268 research outputs found

    Vertical Handover Decision Algorithm in Heterogeneous Wireless Networks

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    [EN] With the recent progress in the area of cellular communication the issue of inter cells handover without dropping an ongoing connection with the base station has arisen. In this paper, the focus is on the performance of vertical handover. Various proposed interconnection architectures for vertical handover in heterogeneous networks were studied. Two different algorithms to make the decision on when and to which network perform a handover were considered. In the first of them the decision is based on the received signal strength (RSS). In the second one a fuzzy logic system that uses RSS, bandwidth, battery power and packet loss as the input parameters is proposed. The simulation results show that the algorithm based on fuzzy logic leads to a reduction of the number of handovers and a minimisation of the power consumption as compared to the first algorithm used here and the existing algorithms.This work was supported by the Spanish Ministry of Economy and Competitiveness through Grants TIN2013-47272-C2-1-R and BES-2011-045551.Benaatou, W.; Latif, A.; Pla, V. (2017). Vertical Handover Decision Algorithm in Heterogeneous Wireless Networks. International Journal of Internet Protocol Technology (Online). 10(4):197-213. https://doi.org/10.1504/IJIPT.2017.08891419721310

    Fuzzy logic-based intelligent scheme for enhancing QoS of vertical handover decision in vehicular ad-hoc networks

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    The design of next generation networks in various technologies under the “Anywhere, Anytime” paradigm offers seamless connectivity across different coverage. A conventional algorithm such as RSSThreshold algorithm, that only uses the received strength signal (RSS) as a metric, will decrease handover performance regarding handover latency, delay, packet loss, and handover failure probability. Moreover, the RSS-based algorithm is only suitable for horizontal handover decision to examine the quality of service (QoS) compared to the vertical handover decision in advanced technologies. In the next generation network, vertical handover can be started based on the user’s convenience or choice rather than connectivity reasons. This study proposes a vertical handover decision algorithm that uses a Fuzzy Logic (FL) algorithm, to increase QoS performance in heterogeneous vehicular ad-hoc networks (VANET). The study uses network simulator 2.29 (NS 2.29) along with the mobility traffic network and generator to implement simulation scenarios and topologies. This helps the simulation to achieve a realistic VANET mobility scenario. The required analysis on the performance of QoS in the vertical handover can thus be conducted. The proposed Fuzzy Logic algorithm shows improvement over the conventional algorithm (RSSThreshold) in the average percentage of handover QoS whereby it achieves 20%, 21% and 13% improvement on handover latency, delay, and packet loss respectively. This is achieved through triggering a process in layer two and three that enhances the handover performance

    Multipoint Relay Selection based on Stability of Spatial Relation in Mobile Ad hoc Networks

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    Increasing stability is one of the main objectives in designing routing protocols for Mobile Ad-Hoc Network (MANETS). Various research schemes have been addressed to this challenge and to support it. In fact, some papers have considered modifications to MPRs selection mechanism in OLSR. In this paper, the author proposes a new mechanism to elect stable and sustainable nodes relay between all nodes in MANETs. In this mechanism, a mobility function is used as the main selection criterion based on the calculation of the spatial relation of a node relative to its neighbor. This mechanism is applied in OLSR protocol to choose stable and supportable MPRs nodes. This mechanism significantly finds more stable MPRs and it promises QoS metrics such as lost packets and delay. Simulation results reveals a significant performance gains and it motivates further examinations to develop the mechanism in order to improve the routing protocol requirements. Performances are evaluated based on Random Waypoint model and network simulator ns3

    An intelligent network selection mechanism for vertical handover decision in vehicular Ad Hoc wireless networks

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    The design of the Vehicular Ad-hoc Network (VANET) technology is a modern paradigm for vehicular communication on movement. However, VANET's vertical handover (VHO) decision in seamless connectivity is a huge challenge caused by the network topology complexity and the large number of mobile nodes that affect the network traffic in terms of the data transmission and dissemination efficiency. Furthermore, the conventional scheme only uses a received signal strength as a metric value, which shows a lack of appropriate handover metrics that is more suitable in horizontal handover compared to VHO. Appropriate VHO decisions will result in an increase in the network quality of service (QoS) in terms of delay, latency, and packet loss. This study aims to design an intelligent network selection to minimize the handover delay and latency, and packet loss in the heterogeneous Vehicle-to- Infrastructure (V2I) wireless networks. The proposed intelligent network selection is known as the Adaptive Handover Decision (AHD) scheme that uses Fuzzy Logic (FL) and Simple Additive Weighting (SAW) algorithms, namely F-SAW scheme. The AHD scheme was designed to select the best-qualified access point (AP) and base station (BS) candidates without degrading the performance of ongoing applications. The F-SAW scheme is proposed to develop a handover triggering mechanism that generates multiple attributes parameters using the information context of vertical handover decision in the V2I heterogeneous wireless networks. This study uses a network simulator (NS-2) as the mobility traffic network and vehicular mobility traffic (VANETMobiSim) generator to implement a topology in a realistic VANET mobility scenario in Wi-Fi, WiMAX, and LTE networks technologies. The proposed AHD scheme shows an improvement in the QoS handover over the conventional (RSS-based) scheme with an average QoS increased of 21%, 20%, and 13% in delay, latency and packet loss, while Media Independent Handover based (MIH-based) scheme with 12.2%, 11%, and 7% respectively. The proposed scheme assists the mobile user in selecting the best available APs or BS during the vehicles’ movement without degrading the performance of ongoing applications

    A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

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    Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this article, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.acceptedVersio

    Empirical Analysis of Privacy Preservation Models for Cyber Physical Deployments from a Pragmatic Perspective

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    The difficulty of privacy protection in cyber-physical installations encompasses several sectors and calls for methods like encryption, hashing, secure routing, obfuscation, and data exchange, among others. To create a privacy preservation model for cyber physical deployments, it is advised that data privacy, location privacy, temporal privacy, node privacy, route privacy, and other types of privacy be taken into account. Consideration must also be given to other types of privacy, such as temporal privacy. The computationally challenging process of incorporating these models into any wireless network also affects quality of service (QoS) variables including end-to-end latency, throughput, energy use, and packet delivery ratio. The best privacy models must be used by network designers and should have the least negative influence on these quality-of-service characteristics. The designers used common privacy models for the goal of protecting cyber-physical infrastructure in order to achieve this. The limitations of these installations' interconnection and interface-ability are not taken into account in this. As a result, even while network security has increased, the network's overall quality of service has dropped. The many state-of-the-art methods for preserving privacy in cyber-physical deployments without compromising their performance in terms of quality of service are examined and analyzed in this research. Lowering the likelihood that such circumstances might arise is the aim of this investigation and review. These models are rated according to how much privacy they provide, how long it takes from start to finish to transfer data, how much energy they use, and how fast their networks are. In order to maximize privacy while maintaining a high degree of service performance, the comparison will assist network designers and researchers in selecting the optimal models for their particular deployments. Additionally, the author of this book offers a variety of tactics that, when used together, might improve each reader's performance. This study also provides a range of tried-and-true machine learning approaches that networks may take into account and examine in order to enhance their privacy performance
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