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In emergency response operations, using uncrewed aerial vehicles (UAVs) has recently become a promising solution due to their flexibility and easy deployment. However, tasks performed by the UAVs, e.g., object detection and human pose recognition, usually require a high computation capacity and energy supply. Furthermore, offloading tasks to the edge server-equipped base stations may not always be possible because of a lack of infrastructure or distance. Therefore, UAV-aided edge servers can be deployed near UAV scouts to provide computing services. However, a UAV can not perform all types of tasks since it has limitations on memory, available software, central processing unit (CPU), and graphics processing unit (GPU) capacity. Therefore, this study focuses on task offloading (TO), power, and computation resource allocation (PRA) problems in a multi-layer MEC-enabled UAV network while taking into account CPU and GPU requirements of tasks, the capacity of the devices (i.e., computational resources, power, and energy), and limitations on the type of tasks a UAVs can perform. The problem is formulated as a non-convex mixedinteger nonlinear problem to minimize the weighted sum of the maximum energy consumption ratio in the network and total task execution latency ratio, and then decomposed and converted into an integer and a convex problem. A messy genetic algorithm (mGA)-based TO and PRA strategy (mGA-TPR) is proposed to solve the problem, where two PRA strategies are based on the Karush-Kuhn-Tucker conditions used to solve the PRA problem. Simulation results verify that the proposed scheme can outperform the baseline methods.INDEX TERMS Multi-access edge computing, task offloading, resource allocation, messy genetic algorithm A solution to reduce the execution latency of the application and UAV energy consumption is to offload the task

With the increasing number of smart device users, data transmission between users is becoming more important, and a network architecture called opportunistic mobile social network (OMSN) is gaining attention. However, routing in OMSNs is a challenging problem due to the frequent disconnection between nodes and the absence of paths from the source to the destination. It results in a complex topology and a low packet transmission success rate. Therefore, we propose a novel routing algorithm called the temporal social interactions-based routing protocol (TSIRP) for solving the problem of low network performance due to the improper selection of message relay nodes. First, we focus on the temporal context of social interactions. Specifically, at a certain time of the day, a person has specific people with whom the person usually interacts (e.g., workers usually meet co-workers during working hours; students usually meet their classmates during class). Based on temporal social interactions between nodes, potential forwarding metrics are proposed and calculated for each time of the day to make forwarding decisions. Second, we propose a new scheme to control the message spreading rate, which allows achieving a balance between delivery latency and overhead ratio. In addition, an analytical model is also designed using an absorbing Markov chain to estimate the performance of TSIRP. Simulations were also conducted, and the results indicate that TSIRP can achieve better performance than existing routing protocols in terms of packet delivery ratio, delivery latency, network overhead ratio, and average hop count.INDEX TERMS Forwarding token, opportunistic mobile social network, potential forwarding metric, spreading rate control value.

The heterogeneous novelty applications present in the 5th generation (5G) era, including machine-type communication (mMTC), enhanced mobile broadband (eMBB) communication, and ultra-reliable low latency communication (URLLC), which required mobile edge computing (MEC) for local computation and services. The next-generation radio networking (NGRN) will rely on new radio (NR) with the millimeter-wavelength (mmWave) technologies that enable ultra-dense connectivities of the deployed heterogeneous mobile terminal gateways (MTG). However, the mission-critical mMTC applications will suffer from inadequate radio resource management and orchestration (MANO), which will diminish end-to-end (E2E) communication reliability in edge areas. This paper proposed optimal MTG selections and resource allocation (RA) based on the complementary between MTG loading prediction based on recurrent neural network-based long short-term memory (RNN-LSTM) and MTG loading adjustment based on the applied deep reinforcement learning (DRL) approaches, respectively. Furthermore, the RNN-LSTM enhances offloading and handover decisions with discrete-time predictions, while the DRL plays an essential role in adjusting the determined MTG during congestion situations. The proposed method contributed to remarkable outcomes in crucial performance metrics over reference approaches regarding computation and communication quality of service (QoS).

Backscatter communication is a battery-less data transmission method for massive IoT devices. These backscatter devices receive an incident signal from an RF-source gateway to harvest energy. These devices can be operated to transmit data after harvesting energy. This technology is widely applied to smart city applications. In general, IoT devices in the smart city applications have insufficient resources. They use narrowband communication to transmit small sizes of data. Thus, a simple channel access approach should be considered for data transmission. In addition, network scalability is also important in the backscatter network for smart city applications. According to energy harvesting and data generation, devices participating in data transmission can change frequently. Providing the network scalability by the changing devices can improve the transmission efficiency in the backscatter network. Therefore, we propose a novel media access scheme for the backscatter network in the smart city applications. The proposed scheme is designed by the contention-based approach to support the network scalability. It controls backscattering signal for energy harvesting and distributes contention in multiple access. It allows additional data transmission in backscattering period for harvesting energy to provide fairness of devices. For performance evaluation, extensive computer simulations are carried out and the proposed method is compared to TDMA that is a typical media access scheme in the backscatter network.

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