One inevitable problem in Ad Hoc networks is the limited battery capacity, which explains why portable devices might shut down suddenly when the power of hardware is depleted. Hence, how to decrease the power consumption is an important issue in ad hoc networks. With the development of wireless technology, mobile devices can transmit voices, surf the Internet, download entertaining stuffs, and even support some P2P applications, like sharing real-time streaming. In order to keep the quality stable, the transmission must be continuous and it is thus necessary to select some managers to coordinate all nodes in a P2P community. In addition to assigning jobs to the staffs (children) when needed, these managers (ancestors) are able to reappoint jobs in advance when employees retire. This paper proposed a mechanism called Cluster-based Power Management (CPM) to stabilize the transmissions and increase Time to Live (TTL) of mobile hosts. In our new proposed method, we establish the clusters according to every node's joining order and capability, and adjust their sleep time dynamically through three different mathematical models. Our simulation results reveal that this proposed scheme not only reduces the power consumption efficiently, but also increases the total TTLs evidently.

When supporting both voice and TCP in a wireless multihop network, there are two conflicting goals: to protect the VoIP traffic, and to completely utilize the remaining capacity for TCP. We investigate the interaction between these two popular categories of traffic and find that conventional solution approaches, such as enhanced TCP variants, priority queues, bandwidth limitation, and traffic shaping do not always achieve the goals. TCP and VoIP traffic do not easily coexist because of TCP aggressiveness and data burstiness, and the (self-) interference nature of multihop traffic. We found that enhanced TCP variants fail to coexist with VoIP in the wireless multihop scenarios. Surprisingly, even priority schemes, including those built into the MAC such as RTS/CTS or 802.11e generally cannot protect voice, as they do not account for the interference outside communication range. We present VAGP (Voice Adaptive Gateway Pacer) - an adaptive bandwidth control algorithm at the access gateway that dynamically paces wired-to-wireless TCP data flows based on VoIP traffic status. VAGP continuously monitors the quality of VoIP flows at the gateway and controls the bandwidth used by TCP flows before entering the wireless multihop. To also maintain utilization and TCP performance, VAGP employs TCP specific mechanisms that suppress certain retransmissions across the wireless multihop. Compared to previous proposals for improving TCP over wireless multihop, we show that VAGP retains the end-to-end semantics of TCP, does not require modifications of endpoints, and works in a variety of conditions: different TCP variants, multiple flows, and internet delays, different patterns of interference, different multihop topologies, and different traffic patterns.

Network coding is a newly developed technology that can cause considerable improvements in network throughput. COPE is the first network coding approach for wireless mesh networks and it is based on opportunistic Wireless Network Coding (WNC). It significantly improves throughput of multi-hop wireless networks utilizing network coding and broadcast features of wireless medium. In this paper we propose a new method, called FENC, for opportunistic WNC that improves the network throughput. In addition, its complexity is lower than other opportunistic WNC approaches. FENC utilizes division and conquer method to find an optimal network coding. The numerical results show that the proposed opportunistic algorithm improves the overall throughput as well as network coding approach.

Wireless mesh networks (WMNs) provide high-speed backbone networks without any wired cable. Many researchers have tried to increase network throughput by using multi-channel and multi-radio interfaces. A multi-radio multi-channel WMN requires channel assignment algorithm to decide the number of channels needed for each link. Since the channel assignment affects routing and interference directly, it is a critical component for enhancing network performance. However, the optimal channel assignment is known as a NP complete problem. For high performance, most of previous works assign channels in a centralized manner but they are limited in being applied for dynamic network environments. In this paper, we propose a simple flow estimation algorithm and a hybrid channel assignment algorithm. Our flow estimation algorithm obtains aggregated flow rate information between routers by packet sampling, thereby achieving high scalability. Our hybrid channel assignment algorithm initially assigns channels in a centralized manner first, and runs in a distributed manner to adjust channel assignment when notable traffic changes are detected. This approach provides high scalability and high performance compared with existing algorithms, and they are confirmed through extensive performance evaluations.

Mobile grid is a combination of grid computing and mobile computing to build grid systems in a wireless mobile environment. The development of network technology is assisting in realizing mobile grid. Mobile grid based on established grid infrastructures needs effective resource management and reliable job scheduling because mobile grid utilizes not only static grid resources but also dynamic grid resources with mobility. However, mobile devices are considered as unavailable resources in traditional grids. Mobile resources should be integrated into existing grid sites. Therefore, this paper presents a mobile grid middleware interconnecting existing grid infrastructures with mobile resources and a mobile service agent installed on the mobile resources. This paper also proposes a mobile resource reliability-based job scheduling model in order to overcome the unreliability of wireless mobile devices and guarantee stable and reliable job processing. In the proposed job scheduling model, the mobile service agent calculates the mobile resource reliability of each resource by using diverse reliability metrics and predicts it. The mobile grid middleware allocated jobs to mobile resources by predicted mobile resource reliability. We implemented a simulation model that simplifies various functions of the proposed job scheduling model by using the DEVS (Discrete Event System Specification) which is the formalism for modeling and analyzing a general system. We also conducted diverse experiments for performance evaluation. Experimental results demonstrate that the proposed model can assist in improving the performance of mobile grid in comparison with existing job scheduling models.

The fundamental design goal of wireless sensor MAC protocols is to minimize unnecessary power consumption of the sensor nodes, because of its stringent resource constraints and ultra-power limitation. In existing MAC protocols in wireless sensor networks (WSNs), duty cycling, in which each node periodically cycles between the active and sleep states, has been introduced to reduce unnecessary energy consumption. Existing MAC schemes, however, use a fixed duty cycling regardless of multi-hop communication and traffic fluctuations. On the other hand, there is a tradeoff between energy efficiency and delay caused by duty cycling mechanism in multi-hop communication and existing MAC approaches only tend to improve energy efficiency with sacrificing data delivery delay. In this paper, we propose two different MAC schemes (ADS-MAC and ELA-MAC) using closed-loop control in order to achieve both energy savings and minimal delay in wireless sensor networks. The two proposed MAC schemes, which are synchronous and asynchronous approaches, respectively, utilize an adaptive timer and a successive preload frame with closed-loop control for adaptive duty cycling. As a result, the analysis and the simulation results show that our schemes outperform existing schemes in terms of energy efficiency and delivery delay.

In this paper, we propose an Energy Efficient Media Access Control (EE-MAC) protocol for wireless sensor networks. The proposed scheme is designed to save power consumption and guarantee quality-of-service for real-time traffic. EE-MAC uses the superframe structure which is bounded by the transmission of a beacon frame and can have an active and an inactive portion. The active period is divided into the contention free period (CFP) for real-time traffic transmission and the contention access period (CAP) for non-real-time traffic transmission. We propose the exclusively allocated backoff scheme which assigns a unique backoff time value to each real-time node based on bandwidth allocation and admission control. This scheme can avoid collision between real-time nodes by controlling distributed fashion and take effect a statistical time division multiple access. We also propose the algorithm to change the duty cycle adaptively according to channel utilization of media depending on network traffic load. This algorithm can prolong network lifetime by reducing the amount of energy wasted on idle listening.

In this paper we develop a link cost function for data delivery in sensor network. Usually most conventional methods determine the optimal coefficients in the cost function without considering the surrounding environment of the node such as the wireless propagation environment or the topological environment. Due to this reason, there are limitations to improve the quality of data delivery such as data delivery ratio and delay of data delivery. To solve this problem, we derive a new cost function using the concept of Partially Connected Neural Network (PCNN) which is modeled according to the input types whether inputs are correlated or uncorrelated. The correlated inputs are connected to the hidden layer of the PCNN in a coupled fashion but the uncoupled inputs are in an uncoupled fashion. We also propose the training technique for finding an optimal weight vector in the link cost function. The link cost function is trained to the direction that the packet transmission success ratio of each node maximizes. In the experimental section, we show that our method outperforms other conventional methods in terms of the quality of data delivery and the energy efficiency.

An interest-based P2P constructs the peer connections based on similarities for efficient search of resources. A clustering technique using peer similarities as data is an effective approach to group the most relevant peers. However, the separation of groups produced from clustering lowers the scalability of a P2P network. Moreover, the interest-based approach is only concerned with user-level grouping where topology-awareness on the physical network is not considered. This paper proposes an efficient scalable search for the interest-based P2P system. A scalable multi-ring (SMR) based on fuzzy clustering handles the grouping of relevant peers and the proposed scalable search utilizes the SMR for scalability of peer queries. In forming the multi-ring, a minimized route function is used to determine the shortest route to connect peers on the physical network. Performance evaluation showed that the SMR acquired an accurate peer grouping and improved the connectivity rate of the P2P network. Also, the proposed scalable search was efficient in finding more replicated files throughout the peer network compared to other traditional P2P approaches.

The purpose of this study is to explore the determinants of actual demand for wireless broadband (WiBro) services in Korea. To this end, this study identifies conceptual definitions of diverse latent variables and determines the significance of the causal relationship between independent and dependent variables. It then presents the nomadic user concept by testing the moderator effect. This study also proposes a revised Unified Theory of Acceptance and Use of Technology (UTAUT) model, which is an appropriate model for wireless communication services and users' attributes. Prior research has concentrated exclusively on the Technology Acceptance Model (TAM) based on future potential users. Compared to previous studies, the current research model and analysis results are useful in not only academic but also industry contexts.

Topology management, which includes neighbor discovery, tracking and updating, is a key area that need to be dealt with appropriately to increase network performance. The use of directional antenna in Wireless Mesh Networks is beneficial in constructing backbone networks viewing the properties of directional antenna. The backbone links must be robust to obtain better network performance. In this paper, a simple yet effective topology protocol is presented that performs well compared to its predecessors. Our protocol constructs the topology with the constraints in the number of links per node. The full topology is constructed in two phases. The resultant topology is termed as Web-topology. The topology formed is robust, efficient, and scalable.

Content Based Image Retrieval (CBIR) is an approach for retrieving similar images from an image database based on automatically-derived image features. The quality of a retrieval system depends on the features used to describe image content. In this paper, we propose an image clustering system that takes a database of images as input and clusters them using k-means clustering algorithm taking into consideration color, texture and shape features. Experimental results show that the combination of the three features brings about higher values of accuracy and precision.

A 3D model based approach for a face representation and recognition algorithm has been investigated as a robust solution for pose and illumination variation. Since a generative 3D face model consists of a large number of vertices, a 3D model based face recognition system is generally inefficient in computation time and complexity. In this paper, we propose a novel 3D face representation algorithm based on a pixel to vertex map (PVM) to optimize the number of vertices. We explore shape and texture coefficient vectors of the 3D model by fitting it to an input face using inverse compositional image alignment (ICIA) to evaluate face recognition performance. Experimental results show that the proposed face representation and recognition algorithm is efficient in computation time while maintaining reasonable accuracy.