The Linking Open Data (LOD) cloud is a collection of linked Resource Description Framework (RDF) data with over 31 billion RDF triples. Accessing linked data is a challenging task because each data set in the LOD cloud has a specific ontology schema, and familiarity with the ontology schema used is required in order to query various linked data sets. However, manually checking each data set is time-consuming, especially when many data sets from various domains are used. This difficulty can be overcome without user interaction by using an automatic method that integrates different ontology schema. In this paper, we propose a Mid-Ontology learning approach that can automatically construct a simple ontology, linking related ontology predicates (class or property) in different data sets. Our Mid-Ontology learning approach consists of three main phases: data collection, predicate grouping, and Mid-Ontology construction. Experiments show that our Mid-Ontology learning approach successfully integrates diverse ontology schema with a high quality, and effectively retrieves related information with the constructed Mid-Ontology.

Fused Blocked Pattern Matching is a kind of approximate matching based on Blocked Pattern Matching, and can be used in identification of fused peptides in tumor genomes. In this paper, we propose a new algorithm for fused blocked pattern matching. We give a comparison between Julio's solution and ours, which shows our algorithm is more efficient.

Multi-Protocol Label Switching (MPLS) can efficiently support the explicit routes setup by the use of Label Switched Paths (LSPs) between an ingress Label Switched Router (LSR) and an egress LSR. Hence it is possible to distribute the network traffic among several paths to achieve load balancing, thus improving the network utilization, and minimizing the congestion. The packet-level traffic characteristics in the Internet is so complex that it is natural to do traffic engineering (TE) and control at the flow level. The emerging Multi-Protocol Label Switching (MPLS) has introduced an attractive solution to TE in IP networks. The main objective of this paper is to balance traffic at the flow level among the parallel Label Switched Paths (LSPs) in MPLS networks. We introduce a multipath load-balancing model at the flow level. In this model, each LSP is modeled as an M/G/1 processor-sharing queue. The load-balancing problem is then considered as an optimization problem. Based on the analysis of the model, we propose a heuristic but efficient mechanism that can make good use of the traffic characteristics at the flow level. Packet disorder is avoided effectively by dispatching packets belonging to one flow to the same path. This mechanism only need to be implemented in the ingress LSRs and the egress LSRs, while the intermediate LSRs only forward the packets. Apart from discussing the traffic allocation granularity, and the implementation issues in details, we have also performed extensive simulations using NS-2 with MPLS modules. The simulation results show that the load through the network is well balanced so that the network throughput is improved and the delay is decreased efficiently.

Scalable video coding with different modulation and coding schemes (MCSs) applied to different video layers is very appropriate for wireless multicast services because it can provide different video quality to different users according to their channel conditions, and a promising solution to handle packet losses induced by fading wireless channels is the use of layered hybrid FEC/ARQ scheme according to light-weight feedback messages from users about how many packets they have received. It is important to choose an appropriate MCS for each layer, decide how many parity packets in one layer should be transmitted, and determine the resources allocated to multiple video sessions to apply scalable video coding to wireless multicast streaming. We prove that such resource allocation problem is NP-hard and propose an approximate optimal algorithm with a polynomial run time. The algorithm can get the optimal transmission configuration to maximize the expected utility for all users where the utility can be a generic non-negative, non-decreasing function of the received rate. The results from simulations revealed that our algorithm offer significant improvements to video quality over a nave algorithm, an optimal algorithm without feedback from users, and an algorithm with feedback from designated users, especially in scenarios with multiple video sessions and limited radio resources.

To improve the data rate in OFDMA-based wireless networks, Carrier Aggregation (CA) technology has been included in the LTE-Advanced standard. Different Carrier Component (CC) capacities of users under the same eNodeB (eNB, i.e. Base Station) make it challenging to allocate resources with CA. In this paper, we jointly consider CC and Resource Block (RB) assignments, and power allocation to achieve proportional fairness in the long term. The goal of the problem is to maximize the overall throughput with fairness consideration. We consider a more general CC assignment framework that each User Equipment (UE) (i.e. Mobile Station) can support any number of CCs. Furthermore, we have proved the problem is NP-hard, even if power is equally allocated to RBs. Thus, first an optimal RB assignment and power allocation algorithm is proposed and then a carrier aggregation enabled joint resource allocation algorithm called CARA is proposed. By jointly considering CC and RB assignments, and power allocation, the proposed approach can achieve better performance. Simulation results show the proposed algorithm can significantly improve performance, e.g., total throughput compared with the existing algorithm.

With the increasing popularity of multicast and real-time streaming service applications, efficient channel assignment algorithms that handle both multicast and unicast traffic in wireless mesh networks are needed. One of the most effective approaches to enhance the capacity of wireless networks is to use systems with multiple channels and multiple radio interfaces. However, most of the past works focus on vertex coloring of a general contention graph, which is NP-Complete, and use the greedy algorithm to achieve a suboptimal result. In this paper, we combine unicast and multicast with a transmission set, and propose a framework named Chordal Graph Based Channel Assignment (CGCA) that performs channel assignment for multicast and unicast traffic in multi-channel multi-radio wireless mesh networks. The proposed framework based on chordal graph coloring minimizes the interference of the network and prevents unicast traffic from starvation. Simulation results show that our framework provides high throughput and low end-to-end delay for both multicast and unicast traffic. Furthermore, our framework significantly outperforms other well-known schemes that have a similar objective in various scenarios.

Let T be a text of length n and P be a pattern of length m, both strings over a fixed finite alphabet. The Pattern Matching with Swaps problem is to find all occurrences of P in T if adjacent pattern characters can be swapped. In the Approximate Pattern Matching problem with Swaps, one seeks for every text location with a swapped match of P, the number of swaps necessary to obtain a match at the location. In this paper we provide the first off-line solution for the swap matching problem and the approximate pattern matching problem with swaps. We present a new data-structure called a Swap-transforming Tree. And we give a precise upper-bond of the number of the swapped versions of a pattern. By using the swap-transforming tree, we can solve both problems in time O(λmlog2 n) on an O(nHk) bits indexing data structure. Here λ is a constant. Our solution is more effective when the pattern is short.

Up until now, the best public key encryption with multi-dimensional range query (PKMDRQ) scheme has two problems which need to be resolved. One is that the scheme is selectively secure. The other is that the time of decryption is long. To address these problems, we present a method of converting a predicate encryption supporting inner product (IPE) scheme into a PKMDRQ scheme. By taking advantage of this approach, an instance is also proposed. The comparison between the previous work and ours shows that our scheme is more efficient over the time complexity. Moreover, our scheme is adaptively secure.

Relay has been incorporated into standards of wireless access networks to improve the system capacity and coverage. However, the resource allocation problem to support scalable video coding (SVC) multicast for wireless relay networks is challenging due to the existence of relay stations (RSs). In this paper, we study the resource allocation problem for SVC multicast over multi-hop wireless relay networks to maximize the total utility of all users with a general non-negative, non-decreasing utility function. Since the problem is NP-hard, we simplify it with RS specification functions which specialize the relay station to receive data for each user, and convert the resource allocation problem with one RS specification function as finding a maximum spanning sub-tree of a directed graph under budget constraint. A heuristic algorithm is proposed to solve the problem with polynomial time complexity. The simulation results reveal that the proposed algorithm outperforms other algorithms under assumptions of two-hop wireless relay networks or separated transmission for relay and access links, and it keeps good approximation to the optimal results.

This paper presents an ontology-based driving decision making system, which can promptly make safety decisions in real-world driving. Analyzing sensor data for improving autonomous driving safety has become one of the most promising issues in the autonomous vehicles research field. However, representing the sensor data in a machine understandable format for further knowledge processing still remains a challenging problem. In this paper, we introduce ontologies designed for autonomous vehicles and ontology-based knowledge base, which are used for representing knowledge of maps, driving paths, and perceived driving environments. Advanced Driver Assistance Systems (ADAS) are developed to improve safety of autonomous vehicles by accessing to the ontology-based knowledge base. The ontologies can be reused and extended for constructing knowledge base for autonomous vehicles as well as for implementing different types of ADAS such as decision making system.

Being different from other networks, the load and direction of data traffic for wireless sensor networks are rather predictable. The relationships between nodes are cooperative rather than competitive. These features allow the design approach of a protocol stack to be able to use the cross-layer interactive way instead of a hierarchical structure. The proposed cross-layer protocol CLWSN optimizes the channel allocation in the MAC layer using the information from the routing tables, reduces the conflicting set, and improves the throughput. Simulations revealed that it outperforms SMAC and MINA in terms of delay and energy consumption.

In this letter, an improved statistical simulation model with a new parameter computation method is proposed for Rayleigh fading channels. Compared with the existing simulators, the proposed model yields much higher simulation efficiency, while it can still obtain adequate approximations of the desired statistical properties.