Ph.D. Dissertation:

Dynamic model for task allocation in the internet of mobile things using fuzzy-logic based routing

The Internet of Things (IoT) is a huge revolution nowadays. IoT networks consist of devices or objects with limited resources, where objects are limited in terms of power, memory, and processing capacity, and usually work with batteries. Mobity is the main property of the things in the IoT networks. The routing in the mobile Internet of Things is one of the challenges because the proposed protocols are not specifically designed for mobile Internet of Things, so mobility management in this network is considered one of the important challenges. Mobile nodes in this network collect and transmit information to the server; The way of allocating work among the nodes, and selecting the most suitable node to allocate the received work in the task allocation is critical in IoT in terms of energy consumption in the network and the execution time and the execution cost for mobile node that is effective on the power consumption in the network. The big challenge in routing is mobility management. In this thesis, a new fuzzy routing protocol based on RPL protocol is proposed based on four important parameters of received signal strength, ETX, Euclidean distance, and Hop count are proposed for mobility management. The simulation results in Cooja compared to RPL, mRPL, and EMA-RPL showed improved energy consumption, packet delivery rate, signaling cost, and handover delay in the network. A new task allocation model based on the new fuzzy RPL-based routing protocol is simulated and compared with the related works, and the results showed the reduction of energy consumption and the social cost of the mobile nodes in the network. The results showed the optimal performance of the network by using the proposed routing protocol, as well as the optimal task allocation in terms of important network parameters and energy consumption.

Keywords: Task allocation, Internet of mobile Things, Routing, RPL, Fuzzy Logic.