Bloom filter (BF) has been widely used to support membership query, i.e., to judge whether a given element x is a member of a given set S or not. Recent years have seen a flourish design explosion of BF due to its characteristic of space-efficiency and the functionality of constant-time membership query. The existing reviews or surveys mainly focus on the applications of BF, but fall short in covering the current trends, thereby lacking intrinsic understanding of their design philosophy. To this end, this survey provides an overview of BF and its variants, with an emphasis on the optimization techniques. Basically, we survey the existing variants from two dimensions, i.e., performance and generalization. To improve the performance, dozens of variants devote themselves to reducing the false positives and implementation costs. Besides, tens of variants generalize the BF framework in more scenarios by diversifying the input sets and enriching the output functionalities. To summarize the existing efforts, we conduct an in-depth study of the existing literature on BF optimization, covering more than 60 variants. We unearth the design philosophy of these variants and elaborate how the employed optimization techniques improve BF. Furthermore, comprehensive analysis and qualitative comparison are conducted from the perspectives of BF components. Lastly, we highlight the future trends of designing BFs. This is, to the best of our knowledge, the first survey that accomplishes such goals.

The emergence of edge computing can effectively tackle the problem of large transmission delays caused by the long-distance between user devices and remote cloud servers. Users can offload tasks to the nearby edge servers to perform computations, so as to minimize the average task response time through effective task dispatching and scheduling methods. However: 1) in the task dispatching phase, the dynamic features of network conditions and server loads make it difficult for the offloaded tasks to select the optimal edge server and 2) in the task scheduling phase, each edge server may face a large number of offloading tasks to schedule, resulting in long average task response time, or even severe task starvation. In this article, we propose an online task dispatching and fair scheduling method OTDS to tackle the above two challenges, which combines online learning (OL) and deep reinforcement learning (DRL) techniques. Specifically, using an OL approach, OTDS performs real-time estimating of network conditions and server loads, and then dynamically assigns tasks to the optimal edge servers accordingly. Meanwhile, at each edge server, by combing the round-robin mechanism with DRL, OTDS is able to allocate appropriate resources to each task according to its time sensitivity and achieve high efficiency and fairness in task scheduling. Evaluation results show that our online method can dynamically allocate network resources and computing resources to those offloaded tasks according to their time-sensitive requirements. Thus, OTDS outperforms the existing methods in terms of the efficiency and fairness on task dispatching and scheduling by significantly reducing the average task response time.

Unmanned aerial vehicle (UAV) networking emerges as an indispensable communication paradigm for the sixth-generation (6G) communication systems to fulfill ubiquitous coverage and near-instant super-connectivity. To fully exploit the merits of various UAVs, based on software-defined networking (SDN) and artificial intelligence (AI) technologies, this article proposes an intelligent air-ground integrated network architecture called Lasagna. It acts as a comprehensive infrastructure that can complement the ground network and support the 6G. It is hierarchically composed of the high-altitude platform, low-altitude platform, and ground platform, to provide ubiquitous intelligent networking, computing, and caching services in a flexible, seamless, and economical manner. From a high-level view, we detail how Lasagna provides smart edge wireless communication, computing, and caching services. We also explore some open research directions and conclude this article.

The increasing availability of autonomous Unmanned Aerial Vehicles (UAVs) has provided an emerging way for extensive surveillance and monitoring in civilian and military with the advantages of high flexibility, mobility, and bird's-eye features. To mitigate the limitations of on-board energy and computation capacity, UAVs usually offload intensive computation tasks (e.g., image/video processing) to base-station-enabled mobile-edge computing (MEC) facilities. However, base stations are not always available in rural areas. To this end, we propose to offload computation-intensive tasks to Unmanned Ground Vehicles (UGVs), which have fixed routes (i.e., highways and roads). However, the UAV-UGV wireless communication may disclose the offloading information to potential eavesdroppers. Besides, such UGVs have sufficient computation slots but may run out of the target area after a period of traveling. In this paper, the tasks cached on UAVs are modeled as a stochastic queue, and a secure communication strategy to offload the cached tasks from UAVs to UGVs is proposed. We try to maximize the average utility of such a UAV-UGV collaboration with respect to latency, power, velocity, anti-collision, and distance. To solve this non-convex mixed integer nonlinear programming problem, a fast converging and computationally efficient iterative algorithm is investigated utilizing the block coordinate descent method and the successive convex approximation technique. Experiment results demonstrate that our algorithm consistently outperforms the state-of-the-art scheme, significantly improving the average utility of the system.

As mobile device usage and data traffic increase, the demand for faster data processing becomes crucial. mobile edge computing (MEC) meets this need by placing servers at the network’s edge for real-time computing. However, fixed terrestrial MEC servers struggle with scalability, limiting their effectiveness. Integrating uncrewed aerial vehicles (UAVs) with MEC technology offers a promising solution, enhancing communication efficiency and service quality. This article proposes a joint communication and computation offloading model for coordinated multipoint (CoMP) UAV-assisted MEC networks utilizing hexagonal cell partitioning. Within each cell, a cluster of UAVs, each equipped with its own MEC server and connected to a central server via a reliable backhaul, collaborates to serve terrestrial user equipment. To analyze this system, we develop a unified analytical framework integrating stochastic geometry and queuing theory. Furthermore, we define the success probability of edge computing (SPEC) metric to quantitatively evaluate communication reliability and computational efficiency. Finally, we explore the effects of critical parameters on network performance. Simulation results closely match the theoretical predictions, confirming our proposed model’s validity and our analysis’s accuracy. Notably, our proposed model demonstrates an improvement in SPEC of approximately 57.24% over non-CoMP model and 24.97% over the user-centric CoMP model.

As resource-intensive and latency-sensitive applications continue to expand, the integration of unmanned aerial vehicles (UAVs) with mobile edge computing (MEC) has emerged as a viable solution, offering flexible, on-demand services for mobile users (MUs) without reliance on terrestrial infrastructure. The adoption of non-orthogonal multiple access (NOMA) further reduces latency by allowing MUs to offload tasks simultaneously over a single subchannel. However, many existing offloading methods do not explicitly incorporate a priority-based task scheduling mechanism and instead optimize task execution based on system constraints such as latency or energy consumption. To bridge this gap, we propose a QoS-oriented task offloading scheme that systematically optimizes task scheduling. We formulate an average system utility maximization problem that jointly optimizes UAVs’ 3D trajectories, MU association, task offloading ratios, and resource allocation. The optimization problem is inherently complex due to its non-convex nature and multiple constraints. To address this, we first employ Lagrange duality to decouple constraints, reducing computational complexity. Subsequently, we propose a novel improved soft actor-critic (ISAC) algorithm, which incorporates a perturbation term into the loss function to guide the training process away from local minima and toward globally optimal solutions. Through extensive simulation, we demonstrate that the ISAC algorithm guarantees convergence and significantly outperforms benchmark methods on offloading transmission rates, task completion rates, and overall system utility.