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Mobile wireless multimedia sensor networks (WMSNs), which consist of mobile sink or sensor nodes and use rich sensing information, require much faster and more reliable wireless links than static wireless sensor networks (WSNs). This paper proposes an adaptive multi-node (MN) multiple input and multiple output (MIMO) transmission to improve the transmission reliability and capacity of mobile sink nodes when they experience spatial correlation. Unlike conventional single-node (SN) MIMO transmission, the proposed scheme considers the use of transmission antennas from more than two sensor nodes. To find an optimal antenna set and a MIMO transmission scheme, a MN MIMO channel model is introduced first, followed by derivation of closed-form ergodic capacity expressions with different MIMO transmission schemes, such as space-time transmit diversity coding and spatial multiplexing. The capacity varies according to the antenna correlation and the path gain from multiple sensor nodes. Based on these statistical results, we propose an adaptive MIMO mode and antenna set switching algorithm that maximizes the ergodic capacity of mobile sink nodes. The ergodic capacity of the proposed scheme is compared with conventional SN MIMO schemes, where the gain increases as the antenna correlation and path gain ratio increase.

During the last several decades, wireless sensor networks (WSNs) have been widely used in various areas, including environmental monitoring, home automation, healthcare, agriculture, unmanned battle fields, public space surveillance and intelligent traffic systems (ITSs) [

A multiple antenna technique may be an appropriate solution to provide reliable and high data rate channels in WMSNs. Multiple input and multiple output (MIMO) systems can support higher data rates than a single input and single output (SISO) system under the same transmit power budget and bit-error-rate (BER) performance requirements. MIMO communication exploits the spatial components of the wireless channel to improve the capacity and error rate performance of communication systems through spatial diversity or multiplexing. Diversity schemes, such as orthogonal space-time block codes (OSTBC) [

To overcome this problem, we propose an adaptive multi-node MIMO transmission scheme for mobile WMSNs. In an adaptive MIMO transmission, various MIMO schemes are dynamically exploited based on the correlation of the given channels. In cellular systems, a number of adaptive MIMO transmission techniques have been proposed [

Unfortunately, there are limitations with regards to applying these works directly to mobile WMSNs. Most of all, they have been designed for cellular systems and considered only single-node (SN) MIMO systems, where all the signals for a target mobile station come from its associated base station. However, the signals for a mobile sink node come from its associated multiple sensor nodes in mobile WMSNs. Thus, conventional SN MIMO switching schemes cannot avoid performance degradation when there is spatial correlation among a mobile sink node and associated sensor nodes. To tackle this problem, we propose an adaptive multi-node (MN) MIMO transmission scheme for mobile WMSNs, which performs spatial MIMO mode adaptation and antenna set switching with associated multiple sensor nodes. The following are the principal contributions of this paper. First, we propose a suitable model for multi-node, spatially-correlated channels that includes the different path gains from each sensor node. This allows us to include the difference between the average received signal-to-noise ratio for each sensor and the mobile sink link. Second, we derive the ergodic link capacity of orthogonal space-time block coding and spatial multiplexing with zero-forcing receivers for MN MIMO systems, using the results from [

The rest of the paper is organized as follows. In Section 2, we introduce the target MN MIMO system and channel models. In Section 3, we analytically derive the ergodic capacity of MN OSTBC and SM schemes. The proposed MN MIMO switching scheme in mobile WMSNs is presented in Section 4. Finally, we provide simulation results and concluding remarks in Sections 5 and 6, respectively.

In

For purposes of analysis, in this paper, we consider a flat Rayleigh fading channel model with path loss, transmit correlation and receive correlation. The results can be extended to frequency selective fading channels with the use of orthogonal frequency division multiplexing (OFDM). Under these assumptions, the received signal at a target MS can be represented in discrete-time as:
_{0} and _{1}_{2} …_{M}_{m}_{m}_{,1}_{m}_{,2} …_{m,N}^{T}

The channel correlation is assumed to have a Kronecker structure [_{1}-th transmit/_{1}-th receive and _{2}-th transmit/_{2}-th receive antenna pairs can be represented [_{SENm,m1,m2} (_{SENm,n1,n2}) is the transmit (receive) antenna correlation between antenna _{1} and _{2} of SEN_{1} and _{2} of a target MS), where _{SENm,m1,m2} (_{SENm,n1,n2}=1) when _{1} = _{2} (_{1} = _{2}). Then, the correlation matrix, Q, of the CIR vector can be decomposed into transmit and received correlation matrices, _{t} and _{r}[_{A,B}_{1} and _{2} account for the path gain difference between the two sensor nodes. In most SN MIMO channel models, _{1} = 1. In the MN case, however, the path gain must be retained to account for differences in loss due to different distances between sensor nodes. As a result, the channel matrix of the MN MIMO system can be represented, like the SN MIMO system, as:
_{w}

The SN and MN MIMO channel models have different structures of their transmit correlation matrices, _{t}_{1} and last _{2} antennas are, respectively, selected from SEN1 and SEN2, the transmit channel correlation matrix is:
_{t}_{,SEN}_{m}

This section presents the link level capacity for different candidate MN MIMO transmission algorithms in mobile WMSNs. We use the term link level capacity to denote the ergodic mutual information assuming that equal power allocation is applied at the transmitter [

When SM with zero forcing is employed, the MIMO channel is effectively decoupled into _{k}_{k}_{m,n}_{0} = _{s}_{0}, where
_{k}_{t}_{SM} can be written concisely [

With _{1} and (_{1}) transmit antennas from SEN1 and SEN2, respectively, the link level capacity of OSTBC can be expressed as [_{C}_{s}_{t}_{s}_{t}_{q,i}_{t} ⨂ _{r}. Using

Note that the capacity of multi-node (MN) OSTBC is slightly lower than that of single-node (SN) OSTBC, even for high transmit antenna correlation, because the ergodic capacity of OSTBC is largely affected not by spatial correlation, but by path gain and the number of transmit antennas, as shown in

In this section, we describe the proposed statistical, multi-node, MIMO mode switching algorithm. We use the term statistical because the mode decision is based on the transmit correlation information and the path gains contained in the transmit correlation matrix. Thus, the link level capacity derived in the previous section can be used as a performance measure, assuming coding and interleaving over a large number of channel realizations. Statistical-based mode switching is effective at the boundary region among different sensor nodes.

Previous works showed that there are many benefits to switching between multiplexing and diversity modes of operation based on the current channel state [_{MN-SN}

Simple switching between SN and MN modes of operation can be improved further using the concept of antenna subset selection [

In this section, we propose a statistical MN MIMO mode switching algorithm based on the capacity analysis described in Section 3. The basic idea of the proposed algorithm is the switching of MIMO mode based on the statistical channel quality to maximize the capacity. To find an appropriate switching point and an optimal MIMO mode, we define a multi-node (1) channel estimation, (2) MIMO mode and antenna set selection and (3) MIMO mode switching.

This section presents the performance evaluation results of the proposed scheme. To demonstrate an application process of the proposed MIMO mode switching algorithm, we describe the special case of

In this case, we can use closed form solutions for the eigenvalues of 2 × 2 matrices to simplify the link level capacity expressions in the previous section. In this case, the simpler form of link level capacity of

Note that correlation and path gain are the main parameters that determine the SM capacity. MN transmission can avoid spatial correlation, increasing the capacity, while power loss is experienced due to

_{SEN1,1,2}| = |_{sen2,1,2}|, as intuitively expected. Assuming SEN1 SM is better than SEN2 SM without loss of generality, the MN SM provides higher capacity than SEN1 SN SM when:

This can be simplified using

Here, channel environments can be classified into three cases as:

The regions for the three cases are depicted in _{2}, _{2} and _{MN-SN}_{2,} ^{4} − _{2,}

We can also calculate SNR crossing points between MN (or SN) SM and SN OSTBC by equating _{OSTBC-SM}, can be represented as:
_{OSTBC-SM} = 4(1+ | ^{2})(1− | ^{2})^{−2}, whereas γ_{ostbc-sm} = 2^{−2}(3 − ^{2}) when MN SM provides higher capacity than SN SM. Therefore, we can select the best MIMO scheme by comparing the capacities for a given SNR and channel condition parameters.

_{OSTBC-SM}, because the capacity of SM increases more rapidly with parallel data transmission [

In this paper, we introduced a MN MIMO switching scheme for spatially-correlated channels. The channel for MN systems is modeled first, and then, the ergodic capacities of diversity-based OSTBC and multiplexing-based SM for multiple sensor nodes are derived in closed form. We then proposed a switching scheme, where the MIMO scheme and transmit antenna set are selected to maximize the link level capacity. While the MN OSTBC is less advantageous for MN MIMO irrespective of transmit antenna correlation, MN SM can provide substantial gain over SN SM, especially when the transmit antenna correlation and path gain ratio are relatively high. The proposed MIMO switching scheme can be directly applied to the slow moving sink node when mode switching occurs infrequently. Further studies should extend the scheme to a fast moving sink node, where MIMO mode switching is performed based on instantaneous and average channel condition parameters. In addition, we will study an adaptive MIMO mode switching with consideration for the mobility of wireless sensor nodes.

This work was supported by the research fund of Hanyang University (HY-2012-N).

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2010-0023326).

The authors declare no conflict of interest.

Mobile wireless multimedia sensor networks (WMSNs) with multi-node (MN) multiple input and multiple output (MIMO).

Spatial multiplexing (SM) 2 × 2 MIMO capacity (

SM 2 × 2 MIMO capacity (

SM 2 × 2 MIMO capacity (

The procedure of a statistical multi-node MIMO switching.

MIMO mode classification for the 2 × 2 case.

MIMO 2 × 2 capacity with high spatial correlation (

MIMO 2 × 2 capacity with low spatial correlation (

MIMO 4 × 4 capacity with high spatial correlation (

MIMO 4 × 4 capacity with low spatial correlation (