Xiaoling Wu

RSSI and LQI Data Clustering Techniques to Determine the Number of Nodes in Wireless Sensor Networks

With the rapid proliferation of wireless sensor networks, different network topologies are likely to exist in the same geographical region, each of which is able to perform its own functions individually. However, these networks are prone to cause interference to neighbor networks, such as data duplication or interception. How to detect, determine, and locate the unknown wireless topologies in a given geographical area has become a significant issue in the wireless industry. This problem is especially acute in military use, such as spy-nodes detection and communication orientation systems. In this paper, three different clustering methods are applied to classify the RSSI and LQI data recorded from the unknown wireless topology into a certain number of groups in order to determine the number of active sensor nodes in the unknown wireless topology. The results show that RSSI and LQI data are capable of determining the number of active communication nodes in wireless topologies.

Touchware: a software-based technique for high-resolution multi-touch sensing devices

Finger pointing on touch screens is a very natural way of human computer interaction. However, for many capacitive touch sensing devices, it may suffer from the nature of direct input since the size of human fingers and the lack of sensing precision make absolute positioning on touch screen difficult, especially on multi-touch sensing devices. Even if high-resolution/precision multi-touch devices become popular in the market, the cost is high and the positioning algorithm is not a flexible module. In this paper, we present Touchware, a software-based technique to overcome these limitations with low cost and to provide support for the development of multi-touch applications for rich input modalities. We introduce the maxima-based clustering algorithm and weight-based geometric algorithm for accurate finger positioning and first-contact-based decision with sniper for ghost pattern elimination. We evaluated the performance and show how the techniques can be successfully used for single-touch and multi-touch applications.

An optimal slicing strategy for SDN based smart home network

Software Defined Network (SDN) has long been a research focus since born from the lab of Stanford University. Researches on traditional home networks are faced with a series of challenges due to the ever more complicated user demands. The application of SDN to the home network is an effective approach in coping with it. Now the research on the SDN based home network is in its preliminary stage. Therefore, for better user experience, it is essential to effectively manage and utilize the resources of the home network. The general slicing strategies dont show much advantage in performance within the home networks due to the increased user demands and applications. In this paper, we introduce an advanced SDN based home network prototype and analyze its compositions and application requirements. By implementing and comparing several slicing strategies in properties, we achieve an optimized slicing strategy according to the specified home network circumstance and our preference.

Energy-Efficient Routing Algorithms Based on OVSF Code and Priority in Clustered Wireless Sensor Networks

Energy awareness is a vital design issue in wireless sensor networks. Since the amount of sensing data may be large and sensor nodes are usually battery-powered, it is critical to design energy-efficient routing algorithms to prolong network lifetime. Given a certain sensor deployment, the routing strategy of sensor data would have profound effects on the communication cost. In this paper, based on low-energy adaptive clustering hierarchy (LEACH) series protocols which are low-energy consumption adaptive clustering routing protocols, we propose the OVSF mechanism based routing protocol and EERPP (Energy-Efficient Routing Protocol based on Priority). Simulation results of OVSF mechanism based protocol and EERPP demonstrate a significant improvement on the network metrics such as the lifetime and the end-to-end delay.

A Survey on Sensor Deployment in Underwater Sensor Networks

Underwater sensor networks are becoming a new field, mainly applied for ocean data collection, ocean sampling, environmental and pollution monitoring, etc. Similar to terrestrial sensor networks, it is essential to provide communication coverage in such a way that the whole monitoring area is covered by the sensor nodes in UWSN. Many important deployment strategies for terrestrial sensor networks have been proposed, most of which cannot be directly applied to UWSN due to its unique 3D characteristics. This paper surveys the different deployment algorithms that can be applied to the domain of UWSN, classified into 3D underwater sensor networks, 2D underwater sensor networks and gateway node deployment. Different schemes are compared and their advantages and disadvantages are discussed.

A Dynamic Underwater Sensor Network Architecture Based on Physical Clustering and Intra-cluster Autonomy

In this paper, a dynamic architecture is presented for underwater sensor networks based on the physical clustering and intra-cluster autonomy according to the traditional logical clustering theory. In this architecture, the cluster headers eliminate the negative effects of the current and improve the stability of the underwater sensor networks by drawing cluster nodes to do circular motion through cluster cables. This wired communication inside cluster and wireless communication among clusters improve the speed of the data transmission and delivering. On the other hand, in order to solve energy-intensive problem, an energy harvesting technology is considered to provide nodes with sustainable energy. This dynamic underwater sensor network architecture provides a basic model to study the high-performance and reusable underwater sensor networks.

Optimal Fusion Set based Clustering in WSN for continuous objects monitoring

Based on the application of continuous objects monitoring (COM) for Wireless Sensor Networks (WSNs), the sampling data collected by the sensors have relatively higher correlation and continuity due to the reason that the characteristic parameters of the monitored objects are continuous both in time and in space. In this paper, an Optimal Fusion Set based Clustering (OFSC) algorithm is presented to address the network clustering problem when monitoring the continuous objects. Different from the traditional clustering algorithms which cluster the network only after the cluster heads have been determined, OFSC algorithm, based on the global routing protocol in which the entire network information can be acquired by each node, the cluster head selection is carried out individually by nodes after the clustering results are firstly determined according to the Optimal Fusion Set theory. The performance evaluation results show that our OFSC algorithm can remarkably reduce the data traffic. On the other hand, our OFSC algorithm is a distributed clustering algorithm, which eliminates the computation of the communication cost during the cluster head selection, hence, decreases the computational complexity. Moreover, compared with the traditional LEACH and LEACH-C, our results show that the energy consumption of the entire network can be better balanced when monitoring the continuous objects.

Improving WSNs sleep scheduling mechanism with SDN-like architecture

We propose a SDN-like architecture based WSN and improve the existing EC-CKN Sleep Scheduling mechanism to implement more efficient energy management. A SDN-like architecture is adopted instead of traditional WSN architecture and EC-CKN algorithm is applied as the fundamental algorithm. This paper presents the design, implementation and evaluation of the proposed SDN-ECCKN on the WSN with SDN-like architecture.

A Granulometric Size Distribution based intrusion detection method for WSN

Security issue in Wireless Sensor Networks (WSNs) has long been the topic of extensive research in the last decade. Data encryption and authentication are the most common methods to address the security issues in WSN. However, these efforts are ineffective in detecting the diverse malicious attacks, especially in intrusion detection. Comparatively, very few attentions have been paid for detecting intrusive nodes in a common WSN topology. Therefore, in this paper we derive an innovative method called GSD method for detecting malicious attack in WSNs, such as intrusion detection. Link Quality Indicator (LQI) data of each node are utilized as the network parameters in this method. The simulation results show their effectiveness in intrusion detection.

Horizontal hierarchy slicing based data compression for WSN

Data compression on sensing data in Wireless Sensor Networks (WSNs) has long been the topic of extensive research in the last decade. Especially, in video sensor networks, the video and image data that need to be transmitted are relatively larger than common data. However WSNs usually have limited power supply and constrained communication bandwidth, it is significant to reduce the video and image data without any distortion before transmission to lower the energy consumption and the transmission delay. Therefore, in this paper we propose Horizontal Hierarchy Slicing (HHS) method based on Mathematical Morphology technology for compressing the data in WSNs. The results show its effectiveness in data compression for video and image data.