342 research outputs found

    Cooperative sensing and compression in vehicular sensor networks for urban monitoring

    Get PDF
    A Vehicular Sensor Network (VSN) may be used for urban environment surveillance utilizing vehicle-based sensors to provide an affordable yet good coverage for the urban area. The sensors in VSN enjoy the vehicle's steady power supply and strong computational capacity not available in traditional Wireless Sensor Network (WSN). However, the mobility of the vehicles results in highly dynamic and unpredictable network topology, leading to packet losses and distorted surveillance results. To resolve these problems, we propose a cooperative data sensing and compression approach with zero inter-sensor collaboration overhead based on sparse random projections. The algorithm provides excellent reconstruction accuracy for the sensed field, and by taking advantage of the spatial correlation of the data, enjoys much smaller communication traffic load compared to traditional sampling algorithms in wireless sensor networks. Real urban environment data sets are used in the experiments to test the reconstruction accuracy and energy efficiency under different vehicular mobility models. The results show that our approach is superior to the conventional sampling and interpolation strategy which propagates data in an uncompressed form, with 4-5dB gain in reconstruction quality and 21-55% savings in communication cost for the same sampling times. ©2010 IEEE.published_or_final_versionThe IEEE International Conference on Communications (ICC) 2010, Cape Town, South Africa, 23-27 May 2010. In Proceedings of the IEEE ICC, 2010, p. 1-

    Robotic Wireless Sensor Networks

    Full text link
    In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

    Optimized Clustering Protocol for Balancing Energy in Wireless Sensor Networks

    Get PDF
    While wireless sensor networks (WSNs) are increasingly equipped to handle more complex functions and in-network processing may require these battery powered sensors to judiciously use their constrained energy to prolong the effective network lifetime. Cluster-based Hierarchical Routing Protocol using compressive sensing (CS) theory (CBHRP-CS) divides the network into several clusters, each managed by a set of CHs called a header. Each member of the header compresses the collected data using CS. This paper proposes an optimized clustering protocol using CS (OCP-CS) to improve the performance of WSNs by exploiting compressibility. In OCP-CS, each cluster is managed by a cluster head (CH). CHs are selected based on node concentration and sensor residual energy, and performs data aggregation using CS to reduce the energy consumed in the process of data sampling and transmission. Simulations show that our proposed protocol is effective in prolonging the network lifetime and supporting scalable data aggregation than existing protocols

    Heterogeneous Networked Data Recovery from Compressive Measurements Using a Copula Prior

    Get PDF
    Large-scale data collection by means of wireless sensor network and internet-of-things technology poses various challenges in view of the limitations in transmission, computation, and energy resources of the associated wireless devices. Compressive data gathering based on compressed sensing has been proven a well-suited solution to the problem. Existing designs exploit the spatiotemporal correlations among data collected by a specific sensing modality. However, many applications, such as environmental monitoring, involve collecting heterogeneous data that are intrinsically correlated. In this study, we propose to leverage the correlation from multiple heterogeneous signals when recovering the data from compressive measurements. To this end, we propose a novel recovery algorithm---built upon belief-propagation principles---that leverages correlated information from multiple heterogeneous signals. To efficiently capture the statistical dependencies among diverse sensor data, the proposed algorithm uses the statistical model of copula functions. Experiments with heterogeneous air-pollution sensor measurements show that the proposed design provides significant performance improvements against state-of-the-art compressive data gathering and recovery schemes that use classical compressed sensing, compressed sensing with side information, and distributed compressed sensing.Comment: accepted to IEEE Transactions on Communication

    Data Compression in Multi-Hop Large-Scale Wireless Sensor Networks

    Get PDF
    Data collection from a multi-hop large-scale outdoor WSN deployment for environmental monitoring is full of challenges due to the severe resource constraints on small battery-operated motes (e.g., bandwidth, memory, power, and computing capacity) and the highly dynamic wireless link conditions in an outdoor communication environment. We present a compressed sensing approach which can recover the sensing data at the sink with good accuracy when very few packets are collected, thus leading to a significant reduction of the network traffic and an extension of the WSN lifetime. Interplaying with the dynamic WSN routing topology, the proposed approach is efficient and simple to implement on the resource-constrained motes without motes storing of a part of random measurement matrix, as opposed to other existing compressed sensing based schemes. We provide a systematic method via machine learning to find a suitable representation basis, for the given WSN deployment and data field, which is both sparse and incoherent with the measurement matrix in the compressed sensing. We validate our approach and evaluate its performance using our real-world multi-hop WSN testbed deployment in situ in collecting the humidity and soil moisture data. The results show that our approach significantly outperforms three other compressed sensing based algorithms regarding the data recovery accuracy for the entire WSN observation field under drastically reduced communication costs. For some WSN scenarios, compressed sensing may not be applicable. Therefore we also design a generalized predictive coding framework for unified lossless and lossy data compression. In addition, we devise a novel algorithm for lossless compression to significantly improve data compression performance for variouSs data collections and applications in WSNs. Rigorous simulations show our proposed framework and compression algorithm outperform several recent popular compression algorithms for wireless sensor networks such as LEC, S-LZW and LTC using various real-world sensor data sets, demonstrating the merit of the proposed framework for unified temporal lossless and lossy data compression in WSNs

    A critical analysis of research potential, challenges and future directives in industrial wireless sensor networks

    Get PDF
    In recent years, Industrial Wireless Sensor Networks (IWSNs) have emerged as an important research theme with applications spanning a wide range of industries including automation, monitoring, process control, feedback systems and automotive. Wide scope of IWSNs applications ranging from small production units, large oil and gas industries to nuclear fission control, enables a fast-paced research in this field. Though IWSNs offer advantages of low cost, flexibility, scalability, self-healing, easy deployment and reformation, yet they pose certain limitations on available potential and introduce challenges on multiple fronts due to their susceptibility to highly complex and uncertain industrial environments. In this paper a detailed discussion on design objectives, challenges and solutions, for IWSNs, are presented. A careful evaluation of industrial systems, deadlines and possible hazards in industrial atmosphere are discussed. The paper also presents a thorough review of the existing standards and industrial protocols and gives a critical evaluation of potential of these standards and protocols along with a detailed discussion on available hardware platforms, specific industrial energy harvesting techniques and their capabilities. The paper lists main service providers for IWSNs solutions and gives insight of future trends and research gaps in the field of IWSNs

    CDAR : contour detection aggregation and routing in sensor networks

    Get PDF
    Wireless sensor networks offer the advantages of low cost, flexible measurement of phenomenon in a wide variety of applications, and easy deployment. Since sensor nodes are typically battery powered, energy efficiency is an important objective in designing sensor network algorithms. These algorithms are often application-specific, owing to the need to carefully optimize energy usage, and since deployments usually support a single or very few applications. This thesis concerns applications in which the sensors monitor a continuous scalar field, such as temperature, and addresses the problem of determining the location of a contour line in this scalar field, in response to a query, and communicating this information to a designated sink node. An energy-efficient solution to this problem is proposed and evaluated. This solution includes new contour detection and query propagation algorithms, in-network-processing algorithms, and routing algorithms. Only a small fraction of network nodes may be adjacent to the desired contour line, and the contour detection and query propagation algorithms attempt to minimize processing and communication by the other network nodes. The in-network processing algorithms reduce communication volume through suppression, compression and aggregation techniques. Finally, the routing algorithms attempt to route the contour information to the sink as efficiently as possible, while meshing with the other algorithms. Simulation results show that the proposed algorithms yield significant improvements in data and message volumes compared to baseline models, while maintaining the integrity of the contour representation
    • …
    corecore