Energy-latency tradeoffs for data gathering in wireless sensor networks

By Yang Yu, Bhaskar Krishnamachari, Viktor K. Prasanna. IEEE INFOCOM 2004, 2004

388 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/aabb38634d329a5f554cfedc41fb881acf40742b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Michael G. Rabbat
2. Ali H. Sayed
3. Soummya Kar
4. José M. F. Moura
5. Anna Scaglione
6. Angelia Nedić
7. Nicolas Loizou
8. A. Dimakis

In Electrical and Electronic Engineering

1. Chau Yuen
2. Lajos Hanzo
3. Anna Scaglione
4. José M. F. Moura
5. Shaoshi Yang
6. Zesong Fei
7. Bo Jiang
8. Yu Gu

Citations in the corpus

• Opportunistic Flooding in Low-Duty-Cycle Wireless Sensor Networks with Unreliable Links (2009, background): “introduce solutions on minimizing energy dissipation for sensor nodes under certain latency constraints [45].”
• Gossip Algorithms for Distributed Signal Processing (2010, methodology): “ to a fusion center, only the results of the computation are transmitted to the end-user. In many situations, in-network computation leads to considerable energy savings over the centralized approach [8], [9]. Many previous approaches to in-network processing assume that the network can provide spec”
• Data-aggregation techniques in sensor networks: a survey (2006, methodology): “The non-monotonic energy model used in [ 26 ] is based on Quadrature Amplitude Modulation (QAM) scheme.”
• In-network aggregation techniques for wireless sensor networks: a survey (2007, background): “In addition, a node may be marked as special depending on many factors such as its position within the data gathering tree [34], its resources [35], the type of data stored in its queue [36], [37], or the processing cost due to aggregation procedures [38].”
• A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems (2016, methodology): “[137] studied the energyversus-latency trade-offs using the so-called data aggregation tree4 in a real-time scenario with a specified latency constraint, and developed algorithms for minimizing the overall energy dissipation of the sensor nodes.”
• Energy Efficiency Tradeoff Mechanism Towards Wireless Green Communication: A Survey (2016, background): “The paper [96] explores the energy-latency tradeoff in wireless communication using modulation scaling”