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One unique architectural benefit of Named Data Networking (NDN) is adaptive forwarding, i.e., the forwarding plane is able to observe past data retrieval performance and use it to adjust forwarding decisions for future Interests. To be effective, adaptive forwarding assumes that Interest Routing Locality is related to Interests' common name prefix, meaning that Interests sharing the same prefix are likely to follow a similar forwarding path within a short period of time. Since Interests can have multiple common prefixes with different lengths, the real challenge is determining which prefix length should be used in adaptive forwarding to record path performance measurements - we refer to this as the Prefix Granularity Problem. The longer the common prefix is, the better the Interest Routing Locality, and the larger the forwarding table. Given the limited FIB size, route names are designed to be considerably shorter than Interest names. Existing adaptive forwarding designs use route names to record path performance measurements, which looses forwarding adaptability as it promises in the event of partial network failures. In this work, we propose to dynamically aggregate and de-aggregate name prefixes in the forwarding table in order to use the prefixes that are the most appropriate given current network situation. In addition, to reduce the overhead of adaptive forwarding, we propose mechanisms to minimize the use of the longest prefix matching in Data packet processing. Simulations demonstrate that the proposed techniques can result in better forwarding decisions in the event of partial network failures with significantly reduced overhead.

Large low Earth orbit (LEO) satellite constellations provide low-latency and high-bandwidth Internet connectivity at the global scale. One major challenge is to handle frequent satellite handovers. Named Data Networking (NDN) adopts a pull-based communication model, which allows users to retrieve data that fail to come back because of satellite handovers by retransmitting the corresponding requests, hence simplifying mobility management when retrieving data. However, we find that relying on such retransmissions alone can be highly inefficient in typical LEO satellite constellations. Specifically, typical inter-satellite topologies and satellite handover strategies may produce bad cases for retransmissions, generating a significant amount of additional traffic. Motivated by this observation, this paper attempts to consolidate NDN's advantage in mobility management with the Data Recovery Link Service (DRLS), a shim layer service operating between the network and link layer in the NDN protocol stack. DRLS hides recurring satellite handovers from forwarding by recovering data from the previously connected satellite via alternative paths, thus ensuring the bidirectional request-response exchange of NDN without retransmitting requests. A prototype of DRLS is implemented in the reference NDN software forwarder and evaluated through simulations. Results prove the efficacy of the proposed mechanism at reducing the overall traffic volume.

Large low Earth orbit (LEO) satellite constellations are intended to provide global low-latency high-bandwidth Internet connectivity. Due to their large scale and high mobility nature, networking is a big challenge. In this paper, we investigate applying Named Data Networking (NDN) to this scenario. Specifically, we discuss that NDN's architectural benefits, such as adaptive forwarding, in-network caching, off-the-grid communication, data mule service, in-network/edge computing, mobility support, and data-centric security, make it a promising candidate. Moreover, we focus on studying NDN's consumer mobility support. Specifically, NDN's in-network Interest retransmission can quickly react to satellite handovers. However, we make an observation that Interest routing paths before and after satellite handover may not overlap, hence underusing NDN's in-network caching. Therefore, we direct retransmitted Interests due to handovers to the previous connected satellite via forwarding hint. Simulation results show that the studied approaches can decently improve the consumers' performance and reduce the network traffic, achieving better consumer mobility support.

One unique architectural benefit of Named Data Networking (NDN) is adaptive forwarding, i.e., the forwarding plane is able to observe data retrieval performance of past Interests and use it to adjust forwarding decisions for future Interests. To be effective, adaptive forwarding assumes Interest Routing Locality, meaning that Interests sharing the same prefix are likely to follow a similar forwarding path within a short period of time. Therefore, past observations can provide insight into how forwarding will likely perform for the same prefix in the near future. Since Interests can have multiple common prefixes with different lengths, the real challenge is determining which prefix length should be used in adaptive forwarding to record path measurements - we refer to this as the Prefix Granularity Problem. The longer the common prefix is, the better Interest Routing Locality. However, finer grained-prefixes cover fewer Interests each and require a larger forwarding table. Existing adaptive forwarding designs use a static prefix length, which is known to encounter issues in the event of partial network failures. In this work, we propose to dynamically aggregate and de-aggregate name prefixes in the forwarding table in order to use the prefixes that are the most appropriate given current network situation. In addition, to reduce the overhead of adaptive forwarding, we propose mechanisms to minimize the use of longest prefix matching during the processing of Data packets. Simulations demonstrate that the proposed techniques can result in better forwarding decisions in the event of partial network failures with significantly reduced overhead.

Named Data Networking (NDN) changes the network communication paradigm from address-centric in TCP/IP to data-centric. This architectural change extends capabilities of the network's forwarding plane, enabling more efficient and adaptive data retrieval. However, it is unclear how to utilize forwarding behaviors, and reduce the deployment complexity of NDN at edge networks. In fact, to run NDN applications between just two nodes involves decent knowledge of the software forwarder (NFD) and non-trivial network configuration. This paper designs self-configured adaptive forwarding plane for NDN at edge networks, to improve data retrieval efficiency and minimize deployment complexity. The designed functionalities are implemented in NFD and tested on real devices. Results show that NFD is able to run out-of-the-box with different applications. More specifically, the new forwarding strategy is able to learn and utilize multiple faces and routes, and to better handle NACK and link failure, achieving good performance.

A major challenge to potential ICN/NDN deployment is the requirement of application support, namely, applications need to be rewritten or modified in order to run on NDN networks and receive the full benefits. Using a proxy to translate between an application-level protocol and NDN offers a viable solution that balances between development cost and architectural benefits. In this paper, we study on the questions of how to facilitate and incentivize the development and deployment of such protocol translation proxies. We propose to enable existing applications to communicate "off the grid", i.e., using only local network connectivity without the global Internet, by translating between conventional clientserver protocols and NDN. This provides deployment incentives by enabling a useful feature with no or minimal changes to existing applications. By giving the experience of a few protocols, we hope to abstract out some common design patterns that can be reused in developing other application-level proxies. This paper reports our work on IMAP/NDN translation for local email access and XMPP/NDN translation for local group chat. Based on this work, we identify and discuss a number of common design issues including application-level framing, namespace design, application protocol semantics, multiparty synchronization, security and privacy, and real-world deployment challenges.

Most of today's applications require an Internet connection to reach cloud servers essential for their functioning. However, in many scenarios, such connectivity is unavailable, such as when a user is in a remote location with no cellular coverage or during a natural disaster. In these situations, nearby devices can still connect to each other, and the applications running on each device can have data useful to each other; however, the applications are unable to exchange data directly between themselves. In this paper, we describe mechanisms to enable off-the- grid communication for existing applications, allowing them to directly exchange data between themselves. To achieve this goal, an application should be capable of discovering, fetching, and verifying data without the help of a server. We believe that Named Data Networking (NDN) can provide an application with these abilities. We utilize email access as a study case and propose mailSync, a framework to the goal of app-to-app communication for this application via NDN. Meanwhile, we identify the various differences between TCP/IP and NDN applications, and we generalize several steps to "NDNize" an existing application, including protocol translation, application layer framing, naming, data discovery and security management. We implemented a prototype of mailSync in Java on laptop and Android as a proof of concept.

In Named Data Networking (NDN) content consumers request data by names instead of sending requests to specific destination addresses. This fits shared media particularly well for benefits such as native multicast, mobility support, and fault tolerance. However, since current network interface cards only filter packets based on destination addresses, all NDN packets have to be delivered to software for name-based filtering, resulting in significant CPU overhead. We propose NDN-NIC, a network interface card that filters packets based on their content names, to minimize the CPU overhead while running NDN over shared media. This paper tackles NDN-NIC's main research challenge: using the limited amount of on-chip memory (in tens of kilobytes) to support packet filtering based on a large number of rulesets (in hundreds of thousands). We use Bloom filters to store various name tables on NDN-NIC, and design a number of mechanisms to further adjust the name prefixes that go into the Bloom filters, minimizing false positives under given memory limit. Using traffic traces collected from a department network, simulations show that NDN-NIC with 16KB of memory can filter out 96.30% of all received packets and reduce the main CPU usage by 95.92%.

The Internet of Things (IoT) is a vision for interconnecting all of the world's "things" -- from vehicles to diet scales, smart homes and electrical grids -- through a common set of networking technologies. Realizing this vision using a host-to-host communication paradigm, such as that of the Internet Protocol (IP), is challenging in the context of highly heterogeneous, constrained devices that connect intermittently to one or more networks, often using multiple interfaces, communicate within various security regimes, and require both local and global communication capability. Using IP and similar protocols as the narrow waist of interoperability for IoT requires managing data exchange and security in terms that are largely orthogonal to application semantics, while simultaneously needing to minimize resource usage. This paper explores how Named Data Networking (NDN), a proposed future Internet architecture, addresses the root causes of these challenges and can help achieve the IoT vision in a more secure, straightforward, and innovation-friendly manner. NDN's data-centric communication model aligns network and application semantics, enabling developers to work with "things" and their data directly, and for IoT networks to be deployed and configured easily. To substantiate the high-level discussion, we give examples of ongoing design and implementation work in IoT over NDN and compare the architecture to well-known existing protocols and frameworks. Finally, we discuss short-and long-term scenarios for employing NDN to enable the Internet of Things.

PurposeThe purpose of this paper is to develop an effective and reliable corner detection algorithm so as to extract all the desired corners successfully. In addition, the influences of edge detection method as well as smoothing technique on the overall performance of corner detection techniques are investigated.Design/methodology/approachIn this paper, an effective corner detection algorithm based on subpixel edge detector and Gaussian filter is presented. First, a subpixel accuracy edge detector is used rather than a pixel accuracy edge detector to detect edges. Second, B‐splines approximation technique is used to eliminate the staircase effect of a digital curve. Third, curvature curve derived from the edges is smoothed by a Gaussian filter. Finally, statistical process control technique is applied to detect vertices.FindingsThe results show that spatial‐moment outperforms chain code as an edge detector. Furthermore, the Gaussian filter should be used to smooth curvature curve instead of smoothing the profile of an object, because the former provides greater impact on the corner detection results.Originality/valueIn addition to object recognition, motion tracking and obstacle avoidance, the proposed method also has many important engineering and manufacturing applications such as dimensional measuring, reverse engineering, and machine vision‐based computer numerical control (CNC) machining of polygonal sheet metal parts.

This study proposes a vision-based automatic raw fish handling system to speed up fish cleaning and weighing. The proposed fish weighing system used a camera to capture projected images of fishes. Applying image processing techniques, physical properties of fishes, such as length, width, perimeter and area were obtained. Followed by regression analysis, weight-length, weight-height, weight-perimeter and weight-area relationships were derived. Analysis results of fifty tilapias show that coefficient of determination of the regression equation relating weight and area is 0.9303. The high value suggests that a tilapia's weight is highly correlated with its projected area. Therefore, use a tilapia's area to estimate its weight is justifiable.

This study proposes a tool wear monitoring system based on machine vision technique. The tool wear of single edge rhombus micro-end-mills with mill parameters (Side Clearance Angle, different coating layer, feed rate and spindle speed) in milling 6061 aluminum alloy was experimentally investigated in this study. A L 9 (34) orthogonal array, analysis of variance (ANOVA) and signal-to-noise (S/N) were determined to know the level of importance of the machining parameters. Using Taguchi method for design of a robust experiment, the interactions among factors are also investigated. The experimental results indicate that Side Clearance Angle and coating layer are recognized to make the most significant contribution to the overall performance. The correlation was obtained by multi-variable nonlinear regression and compared with the experimental results. The experimental results showed that Using TiCN-coated micro-end-mill and setting side clearance angle at 12 degrees, spindle speed at 6000 rpm and feed rate at 0.0125 mm/rev minimized the wear on micro-end mills and maximized tool life. The confirmation tests demonstrated a feasible and an effective method for the evaluation of tool wear in milling of 6061 aluminum alloy.

The purpose of this research is to use the visual inspection technique for the automatic tool wear measurement of different coated drills. The tool wear images with the different coated drilling are captured using a machine vision system incorporating with an effective vertex detection algorithm based on subpixel edge detector and Gaussian filter is presented. The results show that the proposed algorithm is an effective method for the different coated drilling factor is recognized to make the most significant contribution to the over all performance. All drilling tests were carried out under dry cutting conditions without any coolant being used, The TiAlN-coated drilling has the least wear rate amongst these coated drilling cutters and has the longest tool life in this experiment

Mammogram registration is a critical step in automatic detection of breast cancer. Much research has been devoted to registering mammograms using either feature-matching or similarity measure. However, few studies have been done on combining these two methods. In this research, a hybrid mammogram registration method is developed by combining feature-based and intensity-based image registration techniques. Besides, internal and external features were used simultaneously during registration to obtain a global spatial transformation. The experimental results indicate that the similarity between the two mammograms increases significantly after a proper registration using the proposed registration procedures.