An efficient resiliency scheme for data centric storage in wireless sensor networks

Tanushetty, R.; Ngoh, Lek Heng; Keng, Pung Hung

doi:10.1109/vetecf.2004.1400598

Cited by 19 publications

(25 citation statements)

References 14 publications

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“…A great amount of research effort [5], [15], [16], [21], [22] has been put to DCS since it was first introduced in the year 2002 [15]. Compared to LCS, DCS is based on a different view of routing data, in which a type or a name instead of an IP address should be attached to identify the data.…”

Section: Related Workmentioning

confidence: 99%

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On the Performance of Location-Centric Storage in Sensor Networks

Xing

Cheng

2007

International Conference on Wireless Algorithms, Systems and Applications (WASA 2007)

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Abstract-This paper presents Location-Centric Storage (LCS), a novel distributed data storage protocol for sensor networks. In the protocol, each event detected by sensors is associated with an intensity value (σ)(by sensors), where σ is a parameter that depends on the characteristics of the event and the application context. When event information is broadcast, a sensor decides whether to store the record of an event by checking its distance to the event location and the σ. In general, the higher the intensity of an event, the further its information can propagate geographically in the sensor network. Besides, the closer to the event location, the denser the sensors are that store the event information, and thus the quicker and better a user can know about the event (by reading from surrounding sensors). The protocol utilizes network resource efficiently. In particular, the storage load of sensors is independent of the network size, and is evenly distributed across the network. Moreover, the communication distance for getting event information is small. Therefore, the protocol has great scalability. We provide detailed theoretical analysis and simulation study to support the claims. We also ran simulations to show the advantage of our protocol over some previous work. LCS can be used for applications such as context-dependent information mining in pervasive computing and on-demand warning in surveillance sensor networks.

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Section: Related Workmentioning

confidence: 99%

“…A data centric storage scheme [15] based on geographic hash tables [16] maps the data of the same type (name) to a fixed location in the sensor network. The performance of these three methods has been extensively studied [5], [15], [16], [21], [22]. These studies indicate that no one outperforms the other two in all situations.…”

Section: Introductionmentioning

confidence: 99%

On the Performance of Location-Centric Storage in Sensor Networks

Xing

Cheng

2007

International Conference on Wireless Algorithms, Systems and Applications (WASA 2007)

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“…The event model in this paper is similar to those used in [1,7,9,8,10,12] that an event is something of interest and can be any kind of information. Events are independent of each other and occur randomly at anywhere.…”

Section: Related Workmentioning

confidence: 99%

“…To provide different resiliency levels for different events, keeping an event in multiple locations is suggested in [12]. More important information is kept in more hash locations to enhance resiliency.…”

Section: Related Workmentioning

confidence: 99%

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Efficient Event and Query Distribution in Sensor Networks

Chan¹,

Lui²,

Tam³

2005 International Conference on Collaborative Computing: Networking, Applications and Worksharing

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Improving data centric storage with diffuse caching in wireless sensor networks

Cheng

Lui

et al. 2008

Wireless Communications

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In a sensor network that adopts data-centric storage (DCS), information of the same kind is kept in the same set of nodes. That is, when a sensor detects a certain event, no matter where it is, it sends the information to the designated location. When another node wants the information, it can send a query to that location to retrieve it. Unfortunately, existing protocols based on DCS are prone to the hot-spot problem where some nodes have to handle lots of messages. In this paper, we study how to apply diffuse caching on top of DCS. We diffuse popular information to other nodes so as to share the workloads. Our diffusing mechanism is adaptive that the distribution varies based on the popularity of the event type. We evaluate our protocol using simulations and the results show that our protocol successfully alleviates the hot-spot problem and reduces the message overheads.

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An efficient resiliency scheme for data centric storage in wireless sensor networks

Cited by 19 publications

References 14 publications

On the Performance of Location-Centric Storage in Sensor Networks

On the Performance of Location-Centric Storage in Sensor Networks

Efficient Event and Query Distribution in Sensor Networks

Improving data centric storage with diffuse caching in wireless sensor networks

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