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In this paper, we consider the problem of RSS-fingerprinting localization in wireless sensor networks. In particular, inspired by the recent advances in sparse approximation and compressive sensing theory, we propose a localization scheme based on the dictionary design of block-sparse signals. We show via numerical simulations and real experiments that the proposed technique outperforms traditional...
One of the most popular and efficient methods for conserving energy in Wireless Sensor Networks (WSNs) is data aggregation. This technique usually introduces an additional delay in the transmission of data packets. The inherent trade-off between energy consumption and end-to-end delay imposes an important decision to be made by the nodes, mainly to determine the most appropriate time for aggregating...
With the current progress in communication technology, Wireless Sensor Network (WSN)s are extensively setup for plethora of applications such as defense, intelligent transportation systems, healthcare and many more. The limitation of sensor node is in terms of resources namely storage, power, processing potential and communication range. For the sensor network to be operational an efficient routing...
The covering problem and node deployment is a fundamental problem in the research of wireless sensor network. The number of nodes and the coverage of a network can directly affect performance and operating costs. For this reason, we propose an EMAC algorithm (Energy Efficient Multi-target Associate Coverage Algorithm). The algorithm uses the correlation between the nodes and dynamic grouping to adjust...
In distributed networks such as wireless ad hoc networks, local and lossy logs are often available on individual nodes. We propose REFILL, which analyzes lossy and unsynchronized logs collected from individual nodes and reconstructs the network behaviors. We design an inference engine based on protocol semantics to abstract states on each node. Further we leverage inherent and implicit event correlations...
Currently available fault detection approaches generally pursue high fault detection performance but neglect energy consumption due to high volume of messages exchanged during fault detection process. Therefore, in this work we propose a reactive distributed scheme for detecting faulty nodes. The scheme is able to detect transient and permanent faulty nodes accurately with very less false alarm rate...
This work proposes an intelligent decision system for a residential infrastructure based on wireless sensors and actuator networks, called ResiDI. ResiDI is equipped with battery-powered nodes to ensure that they are deployable anywhere in the house without the need for wiring, drilling or any pre-existing infrastructure. The key intelligence of ResiDI is distributed in the decider nodes, which are...
This paper characterizes path loss and spatial correlation for near ground channels in an open indoor and snowy terrain. The study is based on conducting extensive measurements at 2.45 GHz frequency range for Line-of-sight scenarios. The index of reflection of log-distance path-loss is evaluated. The impact of antenna height, and transmitter-receiver distance on pathloss and spatial correlation is...
Wireless links are typically modeled in isolation as independent and parallel links. However, in reality, there is often a correlation in defects and impairments between collocated links. The spatial correlation of shadowing among proximate wireless links has been frequently observed. Although different types of correlated shadowing models have been proposed for wireless channels, these models are...
Channel randomness can be exploited to generate secret keys. However, to ensure secrecy, it is necessary that the channel response of any eavesdropping party remain sufficiently de-correlated with that of the legitimate users'. In this paper, we investigate whether such de-correlation occurs for a body area network (BAN) operating in an indoor environment at 2.45 GHz. The hypothetical BAN configuration...
Fault detection in wireless sensor networks is a crucial and challenging task. Many detection approaches relying on specific rules or inference models have been proposed to distinguish faulty sensors by exploring spatial-temporal correlations among sensor readings. However, these approaches may require high communication overhead or computational cost, and many potential faulty sensors that may not...
Rapid increase of elderly population in the world is motivating innovative technologies for healthcare services that are both high-quality and cost-effective. Wireless sensor networks (WSNs) based smart home for assisted living is one of the most prosperous solutions to this area, since it removes the requirement of deploying wired devices and is easy to be implemented in existing home environments...
One of the main challenges confronted by wireless sensor networks (WSNs) is to reduce energy consumption of nodes for the purpose of network lifetime extension. In the literature, many backbone based protocols such as connected dominating set (CDS) and broadcast or multicast based protocols are employed in order to improve network performance in terms of metrics like energy consumption, number of...
The recently emerged compressive sensing (CS) theory provides a whole new avenue for data gathering (DG) in wireless sensor networks (WSNs) with the benefits of energy efficient. The exiting CS based DG approaches design the routing processes based on the assumption that there is a transform that can sparsely represent the sensor readings. However, most of the real sensor readings are well approximated...
Traditional trust evaluation models in wireless sensor networks separate detection and evaluation from data aggregation. Data accuracy is ignored in these models. Algorithms like WTE alleviate some of the problem, but not all. For example, it cannot resist attacks from nodes with a good reputation. Another ignored problem is the false detection caused by occasional abnormal behavior, hash environment...
This paper proposes a new data processing framework for energy saving, based on a synergy between data compression and data aggregation techniques. This combination allows a more compact representation of the transmitted data in the clusters head nodes compared with the individual use of each technique. For data compression, we use the static Huffman algorithm with Extrapolation prediction that exploits...
Fault detection plays a crucial role in wireless sensor networks (WSNs). Many fault detection approaches requiring a priori knowledge of network faults have been proposed to distinguish faulty sensors by exploring spatial-temporal correlations among sensor readings. However, many faulty sensors that may not generate anomalous sensor readings, and potential failures with unknown types and symptoms...
The sensing of urban environments usually takes into account the deployment of a large number of devices to measure their environmental attributes, such as temperature, pressure, humidity, luminosity and pollution. In such applications, nearby sensors usually produce similar readings due to their spatial and temporal correlation. In the era of big data, management of collected data requires autonomous...
Emerging wireless sensor networks (WSNs) aim to overcome current needs for additional backbone infrastructure in mobile networks. Confronted with enormous requirements in terms of spectrum and energy efficiency, WSNs have been shown to benefit from distributed source coding (DSC) by means of multi-route redundancy. However, DSC in WSNs yields low performance regarding the correlation estimation of...
This paper addresses the problem of model-based source localization using spatially autocorrelated received signal strength (RSS) measurements when the model parameters are not known a priori. This combined problem arises typically in situations in which a large number of observations are collected in positions close to each other in unknown environments. In our approach we model the RSS as a spatial...
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