DIFS: a distributed index for features in sensor networks

Greenstein, Ben; Estrin, Deborah; Govindan, Ramesh; Ratnasamy, Sylvia; Shenker, Scott

doi:10.1016/s1570-8705(03)00010-6

Cited by 141 publications

(117 citation statements)

References 16 publications

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“…The hash mapping hashes nearby sensor data to the same node, and uses k-d tree to support range queries. DIFS [5] relies on geographic hash and quad tree structure for efficient index construction and range searches. DIMENSIONS [3] uses data compression and data aging to reduce redundancy caused by spatial and temporal correlation.…”

Section: Related Workmentioning

confidence: 99%

“…In a mesh network topology, however, there are potentially multiple producers and consumers all seeking to exploit one event simultaneously. In this scenario, some work has focused on the geographical locations [18] of producers and consumers but given no attention to the issue of the data rates [5,8,13]. Once the network topology is fixed, the storage node position is fixed too, which does not apply adaptive storage principle to reduce energy consumption in sensor networks.…”

Section: Introductionmentioning

confidence: 99%

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Achieving optimal data storage position in wireless sensor networks

Zhang

Xiao

Zhou

2010

Computer Communications

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a b s t r a c tData storage in wireless sensor networks (WSNs) involves producers (such as sensor nodes) storing in storage positions a large amount of data which they have collected and consumers (e.g., base stations, users, and sensor nodes) then retrieving that data. When addressing this issue, previous work failed to utilize data rates and locations of multiple producers and consumers to determine optimal data storage positions to be communication cost-effective in a mesh network topology. In this paper, we first formalize the data storage problem into a one-to-one (one producer and one consumer) model and a many-tomany (m producers and n consumers) model with the goal of minimizing the total energy cost. Based on above models, we propose optimal data storage (ODS) algorithms that can produce global optimal data storage position in linear, grid, and mesh network topologies. To reduce the computation of ODS in the mesh network topology, we present a near-optimal data storage (NDS) algorithm, which is an approximation algorithm and can obtain a local optimal position. Both ODS and NDS are locality-aware and are able to adjust the storage position adaptively to minimize energy consumption. Simulation results show that NDS not only provides substantial cost benefit over centralized data storage (CDS) and geographic hash table (GHT), but performs as well as ODS in over 75% cases.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Achieving optimal data storage position in wireless sensor networks

Zhang

Xiao

Zhou

2010

Computer Communications

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“…A major limitation of the earlier DCS systems is that they either supported (support) only an exact match point query, such as GHT [14], or a simple range query concerning a maximum of two attributes, such as the time-indexing scheme [4] and the DIFS [3].…”

Section: Related Workmentioning

confidence: 99%

Scalable multi-dimensional range queries and routing in data-centric sensor networks

Monti¹,

Moro²

2008

Proceedings of the Third International ICST Conference on Scalable Information Systems

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Large scale wireless ad hoc networks of computers, sensors, PDAs etc. (i.e. nodes) are revolutionizing connectivity and leading to a paradigm shift from centralized systems to highly distributed and dynamic environments. An example of ad hoc networks are sensor networks, which are usually composed by small units able to sense and transmit to a sink elementary data which are successively processed by an external machine. Recent improvements in the memory and computational power of sensors, together with the reduction of energy consumptions, are rapidly changing the potential of such systems, moving the attention towards data-centric sensor networks. In these sensor networks, data or events are named by attributes that have scalar values, so one natural way to query events of interest is to use a multidimensional range query. W-Grid acts as a distributed index and allows multi-dimensional data management capability since nodes' virtual coordinates can act as a distributed database without needing neither special implementation or reorganization and supports multi-dimensional range queries. In this paper we introduce range query features in W-Grid and we show, by means of an extensive number of simulations, the performance and network costs in comparison with a well-know competitor solution in literature.

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“…构建索引是指在数据存储时建立索引,而查询访问可通过索引获得数据,典型代表是 DIM [10] ,DIFS [11] 和 DIMENSIONS [12] .DIM [10] 不仅采用特殊性质的哈希函数来确定数据存放位置,而且引入了数据库中的索引结构 …”

Section: 相关工作unclassified

Adaptive information brokerage in wireless sensor networks

Gon¹

2008

Academic Journal of Second Military Medical University

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Information brokerage in wireless sensor networks involves producers (such as sensor nodes) storing in storage positions a large amount of data that they have collected and consumers (e.g. base stations, users, and nodes) retrieving that information. In this paper, first, the data storage problem is formalized into a one-to-one (one producer and one consumer) model, a many-to-one (m producers and one consumer) model, and a many-to-many (m producers and n consumers) model with the goal of minimizing the total energy consumption. Second, based on the above models, two algorithms are proposed to determine the storage positions based on data rates of producers, query rates of consumers, and transmission scheme of information brokerage. The optimal data storage (ODS) scheme, a greedy algorithm, produces the global optimal data storage positions and the near-optimal data storage (NDS) scheme, an approximate algorithm, can greatly reduce the computational overhead while achieving local optimal positions. Both ODS and NDS are able to adjust the storage positions adaptively to minimize energy consumption that includes the costs of storing and querying the data. Simulation results show that NDS not only provides substantial cost benefits but also performs as effective and efficient as ODS in over 70% of the tested cases.

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DIFS: a distributed index for features in sensor networks

Cited by 141 publications

References 16 publications

Achieving optimal data storage position in wireless sensor networks

Achieving optimal data storage position in wireless sensor networks

Scalable multi-dimensional range queries and routing in data-centric sensor networks

Adaptive information brokerage in wireless sensor networks

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