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Efficient Multi-Channel Signal Strength based Localization via Matrix Completion and Bayesian Sparse Efficient Multi-Channel Signal Strength based Localization via Matrix Completion and Bayesian Sparse

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Date added: 02/12/2015
Date modified: 02/12/2015
Filesize: 286 Bytes
Downloads: 1319

Fingerprint-based location sensing technologies play an increasingly important role in pervasive computing applications due to their accuracy and minimal hardware requirements. However, typical fingerprint-based schemes implicitly assume that communication occurs over the same channel (frequency) during the training and the runtime phases. When this assumption is violated, the mismatches between training and runtime fingerprints can significantly deteriorate the localization performance. Additionally, the exhaustive calibration procedure required during training limits the scalability of this class of methods. In this work, we propose a novel, scalable, multi-channel fingerprint-based indoor localization system that employs modern mathematical concepts based on the Sparse Representations and Matrix Completion theories. The contribution of our work is threefold. First, we investigate the impact of channel changes on the fingerprint characteristics and the effects of channel mismatch on state-of-the-art localization schemes. Second, we propose a novel fingerprint collection technique that significantly reduces the calibration time, by formulating the map construction as an instance of the Matrix Completion problem. Third, we propose the use of sparse Bayesian learning to achieve accurate location estimation. Experimental evaluation on real data highlights the superior performance of the proposed framework in terms of reconstruction error and localization accuracy.

Energy Efficient Consensus over Complex Networks Energy Efficient Consensus over Complex Networks

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Date added: 02/12/2015
Date modified: 02/12/2015
Filesize: 286 Bytes
Downloads: 1471

The need to extract large amounts of information from the environment to have precise situation awareness and then react appropriately to certain events has led to the emergence of complex and heterogeneous sensor networks. In this context, where the sensor nodes are usually powered by batteries, the design of new methods to make inference processes efficient in terms of energy consumption is necessary. One of these processes, which is present in many distributed tasks performed by these complex networks, is the consensus process. This is the basis for certain tracking algorithms in monitoring and control applications. To improve the energy efficiency of this process, in this paper we propose a new methodology to optimize the network topology. More specifically, the topologies we obtain are Pareto-optimal solutions in terms of energy consumption and network lifetime metrics. This methodology is first approached from a general point of view, including most network properties at a time. Then, since in the practice not all networks present the same characteristics, we identify three real settings in which the optimization must be tackled differently. This leads to three particularizations of the problem, where the appearance of well-known graph models: small world, scale free and random geometric graphs is related with certain environment and nodes characteristics. Finally, extensive numerical results are presented to show the validity and efficiency of the proposed methodology.

Modelling IEEE 802.15.4 Networks over Fading Channels Modelling IEEE 802.15.4 Networks over Fading Channels

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Date added: 02/12/2015
Date modified: 02/12/2015
Filesize: 286 Bytes
Downloads: 1333

Although the performance of the medium access control (MAC) of the IEEE 802.15.4 has been investigated under the assumption of ideal wireless channel, the understanding of the cross-layer dynamics between MAC and physical layer is an open problem when the wireless channel exhibits path loss, multi-path fading, and shadowing. The analysis of MAC and wireless channel interaction is essential for consistent performance prediction, correct design and optimization of the protocols. In this paper, a novel approach to analytical modeling of these interactions is proposed. The analysis considers simultaneously a composite channel fading, interference generated by multiple terminals, the effects induced by hidden terminals, and the MAC reduced carrier sensing capabilities. Depending on the MAC parameters and physical layer thresholds, it is shown that the MAC performance indicators over fading channels can be far from those derived under ideal channel assumptions. As novel results, we show to what extent the presence of fading may be beneficial for the overall network performance by reducing the multiple access interference, and how this information can be used for joint selection of MAC and physical layer parameters.

Closing the gap between traffic workload and channel occupancy models for 802.11 networks Closing the gap between traffic workload and channel occupancy models for 802.11 networks

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Date added: 02/12/2015
Date modified: 02/12/2015
Filesize: 286 Bytes
Downloads: 1280

The modeling of wireless network traffic is necessary to evaluate the possible gains of spectrum sharing and to support the design of new cognitive protocols that can use spectrum efficiently in network environments where diverse technologies coexist. In this paper we focus on IEEE 802.11 wireless local area networks and close the gap between two popular levels of modeling, macroscopic traffic workload modeling and microscopic channel occupancy modeling. We consider traffic streams generated by established traffic workload models and characterize the networking scenarios where a simple, semi-Markovian channel occupancy model accurately predicts the wireless channel usage. Our results demonstrate that the proposed channel occupancy model can capture the channel idle time distribution in most of the scenarios, while the Markovian assumption cannot be validated in all cases.

Stream Correlation Monitoring for Uncertainty-Aware Data Processing Systems Stream Correlation Monitoring for Uncertainty-Aware Data Processing Systems

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Date added: 02/12/2015
Date modified: 02/12/2015
Filesize: 286 Bytes
Downloads: 1267

In several industrial applications, monitoring large-scale infrastructures in order to provide notifications for abnormal behavior is of high significance. For this purpose, the deployment of large-scale sensor networks is the current trend. However, this results in handling vast amounts of low-level, and often unreliable, data, while an efficient and real-time data manipulation is a strong demand. In this paper, we propose an uncertainty-aware data management system capable of monitoring pairwise correlations of large sensor data streams in real-time. An efficient similarity function based on the truncated DFT is employed instead of the typical correlation coefficient to monitor dynamic phenomena for timely alerting notifications, and to guarantee the validity of detected extreme events. Experimental evaluation with a set of real data recorded by distinct sensors in an industrial water desalination plant reveals a high performance of our proposed approach in terms of achieving significantly reduced execution times, along with increased accuracy in detecting highly correlated pairs of sensor data streams.

Modeling anti-collision protocols for RFID systems with multiple access interference Modeling anti-collision protocols for RFID systems with multiple access interference

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Date added: 02/12/2015
Date modified: 02/12/2015
Filesize: 286 Bytes
Downloads: 1341

Passive tags based on backscattered signals yield low energy consumption and enable a rich set of applications. In this paper, the performance of the dynamic framed ALOHA of the ISO/IEC 18000-6C standard is investigated. A Markov chain model of the protocol is developed by including the effects of the channel under different propagation scenarios. Depending on the air interface parameters and the fading components, it is shown that multiple access interference determines complex and unexpected effects on the system performance. An explicit analysis of the successful interrogation probability is obtained with respect to the number of tags, number of interrogation rounds and fading severity. It is shown that channel statistics may induce significant degradation, although a moderate spread of the fading can be beneficial especially for those contexts that include time-varying conditions.

Distributed random sensing order analysis and optimization in cognitive radio systems Distributed random sensing order analysis and optimization in cognitive radio systems

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Date added: 02/12/2015
Date modified: 02/12/2015
Filesize: 286 Bytes
Downloads: 1289

Developing an efficient spectrum access policy enables cognitive radios to dramatically increase spectrum utilization while assuring predetermined quality of service levels for the primary users. In this paper, modeling, performance analysis, and optimization of a distributed secondary network with random sensing order policy are studied. Specifically, the secondary users create a random order of the available channels and then find a transmission opportunity in a distributed manner. By a Markov chain analysis, the average throughputs of the secondary users and average interference level between the secondary and primary users are evaluated. Then, a maximization of the performance of the secondary network in terms of throughput while keeping under control the average interference is proposed. A simple and practical adaptive algorithm is established to optimize the network. Finally, numerical results are provided to validate the analytical derivations and demonstrate the performance of the proposed schemes. It is shown that distributed algorithms can achieve substantial performance improvements in cognitive radio networks without the need of centralized operations or management.

Optimal topology design for energy efficient consensus in broadcast Wireless Sensor Networks Optimal topology design for energy efficient consensus in broadcast Wireless Sensor Networks

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Date added: 02/12/2015
Date modified: 02/12/2015
Filesize: 286 Bytes
Downloads: 1638

Average consensus algorithms are an essential tool in wireless sensor networks for multiple estimation tasks, being the convergence time and the energy consumption of these algorithms critical for their usability. Most existing work in the related literature focuses on improving these two parameters, assuming generally unicast communications, which are neither realistic nor efficient given the wireless nature of these networks. Instead, broadcast communications allow a greater instantaneous exchange of information between the network nodes, accelerating the consensus and saving energy in communications. In this work, we propose two methods that optimize the network topology to simultaneously improve the total power consumption per iteration, the maximum power consumption per node and the convergence time in a broadcast scenario. The first method is applied to continuous systems, while the second one is more suitable for discrete systems. Numerical results are presented to show the validity and efficiency of the proposed methods.

Achieving energy-efficient distributed consensus in wireless Scale Free Networks Achieving energy-efficient distributed consensus in wireless Scale Free Networks

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Date added: 02/12/2015
Date modified: 02/12/2015
Filesize: 286 Bytes
Downloads: 1274

The so-called scale free property is a feature of many complex systems, including the Internet and World Wide Web, where most nodes have just a few links whereas the rest have a huge number of them. The traditional models conceived to reproduce the formation of these systems do not take into account the topological features of the growing network. Nevertheless, the resulting topology has a major influence on important properties of the network, such as synchronization and consensus time of nodes, network robustness, etc. In this work, we propose a new growth model that, starting from a random deployment of nodes, performs a wiring process using a metric that balances the degree of the nodes (preferential attachment), the euclidean distance (power consumption) and the algebraic connectivity (consensus time among others). Numerical results show that our proposal not only captures the properties of scale free networks, but also achieves a final topology that leads to more energy-efficient processes than traditional approaches.

Optimal Sensor Placement for Bacteria Detection in Water Distribution Networks Optimal Sensor Placement for Bacteria Detection in Water Distribution Networks

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Date added: 02/12/2015
Date modified: 02/12/2015
Filesize: 286 Bytes
Downloads: 1248

The real-time detection of bacteria and other bio-pollutants in water distribution networks and the real-time control of the water quality is made possible by new biosensors. However, the limited communication capabilities of these sensors, which are placed underground, and their limited number, due to their high cost, pose significant challenges in the deployment and the reliable monitoring. This paper presents a preliminary study concerning the problem of the static optimal sensor placement of a wireless biosensor network in a water distribution network for real-time detection of bacterial contamination. An optimal sensor placement strategy is proposed, which maximizes the probability of detection considering a limited number of sensors while ensuring a connected communication topology. A lightweight algorithm that solves the optimal placement problem is developed. The performance of the proposed algorithm is evaluated through simulations, considering different network topologies using a water pipelines emulator. The results indicate that the proposed optimization outperforms more traditional approaches in terms of detection probability. It is concluded that the availability of a dynamic model of the bacterial propagation along with a spatio-temporal correlation of the process could lead to a more advanced real-time control of the water distribution networks.