Ching-Ju Lin

HCDD: hierarchical cluster-based data dissemination in wireless sensor networks with mobile sink

Finding the routing path for disseminating data to mobile sinks in the wireless sensor networks is a challenging problem due to the random mobility of sinks and the limited resources of sensors, such as energy, storage capacity, and computing capability. Although flooding the location information of mobile sinks seems to be a naive method to find the path between the data source and the mobile sink, it drains much power of the sensor nodes. Thus, we propose a Hierarchical Cluster-based Data Dissemination scheme, named HCDD, to disseminate data to the mobile sink with light control overhead. In HCDD, the sensor nodes are self-organized to find the route without the knowledge of nodes location information. That is, unlike other works, the HCDD can operate without any expensive and power-consuming GPS device used for estimating the location information. The simulation results show that our HCDD scheme can greatly alleviate the control overhead, and, at the same time, achieve longer network lifetime and comparable number of received data with previous works, such as the TTDD-like methods.

Performance Study of Optimal Routing and Channel Assignment in Wireless Mesh Networks

In multi-channel wireless mesh networks (WMNs), the routing and channel assignment can interdependently determine network capacity. However, joint routing and channel assignment (JRC) is a challenging problem since it must satisfy the Radio Constraint, the Channel Constraint, and the Co-channel Interference Constraint. Recently, many heuristic or suboptimization algorithms have been proposed to address the JRC problem. The aim of this paper is to preform a comprehensive performance study to fairly compare the different approaches. To achieve this goal, a proper benchmark, a mixed integer linear programming (MILP) model that can optimally configure the routing and channel assignment to get maximum achievable network capacity, is provided in this work as well. The proposed MILP model can help system designers quantitatively evaluate existing heuristic methods and make a better decision by considering the tradeoff between the characteristics of various schemes according to the system requirements.

A trend-loss-density-based differential scheme in wired-cum-wireless networks

Since interests in multimedia services have been growing with the proliferation of wireless networks, there is a need to provide an efficient loss-differential scheme for the wired-cum-wireless networks. This is important because with the aid of the differential scheme, the congestion control protocol can tell the packet loss due to the error-prone wireless link or the network congestion. Then, the congestion control protocol will avoid reducing the sending rate blindly in the present of the packet loss. In this work, we propose an end-to-end loss-differential approach, called Trend-and-Loss-Density-based (TD) scheme. Since the congestion losses are highly co-related to each other, the TD scheme considers (a) the trend to indicate where the packet loss happens, and (b) the loss density to examine how often the packet loss occurs. Specifically, we use the autocorrelation function to measure the dense degree of a packet loss in a given sequence. Moreover, the threshold of each trend in the TD scheme is decided according to the characteristics of the wireless channel so it is easily adapted to different wireless network situations. We evaluate the TD scheme via extensive simulations in ns2. The results show that compared with other differential schemes, the TD scheme can substantially improve wireless and overall misclassification rates while maintaining a comparable throughput in all experiments.

Route-Aware Load-Balanced Resource Allocation for Wireless Mesh Networks

Multi-channel wireless mesh networks (WMNs) aim to perform ubiquitous wireless broadband network access. In WMNs, much attention has been paid to the problem of resource allocation, i.e., how to utilize multiple orthogonal channels and multiple communication radios to enhance the aggregate throughput efficiently. Routing and resource allocation correlatively determine the performance of WMNs. Thus, this paper proposes a route-aware resource allocation algorithm to distribute total traffic load over diverse radios and channels based on route information. The simulation results show that route-aware resource allocation can balance the workload among all available resources (radios and channels) and achieve the higher aggregate throughput and fairness.

Distributed flexible channel assignment in WLANs

Flexible channels enable Access Points (AP) and clients to determine channel widths and their center frequencies. The key challenge in flexible channels is how to determine proper channel width and center frequency for each link demands such that the overall system throughput could be improved. In this work, we introduce a distributed flexible channel assignment algorithm, DFCA. Different from prior researches, DFCA does link-based channel assignment instead of AP-based or packet-based. This is because that the packet-based method could incur heavy overhead, while the AP-based method is not able to determine the interfering relation between links precisely. In addition, the transmission weight of a link is proposed; with the help of the transmission weight, channel width, center frequency and interference relation to neighboring nodes, DFCA is able to accurately predict that link throughput prior to the channel assignment. Results show that DFCA could outperform traditional fixed channel assignment approaches and the AP-based flexible channel assignment algorithm. Even compared with FLUID[1], which is a packet-based channel assignment approach with assist of a central controller, DFCA is able to achieve comparable (at least 93%) throughput by using only local information rather than global knowledge.

A fast and accurate characteristic-based rate-quantization model for video transmission

In this paper, we analyze the limitation of ρ-domain based rate-quantization (R-Q) model. We find out that a characteristic-based R-Q model can be derived from ρ-domain to q-domain. Experimental data show that such a characteristic-based R-Q model can provide a more accurate estimation of the actual bitrate than existing models for both frame-level and macroblock(MB)-level. In addition, a simple analysis of computational complexity of our quantization-free characteristics extraction framework shows that our model is faster than existing variance and ρ-domain based R-Q models.