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.

Mobile cloud computing (MCC) as an emerging and prospective computing paradigm, can significantly enhance computation capability and save energy of smart mobile devices (SMDs) by offloading computation-intensive tasks from resource-constrained SMDs onto the resource-rich cloud. However, how to achieve energy-efficient computation offloading under the hard constraint for application completion time remains a challenge issue. 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 the eDors problem into the energy-efficiency cost (EEC) minimization problem while satisfying the task-dependency requirements and the completion time deadline constraint. To solve the optimization problem, we then propose a distributed eDors algorithm consisting of three subalgorithms of computation offloading selection, clock frequency control and transmission power allocation. More importantly, we find that the 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, our experimental results in a real testbed demonstrate that the eDors algorithm can effectively reduce the EEC by optimally adjusting the CPU clock frequency of SMDs based on the dynamic voltage and frequency scaling (DVFS) technique in local computing, and adapting the transmission power for the wireless channel conditions in cloud computing.

Blockchain technology can be extensively applied in diverse services, including online micro-payments, supply chain tracking, digital forensics, health-care record sharing, and insurance payments. Extending the technology to the Internet of things (IoT), we can obtain a verifiable and traceable IoT network. Emerging research in IoT applications exploits blockchain technology to record transaction data, optimize current system performance, or construct next-generation systems, which can provide additional security, automatic transaction management, decentralized platforms, offline-to-online data verification, and so on. In this article, we conduct a systematic survey of the key components of IoT blockchain and examine a number of popular blockchain applications. In particular, we first give an architecture overview of popular IoT-blockchain systems by analyzing their network structures and protocols. Then, we discuss variant consensus protocols for IoT blockchains, and make comparisons among different consensus algorithms. Finally, we analyze the traffic model for P2P and blockchain systems and provide several metrics. We also provide a suitable traffic model for IoT-blockchain systems to illustrate network traffic distribution.

Sybil attacks have been regarded as a serious security threat to ad hoc networks and sensor networks. They may also impair the potential applications of VANETs (Vehicular Ad hoc Networks) by creating an illusion of traffic congestion. In this paper, we present a lightweight security scheme for detecting and localizing Sybil nodes in VANETs, based on statistic analysis of signal strength distribution. Our scheme is a distributed and localized approach, in which each vehicle on a road can perform the detection of potential Sybil vehicles nearby by verifying their claimed positions. We first introduce a basic signal-strength-based position verification scheme. However, the basic scheme proves to be inaccurate and vulnerable to spoof attacks. In order to compensate for the weaknesses of the basic scheme, we propose a technique to prevent Sybil nodes from covering up for each other. In this technique, traffic patterns and support from roadside base stations are used to our advantage. We, then, propose two statistic algorithms to enhance the accuracy of position verification. The algorithms can detect potential Sybil attacks by observing the signal strength distribution of a suspect node over a period of time. The statistic nature of our algorithms significantly reduces the verification error rate. Finally, we conduct simulations to explore the feasibility of our scheme.