Modelling and solving optimal placement problems in wireless sensor networks

Guerriero, Francesca; Violi, Antonio; Natalizio, Enrico; Loscrì, Valeria; Costanzo, Carmelo

doi:10.1016/j.apm.2010.05.020

Cited by 43 publications

(27 citation statements)

References 23 publications

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“…Recent works focused on multi-objective mathematical models to determine the best placement of mobile nodes for different tasks [20,27]. In [4], authors analyze mobile sensor movement on a circular path with the goal to cover the set of predefined PoIs that lay on the same path, but the position of the PoI is assumed to be known and the approach cannot be used for the multi-path problem (that introduces multiple movement paths and data sink).…”

Section: Introductionmentioning

confidence: 99%

Multiple point of interest discovery and coverage with mobile wireless sensors

Erdelj

Natalizio

Razafindralambo

2012

2012 International Conference on Computing, Networking and Communications (ICNC)

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Section: Introductionmentioning

confidence: 99%

Multiple point of interest discovery and coverage with mobile wireless sensors

Erdelj

Natalizio

Razafindralambo

2012

2012 International Conference on Computing, Networking and Communications (ICNC)

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“…All the cited works consider the deployment of static nodes, whereas in our work nodes are mobile and can reach a different and possibly more conve-nient location in the field in terms of energy efficiency. The movement of nodes involves an energy expenditure that can negatively affect the overall energy budget, therefore algorithms and heuristics for energy-efficient movements have been proposed [7]. The main difference between our work and the mentioned works is that MEGAN bases the node selection on the energy expenditure for movement and transmission, as well as the harvestable energy.…”

Section: Related Workmentioning

confidence: 99%

Beacon-Less Mobility Assisted Energy Efficient Georouting in Energy Harvesting Actuator and Sensor Networks

Mitton

Natalizio

Wolhuter

2013

Ad-Hoc, Mobile, and Wireless Network

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In the next years, wireless sensor networks are expected to be more and more widely deployed. In order to increase their performance without increasing nodes' density, a solution is to add some actuators that have the ability to move. However, even actuators rely on batteries that are not expected to be replaced. In this paper, we introduce MEGAN (Mobility assisted Energy efficient Georouting in energy harvesting Actuator and sensor Networks), a beacon-less protocol that uses controlled mobility, and takes account of the energy consumption and the energy harvesting to select next hop. MEGAN aims at prolonging the overall network lifetime rather than reducing the energy consumption over a single path. When node s needs to send a message to the sink d, it first computes the "ideal" position of the forwarder node based on available and needed energy, and then broadcasts this data. Every node within the transmission range of s in the forward direction toward d will start a backoff timer. The backoff time is based on its available energy and on its distance from the ideal position. The first node whose backoff timer goes off is the forwarder node. This node informs its neighborhood and then moves toward the ideal position. If, on its route, it finds a good spot for energy harvesting, it will actually stop its movement and forward the original message by using MEGAN, which will run on all the intermediate nodes until the destination is reached. Simulations show that MEGAN reduces energy consumption up to 50% compared to algorithms where mobility and harvesting capabilities are not exploited.

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“…Moreover, part of the nodes (both target nodes and sensor nodes) can be mobile, there can be only a partial location awareness, messages can require a routing to reach the central station, energy consumption may affect the activity of the sensors, and obstacles may cause imprecise measurements. Therefore different objective functions are considered in order to take care of all these issues [4][5][6][7]. Another research field concerns sensor placement problems in real-time early warning systems, expecially in water distribution networks [2].…”

Section: Introductionmentioning

confidence: 99%

A Facility Location Model for Air Pollution Detection

Lancia

Rinaldi

Serafini

2018

Mathematical Problems in Engineering

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We describe mathematical models and practical algorithms for a problem concerned with monitoring the air pollution in a large city. We have worked on this problem within a project for assessing the air quality in the city of Rome by placing a certain number of sensors on some of the city buses. We cast the problem as a facility location model. By reducing the large number of data variables and constraints, we were able to solve to optimality the resulting MILP model within minutes. Furthermore, we designed a genetic algorithm whose solutions were on average very close to the optimal ones. In our computational experiments we studied the placement of sensors on 187 candidate bus routes. We considered the coverage provided by 10 up to 60 sensors.

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Modelling and solving optimal placement problems in wireless sensor networks

Cited by 43 publications

References 23 publications

Multiple point of interest discovery and coverage with mobile wireless sensors

Multiple point of interest discovery and coverage with mobile wireless sensors

Beacon-Less Mobility Assisted Energy Efficient Georouting in Energy Harvesting Actuator and Sensor Networks

A Facility Location Model for Air Pollution Detection

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