Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy for smart mobile devices (SMDs) by offloading computation-intensive tasks from resourceconstrained SMDs onto resource-rich cloud. However, how to achieve energy-efficient computation offloading under hard constraint for application completion time remains a challenge. To address such a challenge, in this paper, we provide an energy-efficient dynamic offloading and resource scheduling (eDors) policy to reduce energy consumption and shorten application completion time. We first formulate eDors problem into an energy-efficiency cost (EEC) minimization problem while satisfying task-dependency requirement and completion time deadline constraint. We then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control and transmission power allocation. Next, we show that computation offloading selection depends on not only the computing workload of a task, but also the maximum completion time of its immediate predecessors and the clock frequency and transmission power of the mobile device. Finally, we provide experimental results in a real testbed and demonstrate that eDors algorithm can effectively reduce EEC by optimally adjusting CPU clock frequency of SMDs in local computing, and adapting transmission power for wireless channel conditions in cloud computing.

With the proliferation of mobile devices, indoor fingerprinting based localization has caught considerable interest on account of its high precision. Meanwhile, channel state information (CSI), as a promising positioning characteristic, has been gradually adopted as an enhanced channel metric in indoor positioning schemes. In this paper, we propose a CSI amplitude fingerprinting based localization algorithm in Narrowband Internet of Things (NB-loT) system, in which we optimize centroid algorithm based on CSI propagation model. In particular, in the fingerprint matching, we utilize the method of multidimensional scaling (MDS) analysis to calculate the Euclidean distance and Time-reversal Resonating Strength (TRRS) between the target point and the reference points and then employ the K-Nearest Neighbor (KNN) algorithm for location estimation. By conjugate gradient method, moreover, we optimize the localization error of triangular centroid algorithm and combine the positioning result with MDS and KNN’s estimated position to get the final estimated position. Experiment results show that compared to some existing localization methods, our proposed algorithm can effectively reduce positioning error.

Mobile edge computing (MEC) as an emerging and prospective computing paradigm, offloads the computation-intensive tasks from resources-constrained smart mobile devices (SMDs) to edge cloud or server so as to enhance computation capability of SMDs. Meanwhile, it is expected that wireless power transfer (WPT) is applied to MEC (WPT-MEC) in order to prolong operation time of the battery. However, how to achieve the energy-effective computation offloading in WPT-MEC system under the hard constraint remains a challenge issue. To address this challenge, this paper considers energy-effective resource allocation policy in a two-user WPT-MEC system. We firstly formulate the maximization minimum energy efficiency (EE) problem to ensure the fairness of users. Then, due to the “doubly near-far” problem, we propose a user cooperative scheme in which the near user (NU) can forward the far user’s (FU) tasks to the edge cloud utilizing its more harvested energy. Considering the green network, we attach the scheme to maximize the users’ EE, defined as a ratio of the user throughput to its harvested or consumptive energy, subject to the constraints of the computational tasks in two schemes. We convert two problems into their equivalent parameterized subtractive form and provide the corresponding optimal solutions via two efficient optimization algorithms. Numerical results show that the optimal WPT-MEC system with cooperation has significant performance enhancement over the systems without cooperation.

Mobile edge computing (MEC) has become a promising technique to accommodate demands of resource-constrained mobile devices by offloading the task onto edge clouds nearby. However, most existing works only focus on whether to offload or where to offload the task but ignore the motivations of edge clouds to offer service. In this paper, we first propose an incentive mechanism in a non-competitive environment. We utilize market-based profit maximization pricing model to establish the relationship between the resources provided by edge clouds and the price charged to mobile devices. By solving the optimization problem, we provide a reasonable pricing strategy to not only ensure the profit of resource providers but guarantee the quality of experience (QoE) of mobile devices. Furthermore, we design an online profit maximization multi-round auction (PMMRA) mechanism for the resource trading between edge clouds as sellers and mobile devices as buyers in a competitive environment. The mechanism can effectively determine the price paid by buyers to use the resources provided by sellers and make the corresponding match between edge clouds and mobile devices. Finally, numerical results show that proposed mechanism outperforms other existing algorithms in maximizing the profit of edge clouds.

Mobile edge computing (MEC) can use wireless access network (RAN) to provide the services required by user's information technology (IT) and cloud computing functions nearby, which can create a high-performance and low latency service environment. Performing task offloading and data caching at access points (APs) in a cooperative manner can reduce the heavy backhaul load and the retransmission of content downloading. However, in edge networks (ENs), how to maximize storage utilization while reducing service latency and energy consumption is still a key issue, because the heterogeneity of ENs and the uneven distribution of users make it difficult to determine which MEC server and what data should be cached. In this paper, we study a two-tier MEC system, which enables data caching and computing offloading policy to minimize the network cost at the user equipment (UE) side, while satisfying the constraints of task offloading deadline, the cache capacity at APs and the computing capability of MEC servers. The optimization problem is formulated as a mixed integer nonlinear program (MINLP) problem. In order to solve the problem, we transform it into an equivalent task offloading convex optimization problem by fixing an optimization variable. Furthermore, we solve a cache placement problem by dynamic programming (DP) algorithm. Then we propose a distributed collaborative data caching and computing offloading (CDCCO) iterative algorithm. Simulation results demonstrate that our proposed CDCCO algorithm can significantly reduce the network cost and achieve better performance than other existing schemes.

Despite increasingly emerging applications, a primary concern for blockchain to be fully practical is the inefficiency of data query. Direct queries on the blockchain take much time by searching every block, while indirect queries on a blockchain database greatly degrade the authenticity of query results. To conquer the authenticity problem, we propose a Verifiable Query Layer (VQL) that can be deployed in the cloud to provide both efficient and verifiable data query services for blockchain systems. The middleware layer extracts data from the underlying blockchain system and efficiently reorganizes them in databases. To prevent falsified data from being stored in the middleware, a cryptographic fingerprint is calculated based on each constructed database. The database fingerprint will be first verified by miners and then written into the blockchain. Moreover, public users can verify the entire databases or several databases that interest them in the middleware layer. We implement VQL together with the verification schemes and conduct extensive experiments based on a practical blockchain system. The evaluation results demonstrate that VQL can efficiently support various data query services and guarantee the authenticity of query results for blockchain systems.