Simulating the power consumption of large-scale sensor network applications

By Victor Shnayder, Mark Hempstead, Bor-rong Chen, Geoffrey Werner-Allen, Matt Welsh. ACM International Conference on Embedded Networked Sensor Systems, 2004

1,236 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/8554e34b7d4fdcb6d93a72f339ad3a59c33a63cb

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Citations in the corpus

Trio: enabling sustainable and scalable outdoor wireless sensor network deployments (2006, critical): “Current simulators like TOSSIM [12] and PowerTOSSIM [19] fail to capture the complex physical phenomena that appear in real deployments.”
An analysis of a large scale habitat monitoring application (2004, background): “A more complete and automated approach to power profiling is described in [15] We can improve our lifetime estimate through additional health information from each mote.”
MoteLab: a wireless sensor network testbed (2005, background): “In situ power profiling of sensor applications is another difficult task faced by researchers, since rigging a node up to an appropriate measurement harness is often non-trivial and requires manual data capture [2].”
On maximum lifetime routing in Wireless Sensor Networks (2009, background): “According to [2], the majority of power consumption is due to the radio component.”
Accurate prediction of power consumption in sensor networks (2005, methodology): “Although Power Tossim and our approach base on nearly the same measurements (see Table 1 and [15]), the results in terms of energy consumption are quite different.”
MobiRoute: Routing Towards a Mobile Sink for Improving Lifetime in Sensor Networks (2006, methodology): “(7)Since TOSSIM uses a MAC that never switches off its radio, tools such as Power-TOSSIM [24] always report a flat energy consumption pattern of a network no matter where the sink is located.”
Near-Threshold RISC-V Core With DSP Extensions for Scalable IoT Endpoint Devices (2017, neutral): “d = a[0] · b[0] + a[1] · b[1] + a[2] · b[2] + a[3] · b[3]”
Decentralized intrusion detection in wireless sensor networks (2005, methodology): “WSNs, such as SensorSim [16], TOSSIM [13], and Power-TOSSIM [ 23 ].”
DW-MAC: a low latency, energy efficient demand-wakeup MAC protocol for wireless sensor networks (2008, background): “In evaluating power efficiency, we focus on energy consumed by radios but ignore energy consumed by other components such as CPU and memory [23].”
Cross-Level Sensor Network Simulation with COOJA (2006, methodology): “PowerTOSSIM [8] is an extension to TOSSIM for estimating per-node power consumption.”
Distributed Consensus With Limited Communication Data Rate (2011, background): “Simulations show that for a large-scale sensor network, communications between nodes consume far more power than computation ([37]).”
Telos: enabling ultra-low power wireless research (2005, background): “It eliminates programming and support boards, while enabling experimentation with WSNs in both lab, testbed, and deployment settings.”
A near-threshold RISC-V core with DSP extensions for scalable IoT Endpoint Devices (2016, methodology): “ so does the required processing power. As the power of wireless (and wired) communication from the endpoint to the higher level nodes in the IoT hierarchy is still dominating the overall power budget[2], it is highly desirable to reduce the amount of transmitted data by doing more complex near-sens”
On distributed fault-tolerant detection in wireless sensor networks (2006, methodology): “To validate our theoretical work, we plan to propose a communication protocol for fault corrections and implement the proposed detection algorithms on sensor network simulators, such as PowerTOSSIM [21].”
Data compression algorithms for energy-constrained devices in delay tolerant networks (2006, methodology): “5 mW 21 mW 12,364 CC1000 300 m at 3V [31] at 3V [31] [36] MaxStream 2.”