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Outputs (4)

Load Balancing Objective Function in RPL (Draft 2) (2017)
Report
Qasem, M., Al-Dubai, A., Romdhani, I., Ghaleb, B., Hou, J., & Jadhav, R. A. (2017). Load Balancing Objective Function in RPL (Draft 2). USA: IETF

This document proposes an extended Objective Function(OF) that balances the number of child nodes of the parent nodes to avoid the overloading problem and ensure node lifetime maximization in the IPv6 Routing Protocol for Low-Power and Lossy Ne... Read More about Load Balancing Objective Function in RPL (Draft 2).

Drizzle: Adaptive and fair route maintenance algorithm for Low-power and Lossy Networks in IoT (2017)
Presentation / Conference Contribution
Ghaleb, B., Al-Dubai, A., Romdhani, I., Nasser, Y., & Boukerche, A. (2017). Drizzle: Adaptive and fair route maintenance algorithm for Low-power and Lossy Networks in IoT. In 2017 IEEE International Conference on Communications (ICC) (1-6). https://doi.o

Low-power and Lossy Networks (LLNs) have been a key component in the Internet of Things (IoT) paradigm. Recently, a standardized algorithm, namely Trickle algorithm, is adopted for routing information maintenance in such networks. This algorithm is o... Read More about Drizzle: Adaptive and fair route maintenance algorithm for Low-power and Lossy Networks in IoT.

A new enhanced RPL based routing for Internet of Things (2017)
Presentation / Conference Contribution
Ghaleb, B., Al-Dubai, A., Ekonomou, E., & Wadhaj, I. (2017, June). A new enhanced RPL based routing for Internet of Things. Presented at 2017 IEEE International Conference on Communications Workshops (ICC Workshops)

The IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) has been recently standardized for routing in the constrained Internet of Things networks. Unfortunately, the RPL storing mode has been found restricted by storage limitations in the ro... Read More about A new enhanced RPL based routing for Internet of Things.

STF-RNN: Space Time Features-based Recurrent Neural Network for predicting people next location (2017)
Presentation / Conference Contribution
Al-Molegi, A., Jabreel, M., & Ghaleb, B. (2017). STF-RNN: Space Time Features-based Recurrent Neural Network for predicting people next location. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). https://doi.org/10.1109/ssci.2016.784991

This paper proposes a novel model called Space Time Features-based Recurrent Neural Network (STF-RNN) for predicting people next movement based on mobility patterns obtained from GPS devices logs. Two main features are involved in model operations, n... Read More about STF-RNN: Space Time Features-based Recurrent Neural Network for predicting people next location.