The high-speed satellite-terrestrial network (STN) is an indispensable alternative in future mobile communication systems. In this article, we first introduce the architecture and application scenarios of STNs, and then investigate possible ways to implement mobile edge computing (MEC) technique for QoS improvement in STNs. We propose satellite MEC (SMEC), in which a user equipment without a proximal MEC server can also enjoy MEC services via satellite links. We propose a dynamic network virtualization technique to integrate the network resources, and furtherly design a cooperative computation offloading (CCO) model to achieve parallel computation in STNs. Task scheduling models in SMEC are discussed in detail, and an elemental simulation is conducted to evaluate the performance of the proposed CCO model in SMEC.

In order to optimize the resource utilization of physical machines (PMs), the workload prediction of virtual machines (VMs) is vital but challenging. Most of existing literatures focus on either resource prediction or allocation individually, but both of them are highly correlated. In this paper, we propose a multiobjective genetic algorithm (GA) to dynamically forecast the resource utilization and energy consumption in cloud data center. We formulate a multiobjective optimization problem of resource allocation, which considers the CPU and memory utilization of VMs and PMs, and the energy consumption of data center. The proposed GA forecasts the resource requirement of next time slot according to the historical data in previous time slots. We further propose a VM placement algorithm to allocate VMs for next time slot based on the prediction results of GA. In our simulation-based analysis, the optimal solution for resource prediction under stable and unstable utilization tendency is found by the proposed GA. The prediction result is superior to the previous proposed Grey forecasting model. Results show that the proposed VM placement algorithm not only increases the average utilization level of CPU and memory but also decreases the energy consumption of cloud data center.

The black hole attack is one of the well-known security threats in wireless mobile ad hoc networks. The intruders utilize the loophole to carry out their malicious behaviors because the route discovery process is necessary and inevitable. Many researchers have conducted different detection techniques to propose different types of detection schemes. In this paper, we survey the existing solutions and discuss the state-of-the-art routing methods. We not only classify these proposals into single black hole attack and collaborative black hole attack but also analyze the categories of these solutions and provide a comparison table. We expect to furnish more researchers with a detailed work in anticipation.

With the growth of the human population comes the constantly rising demand for agricultural products. Nevertheless, as the world experiences climate change, many crops are often damaged by weather conditions.This study utilizes Intelligent Agriculture IoT equipment to monitor the environmental factors on a farm. The collected data underwent 3D cluster analysis to yield analysis of the environmental factors of that farm. The proposed scheme bears the following features: (1) data normalization is achieved via the combination of moving average and average variance; (2) we applied 3D cluster analysis to analyze the relation between environmental factors and subsequently examine the rules of thumb held by the farmers; (3) the system determines whether a selected crop has been placed in the appropriate cluster; and (4) the system sets a critical value in the cluster based on future environments and provides advice on whether a crop is suitable for the farm. We placed Intelligent Agriculture IoT equipment in the farm for monitoring purposes and ran an actual-scenario analysis using the algorithm in our study; results confirm that our proposed scheme is indeed feasible.

By collecting and analyzing a vast quantity and different categories of information, traffic flow and road congestion can be predicted and avoided in intelligent transportation system. However, how to tackle with these big data is vital but challenging. Most of the existing literatures utilized batch method to process a bunch of road data that cannot achieve real-time traffic prediction. In this paper, we use the spouts and bolts in Apache Storm to implement a real-time traffic prediction model by analyzing enormous streaming data, such as road density, traffic events, and rainfall volume. The proposed SVM-based real-time highway traffic congestion prediction (SRHTCP) model collects the road data from the Taiwan Area National Freeway Bureau, the traffic events reported by road users from the Police Broadcasting Service in Taiwan, and the weather data from the Central Weather Bureau in Taiwan. We use fuzzy theory to evaluate the traffic level of road section in real time with considering road speed, road density, road traffic volume, and the rainfall of road sections. In addition, the SRHTCP model predicts the road speed of next time period by exploring streaming traffic and weather data. Results showed that the proposed SRHTCP model improves 25.6% prediction accuracy than the prediction method based on weighted exponential moving average method under the measurement of mean absolute relative error.