SingLing Lee

Routing Protocols in Vehicular Ad Hoc Networks: A Survey and Future Perspectives

Vehicular Ad hoc Network (VANET), a subclass of mobile ad hoc networks (MANETs), is a promising approach for the intelligent transportation system (ITS). The design of routing protocols in VANETs is important and necessary issue for support the smart ITS. The key difference of VANET and MANET is the special mobility pattern and rapidly changeable topology. It is not effectively applied the existing routing protocols of MANETs into VANETs. In this investigation, we mainly survey new routing results in VANET. We introduce unicast protocol, multicast protocol, geocast protocol, mobicast protocol, and broadcast protocol. It is observed that carry-and-forward is the new and key consideration for designing all routing protocols in VANETs. With the consideration of multi-hop forwarding and carry-and-forward techniques, min-delay and delay-bounded routing protocols for VANETs are discussed in VANETs. Besides, the temporary network fragmentation problem and the broadcast storm problem are further considered for designing routing protocols in VANETs. The temporary network fragmentation problem caused by rapidly changeable topology influence on the performance of data transmissions. The broadcast storm problem seriously affects the successful rate of message delivery in VANETs. The key challenge is to overcome these problems to provide routing protocols with the low communication delay, the low communication overhead, and the low time complexity. The challenges and perspectives of routing protocols for VANETs are finally discussed.

A Mobicast Routing Protocol in Vehicular Ad-Hoc Networks

In this paper, we present a spatiotemporal multicast, called a mobicast, protocol for supporting applications which require spatiotemporal coordination in VANETs. The spatiotemporal character of a mobicast is to forward a mobicast message to vehicles located in some geographic zone at time t, where the geographic zone is denoted as zone of relevance (ZOR). Vehicles located in ZOR at the time t must keep the connectivity to maintain the real-time data communication between all vehicles in ZOR. The temporal network fragmentation problem is occurred if the connectivity of ZOR is lost such that vehicles in ZOR cannot successfully receive the mobicast messages. To solve the problem, a new mobicast protocol is presented in this work to successfully disseminate mobicast messages to all vehicles in ZOR via a special geographic zone, called as zone of forwarding (ZOF). The main contribution of this work is to develop a new mobicast routing protocol to dynamically estimate the accurate ZOF to successfully disseminate mobicast messages to all vehicles in ZOR. To illustrate the performance achievement, simulation results are examined in terms of dissemination successful rate, Packet overhead multiplication, and packet delivery delay.

A multi-class SVM classification system based on learning methods from indistinguishable chinese official documents

Highlights? Documents of which contents are indistinguishable are improperly classified. ? Indistinguishable documents are identified by utilizing SVM classification models. ? Applying indistinguishable documents for the training set is effective to improve SVM classification accuracy. Support Vector Machines (SVM) has been developed for Chinese official document classification in One-against-All (OAA) multi-class scheme. Several data retrieving techniques including sentence segmentation, term weighting, and feature extraction are used in preprocess. We observe that most documents of which contents are indistinguishable make poor classification results. The traditional solution is to add misclassified documents to the training set in order to adjust classification rules. In this paper, indistinguishable documents are observed to be informative for strengthening prediction performance since their labels are predicted by the current model in low confidence. A general approach is proposed to utilize decision values in SVM to identify indistinguishable documents. Based on verified classification results and distinguishability of documents, four learning strategies that select certain documents to training sets are proposed to improve classification performance. Experiments report that indistinguishable documents are able to be identified in a high probability and are informative for learning strategies. Furthermore, LMID that adds both of misclassified documents and indistinguishable documents to training sets is the most effective learning strategy in SVM classification for large set of Chinese official documents in terms of computing efficiency and classification accuracy.

Certainty-based active learning for sampling imbalanced datasets

Active learning is to learn an accurate classifier within as few queried labels as possible. For practical applications, we propose a Certainty-Based Active Learning (CBAL) algorithm to solve the imbalanced data classification problem in active learning. Without being affected by irrelevant samples which might overwhelm the minority class, the importance of each unlabeled sample is carefully measured within an explored neighborhood. For handling the agnostic case, IWAL-ERM is integrated into our approach without costs. Thus our CBAL is designed to determine the query probability within an explored neighborhood for each unlabeled sample. The potential neighborhood is incrementally explored, and there is no need to define the neighborhood size in advance. In our theoretical analysis, it is presented that CBAL has a polynomial label query improvement over passive learning. And the experimental results on synthetic and real-world datasets show that, CBAL has the ability of identifying informative samples and dealing with the imbalanced data classification problem in active learning.

A mobicast routing protocol in underwater sensor networks

In this paper, we investigate a mobicast, also called a mobile geocast, problem in three-dimensional (3-D) underwater sensor networks (USNs), which aims to overcome the hole problem and minimizes the energy consumption of the sensor nodes while maximizing the data collection. In this paper, all underwater sensor nodes are randomly distributed in a 3-D underwater environment in the sea to form a 3-D USN. Considered a mobile sink or an autonomous underwater vehicle (AUV), all possible sensor nodes near the AUV form a 3-D geographic zone called a 3-D zone of reference (3-D ZOR). The AUV travels a user-defined route and continuously collects data from sensor nodes within a series of 3-D ZORs at different times. The main problem is how to efficiently collect data from sensor nodes within a 3-D ZOR while those sensor nodes are usually in sleep mode for a long period. The routing protocol relies on two phases: the first phase consists of collecting data within a 3-D ZOR, and the second phase consists of waking up those sensor nodes in the next 3-D ZOR to be queried while trying to avoid topology holes. To save power, only sensor nodes in a 3-D ZOR are notified to enter the active mode in order to deliver sensed results to the AUV. The specific characteristics of USNs, including low communication bandwidth, large propagation delay, and ocean current, are significantly different from wireless sensor networks. To consider the characteristics of USNs, a new mobicast routing protocol is developed in 3-D USNs. The key design challenge is to develop a power-saving mobicast protocol in 3-D USNs to overcome the unpredictable 3-D hole problem. To solve the hole problem, an “apple slice” technique is used to build multiple segments to surround the hole and to assure routing path continuity. Finally, performance analysis is derived, and simulation results illustrate the performance improvement in successful delivery rate, power consumption, and message overhead.

Certainty-Enhanced Active Learning for Improving Imbalanced Data Classification

In active learning algorithms, informative samples are usually queried for true labels according to the disagreement of existing hypotheses. However we observed that, when the streaming dataset has skewed class membership, the imbalanced data classification problem is caused in active learning. The Minority class is overwhelmed by the majority class in generating the hypotheses. In this paper, for each unlabeled sample we propose to utilize only local behavior in the certainty-enhanced neighborhood, rather than the entire dataset, to generate the error minimization hypotheses. Consequently, our proposed method enhances the prediction of hypotheses and is able to determine the query probabilities properly. In our experiments, synthetic and real-world datasets are used for presenting the effectiveness of our active learning approach. It is shown that the proposed approach decreases the probability of querying a certain (majority) sample and has the ability of dealing with the imbalanced data classification problem in active learning.

Single-hop all-to-all broadcast in optical star networks with breakdown or power-off transceivers

Packet scheduling provides a way to reduce routing control and guarantee overall throughput. An interesting special case of the packet scheduling is all-to-all broadcast in which every transmitter/receiver pair has exactly one packet to be transferred. Several optimal scheduling algorithms have been proposed for the all-to-all broadcast problem in wavelength division multiplexed (WDM) optical star networks. However, these algorithms do not fit anymore when failures occur in some transceivers. In this paper, we consider the all-to-all broadcast problem in WDM optical star network with some breakdown or power-off transceivers. We focus on the all-optical scheduling problem where the traffic reaches its destination in single-hop without being converted to electronic form for reaching high data transmission rates. The transmitters are tunable with an associated tuning delay, and the receivers are fixed-tuned to one of the available wavelengths. In this model, we first identify the lower bounds of the scheduling length for the problem. Then, we propose single-hop scheduling algorithms to find the optimal solution in both terms of arbitrary number of wavelengths and value of tuning latency.

Improved Approximation Algorithms for Weighted Hypergraph Embedding in a Cycle

We consider the problem of embedding weighted hyperedges of a hypergraph as paths in a cycle on the same number of vertices, such that the maximum congestion of any physical link of the cycle is minimized. The problem, called weighted hypergraph embedding in a cycle (WHEC), is known to be NP-complete even when each hyperedge is unweighted or each weighted hyperedge contains exactly two vertices. In this paper, we propose an improved rounding algorithm for the WHEC problem to provide a solution with an approximation bound of

A Track-Presetting strategy in PCS using hierarchical location databases

1.5(opt+w_{max})

Minimizing energy consumption with QoS constraints over IEEE 802.16e networks

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