24,202 research outputs found
Push & Pull: autonomous deployment of mobile sensors for a complete coverage
Mobile sensor networks are important for several strategic applications
devoted to monitoring critical areas. In such hostile scenarios, sensors cannot
be deployed manually and are either sent from a safe location or dropped from
an aircraft. Mobile devices permit a dynamic deployment reconfiguration that
improves the coverage in terms of completeness and uniformity.
In this paper we propose a distributed algorithm for the autonomous
deployment of mobile sensors called Push&Pull. According to our proposal,
movement decisions are made by each sensor on the basis of locally available
information and do not require any prior knowledge of the operating conditions
or any manual tuning of key parameters.
We formally prove that, when a sufficient number of sensors are available,
our approach guarantees a complete and uniform coverage. Furthermore, we
demonstrate that the algorithm execution always terminates preventing movement
oscillations.
Numerous simulations show that our algorithm reaches a complete coverage
within reasonable time with moderate energy consumption, even when the target
area has irregular shapes. Performance comparisons between Push&Pull and one of
the most acknowledged algorithms show how the former one can efficiently reach
a more uniform and complete coverage under a wide range of working scenarios.Comment: Technical Report. This paper has been published on Wireless Networks,
Springer. Animations and the complete code of the proposed algorithm are
available for download at the address:
http://www.dsi.uniroma1.it/~novella/mobile_sensors
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Optimal coverage multi-path scheduling scheme with multiple mobile sinks for WSNs
Wireless Sensor Networks (WSNs) are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring. These sensors can transmit their monitored data to the sink in a multi-hop communication manner. However, the ‘hot spots’ problem will be caused since nodes near sink will consume more energy during forwarding. Recently, mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission. Even though it is difficult to consider many network metrics such as sensor position, residual energy and coverage rate etc., it is still very important to schedule a reasonable moving trajectory for the mobile sink. In this paper, a novel trajectory scheduling method based on coverage rate for multiple mobile sinks (TSCR-M) is presented especially for large-scale WSNs. An improved particle swarm optimization (PSO) combined with mutation operator is introduced to search the parking positions with optimal coverage rate. Then the genetic algorithm (GA) is adopted to schedule the moving trajectory for multiple mobile sinks. Extensive simulations are performed to validate the performance of our proposed method
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Numerical modeling of shape and topology optimisation of a piezoelectric cantilever beam in an energy-harvesting sensor
Piezoelectric materials are excellent transducers for converting mechanical energy from the environment for use as electrical energy. The conversion of mechanical energy to electrical energy is a key component in the development of self-powered devices, especially enabling technology for wireless sensor networks. This paper proposes an alternative method for predicting the power output of a bimorph cantilever beam using a finite-element method for both static and dynamic frequency analyses. A novel approach is presented for optimising the cantilever beam, by which the power density is maximised and the structural volume is minimised simultaneously. A two-stage optimisation is performed, i.e., a shape optimisation and then a “topology” hole opening optimisation
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