Ting Zhang

Bio: Ting Zhang is an academic researcher from Tianjin University of Technology. The author has contributed to research in topics: Computer science & Node (networking). The author has an hindex of 18, co-authored 35 publications receiving 1239 citations. Previous affiliations of Ting Zhang include Tangshan College.

New Multi-Hop Clustering Algorithm for Vehicular Ad Hoc Networks

Abstract: As a hierarchical network architecture, the cluster architecture can improve the routing performance greatly for vehicular ad hoc networks (VANETs) by grouping the vehicle nodes However, the existing clustering algorithms only consider the mobility of a vehicle when selecting the cluster head The rapid mobility of vehicles makes the link between nodes less reliable in cluster A slight change in the speed of cluster head nodes has a great influence on the cluster members and even causes the cluster head to switch frequently These problems make the traditional clustering algorithms perform poorly in the stability and reliability of the VANET A novel passive multi-hop clustering algorithm (PMC) is proposed to solve these problems in this paper The PMC algorithm is based on the idea of a multi-hop clustering algorithm that ensures the coverage and stability of cluster In the cluster head selection phase, a priority-based neighbor-following strategy is proposed to select the optimal neighbor nodes to join the same cluster This strategy makes the inter-cluster nodes have high reliability and stability By ensuring the stability of the cluster members and selecting the most stable node as the cluster head in the N-hop range, the stability of the clustering is greatly improved In the cluster maintenance phase, by introducing the cluster merging mechanism, the reliability and robustness of the cluster are further improved In order to validate the performance of the PMC algorithm, we do many detailed comparison experiments with the algorithms of N-HOP, VMaSC, and DMCNF in the NS2 environment

A kind of effective data aggregating method based on compressive sensing for wireless sensor network

Abstract: Wireless sensor network (WSN) in the Internet of Things consists of a large number of nodes. The proposal of compressive sensing technology provides a novel way for data aggregation in WSN. Based on the clustering structure of WSN, a kind of effective data aggregating method based on compressive sensing is proposed in this paper. The aggregating process is divided into two parts: in the cluster, the sink node sets the corresponding seed vector based on the distribution of network and then sends it to each cluster head. Cluster head can generate corresponding own random spacing sparse matrix based on its received seed vector and collect data through compressive sensing technology. Among clusters, clusters forward measurement values to the sink node along multi-hop routing tree. Performance analysis and comparison with the relative methods show that our method is effective and superior to other methods regardless of intra-cluster or inter-cluster on the total energy consumption of network.

Novel approach of distributed & adaptive trust metrics for MANET

Abstract: It is known to all that mobile ad hoc network (MANET) is more vulnerable to all sorts of malicious attacks which affects the reliability of data transmission because the network has the characteristics of wireless, multi-hop, etc. We put forward novel approach of distributed & adaptive trust metrics for MANET in this paper. Firstly, the method calculates the communication trust by using the number of data packets between nodes, and predicts the trust based on the trend of this value, and calculates the comprehensive trust by considering the history trust with the predict value; then calculates the energy trust based on the residual energy of nodes and the direct trust based on the communication trust and energy trust. Secondly, the method calculates the recommendation trust based on the recommendation reliability and the recommendation familiarity; adopts the adaptive weighting, and calculates the integrate direct trust by considering the direct trust with recommendation trust. Thirdly, according to the integrate direct trust, considering the factor of trust propagation distance, the indirect trust between nodes is calculated. The feature of the proposed method is its ability to discover malicious nodes which can partition the network by falsely reporting other nodes as misbehaving and then proceeds to protect the network. Simulation experiments and tests of the practical applications of MANET show that the proposed approach can effectively avoid the attacks of malicious nodes, besides, the calculated direct trust and indirect trust about normal nodes are more conformable to the actual situation.

New Multi-Hop Clustering Algorithm for Vehicular Ad Hoc Networks

Abstract: As a hierarchical network architecture, the cluster architecture can improve the routing performance greatly for vehicular ad hoc networks (VANETs) by grouping the vehicle nodes However, the existing clustering algorithms only consider the mobility of a vehicle when selecting the cluster head The rapid mobility of vehicles makes the link between nodes less reliable in cluster A slight change in the speed of cluster head nodes has a great influence on the cluster members and even causes the cluster head to switch frequently These problems make the traditional clustering algorithms perform poorly in the stability and reliability of the VANET A novel passive multi-hop clustering algorithm (PMC) is proposed to solve these problems in this paper The PMC algorithm is based on the idea of a multi-hop clustering algorithm that ensures the coverage and stability of cluster In the cluster head selection phase, a priority-based neighbor-following strategy is proposed to select the optimal neighbor nodes to join the same cluster This strategy makes the inter-cluster nodes have high reliability and stability By ensuring the stability of the cluster members and selecting the most stable node as the cluster head in the N-hop range, the stability of the clustering is greatly improved In the cluster maintenance phase, by introducing the cluster merging mechanism, the reliability and robustness of the cluster are further improved In order to validate the performance of the PMC algorithm, we do many detailed comparison experiments with the algorithms of N-HOP, VMaSC, and DMCNF in the NS2 environment

V2VR: Reliable Hybrid-Network-Oriented V2V Data Transmission and Routing Considering RSUs and Connectivity Probability

Abstract: Vehicular ad hoc networks (VANETs) have been widely used in intelligent transportation systems (ITSs) for purposes such as the control of unmanned aerial vehicles (UAVs) and trajectory prediction. However, an efficient and reliable data routing decision scheme is critical for VANETs due to the feature of self-organizing wireless multi-hop communication. Compared with wireless networks, which are unstable and have limited bandwidth, wired networks normally provide longer transmission distances, higher network speeds and greater reliability. To address this problem, this paper proposes a reliable VANET routing decision scheme based on the Manhattan mobility model, which considers the integration of roadside units (RSUs) into wireless and wired modes for data transmission and routing optimization. First, the problems of frequently moving vehicles and network connectivity are analyzed based on road networks and the motion information of vehicle nodes. Second, an improved greedy algorithm for vehicle wireless communication is used for network optimization, and a wired RSU network is also applied. In addition, routing decision analysis is carried out in accordance with the probabilistic model for various transmission ranges by checking the connectivity among vehicles and RSUs. Finally, comprehensive experiments show that our proposed method can support real-time planning and improve network transmission performance compared with other baseline protocol approaches in terms of several metrics, including package delivery ratio, time delay and wireless hops.

Novel self-adaptive routing service algorithm for application in VANET

Abstract: As a special MANET (mobile ad hoc network), VANET (vehicular ad-hoc network) has two important properties: the network topology changes frequently, and communication links are unreliable. Both properties are caused by vehicle mobility. To predict the reliability of links between vehicles effectively and design a reliable routing service protocol to meet various QoS application requirements, in this paper, details of the motion characteristics of vehicles and the reasons that cause links to go down are analyzed. Then a link duration model based on time duration is proposed. Link reliability is evaluated and used as a key parameter to design a new routing protocol. Quick changes in topology make it a huge challenge to find and maintain the end-to-end optimal path, but the heuristic Q-Learning algorithm can dynamically adjust the routing path through interaction with the surrounding environment. This paper proposes a reliable self-adaptive routing algorithm (RSAR) based on this heuristic service algorithm. By combining the reliability parameter and adjusting the heuristic function, RSAR achieves good performance with VANET. With the NS-2 simulator, RSAR performance is proved. The results show that RSAR is very useful for many VANET applications.

A kind of effective data aggregating method based on compressive sensing for wireless sensor network

Abstract: Wireless sensor network (WSN) in the Internet of Things consists of a large number of nodes. The proposal of compressive sensing technology provides a novel way for data aggregation in WSN. Based on the clustering structure of WSN, a kind of effective data aggregating method based on compressive sensing is proposed in this paper. The aggregating process is divided into two parts: in the cluster, the sink node sets the corresponding seed vector based on the distribution of network and then sends it to each cluster head. Cluster head can generate corresponding own random spacing sparse matrix based on its received seed vector and collect data through compressive sensing technology. Among clusters, clusters forward measurement values to the sink node along multi-hop routing tree. Performance analysis and comparison with the relative methods show that our method is effective and superior to other methods regardless of intra-cluster or inter-cluster on the total energy consumption of network.

A survey on missing data in machine learning.

Abstract: Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur because of various factors like missing completely at random, missing at random or missing not at random. All these may result from system malfunction during data collection or human error during data pre-processing. Nevertheless, it is important to deal with missing values before analysing data since ignoring or omitting missing values may result in biased or misinformed analysis. In literature there have been several proposals for handling missing values. In this paper, we aggregate some of the literature on missing data particularly focusing on machine learning techniques. We also give insight on how the machine learning approaches work by highlighting the key features of missing values imputation techniques, how they perform, their limitations and the kind of data they are most suitable for. We propose and evaluate two methods, the k nearest neighbor and an iterative imputation method (missForest) based on the random forest algorithm. Evaluation is performed on the Iris and novel power plant fan data with induced missing values at missingness rate of 5% to 20%. We show that both missForest and the k nearest neighbor can successfully handle missing values and offer some possible future research direction.