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Abstract: A mobile grid incorporates mobile devices into Grid systems. But mobile devices at present have severe limitations in terms of processing, memory capabilities and energy. Minimizing the energy usage in mobile devices poses significant challenges in mobile grids. This paper presents energy constrained resource allocation optimization for mobile grids. The goal of the paper is not only to reduce energy consumption, but also to improve the application utility in a mobile grid environment with a limited energy charge, ensuring battery lifetime and the deadlines of the grid applications. The application utility not only depends on its allocated resources including computation and communication resources, but also on the consumed energy, this leads to a coupled utility model, where the utilities are functions of allocated resources and consumed energy. Energy constrained resources allocation optimization is formulated as a utility optimization problem, which can be decomposed into two subproblems, the interaction between the two sub-problems is controlled through the use of a pricing variable. The paper proposes a price-based distributed energy constrained resources allocation optimization algorithm. In the simulation, the performance evaluation of our energy constrained resources allocation optimization algorithm is conducted. [Copyright &y& Elsevier]

Abstract: This paper describes our recent improvements to IBM TRANSTAC speech-to-speech translation systems that address various issues arising from dealing with resource-constrained tasks, which include both limited amounts of linguistic resources and training data, as well as limited computational power on mobile platforms such as smartphones. We show how the proposed algorithms and methodologies can improve the performance of automatic speech recognition, statistical machine translation, and text-to-speech synthesis, while achieving low-latency two-way speech-to-speech translation on mobiles. [Copyright &y& Elsevier]

Abstract: In this paper, we address a novel deployment problem in isotropic mobile sensor networks. Sensors are to be relocated uniformly in a region of interest (ROI) centered at a target of interest (TOI) which could be stationary or mobile. With the assumption that relative direction of a sensor to the TOI can be recognized or inferred by devices equipped in the sensor, distributed control algorithms based on first-order and second-order dynamic models are proposed for both stationary and mobile TOI situations. The Lyapunov stabilities and coverage guarantee are provided. To further improve the deployment such as coverage holes inside the network and uniformity of the deployment, four assisted rules are also proposed. Then algorithms proposed for the situation of a stationary TOI are extended to anisotropic sensor networks. Simulations demonstrate the effective performances of the proposed algorithms. [Copyright &y& Elsevier]

Abstract: Recently, one of the most critical issues in mobile ad hoc networks (MANETs) is providing quality of service (QoS) through routing, access/admission control, resource reservation, and mobility management. However, most existing solutions do not provide QoS effectively due to the interference arising from mobility. In this paper, we refer to interference as a quasi-exposed node problem. To solve this problem, a new algorithm, named a distributed channel assignment control, is proposed that focuses on performance enhancements related to QoS and mathematical analysis techniques for the channel bandwidth. This novel algorithm uses channel assignment control with a power control to reduce the negative effects induced by the quasi-exposed node problem, and then the channels are adaptively negotiated to allow communication in the interference region. The proposed algorithm has been evaluated via extensive simulations, and the results show that it can successfully guarantee QoS by maintaining good throughput, reducing control message overhead, and enhancing delay. [ABSTRACT FROM AUTHOR]

Abstract: Detecting and tracking persons in the sequences of monocular images are the important and difficult problems in computer vision and have been well studied in these two decades. Recently, the methods based on stereo vision have attracted great attentions since 3D information can be exploited. This paper presents an approach for multiple-people detection and tracking using stereo vision. Tracking is carried out using a multiple particle filtering approach that combines depth, colour and gradient information. We modify the degree of confidence assigned to depth information, according to the amount of it found in the disparity map, using a novel confidence measure. The greater the amount of disparity information found, the higher the degree of confidence assigned to depth information in the final particles weights is. In the worst case (total absence of disparity), the proposed algorithm makes use of the information available (colour and gradient) to track, thus performing as a pure colour-based tracking algorithm. People are detected combining an adaboost classifier with stereo information. In order to test the validity of our proposal, it is evaluated in several sequences of colour and disparity images where people interact in complex situations: walk at different distances, shake hands, cross their paths, jump, run, embrace each other and even swap their positions quickly trying to confuse the system. The experimental results show that the proposal is able to deal with occlusions and to effectively determine both the 3D position of the people being tracked and their 2D head locations in the camera image, and everything is realized in real time. Besides, as the proposed method does not require the use of a background model, it can be considered particularly appropriate for applications that must run on mobile devices. [Copyright &y& Elsevier]

Abstract: One critical issue in wireless sensor networks is how to gather sensed information in an energy-efficient way since the energy is a scarce resource in a sensor node. Cluster-based architecture is an effective architecture for data-gathering in wireless sensor networks. However, in a mobile environment, the dynamic topology poses the challenge to design an energy-efficient data-gathering protocol. In this paper, we consider the cluster-based architecture and provide distributed clustering algorithms for mobile sensor nodes which minimize the energy dissipation for data-gathering in a wireless mobile sensor network. There are two steps in the clustering algorithm: cluster-head election step and cluster formation step. We first propose two distributed algorithms for cluster-head election. Then, by considering the impact of node mobility, we provide a mechanism to have a sensor node select a proper cluster-head to join for cluster formation. Our clustering algorithms will achieve the following three objectives: (1) there is at least one cluster-head elected, (2) the number of cluster-heads generated is uniform, and (3) all the generated clusters have the same cluster size. Last, we validate our algorithms through an extensive experimental analysis with Random Walk Mobility (RWM) model, Random Direction Mobility (RDM) model, and a Simple Mobility (SM) model as well as present our findings. [Copyright &y& Elsevier]

Abstract: With the emergence of large mobile ad hoc networks, the ability of existing routing protocols to scale well and function satisfactorily comes into question. Clustering has been proposed as a means to divide large networks into groups of suitably smaller sizes such that prevailing MANET routing protocols can be applied. However, the benefits of clustering come at a cost. Clusters take time to form and the clustering algorithms also introduce additional control messages that contend with data traffic for the wireless bandwidth. In this paper, we aim to analyse a distributed multi-hop clustering algorithm, Mobility-based D-Hop (MobDHop), based on two key clustering performance metrics and compare it with other popular clustering algorithms used in MANETs. We show that the overhead incurred by multi-hop clustering has a similar asymptotic bound as 1-hop clustering while being able to reap the benefits of multi-hop clusters. Simulation results are presented to verify our analysis. [Copyright &y& Elsevier]

Abstract: In many large, distributed or mobile networks, broadcast algorithms are used to update information stored at the nodes. In this paper, we propose a new model of communication based on rendezvous and analyze a multi-hop distributed algorithm to broadcast a message in a synchronous setting. In the rendezvous model, two neighbors u and v can communicate if and only if u calls v and v calls u simultaneously. Thus nodes u and v obtain a rendezvous at a meeting point. If m is the number of meeting points, the network can be modeled by a graph of n vertices and m edges. At each round, every vertex chooses a random neighbor and there is a rendezvous if an edge has been chosen by its two extremities. Rendezvous enable an exchange of information between the two entities. We get sharp lower and upper bounds on the time complexity in terms of number of rounds to broadcast: we show that, for any graph, the expected number of rounds is between ln n and O(n 2). For these two bounds, we prove that there exist some graphs for which the expected number of rounds is either O(ln(n)) or Ω(n 2). For specific topologies, additional bounds are given. [Copyright &y& Elsevier]

Abstract: In this paper, issues involved in the design of real-time on-demand broadcast system which maintains data temporal constraints are discussed. We propose a new online scheduling algorithm, called RDDS that incorporates access frequency, data size, request-deadline and data-deadline of pending requests for real-time on-demand broadcast system with dual deadlines. Furthermore, the concepts of deferrable requests and non-deferrable requests are introduced, cases of non-deferrable requests are analyzed, and Non-deferrable Request First policy is proposed and integrated into RDDS to form another new algorithm, called RDDS-W. We have performed a series of simulation experiments to evaluate the performance of our algorithms as compared with other previously proposed methods. The experimental results show that our algorithms can substantially outperform other algorithms under a wide range of scenarios, especially when combining with Non-deferrable Request First policy, which improves the performance significantly. [Copyright &y& Elsevier]

Abstract: In a mobile ad hoc (multi-hop) wireless network, the logical structure of a ring is likely to become volatile or expensive to maintain over time due to changeable network topology. Additional adverse effects take place when a process joins or leaves the computation in the presence of mobility. This paper presents a distributed algorithm that adapts a ring among mobile nodes to the network dynamics to reflect overall communication efficiency. This is achieved by modifying the ring structure in a localized, mutual exclusive fashion, thereby allowing for concurrent segment-wise modifications to proceed. Remarkably our proposal operates without global knowledge of the logical structure and can be embodied as an underlying protocol stratum that supports transparent deployments of conventional algorithms in mobile environment. Subsequent to correctness proof, simulation results show that our proposal is promising in several regards. [Copyright &y& Elsevier]

In a mobile environment, each mobile host should have a home agent on its home network that maintains a registry of the current location of the mobile host. This registry is normally updated every time a mobile host moves from one subnet to another. We study the tradeoff between the cost of updating the registry and the cost of searching for a mobile host while it is away from home. Using a set of special agents, called proxy agents, which implement a two-tier update process, the cost of updates could be reduced; however, the search cost might increase. We study different approaches to identify a set of proxy agents that minimizes the cost of search. In this paper, we use mathematical programming to obtain optimal solutions to the problem. We consider two situations: the cost of search measured by the sum of all search message costs, and the cost of search measured by the maximum cost of such messages. For these two respective cases we formulate the minimization of the cost of search as Min-Sum and Min-Max problems. For large networks in which the optimization problem may be intractable, we study three different approximate approaches: (1) clustering, (2) genetic algorithms, and (3) simulated annealing. Results of a large set of experiments are presented. [Copyright &y& Elsevier]