Distributed Operator Placement and Data Caching in Large-Scale Sensor Networks

Lei, Ying; Liu, Zhen; Towsley, Don; Xia, Cathy H.

doi:10.1109/infocom.2008.151

Cited by 48 publications

(38 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the entities to cache are usually limited to sensor readings. [7] proposed a novel caching scheme in which operators in query graph that carries the semantics from application layer can be pushed down inside the network to perform "innetwork" processing with the intent of reducing data transmission. In our work, to further improve the system scalability and energy efficiency, we extend the traditional caching scheme and propose an optimization framework in which both of the sensor readings and fragments of applications can be cached at different layers of CEB in opposite directions.…”

Section: Related Workmentioning

confidence: 99%

An Optimization Framework for Cloud-Sensor Systems

Helal

2014

2014 IEEE 6th International Conference on Cloud Computing Technology and Science

View full text Add to dashboard Cite

Imminent massive-scale IoT deployments require a Cloud-Sensor architecture to facilitate an ecosystem of friction-free integration and programmability. In addition to these two functional requirements, challenging performance and scalability requirements must be addressed by any such architecture. We have introduced the Cloud-Edge-Beneath (CEB) architecture which addresses scalability and performance through a built-in distributed optimization framework. In this paper, we focus on CEB's optimization framework which follows a bi-directional waterfall model in which not only sensor data can move upward to applications, but applications (fragments) can move downward to lower layers of CEB closer to data sources. The framework enables many optimization ideas and opportunities, including our own. We present the bi-directional waterfall framework along with a sketch of several of our optimization algorithms enabled by the framework. We also present an example of an experimental study to determine dominant resources in the cloud -a variable which as will be seen greatly affects the logic of some of the optimization algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

An Optimization Framework for Cloud-Sensor Systems

Helal

2014

2014 IEEE 6th International Conference on Cloud Computing Technology and Science

View full text Add to dashboard Cite

show abstract

“…Ying et al [6] propose a distributed algorithm to do the same task as above, namely static operator placement. Nodes exchange information with their neighbors iteratively until they find the optimal placement for all given operators.…”

Section: Previous Work and The Differencesmentioning

confidence: 99%

A Distributed Technique for Dynamic Operator Placement in Wireless Sensor Networks

Chatzimilioudis

Mamoulis²,

Gunopulos

2010

2010 Eleventh International Conference on Mobile Data Management

View full text Add to dashboard Cite

Abstract-We present an optimal distributed algorithm to adapt the placement of a single operator in high communication cost networks, such as a wireless sensor network. Our parameterfree algorithm finds the optimal node to host the operator with minimum communication cost overhead. Three techniques, proposed here, make this feature possible: 1) identifying the special, and most frequent case, where no flooding is needed, otherwise 2) limitation of the neighborhood to be flooded and 3) variable speed flooding and eves-dropping. When no flooding is needed the communication cost overhead for adapting the operator placement is negligible. In addition, our algorithm does not require any extra communication cost while the query is executed. In our experiments we show that for the rest of cases our algorithm saves 30%-85% of the energy compared to previously proposed techniques. To our knowledge this is the first optimal and distributed algorithm to solve the 1-median (Fermat node) problem. I. OVERVIEW AND MOTIVATIONNetwork applications often need to perform in-network query processing. Sensor networks are being deployed in the physical or urban environment to benefit scientific research or security surveillance. An example of a query in a network, which is monitoring traffic in a busy downtown area, could be "How many cars took the same route of passing through intersections A, B and C?". To avoid the cost of communicating all the data lists from the nodes in regions A, B and C to the querying node, the query must be executed in-network. Data lists generated on the source nodes are fed into operators on intermediate nodes that combine several lists from different sources. The amount of data is reduced due to the selectivity of the operators and the data that reaches the querying node is the final answer.An operator, that is involved in the in-network processing, can be placed on a node of the network. It takes in elements from source nodes, processes them, and sends the output to either another operator node or to the sink. Shipping elements over an edge in the graph imposes a cost that is dependent on the weight of the elements. Therefore, the placement of an operator can greatly affect the cost of answering a query since it affects the number of edges the elements have to travel over and the weight of the elements, since usually the output weight is not the sum of the input weights. It is typical to have continuous queries that require an answer over a continuous period of epochs. In most applications the sources and operators are not producing the same weight of elements in every epoch. Similarly, nodes in the network might be mobile resulting in different hop-distances between nodes in every epoch. Therefore, the initial operator placement might not be good enough for future epochs. It is a large overhead to re-run the algorithms for finding a good placement for the operators of the query. Instead, the technique followed in literature is to update the placement of just the operators that are affected by the weight...

show abstract

“…Data stream processing as it provided by Data Stream Management Systems (DSMSs) is also an option for distributed processing in WSNs that is more efficient than sending all data to a central processing unit [2]. If operator placement decisions get disadvantageous at runtime, replacement and migration of operators is required.…”

Section: Introductionmentioning

confidence: 99%

Dynamic operator replacement in sensor networks

Strübe

Daum

Kapitza

et al. 2010

The 7th IEEE International Conference on Mobile Ad-Hoc and Sensor Systems (IEEE MASS 2010)

View full text Add to dashboard Cite

Abstract-We present an integrated approach for supporting in-network sensor data processing in dynamic and heterogeneous sensor networks. The concept relies on data stream processing techniques that define and optimize the distribution of queries and their operators. We anticipate a high degree of dynamics in the network, which can for example be expected in the case of wildlife monitoring applications. The distribution of operators to individual nodes demands system level capabilities not available in current sensor node operating systems. In particular, we present a system for seamless and on demand operator migration between sensor nodes. Our framework, which we implemented for Contiki running on TelosB nodes, supports stateful module migration including selected parts of the code and data sections.

show abstract

Distributed Operator Placement and Data Caching in Large-Scale Sensor Networks

Cited by 48 publications

References 15 publications

An Optimization Framework for Cloud-Sensor Systems

An Optimization Framework for Cloud-Sensor Systems

A Distributed Technique for Dynamic Operator Placement in Wireless Sensor Networks

Dynamic operator replacement in sensor networks

Contact Info

Product

Resources

About