Yu-Wu Wang

A power-efficient routing protocol for underwater wireless sensor networks

Underwater wireless sensor networks have attracted significant attention recently from both academia and industry to explore natural undersea resources and gathering of scientific data in aqueous environments. The nature of an underwater sensor network, such as low bandwidth and large propagation latency, floating node mobility, and power efficiency, is significantly different from traditional ground-based wireless sensor networks. Power-efficient communication protocols are thus urgently demanded in the deployment of underwater sensor networks. In this paper, a routing protocol is developed to tackle these problems in underwater wireless sensor networks. A forwarding node selector is employed to determine the appropriate sensors to forward the packets to the destination, and a forwarding tree trimming mechanism is adopted to prevent excess spread of forwarded packets. The proposed protocol is compared with a representative routing protocol for UWSNs in the literature. The experimental results verify the effectiveness and feasibility of the proposed work.

An adaptive multimedia streaming dissemination system for vehicular networks

An adaptive seamless streaming dissemination system for vehicular networks is presented in this work. An adaptive streaming system is established at each local server to prefetch and buffer stream data. The adaptive streaming system computes the parts of prefetched stream data for each user and stores them temporarily at the local server, based on current situation of the users and the environments where they are located. Thus, users can download the prefetched stream data from the local servers instead of from the Internet directly, meaning that the video playing problem caused by network congestion can be avoided. Several techniques such as stream data prefetching, stream data forwarding, and adaptive dynamic decoding were utilized for enhancing the adaptability of different users and environments and achieving the best transmission efficiency. Fuzzy logic inference systems are utilized to determine if a roadside base station or a vehicle can be chosen to transfer stream data for users. Considering the uneven deployment of BSs and vehicles, a bandwidth reservation mechanism for premium users was proposed to ensure the QoS of the stream data premium users received. A series of simulations were conducted, with the experimental results verifying the effectiveness and feasibility of the proposed work.

A self-healing clustering algorithm for underwater sensor networks

Underwater wireless sensor networks (UWSNs) is a novel networking paradigm to explore aqueous environments. The characteristics of mobile UWSNs, such as low communication bandwidth, large propagation delay, floating node mobility, and high error probability, are significantly different from terrestrial wireless sensor networks. Energy-efficient communication protocols are thus urgently demanded in mobile UWSNs. In this paper, we develop a novel clustering algorithm that combines the ideas of energy-efficient cluster-based routing and application-specific data aggregation to achieve good performance in terms of system lifetime, and application-perceived quality. The proposed clustering technique organizes sensor nodes into direction-sensitive clusters, with one node acting as the head of each cluster, in order to fit the unique characteristic of up/down transmission direction in UWSNs. Meanwhile, the concept of self-healing is adopted to avoid excessively frequent re-clustering owing to the disruption of individual clusters. The self-healing mechanism significantly enhances the robustness of clustered UWSNs. The experimental results verify the effectiveness and feasibility of the proposed algorithm.

Using learning style‐based diagnosis tool to enhance collaborative learning in an undergraduate engineering curriculum

In this study, an intelligent learning style aware diagnosis agent for computer‐supported cooperative learning is proposed. Learners are first assigned to heterogeneous groups based on their learning styles questionnaire given right before the beginning of learning activities on the e‐learning platform. The proposed diagnosis agent then scrutinizes each learners learning portfolio on e‐learning platform and automatically issues feedback messages in case some learners behavior that is unfitted to his/her learning styles or devious argument on discussion board or wiki is detected. The Moodle, an open‐source software e‐learning platform, is used to establish the cooperative learning environment for this study. The experimental results reveal that the proposed learning style aware diagnosis agent indeed boosts the performance of the learners.

Applications of machine learning techniques to a sensor-network-based prosthesis training system

Abstract In the past, the utilization of the limb prosthesis has improved the daily life of amputees or patients with movement disorders. However, a leg-amputee has to take a series of training after wearing a limb prosthesis, and the training results determine whether a patient can use the limb prosthesis correctly in her/his daily life. Limb prosthesis vendors thus desire to offer the leg-amputee a complete and well-organized training process, but they often fail to do so owing to the factors such as the limited support of human resource and financial condition of the amputee. This work proposes a prosthesis training system that the amputees can borrow or buy from the limb prosthesis vendors and train themselves at home. Instant feedback messages provided by the prosthesis training system are used to correct their walking postures during the self-training process. An embedded chip is used as a core to establish a body area sensor network for the prosthesis training system. RFID readers and tags are employed to acquire the 3D positioning information of the amputees limbs in this work to assist in diagnosing the amputees walking problem. A series of simulations were conducted and the simulation results exhibit the effectiveness and practicability of the proposed prosthesis training system.

Application of cellular automata and type-2 fuzzy logic to dynamic vehicle path planning

Abstract Nowadays, most road navigation systems’ planning of optimal routes is conducted by the On Board Unit (OBU). If drivers want to obtain information about the real-time road conditions, a Traffic Message Channel (TMC) module is also needed. However, this module can only provide the current road conditions, as opposed to actually planning appropriate routes for users. In this work, the concept of cellular automata is used to collect real-time road conditions and derive the appropriate paths for users. Notably, type-2 fuzzy logic is adopted for path analysis for each cell established in the cellular automata algorithm. Besides establishing the optimal routes, our model is expected to be able to automatically meet the personal demands of all drivers, achieve load balancing between all road sections to avoid the problem of traffic jams, and allow drivers to enjoy better driving experiences. A series of simulations were conducted to compare the proposed approach with the well-known A* Search algorithm and the latest state-of-the-art path planning algorithm found in the literature. The experimental results demonstrate that the proposed approach is scalable in terms of the turnaround times for individual users. The practicality and feasibility of applying the proposed approach in the real-time environment is thus justified.

An Adaptive Rule-Based Intrusion Alert Correlation Detection Method

Intrusion detection system (IDS) is a security layer that is used to discover ongoing intrusive attacks and anomaly activities in information systems and is usually working in a dynamically changing environment. Although increasing IDSs are developed in the literature, network security administrators are faced with the task of analyzing enormous alerts produced from the analysis of different event streams. The intrusion detection model needs to be continuously tuned in order to reduce correlative alerts and help the administrator to determine accurate and critical attacks. In this work, an alert correlation detection module is proposed to analyze the alerts produced by IDSs and provides a more succinct and overall view of intrusions. An automatically tuned IDS rules generation module based on fuzzy logic technique is used to block the highly correlative alerts. The experimental results reveal that the proposed work is effective in achieving alert reduction and abstraction.

Direction-Sensitive Routing Protocols for Underwater Wireless Sensor Networks

Underwater wireless sensor networks are expected to explore natural undersea resources and gathering of scientific data in aqueous environments. The characteristics of mobile underwater sensor networks, such as low communication bandwidth, large propagation delay, floating node mobility, and high error probability, are significantly different from terrestrial wireless sensor networks. Energy-efficient communication protocols are thus urgently demanded in mobile underwater sensor networks. In this paper, two routing protocols are developed to tackle these problems in underwater wireless sensor networks, in which fuzzy logic inference systems and support vector machines are employed to determine the appropriate sensors to forward the packets to the destination. The proposed protocols are compared with a representative routing protocol for mobile underwater sensor networks in the literature. The experimental results verify the effectiveness and feasibility of the proposed work.

A Direction-Sensitive Routing Protocol for Underwater Wireless Sensor Networks

Underwater wireless sensor networks are expected to explore natural undersea resources and gathering of scientific data in aqueous environments. The characteristics of mobile underwater sensor networks, such as low communication bandwidth, large propagation delay, floating node mobility, and high error probability, are significantly different from terrestrial wireless sensor networks. Energy-efficient communication protocols are thus urgently demanded in mobile underwater sensor networks. In this paper, a routing protocol is developed to tackle these problems in underwater wireless sensor networks, in which fuzzy logic inference system is employed to determine the appropriate sensors to forward the packets to the destination. The proposed protocol is compared with a representative routing protocol for mobile underwater sensor networks in the literature. The experimental results verify the effectiveness and feasibility of the proposed work.

A prediction-based joint bandwidth allocation scheme for heterogeneous wireless networks

With advanced network technologies in recent years, people may connect with different types of networks anytime, anywhere. Since wireless network resource distribution is an important issue, we propose a user mobility prediction algorithm, which considers the coverage of different types of base stations and varied mobility of pedestrians, vehicles, and mass transportation. In addition, a novel bandwidth utilization optimization technique is employed in this work to allocate bandwidth more efficiently. Hybrid genetic algorithm, which combines Genetic Algorithm and the local search to improve the frequency of finding Pareto set, is adopted to realize the optimization problem. The performance of our algorithm is compared to two other state-of-the art approaches in the literature. The simulation results show that our algorithms can achieve desirable performance in terms of network utilization, throughput, and QoS quality in the heterogeneous wireless networks.