Dingde Jiang

Collaborative Multi-hop Routing in Cognitive Wireless Networks

The collaboration of nodes in cognitive wireless networks is a large challenge. This paper studies the collaborative multi-hop routing in cognitive networks. We propose a new algorithm to construct the collaborative routing in multi-hop cognitive networks. Our algorithm takes into account the interference among nodes including primary and secondary users. The clustering and collaboration are exploited to improve the performance of collaborative routing in multi-hop cognitive wireless networks with multiple primary and secondary users. By analyzing the maximum transmission distance, collaborations, transmission angle control and power control, and channel allocation, we propose a new clustering-based collaborative multi-hop cognitive routing algorithm to attain better network performance. Simulation results show that our approach is feasible and effective.

A collaborative multi-hop routing algorithm for maximum achievable rate

This paper studies collaborative multi-hop communication technology in next generation wireless communications. We propose a collaborative multi-hop routing algorithm combined with clustering to improve network performance. To build the multi-hop routing with maximum achievable rate, a relation matrix is exploited to describe the possible coverage of network nodes. A clustering-based path strategy is presented to create the effective next-hop link. A collaboration strategy is proposed to construct collaborative matrix. And then by clustering and collaboration, a multi-hop routing with maximum achievable rate is successfully built. The effectiveness and the feasibility of the proposed methods are verified by simulation results.

Energy-Efficient Multi-Constraint Routing Algorithm With Load Balancing for Smart City Applications

Many researches show that the power consumption of network devices of ICT is nearly 10% of total global consumption. While the redundant deployment of network equipment makes the network utilization is relatively low, which leads to a very low energy efficiency of networks. With the dynamic and high quality demands of users, how to improve network energy efficiency becomes a focus under the premise of ensuring network performance and customer service quality. For this reason, we propose an energy consumption model based on link loads, and use the networks bit energy consumption parameter to measure the network energy efficiency. This paper is to minimize the networks bit energy consumption parameter, and then we propose the energy-efficient minimum criticality routing algorithm, which includes energy efficiency routing and load balancing. To further improve network energy efficiency, this paper proposes an energy-efficient multi-constraint rerouting (E2MR2) algorithm. E2MR2 uses the energy consumption model to set up the link weight for maximum energy efficiency and exploits rerouting strategy to ensure network QoS and maximum delay constraints. The simulation uses synthetic traffic data in the real network topology to analyze the performance of our method. Simulation results that our approach is feasible and promising.

Multi-Armed Bandit Channel Access Scheme With Cognitive Radio Technology in Wireless Sensor Networks for the Internet of Things

The wireless sensor network (WSN) is one of the key enablers for the Internet of Things (IoT), where WSNs will play an important role in future internet by several application scenarios, such as healthcare, agriculture, environment monitoring, and smart metering. However, todays radio spectrum is very crowded for the rapid increasing popularities of various wireless applications. Hence, WSN utilizing the advantages of cognitive radio technology, namely, cognitive radio-based WSN (CR-WSN), is a promising solution for spectrum scarcity problem of IoT applications. A major challenge in CR-WSN is utilizing spectrum more efficiently. Therefore, a novel channel access scheme is proposed for the problem that how to access the multiple channels with the unknown environment information for cognitive users, so as to maximize system throughput. The problem is modeled as I.I.D. multi-armed bandit model with M cognitive users and N arms (M<;N). In order to solve the competition and the fairness between cognitive users of WSNs, a fair channel-grouping scheme is proposed. The proposed scheme divides these channels into M groups according to the water-filling principle based on the learning algorithm UCB-K index, the number of channels not less than one in each group and then allocate channel group for each cognitive user by using distributed learning algorithm fairly. Finally, the experimental results demonstrate that the proposed scheme cannot only effectively solve the problem of collision between the cognitive users, improve the utilization rate of the idle spectrum, and at the same time reflect the fairness of selecting channels between cognitive users.

Integrated Wireless Sensor Systems via Near-Space and Satellite Platforms: A Review

Due to extreme conditions, the near-space region is vastly underused and can be utilized for various scientific uses. The unconstrained orbital mechanism and low fuel consumption advantages for using synthetic aperture radar over the satellites and airplanes navigation systems make these conditions superior for a wide range of services, monitoring, earth observation, and sensing applications. The augmented integration within the existing global navigation system can help in measuring the direction-of-arrival, as well as collecting and distributing accurate location information. For wireless sensing applications, it can enable a new range of opportunities, a wide range of smart sensor applications as experimental platforms for deployment of new technologies. Here, we also examine the implementation of near-space platform (NSP) coverage and associated technologies. Then, a brief integration of communication and navigation services using NSP from a top-level system description of how to relay, associated complementary systems, including radar sensor systems, satellite systems, and terrestrial networks can be used.

Spatio-Temporal Kronecker Compressive Sensing for Traffic Matrix Recovery

A traffic matrix is generally used by several network management tasks in a data center network, such as traffic engineering and anomaly detection. It gives a flow-level view of the network traffic volume. Despite the explicit importance of the traffic matrix, it is significantly difficult to implement a large-scale measurement to build an absolute traffic matrix. Generally, the traffic matrix obtained by the operators is imperfect, i.e., some traffic data may be lost. Hence, we focus on the problems of recovering these missing traffic data in this paper. To recover these missing traffic data, we propose the spatio-temporal Kronecker compressive sensing method, which draws on Kronecker compressive sensing. In our method, we account for the spatial and temporal properties of the traffic matrix to construct a sparsifying basis that can sparsely represent the traffic matrix. Simultaneously, we consider the low-rank property of the traffic matrix and propose a novel recovery model. We finally assess the estimation error of the proposed method by recovering real traffic.

Spectral Matrix Decomposition-Based Motion Artifacts Removal in Multi-Channel PPG Sensor Signals

The intelligent wearable heart rate measurement requirement has attracted more and more attention, and the related applications of Internet of Things are emerging. However, under intensive physical exercises, motion artifacts are strong interference sources for wrist-type photoplethysmography (PPG) sensor signals, thus significantly affecting the accurate estimation of heart rate and other physiological parameters. Currently, how to effectively remove the motion artifacts from PPG sensor signals is becoming an active and challenging research realm. In this paper, we propose a multi-channel spectral matrix decomposition (MC-SMD) model to accurately estimate heart rate in the presence of intensive physical activities. Motivated by the observation that the PPG signal spectrum and the acceleration spectrum have almost the same spectral peak positions in the frequency domain, we first model the removal of motion artifacts as a spectral matrix decomposition optimization problem. After removing motion artifacts, we propose a new spectral peak tracking method for estimating heart rate. Experimental results on the well-known PPG data sets recorded from 12 subjects during intensive movements demonstrate that MC-SMD can efficiently remove the motion artifacts and retrieve an accurate heart rate using multi-channel PPG sensor signals.

QoS constraints-based energy-efficient model in cloud computing networks for multimedia clinical issues

For many applications of multimedia medical devices in clinical and medical issues, cloud computing becomes a very useful way. However, high energy consumption of cloud computing networks for these applications brings forth a large challenge. This paper studies the energy-efficient problem with QoS constraints in large-scale cloud computing networks. We use the sleeping and rate scaling mechanism to propose a link energy consumption model to characterize the network energy consumption. If there is no traffic on a link, we will let it be sleeping. Otherwise, it is activated and we divide its energy consumption into base energy consumption and traffic energy consumption. The former describes the constant energy consumption that exists when the link runs, while the later, which is a quadratic function with respect to the traffic, indicates the relations between link energy consumption and the traffic on the link. Then considering the relation among network energy consumption, number of active links, and QoS constraints, we build the multi-constrained energy efficient model to overcome the high energy consumption in large-scale cloud computing networks. Finally, we exploit the NSF and GEANT network topology to validate our model. Simulation results show that our approach can significantly improve energy efficiency of cloud computing networks.

A Cuckoo Search-Support Vector Machine Model for Predicting Dynamic Measurement Errors of Sensors

Sensors play a very important role in the Internet of Things. Error correction is of great significance to achieve sensor precision. Currently, accurately predicting the future dynamic measurement error is an effective way to improve sensor precision. Aiming to solve the problem of low model accuracy in traditional dynamic measurement error prediction, this paper employs the support vector machine (SVM) to predict the dynamic measurement error of sensors. However, the performance of the SVM depends on setting the appropriate parameters. Hence, the cuckoo search (CS) algorithm is adopted to optimize the key parameters to avoid the local minimum value which can occurs when using the traditional method of parameter optimization. To validate the predictive performance of the proposed CS-SVM model, the dynamic measurement error data for two sensors are applied to establish a predictive model. The root mean squared error and the mean absolute percentage error are employed to evaluate the models performances. These results are also compared with those obtained from the SVM optimized by a grid search and the particle swarm optimization method. The experiments show that the SVM model based on the CS algorithm achieves more accurate prediction and is more effective in predicting dynamic measurement errors for sensors than the previous models.

Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks

Network traffic analysis has been one of the most crucial techniques for preserving a large-scale IP backbone network. Despite its importance, large-scale network traffic monitoring techniques suffer from some technical and mercantile issues to obtain precise network traffic data. Though the network traffic estimation method has been the most prevalent technique for acquiring network traffic, it still has a great number of problems that need solving. With the development of the scale of our networks, the level of the ill-posed property of the network traffic estimation problem is more deteriorated. Besides, the statistical features of network traffic have changed greatly in terms of current network architectures and applications. Motivated by that, in this paper, we propose a network traffic prediction and estimation method respectively. We first use a deep learning architecture to explore the dynamic properties of network traffic, and then propose a novel network traffic prediction approach based on a deep belief network. We further propose a network traffic estimation method utilizing the deep belief network via link counts and routing information. We validate the effectiveness of our methodologies by real data sets from the Abilene and GANT backbone networks.