488 research outputs found

    Covert Communication in Autoencoder Wireless Systems

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    The broadcast nature of wireless communications presents security and privacy challenges. Covert communication is a wireless security practice that focuses on intentionally hiding transmitted information. Recently, wireless systems have experienced significant growth, including the emergence of autoencoder-based models. These models, like other DNN architectures, are vulnerable to adversarial attacks, highlighting the need to study their susceptibility to covert communication. While there is ample research on covert communication in traditional wireless systems, the investigation of autoencoder wireless systems remains scarce. Furthermore, many existing covert methods are either detectable analytically or difficult to adapt to diverse wireless systems. The first part of this thesis provides a comprehensive examination of autoencoder-based communication systems in various scenarios and channel conditions. It begins with an introduction to autoencoder communication systems, followed by a detailed discussion of our own implementation and evaluation results. This serves as a solid foundation for the subsequent part of the thesis, where we propose a GAN-based covert communication model. By treating the covert sender, covert receiver, and observer as generator, decoder, and discriminator neural networks, respectively, we conduct joint training in an adversarial setting to develop a covert communication scheme that can be integrated into any normal autoencoder. Our proposal minimizes the impact on ongoing normal communication, addressing previous works shortcomings. We also introduce a training algorithm that allows for the desired tradeoff between covertness and reliability. Numerical results demonstrate the establishment of a reliable and undetectable channel between covert users, regardless of the cover signal or channel condition, with minimal disruption to the normal system operation

    Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

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    Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network and list some target values for the next generation of wireless systems. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.Comment: 25 figures, 12 tables, submitted to IEEE Open Journal of the Communications Societ

    Signal Processing and Learning for Next Generation Multiple Access in 6G

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    Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed

    Maximizing the stable throughput of heterogeneous nodes under airtime fairness in a CSMA environment

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    The stability region of non-persistent CSMA is analyzed in a general heterogeneous network, where stations have different mean packet arrival rates, packet transmission times probability distributions and transmission probabilities. The considered model of CSMA captures the behavior of the well known CSMA/CA, at least as far as stability and throughput evaluation are concerned. The analysis is done both with and without collision detection. Given the characterization of the stability region, throughput-optimal transmission probabilities are identified under airtime fairness, establishing asymptotic upper and lower bounds of the maximum achievable stable throughput. The bounds turn out to be insensitive to the probability distribution of packet transmission times. Numerical results highlight that the obtained bounds are tight not only asymptotically, but also for essentially all values of the number of stations. The insight gained leads to the definition of a distributed adaptive algorithm to adjust the transmission probabilities of stations so as to attain the maximum stable throughput

    Data Collection in Two-Tier IoT Networks with Radio Frequency (RF) Energy Harvesting Devices and Tags

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    The Internet of things (IoT) is expected to connect physical objects and end-users using technologies such as wireless sensor networks and radio frequency identification (RFID). In addition, it will employ a wireless multi-hop backhaul to transfer data collected by a myriad of devices to users or applications such as digital twins operating in a Metaverse. A critical issue is that the number of packets collected and transferred to the Internet is bounded by limited network resources such as bandwidth and energy. In this respect, IoT networks have adopted technologies such as time division multiple access (TDMA), signal interference cancellation (SIC) and multiple-input multiple-output (MIMO) in order to increase network capacity. Another fundamental issue is energy. To this end, researchers have exploited radio frequency (RF) energy-harvesting technologies to prolong the lifetime of energy constrained sensors and smart devices. Specifically, devices with RF energy harvesting capabilities can rely on ambient RF sources such as access points, television towers, and base stations. Further, an operator may deploy dedicated power beacons that serve as RF-energy sources. Apart from that, in order to reduce energy consumption, devices can adopt ambient backscattering communication technologies. Advantageously, backscattering allows devices to communicate using negligible amount of energy by modulating ambient RF signals. To address the aforementioned issues, this thesis first considers data collection in a two-tier MIMO ambient RF energy-harvesting network. The first tier consists of routers with MIMO capability and a set of source-destination pairs/flows. The second tier consists of energy harvesting devices that rely on RF transmissions from routers for energy supply. The problem is to determine a minimum-length TDMA link schedule that satisfies the traffic demand of source-destination pairs and energy demand of energy harvesting devices. It formulates the problem as a linear program (LP), and outlines a heuristic to construct transmission sets that are then used by the said LP. In addition, it outlines a new routing metric that considers the energy demand of energy harvesting devices to cope with routing requirements of IoT networks. The simulation results show that the proposed algorithm on average achieves 31.25% shorter schedules as compared to competing schemes. In addition, the said routing metric results in link schedules that are at most 24.75% longer than those computed by the LP

    An Internet of Things (IoT) based wide-area Wireless Sensor Network (WSN) platform with mobility support.

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    Wide-area remote monitoring applications use cellular networks or satellite links to transfer sensor data to the central storage. Remote monitoring applications uses Wireless Sensor Networks (WSNs) to accommodate more Sensor Nodes (SNs) and for better management. Internet of Things (IoT) network connects the WSN with the data storage and other application specific services using the existing internet infrastructure. Both cellular networks, such as the Narrow-Band IoT (NB-IoT), and satellite links will not be suitable for point-to-point connections of the SNs due to their lack of coverage, high cost, and energy requirement. Low Power Wireless Area Network (LPWAN) is used to interconnect all the SNs and accumulate the data to a single point, called Gateway, before sending it to the IoT network. WSN implements clustering of the SNs to increase the network coverage and utilizes multiple wireless links between the repeater nodes (called hops) to reach the gateway at a longer distance. Clustered WSN can cover up to a few km using the LPWAN technologies such as Zigbee using multiple hops. Each Zigbee link can be from 200 m to 500 m long. Other LPWAN technologies, such as LoRa, can facilitate an extended range from 1km to 15km. However, the LoRa will not be suitable for the clustered WSN due to its long Time on Air (TOA) which will introduce data transmission delay and become severe with the increase of hop count. Besides, a sensor node will need to increase the antenna height to achieve the long-range benefit of Lora using a single link (hop) instead of using multiple hops to cover the same range. With the increased WSN coverage area, remote monitoring applications such as smart farming may require mobile sensor nodes. This research focuses on the challenges to overcome LoRa’s limitations (long TOA and antenna height) and accommodation of mobility in a high-density and wide-area WSN for future remote monitoring applications. Hence, this research proposes lightweight communication protocols and networking algorithms using LoRa to achieve mobility, energy efficiency and wider coverage of up to a few hundred km for the WSN. This thesis is divided into four parts. It presents two data transmission protocols for LoRa to achieve a higher data rate and wider network coverage, one networking algorithm for wide-area WSN and a channel synchronization algorithm to improve the data rate of LoRa links. Part one presents a lightweight data transmission protocol for LoRa using a mobile data accumulator (called data sink) to increase the monitoring coverage area and data transmission energy efficiency. The proposed Lightweight Dynamic Auto Reconfigurable Protocol (LDAP) utilizes direct or single hop to transmit data from the SNs using one of them as the repeater node. Wide-area remote monitoring applications such as Water Quality Monitoring (WQM) can acquire data from geographically distributed water resources using LDAP, and a mobile Data Sink (DS) mounted on an Unmanned Aerial Vehicle (UAV). The proposed LDAP can acquire data from a minimum of 147 SNs covering 128 km in one direction reducing the DS requirement down to 5% comparing other WSNs using Zigbee for the same coverage area with static DS. Applications like smart farming and environmental monitoring may require mobile sensor nodes (SN) and data sinks (DS). The WSNs for these applications will require real-time network management algorithms and routing protocols for the dynamic WSN with mobility that is not feasible using static WSN technologies. This part proposes a lightweight clustering algorithm for the dynamic WSN (with mobility) utilizing the proposed LDAP to form clusters in real-time during the data accumulation by the mobile DS. The proposed Lightweight Dynamic Clustering Algorithm (LDCA) can form real-time clusters consisting of mobile or stationary SNs using mobile DS or static GW. WSN using LoRa and LDCA increases network capacity and coverage area reducing the required number of DS. It also reduces clustering energy to 33% and shows clustering efficiency of up to 98% for single-hop clustering covering 100 SNs. LoRa is not suitable for a clustered WSN with multiple hops due to its long TOA, depending on the LoRa link configurations (bandwidth and spreading factor). This research proposes a channel synchronization algorithm to improve the data rate of the LoRa link by combining multiple LoRa radio channels in a single logical channel. This increased data rate will enhance the capacity of the clusters in the WSN supporting faster clustering with mobile sensor nodes and data sink. Along with the LDCA, the proposed Lightweight Synchronization Algorithm for Quasi-orthogonal LoRa channels (LSAQ) facilitating multi-hop data transfer increases WSN capacity and coverage area. This research investigates quasi-orthogonality features of LoRa in terms of radio channel frequency, spreading factor (SF) and bandwidth. It derived mathematical models to obtain the optimal LoRa parameters for parallel data transmission using multiple SFs and developed a synchronization algorithm for LSAQ. The proposed LSAQ achieves up to a 46% improvement in network capacity and 58% in data rate compared with the WSN using the traditional LoRa Medium Access Control (MAC) layer protocols. Besides the high-density clustered WSN, remote monitoring applications like plant phenotyping may require transferring image or high-volume data using LoRa links. Wireless data transmission protocols used for high-volume data transmission using the link with a low data rate (like LoRa) requiring multiple packets create a significant amount of packet overload. Besides, the reliability of these data transmission protocols is highly dependent on acknowledgement (ACK) messages creating extra load on overall data transmission and hence reducing the application-specific effective data rate (goodput). This research proposes an application layer protocol to improve the goodput while transferring an image or sequential data over the LoRa links in the WSN. It uses dynamic acknowledgement (DACK) protocol for the LoRa physical layer to reduce the ACK message overhead. DACK uses end-of-transmission ACK messaging and transmits multiple packets as a block. It retransmits missing packets after receiving the ACK message at the end of multiple blocks. The goodput depends on the block size and the number of lossy packets that need to be retransmitted. It shows that the DACK LoRa can reduce the total ACK time 10 to 30 times comparing stop-wait protocol and ten times comparing multi-packet ACK protocol. The focused wide-area WSN and mobility requires different matrices to be evaluated. The performance evaluation matrices used for the static WSN do not consider the mobility and the related parameters, such as clustering efficiency in the network and hence cannot evaluate the performance of the proposed wide-area WSN platform supporting mobility. Therefore, new, and modified performance matrices are proposed to measure dynamic performance. It can measure the real-time clustering performance using the mobile data sink and sensor nodes, the cluster size, the coverage area of the WSN and more. All required hardware and software design, dimensioning, and performance evaluation models are also presented

    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

    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

    Time-, Graph- and Value-based Sampling of Internet of Things Sensor Networks

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