Mobile security has become more and more important due to the boom of mobile commerce (m-commerce). However, the development of m-commerce is facing many challenges regarding data security problems. Recently, blockchain has been introduced as an effective security solution deployed successfully in many applications in practice, such as, Bitcoin, cloud computing, and Internet-of-Things. However, the blockchain technology has not been adopted and implemented widely in m-commerce because its mining processes usually require to be performed on standard computing units, e.g., computers. Therefore, in this paper, we introduce a new m-commerce application using blockchain technology, namely, MobiChain, to secure transactions in the m-commerce. Especially, in the MobiChain application, the mining processes can be executed efficiently on mobile devices using our proposed Android core module. Through real experiments, we evaluate the performance of the proposed model and show that blockchain will be an efficient security solution for future m-commerce.

Unmanned aerial vehicles (UAVs), also known as drones, have emerged as a promising mode of fast, energyefficient, and cost-effective package delivery. A considerable number of works have studied different aspects of drone package delivery service by a supplier, one of which is delivery planning. However, existing works addressing the planning issues consider a simple case of perfect delivery without service interruption, e.g., due to accident which is common and realistic. Therefore, this paper introduces the joint ground and aerial delivery service optimization and planning (GADOP) framework. The framework explicitly incorporates uncertainty of drone package delivery, i.e., takeoff and breakdown conditions. The GADOP framework aims to minimize the total delivery cost given practical constraints, e.g., traveling distance limit. Specifically, we formulate the GADOP framework as a three-stage stochastic integer programming model. To deal with the high complexity issue of the problem, a decomposition method is adopted. Then, the performance of the GADOP framework is evaluated by using two data sets including Solomon benchmark suite and the real data from one of the Singapore logistics companies. The performance evaluation clearly shows that the GADOP framework can achieve significantly lower total payment than that of the baseline methods which do not take uncertainty into account.

Recently, unmanned aerial vehicles (UAVs), also known as drones, has emerged as an efficient and cost-effective solution for package delivery. Especially, drones are expected to incur lower cost, and achieve fast and environment friendly delivery. While most of existing drone research concentrates on surveillance applications, few works studied the drone package delivery planning problem. Even so, the previous works only focus on the drone delivery planning of a single supplier. In this paper, thus we propose the supplier cooperation in drone delivery (CoDD) framework. The framework considers jointly package assignment, supplier cooperation, and cost management. The objective of the framework is to help suppliers minimize and achieve fair share of the cost as well as reach a stable cooperation. The trade-off between using drones and outsourcing package delivery to a carrier is also investigated. The performance evaluation of the CoDD framework is conducted by using the Solomon benchmark suite and a real Singapore dataset which evidently confirms the practical findings.

Recently, an unmanned aerial vehicle (UAV), as known as drone, has become an alternative means of package delivery. Although the drone delivery scheduling has been studied in recent years, most existing models are formulated as a single objective optimization problem. However, in practice, the drone delivery scheduling has multiple objectives that the shipper has to achieve. Moreover, drone delivery typically faces with unexpected events, e.g., breakdown or unable to takeoff, that can significantly affect the scheduling problem. Therefore, in this paper, we propose a multi-objective and three-stage stochastic optimization model for the drone delivery scheduling, called multi-objective optimization for drone delivery (MODD) system. To handle the the multi-objective optimization in the MODD system, we apply ε-constraint method. The performance evaluation is performed by using a real dataset from Singapore delivery services.

With an increasing demand from emerging logistics businesses, Vehicle Routing Problem with Private fleet and common Carrier (VRPPC) has been introduced to manage package delivery services from a supplier to customers. However, almost all of existing studies focus on the deterministic problem that assumes all parameters are known perfectly at the time when the planning and routing decisions are made. In reality, some parameters are random and unknown. Therefore, in this paper, we consider VRPPC with hard time windows and random demand, called Optimal Delivery Planning (ODP). The proposed ODP aims to minimize the total package delivery cost while meeting the customer time window constraints. We use stochastic integer programming to formulate the optimization problem incorporating the customer demand uncertainty. Moreover, we evaluate the performance of the ODP using test data from benchmark dataset and from actual Singapore road map.