Experimentation with MANETs of Smartphones

Mobile AdHoc NETworks (MANETs) have been identified as a key emerging technology for scenarios in which IEEE 802.11 or cellular communications are either infeasible, inefficient, or cost-ineffective. Smartphones are the most adequate network nodes in many of these scenarios, but it is not straightforward to build a network with them. We extensively survey existing possibilities to build applications on top of ad-hoc smartphone networks for experimentation purposes, and introduce a taxonomy to classify them. We present AdHocDroid, an Android package that creates an IP-level MANET of (rooted) Android smartphones, and make it publicly available to the community. AdHocDroid supports standard TCP/IP applications, providing real smartphone IEEE 802.11 MANET and the capability to easily change the routing protocol. We tested our framework on several smartphones and a laptop. We validate the MANET running off-the-shelf applications, and reporting on experimental performance evaluation, including network metrics and battery discharge rate.Comment: 6 pages, 7 figures, 1 tabl

Ticket Automation: an Insight into Current Research with Applications to Multi-level Classification Scenarios

odern service providers often have to deal with large amounts of customer requests, which they need to act upon in a swift and effective manner to ensure adequate support is provided. In this context, machine learning algorithms are fundamental in streamlining support ticket processing workflows. However, a large part of current approaches is still based on traditional Natural Language Processing approaches without fully exploiting the latest advancements in this field. In this work, we aim to provide an overview of support Ticket Automation, what recent proposals are being made in this field, and how well some of these methods can generalize to new scenarios and datasets. We list the most recent proposals for these tasks and examine in detail the ones related to Ticket Classification, the most prevalent of them. We analyze commonly utilized datasets and experiment on two of them, both characterized by a two-level hierarchy of labels, which are descriptive of the ticket’s topic at different levels of granularity. The first is a collection of 20,000 customer complaints, and the second comprises 35,000 issues crawled from a bug reporting website. Using this data, we focus on topically classifying tickets using a pre-trained BERT language model. The experimental section of this work has two objectives. First, we demonstrate the impact of different document representation strategies on classification performance. Secondly, we showcase an effective way to boost classification by injecting information from the hierarchical structure of the labels into the classifier. Our findings show that the choice of the embedding strategy for ticket embeddings considerably impacts classification metrics on our datasets: the best method improves by more than 28% in F1- score over the standard strategy. We also showcase the effectiveness of hierarchical information injection, which further improves the results. In the bugs dataset, one of our multi-level models (ML-BERT) outperforms the best baseline by up to 5.7% in F1-score and 5.4% in accuracy

Automated Ticket Routing Helpdesk Portal

This report was commissioned to deliver the understanding of the chosen Final Year Project title "Automated Ticket Routing Helpdesk Portal". The report will be segregated into four chapters which are the introduction including problem statement and objectives; literature review; methodology and conclusion. Helpdesk is a very powerful tool to assist IT users. A good helpdesk system is very crucial to assist users and also improve service by the helpdesk team. At Universiti Teknologi Petronas, we already have an existing helpdesk system. However, I have identified a problem in the system; the current system still uses manual ticket routing and assignment. Automated ticket routing and assignment stop manually assigning tickets to the support personnel that you think is available and has the skill set to address the ticket. Automated ticket routing and assignment uses intelligent business logic to determine which support personnel is assigned to a new ticket using a combination of skill-set, work schedule and work load balancing criteria. In the introduction, problem statements that lead to the idea of developing the project title will be cleared up and the objectives are highlighted. Towards the development of this project, information gathering from the experts will be conducted. A comprehensive research also will determine the relevancy of this project. The literature reviews will explain in depth the understanding of the proposed project

Deliverable JRA1.1: Evaluation of current network control and management planes for multi-domain network infrastructure

This deliverable includes a compilation and evaluation of available control and management architectures and protocols applicable to a multilayer infrastructure in a multi-domain Virtual Network environment.The scope of this deliverable is mainly focused on the virtualisation of the resources within a network and at processing nodes. The virtualization of the FEDERICA infrastructure allows the provisioning of its available resources to users by means of FEDERICA slices. A slice is seen by the user as a real physical network under his/her domain, however it maps to a logical partition (a virtual instance) of the physical FEDERICA resources. A slice is built to exhibit to the highest degree all the principles applicable to a physical network (isolation, reproducibility, manageability, ...). Currently, there are no standard definitions available for network virtualization or its associated architectures. Therefore, this deliverable proposes the Virtual Network layer architecture and evaluates a set of Management- and Control Planes that can be used for the partitioning and virtualization of the FEDERICA network resources. This evaluation has been performed taking into account an initial set of FEDERICA requirements; a possible extension of the selected tools will be evaluated in future deliverables. The studies described in this deliverable define the virtual architecture of the FEDERICA infrastructure. During this activity, the need has been recognised to establish a new set of basic definitions (taxonomy) for the building blocks that compose the so-called slice, i.e. the virtual network instantiation (which is virtual with regard to the abstracted view made of the building blocks of the FEDERICA infrastructure) and its architectural plane representation. These definitions will be established as a common nomenclature for the FEDERICA project. Other important aspects when defining a new architecture are the user requirements. It is crucial that the resulting architecture fits the demands that users may have. Since this deliverable has been produced at the same time as the contact process with users, made by the project activities related to the Use Case definitions, JRA1 has proposed a set of basic Use Cases to be considered as starting point for its internal studies. When researchers want to experiment with their developments, they need not only network resources on their slices, but also a slice of the processing resources. These processing slice resources are understood as virtual machine instances that users can use to make them behave as software routers or end nodes, on which to download the software protocols or applications they have produced and want to assess in a realistic environment. Hence, this deliverable also studies the APIs of several virtual machine management software products in order to identify which best suits FEDERICA’s needs.Postprint (published version

Improving Support Ticket Systems Using Machine Learning: A Literature Review

Processing customer support requests via a support ticket system is a key-element for companies to provide support to their customers in an organized and professional way. However, distributing and processing such tickets is much work, increasing the cost for the support providing company and stretching the resolution time. The advancing potential of Machine Learning has led to the goal of automating those support ticket systems. Against this background, we conducted a Literature Review aiming at determining the present state-of-the-art technology in the field of automated support ticket systems. We provide an overview about present trends and topics discussed in this field. During the Literature Review, we found creating an automated incident management tool being the majority topic in the field followed by request escalation and customer sentiment prediction and identified Random Forrest and Support Vector Machine as best performing algorithms for classification in the field

Delivering knowledge in the field: A telecommunications service provision and maintenance case

This paper proposes a novel approach to providing knowledge management services in a business process wherein field engineers are the main process actors, providing and maintaining telecommunications services. Cooperating multi-agents play a central role for the provision of knowledge management services by integrating heterogeneous systems to collect related knowledge for the execution of mobile tasks. The proposed system is expected to increase both the performance of the mobile workforce and customer satisfaction by supporting and encouraging knowledge sharing

Spartan Daily, March 5, 2003

Volume 120, Issue 29https://scholarworks.sjsu.edu/spartandaily/9825/thumbnail.jp