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Register a new userMobility-Aware Offloading and Resource Allocation in MEC-Enabled IoT Networks
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Mobile edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. IoT with mobility has found tremendous new services in the 5G era and the forthcoming 6G eras such as autonomous driving and vehicular communications. However, mobility of IoT devices has not been studied in the sufficient level in the existing works. In this paper, the offloading decision and resource allocation problem is studied with mobility consideration. The long-term average sum service cost of all the mobile IoT devices (MIDs) is minimized by jointly optimizing the CPU-cycle frequencies, the transmit power, and the user association vector of MIDs. An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on Lyapunov optimization and Semi-Definite Programming (SDP). Simulation results demonstrate that our proposed scheme can balance the system service cost and the delay performance, and outperforms other offloading benchmark methods in terms of the system service cost.
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Energy efficiency and security are two critical issues for mobile edge computing (MEC) networks. With stochastic task arrivals, time-varying dynamic environment, and passive existing attackers, it is very challenging to offload computation tasks securely and efficiently. In this paper, we study the task offloading and resource allocation problem in a non-orthogonal multiple access (NOMA) assisted MEC network with security and energy efficiency considerations. To tackle the problem, a dynamic secure task offloading and resource allocation algorithm is proposed based on Lyapunov optimization theory. A stochastic non-convex problem is formulated to jointly optimize the local-CPU frequency and transmit power, aiming at maximizing the network energy efficiency, which is defined as the ratio of the long-term average secure rate to the long-term average power consumption of all users. The formulated problem is decomposed into the deterministic sub-problems in each time slot. The optimal local CPU-cycle and the transmit power of each user can be given in the closed-from. Simulation results evaluate the impacts of different parameters on the efficiency metrics and demonstrate that the proposed method can achieve better performance compared with other benchmark methods in terms of energy efficiency.
This paper presents a novel unmanned aerial vehicle (UAV) aided mobile edge computing (MEC) architecture for vehicular networks. It is considered that the vehicles should complete latency critical computation intensive tasks either locally with on-board computation units or by offloading part of their tasks to road side units (RSUs) with collocated MEC servers. In this direction, a hovering UAV can serve as an aerial RSU (ARSU) for task processing or act as an aerial relay and further offload the computation tasks to a ground RSU (GRSU). In order to significantly reduce the delay during data offloading and downloading, this architecture relies on the benefits of massive multiple input multiple output (MIMO). Therefore, it is considered that the vehicles, the ARSU, and the GRSU employ large scale antennas. A three dimensional (3D) geometrical representation of the MEC enabled network is introduced and an optimization method is proposed that minimizes the weighted total energy consumption (WTEC) of the vehicles and ARSU subject to transmit power allocation, task allocation, and timeslot scheduling. The numerical results verify the theoretical derivations, emphasize on the effectiveness of the massive MIMO transmission, and provide useful engineering insights.
The combination of non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) can significantly improve the spectrum efficiency beyond the fifth-generation network. In this paper, we mainly focus on energy-efficient resource allocation for a multi-user, multi-BS NOMA assisted MEC network with imperfect channel state information (CSI), in which each user can upload its tasks to multiple base stations (BSs) for remote executions. To minimize the energy consumption, we consider jointly optimizing the task assignment, power allocation and user association. As the main contribution, with imperfect CSI, the optimal closed-form expressions of task assignment and power allocation are analytically derived for the two-BS case. Specifically, the original formulated problem is nonconvex. We first transform the probabilistic problem into a non-probabilistic one. Subsequently, a bilevel programming method is proposed to derive the optimal solution. In addition, by incorporating the matching algorithm with the optimal task and power allocation, we propose a low complexity algorithm to efficiently optimize user association for the multi-user and multi-BS case. Simulations demonstrate that the proposed algorithm can yield much better performance than the conventional OMA scheme but also the identical results with lower complexity from the exhaustive search with the small number of BSs.
With the rapid growth of Internet of Things (IoT) devices, the next generation mobile networks demand for more operating frequency bands. By leveraging the underutilized radio spectrum, the cognitive radio (CR) technology is considered as a promising solution for spectrum scarcity problem of IoT applications. In parallel with the development of CR techniques, Wireless Energy Harvesting (WEH) is considered as one of the emerging technologies to eliminate the need of recharging or replacing the batteries for IoT and CR networks. To this end, we propose to utilize WEH for CR networks in which the CR devices are not only capable of sensing the available radio frequencies in a collaborative manner but also harvesting the wireless energy transferred by an Access Point (AP). More importantly, we design an optimization framework that captures a fundamental tradeoff between energy efficiency (EE) and spectral efficiency (SE) of the network. In particular, we formulate a Mixed Integer Nonlinear Programming (MINLP) problem that maximizes EE while taking into consideration of users buffer occupancy, data rate fairness, energy causality constraints and interference constraints. We further prove that the proposed optimization framework is an NP-Hard problem. Thus, we propose a low complex heuristic algorithm, called INSTANT, to solve the resource allocation and energy harvesting optimization problem. The proposed algorithm is shown to be capable of achieving near optimal solution with high accuracy while having polynomial complexity. The efficiency of our proposal is validated through well designed simulations.
Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA) have been regarded as promising technologies to improve computation capability and offloading efficiency of the mobile devices in the sixth generation (6G) mobile system. This paper mainly focuses on the hybrid NOMA-MEC system, where multiple users are first grouped into pairs, and users in each pair offload their tasks simultaneously by NOMA, and then a dedicated time duration is scheduled to the more delay-tolerable user for uploading the remaining data by orthogonal multiple access (OMA). For the conventional NOMA uplink transmission, successive interference cancellation (SIC) is applied to decode the superposed signals successively according to the channel state information (CSI) or the quality of service (QoS) requirement. In this work, we integrate the hybrid SIC scheme which dynamically adapts the SIC decoding order among all NOMA groups. To solve the user grouping problem, a deep reinforcement learning (DRL) based algorithm is proposed to obtain a close-to-optimal user grouping policy. Moreover, we optimally minimize the offloading energy consumption by obtaining the closed-form solution to the resource allocation problem. Simulation results show that the proposed algorithm converges fast, and the NOMA-MEC scheme outperforms the existing orthogonal multiple access (OMA) scheme.
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