Mong-Fong Horng

The new intrusion prevention and detection approaches for clustering-based sensor networks [wireless sensor networks]

In this paper, we propose two approaches to improve the security of clustering-based sensor networks: authentication-based intrusion prevention and energy-saving intrusion detection. In the first approach, different authentication mechanisms are adopted for two common packet categories in generic sensor networks to save the energy of each node. In the second approach, different monitoring mechanisms are also needed to monitor cluster-heads (CHs) and member nodes according to their importance. When monitoring CHs, member nodes of a CH take turns to monitor this CH. This mechanism reduces the monitor time, and therefore saves the energy of the member nodes. When monitoring member nodes, CHs have the authority to detect and revoke the malicious member nodes. This also saves the node energy because of using CHs to monitor member nodes instead of using all the member nodes to monitor each other. Finally, simulations are performed and compared with LEACH, based on an ns2 LEACH CAD tool. The simulation result shows that the proposed approaches obviously extend the network lifetime when the clustering-based sensor network is under attack.

Using Multivariate Regression Model with Least Absolute Shrinkage and Selection Operator (LASSO) to Predict the Incidence of Xerostomia after Intensity-Modulated Radiotherapy for Head and Neck Cancer

Purpose The aim of this study was to develop a multivariate logistic regression model with least absolute shrinkage and selection operator (LASSO) to make valid predictions about the incidence of moderate-to-severe patient-rated xerostomia among head and neck cancer (HNC) patients treated with IMRT. Methods and Materials Quality of life questionnaire datasets from 206 patients with HNC were analyzed. The European Organization for Research and Treatment of Cancer QLQ-H&N35 and QLQ-C30 questionnaires were used as the endpoint evaluation. The primary endpoint (grade 3+ xerostomia) was defined as moderate-to-severe xerostomia at 3 (XER3m) and 12 months (XER12m) after the completion of IMRT. Normal tissue complication probability (NTCP) models were developed. The optimal and suboptimal numbers of prognostic factors for a multivariate logistic regression model were determined using the LASSO with bootstrapping technique. Statistical analysis was performed using the scaled Brier score, Nagelkerke R2, chi-squared test, Omnibus, Hosmer-Lemeshow test, and the AUC. Results Eight prognostic factors were selected by LASSO for the 3-month time point: Dmean-c, Dmean-i, age, financial status, T stage, AJCC stage, smoking, and education. Nine prognostic factors were selected for the 12-month time point: Dmean-i, education, Dmean-c, smoking, T stage, baseline xerostomia, alcohol abuse, family history, and node classification. In the selection of the suboptimal number of prognostic factors by LASSO, three suboptimal prognostic factors were fine-tuned by Hosmer-Lemeshow test and AUC, i.e., Dmean-c, Dmean-i, and age for the 3-month time point. Five suboptimal prognostic factors were also selected for the 12-month time point, i.e., Dmean-i, education, Dmean-c, smoking, and T stage. The overall performance for both time points of the NTCP model in terms of scaled Brier score, Omnibus, and Nagelkerke R2 was satisfactory and corresponded well with the expected values. Conclusions Multivariate NTCP models with LASSO can be used to predict patient-rated xerostomia after IMRT.

A Transmission Power Optimization with a Minimum Node Degree for Energy-Efficient Wireless Sensor Networks with Full-Reachability

Transmission power optimization is the most significant factor in prolonging the lifetime and maintaining the connection quality of wireless sensor networks. Un-optimized transmission power of nodes either interferes with or fails to link neighboring nodes. The optimization of transmission power depends on the expected node degree and node distribution. In this study, an optimization approach to an energy-efficient and full reachability wireless sensor network is proposed. In the proposed approach, an adjustment model of the transmission range with a minimum node degree is proposed that focuses on topology control and optimization of the transmission range according to node degree and node density. The model adjusts the tradeoff between energy efficiency and full reachability to obtain an ideal transmission range. In addition, connectivity and reachability are used as performance indices to evaluate the connection quality of a network. The two indices are compared to demonstrate the practicability of framework through simulation results. Furthermore, the relationship between the indices under the conditions of various node degrees is analyzed to generalize the characteristics of node densities. The research results on the reliability and feasibility of the proposed approach will benefit the future real deployments.

An extensible particles swarm optimization for energy-effective cluster management of underwater sensor networks

Acoustic communication networks in underwater environment are the key technology to explore global ocean. There are major challenges including (1) lack of stable and sufficient power supply, (2) disable of radio frequency signal and (3) no communication protocol designed for underwater environment. Thus, acoustic so far is the only media suitable to operate for underwater communication. In this paper, we study the technology of underwater acoustic communication to support underwater sensor networks. Toward the energy-effective goal, a cluster-based sensor network is assumed. The energy-dissipation of sensor nodes is optimized by biological computing such as Particle Swarm Optimization (PSO). The objective function of sensor node clustering is formulized to constraint on the network coverage and energy dissipation. The problem of dual-objective optimization is solved by the proposed extensible PSO (ePSO). ePSOis an innovation from traditional PSO. The major innovation is to offer an extensible particle structure and to enable more flexible search for optimal solutions in space. The experimental results demonstrate that the proposed ePSO effectively and fast tackles multi-objective optimization problem. The application of ePSO on underwater acoustic communication systems shows the feasibility in real world.

A vehicle license plate recognition system based on spatial/frequency domain filtering and neural networks

In this paper, we develop an intelligent application based neural networks and image processing to recognize license plate for car management. Through the license recognition, the car number composed of English alphabets and digitals is readable for computers. Recognition of license is processed in two stages including feature extraction and recognition. The feature extraction contains the image locating, segmentation of the region of interest (ROI). Then the extracted ROIs are fed to a trained neural network for recognition. The neural network is a three-layer feed-forward neural network. Test images are produced from real parking lots. There are 500 images of car plates with tile, zooming and various lighting conditions, for verification. The experiment results show that the ratio of successful locating of license plate is around 96.8%, and the ratio of successful segmentation is 91.1%. The overall successful recognition ratio is 87.5%. Therefore, the experimental result shows that the proposed method works effectively, and simultaneously to improve the accuracy for the recognition. This system improves the performance of automatic license plate recognition for future ITS applications.

Scheduling Sporadic, Hard Real-Time Tasks with Resources

A new dynamic voltage scaling (DVS) approach to schedule sporadic, hard real-time tasks with shared resource in a power saving way is proposed. Although DVS algorithms have been recognized as a feasible solution to save energy, the scheduling of such tasks has not been fully explored. Thus, in the paper, the problem of power-saving scheduling for sporadic tasks that share a set of serially reusable, single unit software resources is investigated. To evaluate the performance of DVSSR, a real application, Robotic Highway Safety Marker (RSM), is employed. In this application, DVSSR and other DVS algorithms are simulated and compared. Simulation results show that DVSSR offers reasonable trade-off between cost and power savings. In RSM application, DVSSR achieves 92.03% average power savings while comparing with the classic DVS.

A High Energy Efficiency Approach Based on Fuzzy Clustering Topology for Long Lifetime in Wireless Sensor Networks

Fuzzy logic has been successfully applied in various fields of daily life. Fuzzy logic is based on non-crisp set. The characteristic function of non-crisp set is permitted to have to range value between 0 and 1. In a cluster each node is definitely not only belong a cluster but also belong more than a cluster like as the non-crisp set. Therefore, classification cluster in wireless sensor network (WSN) is a complex problem. Fuzzy c-mean algorithm (FCM) is a highly suitable for classification cluster. The paper proposes for integration of Fuzzy Logic Controller and FCM to give a solution to improve the energy efficiency of WSN. Moreover, through the simulation results the lifetime of cluster is increased by more than 55%. The paper shows that the proposed approach has been confirmed that is the better choice of high energy efficiency for longer lifetime in cluster of WSN.

A Driving Behavior Detection Based on a Zigbee Network for Moving Vehicles

In this paper, a scheme of moving-vehicles behavior detection based on a Zigbee network is proposed. Three-axis accelerometers are installed on vehicles to capture the moving vehicle postures. A fuzzy inference system is developed to infer the six basic states of vehicle posture, such as normal driving, left/right turning, departure, accelerate, braking and bumping. Based on the recognition of vehicle postures, the dangerous driving behaviors of vehicle such as serpentuate will be detected. In this paper, the design and development of hardware, vehicle posture measurement and dangerous driving behavior inferences are presented and realized. Additionally, an Android APP is developed to offer human-machine interface. The detection results and GPS information are showed in this developed system. The system sends message to related user if dangerous driving behavior is detected. The detected data is stored to cloud for further application.

Dosimetric advantages of generalised equivalent uniform dose-based optimisation on dose–volume objectives in intensity-modulated radiotherapy planning for bilateral breast cancer

OBJECTIVE We compared and evaluated the differences between two models for treating bilateral breast cancer (BBC): (i) dose-volume-based intensity-modulated radiation treatment (DV plan), and (ii) dose-volume-based intensity-modulated radiotherapy with generalised equivalent uniform dose-based optimisation (DV-gEUD plan). METHODS The quality and performance of the DV plan and DV-gEUD plan using the Pinnacle(3) system (Philips, Fitchburg, WI) were evaluated and compared in 10 patients with stage T2-T4 BBC. The plans were delivered on a Varian 21EX linear accelerator (Varian Medical Systems, Milpitas, CA) equipped with a Millennium 120 leaf multileaf collimator (Varian Medical Systems). The parameters analysed included the conformity index, homogeneity index, tumour control probability of the planning target volume (PTV), the volumes V(20 Gy) and V(30 Gy) of the organs at risk (OAR, including the heart and lungs), mean dose and the normal tissue complication probability. RESULTS Both plans met the requirements for the coverage of PTV with similar conformity and homogeneity indices. However, the DV-gEUD plan had the advantage of dose sparing for OAR: the mean doses of the heart and lungs, lung V(20) (Gy), and heart V(30) (Gy) in the DV-gEUD plan were lower than those in the DV plan (p<0.05). CONCLUSIONS A better result can be obtained by starting with a DV-generated plan and then improving it by adding gEUD-based improvements to reduce the number of iterations and to improve the optimum dose distribution. Advances to knowledge The DV-gEUD plan provided superior dosimetric results for treating BBC in terms of PTV coverage and OAR sparing than the DV plan, without sacrificing the homogeneity of dose distribution in the PTV.

A new scheme of ant colony system algorithm to discovery optimal solution with flip-flop search

In this study, we propose an ACS with flip-flop search strategy to find the route from source node to destination node in an Ad hoc network topology. A flip-flop search strategy is to alternate the search direction towards either high pheromone area or low pheromone area iteratively in the evolution process. The proposed Flip-Flop search strategy effectively solves the pheromone-excess problem in ACS. The ants are allowed to select reverse path to avoid the ants affected by the high pheromone concentration and disable the ability of discover new search area in routing phase. In simulations, the proposed Flip-Flop Ant Colony System (FFACS) is compared with Traditional Ant Colony System in conditions of various deployment densities and topologies of wireless sensor network. The results show that the FFACS has promising ability of discover new search area to reach the better optimal solution than the TACS has. In addition, the robustness and the stability of FFACS are better than TACS.