In recent years, several kinds of wireless network have been widely deployed. In this work, we focus on the e-bus-system for movements of buses. Passengers can get the bus information through the intelligent bus stop when they are waiting for the bus. In today’s environment of Taiwan, the transportation information is provided through some specific Mobile network operators. In this case, it costs a huge expenditure every month for the charge of data transmission. Therefore, we propose a new network system with Digital Mobile Radio (DMR) and Super Wi-Fi to replace the mobile network operator. Furthermore, when the catastrophe happened, the proposed system can also change to be used for the rescues. The implementation of the system architecture, the application of DMR and super WiFi in the proposed system, and the operation scenario are described in this paper.
The wire harness testing system by Fujian Creation Electronics Co., Ltd (FCE) burdens high system installation costs. FCE introduced IEEE 802.15.4 module to develop a wireless sensor network (WSN). However, the WSN designed by FCE contains numerous sensors and each of them needs to upload mass data to the server. This lengthens the time for data collection1, and hence the detection result refresh time becomes too long to be accepted by their customers. Therefore, a solution for the time constraint from the customers of FCE is a challenge to overcome. Thus, we designed an appropriate WSN without the time constraints for the wireless wire harness testing system of FCE by introducing the edge computing methodology to transfer the mass data into a small one. Based on this method, we proposed necessary network protocols and algorithms for the wireless wire harness testing system. The wireless solution proposed in this study significantly reduces labor costs and improves operational efficiency.
Indoor positioning systems based on wireless local area networks are growing rapidly in importance and gaining commercial interest. Pedestrian dead reckoning (PDR) systems, which rely on inertial sensors, such as accelerometers, gyroscopes, or even magnetometers to estimate users' movement, have also been widely adopted for real-time indoor pedestrian location tracking. Since both kinds of systems have their own advantages and disadvantages, a maximum likelihood-based fusion algorithm that integrates a typical Wi-Fi indoor positioning system with a PDR system is proposed in this paper. The strength of the PDR system should eliminate the weakness of the Wi-Fi positioning system and vice versa. The intelligent fusion algorithm can retrieve the initial user location and moving direction information without requiring any user intervention. Experimental results show that the proposed positioning system has better positioning accuracy than the PDR system or Wi-Fi positioning system alone.
Nowadays, Intelligent Transportation Systems (ITS) technologies are exploring a wide range of services such as freeway management, crash prevention & safety, driver assistance, and infotainment of drivers and/or passengers. In this paper, an agile urban parking recommendation service for vehicular intelligent guiding system is designed to facilitate city citizens with fully efficient, real-time and precise parking lot guiding suggestions for the sustainability of the future green city. The system offers drivers a friendly parking lot recommendation sequence and saves driver's time circling around by the accurate prediction of the successful parking probability in each parking lot. The proposed cost model constructs an optimal recommendation sequence considering successful parking probability and time to reach the parking lot. Through the collection and analysis of realistic records from parking lots in Taipei city, a prediction algorithm is developed to estimate the successful parking probability by using current available space counts. Extensive experiments are conducted to demonstrate the effectiveness of the prediction algorithm.
This paper at first details the resident physician scheduling problem which is important for the hospital. The difficulties of resident physician scheduling problem are how to satisfy the safe schedule constraint, the physician specification constraint and the fair schedule constraint simultaneously. To minimize the penalties violating the above constraints, our study has adopted the evolutionary approach. In addition to employ the ordinary genetic operators, we have proposed a new mutation method called dynamic mutation for solving this problem effectively. The experimental result showed that our proposed algorithm performed well in searching optimal schedules. At last, a physician scheduling system has been designed and implemented according to the proposed algorithm.
This paper introduces a novel scheduling problem called the active interval scheduling problem in hierarchical wireless sensor networks for long-term periodical monitoring applications. To improve the report sensitivity of the hierarchical wireless sensor networks, an efficient scheduling algorithm is desired. In this paper, we propose a compact genetic algorithm (CGA) to optimize the solution quality for sensor network maintenance. The experimental result shows that the proposed CGA brings better solutions in acceptable calculation time.
Background . The mode of combining mobile terminals and mobile digital music has gradually entered a bottleneck period while promoting the development of the mobile terminal industry. Proposing personalized solutions for specific groups of people including subhealthy people has become the current direction for the further long‐term development of this mode. Understanding the influencing factors that affect subhealthy people’s selection of mobile digital music is an urgent problem that needs to be solved in the development of personalized solutions. Objective . This article analyzes the influencing factors of the purchase intent to buy mobile digital music for subhealthy people and provides suggestions for mobile terminal vendors on how to design personalized solutions for this group of people. Methods . In order to achieve the above goals, this article constructs an influencing factor model based on the theory of perceived value, collects data by means of questionnaires, and uses the method of structural equations to verify the proposed model. Results . The results show that perceived quality, perceived price, social value, and emotional value all have a significant effect on users’ purchase intention. Meanwhile, the conditional value and epistemic value have a negative moderating effect on the relationship between the social value and subhealthy people’s digital music purchase intention but strengthen the positive relationship between the emotional value and subhealthy people’s digital music purchase intention. Conclusions . The analysis shows that mobile terminal vendors should have their own unique attitudes and feelings when designing personalized mobile digital music solutions for subhealthy people, clarify their position, find strategies to improve the experience of subhealthy people, and win the reputation of subhealthy people. Let subhealthy people have a sense of belongingness.
The hierarchical network structure significantly reduces the size and maintenance cost of routing table for huge networks. But in ad hoc networks, no fixed host leads to the challenge of the hierarchical structure, since the topology information needs to be updated dynamically due to membership changes caused by mobility. To construct the hierarchical structure of physical locations, we adopt a cluster infrastructure to partition the network into different groups for physical location maintenance. In order to construct the hierarchical structure of logical locations, all hosts are divided into several domains, each one of them has one corresponding domain location server to record all of the member physical locations (cluster locations). With the hierarchical structure, most necessary routing information can be ignored.
Due to insufficient available bandwidth resources and the continuously growing demand for cellular communication services, the channel assignment problem has become increasingly important. To trace the optimal assignment, several heuristic strategies have been proposed. So far, most of them focus on the small-scale systems containing no more than 25 cells and they use an anachronistic cost model, which does not satisfy the requirements of most existing cellular operators, to measure the solution quality. Solving the small-scale channel assignment problems could not be applied into existing large scale cellular networks' practice. This article proposes a decomposition approach to solve the fixed channel assignment problem (FCAP) for large-scale cellular networks through partitioning the whole cellular network into several smaller sub-networks and then designing a sequential branch-and-bound algorithm that is made to solve the FCAP for them sequentially. The key issue of partition is to minimize the dependences of the sub-networks so that the proposed heuristics for solving smaller problems will suffer fewer constraints in searching for better assignments. The proposed algorithms perform well based on experimental results and they were applied to the Taiwan Cellular Cooperation (TCC) in ChungLi city to find better assignments for its network.
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