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Related Concept Videos

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the problem,...
Maximum Power Transfer01:16

Maximum Power Transfer

Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
By substituting the entire circuit with...
Distributed Loads01:19

Distributed Loads

Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
Ampere's Law: Problem-Solving01:31

Ampere's Law: Problem-Solving

Ampere's law states that for any closed looped path, the line integral of the magnetic field along the path equals the vacuum permeability times the current enclosed in the loop. If the fingers of the right hand curl along the direction of the integration path, the current in the direction of the thumb is considered positive. The current opposite to the thumb direction is considered negative.
Specific steps need to be considered while calculating the symmetric magnetic field distribution using...
Optimal Foraging00:48

Optimal Foraging

How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.

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Related Experiment Videos

EACCO: Optimizing the Computation and Communication in Resource-Constrained IoT Devices for Energy-Efficient Swarm

Amir Ijaz1, Hashem Haghbayan1, Ethiopia Nigussie1

  • 1Department of Computing, University of Turku, FI-20014 Turku, Finland.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary

This study introduces an energy-efficient design for Internet of Things (IoT) swarm robots, integrating energy harvesting and optimized communication. The approach ensures robots operate autonomously for extended periods with minimal maintenance.

Keywords:
Industrial Internet of Things (IIoT)Internet of Things (IoT)communicationslow-power and energy-harvesting technologiespower managementswarm robotics

Related Experiment Videos

Area of Science:

  • Robotics
  • Internet of Things (IoT)
  • Energy Systems

Background:

  • Energy consumption is a major challenge for resource-constrained IoT platforms, especially swarm robotic systems.
  • Prolonged collaborative operation of numerous devices necessitates efficient power management.

Purpose of the Study:

  • To present a comprehensive design strategy for enhancing processing and communication efficiency in IoT swarm robotics.
  • To reduce overall energy consumption and improve system longevity.

Main Methods:

  • Incorporation of energy harvesting (photovoltaic, RF) and dynamic power management.
  • Development of energy-efficient communication protocols (duty cycling, power control, data compression).
  • Utilized MCU-based nodes (TI MSP430 with LoRa) for communication validation and edge platforms (Jetson Nano/TX2) for power profiling.

Main Results:

  • Achieved an energy neutrality ratio well above unity, even with limited ambient energy.
  • Demonstrated significant reductions in energy per bit transmitted.
  • Validated reliable long-term operation through simulations and hardware prototypes.

Conclusions:

  • The proposed design strategy enables autonomous IoT-based robot swarms with minimal maintenance.
  • The findings support the deployment of long-lasting, self-sufficient robotic systems.