Dynamic Power Management in Wireless Sensor Networks

By A. Sinha, Anantha P. Chandrakasan. IEEE Design & Test of Computers, 2001

802 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f58a92d0b278bd03117049ba9aa75b43a4026ab8

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Wireless sensor networks: a survey (2002, background): “In [80], the authors propose a workload prediction scheme based on adaptive filtering of the past workload profile and analyze several filtering schemes.”
Energy-Harvesting Wireless Sensor Networks (EH-WSNs) (2018, background): “In Sinha and Chandrakasan (2001), several sleep states are defined for each node based on which components are put in the sleep mode.”
Power management in energy harvesting sensor networks (2007, background): “Another issue that becomes important in networked systems with multiple harvesting nodes is that different nodes may have different harvesting opportunity.”
Piezoelectric Energy Harvesting Devices: An Alternative Energy Source for Wireless Sensors (2012, background): “The operation principle of DVS technique is that increasing a circuit’s voltage allows it to switch faster, but with an increase in energy consumption, and conversely the decrease in circuit voltage causes the circuit to have a low switching time with an accompanied decrease in energy consumption [5”
Wireless MEMS-Based Accelerometer Sensor Boards for Structural Vibration Monitoring: A Review (2017, background): “High-resolution MCUs, suitable for resolving microvibration, may take up to 25% of the board’s total power consumption [19]; therefore, researchers have developed systems with off-board”
Low energy adaptive clustering hierarchy with deterministic cluster-head selection (2002, background): “In [7] particular modules of the sensor hardware are turned off when not needed.”
Sensor Networks (2002, background): “A dynamic power management scheme for wireless sensor networks is discussed in [14] where five power-saving modes are proposed and intermode transition policies are investigated.”
Power efficient data gathering and aggregation in wireless sensor networks (2003, background): “going research on power management issues in order to reduce the power consumption when the nodes become idle [2].”
A survey on power control issues in wireless sensor networks (2007, methodology): “Each sensor node consists of N embedded components (the sensor and the A/D converter, the processor with memory and the transceiver) which can be in different sleep-states [ 19 ].”
Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring (2011, methodology): “As an example, a health smart home was designed by the Faculty of Medicine in Grenoble-France to check the feasibility of such systems [38].”
Optimal energy management policies for energy harvesting sensor nodes (2008, background): “, reducing the number of bits to transmit ([2], [3]), by choosing the best modulation strategy ([4]), by using efficient transmission scheduling to take advantage of charge recovery phenomenon ([5]), by exploiting power saving modes (sleep/listen) periodically ([6]), using energy efficient routing (”
Random key-assignment for secure Wireless Sensor Networks (2003, background): “Finally, note that we do not need to assume that sensors in the WSN are tamper-resistant, thus relaxing the assumptions in [3, 17, 8].”
Smart Cities: A Survey on Data Management, Security, and Enabling Technologies (2017, background): “As for data acquisition techniques that make use of machine learning, several works have considered the use of machine learning in WSNs in order to solve network associated problem, such as energy-aware communication [171]–[175], optimal deployment and localization [176]–[179], resource allocation a”