IP mobility ensures network reachability and session continuity while IPv6 networks are on the move. In the Network Mobility (NEMO) model, the potential for NEMO Mobile Routers (MRs) to interconnect and extend Internet connectivity allows the formation Nested NEMO networks. With MANEMO, nested MRs can be efficiently interconnected in a tree-based structure with Internet access being maintained via a designated Gateway. However, this only supports single-homed Internet connectivity.With the span of wireless access technologies and the popularity of multi-interfaced devices, multihoming support in this scenario becomes critical. A Nested Mobile Network with heterogeneous available Internet access options would allow better overall network performance and optimal utilisation of available resources. In this paper, we present the Multihomed Mobile Network Architecture (MMNA), a comprehensive multihomed mobility solution. It provides a multihoming management mechanism for Gateway Discovery and Selection on top of a multihomed mobility model integrating different mobility and multihoming protocols. It enables a complex nested multihomed topology to be established with multiple gateways supporting heterogeneous Internet access. The results demonstrate that the proposed solution achieves better overall throughput, load sharing, and link failure recovery.
Introducing an IP-based communication system into the mountain rescue domain would enable carrying out search and rescue missions in an effective way. With efficient mobility and multihoming support, a Mountain Rescue Team would be able to establish more effective and reliable Internet communication. In this paper, we present the Multihomed Mobile Network Architecture (MMNA), a comprehensive multihomed mobility solution for complex nested mobility scenarios. It provides a multihoming management mechanism for gateway discovery and selection, on top of an efficient multihomed mobility model integrating different mobility and multihoming protocols. The design of the MMNA solution is first presented. We then describe how the MMNA was experimentally implemented and evaluated in a testbed setup to examine its effectiveness and feasibility considering a use case example of a mountain rescue scenario. The results highlight the practicality and advantages of deploying the MMNA into such a critical real-world scenario.
This research article reports a compact fractal 4 × 4 UWB extended bandwidth MIMO antenna with physical dimensions of 44 × 44 mm2 for high-speed wireless applications. The reported antenna comprises four fractal radiating elements that are symmetrical and placed orthogonal to each other with a respective rectangular ground printed on the opposite plane. A higher isolation is achieved between the radiating elements by the placement of a fractal patch orthogonally and no separate decoupling structure is required. The antenna offers a −10 dB transmission capacity of 2.84–15.88 GHz. The fractal radiating element, which is embedded by an inverted T-type stub placed within a rectangular slot and an etched rotated C-type slot, provides band-stop filters for WiMAX (Worldwide inter-operability for Microwave Access) and WLAN (wireless local area network)-interfering bands. The key parameters of diversity performance are compared by simulation and measurement (fabricated prototype) of ECC (envelope correlation coefficient), DG (directive gain), TARC (total active reflection coefficient) and CCL (channel capacity loss). The antenna offers an omnidirectional radiation pattern with an average gain of 3.52 dBi.
The development of IPv6-based network architectures for Internet of Things (IoT) systems is a feasible approach to widen the horizon for more effective applications, but remains a challenge. Network routing needs to be effectively addressed in such environments of scarce computational and energy resources. The Internet Engineering Task Force (IETF) specified the IPv6 Routing Protocol for Low Power and Lossy Network (RPL) to provide a basic IPv6-based routing framework for IoT networks. However, the RPL design has the potential of extending its functionality to a further limit and incorporating the support of advanced routing mechanisms. These include multipath routing which has opened the doors for great improvements towards efficient energy balancing, load distribution, and even more. This paper fulfilled a need for an effective review of recent advancements in Internet of Things (IoT) networking. In particular, it presented an effective review and provided a taxonomy of the different multipath routing solutions enhancing the RPL protocol. The aim was to discover its current state and outline the importance of integrating such a mechanism into RPL to revive its potentiality to a wider range of IoT applications. This paper also discussed the latest research findings and provided some insights into plausible follow-up researches.
<p class="0abstract">Innovative applications are employed to enhance human-style life. The Internet of Things (IoT) is recently utilized in designing these environments. Therefore, security and privacy are considered essential parts to deploy and successful intelligent environments. In addition, most of the protection systems of IoT are vulnerable to various types of attacks. Hence, intrusion detection systems (IDS) have become crucial requirements for any modern design. In this paper, a new detection system is proposed to secure sensitive information of IoT devices. However, it is heavily based on deep learning networks. The protection system can provide a secure environment for IoT. To prove the efficiency of the proposed approach, the system was tested by using two datasets; normal and fuzzification datasets. The accuracy rate in the case of the normal testing dataset was 99.30%, while was 99.42% for the fuzzification testing dataset. The experimental results of the proposed system reflect its robustness, reliability, and efficiency.</p>
Internet of Things (IoT) systems incorporate a multitude of resource-limited devices typically interconnected over Low Power and Lossy Networks (LLNs). Robust IP-based network routing among such constrained IoT devices can be effectively realized using the IPv6 Routing Protocol for LLN (RPL) which is an IETF-standardized protocol. The RPL design features a topology maintenance mechanism based on a version numbering system. However, such a design property makes it easy to initiate Version Number (VN) attacks targeting the stability, lifetime, and performance of RPL networks. Thus the wide deployment of RPL-based IoT networks would be hindered significantly unless internal routing attacks such as the VN attacks are efficiently addressed. In this research work, a lightweight and effective detection and mitigation solution against RPL VN attacks is introduced. With simple modifications to the RPL functionality, a collaborative and distributed security scheme is incorporated into the protocol design (referred to as CDRPL). As the experimental results indicated, it provides a secure and scalable solution enhancing the resilience of the protocol against simple and composite VN attacks in different experimental setups. CDRPL guaranteed fast and accurate attack detection as well as quick topology convergence upon any attack attempt. It also efficiently maintained network stability, control traffic overhead, QoS performance, and energy consumption during different scenarios of the VN attack. Compared to other similar approaches, CDRPL yields better performance results with lightweight node-local processing, no additional entities, and less communication overhead.
In the dynamic and ever-evolving realm of network security, the ability to accurately identify and classify portscan attacks both inside and outside networks is of paramount importance. This study delves into the underexplored potential of fusing graph theory with machine learning models to elevate their anomaly detection capabilities in the context of industrial Internet of things (IIoT) network data analysis. We employed a comprehensive experimental approach, encompassing data preprocessing, visualization, feature analysis, and machine learning model comparison, to assess the efficacy of graph theory representation in improving classification accuracy. More specifically, we converted network traffic data into a graph-based representation, where nodes represent devices and edges represent communication instances. We then incorporated these graph features into our machine learning models. Our findings reveal that incorporating graph theory into the analysis of network data results in a modest-yet-meaningful improvement in the performance of the tested machine learning models, including logistic regression, support vector machines, and K-means clustering. These results underscore the significance of graph theory representation in bolstering the discriminative capabilities of machine learning algorithms when applied to network data.
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