Efficient performance monitoring for ubiquitous virtual networks based on matrix completion

Inspired by the concept of software-defined network and network function virtualization, vast virtual networks are generated to isolate and share wireless resources for different network operators. To achieve fine-grained resource control and scheduling among virtual networks (VNs), network performance monitoring is essential. However, due to limitation of hardware, real-time performance monitoring is impossible for a complete virtual network. In this paper, taking advantage of the low-rank characteristic of 90 virtual access points (VAPs) measurement data, we propose an intelligent measurement scheme, namely, adaptive and sequential sampling based on matrix completion (MC), which exploits from the MC to construct the complete data of VN performance from a partial direct monitoring data. First, to construct the initial measurement matrix, we propose a sampling correction model based on dispersion and coverage. Second, a stopping condition for the sequential sampling is introduced, based on the stopping condition, the sampling process for a period can stop without waiting for the matrix reconstruction to reach certain of accuracy level. Finally, the sampled VAPs are determined by referring the back-forth completed matrixes\u27 normalized mean absolute error. The experiments show that our approach can achieve a constant network perception and maintain a relatively low error rate with a small sampling rate

Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch

Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, in a wireless environment, the main controller node may leave the sensor network due to the dynamic topology change or the unstable wireless signal, leaving the rest of network devices without control, e.g., a sensor node as a switch may forward packets according to stale rules until the controller updates the flow table entries. To solve this problem, this paper proposes a novel SDN-based vehicular sensor networks architecture which can minimize the performance penalty of controller connection loss. We achieve this by designing a connection state detection and self-learning mechanism. We build prototypes based on extended Open vSwitch and Ryu. The experimental results show that the recovery time from controller connection loss is under 100 ms and it keeps rule updating in real time with a stable throughput. This architecture enhances the survivability and stability of SDN-based vehicular sensor networks in precision agriculture

Proposal of architecture for IoT solution for monitoring and management of plantations

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production This work presents a systematic review of the existing literature on smart farming with IoT. The systematic review reveals an evolution in the way data are processed by IoT solutions in recent years. Traditional approaches mostly used data in a reactive manner. In contrast, recent approaches allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis. Based on the finds of the systematic review, this work proposes an architecture of an IoT solution that enables monitoring and management of crops in real time. The proposed architecture allows the usage of big data and machine learning to process the collected data. A prototype is implemented to validate the operation of the proposed architecture and a security risk assessment of the implemented prototype is carried out. The implemented prototype successfully validates the proposed architecture. The architecture presented in this work allows the implementation of IoT solutions in different scenarios of farming, such as indoor and outdoor