518 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    EASND: Energy Adaptive Secure Neighbour Discovery Scheme for Wireless Sensor Networks

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    Wireless Sensor Network (WSN) is defined as a distributed system of networking, which is enabled with set of resource constrained sensors, thus attempt to providing a large set of capabilities and connectivity interferences. After deployment nodes in the network must automatically affected heterogeneity of framework and design framework steps, including obtaining knowledge of neighbor nodes for relaying information. The primary goal of the neighbor discovery process is reducing power consumption and enhancing the lifespan of sensor devices. The sensor devices incorporate with advanced multi-purpose protocols, and specifically communication models with the pre-eminent objective of WSN applications. This paper introduces the power and security aware neighbor discovery for WSNs in symmetric and asymmetric scenarios. We have used different of neighbor discovery protocols and security models to make the network as a realistic application dependent model. Finally, we conduct simulation to analyze the performance of the proposed EASND in terms of energy efficiency, collisions, and security. The node channel utilization is exceptionally elevated, and the energy consumption to the discovery of neighbor nodes will also be significantly minimized. Experimental results show that the proposed model has valid accomplishment

    Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches

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    Traditional networking devices support only fixed features and limited configurability. Network softwarization leverages programmable software and hardware platforms to remove those limitations. In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms. This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0. P4 is the most popular technology to implement programmable data planes. However, programmable data planes, and in particular, the P4 technology, emerged only recently. Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking. The research of this thesis focuses on two open issues of programmable data planes. First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet. Second, it enables BIER in high-performance P4 data planes. BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet. The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study. Two more peer-reviewed papers contain additional content that is not directly related to the main results. They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts

    Hybridization of Energy Optimization Technique for Cluster Based Routing using Various Computational Intelligence Methods in WSN

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    Approaches in WSN technology has determined by opportunity of tiny and inexpensive sensor nodes with adequacy of sensing multiple kinds of information processing and wireless communication. Network lifetime and energy efficiency are major indexes of WSN. Several clustering techniques are intended to extend the network lifetime but whereas there is an issue of incompetent Cluster Head (CH) election. To overcome this issue, an Integration of Novel Memetic and Brain Storm Optimization approach with Levy Distribution (IoNM-BSOLyD) has been proposed for clustering using fitness function. In the meanwhile, election of CH is done by utilizing fitness function, which incorporates following amplitude such as energy, distance to adjacent nodes, distance to BS, and network load. After clustering, routing techniques decides the detecting and pursuing the route in WSN. In this proposed work, a Water Wave Optimization with Hill Climbing technique (WWO-HCg) is introduced for routing purpose. This proposed methodology deals with ternary QoS aspect such as network delay, energy consumption, packet delivery ratio, network lifetime and security to select optimal path and enhance QoS as well. This proposed protocol provides better performance result than other contemporary protocols

    Mobile Sink Node with Discerning Motility Approach for Energy Efficient Delay Sensitive Data Communication over Wireless Sensor Body Area Networks

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    The sensors nearby the static sink drains their energy resources rapidly, since they continuously involve to build routes in Wireless sensor networks, which are between data sources and static sink. Hence, the sensors nearby the sink having limited lifespan, which axing the network lifetime.The mobile-sink strategy that allows the sink to move around the network area to distribute the transmission overhead to multiple sensor nodes. However, the mobile-sink strategy is often tall ordered practice due to the continuous need of establishing routes between source nodes and the mobile sink (MS) at new position occurred due to its random mobility. In regard to above stated argument, this manuscript proposed a novel energy data transmission strategy which is effective for WSN with mobile sink. Unlike the traditional contributions, which relies on mobile sink with random mobility strategies, the proposal defines a discerning path for mobile sink routing between sectioned clusters of the WSN. The proposal of the manuscript titled “Mobile Sink Node with Discerning Motility Approach (MSDMA) for Energy Efficient Data Communication over WBAN”. The method defined in proposed model sections the target network in to multiple geographical clusters and prioritize these clusters by the delay sensitivity of the data transmitted by the sensor nodes of the corresponding clusters. Further, discriminating these clusters by their delay sensitive priority to define mobile sink route. For estimation of the delay sensitive priority of the clusters, set of metrics are proposed. The experimental study carried on simulation to assess the significance of the suggested method. The performance improvement of the suggested method is ascended through comparative analysis performed against benchmark model under divergent metrics

    MAC protokol adaptivnog faktora ispune zasnovan na predviđanju u bežičnim senzorskim mrežama sa prikupljanjem solarne energije

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    Harvesting ambient energy has enabled the development of energy-harvesting wireless sensor networks (EH-WSNs). However, in these networks, the uncertainty in harvesting rate due to dynamic weather conditions raises new challenges. Therefore, this drives the development of energy harvesting-aware solutions. Formerly, many MAC protocols have been developed for EH-WSNs, which offer various features based on available harvested energy to support different applications. Nevertheless, optimizing MAC performance by incorporating predicted future energy intake is relatively new in EH-WSNs. Therefore, this thesis presents a machine learning prediction based adaptive duty cycle medium access control (MAC) protocol for solar energy harvesting wireless sensor networks WSNs. The developed protocol incorporates information about the current and future harvested energy using mathematical formulations to improve network performance. By doing so, the proposed MAC protocol effectively addresses the primary goals of solar energy harvesting WSNs: ensuring long-term network sustainability and efficient utilization of harvested energy to enhance the application performance under dynamically changing energy harvesting conditions.Сакупљање амбијенталне енергије омогућило је развој бежичних сензорских мрежа (EH-WSN) за прикупљање енергије. Међутим, у овим мрежама, неизвесност у стопи жетве услед динамичних временских услова поставља нове изазове. Стога, ово покреће развој решења која су свесна прикупљања енергије. Раније су развијени многи MAC протоколи за EH-WSN, који нуде различите карактеристике засноване на доступној прикупљеној енергији за подршку различитим апликацијама. Ипак, оптимизација перформанси MAC-а укључивањем предвиђеног будућег уноса енергије је релативно нова у EH-WSN-овима. Стога, ова теза представља протокол адаптивног радног циклуса за контролу приступа медијуму (MAC) заснован на предвиђању заснованом на машинском учењу за бежичне WSN мреже за прикупљање соларне енергије. Развијени протокол укључује информације о тренутној и будућој прикупљеној енергији користећи математичке формулације за побољшање перформанси мреже. На тај начин, предложени MAC протокол ефикасно се бави примарним циљевима WSN-а за прикупљање соларне енергије: обезбеђивање дугорочне одрживости мреже и ефикасно коришћење прикупљене енергије за побољшање перформанси апликације под динамички променљивим условима прикупљања енергије.Sakupljanje ambijentalne energije omogućilo je razvoj bežičnih senzorskih mreža (EH-WSN) za prikupljanje energije. Međutim, u ovim mrežama, neizvesnost u stopi žetve usled dinamičnih vremenskih uslova postavlja nove izazove. Stoga, ovo pokreće razvoj rešenja koja su svesna prikupljanja energije. Ranije su razvijeni mnogi MAC protokoli za EH-WSN, koji nude različite karakteristike zasnovane na dostupnoj prikupljenoj energiji za podršku različitim aplikacijama. Ipak, optimizacija performansi MAC-a uključivanjem predviđenog budućeg unosa energije je relativno nova u EH-WSN-ovima. Stoga, ova teza predstavlja protokol adaptivnog radnog ciklusa za kontrolu pristupa medijumu (MAC) zasnovan na predviđanju zasnovanom na mašinskom učenju za bežične WSN mreže za prikupljanje solarne energije. Razvijeni protokol uključuje informacije o trenutnoj i budućoj prikupljenoj energiji koristeći matematičke formulacije za poboljšanje performansi mreže. Na taj način, predloženi MAC protokol efikasno se bavi primarnim ciljevima WSN-a za prikupljanje solarne energije: obezbeđivanje dugoročne održivosti mreže i efikasno korišćenje prikupljene energije za poboljšanje performansi aplikacije pod dinamički promenljivim uslovima prikupljanja energije

    MAC protokol adaptivnog faktora ispune zasnovan na predviđanju u bežičnim senzorskim mrežama sa prikupljanjem solarne energije

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    Harvesting ambient energy has enabled the development of energy-harvesting wireless sensor networks (EH-WSNs). However, in these networks, the uncertainty in harvesting rate due to dynamic weather conditions raises new challenges. Therefore, this drives the development of energy harvesting-aware solutions. Formerly, many MAC protocols have been developed for EH-WSNs, which offer various features based on available harvested energy to support different applications. Nevertheless, optimizing MAC performance by incorporating predicted future energy intake is relatively new in EH-WSNs. Therefore, this thesis presents a machine learning prediction based adaptive duty cycle medium access control (MAC) protocol for solar energy harvesting wireless sensor networks WSNs. The developed protocol incorporates information about the current and future harvested energy using mathematical formulations to improve network performance. By doing so, the proposed MAC protocol effectively addresses the primary goals of solar energy harvesting WSNs: ensuring long-term network sustainability and efficient utilization of harvested energy to enhance the application performance under dynamically changing energy harvesting conditions.Сакупљање амбијенталне енергије омогућило је развој бежичних сензорских мрежа (EH-WSN) за прикупљање енергије. Међутим, у овим мрежама, неизвесност у стопи жетве услед динамичних временских услова поставља нове изазове. Стога, ово покреће развој решења која су свесна прикупљања енергије. Раније су развијени многи MAC протоколи за EH-WSN, који нуде различите карактеристике засноване на доступној прикупљеној енергији за подршку различитим апликацијама. Ипак, оптимизација перформанси MAC-а укључивањем предвиђеног будућег уноса енергије је релативно нова у EH-WSN-овима. Стога, ова теза представља протокол адаптивног радног циклуса за контролу приступа медијуму (MAC) заснован на предвиђању заснованом на машинском учењу за бежичне WSN мреже за прикупљање соларне енергије. Развијени протокол укључује информације о тренутној и будућој прикупљеној енергији користећи математичке формулације за побољшање перформанси мреже. На тај начин, предложени MAC протокол ефикасно се бави примарним циљевима WSN-а за прикупљање соларне енергије: обезбеђивање дугорочне одрживости мреже и ефикасно коришћење прикупљене енергије за побољшање перформанси апликације под динамички променљивим условима прикупљања енергије.Sakupljanje ambijentalne energije omogućilo je razvoj bežičnih senzorskih mreža (EH-WSN) za prikupljanje energije. Međutim, u ovim mrežama, neizvesnost u stopi žetve usled dinamičnih vremenskih uslova postavlja nove izazove. Stoga, ovo pokreće razvoj rešenja koja su svesna prikupljanja energije. Ranije su razvijeni mnogi MAC protokoli za EH-WSN, koji nude različite karakteristike zasnovane na dostupnoj prikupljenoj energiji za podršku različitim aplikacijama. Ipak, optimizacija performansi MAC-a uključivanjem predviđenog budućeg unosa energije je relativno nova u EH-WSN-ovima. Stoga, ova teza predstavlja protokol adaptivnog radnog ciklusa za kontrolu pristupa medijumu (MAC) zasnovan na predviđanju zasnovanom na mašinskom učenju za bežične WSN mreže za prikupljanje solarne energije. Razvijeni protokol uključuje informacije o trenutnoj i budućoj prikupljenoj energiji koristeći matematičke formulacije za poboljšanje performansi mreže. Na taj način, predloženi MAC protokol efikasno se bavi primarnim ciljevima WSN-a za prikupljanje solarne energije: obezbeđivanje dugoročne održivosti mreže i efikasno korišćenje prikupljene energije za poboljšanje performansi aplikacije pod dinamički promenljivim uslovima prikupljanja energije

    Energy efficient data transmission using multiobjective improved remora optimization algorithm for wireless sensor network with mobile sink

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    A wireless sensor network (WSN) is a collection of nodes fitted with small sensors and transceiver elements. Energy consumption, data loss, and transmission delays are the major drawback of creating mobile sinks. For instance, battery life and data latency might result in node isolation, which breaks the link between nodes in the network. These issues have been avoided by means of mobile data sinks, which move between nodes with connection issues. Therefore, energy aware multiobjective improved remora optimization algorithm and multiobjective ant colony optimization (EA-MIROA-MACO) is proposed in this research to improve the WSN’s energy efficiency by eliminating node isolation issue. MIRO is utilized to pick the optimal cluster heads (CHs), while multiobjective ant colony optimization (MACO) is employed to find the path through the CHs. The EA-MIROA-MACO aims to optimize energy consumption in nodes and enhance data transmission within a WSN. The analysis of EA-MIROA-MACO’s performance is conducted by considering the number of alive along with dead nodes, average residual energy, and network lifespan. The EA-MIROA-MACO is compared with traditional approaches such as mobile sink and fuzzy based relay node routing (MSFBRR) protocol as well as hybrid neural network (HNN). The EA-MIROA-MACO demonstrates a higher number of alive nodes, specifically 192, over the MSFBRR and HNN for 2,000 rounds
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