Abstract
Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude.
Highlights
The Multi-Input Multi-Output (MIMO) technique can boost network throughput, conserve energy, and improve network coverage, which has been supported by various international standards such as3rd Generation Partnership Project Long Term Evolution (3GPP LTE), LTE Advanced (LTE-A), the or fifth generation (5G) cellular networks and IEEE 802.16e and IEEE 802.16m
For any link i→j in a connected network formed by the ICV-MIMO scheme, where the MCHs of node i and j are node u and v respectively, if there exists the inter-cluster link u→v in the network, the clustering ranges of node u and v are at most du,max /3
In the first study thethe impact of the number of nodes (that(that is, it is first group groupofofsimulations, simulations,we we study impact of the number of nodes is,node it is density since since the simulation field is fixed) on the transmission power, the average path node density the simulation field is fixed) onaverage the average transmission power, the average hop-count, the network lifetime, the the network connectivity ratio, the path hop-count, the network lifetime, network connectivity ratio, theaverage averagepath pathdelay, delay, and the average packet delivery ratio for the and the three schemes
Summary
The Multi-Input Multi-Output (MIMO) technique can boost network throughput, conserve energy, and improve network coverage, which has been supported by various international standards such as3rd Generation Partnership Project Long Term Evolution (3GPP LTE), LTE Advanced (LTE-A), the or fifth generation (5G) cellular networks and IEEE 802.16e and IEEE 802.16m. Each wireless device has to be equipped with multiple antennas to exploit the benefits of MIMO. Due to the high hardware complexity and cost, implementation of multiple transmitting antennas sometimes cannot be available, especially in small wireless devices (e.g., sensors). Cooperative MIMO emulates the functionality of multi-antenna systems by grouping wireless devices to operate as virtual multi-antenna nodes [1], which is called Virtual MIMO (VMIMO). It is desirable for single-antenna devices to take advantage of MIMO gains efficiently, which enables one to make use of all the neighboring terminals and amortize the cost of multiple antennas.
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