Uncovering Ecosystem Service Bundles through Social Preferences

11 p.

Extending Distance-based Ranking Models In Estimation of Distribution Algorithms

Recently, probability models on rankings have been proposed in the field of estimation of distribution algorithms in order to solve permutation-based combinatorial optimisation problems. Particularly, distance-based ranking models, such as Mallows and Generalized Mallows under the Kendall’s-t distance, have demonstrated their validity when solving this type of problems. Nevertheless, there are still many trends that deserve further study. In this paper, we extend the use of distance-based ranking models in the framework of EDAs by introducing new distance metrics such as Cayley and Ulam. In order to analyse the performance of the Mallows and Generalized Mallows EDAs under the Kendall, Cayley and Ulam distances, we run them on a benchmark of 120 instances from four well known permutation problems. The conducted experiments showed that there is not just one metric that performs the best in all the problems. However, the statistical test pointed out that Mallows-Ulam EDA is the most stable algorithm among the studied proposals.

The Slow Search for Solutions: Lessons from Historical Energy Transitions by Sector and Service

28 p.

Radio-aware service-level scheduling to minimize downlink traffic delay through Mobile Edge Computing

One of the most challenging problems in mobile broadband networks is how to assign the available radio resources among the different mobile users. Traditionally, research proposals are either speci c to some type of traffic or deal with computationally intensive algorithms aimed at optimizing the delivery of general purpose traffic. Consequently, commercial networks do not incorporate these mechanisms due to the limited hardware resources at the mobile edge. Emerging 5G architectures introduce cloud computing principles to add flexible computational resources to Radio Access Networks. This paper makes use of the Mobile Edge Computing concepts to introduce a new element, denoted as Mobile Edge Scheduler, aimed at minimizing the mean delay of general traffic flows in the LTE downlink. This element runs close to the eNodeB element and implements a novel flow-aware and channel-aware scheduling policy in order to accommodate the transmissions to the available channel quality of end users.

RESTful Discovery and Eventing for Service Provisioning in Assisted Living Environments

Service provisioning in assisted living environments faces distinct challenges due to the heterogeneity of networks, access technology, and sensing/actuation devices in such an environment. Existing solutions, such as SOAP-based web services, can interconnect heterogeneous devices and services, and can be published, discovered and invoked dynamically. However, it is considered heavier than what is required in the smart environment-like context and hence suffers from performance degradation. Alternatively, REpresentational State Transfer (REST) has gained much attention from the community and is considered as a lighter and cleaner technology compared to the SOAP-based web services. Since it is simple to publish and use a RESTful web service, more and more service providers are moving toward REST-based solutions, which promote a resource-centric conceptualization as opposed to a service-centric conceptualization. Despite such benefits of REST, the dynamic discovery and eventing of RESTful services are yet considered a major hurdle to utilization of the full potential of REST-based approaches. In this paper, we address this issue, by providing a RESTful discovery and eventing specification and demonstrate it in an assisted living healthcare scenario. We envisage that through this approach, the service provisioning in ambient assisted living or other smart environment settings will be more efficient, timely, and less resource-intensive.