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Iron-functionalized titanium dioxide (TiO2) composites with various Fe-to-Ti ratios were prepared on flexible glass fibers (GF-Fe-TiO2) via a sol-gel method, followed by a dip-coating process. The photocatalytic ability of these composites in degrading selected volatile organic compounds (VOCs; benzene, toluene, ethylbenzene, ando-xylene [BTEX]) at indoor concentration levels was examined. The GF-Fe-TiO2composites were characterized using scanning electron microscopy, energy-dispersive X-ray elemental analysis, ultraviolet (UV)-visible spectroscopy, and X-ray diffraction. The GF-Fe-TiO2composites showed superior photocatalytic performance to that of a reference glass fiber–supported TiO2photocatalyst for the treatment of BTEX under visible light. However, this trend was reversed under UV irradiation. Specifically, the average BTEX photocatalytic efficiencies of the 0.01-GF-Fe-TiO2composite in a 3-hr visible-light photocatalytic process were 4%, 33%, 51%, and 74%, respectively. Conversely, the average BTEX photocatalytic efficiencies obtained for GF-TiO2were close to 0%, 5%, 16%, and 29%, respectively. These findings demonstrated that the GF-Fe-TiO2composites could be applied to photocatalytically purify BTEX, especially under visible-light exposure. Moreover, the GF-Fe-TiO2composites prepared with different Fe-to-Ti ratios displayed different BTEX photocatalytic decomposition efficiencies under visible or UV light, allowing for optimization of the Fe-to-Ti ratio (which was found to be 0.01). Implications: The application of nanomaterials for air purification necessitates a supporting material to stabilize them while in contact with the treated air in the photocatalytic chamber. Glass fibers have an obvious advantage over other supporting materials mainly because of its flexibility, which makes it much easier to handle. However, the applications of glass fiber–supported, visible light–activated photocatalysts to the treatment of air pollutants are rarely reported in literature. Accordingly, this study aimed to investigate the applicability of glass fiber–supported Fe-TiO2 for the purification of VOCs under visible- as well as UV-light exposure. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we propose an automated negotiation system that can efficiently carry out multilateral negotiations with multi-attributes in pervasive computing environments. For the multilateral negotiation system proposed in our study, an e-commerce framework for pervasive computing environments in which the components can dynamically join and disjoin a virtual market is also introduced. This framework overcomes the locality of pure ad hoc networks and supports efficiently multilateral negotiation models in pervasive computing environments. In order to achieve the applicability toward multilateral negotiations, the concept of a mediator agent and the bilateral negotiation scheme based on linear programming is utilized for the proposed system. The experimental results show that the proposed system produces higher joint profits in negotiations about 5% and is about 96 times faster in reaching agreements on the average under the condition of agreement for reciprocity than a negotiation system based on the trade-off mechanism. [Copyright &y& Elsevier]

Due to fast-growing mobile devices usage such as smartphones and wearable devices, the rapid information propagation in the Mobile Social Networks environment is very important. In particular, information transmission of people with repeated daily patterns in complex areas such as big cities requires a very meaningful analysis. We address the problem of identifying a key player who can quickly propagate the information to the whole network. This problem, in other words, often refer as the information propagation problem. In this research, we selected the top-k influential nodes to learn the knowledge-based movements of people by using a Markov chain process in a real-life environment. Subsequently, their movement probabilities according to virtual regions were used to ensure appropriate clustering based on the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Since moving patterns in a university campus data have a dense collection of people, the DBSCAN algorithm was useful for producing very dense groupings. After clustering, we also elected the top-k influential nodes based on the results learned from the score of each node according to groups. We determined the rate at which information spreads by using trace data from a real network. Our experiments were conducted in the Opportunistic Network Environment simulator. The results showed that the proposed method has outstanding performance for the level of spreading time in comparison to other methods such as Naïve, Degree, and K-means. Furthermore, we compared the performance of RandomDBC with that of ActiveDBC, proving that the latter method was important to extract the influential top-k nodes, and showed superior performance. [ABSTRACT FROM AUTHOR]

In mobile social networks, forwarding is a challenging problem since there may be no end-to-end paths. Existing schemes using overlays are not applicable to pure mobile social networks since they use environments different from the mobile social networks. To resolve this problem, we propose a novel forwarding scheme that considers the common neighbor similarity and contact probability. In the proposed scheme, the more nodes have in common, the more likely they are to be friends, and higher contact probabilities between nodes increase the chances they will arrive at the destination. The proposed scheme constructs an overlay using two layers with the common neighbor similarity and the contact probability and forwards messages to the destinations through the overlay. Experimental results show that the proposed scheme outperforms most known forwarding schemes in terms of balancing the network traffic and transmission delay. [ABSTRACT FROM AUTHOR]

With the increase in the number of mobile devices such as tablets and smart watches, mobile social networks (MSNs) provide great opportunities for people to exchange information. As a result, information diffusion has become a critical issue in the emerging MSNs. In this paper, we address the problem of finding the top-k influential users who can effectively spread information in a network, which is referred to as the diffusion minimization problem. In order to minimize the spreading period, we can utilize the k-center problem, but which has a time complexity of NP-hard. We propose a community-based diffusion scheme using Markov chain and spectral clustering (CDMS) to minimize the spreading time by adopting a community concept based on the geographic regularity of human mobility in the MSNs. We exploit the Markov chain to predict a node's mobility patterns and cluster the predicted patterns using the spectral graph theory. Finally, we select the top-k influential nodes in each community. Simulations are performed using the NS-2, based on the home-cell community-based mobility model, to show that the proposed scheme results in MSNs. In addition, we demonstrate that CDMS outperforms the noncommunity-based algorithms in terms of the number of nodes and ratio of k influential nodes. [ABSTRACT FROM AUTHOR]

Incorporating social relationship properties into forwarding schemes in opportunistic networks has become a more and more important paradigm. Communication among the nodes in an opportunistic network relies on intermittent contacts without complete end-to-end paths. Most of these forwarding schemes take advantage of social information such as contact information and social relationship among the nodes in the network. In this paper, we propose a social information based forwarding scheme in opportunistic networks through mimicking honey bees' behaviors in an artificial bee colony. In the proposed scheme, we adopt the percolation centrality in the social networks to assign certain nodes as 'influential' bees. The proposed forwarding scheme is aim at balancing the level between network traffic and transmission delay using the home-cell community-based mobility model (HCMM). Experiments were performed on the network simulator NS-2. The results show that the proposed scheme has outstanding performance for the level of network traffic and transmission delay in comparison to other schemes such as Epidemic, PRoPHET and SimBet when using the HCMM. [ABSTRACT FROM AUTHOR]

An opportunistic network (OPPNET) is a wireless networks without an infrastructure. In OPPNET , communication intermittently occurs when one node meets with another node. Thus, a connected path between the source and destination nodes rarely exists. For this reason, nodes need not only to forward messages but are also to store and carry messages as relay nodes. In OPPNET, several routing algorithms that rely on relay nodes with appropriate behavior have been proposed. Some of these are referred to as context- ignorant routing algorithms, which manipulate flooding, and others are referred to as context- aware routing algorithms, which utilize the contextual information. We propose a routing algorithm that employs a novel similarity based on both position and social information. We combine the position similarity with the social similarity using the fuzzy inference method to obtain the enhanced performance. Through this method, the proposed algorithm utilizes more proper relay nodes in forwarding adaptively and achieves significant improvement on the performance especially under memory constrained environment. We analyze the proposed algorithm on the NS-2 network simulator with the home-cell community-based mobility model. Experimental results show that the proposed algorithm outperforms typical routing algorithms in terms of the network traffic and delivery delay. [ABSTRACT FROM AUTHOR]

Most schemes in mobile social networks (MSNs) assume that nodes simply forward messages without considering selfishness. We therefore first devise social preference-based selfishness for MSNs by which nodes decide to drop or keep (forward) and replace messages to save buffer space according to the message preference and the communities of nodes. We then propose a novel cooperative forwarding scheme for social preference-based selfishness in MSNs, the social preference- aware forwarding scheme (SPF) incorporates the proposed message forwarding scheme and a buffer replacement policy for the message preference. It takes advantage of social information with the home-cell community-based mobility model. Considering the contact probability and buffer replacement policy for the message preferences, SPF, therefore, efficiently delivers messages to the destination by reflecting the degree of selfishness to which nodes cooperatively manage their buffer spaces and how frequently and how recently they meet. Consequently, all nodes can cooperatively drop or keep (forward) and replace the messages in the buffer spaces for the message preferences in SPF. SPF outperforms Epidemic, PRoPHET, and SimBet in terms of delivery ratio, network traffic, buffer space, hop count, and replacement frequency. [ABSTRACT FROM AUTHOR]

We study a forwarding strategy in opportunistic networks which are one of the most challenging networks among mobile ad-hoc networks. In opportunistic networks, a node does not have knowledge about the entire network topology, which is essential in the mobile ad-hoc network's forwarding strategy. Thus, node behavior is exploited to calculate future contact opportunities for forwarding a message. Utilizing social network analysis (e.g., similarity and centrality) has been proposed to improve the accuracy of the calculation task. This paper proposes a forwarding strategy based on an analysis of network topology. In the proposed strategy, each node takes a sequence of snapshots of its first-order neighbors during a warm-up period. Each node exchanges its own snapshots with each other, and then aggregates the snapshots in order to extract the network topology information. The extracted network topology is analyzed by social network analysis methods: compactness and algebraic connectivity. Forwarding decisions are made using the analysis of the features (compactness and algebraic connectivity). We present simulations using NS-2 and the home-cell community-based mobility model to show that the proposed forwarding strategy results in delay performances similar to the epidemic forwarding scheme, while maintaining reasonable network traffic. In addition, we demonstrate that the proposed strategy outperforms the SimBet and PRoPHET forwarding schemes with various communication ranges and memory space. [ABSTRACT FROM AUTHOR]

Clustering has been shown to be a highly effective way to reduce network traffic in mobile ad hoc networks. Many clustering schemes have been proposed. However, none of these schemes can be directly applied to a mobile social network because they are designed for well-connected networks and require timely information sharing among the nodes. In this paper, we propose the hierarchical clustering-based forwarding scheme (HCS), which implements hierarchical clustering on social information. Each node constructs hierarchical clusters based on common neighbor similarity at the end of the warm-up period. The nodes then forward a message to other nodes based on the clustering information and similarity scores. HCS exploits the shortcuts on the path toward the destination node with the help of social similarity and node movement patterns. Experiments were performed on an NS-2 network simulator. The results show that HCS reduces network traffic compared to non-clustering schemes, such as Epidemic, SimBet, PRoPHET, and common neighbor similarity schemes, while maintaining acceptable transmission delay compared to the Wait scheme. [ABSTRACT FROM AUTHOR]

An opportunistic network (OPPNET) consists of diverse mobile nodes with various mobility patterns. Numerous mobility patterns and the resource constraints of mobile nodes lead to network partitioning that result in system performance degradation including low data accessibility. In a traditional mobile ad hoc network (MANET) which is similar to an OPPNET, replica allocation schemes have been proposed to increase data accessibility. Although the schemes are efficient in a MANET, they may not be directly applicable to an OPPNET because the schemes are based on a grouping of mobile nodes. It is very difficult to build groups based on network topology in an OPPNET because a node in an OPPNET does not keep its network topology information. In this paper, we propose a novel replica allocation scheme for an opportunistic network called the Snooping- based Fully Distributed replica allocation scheme. The proposed scheme allocates replicas in a fully distributed manner without grouping to reduce the communication cost, and fetches allocated replicas utilizing a novel candidate list concept to achieve high data accessibility. In the proposed scheme, a node can fetch replicas opportunistically based on the candidate list. Consequently, the proposed replica allocation scheme achieves high data accessibility while reducing the communication cost significantly. Extensive simulation results demonstrate that the proposed scheme reduces the communication cost and improves data accessibility over traditional schemes. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes a new group-based acceleration data structure called gkDtree for interactive ray tracing of dynamic scenes. The main idea of the gkDtree is to construct the acceleration structure with a multi-level hierarchy, and to integrate a parallelization approach to result in a faster update and a more efficient tree traversal. A gkDtree can be viewed as a set of kd-trees, each of which is a local acceleration structure corresponding to a group. For a gkDtree, a scene is divided into several groups based on a scene graph. The local acceleration structure of each group involving only dynamic primitives is rebuilt. To achieve higher parallelization, dependencies among groups in different levels are removed before rebuilding occurs in parallel. To enhance the scalability of parallelization, a simple and fast load-balancing scheme is introduced. Furthermore, we apply a variety of accurate SAH (surface area heuristic) algorithms into tree generation for both static and dynamic groups. The experimental results show that a gkDtree has a real-time update performance. It has an update performance that is up to 166 times faster than a kd-tree for our test scenes in a six-core hardware system environment. Furthermore, the results also show that tree traversal performance of a gkDtree is competitive with that of a kd-tree. [Copyright &y& Elsevier]

This paper proposes an out-of-order execution control scheme that can be effectively applied to a coprocessor for embedded systems. A floating-point coprocessor has generally multiple pipelines such as a floating-point adder, a floating-point multiplier, a floating-point divider and a load/store pipelines. In order to utilize fully these pipelines, a constraint-based dynamic control scheme is designed for a coprocessor. This control scheme can be achieved by a data dependency checking, a resource conflict checking, and an exception prediction technique. With this control scheme a coprocessor can execute its instructions out of order without an extra hardware unit for out-of-order execution control. [Copyright &y& Elsevier]