Wireless sensor networks (WNSs) are gradually evolving from a promising technology to a well-established reality in a large set of different domains. In order to fulfill the requirements of the specific scenario, a WSN must provide the right tradeoff between performance and lifetime, which is heavily determined by the network design. However, although the complexity of WSNs is increasing, the design space exploration is often carried out manually without the support of a general analytical methodology. In this paper, we advocate a model-based approach as an efficient and scalable way to explore the energy-performance tradeoffs during the design. In particular, we show that it is possible to define system-level models to describe wide classes of WSNs, providing a quick and accurate network evaluation. As a proof of concept, we propose a general model that describes the main characteristics of a class of WSNs for human health monitoring, and we apply it to a real case study. The results show that the energy-performance estimation error of the model never exceeds 1.74% compared to real data, while the evaluation time is reduced by up to 6 orders of magnitude with respect to an accurate network simulation.

Wireless body sensor networks (WBSN) hold the promise to enable next-generation patient-centric mobile-cardiology systems. A WBSN-enabled electrocardiogram (ECG) monitor consists of wearable, miniaturized and wireless sensors able to measure and wirelessly report cardiac signals to a WBSN coordinator, which is responsible for reporting them to the tele-health provider. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization and energy efficiency. Among others, energy efficiency can be significantly improved through embedded ECG compression, which reduces airtime over energy-hungry wireless links. In this paper, we propose a novel real-time energy-aware ECG monitoring system based on the emerging compressed sensing (CS) signal acquisition/compression paradigm for WBSN applications. For the first time, CS is demonstrated as an advantageous real-time and energy-efficient ECG compression technique, with a computationally light ECG encoder on the state-of-the-art Shimmer™ wearable sensor node and a realtime decoder running on an iPhone (acting as a WBSN coordinator). Interestingly, our results show an average CPU usage of less than 5% on the node, and of less than 30% on the iPhone.

Wireless body sensor networks (WBSN) hold the promise to enable next-generation patient-centric tele-cardiology systems. A WBSN-enabled electrocardiogram (ECG) monitor consists of wearable, miniaturized and wireless sensors able to measure and wirelessly report cardiac signals to a WBSN coordinator, which is responsible for reporting them to the tele-health provider. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization and energy efficiency. Among others, energy efficiency can be significantly improved through embedded ECG compression, which reduces airtime over energy-hungry wireless links. In this paper, we quantify the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low complexity energy-aware ECG compression on the state-of-the-art Shimmer™ WBSN mote. Interestingly, our results show that CS represents a competitive alternative to state-of-the-art digital wavelet transform (DWT)-based ECG compression solutions in terms of overall energy efficiency and Shimmer™ node lifetime extension.

This work is devoted to the evaluation of multilead digital wavelet transform (DWT)-based electrocardiogram (ECG) wave delineation algorithms, which were optimized and ported to a commercial wearable sensor platform. More specifically, we investigate the use of root-mean squared (RMS)-based multilead followed by a single-lead online delineation algorithm, which is based on a state-of-the-art offline single-lead delineator. The algorithmic transformations and software optimizations necessary to enable embedded ECG delineation notwithstanding the limited processing and storage resources of the target platform are described, and the performance of the resulting implementations are analyzed in terms of delineation accuracy, execution time, and memory usage. Interestingly, RMS-based multilead delineation is shown to perform equivalently to the best single-lead delineation for the 2-lead QT database (QTDB), within a fraction of a sample duration of the Common Standards for Electrocardiography (CSE) committee tolerances. Finally, a comprehensive evaluation of the energy consumption entailed by the considered algorithms is proposed, which allows very relevant insights into the dominant energy-draining functionalities and which suggests suitable design guidelines for long-lasting wearable ECG monitoring systems.

The IEEE 802.15.4 standard is poised to become the global standard for low data rate, low energy consumption wireless sensor networks (WSN). By assigning the same sets of contention access parameters for all data frames and nodes, the contention access period (CAP) of the slotted IEEE 802.15.4 medium access control (MAC) currently provides a priority-independent channel access functionality and no service differentiation. Several recent WSN applications such as wireless body sensor networks, however, may require service differentiation and traffic prioritization support to accommodate potential high-priority traffic (e.g., alarms or emergency alerts). By allowing different sets of access parameters and data frame lengths for different priority classes, this paper develops a Markov-chain-based analytical model of the CAP of the IEEE 802.15.4 MAC with service differentiation, under unsaturated traffic conditions. In particular, given two priority classes, our analytical model is used to evaluate the performance of a simple, yet effective, contention-window-based service differentiation strategy, in terms of the resulting throughput, average frame service time and access priority for each priority class. The accuracy of the analytical model is validated by extensive ns-2 simulation.

Spatial multiplexing with multi-mode precoding provides a means to achieve both high capacity and high reliability in multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. Multi-mode precoding uses linear transmit precoding that adapts the number of spatial multiplexing data streams or modes, according to the transmit channel state information (CSI). As such, it typically requires complete knowledge of the multi-mode precoding matrices for each subcarrier at the transmitter. In practical scenarios where the CSI is acquired at the receiver and fed back to the transmitter through a low-rate feedback link, this requirement may entail a prohibitive feedback overhead. In this paper, we propose to reduce the feedback requirement by combining codebook-based precoder quantization, to efficiently quantize and represent the optimal precoder on each subcarrier, and multi-mode precoder frequency down-sampling and interpolation, to efficiently reconstruct the precoding matrices on all subcarriers based on the feedback of the indexes of the quantized precoders only on a fraction of the subcarriers. To enable this efficient interpolation-based quantized multimode precoding solution, we introduce (1) a novel precoder codebook design that lends itself to precoder interpolation, across subcarriers, followed by mode selection, (2) a new precoder interpolator and, finally, (3) a clustered mode selection approach that significantly reduces the feedback overhead related to the mode information on each subcarrier. Monte-Carlo bit-error rate (BER) performance simulations demonstrate the effectiveness of the proposed quantized multi-mode precoding solution, at reasonable feedback overhead

Transmit maximum-ratio combining (transmit MRC) is a popular antenna diversity technique that provides both spatial diversity and array gain in downlink multiple-input single-output (MISO) links. These gains, however, critically depend on the availability of the downlink channel state information (CSI). In time-division duplexing systems, channel reciprocity has been commonly put forth to justify the convenient use of the CSI already acquired from the uplink, in the calculation of the transmit-MRC weights. Recent work has questioned this practice, based on the non-reciprocity of multi-antenna RF transceivers, due to significant amplitude and phase mismatches across the antennas. Furthermore, expensive digital calibration solutions have been proposed to enforce the reciprocity of the multi-antenna RF transceivers. Both the impact of multi-antenna amplitude and phase mismatches and the performance of the proposed calibration approaches have only been assessed via simulations. In this contribution, we propose an alternative statistical analysis of the impact of these mismatches on transmit MRC. This analysis allows a faster and more reliable characterization as well as provides insight into the relative importance of these mismatches. Consequently, sufficient matching requirements can be extracted for the multi-antenna RF transceivers, for which simpler and cheaper calibration solutions can be devised.

To approach the potential multiple-input multiple-output (MIMO) capacity while optimizing the system bit-error rate (BER) performance, the joint transmit and receive minimum mean squared error (joint Tx/Rx MMSE) design has been proposed. It is the optimal linear scheme for spatial multiplexing MIMO systems, assuming a fixed number of spatial streams p as well as fixed modulation and coding across these spatial streams. However, the number of spatial streams has been arbitrarily chosen and fixed, which may lead to an inefficient power allocation strategy and a poor BER performance. In this paper, we relax the constraint of fixed number of streams p and optimize this value for the current channel realization, under the constraints of fixed average total transmit power P/sub T/ and fixed rate R, what we refer to as mode selection . Based on the observation of the existence of a dominant optimal number of streams value for the considered Rayleigh flat-fading MIMO channel model, we further propose an "average" mode selection that avoids the per-channel adaptation through using the latter dominant value for all channel realizations. Finally, we exhibit the significant BER improvement provided by our mode selection over the conventional joint Tx/Rx MMSE design. Such significant improvement is due to the better exploitation of the MIMO spatial diversity and the more efficient power allocation enabled by our mode selection.

The joint linear precoder and decoder minimum mean squared error (MMSE) design represents a low complexity yet powerful solution for spatial multiplexing MIMO systems. Its performance, however, critically depends on the availability of timely channel state information (CSI) at both transmitter and receiver. In practice, the latter assumption can be severely challenged, due to channel time variations that lead to outdated CSI at the transmitter. State-of-the-art designs mistakenly use the outdated CSI to design the linear precoder and rely on the receiver to reduce the induced degradation. In this paper, we propose a robust Bayesian joint linear precoder and decoder solution that takes into account the uncertainty of the true channel, given the outdated CSI at the transmitter. We finally assess the robustness of our design to channel time variations through Monte-Carlo analysis of the system's MMSE and average bit-error rate (BER) performance.

To approach the potential MIMO capacity while optimizing the system bit-error rate (BER) performance, the joint transmits and receive minimum mean squared error (MMSE) design has been proposed. It is the optimal linear scheme for spatial multiplexing MIMO systems, assuming a fixed number of spatial streams p as well as a fixed modulation and coding across these spatial streams. However, state-of-the-art designs arbitrarily choose and fix the value of the number of spatial streams p, which may lead to an inefficient power allocation strategy and a poor BER performance. We have previously proposed to relax the constraint of fixed number of streams p and to optimize this value under the constraints of fixed average total transmit power and fixed spectral efficiency, what we referred to as spatial-mode selection. Our previous selection criterion was the minimization of the system sum MMSE. In the present contribution, we introduce a new and better spatial-mode selection criterion that targets the minimization of the system BER. We also provide a detailed performance analysis, over flat-fading channels, that confirms that our proposed spatial-mode selection significantly outperforms the state-of-the-art joint Tx/Rx MMSE design both for uncoded and coded systems, thanks to its better exploitation of the MIMO spatial diversity and more efficient power allocation.