In this paper, we investigate a fundamental assignment problem for a type of constrained group service. A group is considered successfully formed only if the number of assigned patrons falls within the specified lower and upper-bound constraints. We introduce the concept of rank-consistent to characterize a useful subset of the assignment problem. Although the problem is still NP-hard, we introduce the technique of allocation vectors to handle the complexity. This technique not only allows us to exploit several characteristics for performance optimization but also enables us to design a generic procedure to obtain the optimal physical assignment under a given allocation vector. We lay the foundation of a 1/2 approximation algorithm and a branch and bound algorithm for seeking the optimal solution. The branch and bound algorithm employs a specific pattern of optimal allocation vector and several newly proposed pruning techniques, especially the one that utilizes the dominance relations between allocation vectors. Extensive experiments demonstrate that our algorithm is much more effective than a set of heuristic greedy algorithms.

The accuracy of earthquake localization is of great importance for earthquake monitoring systems. Traditionally, numerical optimization methods are used to estimate the hypocenter location and the origin time of an earthquake in an iterative manner. The traditional methods usually depend on certain theoretical models, but the geological conditions in practice can be quite different from the presumed models. In this study, an attention-based hypocenter estimation (AHE) model was proposed to locate the hypocenter and origin time of the earthquake. Rather than using the raw waveforms, the phase picking times and the positions of the triggered stations are used as the input. The attention mechanism is adapted to reveal the correlations among the input sequence. An experimental model was trained using data collected from earthquakes in Taiwan in 2016 and 2017 and tested on data in 2018. From the results, AHE is capable of locating the hypocenter with a high degree of accuracy in terms of the distance, depth, and origin time of earthquakes.

Taiwan that is located at the junction of the Eurasian Plate and the Philippine Sea Plate is one of the most active seismic zones in the world. Devastating earthquakes have occurred around the island and have caused severe damages from time to time. To avoid the severe loss, earthquake early warning (EEW) is of great importance, and one of the most critical issues of EEW is fast and reliable detection for the presence of earthquakes. Traditional methods for earthquake detection usually use criterion-based algorithms to detect the onset of the earthquake waves. Currently, the thresholds for those criteria are usually decided empirically and may result in excessive false alarms. Obviously, false alarms can cause undue panics and diminish the credibility of the system. In this article, the recurrent neural network (RNN) models are adopted to develop a real-time EEW system. The developed system is designed to identify the occurrence of an earthquake event, and the duration of the P-wave and the S-wave. It was trained and tested using the seismograms recorded in Taiwan from 2016 to 2017. From the simulation results, the proposed scheme outperforms the traditional criterion-based schemes in terms of detection accuracy and processing time.

Cloud storage provides the great convenience for people to access their data at anytime from any place. Since cloud storage is usually run by the third-party service provider, keyword search over cloud data with privacy protection is of great importance. Many studies in the literature have proposed keyword search scheme for document search, but, in most schemes, the query keywords must exactly match those in the document indexes. However, it is impractical to restrict query keywords provided by the user when performing the search. This paper proposes the scheme for semantic multi-keyword search over encrypted cloud data. Users are able to select query keywords on their own choice. In addition, the query privacy of the user and the security of the documents are protected simultaneously through encrypted document search to prevent snooping from the cloud service provider. Experiments are conducted using a dataset of massive real world papers. The results show that the proposed scheme can effectively perform the semantic multi-keyword search over encrypted cloud data with great efficiency.

Network virtualization is a promising technology for the emerging 5G and cloud computing networks where the virtual network is a logical topology consisting of virtual nodes and virtual links. In network virtualization, how to efficiently assign resources of the physical network to the virtual networks is of great significance and is known as the Virtual Network Embedding (VNE) problem. This paper presents an efficient algorithm tackling with the coordinated VNE problem. Specifically, a Mod-MaxMatch approach is presented which takes the global link resources into considerations when mapping the virtual nodes. Furthermore, a path splitting scheme based on the genetic algorithm is proposed while mapping the virtual links. The proposed algorithm minimizes the redundant reutilization of physical links and mitigates the demand for network bandwidths. A well-known link cost function is used to evaluate the network performance. The experimental results show that the link cost for the proposed approach is reduced by 77% compared to the traditional methodology and by 21% compared to the state-of-art design.

In this paper, we propose a user centric bandwidth allocation scheme for low latency data transmission in ultra dense vehicular networks. Various mobile devices such as mobile phones, sensors of vehicles or autonomous driving systems,require low latency and bandwidth intensive packet delivery between the devices and the Internet aiming to support real-time applications. In the ultra dense vehicular network, small base stations (SBSs) are densely deployed in a fixed geographic area to provides higher date rate. In further, each SBS cooperates with others to form clusters to better support seamless wireless data transmissions, and each mobile device dynamically plans its wireless connectivity for data transmission when it moves in the network. Specifically, each mobile device pre-allocates the amount of bandwidth from a cluster, formed by several SBSs, based on its expected movement, the delay and band-width requirement of the packet transmission and the resource availability of each cluster. Moreover, to effectively utilize the available network resource, each cluster also redistributes its residual bandwidth to the mobile devices pre-allocate bandwidth from it. Consequently, the latency of the data transmission can be better sustained in the ultra dense vehicular network.

Earthquake early warning system uses high-speed computer network to transmit earthquake information to population center ahead of the arrival of destructive earthquake waves. This short (10 s of seconds) lead time will allow emergency responses such as turning off gas pipeline valves to be activated to mitigate potential disaster and casualties. However, the excessive false alarm rate of such a system imposes heavy cost in terms of loss of services, undue panics, and diminishing credibility of such a warning system. At the current, the decision algorithm to issue an early warning of the onset of an earthquake is often based on empirically chosen features and heuristically set thresholds and suffers from excessive false alarm rate. In this paper, we experimented with three advanced machine learning algorithms, namely, K-nearest neighbor (KNN), classification tree, and support vector machine (SVM) and compared their performance against a traditional criterion-based method. Using the seismic data collected by an experimental strong motion detection network in Taiwan for these experiments, we observed that the machine learning algorithms exhibit higher detection accuracy with much reduced false alarm rate.

With the growing popularity of cloud computing, more and more data owners outsource their data to cloud storages because of the convenience for management. Since cloud storages are usually run by third party service providers, data security is definitely an important problem. A simple way to protect the security is to encrypt the data before outsourcing them to the cloud. Some existing techniques provide keyword search over encrypted data. However, most of the techniques use a long index for the documents and do exact keyword search over the dataset. Those techniques may suffer from the computation and communication time as well as the storage space. In this paper, a keyword search over encrypted data scheme is proposed to improve the search performance in terms of computation time and required space. Essentially, the relations between the documents and selected keywords are first analyzed. Then, the principal information in the keyword-document relations is extracted to reduce the document indexes. The relevance of users' query and the documents is quantitatively evaluated to select the final documents of interest. Experiments on real-world dataset show that the proposed scheme effectively improves the computation performance.

Group services are highly important for a variety of computing application domains. In this paper, we study the fundamental problem of allocating a set of service patrons to a set of service groups in an attempt to maximize the total profit gained by the grouping platform. The problem under consideration is unique in that group service is not provided at all unless its lower bound requirement is satisfied. In addition, we allow each service patron to join multiple groups. In this paper, after proving the hardness property of the problem, we focus first on a special case of the problem. To this end, we propose two approaches. One aims at providing a suboptimal solution using a 1/2-approximation algorithm. The other approach turns to seeking an optimal solution using a branch and bound technique. For this purpose, we introduce a theorem that captures a useful property of an optimal allocation. Based on this theorem, we design an efficient branch and bound algorithm to find an optimal solution. We then extend these methods to solve the general problem. Extensive experiments show that our branch and bound algorithm is able to obtain an optimal solution with a small amount of computation time in many different settings.

Target detection is one of the most important topics in wireless sensor networks. Many studies in the literature have addressed the problem of evaluating the performance of a sensor network based on detection probability. However, it is difficult to guarantee detection probability in a sensor network since it depends on the topology of the sensor deployment and the location of the target. A sensor network without a careful sensor location arrangement may experience very low detection probability. This paper integrates collaborative fusion and sequential detection to guarantee the quality of the decisions made by a sensor network and analytically derives the average detection latency based on value fusion and decision fusion. Specifically, sensors periodically report their local measurements or decisions to a fusion center. The fusion center makes final decisions only when both the pre-defined false alarm probability and missing probability are satisfied. Otherwise, it will continue to collect data and repeat the decision making operations. Simple and elegant detection rules are provided for the collaborative sequential detection operations. Extensive simulations are conducted to show the performance of a sensor network in terms of detection latency. The correctness of the analytical results for detection latency is also verified by simulations.

Target detection is an important problem in wireless sensor networks where a number of sensors form a network to detect the presence or absence of a certain target or event. Data fusion is a potential method broadly used to improve detection performance when the sampling data are noisy. However, low detection probability cannot be avoided if detection decisions are made based on a collection of sampling data taken at just one particular moment. This paper adopts fusion-based sequential detection to guarantee the quality of detection results. A fusion center is used to collect local data from individual sensors periodically. A final detection decision is made only after the pre-defined constraints of false alarm and missing probability are satisfied. Rules for each sensor to make local decisions and for the fusion center to make global decisions are derived. Simulations are conducted to show the latency of making the final decisions based on the proposed fusion scheme.

Wireless sensor networks have been used for a variety of purposes such as habitat monitoring, malicious target detection, and climate observation. Conventionally, stationary sensors are used to carry out sensing tasks in a region of interest. However, if the region is much larger than the sensing range of a single sensor, stationary sensor networks could incur many problems such as the complicated reciprocal effects between coverage and communication ranges. This paper proposes a method to collect sensing data by mobile sensors. The method first selects critical sensing locations using detection performance as a metric. The sensing tasks are assigned to a number of mobile sensors. These mobile sensors are arranged to collect sensing data through routes as short as possible. In particular, to balance the load, the routes should be planed in approximately equal in length. An approach, namely Balanced Route Planning (BRP), is developed to arrange mobile sensors' patrolling paths. The load balance condition of BRP is evaluated by simulations. The results show that BRP is a fair and effective scheme for allotting sensing tasks for mobile sensors.

This paper investigates target detection problem in wireless sensor networks. Sensors carry out sensing operations and make consensus decisions about the presence or absence of a target or event. Most of previous studies for target detection either assume an unrealistic disk model for making detection decision or provide complicated numerical methods to evaluate detection performance. This paper develops the Uniformly Most Powerful(UMP) detector based on likelihood ratio test and derives simple and elegant test rules for target presence and absence. Moreover, detection performance measured by missing rate is also derived analytically. Simulations are conducted to show the performance of the UMP detector compared to a detector developed previously based on value fusion. The results show that the proposed detector dramatically outperforms the value fusion detector even in vulnerable locations.

This work considers data query applications in tree-structured networks, where a given set of source nodes generate (or collect) data and forward the data to some halfway storage nodes for satisfying queries that call for data generated by all source nodes. The goal is to determine an optimal set of storage nodes that minimizes overall communication cost. Prior work toward this problem assumed homogeneous channel cost, which may not be the case in many network environments. We generalize the optimal storage problem for a tree-structured network by considering heterogeneous channel costs. The necessary and sufficient conditions for the optimal solution are identified, and an algorithm that incurs a linear time cost is proposed. We have also conducted extensive simulations to validate the algorithm and to evaluate its performance.

Sensor deployment is an important issue for surveillance networks especially when obstacles are present in the monitored region. An intruder can hide beyond obstacles or stay at a low-covered location in order to reduce the probability of being detected. A good sensor deployment should maximize the worst-case detection probability over all possible locations of the intruder. Many heuristics have been proposed in the literature for sensor placement to achieve better detection performance. However, none of them deploy sensors based on the optimal distribution of sensors tailored for the given terrain. In this paper, a sensor deployment approach is proposed using the optimal sensor distribution as a reference. The optimal sensor distribution for the given terrain is first calculated and, then, a clustering-based approach is developed to guide sensors to appropriate locations. The effectiveness of the proposed approach is shown by simulations for regions with and without obstacles. The final deployments show that some sensors' final locations are very close to the obstacles. This is in contrast to the conventional assumption used in many previous studies.

Detection latency, which is defined as the time from the target arrival to the time of the first detection, is an important metric for the performance of sensor networks carrying out target detection, especially when the target is malicious or hostile. It characterizes the efficiency of detecting the presence of a target in a region of interest. Traditionally, stationary sensor networks are used to perform such sensing tasks. Consequently, nearly all research literature for the target detection problem has focused on stationary sensor networks. This paper addresses the problem of detecting the presence/absence of a target using a mobile sensor network. An analytic method is proposed to model the detection latency based on a collaborative sensing architecture. Detection latency for different node mobility models is presented. The accuracy of the analytic model is verified by simulations. This paper also compares the performance of mobile and stationary sensor networks. The comparison shows that if the target is present at the worst possible location in a given deployment, then detection latency of mobile sensor networks is considerably shorter as compared to that of stationary networks with the same number of nodes.

Detecting the presence/absence of an object in a region of interest is one of the important applications for sensor networks. A considerable amount of work has been seen in the literature for detecting events or objects using wireless sensor networks. Most of the prior work uses a simple binary detection model or an average signal strength model to make decisions of detection. Such methods are not optimal in terms of detection probability. This paper derives a detection approach which is optimal in the sense of Neyman-Pearson test and shows that the detection performance of the traditional average based method is much lower than the optimal. To reduce power consumption and communication cost, a localized fusion method is also developed by carefully selecting sensors in the vicinity of a target location. The paper shows that the localized fusion can dramatically reduce the number of sensors participating the fusion while maintain high detection performance.

Sensor networks have been envisioned to enhance the ability of human beings in observing the environment and understanding the world. A potential application of a sensor network is to detect the presence or absence of a target in a region of interest. Many heuristics have been proposed in literature for placing sensors to achieve better coverage in the monitored region. However, none of them guarantee an optimal sensor deployment especially when there are obstacles in the region. Unlike the prior work, this paper focuses on the problem of determining the optimal sensor distribution in a region with or without obstacles. The detection performance is characterized using a metric called ldquoexposurerdquo, which is defined as the least probability of detecting a target over all possible target locations subject to a fixed false alarm probability. A linear programming based approach is proposed to find the optimal sensor distribution by maximizing the exposure in a given region with or without obstacles. The optimal sensor distribution can also be used as weights of sensor measurements taken at different locations for decision-making.

An envisioned usage of sensor networks is in surveillance systems for detecting a target or monitoring a physical phenomenon in a region. Traditionally, stationary sensor networks are deployed to carry out the sensing operations. In many applications, if the monitored region is relatively large compared to the sensing range of a node, a large number of nodes are required in the region to achieve high coverage. Using mobile nodes in such situations can be an attractive alternative. Mobility of sensor nodes has been studied in sensor networks for many purposes such as power saving, data collection, and packet delivery. However, nearly all research literature for the target detection problem has focused on stationary sensor networks. This paper investigates the problem of detecting the presence/absence of a target using mobile sensor networks. It presents an analytic method to evaluate the detection latency based on a collaborative sensing approach using nodes with uncoordinated mobility. We verify the analytic model through simulations. The analytic method provides a simple way of analyzing the tradeoff between number of nodes and detection latency in a mobile sensor network. The analysis is also used to compare the performance of mobile and stationary sensor networks with respect to these measures. Results show that if the target is present at the worst possible location in a given deployment, then detection latency of mobile sensor networks is considerably less as compared to that of stationary networks with the same number of nodes.

Sensor networks possess the inherent potential to detect the presence of a target in a monitored region. Although a stationary sensor network is often adequate to meet application requirements, it is not suited to many situations, for example, a huge number of nodes are required to monitor a large region. In such situations, mobile sensor networks can be used to resolve the communication and sensing coverage problems. This paper addresses the problem of detecting a target using mobile sensor networks. One of the fundamental issues in target detection problems is exposure, which measures how the region is covered by the sensor network. While traditional studies focus on stationary sensor networks, this paper formally defines and evaluates exposure in mobile sensor networks with the presence of obstacles and noise. To conform with practical situations, detection is conducted without presuming the target's activities and moving directions. As there is no fixed layout of node positions, a time expansion technique is developed to evaluate exposure. Since determining exposure can be computationally expensive, algorithms to calculate the upper and lower bounds on exposure are developed. Simulation results are also presented to illustrate the effectiveness of the algorithms