Chi-Cheng Chuang

A new spatial IP assignment method for IP-based wireless sensor networks

Wireless sensor networks (WSNs), one of the commercial wireless mesh networks (WMNs), are envisioned to provide an effective solution for sensor-based AmI (Ambient Intelligence) systems and applications. To enable the communications between AmI sensor networks and the most popular TCP/IP networks seamlessly, the best solution model is to run TCP/IP directly on WSNs (Mulligan et al. 2009; Hui and Culler 2008; Han and Mam 2007; Kim et al. 2007; Xiaohua et al. 2004; Dunkels et al. 2004; Dunkels et al. 2004; Dunkels 2001; Dunkels et al. 2004). In this case, an IP assignment method is required to assign each sensor node a unique IP address. SIPA (Dunkels et al. 2004) is the best known IP assignment method that uses spatial relations and locations of sensor nodes to assign their IP addresses. It has been applied in Contiki (Dunkels et al. 2004), a famous WSN operating system, to support the 6LowPAN protocol. In Chang et al. (2009), we proposed the SLIPA (Scan-Line IP Assignment) algorithm to improve the assignment success rate (ASR) obtained by SIPA. SLIPA can achieve a good ASR when sensor nodes are uniformly distributed. However, if sensor nodes are deployed by other distributions, the improvements would be limited. This paper proposes a new spatial IP assignment method, called SLIPA-Q (SLIPA with equal-quantity partition), to improve SLIPA. Experiments show that, by testing the proposed method 1,000 times with 1,000 randomly deployed sensor nodes, the average ASR obtained by SLIPA-Q is over two times of that obtained by SLIPA. Under the same 88% ASR, the average numbers of sensor nodes those can be successfully assigned by SLIPA-Q, SLIPA, and SIPA are 950, 850, and 135, respectively. Comparing to previous spatial IP assignment methods, SLIPA-Q can achieve dramatic improvements in ASR for assigning IP addresses to a large set of sensor nodes.

Error-Bounded Data Compression Using Data, Temporal and Spatial Correlations in Wireless Sensor Networks

Due to the power is limited on each node of wireless sensor networks (WSNs), a data compression mechanism is required to achieve the purpose of power saving. In this paper, an efficient data compression method is proposed to reduce the size of transmission data under the given error bound. We first apply the observed transmission data to construct a static Huffman codebook which is related to the data correlation of the monitoring environment. Given an error bound, the proposed method determines whether the new sensed data should be sent or not by comparing it with the reference data such as the previous sensed data (for temporal correlation), the neighboring sensed data (for spatial correlation) and the codebook encoded data (for data correlation). Thus, the total size of transmission data can be minimized for power saving. Simulation results show that the proposed method can make WSNs more efficient in energy consumption. Even the error bound is set as a small value (under 0.009), the proposed method can reduce a lot of the transmission data (over 65%) to cut down the total energy consumption. Comparing to DF-TS, our improvement is nearly 70% in the total energy consumed

Three-Dimensional Location-Based IPv6 Addressing for Wireless Sensor Networks in Smart Grid

Smart grid is one of the most important applications of the Internet of Things (IoT) for environmental sustainability and energy efficiency issues in recent years. IP-based wireless sensor networks (IP-WSNs) are considered as one of the promising wireless communication technologies applied in smart grid for providing pervasive communications and control capabilities at low cost as well as connecting metering devices with wired area network (WAN) infrastructures seamlessly without a requirement for deploying proxies. In addition, the IPv6 Internet has become an inevitable trend for all-IP communication because of its large address space and others advantages over IPv4. One of the main challenges for connecting WSNs and IPv6 Internet is IPv6 address configuration since nodes with unique address are a prerequisite for reliability and end to end communication. Hence, an IPv6 address configuration scheme called MPIPA is proposed in this paper. MPIPA utilizes three-dimensional locations coordinates to assign each node a unique spatial IPv6 address based on grouping methods and scan-line scheme. Besides, Assignment Success Rate (ASR) is used in this paper to evaluate the probability that assigns unique IP address to nodes successfully. The simulation results show that MPIPA enables over 8, 000 nodes to be assigned IP address successfully when ASR is still nearly 90%.

Scan-Line IP Assignment for Wireless Sensor Networks

Connecting wireless sensor networks (WSN) with TCP/IP is an important issue in many WSN applications. Recently, there are many solutions proposed to integrate WSN and TCP/IP network [1-5]. Running the TCP/IP protocol directly on WSN is one of the most important solution models. In this model, a unique IP address is needed for every node in WSN. The spatial IP assignment (SIPA) method enables every sensor node to have an IP address by constructing address via node location [6-7]. However, it does not guarantee to assign a unique IP address to every no1de. This paper proposes the scan-line IP assignment (SLIPA) method which makes sure that not only every sensor node will generate a unique IP address but also the spatial relation between nodes is maintained after assignment. Our experiments show that SLIPA can achieve dramatic improvements in the maximum number of nodes that can be successfully assigned under different distribution patterns of nodes.

Video-Like Compression for High Efficiency Database Storage of Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consist of group sensor nodes which are placed in an area to monitor the changes of environment. Usually, sensing data are gathered and stored in a data server which maintains a database to organize and manage numerous of WSNs data. It allows researchers to retrieve these data for further study or analysis. Since the size of WSNs data is huge and the storage resource is limited, this database needs compression to lower the data size. In this paper, we propose a video-like compression method for high efficiency database storage of WSNs. First, the raw data are arranged according to the spatial correlation as an image frame. Then, several image frames with temporal correlation are maintained as a sequence of frames and lossless video compression is adopt for lowering the data size. Based on this idea, we also propose a data retrieve/query algorithm for parallel processing. The trade-off between space saving and query time is discussed after experiencing with real-world data. At last, we compare our proposed method to MySQL, a well-known database which compression is supported. The experimental results reveal that our method achieves over 96% of the space savings. It is over 13% more than that achieved by MySQL.

Comparing LR, GP, BPN, RBF and SVR for Self-Learning Pattern Matching in WSN Indoor Localization

It is a challenging issue to apply WSN (Wireless Sensor Network) to achieve accurate location information. PM (Pattern Matching), known as one of the most famous localization methods, has the drawback of requiring high initialization effort to predict/train MF (Matching Function). In this paper, the authors propose SPM (Self-learning PM) to improve not only the localization accuracy but also the initialization effort of PM. SPM applies a divide-and-conquer self-learning scheme to reduce the number of training patterns in training. Additionally, it introduces a Bayesian filtering scheme to remove the noise signal caused by multipath effects so as to enhance localization accuracy accordingly. This paper applies different training methods (linear regression, Gaussian process, backpropagation network, radial basis function, and support vector regression) to evaluate the performances of SPM and PM in a complicated indoor environment. Experiments show that SPM is better than PM for all training methods applied. SPM can use up to 72% fewer training patterns than PM to achieve the same localization accuracy. If the same number of training patterns is utilized, SPM can achieve up to 58% higher localization accuracy than PM.