Collaborative Scheduling in Highly Dynamic Environments Using Error Inference

Zhang, Qingquan; Gü, Yü; Gu, Lin; Cao, Qing; He, Tian

doi:10.1109/msn.2011.25

Cited by 9 publications

(7 citation statements)

References 27 publications

(31 reference statements)

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“…Sink or CHs performs reclustering in case of centralized schemes. In [10] scheduling mechanism for collaborating sensors and attain error-bound scheduling control to monitor applications is utilized and the advantages of both methods to strike a compromise between energy consumed and prediction accuracy. Sensing planned based on two error information, a) error calculated locally b) error predicted by neighboring nodes.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Centralized Dynamic Clustering Reservation Mac Protocol for MWSN

Suma¹,

Siddappa²

2017

IJCT

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Mobile wireless sensor networks (MWSN) have recently emerged as a hot research topic as their communication links and topology change due to node mobility. In a dynamically changing environment it is essential to build energy efficient communication routes with least communication overheads. Energy is one of the most important concerns for MWSNs. To have a long lasting network lifetime, we propose a centralized, periodic, selective sensing and dynamic clustering algorithm for delay tolerant and high integrity MWSNs, aimed to enhance the connectivity and performance. All dynamically varying parameters such as location, number of nodes present in each virtual grid, residual energy are considered while forming clusters and selecting cluster head (CH). Reclustering and selection of CH is centralized and performed by sink. Cluster size is not constant and may vary from cluster to cluster in every round. If nodes in any grid is less, then nodes of neighboring grids are clubbed to form clusters. Reclustering is done in every round and to avoid data duplication, some nodes in every cluster are selected based on proximity and are not allowed to sense and transmit in that round. It is assumed in a close vicinity if more than one sensors are present they all generate same data as they sense the same physical quantity, this reduce redundant data generation sensing is performed only by selected nodes. Inter and intra communication slots are reserved depending on size of cluster by sink in every round. The algorithm is evaluated using stationary and mobile sink.

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Section: Related Workmentioning

confidence: 99%

Energy Efficient Centralized Dynamic Clustering Reservation Mac Protocol for MWSN

Suma¹,

Siddappa²

2017

IJCT

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“…We developed a prototype system by adding a combination of internal group and inter-group data reduction while trying to maintain data integrity at an acceptable level. Our previous work [10] has shown that a statistical approach can help to predict the future sensing data under predefined tolerance. However using machine learning approach in data reduction is new as the data processing center can predict the data both inside group and inter-group with sending request which normally consume large amount of energy in packets dissemination.…”

Section: A Data Management In Dr3mentioning

confidence: 99%

Cooperative Data Reduction in Wireless Sensor Network

Zhang

Xiao

et al. 2015

ACM Trans. Embed. Comput. Syst.

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Abstract-Due to the limited power constraint in sensors, dynamic scheduling with data quality management is strongly preferred in long lifetime monitoring applications. But typical techniques treat data management as an isolated process on only selected individual nodes, e.g. the centroid node. In this paper, we propose and evaluate an aggressive data reduction algorithm based on error inference within sensor segments. The architecture integrates three parallel dynamic error control mechanisms to optimize the trade-off between energy saving and data validity. We demonstrate that not only substantial energy savings can be achieved but also that an error bound specified by the application can be guaranteed. Moreover, we have investigate the system performance by using the realistic historical soil temperature data as an experimental context. The experimental results demonstrate that the system error meets the specified error tolerance and produces up to a 50 percent of the energy savings compared to several sensing schemes.

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“…The collaborative scheduling was studied in [24]. The authors provided a collaborative scheme based on the concept of error interference.…”

Section: Collaborative Schedulingmentioning

confidence: 99%

Utility-Based Routing in Wireless Sensor Networks

2013

The Art of Wireless Sensor Networks

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Wireless sensor networks (WSNs) have been proposed for monitoring physical environments. The applications in WSNs have comprised a wide variety of scenarios. The design of routing protocols in WSNs becomes more complicated than the traditional network when we consider the energy cost, throughput, reliability, and delay as routing metrics. Selecting a particular routing protocol mainly depends on the capabilities of the nodes, and on the requirements of the application. In this chapter, we will briefly discuss the existing utility-based routing protocols for WSNs. We put them into several categories according to their utility properties, such as delay, cost, and packet delivery ratio. In addition, we will also cover the composition-based utility for wireless networks and its extensions in low duty-cycle WSNs.

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Collaborative Scheduling in Highly Dynamic Environments Using Error Inference

Cited by 9 publications

References 27 publications

Energy Efficient Centralized Dynamic Clustering Reservation Mac Protocol for MWSN

Energy Efficient Centralized Dynamic Clustering Reservation Mac Protocol for MWSN

Cooperative Data Reduction in Wireless Sensor Network

Utility-Based Routing in Wireless Sensor Networks

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