- Research Article
- Open Access
Random Field Estimation with Delay-Constrained and Delay-Tolerant Wireless Sensor Networks
© J. Matamoros and C. Antón-Haro. 2010
- Received: 23 February 2010
- Accepted: 3 May 2010
- Published: 1 June 2010
In this paper, we study the problem of random field estimation with wireless sensor networks. We consider two encoding strategies, namely, Compress-and-Estimate (C&E) and Quantize-and-Estimate (Q&E), which operate with and without side information at the decoder, respectively. We focus our attention on two scenarios of interest: delay-constrained networks, in which the observations collected in a particular timeslot must be immediately encoded and conveyed to the Fusion Center (FC); delay-tolerant (DT) networks, where the time horizon is enlarged to a number of consecutive timeslots. For both scenarios and encoding strategies, we extensively analyze the distortion in the reconstructed random field. In DT scenarios, we find closed-form expressions of the optimal number of samples to be encoded in each timeslot (Q&E and C&E cases). Besides, we identify buffer stability conditions and a number of interesting distortion versus buffer occupancy tradeoffs. Latency issues in the reconstruction of the random field are addressed, as well. Computer simulation and numerical results are given in terms of distortion versus number of sensor nodes or SNR, latency versus network size, or buffer occupancy.
- Sensor Node
- Wireless Sensor Network
- Random Field
- Side Information
- Fusion Center
In recent years, research Wireless Sensor Networks (WSNs) has attracted considerable attention. This is in part motivated by the large number of applications in which WSNs are called to play a pivotal role, such as parameter estimation (i.e., moisture, temperature), event detection (leakage of pollutants, earthquakes, fires), or localization and tracking (e.g., border control, inventory tracking), to name a few .
Typically, a WSN consists of one Fusion Center (FC) and a potentially large number of sensor nodes capable of collecting and transmitting data to the FC over wireless links. In many cases, the underlying phenomenon being monitored can be modeled as a spatial random field. In these circumstances, the set of sensor observations are correlated, with such correlation being typically a function of their spatial locations (see, e.g., ). By effectively handling correlation in the data encoding process, substantial energy savings can be achieved.
In a source coding context, the work in  constitutes a generalization to sensor trees of Wyner-Ziv's pioneering studies . The authors propose two coding strategies, namely Quantize-and-Estimate (Q&E) and Compress-and-Estimate (C&E), and analyze their performance for various networks topologies. The Q&E encoding scheme is a particularization of Wyner-Ziv's to scenarios with no side information at the decoder. Consequently, each sensor observation is encoded (and decoded) independently. Conversely, C&E turns out to be a successive Wyner-Ziv-based coding scheme and, for this reason, it is capable of exploiting spatial correlation.
In a context of random field estimation with WSNs, the pioneering work of  introduced the so-called "bit-conservation principle". The authors prove that, for spatially bandlimited processes, the bit budget per Nyquist-period can be arbitrarily reallocated along the quantization precision and/or the space (by adding more sensor nodes) axes, while retaining the same decay profile of the reconstruction error. In  and, again, for bandlimited processes with arbitrary statistical distributions, the authors propose a mathematical framework to study the impact of the random sampling effect (arising from the adoption of contention-based multiple-access schemes) on the resulting estimation accuracy. For Gaussian observations,  presents a feedback-assisted Bayesian framework for adaptive quantization at the sensor nodes.
From a different perspective but still in a context of random field estimation,  proposes a novel MAC protocol which minimizes the attempts to transmit correlated data. By doing so, not only energy but also bandwidth is preserved. Besides, in , the authors investigate the impact of random sampling, as opposed to deterministic sampling (i.e., equally-spaced sensors) which is difficult to achieve in practice, in the reconstruction of the field. The main conclusion is that, whereas deterministic sampling pays off in the high-SNR regime, both schemes exhibit comparable performances in the low-SNR regime.
In this paper, we address the problem of (nonnecessarily bandlimited) random field estimation with wireless sensor networks. To that aim, we adopt the Q&E and C&E encoding schemes of  and analyze their performance in two scenarios of interest: delay-constrained (DC) and delay-tolerant (DT) sensor networks. In DC scenarios, the observations collected in a particular timeslot must be immediately encoded and conveyed to the FC. In DT networks, on the contrary, the time horizon is enlarged to consecutive timeslots. Clearly, this entails the use of local buffers but, in exchange, the distortion in the reconstructed random field is lower. To capitalize on this, we derive closed-form expressions of the distortion attainable in DT scenarios (unlike in [2, 6, 8], we explicitly take into account quantization effects). From this, we determine the optimal number of samples to be encoded in each of the timeslots as a function of the channel conditions of that particular timeslot. This constitutes the first original contribution of the paper. Along with that, we identify under which circumstances buffers are stable (i.e., buffer occupancy does not grow without bound) and, besides, we study a number of distortion versus buffer occupancy tradeoffs. To the best of our knowledge, such analysis has not been conducted before in a context of random field estimation. Complementarily, we analyze the latency in the reconstruction of consecutive realizations (i.e., those collected in one timeslot) of the random field, this being an original contribution, as well.
The paper is organized as follows. First, in Section 2, we present the signal and communication models, and provide a general framework for distortion analysis. Next, in Section 3, we focus on delay-constrained scenarios and particularize the aforementioned distortion analysis. In Sections 4 and 5 instead, we address delay-tolerant scenarios and analyze the behavior of the Q&E and C&E encoding schemes, respectively. Next, Section 6 investigates latency issues associated with DT networks. In Section 7, we present some computer simulations and numerical results and, finally, we close the paper by summarizing the main findings in Section 8.
where, by definition, with , denotes Brownian Motion with unit variance parameter, and , are constants reflecting the (spatial) variability of the field and its noisy behaviour, respectively. According to this model, the autocorrelation function is given by and, hence, the process is not (spatially) bandlimited.
2.1. Communication Model
2.2. Distortion Analysis: A General Framework
In delay-constrained (DC) networks, the samples collected in the sensing phase of a given timeslot must be necessarily encoded and transmitted to the FC in the corresponding transmission phase. Bearing this in mind, we particularize the analysis of Section 2.2 and compute the average distortion for the cases of Delay-Constrained Quantize-and-Estimate (QEDC) and Compress-and-Estimate (CEDC) encoding strategies.
3.1. Quantize-and-Estimate: Average Distortion
3.2. Compress-and-Estimate: Average Distortion
Here, we impose a long-term delay constraint: the samples collected in consecutive timeslots must be conveyed to the FC in such timeslots. In other words, sensors have now the flexibility to encode and transmit a variable number of samples in each time slot (according to channel conditions) and, by doing so, attain a lower distortion.
which evidences that the encoding rate is constant over timeslots (as initially assumed) and over sensors too.
4.1. Average Distortion in the Reconstructed Random Field
Interestingly, distortion is not a function of the channel gain experienced by the th sensor in timeslot (i.e., distortion does not depend on ). As a result and unlike in QEDC encoding, the distortion experienced in every timeslot is identical. This can be useful in applications where a constant distortion level is needed.
4.2. Buffer Stability Considerations
with . By doing so, one can prove (see the appendix) that buffers are stable. Unsurprisingly, this come at the expense of an increased distortion in the estimates (see computer simulation results in Section 7).
that is, the encoding rate in CEDT networks is constant over sensors and timeslots, as implicitly assumed in the score function (43). To remark, the stability analysis of Section 4.2 also applies here.
5.1. Average Distortion in the Reconstructed Random Field
In delay-tolerant networks, each sensor encodes and transmits a variable number of samples per timeslot. As a result, the time elapsed until the FC receives the first samples from all the sensors in the network (which allows for the reconstruction of the first realizations of the random field) is unavoidably larger than in delay-constrained networks. In this section, we attempt to characterize such latency. To that aim, we start by analyzing the time needed for one sensor to transmit consecutive samples of the random field. Next, we derive the latency of the QEDT and CEDT encoding strategies, respectively.
6.1. Latency Analysis for a Single Sensor Node
6.2. Latency Analysis for QEDT Encoding
Intuitively, latency is a monotonically increasing function in the number of sensors (the more sensors, the larger the time needed to collect all samples). This extent will be verified in Section 7 (Simulation and numerical results).
6.3. Latency Analysis for CEDT Encoding
The latency analysis for CEDT strategies if far more involved due to the successive encoding of data that C&E schemes entail. In general, this does not allow for the derivation of closed-form expressions and, thus, we will resort to an approximate (yet accurate) model.
In this paper, we have extensively analyzed the problem of random field estimation with wireless sensor networks. In order to characterize the dynamics and spatial correlation of the random field, we have adopted a stationary homogeneous Gaussian Markov Ornstein-Uhlenbeck model. We have considered two scenarios of interest: delay-constrained (DC) and delay-tolerant (DT) networks. For each scenario, we have analyzed two encoding schemes, namely, quantize-and-estimate (QE) and compress-and-estimate (CE). In all cases (QEDC, QEDT, CEDC and CEDT), we have carried out an extensive analysis of the average distortion experienced in the reconstructed random field. Moreover, for the QEDT and CEDT strategies we have derived closed-form expressions of (i) the average distortion in the estimates, and (ii) the optimal number of samples of the random field to be encoded in each timeslot (under some simplifying assumptions). Interestingly, the resulting pertimeslot distortion in DT scenarios is deterministic and constant whereas, in DC scenarios, it ultimately depends on the fading conditions experienced in each timeslot. Next, we have focused on the latency associated to the QEDT and CEDT strategies. We have modeled our system as an absorbing Markov chain and, on that basis, we have fully characterized the pdf, CDF, and the average latency for the QEDT case. For CEDT encoding, we have identified an approximate system model suitable for the computation of the average latency. Simulation results reveal that, under a total bandwidth constraint, there exists an optimal number of sensors for which the distortion in the reconstructed random field can be minimized (QEDC, QEDT, CEDC and CEDT cases). This constitutes the best trade-off in terms of, on the one hand, the ability to capture the spatial variations of the random field and, on the other, the persensor channel bandwidth available to encode observations. Besides, the distortion associated to delay-tolerant strategies is, as expected, lower than for delay-constrained ones: some 2-3 dB for both the QE and CE encoding schemes. Moreover, buffer occupancy can be kept at very moderate levels (3 timeslots) with a marginal penalty in terms of distortion (less than 0.3 dB). We also observe that CE schemes effectively exploit the spatial correlation and, by doing so, attain a lower distortion than their QE counterparts (DC and DT scenarios). As far as latency is concerned, we have empirically shown that CEDT exhibits a linear increase in the number of sensors whereas in QEDT encoding latency grows logarithmically (i.e., more slowly). However, CEDT schemes attain a lower distortion than QEDT ones. Besides, for the QEDT case, there is a perfect match between simulations and the theoretical model and, for the CEDT case, latency can be accurately represented by adequately parameterizing the aforementioned approximate system model.
This work is partly supported by the Catalan Government (2009 SGR 1046), the EC-funded project NEWCOM++ (216715), and the Spanish Ministry of Science and Innovation (FPU grant AP2007-01654).
- Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E: Wireless sensor networks: a survey. Computer Networks 2002, 38(4):393-422. 10.1016/S1389-1286(01)00302-4View ArticleGoogle Scholar
- Vuran MC, Akyildiz IF: Spatial correlation-based collaborative medium access control in wireless sensor networks. IEEE/ACM Transactions on Networking 2006, 14(2):316-329.View ArticleGoogle Scholar
- Draper SC, Wornell GW: Side information aware coding strategies for sensor networks. IEEE Journal on Selected Areas in Communications 2004, 22(6):966-976. 10.1109/JSAC.2004.830875View ArticleGoogle Scholar
- Wyner AD, Ziv J: The rate-distortion function for source coding with side information at the decoder. IEEE Transactions on Information Theory 1976, 22(1):1-10. 10.1109/TIT.1976.1055508MathSciNetView ArticleMATHGoogle Scholar
- Ishwar P, Kumar A, Ramchandran K: Distributed sampling for dense sensor networks: a bit-conservation principle. Proceedings of the 2nd International Workshop on Information Processing in Sensor Networks, April 2003, Lecture Notes in Computer Science 2634: 17-31.View ArticleMATHGoogle Scholar
- Dardari D, Conti A, Buratti C, Verdone R: Mathematical evaluation of environmental monitoring estimation error through energy-efficient wireless sensor networks. IEEE Transactions on Mobile Computing 2007, 6(7):790-802.View ArticleGoogle Scholar
- Dogandžić A, Qiu K: Decentralized random-field estimation for sensor networks using quantized spatially correlateddata and fusion-center feedback. IEEE Transactions on Signal Processing 2008, 56(12):6069-6085.MathSciNetView ArticleGoogle Scholar
- Dong M, Tong L, Sadler BM: Impact of data retrieval pattern on homogeneous signal field reconstruction in dense sensor networks. IEEE Transactions on Signal Processing 2006, 54(11):4352-4364.View ArticleGoogle Scholar
- Marco D, Neuhoff DL: Reliability vs. efficiency in distributed source coding for field-gathering sensor networks. Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN '04), April 2004, Berkeley, Calif, USA 161-168.Google Scholar
- Karatzas I, Shreve SE: Brownian Motion and Stochastic Calculus. Springer; 1988.View ArticleMATHGoogle Scholar
- Finch S: Ornstein-uhlenbeck process. May 2004, http://algo.inria.fr/csolve/ou.pdf
- Kay SM: Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice-Hall Signal Processing Series. Prentice-Hall, Englewood Cliffs, NJ, USA; 1993.MATHGoogle Scholar
- Cover TM, Thomas JA: Elements of Information Theory, Wiley Series in Telecommunications. Wiley, New York, NY, USA; 1993.Google Scholar
- Ishwar P, Puri R, Ramchandran K, Pradhan SS: On rate-constrained distributed estimation in unreliable sensor networks. IEEE Journal on Selected Areas in Communications 2005, 23(4):765-775.View ArticleMATHGoogle Scholar
- Corless RM, Gonnet GH, Hare DEG, Jeffrey DJ, Knuth DE:On the Lambert function. Advances in Computational Mathematics 1996, 5(4):329-359.MathSciNetView ArticleMATHGoogle Scholar
- Mayer CD: Matrix Analysis and Applied Linear Algebra. SIAM; 2001.Google Scholar
- Neuts MF: Matrix-Geometric Solutions in Stochastic Models: An Algorithmic Approach, Chapter 2: Probability Distributionsof Phase Type. Dover; 1981.Google Scholar
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