The thesis focuses on collecting data from wireless sensors which are deployed randomly in a regi... more The thesis focuses on collecting data from wireless sensors which are deployed randomly in a region. These sensors are widely used in applications ranging from tracking to the monitoring of environment, traffic and health among others. These energy constrained sensors, once deployed may receive little or no maintenance. Hence gathering data in the most energy efficient manner becomes critical for the longevity of wireless sensor networks (WSNs). Recently, Compressive data gathering (CDG) has emerged as a useful method for collecting sensory data in WSN; this technique is able to reduce global scale communication cost without introducing intensive computation, and is capable of extending the lifetime of the entire sensor network by balancing the forwarding load across the network. This is particularly true due to the benefits obtained from in-network data compression. With CDG, the central unit, instead of receiving data from all sensors in the network, it may receive very few compre...
International Journal of Information Technology and Web Engineering (IJITWE), 2006
The continuous K nearest neighbor (CKNN) query is an important type of query that continuously fi... more The continuous K nearest neighbor (CKNN) query is an important type of query that continuously finds the KNN to a query point on a given path. In this article we focus on moving queries issued on stationary objects in spatial network database (SNDB). The result of this type of query is a set of intervals (defined by split points) and their corresponding KNNs. This means that the KNN of an object travelling on one interval of the path remains the same all through that interval until it reaches a split point where its KNNs change. ...
The use of Voronoi diagram has traditionally been applied to computational geometry and multimedi... more The use of Voronoi diagram has traditionally been applied to computational geometry and multimedia problems. In this paper, we will show how Voronoi diagram can be applied to spatial query processing, and in particular to Reverse Nearest Neighbor (RNN) queries. Spatial and geographical query processing, in general, and RNN in particular, are becoming more important, as online maps are now
ABSTRACT This paper proposes a novel data gathering method using Compressive Sensing (CS) and ran... more ABSTRACT This paper proposes a novel data gathering method using Compressive Sensing (CS) and random projection to improve the lifetime of large Wireless Sensor Networks (WSNs). To increase the network lifetime, one needs to decrease the overall network energy consumption and distribute the energy load more evenly throughout the network. By using compressive sensing in data aggregation, referred to as Compressive Data Gathering (CDG), one can dramatically improve the energy efficiency, and this is particularly attributed to the benefits obtained from data compression. Random projection, together with compressive data gathering, helps further in balancing the energy consumption load throughout the network. In this paper, we propose a new compressive data gathering method called Minimum Spanning Tree Projection (MSTP). MSTP creates a number of Minimum-Spanning-Trees (MSTs), each rooted at a randomly selected projection node, which in turn aggregates sensed data from sensors using compressive sensing. We compare through simulations our method with the existing data gathering schemes. We further extend our method and introduce eMSTP, which joins the sink node to each MST and makes the sink node as the root for each tree. Our simulation results show that MSTP and eMSTP outperform the existing data gathering schemes in decreasing the communication cost and distributing the energy consumption loads and hence improving the overall lifetime of the network.
The thesis focuses on collecting data from wireless sensors which are deployed randomly in a regi... more The thesis focuses on collecting data from wireless sensors which are deployed randomly in a region. These sensors are widely used in applications ranging from tracking to the monitoring of environment, traffic and health among others. These energy constrained sensors, once deployed may receive little or no maintenance. Hence gathering data in the most energy efficient manner becomes critical for the longevity of wireless sensor networks (WSNs). Recently, Compressive data gathering (CDG) has emerged as a useful method for collecting sensory data in WSN; this technique is able to reduce global scale communication cost without introducing intensive computation, and is capable of extending the lifetime of the entire sensor network by balancing the forwarding load across the network. This is particularly true due to the benefits obtained from in-network data compression. With CDG, the central unit, instead of receiving data from all sensors in the network, it may receive very few compre...
International Journal of Information Technology and Web Engineering (IJITWE), 2006
The continuous K nearest neighbor (CKNN) query is an important type of query that continuously fi... more The continuous K nearest neighbor (CKNN) query is an important type of query that continuously finds the KNN to a query point on a given path. In this article we focus on moving queries issued on stationary objects in spatial network database (SNDB). The result of this type of query is a set of intervals (defined by split points) and their corresponding KNNs. This means that the KNN of an object travelling on one interval of the path remains the same all through that interval until it reaches a split point where its KNNs change. ...
The use of Voronoi diagram has traditionally been applied to computational geometry and multimedi... more The use of Voronoi diagram has traditionally been applied to computational geometry and multimedia problems. In this paper, we will show how Voronoi diagram can be applied to spatial query processing, and in particular to Reverse Nearest Neighbor (RNN) queries. Spatial and geographical query processing, in general, and RNN in particular, are becoming more important, as online maps are now
ABSTRACT This paper proposes a novel data gathering method using Compressive Sensing (CS) and ran... more ABSTRACT This paper proposes a novel data gathering method using Compressive Sensing (CS) and random projection to improve the lifetime of large Wireless Sensor Networks (WSNs). To increase the network lifetime, one needs to decrease the overall network energy consumption and distribute the energy load more evenly throughout the network. By using compressive sensing in data aggregation, referred to as Compressive Data Gathering (CDG), one can dramatically improve the energy efficiency, and this is particularly attributed to the benefits obtained from data compression. Random projection, together with compressive data gathering, helps further in balancing the energy consumption load throughout the network. In this paper, we propose a new compressive data gathering method called Minimum Spanning Tree Projection (MSTP). MSTP creates a number of Minimum-Spanning-Trees (MSTs), each rooted at a randomly selected projection node, which in turn aggregates sensed data from sensors using compressive sensing. We compare through simulations our method with the existing data gathering schemes. We further extend our method and introduce eMSTP, which joins the sink node to each MST and makes the sink node as the root for each tree. Our simulation results show that MSTP and eMSTP outperform the existing data gathering schemes in decreasing the communication cost and distributing the energy consumption loads and hence improving the overall lifetime of the network.
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Papers by Dariush Ebrahimi