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For data collection in large-scale wireless sensor networks, dynamic clustering provides a scalable and energy efficient solution which uses cluster head rotation and cluster range assignment algorithms to balance the energy consumption. Nevertheless, most existing works consider the clustering and routing as two isolated issues, which is harmful to the connectivity and energy efficiency of the network.In this paper, we provide detailed analysis on the relations between clustering and routing, and then propose a joint clustering and routing (JCR) protocol for reliable and efficient data collection in large-scale wireless sensor network. JCR adopts the backoff timer and gradient routing to generate connected and efficient inter-cluster topology with the constraint of maximum transmission range. The relations between clustering and routing in JCR are further exploited by theoretical and numerical analysis. The results show that the multi-hop routing in JCR may lead to the unbalanced cluster head selection. Then the solution is provided to optimize the network lifetime by considering the gradient of one-hop neighbor nodes in the setting of backoff timer. Theoretical analysis and simulation results prove the connectivity and efficiency of the network topology generated by JCR.are hard to be satisfied in large-scale WSN. The node has maximum transmission range R max which is bounded by its hardware capability. If the network exists any edge that is longer than R max , the network will be disconnected.Motivated by these observations, we propose a joint clustering and routing protocol (JCR) which adopts backoff timer [12] and gradient routing [20], [21] to generate connected and efficient network topology for data collection in large-scale WSN. Specifically, the major contributions are summarized as follows.1. Detailed analysis on the relations between clustering and routing are provided based on several typical dynamic clustering algorithms. The results show that, if the clustering and routing decisions are made separately, the clustering range R c affects both the connectivity and energy efficiency of the network. In this case, the energy efficiency can hardly be achieved with the constraint of network connectivity.2. The JCR protocol is proposed to build the framework of joint clustering and routing design. Taking advantages of the traffic pattern in data collection scenario, the JCR protocol establishes the gradient field to present the direction through which the sink can be reached. Then the gradient can be exchanged among one-hop neighbors for making the decisions of cluster head selection and routing discovery. With the help of gradient field, JCR ensures the network connectivity with any value of R c . Therefore, in JCR, the R c can be freely adjusted to achieve energy efficiency.3. The relations between clustering and routing in JCR are further exploited by theoretical and numerical analysis. The results show that the multi-hop routing in JCR may lead to unbalanced cluster head selection. Then the solution is ...

Abstract-Traffic monitoring in urban transportation systems can be carried out based on vehicular sensor networks (VSNs). Probe Vehicles (PVs), such as taxis and buses, and Floating Cars (FCs), such as patrol cars for surveillance, can act as mobile sensors for sensing the urban traffic and send the reports to traffic monitoring center (TMC) for traffic estimation. In TMC, sensing reports are aggregated to form traffic matrix, which is used to extract traffic information. Since the sensing vehicles cannot cover all the roads for all the time, TMC needs to estimate the un-sampled data in traffic matrix. As this matrix can be approximated to be of low-rank, Matrix Completion (MC) is an effective method to estimate the un-sampled data. However, our previous analysis on the real traces of taxis in Shanghai reveals that MC methods do not work well due to the uneven samples of PVs, which is common in urban traffic. To exploit the intrinsic relationship between unevenness of samples and traffic estimation error, we study the temporal and spatial entropies of samples and successfully define the important criterion, i.e. average entropy of the sampling process. A new sampling rule based on this relationship is proposed to improve the performance of estimation and monitoring. With the sampling rule, two new patrol algorithms are introduced to plan the paths of controllable FCs to proactively participate in traffic monitoring. By utilizing the patrol algorithms for real dataset analysis, the estimation error reduces from 35% to about 10%, compared with random patrol or interpolation method in traffic estimation. Both the validity of the exploited relationship and the effectiveness of the proposed patrol control algorithms are demonstrated.