The task offloading and resource allocation strategies aim to provide efficient edge computing services for ground mobile devices in mobile edge computing (MEC) environments. In this paper, we propose a UAV-assisted dynamic deployment and decision-making framework (dDDM) to address the issue of insufficient base station coverage in remote areas or emergency scenarios, which leverages the flexibility and computational capabilities of unmanned aerial vehicle (UAV) to supplement terrestrial cellular networks. We focus on optimizing the UAV trajectory to support and enhance the effectiveness of task offloading and resource allocation strategies under conditions of user mobility and stochastic task arrivals. Therefore, a joint optimization problem is formulated with the objective of minimizing the weighted sum of the total latency of ground mobile devices and the overall energy consumption of UAV. To solve this problem, the original optimization problem is decomposed into two subproblems: UAV trajectory planning, and decision-making for task offloading & computing resource allocation. For the UAV trajectory planning subproblem, we propose an improved particle swarm optimization(PSO) algorithm integrated withK-means clustering, i.e., KMPSO algorithm, enabling dynamic UAV deployment in response to user mobility. For the task offloading and resource allocation decision-making, an improved black-winged kite algorithm (IBKA) is developed to determine optimal offloading and allocation strategies. Simulation results demonstrate that the proposed approach outperforms existing benchmark algorithms in terms of both latency and energy consumption.

In Vehicle Edge Computing Network (VECN), task offloading is a key technique to provide the satisfactory quality of service (QoS) for latency-sensitive tasks. However, the diversity of computational resources in edge nodes (i.e., RSU and idle vehicles) and the mobility of vehicles present significant challenges to task offloading. Hence, to address these challenges, we propose an offloading scheme that jointly allocates RSU nodes (including MEC servers) and idle service vehicle resources in this paper. We first prioritize these tasks based on their maximum tolerable latency and design a utility function to capture the executing cost for latency-sensitive tasks. Then, we propose a joint optimization algorithm of task offloading location and proportion based on Multi-agent Deep Reinforcement Learning (jTOLP-MADRL algorithm) for latency-sensitive tasks in VECN, which consists of two sub-algorithms: the Offloading Location Selection (OLS) algorithm and the Offloading Proportion Allocation (OPA) algorithm. Additionally, we design a Convolutional Recurrent Actor-Critic Network (CRACN) to enhance the learning efficiency of the OLS algorithm. Finally, we indicate our algorithm is effective based on simulation results. Compared with the other benchmark algorithms, jTOLP-MADRL can significantly reduce latency and enhance system utility.

Low Earth Orbit (LEO) satellite-based edge computing offers an innovative paradigm for offloading the computing tasks of resource-limited User Equipments (UEs) in the environment lacking terrestrial networks. By offloading a portion of the computing tasks to satellite-mounted servers in space, it can significantly reduce the energy consumption of the UEs while reducing the delay. However, most existing works focus on the performance and energy consumption from the system point of view, neglecting the dynamic Quality of Experience (QoE) of the UEs: the UEs tend to place a higher value on saving energy when their remaining battery level is low, while seeking a lower delay when the battery power is sufficient. To this end, we build a QoE-aware task offloading model in space-terrestrial integrated networks where the UEs can adjust the preference on the delay and energy consumption based on their remaining battery level. We then propose a Self-Adaptive Multi-Agent Deep Deterministic Policy Gradient (SA-MADDPG) task offloading scheme based on Deep Reinforcement Learning (DRL). SA-MADDPG learns the status of the satellite-mounted edge servers and makes different offloading decisions at different battery levels. When the battery level is high, UEs will be more likely to perform computation locally to reduce the delay; on the contrary, when the UE battery level is low, they will seek to offload the computation tasks to the satellites to save energy. Through simulation experiments, we demonstrate that SA-MADDPG can dynamically adjust the UE offloading decision based on their current battery level and effectively improve the user QoE.

Edge computing pushes computation and storage resources to the network edge, which is close to end users, and thus, is critical for latency-sensitive applications, e.g., intelligent vehicularad hocnetworks (VANETs). To enable these services, a set of edge servers needs to be deployed to the roadsides. Such deployment should offer low-latency services to end users, while keeping a low deployment or maintenance cost, which is a nontrivial task. In this article, we study the edge server placement problem in a metropolitan area. This problem is composed of two parts to determine: 1) the locations of the servers and 2) the coverage of each server, with multiple optimization objectives. First, we study the Shanghai Taxi Trace to gain insights into the traffic pattern of taxis, especially how vehicles move between different locations. Second, we build multiobjective optimization models to characterize the tradeoff among three critical performance metrics, namely, the initial deployment cost, the runtime cost (i.e., number of hand-offs between different servers), and the average delay of tasks. Due to the intractability of these NP-hard problems, we propose a heuristic multiobjective optimization method to decompose the global problem into a set of local problems with tractable scale. Numerical results verify that our heuristic strategy achieves a desirable balance among the three performance metrics, e.g., a 5% compromise of the delay can reduce up to 50% of the hand-offs for small local areas, and 10%+ for the entire global area, compared with the best existing algorithms.

Large-scale urban pipeline systems (LSUPSs) are complex pipeline networks of flows (e.g., water, oil, or gas). Flow capacity intelligent monitoring, e.g., automatically monitor the sum of flows in an LSUPS, is an important task in smart cities. Recently, mobile sensors and static receiver nodes are used to perform the task. Mobile sensors are released into the network at selected entrances to collect data, and upload the data to receiver nodes deployed at selected locations of the network for further analysis. Due to cost constraints, the numbers of mobile sensors and receiver nodes are limited, which cause the problem that some pipelines may not be monitored. However, applications normally require that some Zones of Interest (ZoIs) in the network have to be monitored. Therefore, how to select optimal entrances and locations for given numbers of mobile sensors and receiver nodes, so that the capacity of monitored flow is maximized within a given time under the constraint that all ZoIs are also monitored with expected probabilities, is a challenging problem. First, we prove the problem is NP-complete. Then, we design two algorithms based on submodular set function optimization to solve it. The first algorithm can obtain an approximate optimal solution with high time complexity, while the second algorithm can obtain a suboptimal solution with much lower time complexity. Finally, we analyze time complexity and approximate ratio of the two algorithms. Theoretical analyses and simulation results show that the proposed algorithms outperform the state-of-the-art algorithms.

Fault-Prone Mobile Edge Cloud (FP-MEC) is a new type of distributed network composed of mobile edge computing and network function virtualization, where virtual network services can be provided in the form of service function chains (SFCs) that are a sequence of virtual network functions (VNFs) on-demand deployed on resource-limited edge servers. FP-MEC has a characteristic that the fault probability of each VNF is dynamic and fluctuates with time and workloads, making SFCs temporarily unreliable. To increase the reliabilities, redundant Backup VNFs (BVNFs) need to be deployed near the VNFs and activated when they experience faults. Different mobile users would request different SFCs with reliability and service time demands to process their data. However, workloads of VNFs are dynamic and unpredictable in FP-MEC due to random arrival of user requests. How to optimally deploy VNFs and corresponding BVNFs on a set of edge servers to form expected SFCs that have higher reliabilities than user demand values, meanwhile throughput of receiving requests is maximized while receiving cost is minimized in real-time, is a challenging problem. The receiving cost is composed of deployment cost of instantiating VNFs and BVNFs, and communication cost of routing data among users, VNFs and BVNFs. In this paper, the long-term provisioning problem is first formulated as an integer linear program and proved to be NP-hard. Then, it is discretized into a sequence of one-slot optimization problems to handle practical time-varying fault probability, where a set of SFC requests are given at each time slot, and receiving or rejecting decisions are executed immediately without any future information. Finally, an online approximation scheme with a constant approximation ratio is proposed to solve the one-slot problems in polynomial time. Theoretical analyses and experiments based on real network topology of CERNET in China demonstrate that the scheme is promising compared to existing works.

Integrity and privacy are two important secure matrices in cyber security. Due to the limited resources and computing capability of the sensor nodes, it is challenging to simultaneously satisfy these two matrices for top-k querying in two-tiered sensor networks. To solve this problem, this paper proposes a weight-bind-based secure top-k query processing scheme (WBB-TQ), which utilises both the order-preserving symmetric encryption scheme (OPES) and the pairwise-key encryption technique to ensure data privacy in top-k querying. Since OPES can keep the size orders of the sensed data items unchanged before and after they are encrypted, the upper-layer storage nodes in the network can process top-k queries without knowing the exact values of the sensed data items. To guarantee the completeness of query results, we propose a novel method to establish chaining relationship among all the data items generated by each sensor node. By checking whether the relationship holds on not, Sink can find out whether adversaries drop and/or tamper with part or all of the qualified top-k data items in the query results. Theoretical analyses show that WBB-TQ can preserve data integrity and privacy of the top-k query results. Extensive simulation results further demonstrate that, WBB-TQ incurs very low computational and communication cost in securing top-k querying.

By leveraging node mobility and exploring a store-carry-and-forward paradigm, delay-tolerant networking enables and assists end-to-end message delivery in many scenarios, e.g., vehicular ad hoc networks and mobile social networks. Most existing work in the literature either focuses on the routing strategies for unicast, or history-based routing for multicast communications. In this paper, we discover the most important and independent social features from the Infocom 06 trace data, and propose a social profile-based multicast routing scheme. Our proposed scheme reduces the delivery cost greatly compared with flooding-based schemes and achieves a similar performance to the history-based schemes, without the cost of maintaining the contact history. The efficiency of the proposed scheme has been confirmed by trace-driven simulation, which also reflects the efficacy of exploring social features in delay-tolerant networks.