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Interconnecting Smart Objects with IP: The Next Internet explains why the Internet Protocol (IP) has become the protocol of choice for smart object networks. IP has successfully demonstrated the ability to interconnect billions of digital systems on the global Internet and in private IP networks. Once smart objects can be easily interconnected, a whole new class of smart object systems can begin to evolve. The book discusses how IP-based smart object networks are being designed and deployed. The book is organized into three parts. Part 1 demonstrates why the IP architecture is well suited to smart object networks, in contrast to non-IP based sensor network or other proprietary systems that interconnect to IP networks (e.g. the public Internet of private IP networks) via hard-to-manage and expensive multi-protocol translation gateways that scale poorly. Part 2 examines protocols and algorithms, including smart objects and the low power link layers technologies used in these networks. Part 3 describes the following smart object network applications: smart grid, industrial automation, smart cities and urban networks, home automation, building automation, structural health monitoring, and container tracking. Shows in detail how connecting smart objects impacts our lives with practical implementation examples and case studies Provides an in depth understanding of the technological and architectural aspects underlying smart objects technology Offers an in-depth examination of relevant IP protocols to build large scale smart object networks in support of a myriad of new services

This study explores the important task of validating data exchange between a control box, a Programmable Logic Controller (PLC), and a robot in an industrial setting. To achieve this, we adopt a unique approach utilizing both a virtual PLC simulator and an actual PLC device. We introduce an innovative industrial communication module to facilitate the efficient collection and storage of data among these interconnected entities. The main aim of this inquiry is to examine the implementation of Ethernet/IP (EIP), a relatively new addition to the industrial network scenery. It was designed using ODVA’s Common Industrial Protocol (CIP™). The Costumed real-time data communication module was programmed in C++ for the Linux Debian platform and elegantly demonstrates the impressive versatility of EIP as a means for effective data transfer in an industrial environment. The study’s findings provide valuable insights into Ethernet/IP’s functionalities and capabilities in industrial networks, bringing attention to its possible applications in industrial robotics. By connecting theoretical knowledge and practical implementation, this research makes a significant contribution to the continued development of industrial communication systems, ultimately improving the efficiency and effectiveness of automation processes.

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.

In the recent past, Distributed Denial of Service (DDoS) attacks have become more abundant and present one of the most serious security threats. In a DDoS attack, the attacker controls a botnet of daemons residing in vulnerable hosts that send a significant amount of traffic to flood the victim or the network infrastructure. In this paper, a common type of DDoS attacks known as “TCP SYN-Flood” is studied. This type of attack uses spoofed Internet Protocol (IP) addresses for SYN packets by exploiting the weakness in Transmission Control Protocol (TCP) 3-Way handshake used by the TCP/IP suite of protocols, which make the web servers unreachable for legitimate users or even worse, it might lead to server crash. In this paper, a resilient, efficient, lightweight, and robust IP traceback algorithm is proposed using an IP tracing packet for each attack path. The proposed algorithm suggests that edge routers—where the attack starts from—observe the traffic pattern passing through, and if the observed traffic carries the signature of TCP SYN-Flood DDoS attack and a high percentage of it is destined to a particular web server(s), it starts the tracing process by generating an IP trace packet, which accompanies the attack path recording the routers’ IP addresses on the path between the attacker/daemon and the victim, which can extract the path and react properly upon receiving it by discarding any SYN packets originating from that attacker/daemon. To our knowledge, this is the first research that efficiently traces these kinds of attacks while they are running. The proposed solution has low computation and message overhead, efficient detection and tracing time, and converges in near optimal time. The results are validated using extensive simulation runs.

\"If we had computers that knew everything there was to know about things—using data they gathered without any help from us—we would be able to track and count everything, and greatly reduce waste, loss, and cost. We would know when things needed replacing, repairing or recalling, and whether they were fresh or past their best. The Internet of Things has the potential to change the world, just as the Internet did. Maybe even more so.\" —Kevin Ashton, originator of the term, Internet of Things An examination of the concept and unimagined potential unleashed by the Internet of Things (IoT) with IPv6 and MIPv6 What is the Internet of Things? How can it help my organization? What is the cost of deploying such a system? What are the security implications? Building the Internet of Things with IPv6 and MIPv6: The Evolving World of M2M Communications answers these questions and many more. This essential book explains the concept and potential that the IoT presents, from mobile applications that allow home appliances to be programmed remotely, to solutions in manufacturing and energy conservation. It features a tutorial for implementing the IoT using IPv6 and Mobile IPv6 and offers complete chapter coverage that explains: * What is the Internet of Things? * Internet of Things definitions and frameworks * Internet of Things application examples * Fundamental IoT mechanisms and key technologies * Evolving IoT standards * Layer 1/2 connectivity: wireless technologies for the IoT * Layer 3 connectivity: IPv6 technologies for the IoT * IPv6 over low power WPAN (6lowpan) Easily accessible, applicable, and not overly technical, Building the Internet of Things with IPv6 and MIPv6 is an important resource for Internet and ISP providers, telecommunications companies, wireless providers, logistics professionals, and engineers in equipment development, as well as graduate students in computer science and computer engineering courses.

The TCP protocol is a connection-oriented and reliable transport layer communication protocol which is widely used in network communication. With the rapid development and popular application of data center networks, high-throughput, low-latency, and multi-session network data processing has become an immediate need for network devices. If only a traditional software protocol stack is used for processing, it will occupy a large amount of CPU resources and affect network performance. To address the above issues, this paper proposes a double-queue storage structure for a 10G TCP/IP hardware offload engine based on FPGA. Furthermore, a TOE reception transmission delay theoretical analysis model for interaction with the application layer is proposed, so that the TOE can dynamically select the transmission channel based on the interaction results. After board-level verification, the TOE supports 1024 TCP sessions with a reception rate of 9.5 Gbps and a minimum transmission latency of 600 ns. When the TCP packet payload length is 1024 bytes, the latency performance of TOE’s double-queue storage structure improves by at least 55.3% compared to other hardware implementation approaches. When compared with software implementation approaches, the latency performance of TOE is only 3.2% of the software approaches.

The growing demand for IP addresses, driven by the proliferation of devices, has depleted the internet protocol (IP) version 6 (IPv6) reserves of some regional internet registries (RIRs). It is imperative to migrate to IPv6, offering an extended addressing space. This transition is no longer a choice but a necessity due to the exhaustion of IP version 4 (IPv4) addresses. The internet engineering task force (IETF) has implemented various transition strategies, such as the use of dual stack, IPv6-in-IPv4 tunnels, and address translation, due to the inconsistency between the two versions of the IP (IPv4 and IPv6). IPv4/IPv6 address translation mechanisms are crucial for the coexistence of networks using both protocols, with scalability playing a central role. Although these mechanisms offer advantages such as optimizing addressing space, their ability to scale effectively must be evaluated, especially in demanding scenarios such as voice over IP (VoIP). This article examines the scalability of two mechanisms, network address translation 64 (NAT64) and stateless IP/internet control message protocol (ICMP) translation (SIIT), in terms of VoIP clients in the graphical network simulator 3 (GNS3) environment. The results indicate that the SIIT mechanism is more performant and scalable than NAT64 in all measured parameters.

Best‐effort service model of traditional routing is gradually hard to meet the personalized demands under the rapid development of network technologies (e.g. 5G and IPv6). Therefore, service customization should be considered. In this work, a service customized routing mechanism based on deep learning in IPv6 network is proposed, which includes deep learning‐based service customization module, reliability evaluation module, and routing calculation module. The first module uses neural network to learn the complex service customization function, which can quickly output win‐win customized service strategies based on user demands. The second module can quantify the reliability of service routing paths, where not only the link status of IPv6 Neighbor Unreachable Detection (NUD) is considered, but also propose link performance weights to ensure the reliability of differentiated service performance. The third module uses the gray wolf optimization algorithm to calculate an optimal routing path to forward services with the customized strategies as the constraints and the maximum reliability and minimum cost as the goal. Finally, the mechanism is tested on the IPv6 Source Address Validation Improvement (SAVI) platform, which can reduce the execution time by 12.25% and improve the average routing reliability, user and ISP satisfaction by 9.0%, 40.45% and 7.4%, respectively.

The growing adoption of IPv6 protocol presents new security challenges. One of the core components of IPv6 is the neighbor discovery protocol (NDP) which manages the communications between neighboring devices and include the duplicate address detection (DAD) process, that ensures the uniqueness of every configured IPv6 address. Even though it is designed to improve the efficiency of communications, the DAD process presents significant vulnerabilities that can be exploited by attackers. These limitations are linked to ICMPv6 (Internet Control Message Protocol for IPv6) messages and can lead to severe attacks as spoofing and flooding, these attacks are not only compromising the security of communications but also affect the network availability, posing risks to network infrastructure. The present paper investigates these vulnerabilities by simulating two specific types of attacks: Spoofing and flooding. The simulations are conducted in a controlled environment and highlight the real-world impact of these attacks on network performance and service availability. This study allowed us to better understand the underlying mechanisms of these attacks to propose effective prevention and mitigation strategies. The goal of this research is the experimental evaluation of NDP vulnerabilities, especially within the DAD process, and the demonstration of their impact. Furthermore, it proposes a discussion of many practical solutions that are used in literature, such as cryptographic techniques and message filtering mechanisms, to secure IPv6 networks against these threats.

This study outlines a method for using surveillance cameras and an algorithm that calls a deep learning model to generate video segments featuring salmon and trout in small streams. This automated process greatly reduces the need for human intervention in video surveillance. Furthermore, a comprehensive guide is provided on setting up and configuring surveillance equipment, along with instructions on training a deep learning model tailored to specific requirements. Access to video data and knowledge about deep learning models makes monitoring of trout and salmon dynamic and hands‐on, as the collected data can be used to train and further improve deep learning models. Hopefully, this setup will encourage fisheries managers to conduct more monitoring as the equipment is relatively cheap compared with customized solutions for fish monitoring. To make effective use of the data, natural markings of the camera‐captured fish can be used for individual identification. While the automated process greatly reduces the need for human intervention in video surveillance and speeds up the initial sorting and detection of fish, the manual identification of individual fish based on natural markings still requires human effort and involvement. Individual encounter data hold many potential applications, such as capture–recapture and relative abundance models, and for evaluating fish passages in streams with hydropower by spatial recaptures, that is, the same individual identified at different locations. There is much to gain by using this technique as camera captures are the better option for the fish's welfare and are less time‐consuming compared with physical captures and tagging. This study outlines a method for using surveillance cameras and an algorithm that calls a deep learning model to generate video segments featuring salmon and trout in small streams. This automated process greatly reduces the need for human intervention in video surveillance. A comprehensive guide is provided on setting up and configuring surveillance equipment, along with instructions on training a deep learning model tailored to specific requirements. There is much to gain by using this technique as camera captures are the better option for the fish's welfare and are less time‐consuming compared with physical captures and tagging.