Disastrous events are one of the most challenging applications of multihop ad hoc networks due to possible damages of existing telecommunication infrastructure. The deployed cellular communication infrastructure might be partially or completely destroyed after a natural disaster. Multihop ad hoc communication is an interesting alternative to deal with the lack of communications in disaster scenarios. They have evolved since their origin, leading to different ad hoc paradigms such as MANETs, VANETs, DTNs, or WSNs. This paper presents a survey on multihop ad hoc network paradigms for disaster scenarios. It highlights their applicability to important tasks in disaster relief operations. More specifically, the paper reviews the main work found in the literature, which employed ad hoc networks in disaster scenarios. In addition, it discusses the open challenges and the future research directions for each different ad hoc paradigm.

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This study proposes a model for supporting the decision making process of the cloud policy for the deployment of virtual machines in cloud environments. We explore two configurations, the static case in which virtual machines are generated according to the cloud orchestration, and the dynamic case in which virtual machines are reactively adapted according to the job submissions, using migration, for optimizing performance time metrics. We integrate both solutions in the same simulator for measuring the performance of various combinations of virtual machines, jobs and hosts in terms of the average execution and total simulation time. We conclude that the dynamic configuration is prosperus as it offers optimized job execution performance.

Modern cloud computing applications developed from different interoperable services that are interfacing with each other in a loose coupling approach. This work proposes the concept of the Virtual Machine (VM) cluster migration, meaning that services could be migrated to various clouds based on different constraints such as computational resources and better economical offerings. Since cloud services are instantiated as VMs, an application can be seen as a cluster of VMs that integrate its functionality. We focus on the VM cluster migration by exploring a more sophisticated method with regards to VM network configurations. In particular, networks are hard to managed because their internal setup is changed after a migration, and this is related with the configuration parameters during the re-instantiation to the new cloud platform. To address such issue, we introduce a Software Defined Networking (SDN) service that breaks the problem of network configuration into tractable pieces and involves virtual bridges instead of references to static endpoints. The architecture is modular, it is based on the SDN OpenFlow protocol and allows VMs to be paired in cluster groups that communicate with each other independently of the cloud platform that are deployed. The experimental analysis demonstrates migrations of VM clusters and provides a detailed discussion of service performance for different cases.

Mining large amounts of unstructured data for extracting meaningful, accurate, and actionable information, is at the core of a variety of research disciplines including computer science, mathematical and statistical modelling, as well as knowledge engineering. In particular, the ability to model complex scenarios based on unstructured datasets is an important step towards an integrated and accurate knowledge extraction approach. This would provide a significant insight in any decision making process driven by Big Data analysis activities. However, there are multiple challenges that need to be fully addressed in order to achieve this, especially when large and unstructured data sets are considered. In this article we propose and analyse a novel method to extract and build fragments of Bayesian networks (BNs) from unstructured large data sources. The results of our analysis show the potential of our approach, and highlight its accuracy and efficiency. More specifically, when compared with existing approaches, our method addresses specific challenges posed by the automated extraction of BNs with extensive applications to unstructured and highly dynamic data sources. The aim of this work is to advance the current state-of-the-art approaches to the automated extraction of BNs from unstructured datasets, which provide a versatile and powerful modelling framework to facilitate knowledge discovery in complex decision scenarios

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This paper focuses on defining the minimum requirements to support the inter-cooperation between various scales, dynamically evolved Virtual Organizations (VOs). This proposed method is able to assign a weighted value to each pair-wise path that each member (node) can select in order to locate neighbouring nodes according to their preferences. The method also takes into account the communication overhead between each node interaction. The weight of each path is to be measured by the analysis of prerequisites in order to achieve a mutually agreed interaction between nodes. Requirements are defined as the least parameters or conditions that a node needs to achieve in order to determine its accessibility factor. The motivation behind this work is the vision of the Critical Friends Community model, which is a suitable topology for interoperable grid environments. The topology suggests that capturing inter-cooperated nodes interactions that can be publicly available could lead to knowledge of neighbouring VO members which, in turn, could be used for facilitating a more effective resource discovery and selection decision.