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"Computer Communication Networks - economics"
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Signal traffic : critical studies of media infrastructures
\"The contributors to Signal Traffic investigate how the material artifacts of media infrastructure--transoceanic cables, mobile telephone towers, Internet data centers, and the like--intersect with everyday life. Essayists confront the multiple and hybrid forms networks take, the different ways networks are imagined and engaged with by publics around the world, their local effects, and what human beings experience when a network fails. Some contributors explore the physical objects and industrial relations that make up an infrastructure. Others venture into the marginalized communities orphaned from the knowledge economies, technological literacies, and epistemological questions linked to infrastructural formation and use. The wide-ranging insights delineate the oft-ignored contrasts between industrialized and developing regions, rich and poor areas, and urban and rural settings, bringing technological differences into focus. Contributors include Charles R. Acland, Paul Dourish, Sarah Harris, Jennifer Holt and Patrick Vonderau, Shannon Mattern, Toby Miller, Lisa Parks, Christian Sandvig, Nicole Starosielski, Jonathan Sterne, and Helga Tawil-Souri\"-- Provided by publisher.
Book
Using videotelephony to support paediatric oncology-related palliative care in the home: from abandoned RCT to acceptability study
Videotelephony (real-time audio-visual communication) has been used successfully in adult palliative home care. This paper describes two attempts to complete an RCT (both of which were abandoned following difficulties with family recruitment), designed to investigate the use of videotelephony with families receiving palliative care from a tertiary paediatric oncology service in Brisbane, Australia. To investigate whether providing videotelephone-based support was acceptable to these families, a 12-month non-randomised acceptability trial was completed. Seventeen palliative care families were offered access to a videotelephone support service in addition to the 24 hours ‘on-call’ service already offered. A 92% participation rate in this study provided some reassurance that the use of videotelephones themselves was not a factor in poor RCT participation rates. The next phase of research is to investigate the integration of videotelephone-based support from the time of diagnosis, through outpatient care and support, and for palliative care rather than for palliative care in isolation. Trial registration ACTRN 12606000311550
Journal Article
Cost analysis of IPv6 distributed mobility management protocols in comparison with TFMIPv6
The past decade has witnessed a significant evolution in the role of the Internet, transitioning from individual connectivity to an integral aspect of various domains. This shift has prompted a move in IP paradigms from hierarchical to distributed architectures characterized by decentralized structures. This transition empowers efficient data routing and management across diverse networks. However, traditional distributed mobility management (DMM) protocols, reliant on tunneling mechanisms, incur overheads, costs, and delays, exacerbating challenges in managing the exponential growth of mobile data traffic. This research proposes Tunnel-Free Mobility for IPv6 (TFMIPv6) as a solution to address the shortcomings of existing DMM protocols. TFMIPv6 eliminates the need for tunneling, simplifying routing processes and reducing latency. A comprehensive cost analysis and performance evaluation are conducted, comparing TFMIPv6 with traditional protocols such as MIPv6, PMIPv6, FMIPv6, and HMIPv6. The study reveals significant improvements with TFMIPv6. Signaling costs are reduced by 50%, packet delivery costs by 23%, and tunneling costs are completely eliminated (100%). Real-world network traffic datasets are used for simulation, providing statistical evidence of TFMIPv6’s efficacy in supporting an uninterrupted movement of IPv6 data across networks.
Journal Article
A cost effective machine learning based network intrusion detection system using Raspberry Pi for real time analysis
In an increasingly interconnected world, the security of sensitive data and critical operations is paramount. This study presents the development of a Network Intrusion Detection System (NIDS) that analyzes both inbound and outbound network traffic to detect and classify various cyber attacks. The research begins with an extensive review of existing intrusion detection techniques, highlighting the limitations of traditional methods when addressing the unique security challenges posed by distributed networks. To overcome these limitations, advanced machine learning algorithms, including Random Forest, Long Short Term Memory (LSTM) networks, Artificial Neural Networks (ANN), XGBoost, and Naive Bayes, are employed to create a robust and adaptive intrusion detection system. The practical implementation utilizes a Raspberry Pi as the central processing unit for real time traffic analysis, supported by hardware components such as Ethernet cables, LEDs, and buzzers for continuous monitoring and immediate threat response. A comprehensive alert system is developed, sending email notifications to administrators and activating physical indicators to signify detected threats. Our proposed NIDS achieves 96.5 detection accuracy on the NF-UQ-NIDS dataset, with a significantly reduced false positive rate after applying SMOTE. The system processes real time network traffic with an average response time of 50 milliseconds, outperforming traditional IDS solutions in accuracy and efficiency. Evaluation using the NF-UQ-NIDS dataset demonstrates a significant improvement in detection accuracy and response time, establishing the system as an effective tool for safeguarding networks against emerging cyber threats.
Journal Article
A cost-effective adaptive repair strategy to mitigate DDoS-capable IoT botnets
Distributed denial of service (DDoS) is a type of cyberattack in which multiple compromised systems flood the bandwidth or resources of a single system, making the flooded system inaccessible to legitimate users. Since large-scale botnets based on the Internet of Things (IoT) have been hotbeds for launching DDoS attacks, it is crucial to defend against DDoS-capable IoT botnets effectively. In consideration of resource constraints and frequent state changes for IoT devices, they should be equipped with repair measures that are cost-effective and adaptive to mitigate the impact of DDoS attacks. From the mitigation perspective, we refer to the collection of repair costs at all times as a repair strategy. This paper is then devoted to studying the problem of developing a cost-effective and adaptive repair strategy (ARS). First, we establish an IoT botware propagation model that fully captures the state evolution of an IoT network under attack and defense interventions. On this basis, we model the ARS problem as a data-driven optimal control problem, aiming to realize both learning and prediction of propagation parameters based on network traffic data observed at multiple discrete time slots and control of IoT botware propagation to a desired infection level. By leveraging optimal control theory, we propose an iterative algorithm to solve the problem, numerically obtaining the learned time-varying parameters and a repair strategy. Finally, the performance of the learned parameters and the resulting strategy are examined through computer experiments.
Journal Article
A Secure Region-Based Geographic Routing Protocol (SRBGR) for Wireless Sensor Networks
Due to the lack of dependency for routing initiation and an inadequate allocated sextant on responding messages, the secure geographic routing protocols for Wireless Sensor Networks (WSNs) have attracted considerable attention. However, the existing protocols are more likely to drop packets when legitimate nodes fail to respond to the routing initiation messages while attackers in the allocated sextant manage to respond. Furthermore, these protocols are designed with inefficient collection window and inadequate verification criteria which may lead to a high number of attacker selections. To prevent the failure to find an appropriate relay node and undesirable packet retransmission, this paper presents Secure Region-Based Geographic Routing Protocol (SRBGR) to increase the probability of selecting the appropriate relay node. By extending the allocated sextant and applying different message contention priorities more legitimate nodes can be admitted in the routing process. Moreover, the paper also proposed the bound collection window for a sufficient collection time and verification cost for both attacker identification and isolation. Extensive simulation experiments have been performed to evaluate the performance of the proposed protocol in comparison with other existing protocols. The results demonstrate that SRBGR increases network performance in terms of the packet delivery ratio and isolates attacks such as Sybil and Black hole.
Journal Article
Wireless multihop backhauls for rural areas: A preliminary study
Rural areas have very low revenue potential. The major issue in providing low-cost broadband to rural areas is to provide reliable backhaul connections that spread over tens or even hundreds of miles, connecting villages to the nearest service provider. Along with aerial networks of Google and Facebook, there has been a considerable amount of research toward long-distance terrestrial WiFi links. As a comparison, WiFi routers are easier to be deployed and maintained by non-technical people from the local communities, whereas the aerial networks require professional support to operate. Moreover, they are still in the experimentation phase. However, the long distance WiFi links require high-gain directional antennas and very expensive tall towers for high data rates. On the other hand, multihop paths with stronger links may provide better data rates without the need of tall towers. In this paper, we evaluated the concept of using such multihop WiFi links for long backhaul connections. Our simulation results show that these networks can possibly be a cost-effective and practical solution for rural connectivity. These initial results can serve as a first step to understand the comprehensive feasibility of using multihop WiFi networks for backhaul connections in rural area.
Journal Article
VMCast: A VM-Assisted Stability Enhancing Solution for Tree-Based Overlay Multicast
Tree-based overlay multicast is an effective group communication method for media streaming applications. However, a group member's departure causes all of its descendants to be disconnected from the multicast tree for some time, which results in poor performance. The above problem is difficult to be addressed because overlay multicast tree is intrinsically instable. In this paper, we proposed a novel stability enhancing solution, VMCast, for tree-based overlay multicast. This solution uses two types of on-demand cloud virtual machines (VMs), i.e., multicast VMs (MVMs) and compensation VMs (CVMs). MVMs are used to disseminate the multicast data, whereas CVMs are used to offer streaming compensation. The used VMs in the same cloud datacenter constitute a VM cluster. Each VM cluster is responsible for a service domain (VMSD), and each group member belongs to a specific VMSD. The data source delivers the multicast data to MVMs through a reliable path, and MVMs further disseminate the data to group members along domain overlay multicast trees. The above approach structurally improves the stability of the overlay multicast tree. We further utilized CVM-based streaming compensation to enhance the stability of the data distribution in the VMSDs. VMCast can be used as an extension to existing tree-based overlay multicast solutions, to provide better services for media streaming applications. We applied VMCast to two application instances (i.e., HMTP and HCcast). The results show that it can obviously enhance the stability of the data distribution.
Journal Article
Distributed Data Networks That Support Public Health Information Needs
Data networks, consisting of pooled electronic health data assets from health care providers serving different patient populations, promote data sharing, population and disease monitoring, and methods to assess interventions. Better understanding of data networks, and their capacity to support public health objectives, will help foster partnerships, expand resources, and grow learning health systems.
We conducted semistructured interviews with 16 key informants across the United States, identified as network stakeholders based on their respective experience in advancing health information technology and network functionality. Key informants were asked about their experience with and infrastructure used to develop data networks, including each network's utility to identify and characterize populations, usage, and sustainability.
Among 11 identified data networks representing hundreds of thousands of patients, key informants described aggregated health care clinical data contributing to population health measures. Key informant interview responses were thematically grouped to illustrate how networks support public health, including (1) infrastructure and information sharing; (2) population health measures; and (3) network sustainability.
Collaboration between clinical data networks and public health entities presents an opportunity to leverage infrastructure investments to support public health. Data networks can provide resources to enhance population health information and infrastructure.
Journal Article
A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks
This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN). Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP) model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.
Journal Article