The upcoming development of 5G network is envisioned to offer various types of services like virtual reality/augmented reality and autonomous vehicles applications with low latency requirements in Internet of Things (IoT) networks. Mobile edge computing (MEC) has become a promising solution for enhancing the computation capacity of mobile devices at the edge of network in 5G wireless network. Additionally, multiple radio access technologies (multi-RATs) have been verified with the potential in lowering the transmission latency and energy consumption, while improving the quality of services (QoS). Benefiting from the cooperation of multi-RATs, large latency-sensitive computing service tasks (L2SC) can be offloaded by different RATs simultaneously, which has great practical significance for data partitioned oriented applications with large task sizes. In this paper, to enhance the L2SC offloading services for satisfying low latency requirements with low energy consumption, we investigate the energy-latency tradeoff problem for partial task offloading in the MEC-enhanced multi-RAT network, considering the limitation of energy and computing in capability-constrained end devices in IoT networks. Specifically, we formulated the L2SC task computation offloading problem to minimize the weighted sum of the latency cost and the energy consumption by jointly optimizing the local computing frequency, task splitting and transmit power, while guaranteeing the stringent latency requirement and the residual energy constraint. Due to the non-smoothness and non-convexity of the formulated problem with high complexity, we convert the tradeoff problem into a smooth biconvex problem, and propose an alternate convex search based algorithm, which can greatly reduce the computational complexity. Numerical simulation results show the effectiveness of the proposed algorithm with various performance parameters.

Recently, mobile edge computing (MEC) has been recognized as an emerging paradigm to meet the ever-increasing computation demands. Besides, multiple radio access technology (multi-RAT) has been proposed to enhance the network throughput and service reliability. However, the two technologies have been evolving independently. In this paper, we will develop an integrated multi-RAT and MEC network framework, which enables smart devices (SDs) to offload computation tasks over multiple RAT links in parallel. Considering the resource-limited nature as well as the time-varying property of the proposed network, we then focus on investigating long-term fairness guarantee mechanisms to facilitate fair resource sharing/allocation between SDs. Specifically, we formulate a max-min stochastic optimization problem with the objective of maximizing the minimum long-term time-average offloading utility. An adaptive task splitting and resource allocation algorithm is proposed based on the Lyapunov optimization technology, which jointly optimizes the SD task splitting and uplink transmit power allocation, the RAT subcarrier allocation, and the MEC computation frequency allocation. The adaptive algorithm can accommodate to the time-varying network dynamics without requiring their distribution information. Moreover, the proposed algorithm is shown to have polynomial computation complexity and be asymptotically optimal by rigorous analysis. Simulation results verify the theoretical analysis and show that the Jain's fairness index of the proposed algorithm converges to 1 as the number of time-slots grows, which outperforms the benchmark algorithms.

Large-scale Internet of things (IoT) networks have shown great advantages and attracted much attention in 5G networks due to its high-speed Internet connectivity, high data rate, ultra reliability and low latency. However, the diversity of ubiquitous IoT devices and complex transmission environment lead to the difficulty of theoretical analysis of delay performance. In order to capture the characteristics of large IoT networks, a novel integrated analysis framework is developed to build the performance model and analyze the statistical delay performance in this paper. Specifically, we model the spatial distribution of IoT devices with heterogeneous Poisson point processes. With the Laplace transform of signal-to-interference-plus-noise ratio, the service capability of the wireless channel with multiple interferes is determined with a mathematical stochastic-network-calculus aided approach in the exponential domain for Poisson bipolar and cellular networks. Then, the upper bound of delay is achieved by the Mellin transform of the service incremental process. We extend our work to a $k$ -tier heterogeneous network and apply the analysis results to a practical two-tier cognitive radio IoT network. Simulation results validate the theoretical analysis of delay performance with various network parameters.

Unmanned aerial vehicle (UAV) is expected to bring transformative improvements to the integrated sensing and communication (ISAC) systems, due to its high flexibility, high autonomy, large coverage and strong adaptability to various terrains. Sensory data is gathered by sensing UAVs (SUs) from the coverage area and then relayed to the corresponding fusion center UAVs (FCUs). Afterwards, terrestrial base stations receive the sensory data from FCUs in such air-ground networks. However, due to complex task execution environment and transmission environment, it is challenging to capture the network-layer performance of the sensory data transmission and evaluate the trade-off relationship between sensing and communication. In this work, we model and analyze the network-layer delay violation for an ISAC UAV network to address this challenge. Specifically, the UAV formation is distributed according to a Poisson cluster process (PCP). Then, the successful sensing probability is derived, with which the sensory data traffic can be captured. Under the sensory data flow, the delay violation probability is calculated for the two-stage sensory data transmission queue by exploiting stochastic network calculus (SNC). Furthermore, a delay minimization problem is proposed to reveal the trade-off relationship between sensing and communication under the power allocation strategy. Based on the long-term network-layer queue backlog evaluated, we are devoted to analyze the delay violation probability under an emergency that results in the antenna misalignment for one typical sensing UAV during a certain period. The steady-state and transient analysis for the ISAC UAV network not only illustrate the trade-off relationship between sensing and communication for the network, but also provide insights for on-demand power allocation, network deployment, control module provisioning and sensory data flow control under certain performance requirements.

Cooperative task offloading has emerged as a compelling computing paradigm for balancing spatially uneven task workloads and computational resources in distributed mobile edge computing (MEC) systems. However, enabling cooperation among multiple MEC nodes inevitably requires extra communication and computational energy overheads which might counteract the cooperation gain without energy-efficient offloading mechanisms. This paper presents an adaptive cooperative task offloading algorithm aiming at maximizing the time-averaged energy efficiency for small cell MEC networks enabled by millimeter-wave backhauls. With the considered network dynamics, the proposed algorithm makes a good tradeoff between the harvested cooperation utility and the total energy consumption in the long term. In addition, our algorithm ensures the network stability and fulfills the task admission rate requirement of each individual user equipment, by making slot-based decisions over time without requiring a-priori knowledge of the network dynamics. Simulation results verify the outstanding performance of the proposed algorithm by comparing with the static cooperative and adaptive non-cooperative schemes.