The MobyDick Project: A Mobile Heterogeneous All-IP Architecture

Proceedings of Advanced Technologies, Applications and Market Strategies for 3G (ATAMS 2001). Cracow, Poland: 17-20 June, 2001.This paper presents the current stage of an IP-based architecture for heterogeneous environments, covering UMTS-like W-CDMA wireless access technology, wireless and wired LANs, that is being developed under the aegis of the IST Moby Dick project. This architecture treats all transmission capabilities as basic physical and data-link layers, and attempts to replace all higher-level tasks by IP-based strategies. The proposed architecture incorporates aspects of mobile-IPv6, fast handover, AAA-control, and Quality of Service. The architecture allows for an optimised control on the radio link layer resources. The Moby dick architecture is currently under refinement for implementation on field trials. The services planned for trials are data transfer and voice-over-IP.Publicad

Mobile IP movement detection optimisations in 802.11 wireless LANs

The IEEE 802.11 standard was developed to support the establishment of highly flexible wireless local area networks (wireless LANs). However, when an 802.11 mobile node moves from a wireless LAN on one IP network to a wireless LAN on a different network, an IP layer handoff occurs. During the handoff, the mobile node's IP settings must be updated in order to re-establish its IP connectivity at the new point of attachment. The Mobile IP protocol allows a mobile node to perform an IP handoff without breaking its active upper-layer sessions. Unfortunately, these handoffs introduce large latencies into a mobile node's traffic, during which packets are lost. As a result, the mobile node's upper-layer sessions and applications suffer significant disruptions due to this handoff latency. One of the main components of a Mobile IP handoff is the movement detection process, whereby a mobile node senses that it is attached to a new IP network. This procedure contributes significantly to the total Mobile IP handover latency and resulting disruption. This study investigates different mechanisms that aim to lower movement detection delays and thereby improve Mobile IP performance. These mechanisms are considered specifically within the context of 802.11 wireless LANs. In general, a mobile node detects attachment to a new network when a periodic IP level broadcast (advertisement) is received from that network. It will be shown that the elimination of this dependence on periodic advertisements, and the reliance instead on external information from the 802.11 link layer, results in both faster and more efficient movement detection. Furthermore, a hybrid system is proposed that incorporates several techniques to ensure that movement detection performs reliably within a variety of different network configurations. An evaluation framework is designed and implemented that supports the assessment of a wide range of movement detection mechanisms. This test bed allows Mobile IP handoffs to be analysed in detail, with specific focus on the movement detection process. The performance of several movement detection optimisations is compared using handoff latency and packet loss as metrics. The evaluation framework also supports real-time Voice over IP (VoIP) traffic. This is used to ascertain the effects that different movement detection techniques have on the output voice quality. These evaluations not only provide a quantitative performance analysis of these movement detection mechanisms, but also a qualitative assessment based on a VoIP application

A survey on subjecting electronic product code and non-ID objects to IP identification

Over the last decade, both research on the Internet of Things (IoT) and real-world IoT applications have grown exponentially. The IoT provides us with smarter cities, intelligent homes, and generally more comfortable lives. However, the introduction of these devices has led to several new challenges that must be addressed. One of the critical challenges facing interacting with IoT devices is to address billions of devices (things) around the world, including computers, tablets, smartphones, wearable devices, sensors, and embedded computers, and so on. This article provides a survey on subjecting Electronic Product Code and non-ID objects to IP identification for IoT devices, including their advantages and disadvantages thereof. Different metrics are here proposed and used for evaluating these methods. In particular, the main methods are evaluated in terms of their: (i) computational overhead, (ii) scalability, (iii) adaptability, (iv) implementation cost, and (v) whether applicable to already ID-based objects and presented in tabular format. Finally, the article proves that this field of research will still be ongoing, but any new technique must favorably offer the mentioned five evaluative parameters.Comment: 112 references, 8 figures, 6 tables, Journal of Engineering Reports, Wiley, 2020 (Open Access

Supporting Service Differentiation with Enhancements of the IEEE 802.11 MAC Protocol: Models and Analysis

As one of the fastest growing wireless access technologies, Wireless LANs must evolve to support adequate degrees of service differentiation. Unfortunately, current WLAN standards like IEEE 802.11 Distributed Coordination Function (DCF) lack this ability. Work is in progress to define an enhanced version capable of supporting QoS for multimedia traffic at the MAC layer. In this paper, we aim at gaining insight into three mechanisms to differentiate among traffic categories, i.e., differentiating the minimum contention window size, the Inter-Frame Spacing (IFS) and the length of the packet payload according to the priority of different traffic categories. We propose an analysis model to compute the throughput and packet transmission delays. In additions, we derive approximations to get simpler but more meaningful relationships among different parameters. Comparisons with discrete-event simulation results show that a very good accuracy of performance evaluation can be achieved by using the proposed analysis model