Co-EEORS : cooperative energy efficient optimal relay selection protocol for underwater wireless sensor networks
- Khan, Anwar, Ali, Ihsan, Rahman, Atiq, Imran, Muhammad, Amin, Fazal, Mahmood, Hasan
- Authors: Khan, Anwar , Ali, Ihsan , Rahman, Atiq , Imran, Muhammad , Amin, Fazal , Mahmood, Hasan
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Access Vol. 6, no. (2018), p. 28777-28789
- Full Text:
- Reviewed:
- Description: Cooperative routing mitigates the adverse channel effects in the harsh underwater environment and ensures reliable delivery of packets from the bottom to the surface of water. Cooperative routing is analogous to sparse recovery in that faded copies of data packets are processed by the destination node to extract the desired information. However, it usually requires information about the two or three position coordinates of the nodes. It also requires the synchronization of the source, relay, and destination nodes. These features make the cooperative routing a challenging task as sensor nodes move with water currents. Moreover, the data packets are simply discarded if the acceptable threshold is not met at the destination. This threatens the reliable delivery of data to the final destination. To cope with these challenges, this paper proposes a cooperative energy-efficient optimal relay selection protocol for underwater wireless sensor networks. Unlike the existing routing protocols involving cooperation, the proposed scheme combines location and depth of the sensor nodes to select the destination nodes. Combination of these two parameters does not involve knowing the position coordinates of the nodes and results in selection of the destination nodes closest to the water surface. As a result, data packets are less affected by the channel properties. In addition, a source node chooses a relay node and a destination node. Data packets are sent to the destination node by the relay node as soon as the relay node receives them. This eliminates the need for synchronization among the source, relay, and destination nodes. Moreover, the destination node acknowledges the source node about the successful reception or retransmission of the data packets. This overcomes the packets drop. Based on simulation results, the proposed scheme is superior in delivering packets to the final destination than some existing techniques. © 2013 IEEE.
- Authors: Khan, Anwar , Ali, Ihsan , Rahman, Atiq , Imran, Muhammad , Amin, Fazal , Mahmood, Hasan
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Access Vol. 6, no. (2018), p. 28777-28789
- Full Text:
- Reviewed:
- Description: Cooperative routing mitigates the adverse channel effects in the harsh underwater environment and ensures reliable delivery of packets from the bottom to the surface of water. Cooperative routing is analogous to sparse recovery in that faded copies of data packets are processed by the destination node to extract the desired information. However, it usually requires information about the two or three position coordinates of the nodes. It also requires the synchronization of the source, relay, and destination nodes. These features make the cooperative routing a challenging task as sensor nodes move with water currents. Moreover, the data packets are simply discarded if the acceptable threshold is not met at the destination. This threatens the reliable delivery of data to the final destination. To cope with these challenges, this paper proposes a cooperative energy-efficient optimal relay selection protocol for underwater wireless sensor networks. Unlike the existing routing protocols involving cooperation, the proposed scheme combines location and depth of the sensor nodes to select the destination nodes. Combination of these two parameters does not involve knowing the position coordinates of the nodes and results in selection of the destination nodes closest to the water surface. As a result, data packets are less affected by the channel properties. In addition, a source node chooses a relay node and a destination node. Data packets are sent to the destination node by the relay node as soon as the relay node receives them. This eliminates the need for synchronization among the source, relay, and destination nodes. Moreover, the destination node acknowledges the source node about the successful reception or retransmission of the data packets. This overcomes the packets drop. Based on simulation results, the proposed scheme is superior in delivering packets to the final destination than some existing techniques. © 2013 IEEE.
Treating class imbalance in non-technical loss detection : an exploratory analysis of a real dataset
- Ghori, Khawaja, Awais, Muhammad, Khattak, Akmal, Imran, Muhammad, Amin, Fazal, Szathmary, Laszlo
- Authors: Ghori, Khawaja , Awais, Muhammad , Khattak, Akmal , Imran, Muhammad , Amin, Fazal , Szathmary, Laszlo
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 98928-98938
- Full Text:
- Reviewed:
- Description: Non-Technical Loss (NTL) is a significant concern for many electric supply companies due to the financial impact caused as a result of suspect consumption activities. A range of machine learning classifiers have been tested across multiple synthesized and real datasets to combat NTL. An important characteristic that exists in these datasets is the imbalance distribution of the classes. When the focus is on predicting the minority class of suspect activities, the classifiers' sensitivity to the class imbalance becomes more important. In this paper, we evaluate the performance of a range of classifiers with under-sampling and over-sampling techniques. The results are compared with the untreated imbalanced dataset. In addition, we compare the performance of the classifiers using penalized classification model. Lastly, the paper presents an exploratory analysis of using different sampling techniques on NTL detection in a real dataset and identify the best performing classifiers. We conclude that logistic regression is the most sensitive to the sampling techniques as the change of its recall is measured around 50% for all sampling techniques. While the random forest is the least sensitive to the sampling technique, the difference in its precision is observed between 1% - 6% for all sampling techniques. © 2013 IEEE.
Treating class imbalance in non-technical loss detection : an exploratory analysis of a real dataset
- Authors: Ghori, Khawaja , Awais, Muhammad , Khattak, Akmal , Imran, Muhammad , Amin, Fazal , Szathmary, Laszlo
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 98928-98938
- Full Text:
- Reviewed:
- Description: Non-Technical Loss (NTL) is a significant concern for many electric supply companies due to the financial impact caused as a result of suspect consumption activities. A range of machine learning classifiers have been tested across multiple synthesized and real datasets to combat NTL. An important characteristic that exists in these datasets is the imbalance distribution of the classes. When the focus is on predicting the minority class of suspect activities, the classifiers' sensitivity to the class imbalance becomes more important. In this paper, we evaluate the performance of a range of classifiers with under-sampling and over-sampling techniques. The results are compared with the untreated imbalanced dataset. In addition, we compare the performance of the classifiers using penalized classification model. Lastly, the paper presents an exploratory analysis of using different sampling techniques on NTL detection in a real dataset and identify the best performing classifiers. We conclude that logistic regression is the most sensitive to the sampling techniques as the change of its recall is measured around 50% for all sampling techniques. While the random forest is the least sensitive to the sampling technique, the difference in its precision is observed between 1% - 6% for all sampling techniques. © 2013 IEEE.
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