Dionysios Efstathiou

Exploiting limited density information towards near-optimal energy balanced data propagation

In this work, we investigate the problem of achieving energy-balanced data propagation in a distributed wireless sensor network. The energy balance property is essential for prolonging the network lifetime by maximizing the functional lifetime of a large portion of sensors. We propose a distributed, adaptive data propagation algorithm that exploits limited, local density information for achieving energy-balanced propagation while at the same time keeping energy dissipation at low levels. Apart from traditional studies considering uniform sensor distribution, we investigate heterogeneous sensor placement distributions. We conduct a detailed experimental evaluation and comparison with state-of-the-art energy-balanced protocols in order to demonstrate that our density-based protocol improves energy efficiency significantly while also having better energy balance properties. To illustrate that our protocol has near-optimal performance, we compare our protocol with a centralized, off-line optimum solution derived by a linear program which maximizes the network lifetime and show that it achieves near-optimal performance for uniform sensor deployments.

Surrogate-assisted optimisation of composite applications in mobile ad hoc networks

Infrastructure-less mobile ad-hoc networks enable the development of collaborative pervasive applications. Within such dynamic networks, collaboration between devices can be realised through service-orientation by abstracting device resources as services. Recently, a framework for QoS-aware service composition has been introduced which takes into account a spectrum of orchestration patterns, and enables compositions of a better QoS than traditional centralised orchestration approaches. In this paper, we focus on the automated exploration of trade-off compositions within the search space defined by this flexible composition model. For the studied problem, the evaluation of the fitness functions guiding the search process is computationally expensive because it either involves a high-fidelity simulation or actually requires calling the composite service. To overcome this limitation, we have developed efficient surrogate models for estimating the QoS metrics of a candidate solution during the search. Our experimental results show that the use of surrogates can produce solutions with good convergence and diversity properties at a much lower computational effort.

Exploring Optimal Service Compositions in Highly Heterogeneous and Dynamic Service-Based Systems

Dynamic and heterogeneous service-oriented systems present challenges when developing composite applications that exhibit specified quality properties. Resource heterogeneity, mobility, and a large number of spatially distributed service providers complicate the process of composing complex applications with specified QoS requirements. This PhD project aims at enabling the efficient run-time generation of service compositions that share functionality, but differ in their trade-offs between multiple competing and conflicting quality objectives such as application response time, availability and consumption of resources. In this paper we present a research roadmap towards an approach for flexible service composition in dynamic and heterogeneous environments.