Abstract Current trends in computing indicate that there is a great potential for service‐oriented computing and similar technologies, such as application‐oriented networks (AONs), where services can relocate to adapt to the conditions of the underlying network. In such environments, providing and consuming services and establishing a relationship between consumers (users of services) and producers (providers of services) are still challenging and vastly researched aspects. Bearing this in mind, we define a service location and planning (SLP) problem that uniquely matches producers to consumers and accounts for realistic parameters such as, quality of service (QoS) constraints of throughput and delay, and network constraints of underlying link layer bandwidth capacities, and cost of meeting consumer requests. Our contribution lies in the mathematical formulation of the SLP problem as an integer linear programming (ILP) problem that can be solved optimally for small‐scale networks and extending this work using Lagrangean relaxation (LR) approximation techniques to solve the SLP problem for large‐scale networks. Copyright © 2009 John Wiley & Sons, Ltd.

Current trends in computing indicate that there is a great potential for service oriented computing and similar technologies, such as Cisco's Application Oriented Networks, where services provide a higher-level of abstraction to traditional applications. In such cases, providing and consuming services and establishing a relationship between consumers (users of services) and producers (providers of services) are still challenging and vastly researched aspects. We define a service location planning problem (SLP) that is novel in the approach it takes to match consumers to producers, such that the cost of providing services is minimized while taking into account quality of service constraints of throughput and delay. In previous work, we presented an integer linear programming (ILP) model to formulate the service location planning problem. In this paper, we present Lagrangean Relaxation of the model for large-scale networks. We use the approach of Trick [3] to decrease computational overheads.

In tracking applications of wireless sensor networks (WSNs), the classification of an object or event of interest is envisaged to be one of the most computationally intensive tasks that recur frequently over the lifetime of the network. It is imperative that the implementations of such tasks be power efficient and computationally feasible for resource-constrained WSNs. Existing implementations of the best known classifiers such as maximum Aposterior (MAP) classifier are computationally infeasible for WSN environments. The focus of this paper is to investigate computational techniques to realize power efficient distributed implementation of the MAP classifier. In the MAP classifier, one of the most computationally challenging steps is the computation of the inverse of the covariance matrices. In this paper, we study computationally efficient methods for realizing the inverse of a matrix. We present a detailed discussion of one-sided Jacobi Iterations and LU Decomposition for approximating and computing the inverse of the covariance matrices. For LU Decomposition-based solutions, we also apply folding techniques to ensure equal power dissipation among the sensor network nodes. Our contribution lies in the power consumption analysis of executing the MAP classifier using LU decomposition and one-sided Jacobi Iterations to substitute the inverse of the covariance matrices. We show that MAP with one-sided Jacobi Iterations greatly simplifies classification process and makes it more feasible and efficient choice for sensor network applications.

Class 2 opportunistic networks (oppnets) are a new paradigm for Collaborative Computing that aims at integrating communication, computation, sensing, actuation, storage, and other resources and services. Oppnets achieve global tasks and goals through the collaboration and coordination of their nodes (some of which join an oppnet dynamically). We describe the concept of oppnets, discuss related work, and present the standard API framework for oppnets name Oppnet Virtual Machine (OVM). We present the design and implementation details of a small-scale proof-of-concept system, named MicroOppnet, in terms of the OVM primitives. We describe both the design and implementation of MicroOppnet, which not only is a proof of concept but also constitutes our tool for experiments in collaborative computing with oppnets. We are currently working on extending MicroOppnet into a larger oppnet prototype and an oppnet testbed.

In this paper, we present and discuss a novel service location problem that satisfies user demand for services, by installing services on nodes in the network, which can also meet quality of service (QoS) constraints of throughput and delay defined by the user. Service location problems can arise in various different networks, we specifically consider service location in Cisco's Application Oriented Networks (AON). Our goal is to formulate and present the service location problem as an Integer Linear Programming (ILP) problem and solve it optimally for networks with small number of nodes. Our ILP and results meet "real-world" demands of traffic splitting (due to link layer capacity) and service federation (to incur minimal service installation cost). This will enable service providers to offer services in a cost-effective manner while meeting consumer's demand for quality of service for throughput and delay.