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Journal articles on the topic 'Multi-hop MIMO Networks'

Author: Grafiati

Published: 6 September 2023

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1

ONO, Fumie, and Kei SAKAGUCHI. "STBC MIMO Network Coding for Bi-directional Multi-Hop Relay Networks." IEICE Transactions on Communications E92-B, no. 12 (2009): 3676–82. http://dx.doi.org/10.1587/transcom.e92.b.3676.

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2

Smarra, F., A. D'Innocenzo, and M. D. Di Benedetto. "Fault Tolerant Stabilizability of MIMO Multi-Hop Control Networks." IFAC Proceedings Volumes 45, no. 26 (September 2012): 79–84. http://dx.doi.org/10.3182/20120914-2-us-4030.00048.

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3

Zeng, Huacheng, Yi Shi, Y. Hou, Rongbo Zhu, and Wenjing Lou. "A novel MIMO DoF model for multi-hop networks." IEEE Network 28, no. 5 (September 2014): 81–85. http://dx.doi.org/10.1109/mnet.2014.6915444.

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4

Peng, Yu Yang, Jaeho Choi, Zi Chen Ren, and Jae Ho Choi. "Energy Efficient Multi-Hop Wireless Sensor Networks with Cooperative MIMO Scheme." Advanced Materials Research 660 (February 2013): 124–29. http://dx.doi.org/10.4028/www.scientific.net/amr.660.124.

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For wireless sensor networks, energy efficiency is one of the most important subjects in recent research. In this paper, an energy-efficient multi-hop scheme based on cooperative MIMO (multiple-input multiple-output) technique is proposed for wireless sensor networks. Different from other papers, we consider a single cluster transmission scenario in which energy consumption is optimized by selecting the hop length and modulation constellation size. The optimal energy consumption formula is derived and proved mathematically. In addition, the minimum energy consumption per bit is calculated numerically.

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5

Gunduz, Deniz, Mohammad A. Khojastepour, Andrea Goldsmith, and H. Vincent Poor. "Multi-hop MIMO relay networks: diversity-multiplexing trade-off analysis." IEEE Transactions on Wireless Communications 9, no. 5 (May 2010): 1738–47. http://dx.doi.org/10.1109/twc.2010.05.090915.

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6

Chen, Chih-Liang, Wayne E. Stark, and Sau-Gee Chen. "Energy-Bandwidth Efficiency Tradeoff in MIMO Multi-Hop Wireless Networks." IEEE Journal on Selected Areas in Communications 29, no. 8 (September 2011): 1537–46. http://dx.doi.org/10.1109/jsac.2011.110904.

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7

Kwon, Beom, Jongrok Park, and Sanghoon Lee. "Virtual MIMO broadcasting transceiver design for multi-hop relay networks." Digital Signal Processing 46 (November 2015): 97–107. http://dx.doi.org/10.1016/j.dsp.2015.08.003.

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8

Lee, Kyu-haeng, and Daehee Kim. "Cross-Layer Optimization for Heterogeneous MU-MIMO/OFDMA Networks." Sensors 21, no. 8 (April 13, 2021): 2744. http://dx.doi.org/10.3390/s21082744.

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To enable the full benefits from MU-MIMO (Multiuser-Multiple Input Multiple Output) and OFDMA (Orthogonal Frequency Division Multiple Access) to be achieved, the optimal use of these two technologies for a given set of network resources has been investigated in a rich body of literature. However, most of these studies have focused either on maximizing the performance of only one of these schemes, or have considered both but only for single-hop networks, in which the effect of the interference between nodes is relatively limited, thus causing the network performance to be overestimated. In addition, the heterogeneity of the nodes has not been sufficiently considered, and in particular, the joint use of OFDMA and MU-MIMO has been assumed to be always available at all nodes. In this paper, we propose a cross-layer optimization framework that considers both OFDMA and MU-MIMO for heterogeneous wireless networks. Not only does our model assume that the nodes have different capabilities, in terms of bandwidth and the number of antennas, but it also supports practical use cases in which nodes can support either OFDMA or MU-MIMO, or both at the same time. Our optimization model carefully takes into account the interactions between the key elements of the physical layer to the network layer. In addition, we consider multi-hop networks, and capture the complicated interference relationships between nodes as well as multi-path routing via multi-user transmissions. We formulate the proposed model as a Mixed Integer Linear Programming (MILP) problem, and initially model the case in which each node can selectively use either OFDMA or MU-MIMO; we then extend this to scenarios in which they are jointly used. As a case study, we apply the proposed model to sum-rate maximization and max–min fair allocation, and verify through MATLAB numerical evaluations that it can take appropriate advantage of each technology for a given set of network resources. Based on the optimization results, we also observe that when the two technologies are jointly used, more multi-user transmissions are enabled thanks to flexible resource allocation, meaning that greater use of the link capacity is achieved.

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9

Erdogan, Eylem, Sultan Çolak, Hakan Alakoca, Mustafa Namdar, Arif Basgumus, and Lutfiye Durak-Ata. "Interference Alignment in Multi-Hop Cognitive Radio Networks under Interference Leakage." Applied Sciences 8, no. 12 (December 4, 2018): 2486. http://dx.doi.org/10.3390/app8122486.

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In this work, we examine the interference alignment (IA) performance of a multi-input multi-output (MIMO) multi-hop cognitive radio (CR) network in the presence of multiple primary users. In the proposed architecture, it is assumed that linear IA is adopted at the secondary network to alleviate the interference between primary and secondary networks. By doing so, the secondary source can communicate with the secondary destination via multiple relays without causing any interference to the primary network. Even though linear IA can suppress the interference in CR networks considerably, interference leakages may occur due to a fast fading channel. To this end, we focus on the performance of the secondary network for two different cases: (i) the interference is perfectly aligned; (ii) the impact of interference leakages. For both cases, closed-form expressions of outage probability and ergodic capacity are derived. The results, which are validated by Monte Carlo simulations, show that interference leakages can deteriorate both system performance and the diversity gains considerably.

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10

ZHOU, Chao, Xing-Gong ZHANG, and Zong-Ming GUO. "Optimization Scheme for Multi-User Video Transmission Over MIMO Multi-Hop Wireless Networks." Journal of Software 24, no. 2 (December 27, 2013): 279–94. http://dx.doi.org/10.3724/sp.j.1001.2013.04201.

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11

Fawaz, Nadia, Keyvan Zarifi, Mérouane Debbah, and David Gesbert. "Asymptotic Capacity and Optimal Precoding in MIMO Multi-Hop Relay Networks." IEEE Transactions on Information Theory 57, no. 4 (April 2011): 2050–69. http://dx.doi.org/10.1109/tit.2011.2111830.

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12

Jiang, Canming, Yi Shi, Y. Thomas Hou, and Sastry Kompella. "On the Asymptotic Capacity of Multi-Hop MIMO Ad Hoc Networks." IEEE Transactions on Wireless Communications 10, no. 4 (April 2011): 1032–37. http://dx.doi.org/10.1109/twc.2011.020111.100528.

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13

Shi, Yi, Jia Liu, Canming Jiang, Cunhao Gao, and Y. Thomas Hou. "A DoF-Based Link Layer Model for Multi-Hop MIMO Networks." IEEE Transactions on Mobile Computing 13, no. 7 (July 2014): 1395–408. http://dx.doi.org/10.1109/tmc.2013.122.

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14

Zeng, Huacheng, Yi Shi, Y. Thomas Hou, Wenjing Lou, Sastry Kompella, and Scott F. Midkiff. "An Analytical Model for Interference Alignment in Multi-Hop MIMO Networks." IEEE Transactions on Mobile Computing 15, no. 1 (January 1, 2016): 17–31. http://dx.doi.org/10.1109/tmc.2015.2410772.

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15

Zeng, Huacheng, Yi Shi, Y. Thomas Hou, Wenjing Lou, Hanif D. Sherali, Rongbo Zhu, and Scott F. Midkiff. "A Scheduling Algorithm for MIMO DoF Allocation in Multi-Hop Networks." IEEE Transactions on Mobile Computing 15, no. 2 (February 1, 2016): 264–77. http://dx.doi.org/10.1109/tmc.2015.2413788.

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16

Zhang, Guanghui, Jiandong Li, Min Zhao, and Changle Li. "Broadcast scheduling with MIMO links in multi-hop ad hoc networks." Journal of Electronics (China) 24, no. 4 (July 2007): 477–83. http://dx.doi.org/10.1007/s11767-005-0246-z.

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17

TANG, BO, YANDONG WANG, and MINGTIAN ZHOU. "SYSTEM DESIGN OF ENERGY-EFFICIENT COOPERATIVE MIMO SCHEME FOR CLUSTER-BASED WIRELESS SENSOR NETWORKS." International Journal of Wavelets, Multiresolution and Information Processing 09, no. 02 (March 2011): 181–96. http://dx.doi.org/10.1142/s0219691311004043.

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Wireless Sensor Networks (WSNs) with limited energy resource demand the implementation of energy-efficient techniques in applications. Recently, the scheme based on cooperative MIMO (Multiple-Input-Multiple-Output) has been proposed to enhance energy saving in WSNs. In this paper, we analyze the performance of energy consumption of inter-cluster communication based on cooperative MIMO techniques in cluster-based WSNs, and then propose a novel method to optimize the energy consumption analysis by changing the network topology into an equivalent double-string topology which can be easily solved by using optimization programming problem in SISO (Single-Input-Single-Output) system model, where cooperating nodes within each MIMO scheme transmission steps are treated as a virtual node. The simulation results show that our proposed data transmission with cooperative MIMO scheme outperforms the protocol without MIMO techniques significantly in terms of energy, lifetime and delay in multi-hop WSNs.

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18

Cui, Miao. "A MAC Protocol with MPR in Multi-hop MIMO Ad Hoc Networks." Journal of Information and Computational Science 11, no. 7 (May 1, 2014): 2201–12. http://dx.doi.org/10.12733/jics20104468.

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19

Qi, Xiaohui, Kaizhi Huang, Zhihao Zhong, Xiaolei Kang, and Zhou Zhong. "Physical layer security of multi-hop aided downlink MIMO heterogeneous cellular networks." China Communications 13, no. 2 (2016): 120–30. http://dx.doi.org/10.1109/cc.2016.7405728.

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20

Qi, Xiaohui, Kaizhi Huang, Zhihao Zhong, Xiaolei Kang, and Zhou Zhong. "Physical layer security of multi-hop aided downlink MIMO heterogeneous cellular networks." China Communications 13, Supplement2 (2016): 120–30. http://dx.doi.org/10.1109/cc.2016.7833466.

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21

Park, Jongrok, and Sanghoon Lee. "M2-m2 Beamforming for Virtual MIMO Broadcasting in Multi-Hop Relay Networks." IEEE Journal on Selected Areas in Communications 30, no. 8 (September 2012): 1358–69. http://dx.doi.org/10.1109/jsac.2012.120906.

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22

Wang, Zhao, Ming Xiao, Chao Wang, and Mikael Skoglund. "Degrees of Freedom of Multi-Hop MIMO Broadcast Networks with Delayed CSIT." IEEE Wireless Communications Letters 2, no. 2 (April 2013): 1–4. http://dx.doi.org/10.1109/wcl.2013.13.120857.

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23

Li, Quanzhong, Jiayin Qin, and Renhai Feng. "Optimal relay precoding for spectrum sharing multi-hop MIMO cognitive radio networks." Electronics Letters 48, no. 24 (November 22, 2012): 1562–64. http://dx.doi.org/10.1049/el.2012.3598.

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24

Gomez-Cuba, Felipe, and Michele Zorzi. "Optimal Link Scheduling in Millimeter Wave Multi-Hop Networks With MU-MIMO Radios." IEEE Transactions on Wireless Communications 19, no. 3 (March 2020): 1839–54. http://dx.doi.org/10.1109/twc.2019.2959295.

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25

LEE, Jonghyun, Gia Khanh TRAN, Kei SAKAGUCHI, and Kiyomichi ARAKI. "Effect of Power Allocation Schemes on MIMO Two-Way Multi-Hop Network." IEICE Transactions on Communications E93-B, no. 12 (2010): 3362–70. http://dx.doi.org/10.1587/transcom.e93.b.3362.

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26

Gao, Yating, Guixia Kang, and Jianming Cheng. "An Opportunistic Cooperative Packet Transmission Scheme in Wireless Multi-Hop Networks." Sensors 19, no. 21 (November 5, 2019): 4821. http://dx.doi.org/10.3390/s19214821.

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Cooperative routing, combining cooperative communication in the physical layer and routing technology in the network layer, is one of the most widely used technologies for improving end-to-end transmission reliability and delay in the wireless multi-hop networks. However, the existing cooperative routing schemes are designed based on an optimal fixed-path routing so that the end-to-end performance is greatly restricted by the low spatial efficiency. To address this problem, in this paper an opportunistic cooperative packet transmission (OCPT) scheme is explored by combining cooperative communication and opportunistic routing. The proposed scheme divides the multi-hop route into multiple virtual multiple-input-multiple-output (MIMO) transmissions. Before each transmission, based on the idea of opportunistic routing, a cluster head (CH) is introduced to determine the multiple transmitters and multiple receivers to form a cluster. Then, the single-hop transmission distance is defined as the metric of forward progress to the destination. Each intra-cluster cooperative packet transmission is formulated as a transmit beamforming optimization problem, and an iterative optimal beamforming policy is proposed to solve the problem and maximize the single-hop transmission distance. CH organizes multiple transmitters to cooperatively transmit packets to multiple receivers with the optimized transmit beamforming vector. Finally, according to the transmission results, the cluster is updated and the new cooperative transmission is started. Iteratively, the transmission lasts until the destination has successfully received the packet. We comprehensively evaluate the OCPT scheme by comparing it with conventional routing schemes. The simulation results demonstrate that the proposed OCPT scheme is effective on shortening the end-to-end transmission delay, increasing the number of successful packet transmissions and improving the packet arrival ratio and transmission efficiency.

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27

FU, Youhua, Wei-Ping ZHU, Chen LIU, Feng LU, and Hua-An ZHAO. "Joint MMSE Design of Relay and Destination in Two-Hop MIMO Multi-Relay Networks." IEICE Transactions on Communications E96.B, no. 3 (2013): 836–46. http://dx.doi.org/10.1587/transcom.e96.b.836.

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28

Peng, Yu-Yang, Seong-Beom Abn, and Jae-Kyung Pan. "An Energy-Efficient Multi-Hop Scheme Based on Cooperative MIMO for Wireless Sensor Networks." Journal of Korean Institute of Communications and Information Sciences 36, no. 9A (September 30, 2011): 796–800. http://dx.doi.org/10.7840/kics.2011.36a.9.796.

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29

Kim, Daeun, Song-Nam Hong, and Namyoon Lee. "Supervised-Learning for Multi-Hop MU-MIMO Communications With One-Bit Transceivers." IEEE Journal on Selected Areas in Communications 37, no. 11 (November 2019): 2559–72. http://dx.doi.org/10.1109/jsac.2019.2933965.

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30

Rahim, Abdul, and Dr V. A. Sankar Ponnapalli. "Geographic information-based Data Transmission and Cooperative Communication for Vehicular Networks." International Journal of Intelligent Communication, Computing and Networks 1, no. 1 (August 25, 2020): 27–34. http://dx.doi.org/10.51735/ijiccn/001/05.

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With the advancements in Vehicular communication technologies in automobile engineering leads to enhancement of modern societies by utilizing Internet based data communication in a vehicular network to effectively avoid accidents and traffic congestions using Multi Input Multi Output (MIMO) cooperative relay technique for enhancing the aspects of performance by reduction of transmission energy consumption by taking the advantage of spatial and temporal diversity gain in a vehicular network as the conventional routing based on topology is merely not suitable over a dynamic vehicular network environment as GPS is used to identify effective route [4].In this paper we propose applications of cooperative communication techniques and their survey for identifying close relationship between forwarding and addressing techniques in a vehicular network and further we compare performance and energy consumption of cooperative techniques with the traditional multi-hop technique over Rayleigh channel using MQAM for optimization.

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31

Qin, Cai, Chaowei Wang, Du Pan, Weidong Wang, and Yinghai Zhang. "A Cross Time Slot Partial Interference Alignment Scheme in Two-Cell Relay Heterogeneous Networks." Applied Sciences 9, no. 4 (February 15, 2019): 652. http://dx.doi.org/10.3390/app9040652.

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The interference of a two-cell two-hop multi-input–multi-output (MIMO) heterogeneous network where the cell-center user (CCU) coexists with the cell-edge user (CEU) is complex to characterize and costly to suppress. In downlink, the CEU is assumed to be served by a half-duplex relay with amplify-and-forward mode. To suppress the intra-cell and inter-cell interference, a cross time slot partial interference alignment (TPIA) scheme, which aligns the interference terms generated in different sub-time slots to the same subspace, is proposed in this paper. Furthermore, the feasibility condition of antenna configuration is analyzed. Simulation results indicate that the TPIA scheme outperforms other existing conventional techniques in achievable sum degrees of freedom and sum-rate performance.

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32

TRAN, Gia Khanh, Rindranirina RAMAMONJISON, Kei SAKAGUCHI, and Kiyomichi ARAKI. "An Efficient Relay Placement Method with Power Allocation for MIMO Two-Way Multi-Hop Networks." IEICE Transactions on Communications E96.B, no. 5 (2013): 1176–86. http://dx.doi.org/10.1587/transcom.e96.b.1176.

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33

Fu, Youhua, Luxi Yang, and Wei-Ping Zhu. "A nearly optimal amplify-and-forward relaying scheme for two-hop MIMO multi-relay networks." IEEE Communications Letters 14, no. 3 (March 2010): 229–31. http://dx.doi.org/10.1109/lcomm.2010.03.091779.

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34

Hamdaoui, Bechir, and Parameswaran Ramanathan. "Cross-Layer Optimized Conditions for QoS Support in Multi-Hop Wireless Networks with MIMO Links." IEEE Journal on Selected Areas in Communications 25, no. 4 (May 2007): 667–77. http://dx.doi.org/10.1109/jsac.2007.070504.

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35

Kei Sakaguchi, Takumi Yoneda, Masashi Iwabuchi, and Tomoki Murakami. "mmWave massive analog relay MIMO." ITU Journal on Future and Evolving Technologies 2, no. 6 (September 24, 2021): 43–55. http://dx.doi.org/10.52953/wzof2275.

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Millimeter-Wave (mmWave) communications are a key technology to realize ultra-high data rate and ultra-low latency wireless communications. Compared with conventional communication systems in the microwave band such as 4G/LTE, mmWave communications employ a higher frequency band which allows a wider bandwidth and is suitable for large capacity communications. It is expected to be applied to various use cases such as mmWave cellular networks and vehicular networks. However, due to the strong diffraction loss and the path loss in the mmWave band, it is difficult or even impossible to achieve high channel capacity for User Equipment (UE) located in Non-Line-Of-Sight (NLOS) environments. To solve the problem, the deployment of relay nodes has been considered. In this paper, we consider the use of massive analog Relay Stations (RSs) to relay the transmission signals. By relaying the signals by a large number of RSs, an artificial Multiple-Input Multiple-Output (MIMO) propagation environment can be formed, which enables mmWave MIMO communications to the NLOS environment. We describe a theoretical study of a massive relay MIMO system and extend it to include multi-hop relays. Simulations are conducted, and the numerical results show that the proposed system achieves high data rates even in a grid-like urban environment.

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36

Cho, Hee-Nam, Jin-Woo Lee, and Yong-Hwan Lee. "Cooperative power allocation with partial channel information in multi-cell multi-user dual-hop MIMO relay systems." International Journal of Communication Systems 24, no. 5 (September 26, 2010): 586–606. http://dx.doi.org/10.1002/dac.1174.

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37

Bing, Justin Lee, Lenin Gopal, Yue Rong, Choo W. R. Chiong, and Zhuquan Zang. "Robust Transceiver Design for Multi-Hop AF MIMO Relay Multicasting From Multiple Sources." IEEE Transactions on Vehicular Technology 70, no. 2 (February 2021): 1565–76. http://dx.doi.org/10.1109/tvt.2021.3054481.

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38

Wang, Zhiqiang, Yunlong Cai, An Liu, Jun Wang, and Guangrong Yue. "Two-Timescale Uplink Channel Estimation for Dual-Hop MIMO Relay Multi-User Systems." IEEE Transactions on Vehicular Technology 70, no. 5 (May 2021): 4724–39. http://dx.doi.org/10.1109/tvt.2021.3072658.

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39

Minh Nam, Pham, Thanh-Long Nguyen, Ha Duy Hung, Tran Trung Duy, Nguyen Thanh Binh, and Nguyen Luong Nhat. "Throughput analysis of power beacon-aided multi-hop MIMO relaying networks employing NOMA and TAS/SC." TELKOMNIKA (Telecommunication Computing Electronics and Control) 20, no. 4 (August 1, 2022): 731. http://dx.doi.org/10.12928/telkomnika.v20i4.23769.

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40

Liu, Jain-Shing. "Energy-Efficient Cross-Layer Design of Cooperative MIMO Multi-Hop Wireless Sensor Networks Using Column Generation." Wireless Personal Communications 66, no. 1 (June 1, 2011): 185–205. http://dx.doi.org/10.1007/s11277-011-0332-5.

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41

Liu, An, Vincent K. N. Lau, and Youjian Liu. "Duality and Optimization for Generalized Multi-Hop MIMO Amplify-and-Forward Relay Networks With Linear Constraints." IEEE Transactions on Signal Processing 61, no. 9 (May 2013): 2356–65. http://dx.doi.org/10.1109/tsp.2013.2245126.

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42

Peng, Yuyang, and Chan-Hyun Youn. "Lifetime and energy optimization in multi-hop wireless sensor networks with spatial modulation based cooperative MIMO." IEEJ Transactions on Electrical and Electronic Engineering 10, no. 6 (September 23, 2015): 731–32. http://dx.doi.org/10.1002/tee.22155.

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43

LEE, In-Ho, Joong-Hoo PARK, and Dongwoo KIM. "Outage Performance of Multi-Hop Decouple-and-Forward Relaying in Spatially Correlated MIMO Channels." IEICE Transactions on Communications E93-B, no. 5 (2010): 1298–301. http://dx.doi.org/10.1587/transcom.e93.b.1298.

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44

Tin, Phu Tran, Duy-Hung Ha, Pham Minh Quang, Nguyen Thanh Binh, and Nguyen Luong Nhat. "Performance of multi-hop cognitive MIMO relaying networks with joint constraint of intercept probability and limited interference." TELKOMNIKA (Telecommunication Computing Electronics and Control) 19, no. 1 (February 1, 2021): 44. http://dx.doi.org/10.12928/telkomnika.v19i1.18006.

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45

Lertwiram, Namzilp, Gia Khanh Tran, Kei Sakaguchi, and Kiyomichi Araki. "An Efficient Relay Node Placement Scheme for Two-Way MIMO Multi-Hop Networks in Practical Indoor Environments." IEEE Transactions on Wireless Communications 12, no. 6 (June 2013): 2977–87. http://dx.doi.org/10.1109/twc.2013.040413.121423.

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46

Rachedi, Abderrezak, Hakim Badis, and Abderrahim Benslimane. "How MIMO cross-layer design enables QoS while detecting non-cooperative nodes in wireless multi-hop networks." Journal of Network and Computer Applications 46 (November 2014): 395–406. http://dx.doi.org/10.1016/j.jnca.2014.07.011.

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47

Kanithan, S., N. Arun Vignesh, E. Karthikeyan, and N. Kumareshan. "An intelligent energy efficient cooperative MIMO-AF multi-hop and relay based communications for Unmanned Aerial Vehicular networks." Computer Communications 154 (March 2020): 254–61. http://dx.doi.org/10.1016/j.comcom.2020.01.029.

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48

Alam, Md Zahangir, Iwan Adhicandra, and Abbas Jamalipour. "Optimal Best Path Selection Algorithm for Cluster-Based Multi-Hop MIMO Cooperative Transmission for Vehicular Communications." IEEE Transactions on Vehicular Technology 68, no. 9 (September 2019): 8314–21. http://dx.doi.org/10.1109/tvt.2019.2917695.

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49

Kalaivani, S. "Sencar Based Load Balanced Clustering With Mobile Data Gathering In Wireless Sensor Networks." International Journal of Students' Research in Technology & Management 4, no. 2 (July 19, 2016): 38–43. http://dx.doi.org/10.18510/ijsrtm.2016.424.

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The wireless sensor networks consist of static sensors, which can be deployed in a wide environment for monitoring applications. While transmitting the data from source to static sink, the amount of energy consumption of the sensor node is high. This results in reduced lifetime of the network. Some of the WSN architectures have been proposed based on Mobile Elements such as three-layer framework is for mobile data collection, which includes the sensor layer, cluster head layer, and mobile collector layer (called SenCar layer). This framework employs distributed load balanced clustering and dual data uploading, it is referred to as LBC-DDU.In the sensor layer a distributed load balanced clustering algorithm is used for sensors to self-organize themselves into clusters. The cluster head layer use inter-cluster transmission range it is carefully chosen to guarantee the connectivity among the clusters. Multiple cluster heads within a cluster cooperate with each other to perform energy-saving in the inter-cluster communications. Through this transmissions cluster head information is send to the SenCar for its moving trajectory planning.This is done by utilizing multi-user multiple-input and multiple-output (MU-MIMO) technique. Then the results show each cluster has at most two cluster heads. LBC-DDU achieves higher energy saving per node and energy saving on cluster heads comparing with data collection through multi-hop relay to the static data sinks.

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50

Prof. Dr. Noorullah Shariff, Ms Tejashri H. Mohite,. "Evaluation and Analysis for Maximum Lifespan of Wireless Sensor Networks by Energy-Efficient Design." International Journal on Recent and Innovation Trends in Computing and Communication 9, no. 4 (April 30, 2021): 01–05. http://dx.doi.org/10.17762/ijritcc.v9i4.5470.

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Abstract:

Wireless Sensor Networks (WSNs) have used worldwide in the past few years and are now being used in health monitoring ,disaster management, defense, telecommunications, etc. Such networks are used in many industrial and consumer applications such as industrial process and environment monitoring, among others. A WSN network is a collection of specialized transducers known as sensor nodes with a communication link distributed randomly in any locations to monitor environmental parameters such as water level, and temperature. Each sensor node is equipped with a transducer, a signal processor, a power unit, and a transceiver. WSNs are now being widely used to monitor environmental parameters, including the amount of gas, water, temperature, humidity, oxygen level, dust, etc. The WSN for environment monitoring can be equivalently replaced by a multiple-input multiple-output (MIMO) relay network. Multi-hop relay networks have attracted significant research interest in recent years for their capability in increasing the coverage range. The network communication link from a source to a destination is implemented using the amplify-and-forward (AF) or decode-and-forward (DF) schemes. The AF relay receives information from the previous relay and simply amplifies the received signal and then forwards it to the next relay. On the other hand, the DF relay first decodes the received signal and then forwards it to the next relay in the second stage if it can perfectly decode the incoming signal. For analytical simplicity, in this thesis, we consider the AF relaying scheme and the results of this work can also be developed for the DF relay.

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