An evaluation of multi-resolution storage for sensor networks

Ganesan, Deepak; Greenstein, Ben; Perelyubskiy, Denis; Estrin, Deborah; Heidemann, John

doi:10.1145/958491.958502

Cited by 162 publications

(49 citation statements)

References 22 publications

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“…TSAR is different from our design in that storage is not a cooperative activity among nodes. DIMENSIONS [7] is another system that is designed to store long-term information by constructing summaries at different spatial resolutions using various compression techniques. Our previous work EnviroStore [19] presents a storage service that tries to maximize the effective storage capactiy of disconnected sensor networks, but it focuses on how to take the best advantage of uploading opportunities when data mules [27] become close.…”

Section: ) Acoustic Applicationsmentioning

confidence: 99%

EnviroMic: Towards Cooperative Storage and Retrieval in Audio Sensor Networks

Luo

Cao

Huang

et al. 2007

27th International Conference on Distributed Computing Systems (ICDCS '07)

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This paper presents the design, implementation, and evaluation of EnviroMic, a novel distributed acoustic monitoring, storage, and trace retrieval system. Audio represents one of the least exploited modalities in sensor networks to date. The relatively high frequency and large size of audio traces motivate distributed algorithms for coordinating recording tasks, reducing redundancy of data stored by nearby sensors, filtering out silence, and balancing storage utilization in the network. Applications of acoustic monitoring with EnviroMic range from the study of mating rituals and social behavior of animals in the wild to audio surveillance of military targets. EnviroMic is designed for disconnected operation, where the luxury of having a basestation cannot be assumed.We implement the system on a TinyOS-based platform and systematically evaluate its performance through both indoor testbed experiments and a preliminary outdoor deployment. Results demonstrate up to a 4-fold improvement in effective storage capacity of the network compared to uncoordinated recording.

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Section: ) Acoustic Applicationsmentioning

confidence: 99%

EnviroMic: Towards Cooperative Storage and Retrieval in Audio Sensor Networks

Luo

Cao

Huang

et al. 2007

27th International Conference on Distributed Computing Systems (ICDCS '07)

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“…Ganesan et al [26,25] proposed in-network storage of wavelet-based summaries of sensor data. Recently, discrete wavelet transform (DWT) was also proposed in [53,7] for sensor data compression.…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

A Survey of Model-based Sensor Data Acquisition and Management

Sathe

Papaioannou

Jeung³

et al. 2012

Managing and Mining Sensor Data

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In recent years, due to the proliferation of sensor networks, there has been a genuine need of researching techniques for sensor data acquisition and management. To this end, a large number of techniques have emerged that advocate model-based sensor data acquisition and management. These techniques use mathematical models for performing various, day-to-day tasks involved in managing sensor data. In this chapter, we survey the state-of-the-art techniques for model-based sensor data acquisition and management. We start by discussing the techniques for acquiring sensor data. We, then, discuss the application of models in sensor data cleaning; followed by a discussion on model-based meth-

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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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An evaluation of multi-resolution storage for sensor networks

Cited by 162 publications

References 22 publications

EnviroMic: Towards Cooperative Storage and Retrieval in Audio Sensor Networks

EnviroMic: Towards Cooperative Storage and Retrieval in Audio Sensor Networks

A Survey of Model-based Sensor Data Acquisition and Management

Adaptive information brokerage in wireless sensor networks

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