In recent years, the unmanned aerial vehicles (UAVs) have exhibited significant market potential to greatly reduce the cost and time in the field of logistics. The use of UAVs to provide commercial courier has become an emerging industry, remarkably shifting the energy use of the freight sector. However, due to limited battery capacities, the flight duration of civilian rotorcraft UAVs is still short, hindering them from performing remote jobs. In this case, people customarily utilize ground vehicles to carry and assist UAVs in various applications, including cargo delivery. Most previous studies on vehicle-drone cooperative parcel delivery considered only one UAV, thereby suffering from low efficiency when serving a large number of customers. In this paper, we propose a novel hybrid genetic algorithm, which supports the cooperation of a ground vehicle and multiple UAVs for efficient parcel delivery. Our routing and scheduling algorithm allows multiple UAVs carried by the vehicle to simultaneously deliver multiple parcels to customers residing in different locations. The proposed algorithm consists of a pipeline of several modules: population management, heuristic population initialization, and population education. The performance evaluation results show that the proposed algorithm has significant efficiency over existing algorithms.

Smart contracts, one of the success stories in blockchain 2.0, have been widely utilized in a broad range of applications, including those involving Internet of Things (IoT). Given the fast-pace nature of the topic, it can be challenging for the research community to keep track of the latest advances. Hence, in this article, we perform a comprehensive, in-depth review of known security challenges (e.g., inherently vulnerable particularities, programming vulnerabilities, and attacks) and potential research opportunities associated with the deploying of smart contracts in an IoT setting. We hope this survey will serve as a starting point for the readers seeking to understand and explore the potential applications of smart contracts.

In recent decades, Unmanned Aerial Vehicles (UAVs, also known as drones) equipped with multiple sensors have been widely utilized in various applications. Nevertheless, constrained by limited battery capacities, the hovering time of UAVs is quite limited, prohibiting them from serving a wide area. To cater with remote sensing applications, people often employ vehicles to transport, launch, and recycle them. The so-called vehicle-drone cooperation (VDC) benefits from both the far driving distance of vehicles and the high mobility of UAVs. Efficient routing and scheduling can greatly reduce time consumption and financial expenses incurred in VDC. However, previous works in vehicle-drone cooperative sensing considered only one drone, thus unable to simultaneously cover multiple targets distributed in an area. Using multiple drones to sense different targets in parallel can significantly promote efficiency and expand service areas. Therefore, we propose a novel problem, referred to as vehicle-assisted multi-drone routing and scheduling problem. To tackle the problem, we contribute an efficient algorithm, referred to as Vehicle-assisted multi-UAV Routing and scheduling Algorithm, (VURA). In VURA we maintain and iteratively update a memory containing candidate UAV routes. VURA works by iteratively deriving solutions based on UAV routes picked from the memory. In every iteration, VURA jointly optimizes anchor point (AP) selection, path planning, and tour assignment via nested optimization operations. To the best of our knowledge, we are the first to tackle this novel yet challenging problem. Finally, performance evaluation is presented to demonstrate the effectiveness and efficiency of our algorithm when compared with existing solutions.

Microservices as an emerging architecture are creating new opportunities to enable superior network services in Mobile Edge Computing (MEC). In the presence of huge amounts of user requests, the massive communications among microservices have become notoriously complicated. Due to the intricate data dependencies of the microservices, the overall performance of large-scale MEC applications simultaneously depends on both service deployment and request routing. However, most existing work ignores the interdependencies of microservices and studies the deployment and routing as two isolated problems. In this case, this paper investigates the joint optimization of service deployment and request routing in edge computing. We first formulate a delay minimization problem via mixed integer linear programming and queuing analysis, and then provide a hardness proof on the problem. In addition, this paper presents a 2-approximation algorithm, followed with rigorous mathematical proofs to demonstrate the approximation ratio. The proposed two-phase algorithm consists of rounding based service deployment and adaptive-scaling-based request routing policies, which employ fine grained joint optimization to minimize service response delay. Finally, we illustrate the near-optimal performance of the proposed algorithm via comprehensive experiments.

Machine learning (ML) has achieved huge success in recent years, but is also vulnerable to various attacks. In this article, we concentrate on membership inference attacks and propose Aster, which merely requires the target model's black-box API and a data sample to determine whether this sample was used to train the given ML model or not. The key idea of Aster is that the training data of a fully trained ML model usually has lower prediction sensitivities compared with that of the non-training data (i.e., testing data). Less sensitivity means that when perturbing a training sample's feature value in the corresponding feature space, the prediction of the perturbed sample obtained from the target model tends to be consistent with the original prediction. In this article, we quantify the prediction sensitivity with the Jacobian matrix which could reflect the relationship between each feature's perturbation and the corresponding prediction's change. Then we regard the samples with a lower as training data. Aster can breach the membership privacy of the target model's training data with no prior knowledge about the target model or its training data. The experiment results on four datasets show that our method outperforms three state-of-the-art inference attacks.