scholarly journals Distributed M-ary hypothesis testing for decision fusion in multiple-input multiple-output wireless sensor networks

2020 ◽  
Vol 14 (18) ◽  
pp. 3256-3260
Author(s):  
Ali Jamoos ◽  
Rushdi Abuawwad
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Hypothesis Testing ◽  
Multiple Input Multiple Output ◽  
Decision Fusion ◽  
Wireless Sensor ◽  
Multiple Input ◽  
Input Multiple Output

scholarly journals Joint Source-Relay Optimization for MIMO Full-Duplex Bidirectional Wireless Sensor Networks with SWIPT

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1827 ◽  
Author(s):  
Dan Liu ◽  
Zhigang Wen ◽  
Xiaoqing Liu ◽  
Shan Li ◽  
Junwei Zou
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Full Duplex ◽  
Wireless Sensor ◽  
Local Optimum ◽  
Relay Nodes ◽  
Multiple Input ◽  
Input Multiple Output

The simultaneous wireless information and power transfer (SWIPT) technique has been considered as a promising approach to prolong the lifetime of energy-constraint wireless sensor networks (WSNs). In this paper, a multiple-input multiple-output (MIMO) full-duplex (FD) bidirectional wireless sensor network (BWSN) with SWIPT is investigated. Based on minimum total mean-square-error (total-MSE) criterion, a joint optimization problem for source and relay beamforming and source receiving subject to transmitting power and harvesting energy constraints is established. Since this problem is non-convex, an iterative algorithm based on feasible point pursuit-successive convex approximation (FPP-SCA) is derived to obtain a local optimum. Moreover, considering the scenarios in which source and relay nodes equipped with the same and different numbers of antennas, a low-complexity diagonalizing design-based scheme is employed to simplify each non-convex subproblem into convex problems and to reduce the computational complexity. Numerical results of the total-MSE and bit error rate (BER) are implemented to demonstrate the performance of the two different schemes.

Energy Efficient Multi-Hop Wireless Sensor Networks with Cooperative MIMO Scheme

2013 ◽  
Vol 660 ◽  
pp. 124-129
Author(s):  
Yu Yang Peng ◽  
Jaeho Choi ◽  
Zi Chen Ren ◽  
Jae Ho Choi
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Minimum Energy ◽  
Multiple Input Multiple Output ◽  
Wireless Sensor ◽  
Cooperative Mimo ◽  
Minimum Energy Consumption ◽  
Input Multiple Output

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.

Cross layer design with extensive virtual MIMO: FS-MUP optimization model for wireless sensor network

2020 ◽  
pp. 1-16
Author(s):  
Monali Prajapati ◽  
Dr. Jay Joshi
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Multiple Input Multiple Output ◽  
Wireless Sensor ◽  
Cross Layer ◽  
Cross Layer Design ◽  
Virtual Mimo ◽  
Proposed Model ◽  
Multiple Input ◽  
Input Multiple Output

In the wireless sensor network (WSN), wireless communication is said to be the dominant power-consuming operation and it is a challenging one. Virtual Multiple-Input–Multiple-Output (V-MIMO) technology is considered to be the energy-saving method in the WSN. In this paper, a novel multihop virtual MIMO communication protocol is designed in the WSN via cross-layer design to enhance the energy efficiency, reliability, and end-to-end (ETE) and Quality of Service (QoS) provisioning. On the basis of the proposed protocol, the optimal set of parameters concerning the transmission and the overall consumed energy by each of the packets is found. Furthermore, the modeling of ETE latency and throughput of the protocol takes place with respect to the bit-error-rate (BER). A novel hybrid optimization algorithm referred as Flight Straight Moth Updated Particle Swarm Optimization (FS-MUP) is introduced to find the optimal BER that meets the QoS, ETE requirements of each link with lower power consumption. Finally, the performance of the proposed model is evaluated over the extant models in terms of Energy Consumption and BER as well.

scholarly journals Massive MIMO-Based Distributed Signal Detection in Multi-Antenna Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2005
Author(s):  
Guofeng Wei ◽  
Bangning Zhang ◽  
Guoru Ding ◽  
Bing Zhao ◽  
Yimin Wei ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Network ◽  
Detection Probability ◽  
Massive Mimo ◽  
Large Scale ◽  
Multiple Input Multiple Output ◽  
Wireless Sensor ◽  
Channel State ◽  
Single Antenna

For massive multiple-input multiple-output (MIMO) distributed wireless sensor networks, this paper investigates the role of multi-antenna sensors in improving network perception performance. First, we construct a distributed multi-antenna sensor network based on massive MIMO. By using the anti-fading characteristics of multi-antennas, it is better to achieve accurate detection than the single-antenna sensor network. Based on this, we derive a closed-loop expression for the detection probability of the best detector. Then, we consider the case that the sensor power resources are limited, and thus we want to use finite power to achieve higher detection probability. For this reason, the power was optimized by the alternating direction method of multipliers (ADMM). Moreover, we also prove that only statistical channel state is needed in large-scale antenna scenarios, which avoid the huge overhead of channel state information. Finally, according to the simulation results, the multi-antenna sensor network has better detection performance than the single-antenna sensor network which demonstrates the improved performance of the proposed schemes and also validates the theoretical findings.

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