Distributed Joint Source-Channel Coding With Copula-Function-Based Correlation Modeling for Wireless Sensors Measuring Temperature

Wireless sensor networks (WSNs) deployed for temperature monitoring in indoor environments call for systems that perform efficient compression and reliable transmission of the measurements. This is known to be a challenging problem in such deployments, as highly efficient compression mechanisms impose a high computational cost at the encoder. In this paper, we propose a new distributed joint source-channel coding (DJSCC) solution for this problem. Our design allows for efficient compression and error-resilient transmission, with low computational complexity at the sensor. A new Slepian-Wolf code construction, based on non-systematic Raptor codes, is devised that achieves good performance at short code lengths, which are appropriate for temperature monitoring applications. A key contribution of this paper is a novel Copula-function-based modeling approach that accurately expresses the correlation amongst the temperature readings from colocated sensors. Experimental results using a WSN deployment reveal that, for lossless compression, the proposed Copula-function-based model leads to a notable encoding rate reduction (of up to 17.56%) compared with the state-of-the-art model in the literature. Using the proposed model, our DJSCC system achieves significant rate savings (up to 41.81%) against a baseline system that performs arithmetic entropy encoding of the measurements. Moreover, under channel losses, the transmission rate reduction against the state-of-the-art model reaches 19.64%, which leads to energy savings between 18.68% to 24.36% with respect to the baseline system

Data aggregation and recovery for the Internet of Things: A compressive demixing approach

Large-scale wireless sensor networks (WSNs) and Internet-of-Things (IoT) applications involve diverse sensing devices collecting and transmitting massive amounts of heterogeneous data. In this paper, we propose a novel compressive data aggregation and recovery mechanism that reduces the global communication cost without introducing computational overhead at the network nodes. Following the principles of compressive demixing, each node of the network collects measurement readings from multiple sources and mixes them with readings from other nodes into a single low-dimensional measurement vector, which is then relayed to other nodes; the constituent signals are recovered at the sink using convex optimization. Our design achieves significant reduction in the overall network data rates compared to prior schemes based on (distributed) compressed sensing or compressed sensing with (multiple) side information. Experiments using real large-scale air-quality data demonstrate the superior performance of the proposed framework against state-of-the-art solutions, with and without the presence of measurement and transmission noise

Analysis of Shear-critical Reinforced Concrete Columns under Variable Axial Load

Older existing reinforced concrete (R/C) frame structures often contain shear-dominated vertical structural elements, which can experience loss of axial load-bearing capacity after a shear failure, hence initiating progressive collapse. An experimental investigation previously reported by the authors focused on the effect of increasing compressive axial load on the non-linear post-peak lateral response of shear, and flexure-shear, critical R/C columns. These results and findings are used here to verify key assumptions of a finite element model previously proposed by the authors, which is able to capture the full-range response of shear-dominated R/C columns up to the onset of axial failure. Additionally, numerically predicted responses using the proposed model are compared with the experimental ones of the tested column specimens under increasing axial load. Not only global, but also local response quantities are examined, which are difficult to capture in a phenomenological beam-column model. These comparisons also provide an opportunity for an independent verification of the predictive capabilities of the model, because these specimens were not part of the initial database that was used to develop it