Balancing Push and Pull for Efficient Information Discovery in Large-Scale Sensor Networks

Liu, Xin; Huang, Qingfeng; Zhang, Ying

doi:10.1109/tmc.2007.34

Cited by 98 publications

(132 citation statements)

References 16 publications

Supporting

Mentioning

132

Contrasting

Order By: Relevance

“…In contrast, a bottom-up sensor-driven model [12] has also been proposed, assuming that sensors are capable of pushing data to applications when an event occurs. To improve the efficiency of data delivery and enable data sharing, messaging paradigms such as publish/subscribe [9] and push-pull [10] have been widely adopted in sensor data acquisition. Optimization techniques to balance push and pull have been extensively discussed in [10][13] [14], which focus on network topology and routing algorithms.…”

Section: Related Workmentioning

confidence: 99%

“…To improve the efficiency of data delivery and enable data sharing, messaging paradigms such as publish/subscribe [9] and push-pull [10] have been widely adopted in sensor data acquisition. Optimization techniques to balance push and pull have been extensively discussed in [10][13] [14], which focus on network topology and routing algorithms. Furthermore, a new model discussed in [15] utilizes the mixed push/pull strategy and takes advantage of the optimization opportunity provided by the event structure and its time-frequency relaxations.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing push/pull envelopes for energy-efficient cloud-sensor systems

Helal

Thai

et al. 2011

Proceedings of the 14th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

View full text Add to dashboard Cite

Unlike traditional distributed systems, where the resources/needs of computation and communication dominate the performance equation, sensor-based systems (SBS) raise new metrics and requirements for sensors as well as for computing and communication. This includes sensing latency and energy consumption. In this paper, we present a performance model for SBS based on a three-tier architecture that uses edge devices to connect massive-scale networks of sensors to the cloud. In this architecture, which we call Cloud, Edge, and Beneath (CEB), initial processing of sensor data occurs in-and near-network, in order to achieve system sentience and energy efficiency. To optimize CEB performance, we propose the concept of optimal push/pull envelope (PPE). PPE dynamically and minimally adjusts the base push and pull rates for each sensor, according to the relative characteristics of sensor requests (demand side from the Cloud) and sensor data change (supply side from Beneath). We demonstrate the CEB architecture and its push/pull envelope optimization algorithm in an experimental evaluation that measures energy savings and sentience efficiency over a wide range of practical constraints. In addition, from the experiments we demonstrate that by combining PPE optimization algorithm with lazy sampling algorithm, we can achieve further energy saving.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Optimizing push/pull envelopes for energy-efficient cloud-sensor systems

Helal

Thai

et al. 2011

Proceedings of the 14th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

View full text Add to dashboard Cite

show abstract

“…A number of data-centric push-pull query processing techniques have been proposed and examined [6,8,23,28,29,31], which can 5/44 be categorized to two main approaches: structured and unstructured, which can be represented by the geographic hash-based data centric storage technique [29] and the comb-needle method [23] respectively. Kapadia and Krishnamachari [20] present a comparative mathematical analysis of these two approaches based on two types of simple one-shot queries (ALL-type and ANY-type) in singlesink square-grid sensor networks, and later on, Ahn and Krishnamachari [2] find that the scalability of a data-centric sensor networks performance depends upon whether or not the increase in energy and storage resources with more nodes is outweighed by the resulting application-specific increase in event and query loads.…”

Section: Related Workmentioning

confidence: 99%

Maximum lifetime continuous query processing in wireless sensor networks

Kalpakis

Tang

2010

Ad Hoc Networks

View full text Add to dashboard Cite

Monitoring applications emerge as one of the most important applications of wireless sensor networks (WSNs). Such applications typically have long-running complex queries that are continuously evaluated over the sensor measurement streams. Due to the limited energy of the sensors in WSNs, energy efficient query evaluation is critical to prolong the system lifetime -the earliest time that the network can not perform its intended task anymore. We analyze the complexity and provide a simple and effective algorithm for the placement of the nodes of T onto the sensor nodes of a WSN. Our algorithm of operator placement attempts to minimize the total amount of data that need to be communicated. A placement of T induces a certain Maximum Lifetime Concurrent-Flow (MLCF) problem. We provide an efficient algorithm that finds near-optimal integral solutions to the MLCF problem, where a solution is a collection of paths on which certain amount of integral flow is routed. Our approach to the continuous in-network evaluation of T consists of utilizing both our placement and routing algorithms above. * Corresponding author. We demonstrate experimentally that our approach consistently and effectively find the maximum lifetime solutions for the continuous in-network evaluation of expression trees in wireless sensor networks.

show abstract

“…Different from a pure 'pull' or pure 'push' approach (both of which involve expensive flooding operations), a double ruling scheme adopts a symmetric information discovery principle with both the source and sink actively searching for each other, as first suggested in rumor-routing [1]. The notion of trails has appeared in a number of other papers as well, including in trajectory forwarding [14], in the asymptotics of query strategies [16], and the combs and needles work [10]. A number of double ruling schemes that guarantee distance sensitivity have been devised in the literature [15].…”

Section: Double Rulingsmentioning

confidence: 99%

Sparse Data Aggregation in Sensor Networks

Gao

Guibas

Milosavljević

et al. 2007

2007 6th International Symposium on Information Processing in Sensor Networks

View full text Add to dashboard Cite

We study the problem of aggregating data from a sparse set of nodes in a wireless sensor network. This is a common situation when a sensor network is deployed to detect relatively rare events. In such situations, each node that should participate in the aggregation knows this fact based on its own sensor readings, but there is no global knowledge in the network of where all these interesting nodes are located. Instead of blindly querying all nodes in the network, we show how the interesting nodes can autonomously discover each other in a distributed fashion and form an ad hoc aggregation structure that can be used to compute cumulants, moments, or other statistical summaries. Key to our approach is the capability for two nodes that wish to communicate at roughly the same time to discover each other at a cost that is proportional to their network distance. We show how to build nearly optimal aggregation structures that can further deal with network volatility and compensate for the loss or duplication of data by exploiting probabilistic techniques.

show abstract

Balancing Push and Pull for Efficient Information Discovery in Large-Scale Sensor Networks

Cited by 98 publications

References 16 publications

Optimizing push/pull envelopes for energy-efficient cloud-sensor systems

Optimizing push/pull envelopes for energy-efficient cloud-sensor systems

Maximum lifetime continuous query processing in wireless sensor networks

Sparse Data Aggregation in Sensor Networks

Contact Info

Product

Resources

About