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Data forwarding from a source to a sink node when they are not within the communication range is a challenging problem in wireless networking. With the increasing demand of wireless networks, several applications have emerged where a group of users are disconnected from their targeted destinations. Therefore, we consider in this paper a multi-Unmanned Aerial Vehicles (UAVs) system to convey collected data from isolated fields to the base station. In each field, a group of sensors or Internet of Things devices are distributed and send their data to one UAV. The UAVs collaborate in forwarding the collected data to the base station in order to maximize the minimum battery level for all UAVs by the end of the service time. Hence, a group of UAVs can meet at a waypoint along their path to the base station such that one UAV collects the data from all other UAVs and moves forward to another meeting point or the base station. All other UAVs that relayed their messages return back to their initial locations. All collected data from all fields reach to the base station within a certain maximum time to guarantee a certain quality of service. We formulate the problem as a Mixed Integer Nonlinear Program (MINLP), then we reformulated the problem as Mixed Integer Linear Program (MILP) after we linearize the mathematical model. Simulations results show the advantages of adopting the proposed model in using the UAVs' energy more efficiently.

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The exponential growth of data traffic from mobile devices requires the implementation of heterogeneous networks (HetNets), which densely deploy multiple radio access technologies to match such demands. The deployment of many small base stations (SBSs) leads to backhauling problems where wired backhauling is neither available nor efficient. millimeter waves (mmWaves) can potentially mitigate the backhauling problem by providing high throughput and low capital expenditure (CAPEX). However, due to the high attenuation rate in mmWaves, the increase in the distance between SBS and macro base station (MBS) can severely degrade the system's overall performance. On the other hand, densely deployed SBSs with wireless backhauling can cause high energy consumption in the system. Therefore, in this work, a novel network model is presented in which a combination of SBS, active antenna units (AAUs), and edge computing units (ECUs) are deployed to minimize the overall energy consumption of the network while maintaining the sufficient quality of service (QoS). A mathematical model based on optimization modeling is introduced to solve user equipments (UEs) association, dynamic sleeping, backhauling and fronthauling, and power transmission. Due to the complexity of the formulated problem, a heuristic algorithm is introduced. Namely, integrated access and backhauling (IAB) with Edge Computing and Dynamic Sleeping Algorithm (IEDS) algorithm is introduced to decompose the formulated problem into two parts and solve them iteratively. Finally, computer simulation results that demonstrate the model's performance are presented for comparison between optimal solution, IEDS, and HBDS, which shows that IEDS outperformed HBDS in performance with negligible computation difference.