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19
Information fusion for wireless sensor networks: methods, models, and classifications,”
 Article ID 1267073,
, 2007
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Balancing push and pull for efficient information discovery in largescale sensor networks
 IEEE Transactions on Mobile Computing. March
, 2007
"... Abstract—In this paper, we investigate efficient strategies for supporting ondemand information dissemination and gathering in largescale wireless sensor networks. In particular, we propose a “combneedle ” discovery support model resembling an ancient method: Use a comb to help find a needle in sa ..."
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Abstract—In this paper, we investigate efficient strategies for supporting ondemand information dissemination and gathering in largescale wireless sensor networks. In particular, we propose a “combneedle ” discovery support model resembling an ancient method: Use a comb to help find a needle in sand or a haystack. The model combines push and pull for information dissemination and gathering. The push component features data duplication in a linear neighborhood of each node. The pull component features a dynamic formation of an ondemand routing structure resembling a comb. The combneedle model enables us to investigate the cost of a spectrum of push and pull combinations for supporting query and discovery in largescale sensor networks. Our result shows that the optimal routing structure depends on the frequency of query occurrence and the spatialtemporal frequency of related events in the network. The benefit of balancing push and pull for information discovery is demonstrated. Index Terms—Information discovery, query, geographical routing, wireless sensor networks. Ç 1
Modeling search costs in wireless sensor networks
, 2007
"... Abstract — We develop approximate closedform expressions of expected minimum search energy costs for datacentric wireless sensor networks showing the search performance with respect to the network size N and the number of randomly placed copies of the target event r. We consider both unstructured ..."
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Abstract — We develop approximate closedform expressions of expected minimum search energy costs for datacentric wireless sensor networks showing the search performance with respect to the network size N and the number of randomly placed copies of the target event r. We consider both unstructured sensor networks, which use blind sequential search for querying, and structured sensor networks, which use efficient hashbased querying. We also consider two kinds of deployments: a fixed transmit power (FTP) model and the geometric random graph (GRG) model. We find that the search cost of unstructured networks under the FTP deployment is proportional to N and inversely proportional to (r + 1) regardless of the spatial dimension d in which nodes are deployed, while that of the GRG is proportional to N(log N) η d r+1 where η is the pathloss exponent. The search cost of structured networks under the FTP deployment is found to be proportional to d √ N / d √ r, while that of the GRG deployment is proportional to d √ N(log N) η−1 d √. In all r cases, we also provide bounds on the coefficient of proportionality. I.
Optimize Storage Placement in Sensor Networks
"... Data storage has become an important issue in sensor networks as a large amount of collected data need to be archived for future information retrieval. Storage nodes are introduced in this paper to store the data collected from the sensors in their proximities. The storage nodes alleviate the heavy ..."
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Data storage has become an important issue in sensor networks as a large amount of collected data need to be archived for future information retrieval. Storage nodes are introduced in this paper to store the data collected from the sensors in their proximities. The storage nodes alleviate the heavy load of transmitting all data to a central place for archiving and reduce the communication cost induced by the network query. The objective of this paper is to address the storage node placement problem aiming to minimize the total energy cost for gathering data to the storage nodes and replying queries. We examine deterministic placement of storage nodes and present optimal algorithms based on dynamic programming. Further, we give stochastic analysis for random deployment and conduct simulation evaluation for both deterministic and random placements of storage nodes.
Derivations of the Expected Energy Costs of Search and Replication in Wireless Sensor Networks
 USC COMPUTER ENGINEERING
, 2006
"... We develop closedform expressions of the expected minimum search energy cost and replication energy cost for both unstructured sensor networks (which use blind sequential search for querying) and structured sensor networks (which use efficient hashbased querying). We use both the square grid and r ..."
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We develop closedform expressions of the expected minimum search energy cost and replication energy cost for both unstructured sensor networks (which use blind sequential search for querying) and structured sensor networks (which use efficient hashbased querying). We use both the square grid and random topology to derive each cost modeling. We find that the search cost of unstructured networks is proportional to the number of nodes N and inversely proportional to (r + 1) (where r denotes the number of copies of the target event). The search cost of structured networks is proportional to N/ r while the replication cost of both structured and unstructured networks is proportional to N(r − 1). Furthermore, the proportionality of those costs is independent of whether the topology is grid or random, which implies that the two topologies have common structural characteristics in terms of search and replication costs.
Dynamic Random Replication for Data Centric Storage
"... This paper presents a novel framework for Data Centric Storage in a wireless sensor and actor network that enables the use of a randomlyselected set of data replication nodes which also change over the time. This allows reducing the average network traffic and energy consumption by adapting the num ..."
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This paper presents a novel framework for Data Centric Storage in a wireless sensor and actor network that enables the use of a randomlyselected set of data replication nodes which also change over the time. This allows reducing the average network traffic and energy consumption by adapting the number of replicas to applications ’ traffic, while balancing energy burdens by varying their location. To that end we propose and validate a simple model to determine the optimal number of replicas, in terms of minimizing average traffic/energy consumption, from the measured applications’ production/consumption traffic. Simple protocols/mechanisms are proposed to decide when the current set of replication nodes should be changed, to enable new applications and sensor nodes to efficiently bootstrap into a working sensor network, to recover from failing nodes, and to adapt to changing conditions. Extensive simulations demonstrate that our approach can extend a sensor network’s lifetime by at least a 60%, and up to a factor of 10x depending on the lifetime criterion being considered.
EEBASS: EnergyEfficient Balanced Storage Scheme for Sensor Networks
 In GLOBECOM, IEEE
, 2008
"... Abstract—Datacentric storage is an effective and important technique in sensor networks, however, most datacentric storage schemes may not be energy efficient and load balanced due to nonuniform event and query distributions. This paper proposes EEBASS, it utilizes an approximation algorithm to s ..."
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Abstract—Datacentric storage is an effective and important technique in sensor networks, however, most datacentric storage schemes may not be energy efficient and load balanced due to nonuniform event and query distributions. This paper proposes EEBASS, it utilizes an approximation algorithm to solve the optimal storage placement problem according to the variance of event and query distributions, aiming to minimize the total energy consumption for datacentric storage scheme. And it further leverages a ring based replication structure to achieve the load balance goal. Simulation results show that EEBASS is more energy efficient and balanced than traditional datacentric storage mechanisms in sensor network. I.
Acknowledgments
, 2009
"... This thesis studies the problem of determining achievable rates in heterogeneous wireless networks. We analyze the impact of location, traffic, and service heterogeneity. Consider a wireless network with n nodes located in a square area of size n communicating with each other over Gaussian fading ..."
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This thesis studies the problem of determining achievable rates in heterogeneous wireless networks. We analyze the impact of location, traffic, and service heterogeneity. Consider a wireless network with n nodes located in a square area of size n communicating with each other over Gaussian fading channels. Location heterogeneity is modeled by allowing the nodes in the wireless network to be deployed in an arbitrary manner on the square area instead of the usual random uniform node placement. For traffic heterogeneity, we analyze the n x n dimensional unicast capacity region. For service heterogeneity, we consider the impact of multicasting and caching. This gives rise to the n x 2n dimensional multicast capacity region and the 2 " x n dimensional caching capacity region. In each of these cases, we obtain an explicit informationtheoretic characterization of the scaling of achievable rates by providing a converse and a matching (in the scaling sense) communication architecture.
Scalability of the Channel Capacity in Grapheneenabled Wireless Communications to the Nanoscale
"... Graphene is a promising material which has been proposed to build graphene plasmonic miniaturized antennas, or graphennas, which show excellent conditions for the propagation of Surface Plasmon Polariton (SPP) waves in the terahertz band. Due to their small size of just a few µm, graphennas allow t ..."
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Graphene is a promising material which has been proposed to build graphene plasmonic miniaturized antennas, or graphennas, which show excellent conditions for the propagation of Surface Plasmon Polariton (SPP) waves in the terahertz band. Due to their small size of just a few µm, graphennas allow the implementation of wireless communications among nanosystems, leading to a novel paradigm known as Grapheneenabled Wireless Communications (GWC). In this paper, an analytical framework is developed in order to evaluate how the channel capacity of a GWC system scales as its dimensions shrink. In particular, we study how the unique propagation of SPP waves in graphennas will impact the channel capacity. Next, we further compare these results with respect to the case when metallic antennas are used, in which these plasmonic effects do not appear. In addition, asymptotic expressions for the channel capacity are derived in the limit when the system dimensions tend to zero. In this scenario, necessary conditions to ensure the feasibility of GWC networks are found. Finally, using these conditions, new guidelines are derived to explore the scalability of various parameters, such as transmission range and transmitted power. These results may be helpful for designers of future GWC systems and networks.
Poster Abstract: Is DataCentric Storage and Querying Scalable?
"... The scalability of a wireless sensor network has been of interest and importance. We use a constrained optimization framework to derive fundamental scaling laws for both unstructured sensor networks (which use blind sequential search for querying) and structured sensor networks (which use efficient ..."
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The scalability of a wireless sensor network has been of interest and importance. We use a constrained optimization framework to derive fundamental scaling laws for both unstructured sensor networks (which use blind sequential search for querying) and structured sensor networks (which use efficient hashbased querying). We find that the scalability of a sensor network’s performance depends upon whether or not the increase in energy and storage resources with more nodes is outweighed by the concomitant applicationspecific increase in event and query loads. We have figured out the theoretical scaling laws for the networks of 2 dimensional deployment in our previous work [2]. We report on our workinprogress aimed at extending the scaling laws to networks of various dimensional deployment. As a recent achievement, we find that m · q1/2 must be O(N d−1