Browsing by Subject "Energy efficient"

Now showing 1 - 4 of 4

Open Access
Distributed and location-based multicast routing algorithms for wireless sensor networks
(SpringerOpen, 2009-01) Korpeoglu, I.; Bagci, H.
Multicast routing protocols in wireless sensor networks are required for sending the same message to multiple different destinations. In this paper, we propose two different distributed algorithms for multicast routing in wireless sensor networks which make use of location information of sensor nodes. Our first algorithm groups the destination nodes according to their angular positions and forwards the multicast message toward each group in order to reduce the number of total branches in multicast tree which also reduces the number of messages transmitted. Our second algorithm calculates an Euclidean minimum spanning tree at the source node by using the positions of the destination nodes. The multicast message is forwarded to destination nodes according to the calculated MST. This helps in reducing the total energy consumed for delivering the message to all destinations by decreasing the number of total transmissions. Evaluation results show that the algorithms we propose are scalable and energy efficient, so they are good candidates to be used for multicasting in wireless sensor networks. Copyright © 2009 H. Bagci and I. Korpeoglu.
Open Access
An energy efficient additive neural network
(IEEE, 2017) Afrasiyabi, A.; Nasir, B.; Yıldız, O.; Yarman-Vural, F. T.; Çetin, A. Enis
In this paper, we propose a new energy efficient neural network with the universal approximation property over space of Lebesgue integrable functions. This network, called additive neural network, is very suitable for mobile computing. The neural structure is based on a novel vector product definition, called ef-operator, that permits a multiplier-free implementation. In ef-operation, the 'product' of two real numbers is defined as the sum of their absolute values, with the sign determined by the sign of the product of the numbers. This 'product' is used to construct a vector product in n-dimensional Euclidean space. The vector product induces the lasso norm. The proposed additive neural network successfully solves the XOR problem. The experiments on MNIST dataset show that the classification performances of the proposed additive neural networks are very similar to the corresponding multi-layer perceptron.
Open Access
An energy efficient scatternet formation algorithm for Bluetooth-based sensor networks
(IEEE, 2005-02) Saginbekov, Sain; Körpeoğlu, İbrahim
In this paper, we propose an energy-efficient scatternet formation algorithm for Bluetooth based sensor networks. The algorithm is based on first computing a shortest path tree from the base station to all sensor nodes and then solving the degree constraint problem so that the degree of each node in the network is not greater than seven, which is a Bluetooth constaint. In this way, less amount of energy is spent in each round of communication in the sensor network. The algorithm also tries to balance the load evenly on the high-energy consuming nodes which are the nodes that are close to the base station. In this way, the lifetime of the first dying node is also prolonged. We obtained promising results in the simulations. © 2005 IEEE.
Open Access
LineKing: Crowdsourced line wait-time estimation using smartphones
(Springer, 2013) Bulut, M. F.; Yilmaz, Y. S.; Demirbaş, M.; Ferhatosmanoğlu, N.; Ferhatosmanoğlu, Hakan
This paper describes the design, implementation and deployment of LineKing (LK), a crowdsourced line wait-time monitoring service. LK consists of a smartphone component (that provides automatic, energy-efficient, and accurate wait-time detection), and a cloud backend (that uses the collected data to provide accurate wait-time estimation). LK is used on a daily basis by hundreds of users to monitor the wait-times of a coffee shop in our university campus. The novel wait-time estimation algorithms deployed at the cloud backend provide mean absolute errors of less than 2-3 minutes. © 2013 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering.