Current Issue : July-September Volume : 2022 Issue Number : 3 Articles : 5 Articles
In wireless sensor networks (WSNs), the efficiency of data transmission within a limited time is critical, especially for sensors designed with small batteries. In this paper, we design a cooperative transmission scheme with an energy-charging function in a WSN where an unmanned aerial vehicle (UAV) is considered for sensory data collection and energy charging. Specially, the sensor nodes are powered by the UAV for their data transmission. In the first phase, the UAV transmits the energy signal to the sensor nodes distributed on the ground. All the energy received by the sensor nodes is used to collect and transmit the sensory data to the UAV. In the second phase, local data transmissions are conducted among the collaborating sensor nodes in one cluster. In the third phase, the cooperative nodes send the collected sensory data to the UAV in the form of cooperative transmission. In the proposed scheme, we discovered that the size of the modulation constellation and the assigned time ratio of each phase were the key factors affecting the data transmission efficiency. In order to achieve the maximum data transmission, the optimal modulation constellation size and the optimal time ratio of each phase were found using the Lagrange multiplier method. Numerical results show that the proposed scheme with the optimal constellation size and the optimal time ratio can outperform the existing scheme in terms of the data transmission efficiency....
In recent years, along with microelectromechanical (MEMS) technique and wireless body area network (WBAN), wearable health monitoring systems have emerged. With sustainability posture detection technique, people pay more and more attention to mankind posture detection. Human posture detection technique has been widely used in medical, film and television, industry, sports, and other fields. Motion capture is a technique for measuring the motion of moving objects in three-dimensional space with great accuracy. It is the most efficient method for producing computer 3D animation and collecting human motion data. The use of motion capture systems in animation is becoming increasingly common. The problem of a single sensor having a large error in monitoring human motion and attitude is addressed. The use of multisensor data fusion technology is used to propose a human motion pattern recognition method based on data fusion of accelerometer and gyroscope. The system must effectively integrate the information of various sensors in order to achieve the goal of accuracy, timeliness, and reliability processing, and multisensor information fusion systems for various complex application objects are constantly emerging....
According to the development of landscape digitization and the actual market demand, a digital landscape system based on intelligent sensor network is designed and implemented. The system consists of two parts: sensor node and display terminal, forming a star intelligent sensor network. Sensor node measurement is sent to display control terminal through intelligent sensor network. The display control terminal serves as the aggregation node. Based on geometry transformation, free form, and bionics, the method of constructing complex surface and the method strategy of optimizing complex surface are put forward from geometry and bionics theory. Then, the material types and construction methods of landscape composite surface are discussed and studied. According to the process and project of site cognition and landscape planning and design, six special models are established: ecological sensitivity evaluation model, construction suitability evaluation model, project site selection model, road line selection model, quasinatural waterscape construction model, and vertical design model. According to the characteristics of each landscape planning and design project, the logic generation and parameter composition of the model are discussed, and the application of the model is empirically studied and discussed based on actual cases....
In recent years, as a new subject in the computer field, artificial intelligence has developed rapidly, especially in reinforcement learning (RL) and deep reinforcement learning. Combined with the characteristics of Software Defined Network (SDN) for centralized control and scheduling, resource scheduling based on artificial intelligence becomes possible. However, the current SDN routing algorithm has the problem of low link utilization and is unable to update and adjust according to the real-time network status. This paper aims to address these problems by proposing a reinforcement learning-based multipath routing for SDN (RLMR) scheme. RLMR uses Markov Decision Process (MDP) and Q-Learning for training. Based on the real-time information of network state and flow characteristics, RLMR performs routing for different flows. When there is no link that meets the bandwidth requirements, the remaining flows are redistributed according to the Quality of Service (QoS) priority to complete the multipath routing. In addition, this paper defines the forward efficiency (FE) to measure the link bandwidth utilization (LBU) undermultipath routing. Simulation results show that compared with the currentmainstreamshortest path algorithmand ECMP algorithm, the routing algorithmin RLMR has advantages in FE, jitter, and packet loss rate. It can effectively improve the efficiency and quality of routing....
Network slicing- (NS-) based cloud radio access networks (C-RANs) have emerged as a key paradigm to support various novel applications in 5G and beyond networks. However, it is still a challenge to allocate resources efficiently due to heterogeneous quality of service (QoS) requirements of diverse services as well as competition among different network slices. In this paper, we consider a service provisioning allocation framework to guarantee resource utilization while ensuring the QoS of users. Specifically, an inter/intraslice bandwidth optimization strategy is developed to maximize the revenue of the system with multiple network slices. The proposed strategy is hierarchically structured, which decomposes into network-level slicing and packet scheduling level slicing. At the network level, resources are allocated to each slice. At the packet scheduling level, each slice allocates physical resource blocks (PRBs) among users associated with the slice. Numerical results show that the proposed strategy can effectively improve the revenue of the system while guaranteeing heterogeneous QoS requirements. For example, the revenue of the proposed strategy is 21% higher than that of the average allocation strategy....
Loading....