Asymptotically-Optimal Incentive-Based En-Route Caching Scheme

Content caching at intermediate nodes is a very effective way to optimize the operations of Computer networks, so that future requests can be served without going back to the origin of the content. Several caching techniques have been proposed since the emergence of the concept, including techniques that require major changes to the Internet architecture such as Content Centric Networking. Few of these techniques consider providing caching incentives for the nodes or quality of service guarantees for content owners. In this work, we present a low complexity, distributed, and online algorithm for making caching decisions based on content popularity, while taking into account the aforementioned issues. Our algorithm performs en-route caching. Therefore, it can be integrated with the current TCP/IP model. In order to measure the performance of any online caching algorithm, we define the competitive ratio as the ratio of the performance of the online algorithm in terms of traffic savings to the performance of the optimal offline algorithm that has a complete knowledge of the future. We show that under our settings, no online algorithm can achieve a better competitive ratio than $\Omega(\log n)$, where $n$ is the number of nodes in the network. Furthermore, we show that under realistic scenarios, our algorithm has an asymptotically optimal competitive ratio in terms of the number of nodes in the network. We also study an extension to the basic algorithm and show its effectiveness through extensive simulations

Survey on wireless technology trade-offs for the industrial internet of things

Aside from vast deployment cost reduction, Industrial Wireless Sensor and Actuator Networks (IWSAN) introduce a new level of industrial connectivity. Wireless connection of sensors and actuators in industrial environments not only enables wireless monitoring and actuation, it also enables coordination of production stages, connecting mobile robots and autonomous transport vehicles, as well as localization and tracking of assets. All these opportunities already inspired the development of many wireless technologies in an effort to fully enable Industry 4.0. However, different technologies significantly differ in performance and capabilities, none being capable of supporting all industrial use cases. When designing a network solution, one must be aware of the capabilities and the trade-offs that prospective technologies have. This paper evaluates the technologies potentially suitable for IWSAN solutions covering an entire industrial site with limited infrastructure cost and discusses their trade-offs in an effort to provide information for choosing the most suitable technology for the use case of interest. The comparative discussion presented in this paper aims to enable engineers to choose the most suitable wireless technology for their specific IWSAN deployment

Healthcare digitalization and pay-for-performance incentives in smart hospital project financing

This study aims to explore the impact of healthcare digitalization on smart hospital project financing (PF) fostered by pay-for-performance (P4P) incentives. Digital platforms are a technology-enabled business model that facilitates exchanges between interacting agents. They represent a bridging link among disconnected nodes, improving the scalable value of networks. Application to healthcare public-private partnerships (PPPs) is significant due to the consistency of digital platforms with health issues and the complexity of the stakeholder’s interaction. In infrastructural PPPs, public and private players cooperate, usually following PF patterns. This relationship is complemented by digitized supply chains and is increasingly patient-centric. This paper reviews the literature, analyzes some supply chain bottlenecks, addresses solutions concerning the networking effects of platforms to improve PPP interactions, and investigates the cost-benefit analysis of digital health with an empirical case. Whereas diagnostic or infrastructural technology is an expensive investment with long-term payback, leapfrogging digital applications reduce contingent costs. “Digital” savings can be shared by key stakeholders with P4P schemes, incentivizing value co-creation patterns. Efficient sharing may apply network theory to a comprehensive PPP ecosystem where stakeholding nodes are digitally connected. This innovative approach improves stakeholder relationships, which are re-engineered around digital platforms that enhance patient-centered satisfaction and sustainability. Digital technologies are useful even for infectious disease surveillance, like that of the coronavirus pandemic, for supporting massive healthcare intervention, decongesting hospitals, and providing timely big data

Adaptive Recovery Mechanism for SDN Controllers in Edge-Cloud supported FinTech Applications

Financial Technology have revolutionized the delivery and usage of the autonomous operations and processes to improve the financial services. However, the massive amount of data (often called as big data) generated seamlessly across different geographic locations can end end up as a bottleneck for the underlying network infrastructure. To mitigate this challenge, software-defined network (SDN) has been leveraged in the proposed approach to provide scalability and resilience in multi-controller environment. However, in case if one of these controllers fail or cannot work as per desired requirements, then either the network load of that controller has to be migrated to another suitable controller or it has to be divided or balanced among other available controllers. For this purpose, the proposed approach provides an adaptive recovery mechanism in a multi-controller SDN setup using support vector machine-based classification approach. The proposed work defines a recovery pool based on the three vital parameters, reliability, energy, and latency. A utility matrix is then computed based on these parameters, on the basis of which the recovery controllers are selected. The results obtained prove that it is able to perform well in terms of considered evaluation parameters

Online algorithms for content caching: an economic perspective

Content Caching at intermediate nodes, such that future requests can be served without going back to the origin of the content, is an effective way to optimize the operations of computer networks. Therefore, content caching reduces the delivery delay and improves the users’ Quality of Experience (QoE). The current literature either proposes offline algorithms that have complete knowledge of the request profile a priori, or proposes heuristics without provable performance. In this dissertation, online algorithms are presented for content caching in three different network settings: the current Internet Network, collaborative multi-cell coordinated network, and future Content Centric Networks (CCN). Due to the difficulty of obtaining a prior knowledge of contents’ popularities in real scenarios, an algorithm has to make a decision whether to cache a content or not when a request for the content is made, and without the knowledge of any future requests. The performance of the online algorithms is measured through a competitive ratio analysis, comparing the performance of the online algorithm to that of an omniscient optimal offline algorithm. Through theoretical analyses, it is shown that the proposed online algorithms achieve either the optimal or close to the optimal competitive ratio. Moreover, the algorithms have low complexity and can be implemented in a distributed way. The theoretical analyses are complemented with simulation-based experiments, and it is shown that the online algorithms have better performance compared to the state of the art caching schemes

Overcoming barriers towards Sustainable Product-Service Systems in Small and Medium-sized enterprises: State of the art and a novel Decision Matrix

The Sustainable Product-Service Systems are a promising approach based on a Triple Bottom Line perspective of the sustainability. However, its practical and effective adoption is still very limited and addresses significant barriers for the manufacturing firms. Furthermore, this emergent topic has been discussed by literature mainly in large company's context, turning in a very limited and immature stage the current body of knowledge for the Small and Medium-sized Enterprises (SMEs). Thus, considering the significance of small companies to the global economy and their intrinsic difficulties, the purpose of this study was to identify the main barriers involving the transition towards Sustainable Product-Service Systems in manufacturing Small and Medium-sized Enterprises as well as the strategies to overcome them. A systematic literature review of the past two decades was organized capturing the state of the art of the area. Findings reveal that internal barriers associated with intrinsic characteristics of SMEs become still more sensitive during the transition (e.g., limited financial resources, the lack of competences, follower mentality and resistance to change). As well as, barriers related with the novelty of Sustainable Product-Service Systems models require new attitudes to small companies (e.g., changing mindsets from product ownership to use, replacing the value of exchange by value in use involving long-term relations, understanding the Product-Service Systems concept) and particularly highlight the lack of models/methods supporting this transition. The practical contribution of this study is in organise a comprehensive body of knowledge on strategies to overcome barriers towards Sustainable Product-Service offering. Moreover, an innovative decision matrix supporting decision-makers during the Sustainable Product-Service System development was proposed from the literature review findings. (C) 2019 Elsevier Ltd. All rights reserved

Synthetic Modeling of Power Grids Based on Statistical Analysis

The development of new concepts and methods for improving the efficiency of power networks needs performance evaluation with realistic grid topology. However, much of the realistic grid data needed by researchers cannot be shared publicly due to the security and privacy challenges. With this in mind, power researchers studied statistical properties of power grids and introduced synthetic power grid topology as appropriate methodology to provide enough realistic power grid case studies. If the synthetic networks are truly representative and if the concepts or methods test well in this environment they would test well on any instance of such a network as the IEEE model systems or other existing grid models. In the past, power researchers proposed a synthetic grid model, called RT-nested-smallworld, based on the findings from a comprehensive study of the topology properties of a number of realistic grids. This model can be used to produce a sufficiently large number of power grid test cases with scalable network size featuring the same kind of small-world topology and electrical characteristics found in realistic grids. However, in the proposed RT-nested-smallworld model the approaches to address some electrical and topological settings such as (1) bus types assignment, (2) generation and load settings, and (3) transmission line capacity assignments, are not sufficient enough to apply to realistic simulations. In fact, such drawbacks may possibly cause deviation in the grid settings therefore give misleading results in the following evaluation and analysis. To address this challenges, the first part of this thesis proposes a statistical methodology to solve the bus type assignment problem. This method includes a novel measure, called the Bus Type Entropy, the derivation of scaling property, and the optimized search algorithm. The second part of this work includes a comprehensive study on generation/Load settings based on both topology metrics and electrical characteristics. In this section a set of approaches has been developed to generate a statistically correct random set of generation capacities and assign them to the generation buses in a grid. Then we determine the generation dispatch of each generation unit according to its capacity and the dispatch ratio statistics, which we collected and derived from a number of realistic grid test cases. The proposed approaches is readily applied to determining the load settings in a synthetic grid model and to studying the statistics of the flow distribution and to estimating the transmission constraint settings. Considering the results from the first two sections, the third part of this thesis will expand earlier works on the RT-nested-smallworld model and develop a new methodology to appropriately characterize the line capacity assignment and improve the synthetic power grid modeling

Towards Universal Object Detection

Object detection is one of the most important and challenging research topics in computer vision. It is playing an important role in our everyday life and has many applications, e.g. surveillance, autonomous driving, robotics, drone, medical imaging, etc. The ultimate goal of object detection is a universal object detector that can work very well in any case under any condition like human vision system. However, there are multiple challenges on the universality of object detection, e.g. scale-variance, high-quality requirement, domain shift, computational constraint, etc. These will prevent the object detector from being widely used for various scales of objects, critical applications requiring extremely accurate localization, scenarios with changing domain priors, and diverse hardware settings. To address these challenges, multiple solutions have been proposed in this thesis. These include an efficient multi-scale architecture to achieve scale-invariant detection, a robust multi-stage framework effective for high-quality requirement, a cross-domain solution to extend the universality over various domains, and a design of complexity-aware cascades and a novel low-precision network to enhance the universality under different computational constraints. All these efforts have substantially improved the universality of object detection, and the advanced object detector can be applied to broader environments

Digitalization based innovation - A Case Study Framework

Small- and medium-sized enterprises (SMEs) have been leading innovation processes, where the upsurge of digital technology has overpowering implications on competitive positioning, ̄rm's value chains and overall business model. Value creation facilitated by emerging digital technologies alters costs, as well as process performance. Due to ̄eld research and in-depth interviews with owners and managers of SMEs in North-East Italy area, we combine and analyze evidence of the contingent challenges companies face while trying to redesign their business model. Our results point out that being able to accumulate and put into action external ideas can be vital in supplementing internal knowledge base and therefore crucial in escaping technological lock-ins; thus, imposing efforts toward digital transformation offers favorable outcomes