M. Dogruel | Marmara University (original) (raw)
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Papers by M. Dogruel
Proceedings of the 1992 IEEE International Symposium on Intelligent Control, 1992
Proceedings of IEEE Sensors, 2002
... environments. The primary cause of these uncer-tainties is the delay encountered by data as t... more ... environments. The primary cause of these uncer-tainties is the delay encountered by data as they tra-verse the communication links. In addition, the feed-back mechanism employed at each node can also intro-duce delays [5, lo]. ...
Proceedings of 1994 9th IEEE International Symposium on Intelligent Control, 1994
We introduce a model of hybrid systems as a combination of discrete state and continuous state sy... more We introduce a model of hybrid systems as a combination of discrete state and continuous state systems. The continuous state space is divided into regions so that in every region, depending on the discrete state of hybrid system, the continuous state system dynamic functions which are called representative functions are found. The switching situation in a region is studied and the continuous state dynamics to be used in this situation is defined. It is shown that after a switching mode the system behavior may be uncertain due to the high frequency discrete state switchings. The classical stability of the origin of the continuous state space is defined. Following the Lyapunov theory, some stability theorems are provided
IEEE Transactions on Automatic Control, 1996
IEEE Sensors Journal, 2004
A novel task-oriented self-organization algorithm that accounts for mostly location-dependent tas... more A novel task-oriented self-organization algorithm that accounts for mostly location-dependent tasks and heterogeneous sensors inherent in dense ad-hoc sensor systems is proposed. It forms a sensor group for an announced task by sequentially selecting the best matched sensors using a leader election algorithm and a residual task calculation algorithm. To improve the associated communication overhead, the sensor node location information is used in task broadcasting, thus confining the algorithm implementation to a dynamically maintained contributor group which comprises of those sensors which may contribute to the task. Sensor localization is based on a refinement of an algorithm in [1] which utilizes only the neighborhood information of each sensor node corresponding to its each preset radio transmission power level. The proposed self-organization algorithm and how various system parameters affect its performance are examined via extensive simulations. In a densely deployed sensor system, when the refined localization scheme is demonstrated to achieve very good localization, the proposed self-organization algorithm consistently yields a sensor group that covers the announced task.
Proceedings of the 1992 IEEE International Symposium on Intelligent Control, 1992
Proceedings of IEEE Sensors, 2002
... environments. The primary cause of these uncer-tainties is the delay encountered by data as t... more ... environments. The primary cause of these uncer-tainties is the delay encountered by data as they tra-verse the communication links. In addition, the feed-back mechanism employed at each node can also intro-duce delays [5, lo]. ...
Proceedings of 1994 9th IEEE International Symposium on Intelligent Control, 1994
We introduce a model of hybrid systems as a combination of discrete state and continuous state sy... more We introduce a model of hybrid systems as a combination of discrete state and continuous state systems. The continuous state space is divided into regions so that in every region, depending on the discrete state of hybrid system, the continuous state system dynamic functions which are called representative functions are found. The switching situation in a region is studied and the continuous state dynamics to be used in this situation is defined. It is shown that after a switching mode the system behavior may be uncertain due to the high frequency discrete state switchings. The classical stability of the origin of the continuous state space is defined. Following the Lyapunov theory, some stability theorems are provided
IEEE Transactions on Automatic Control, 1996
IEEE Sensors Journal, 2004
A novel task-oriented self-organization algorithm that accounts for mostly location-dependent tas... more A novel task-oriented self-organization algorithm that accounts for mostly location-dependent tasks and heterogeneous sensors inherent in dense ad-hoc sensor systems is proposed. It forms a sensor group for an announced task by sequentially selecting the best matched sensors using a leader election algorithm and a residual task calculation algorithm. To improve the associated communication overhead, the sensor node location information is used in task broadcasting, thus confining the algorithm implementation to a dynamically maintained contributor group which comprises of those sensors which may contribute to the task. Sensor localization is based on a refinement of an algorithm in [1] which utilizes only the neighborhood information of each sensor node corresponding to its each preset radio transmission power level. The proposed self-organization algorithm and how various system parameters affect its performance are examined via extensive simulations. In a densely deployed sensor system, when the refined localization scheme is demonstrated to achieve very good localization, the proposed self-organization algorithm consistently yields a sensor group that covers the announced task.