女,河南安阳人,助理研究员,博士,主要研究方向:移动通信网络优化、网络流量管控;邱大伟(1991-),男,内蒙古赤峰人,博士研究生,主要研究方向:无线移动通信、网络安全;胡茜(1986-),女,湖南益阳人,博士研究生,主要研究方向:复杂无线网络系统、资源控制。
基金资助:
长江学者和创新团队发展计划资助项目（IRT1299）；重庆市科委重点实验室专项经费资助项目（cstc2013yykfA40010）。

Abstract

Abstract: The battery capacity of a terminal device is limited and the data transmission energy consumption is too large between devices in the Device to Device (D2D) cache communication, which lead to the decline of the file unloading rate. In order to solve the problem, a Caching communication content Sharing Algorithm for minimizing inner-Cluster node energy consumption (CCSA)was proposed. Firstly, the user nodes in the network were modeled as Poisson cluster process in view of the random distribution characteristics of user terminals. The unloading model was established based on the energy and communication distance of the node devices, and an adaptive cluster head selection weighting formula was designed. Secondly, the energy and distance weighted sum of nodes were traversed, and the local optimal principle of greedy algorithm was used to select cluster head node. Thus, the user node communication distance was optimized to ensure that users' energy consumption was the lowest to prolong their survival cycles, and the unloading rate of the system was improved. The experimental results show that, compared with the clustered Random cluster head (Random) and the non-clustered Energy Cost optimal (EC) energy consumption optimization algorithms, when the network energy consumption is optimal, the proposed algorithm prolongs the system survival cycle by about 60 percentage points and 72 percentage points. The proposed CCSA can improve the unloading rate and reduce the unloading energy consumption of the system.

Key words: Device-to-Device (D2D), cluster, content sharing, unloading rate, energy consumption optimal

摘要： 针对设备到设备（D2D）缓存通信中，终端设备电池容量有限且设备之间数据传输能耗过大导致文件卸载率下降的问题，提出一种簇内节点能耗最优的缓存通信内容共享算法（CCSA）。首先，鉴于用户终端的随机分布特性，将网络中的用户节点建模为泊松簇过程，基于节点设备能量、通信距离建立卸载模型，设计自适应簇首选择权值公式；其次，遍历节点的能量与距离加权和并采用贪婪算法局部最优原则选取簇首节点，从而对用户节点通信距离进行优化，确保用户能耗最低以延长其生存周期，同时提高系统的卸载率。实验结果表明，与成簇随机选簇首（Random）、非成簇的能耗优化（EC）算法相比，所提算法在网络能耗最优时，系统生存周期延长了约60个百分点、72个百分点。CCSA能够提高卸载率且降低系统的卸载能耗。

关键词: 设备到设备, 簇, 内容共享, 卸载率, 能耗最优

CLC Number:

TONG Piao, LONG Long, HAN Xue, QIU Dawei, HU Qian. Content sharing algorithm for device to device cache communication with minimum inner-cluster energy consumption[J]. Journal of Computer Applications, 2018, 38(6): 1703-1708.

佟飘, 龙隆, 韩雪, 邱大伟, 胡茜. 基于簇内能耗最优的设备到设备缓存通信内容共享算法[J]. 计算机应用, 2018, 38(6): 1703-1708.

References

[1] Cisco. Cisco visual networking index:global mobile data traffic forecast update, 2016-2021 white paper[EB/OL].[2017-09-25]. https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/mobile-white-paper-c11-520862.html.
[2] GOLREZAEI N, DIMAKIS A G, MOLISCH A F. Device-to-device collaboration through distributed storage[C]//GLOBECOM 2012:Proceedings of the 2012 IEEE Global Communications Conference. Piscataway, NJ:IEEE, 2012:2397-2402.
[3] RAO J, FENG H, YANG C C, et al. Optimal caching placement for D2D assisted wireless caching networks[C]//ICC 2016:Proceedings of the 2016 IEEE International Conference on Communications. Piscataway,NJ:IEEE, 2016:2438-2452.
[4] BLASZCZYSZYN B, GIOVANIDIS A. Optimal geographic caching in cellular networks[C]//ICC 2015:Proceedings of the 2015 IEEE International Conference on Communications. Piscataway, NJ:IEEE, 2015:3358-3363.
[5] JI M Y, CAIRE G, MOLISCH A F. The throughput-outage tradeoff of wireless one-hop caching networks[EB/OL].[2017-09-25]. http://pdfs.semanticscholar.org/8166/e32419aa2a89a1125b403d6c093e7f83cf59.pdf.
[6] WU H Q, WANG L, SVENSSON T, et al. Resource allocation for wireless caching in socially-enabled D2D communications[C]//ICC 2016:Proceedings of the 2016 IEEE International Conference on Communications. Piscataway, NJ:IEEE, 2016:1-6.
[7] MALAK D, AL-SHALASH M. Optimal caching for device-to-device content distribution in 5G networks[C]//GC Wkshps 2014:Proceedings of the 2014 IEEE Globecom Workshops. Piscataway, NJ:IEEE, 2014:863-868.
[8] HOSNY S, ERYILMAZ A, ABOUZEID A A, et al. Mobility-aware centralized D2D caching networks[C]//Proceedings of the 201654th Annual Allerton Conference on Communication, Control, and Computing. Piscataway, NJ:IEEE, 2016:725-732.
[9] SAHA C, DHILLON H S. D2D underlaid cellular networks with user clusters:load balancing and downlink rate analysis[C]//WCNC 2017:Proceedings of the 2017 IEEE Wireless Communications and Networking Conference. Piscataway, NJ:IEEE, 2017:1-6.
[10] DOUMIATI S, ARTAIL H, KABALAN K. A framework for clustering LTE devices for implementing group D2D communication and multicast capability[C]//ICICS 2017:Proceedings of the 20178th International Conference on Information and Communication Systems. Piscataway, NJ:IEEE, 2017:216-221.
[11] BRESLAU L, CAO P, FAN L, et al. Web caching and Zipf-like distributions:evidence and implications[C]//INFOCOM 1999:Proceedings of the 1999 Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Piscataway, NJ:IEEE, 1999:126-134.
[12] 张正一,李莉,罗汉文,等.基于容量最优化的设备间通信资源分配方案[J].上海师范大学学报(自然科学版),2017,46(1):37-41.(ZHANG Z Y, LI L, LUO H W, et al. Resource allocation based on capacity optimization fordevice-to-device communication[J]. Journal of Shanghai Normal University (Natural Sciences), 2017, 46(1):37-41.)
[13] AFSHANG M, DHILLON H S, CHONG P H J. k-Closest coverage probability and area spectral efficiency in clustered D2D networks[C]//ICC 2016:Proceedings of the 2016 IEEE International Conference on Communications. Piscataway, NJ:IEEE, 2016:1-6.
[14] HEATH R W, Jr, WU T, KWON Y H, et al. Multiuser MIMO in distributed antenna systems with out-of-cell interference[J]. IEEE Transactions on Signal Processing, 2011, 59(10):4885-4899.
[15] CHEN B Q, YANG C Y. Energy costs for traffic offloading by cache-enabled D2D communications[C]//WCNC 2016:Proceedings of the 2016 IEEE Wireless Communications and Networking Conference. Piscataway, NJ:IEEE, 2016:1-6.

Content sharing algorithm for device to device cache communication with minimum inner-cluster energy consumption

基于簇内能耗最优的设备到设备缓存通信内容共享算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	Shunyong LI, Shiyi LI, Rui XU, Xingwang ZHAO. Incomplete multi-view clustering algorithm based on self-attention fusion [J]. Journal of Computer Applications, 2024, 44(9): 2696-2703.
[2]	Chuanlin PANG, Rui TANG, Ruizhi ZHANG, Chuan LIU, Jia LIU, Shibo YUE. Distributed power allocation algorithm based on graph convolutional network for D2D communication systems [J]. Journal of Computer Applications, 2024, 44(9): 2855-2862.
[3]	Lichao PANG, Xiaofeng WANG, Zhixin XIE, Yi YANG, Xingyu ZHAO, Lan YANG. Solution cluster structure analysis of random regular 3-satisfiability problems [J]. Journal of Computer Applications, 2024, 44(7): 2137-2143.
[4]	Qing WANG, Jieyu ZHAO, Xulun YE, Nongxiao WANG. Enhanced deep subspace clustering method with unified framework [J]. Journal of Computer Applications, 2024, 44(7): 1995-2003.
[5]	Yao DONG, Yixue FU, Yongfeng DONG, Jin SHI, Chen CHEN. Survey of incomplete multi-view clustering [J]. Journal of Computer Applications, 2024, 44(6): 1673-1682.
[6]	Xiaoxia JIANG, Ruizhang HUANG, Ruina BAI, Lina REN, Yanping CHEN. Deep event clustering method based on event representation and contrastive learning [J]. Journal of Computer Applications, 2024, 44(6): 1734-1742.
[7]	Tianyu HUANG, Yuanxing LI, Hao CHEN, Zijia GUO, Mingjun WEI. User cluster partitioning method based on weighted fuzzy clustering in ground-air collaboration scenarios [J]. Journal of Computer Applications, 2024, 44(5): 1555-1561.
[8]	Tongtong XU, Bin XIE, Chunhao ZHANG, Ximei ZHANG. Multi-order nearest neighbor graph clustering algorithm by fusing transition probability matrix [J]. Journal of Computer Applications, 2024, 44(5): 1527-1538.
[9]	Rui TANG, Chuanlin PANG, Ruizhi ZHANG, Chuan LIU, Shibo YUE. DDPG-based resource allocation in D2D communication-empowered cellular network [J]. Journal of Computer Applications, 2024, 44(5): 1562-1569.
[10]	Lin GAO, Yu ZHOU, Tak Wu KWONG. Evolutionary bi-level adaptive local feature selection [J]. Journal of Computer Applications, 2024, 44(5): 1408-1414.
[11]	Yu DING, Hanlin ZHANG, Rong LUO, Hua MENG. Fuzzy clustering algorithm based on belief subcluster cutting [J]. Journal of Computer Applications, 2024, 44(4): 1128-1138.
[12]	Xiaoyan ZHAO, Yan KUANG, Menghan WANG, Peiyan YUAN. Device-to-device content sharing mechanism based on knowledge graph [J]. Journal of Computer Applications, 2024, 44(4): 995-1001.
[13]	Long CHEN, Xuanlin YU, Wen CHEN, Yi YAO, Wenjing ZHU, Ying JIA, Denghong LI, Zhi REN. Efficient clustered routing protocol for intelligent road cone ad-hoc networks based on non-random clustering [J]. Journal of Computer Applications, 2024, 44(3): 869-875.
[14]	Lin SUN, Menghan LIU. K-means clustering based on adaptive cuckoo optimization feature selection [J]. Journal of Computer Applications, 2024, 44(3): 831-841.
[15]	Zhuo ZHANG, Huazhu CHEN. Deep subspace clustering based on multiscale self-representation learning with consistency and diversity [J]. Journal of Computer Applications, 2024, 44(2): 353-359.