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German Montoya published an article in February 2019.
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(2013 - 2019)
(2013 - 2019)
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Article 0 Reads 0 Citations A Prediction Algorithm based on Markov Chains for finding the Minimum Cost Path in a Mobile WSNs Published: 14 February 2019
International Journal of Computers Communications & Control, doi: 10.15837/ijccc.2019.1.3487
In this paper we propose the usage of a prediction technique based on Markov Chains to predict nodes positions with the aim of obtain short paths at minimum energy consumption. Specifically, the valuable information from the mobility prediction method is provided to our distributed routing algorithm in order to take the best network decisions considering future states of network resources. In this sense, in each network node, the mobility method employed is based on a Markov model to forecast future RSSI states of neighboring nodes for determining if they will be farther or closer within the next steps. The approach is evaluated considering different algorithms such as: Distance algorithm, Distance Away algorithm and Random algorithm. In addition, with the aim of performing comparisons against optimal values, we present a mathematical optimization model for finding the minimum cost path between a source and a destination node considering all network nodes are mobile. This paper is an extended variant of .
PROCEEDINGS-ARTICLE 0 Reads 0 Citations A prediction algorithm based on Markov chains for finding the minimum cost path in a mobile wireless sensor network Published: 01 May 2018
2018 7th International Conference on Computers Communications and Control (ICCCC), doi: 10.1109/icccc.2018.8390455
Article 2 Reads 0 Citations Delay-Sensitive Optimization Models and Distributed Routing Algorithms for Mobile Wireless Sensor Networks Published: 17 October 2016
International Journal of Computers Communications & Control, doi: 10.15837/ijccc.2016.6.2745
Communication disruptions caused by mobility in wireless sensor networks introduce undesired delays which affect the network performance in delay sensitive applications in MWSN. In order to study the negative effects caused by mobility, we propose two mathematical models to find the minimum cost path between a source node and a destination node considering the nodes position changes across time. Our mathematical models consider the usage of buffers in the nodes to represent the fact of storing a message if there is not an appropriate forwarding node for transmitting it. In order to contrast our mathematical models results we have designed two kinds of algorithms: the first one takes advantage of the closest neighbours to the destination node in order to reach it as fast as possible from the source node. The second one simply reaches the destination node if a neighbour node is precisely the destination node. Finally, we compare the delay performance of these algorithms against our mathematical models to show how efficient they are for reaching a destination node. This paper is an extension of .a The mathematical model proposed in  is improved by adding two new binary variables with the aim of make it more readable and compact mathematically. This means a post-processing algorithm is added only for evaluating if a solution is at the first network state.
Conference 3 Reads 0 Citations A delay-sensitive mathematical model approach and a distributed algorithm for mobile wireless sensor networks Published: 01 May 2016
2016 6th International Conference on Computers Communications and Control (ICCCC), doi: 10.1109/icccc.2016.7496736
Article 5 Reads 0 Citations An Energy-Efficient and Routing Approach for Position Estimation using Kalman Filter Techniques in Mobile WSNs Published: 01 August 2015
International Journal of Computers Communications & Control, doi: 10.15837/ijccc.2015.4.1990
Mobile Wireless Sensor Networks is being an attractive field due to its applicability to an increasingly amount of mobile scenarios such as wild monitoring, disaster prevention, object guidance and health monitoring. In addition, since the sensors have limited batteries, data routing has to be planned strategically in order to extend the battery lifetime as much as possible  . In this paper, we assume GPS free sensor devices, where considering a predictive technique to estimate the sensor position in a circular trajectory scenario can be useful to know when the sensor will be as close as possible to a sink, and then, help us to reduce the energy consumption by the fact of transmitting data at a short distance respect to the sink. In this paper, we propose an predictive algorithm based on Kalman filter techniques to estimate the proper time at which the sensor is close as much as possible to a sink, in order to reduce the energy consumption in the sensor. Specifically, we propose the usage of two Kalman Filters. One Kalman Filter is used for estimating the Received Signal Strength Indicator (RSSI) level based on several control packets received at the sensor device. This RSSI estimation indicates the distance from the mobile sensor device to the sink at a given time. The second Kalman Filter, based on the outputs from the first Kalman Filter, estimates the angular velocity and the angle of the mobile sensor device at a given time. Once this information is processed, it is possible to estimate the mobile sensor position in a circular trajectory in order to determine how much close is the mobile sensor device respect to the sink. In addition, the communication channel noise may affect the packet content, generating non-accurate information measurements at the receptor. For this reason, our proposal is evaluated under different noise channel levels and compared against a traditional technique. Our predictive routing algorithm shows better results in terms of distance accuracy to the sink and energy consumption in noisy communication channels.
Article 0 Reads 1 Citation Energy Optimization in Mobile Wireless Sensor Networks with Mobile Targets Achieving Efficient Coverage for Critical App... Published: 18 February 2013
International Journal of Computers Communications & Control, doi: 10.15837/ijccc.2013.2.305