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Radiometric fingerprint schemes have been shown effective in identifying wireless devices based on imperfections in their hardware electronics. The robustness of fingerprint systems under complex channel conditions, however, is a critical challenge that makes their application in real-world scenarios difficult. We systematically evaluate the wireless channel's impact on radiometric fingerprints and find that the channel impacts fingerprint features in a very particular way that depends on the channel's properties. Based on the insights, we present RRF, a system that provides a robust identification/authentication service even under complex channel fading disturbance. Our design deploys a hybrid architecture that combines wireless channel simulation, signal processing and machine learning. In this pipeline, RRF first utilizes a series of structured channel simulations to strategically improve system tolerance towards multipath channel interference. On top of that, in the identification phase, RRF relies on noise compensation and a feature denoising filter to augment the system's stability in noisy conditions with weak signals. Our experimental results show that RRF achieves an average accuracy consistently above 99% in empirical scenarios with complex channels, where the baseline approach from previous work rarely exceeds 50%.

Radiometric fingerprint schemes have been shown effective in identifying wireless devices based on imperfections in their hardware electronics. The robustness of fingerprint systems under complex channel conditions, however, is a critical challenge that makes their application in real-world scenarios difficult. We systematically evaluate the wireless channel's impact on radiometric fingerprints and find that the channel impacts fingerprint features in a very particular way that depends on the channel's properties. Based on the insights, we present RRF, a system that provides a robust identification/authentication service even under complex channel fading disturbance. Our design deploys a hybrid architecture that combines wireless channel simulation, signal processing and machine learning. In this pipeline, RRF first utilizes a series of structured channel simulations to strategically improve system tolerance towards multipath channel interference. On top of that, in the identification phase, RRF relies on noise compensation and a feature denoising filter to augment the system's stability in noisy conditions with weak signals. Our experimental results show that RRF achieves an average accuracy consistently above 99% in empirical scenarios with complex channels, where the baseline approach from previous work rarely exceeds 50%.

Radiometric fingerprint schemes have been shown effective in identifying wireless devices based on imperfections in their hardware electronics. The robustness of fingerprint systems under complex channel conditions, however, is a critical challenge that makes their application in real-world scenarios difficult. We systematically evaluate the wireless channel's impact on radiometric fingerprints and find that the channel impacts fingerprint features in a very particular way that depends on the channel's properties. Based on the insights, we present RRF, a system that provides a robust identification/authentication service even under complex channel fading disturbance. Our design deploys a hybrid architecture that combines wireless channel simulation, signal processing and machine learning. In this pipeline, RRF first utilizes a series of structured channel simulations to strategically improve system tolerance towards multipath channel interference. On top of that, in the identification phase, RRF relies on noise compensation and a feature denoising filter to augment the system's stability in noisy conditions with weak signals. Our experimental results show that RRF achieves an average accuracy consistently above 99% in empirical scenarios with complex channels, where the baseline approach from previous work rarely exceeds 50%.

Radiometric fingerprint schemes have been shown effective in identifying wireless devices based on imperfections in their hardware electronics. The robustness of fingerprint systems under complex channel conditions, however, is a critical challenge that makes their application in real-world scenarios difficult. We systematically evaluate the wireless channel's impact on radiometric fingerprints and find that the channel impacts fingerprint features in a very particular way that depends on the channel's properties. Based on the insights, we present RRF, a system that provides a robust identification/authentication service even under complex channel fading disturbance. Our design deploys a hybrid architecture that combines wireless channel simulation, signal processing and machine learning. In this pipeline, RRF first utilizes a series of structured channel simulations to strategically improve system tolerance towards multipath channel interference. On top of that, in the identification phase, RRF relies on noise compensation and a feature denoising filter to augment the system's stability in noisy conditions with weak signals. Our experimental results show that RRF achieves an average accuracy consistently above 99% in empirical scenarios with complex channels, where the baseline approach from previous work rarely exceeds 50%.

Radiometric fingerprint schemes have been shown effective in identifying wireless devices based on imperfections in their hardware electronics. The robustness of fingerprint systems under complex channel conditions, however, is a critical challenge that makes their application in real-world scenarios difficult. We systematically evaluate the wireless channel's impact on radiometric fingerprints and find that the channel impacts fingerprint features in a very particular way that depends on the channel's properties. Based on the insights, we present RRF, a system that provides a robust identification/authentication service even under complex channel fading disturbance. Our design deploys a hybrid architecture that combines wireless channel simulation, signal processing and machine learning. In this pipeline, RRF first utilizes a series of structured channel simulations to strategically improve system tolerance towards multipath channel interference. On top of that, in the identification phase, RRF relies on noise compensation and a feature denoising filter to augment the system's stability in noisy conditions with weak signals. Our experimental results show that RRF achieves an average accuracy consistently above 99% in empirical scenarios with complex channels, where the baseline approach from previous work rarely exceeds 50%.

Network operators are accustomed to use IP-layer active measurements for assessing end-to-end network performance and expect that new technology, such as IoT, provides similar means. This paper investigates the potential and also overhead associated with active measurements in IoT environments, with emphasis on the wireless part. An experimental approach provides insights into RTT accuracy for IoT devices, overhead in terms of energy consumption and network impact, and visualization of network-wide results for bottleneck localization.

We present a hardware platform for performing experimental studies of energy storage devices for low power wireless networks. It is based on a low-cost custom card that can apply fine-grain synthetic loads ? both charge and discharge ? to a set of batteries or capacitors and measure their response in detail. Loads can be defined from a "live" trace of a running wireless device, from a recorded trace, or programmatically via a script. This approach makes it practical to run well controlled, large scale, long running experiments and to obtain high precision and accuracy. We describe two proof-of-concept experiments using rechargeable Li coin cells and capacitors to demonstrate the capabilities of our platform.

We present LoRea an architecture consisting of a backscatter tag, a reader and multiple carrier generators that overcomes the power, cost and range limitations of existing backscatter systems such as Computational Radio Frequency Identification (CRFID). LoRea achieves this by: First, generating narrow-band backscatter transmissions. Second, by mitigating self-interference without the complex designs employed on RFID readers by keeping carrier signal and backscattered signal apart in frequency. Finally, by decoupling carrier generation from the reader and using devices such as WiFi routers and sensor nodes as a source of the carrier signal. LoRea's communication range scales with the carrier strength, and proximity to the carrier source and achieves a maximum range of 3.4 km when the tag is located 1 m from the carrier source while consuming 70 mu Ws at the backscatter tag. We present various ultra-low power and long-range features of the LoRea architecture.

There is the long-standing assumption that radio communication in the range of hundreds of meters needs to consume mWs of power at the transmitting device. In this paper, we demonstrate that this is not necessarily the case for some devices equipped with backscatter radios. We present LoRea an architecture consisting of a tag, a reader and multiple carrier generators that overcomes the power, cost and range limitations of existing systems such as Computational Radio Frequency Identification (CRFID). LoRea achieves this by: First, generating narrow-band backscatter transmissions that improve receiver sensitivity. Second, mitigating self-interference without the complex designs employed on RFID readers by keeping carrier signal and backscattered signal apart in frequency. Finally, decoupling carrier generation from the reader and using devices such as WiFi routers and sensor nodes as a source of the carrier signal. An off-the-shelf implementation of LoRea costs 70 USD, a drastic reduction in price considering commercial RFID readers cost 2000 USD. LoRea's range scales with the carrier strength, and proximity to the carrier source and achieves a maximum range of 3.4 km when the tag is located at 1m distance from a 28 dBm carrier source while consuming 70 mu W at the tag. When the tag is equidistant from the carrier source and the receiver, we can communicate upto 75m, a significant improvement over existing RFID readers.

There is the long-standing assumption that radio communication in the range of hundreds of meters needs to consume mWs of power at the transmitting device. In this paper, we demonstrate that this is not necessarily the case for some devices equipped with backscatter radios. We present LoRea an architecture consisting of a tag, a reader and multiple carrier generators that overcomes the power, cost and range limitations of existing systems such as Computational Radio Frequency Identification (CRFID). LoRea achieves this by: First, generating narrow-band backscatter transmissions that improve receiver sensitivity. Second, mitigating self-interference without the complex designs employed on RFID readers by keeping carrier signal and backscattered signal apart in frequency. Finally, decoupling carrier generation from the reader and using devices such as WiFi routers and sensor nodes as a source of the carrier signal. An off-the-shelf implementation of LoRea costs 70 USD, a drastic reduction in price considering commercial RFID readers cost 2000 USD. LoRea's range scales with the carrier strength, and proximity to the carrier source and achieves a maximum range of 3.4 km when the tag is located at 1m distance from a 28 dBm carrier source while consuming 70 mu W at the tag. When the tag is equidistant from the carrier source and the receiver, we can communicate upto 75m, a significant improvement over existing RFID readers.

There is the long-standing assumption that radio communication in the range of hundreds of meters needs to consume mWs of power at the transmitting device. In this paper, we demonstrate that this is not necessarily the case for some devices equipped with backscatter radios. We present LoRea an architecture consisting of a tag, a reader and multiple carrier generators that overcomes the power, cost and range limitations of existing systems such as Computational Radio Frequency Identification (CRFID). LoRea achieves this by: First, generating narrow-band backscatter transmissions that improve receiver sensitivity. Second, mitigating self-interference without the complex designs employed on RFID readers by keeping carrier signal and backscattered signal apart in frequency. Finally, decoupling carrier generation from the reader and using devices such as WiFi routers and sensor nodes as a source of the carrier signal. An off-the-shelf implementation of LoRea costs 70 USD, a drastic reduction in price considering commercial RFID readers cost 2000 USD. LoRea's range scales with the carrier strength, and proximity to the carrier source and achieves a maximum range of 3.4 km when the tag is located at 1m distance from a 28 dBm carrier source while consuming 70 mu W at the tag. When the tag is equidistant from the carrier source and the receiver, we can communicate upto 75m, a significant improvement over existing RFID readers.

There is the long-standing assumption that radio communication in the range of hundreds of meters needs to consume mWs of power at the transmitting device. In this paper, we demonstrate that this is not necessarily the case for some devices equipped with backscatter radios. We present LoRea an architecture consisting of a tag, a reader and multiple carrier generators that overcomes the power, cost and range limitations of existing systems such as Computational Radio Frequency Identification (CRFID). LoRea achieves this by: First, generating narrow-band backscatter transmissions that improve receiver sensitivity. Second, mitigating self-interference without the complex designs employed on RFID readers by keeping carrier signal and backscattered signal apart in frequency. Finally, decoupling carrier generation from the reader and using devices such as WiFi routers and sensor nodes as a source of the carrier signal. An off-the-shelf implementation of LoRea costs 70 USD, a drastic reduction in price considering commercial RFID readers cost 2000 USD. LoRea's range scales with the carrier strength, and proximity to the carrier source and achieves a maximum range of 3.4 km when the tag is located at 1m distance from a 28 dBm carrier source while consuming 70 mu W at the tag. When the tag is equidistant from the carrier source and the receiver, we can communicate upto 75m, a significant improvement over existing RFID readers.

There is the long-standing assumption that radio communication in the range of hundreds of meters needs to consume mWs of power at the transmitting device. In this paper, we demonstrate that this is not necessarily the case for some devices equipped with backscatter radios. We present LoRea an architecture consisting of a tag, a reader and multiple carrier generators that overcomes the power, cost and range limitations of existing systems such as Computational Radio Frequency Identification (CRFID). LoRea achieves this by: First, generating narrow-band backscatter transmissions that improve receiver sensitivity. Second, mitigating self-interference without the complex designs employed on RFID readers by keeping carrier signal and backscattered signal apart in frequency. Finally, decoupling carrier generation from the reader and using devices such as WiFi routers and sensor nodes as a source of the carrier signal. An off-the-shelf implementation of LoRea costs 70 USD, a drastic reduction in price considering commercial RFID readers cost 2000 USD. LoRea's range scales with the carrier strength, and proximity to the carrier source and achieves a maximum range of 3.4 km when the tag is located at 1m distance from a 28 dBm carrier source while consuming 70 mu W at the tag. When the tag is equidistant from the carrier source and the receiver, we can communicate upto 75m, a significant improvement over existing RFID readers.

CNDS

The performance of an outdoor wireless sensor network for remote monitoring is dictated by its ability to deliver sensed data to a sink. The often isolated location means that radio interference is typically low and with different patterns in transmission errors compared to an indoor deployment. To better understand these processes, we investigate packet errors in such a network. Specically, we study characteristics of decoding errors within 802.15.4 transmissions. We describe the experimental deployment of an outdoor sensor network, located above the Arctic circle, where we log both successfully received and broken packets. Results indicate that a substantial amount of received packets contain errors, where the errors in each packet are typically few. We distinguish between transmission errors and payload errors, and find that transmission errors are equally probable over all positions of a packet, whereas bit errors in the payload are not. This results in some bits having a 25% higher risk of being corrupted than others., CNDS, WISENET

ProFuN, WISENET

Participatory sensing applications typically bind sensor data to locations. Location privacy preserving mechanisms protecting the location of users introduce therefore an uncertainty in the collected data distributions. We consider two strategies to reconstruct a data distribution after k-anonymity has been applied to the users' location information. We investigate how different parameters for the location privacy preserving mechanism influence both the quality of information and the location privacy of the users. Our results show that the cloak area resulted from applying k-anonymity has a higher impact on both the quality of information and the location privacy than the number of users, k, that are cloaked together., ProFuN, WISENET

In Delay Tolerant Networks (DTNs) as an emerging content dissemination platform, mobile nodes opportunistically exchange content as they meet, with the intent of disseminating content among nodes that share common interests. During a meeting, nodes can exchange both content of direct interest to themselves as well as content that is of interest to a larger set of nodes that may be encountered in the future. The utility of DTN is governed by the content exchange opportunity (the amount of content that can be exchanged during a meeting) as well as the selection of content to be exchanged in order to maximise the interest nodes will have in information they are exposed to. Considering that there is a cost associated with the content exchange (e.g. battery usage, buffer occupancy or consumed transmission opportunity) the aim for nodes participating in content dissemination should be to maximise their payoff. In this paper, we contribute a generic framework for describing the characteristics of content exchange among participating nodes in a network. We incorporate a distributed information popularity measurement and the pairwise interaction of nodes modelled as a bargaining problem. The outcome of this process is the fair split up of dwelling time as a network resource and the selection of which content objects to exchange in order to maximise the nodes’ payoff. The framework is generally intended to be used as a capstone for investigation of content dissemination properties and various content exchange strategies in a DTN, a topic addressed in this paper and experiments conducted to validate the function and correctness of the proposed framework., wisenet, resumenet

In Delay Tolerant Networks (DTNs) as an emerging content dissemination platform, mobile nodes opportunistically exchange content as they meet, with the intent of disseminating content among nodes that share common interests. During a meeting, nodes can exchange both content of direct interest to themselves as well as content that is of interest to a larger set of nodes that may be encountered in the future. The utility of DTN is governed by the content exchange opportunity (the amount of content that can be exchanged during a meeting) as well as the selection of content to be exchanged in order to maximise the interest nodes will have in information they are exposed to. Considering that there is a cost associated with the content exchange (e.g. battery usage, buffer occupancy or consumed transmission opportunity) the aim for nodes participating in content dissemination should be to maximise their payoff. In this paper, we contribute a generic framework for describing the characteristics of content exchange among participating nodes in a network. We incorporate a distributed information popularity measurement and the pairwise interaction of nodes modelled as a bargaining problem. The outcome of this process is the fair split up of dwelling time as a network resource and the selection of which content objects to exchange in order to maximise the nodes’ payoff. The framework is generally intended to be used as a capstone for investigation of content dissemination properties and various content exchange strategies in a DTN, a topic addressed in this paper and experiments conducted to validate the function and correctness of the proposed framework., wisenet, resumenet

In Delay Tolerant Networks (DTNs) as an emerging content dissemination platform, mobile nodes opportunistically exchange content as they meet, with the intent of disseminating content among nodes that share common interests. During a meeting, nodes can exchange both content of direct interest to themselves as well as content that is of interest to a larger set of nodes that may be encountered in the future. The utility of DTN is governed by the content exchange opportunity (the amount of content that can be exchanged during a meeting) as well as the selection of content to be exchanged in order to maximise the interest nodes will have in information they are exposed to. Considering that there is a cost associated with the content exchange (e.g. battery usage, buffer occupancy or consumed transmission opportunity) the aim for nodes participating in content dissemination should be to maximise their payoff. In this paper, we contribute a generic framework for describing the characteristics of content exchange among participating nodes in a network. We incorporate a distributed information popularity measurement and the pairwise interaction of nodes modelled as a bargaining problem. The outcome of this process is the fair split up of dwelling time as a network resource and the selection of which content objects to exchange in order to maximise the nodes’ payoff. The framework is generally intended to be used as a capstone for investigation of content dissemination properties and various content exchange strategies in a DTN, a topic addressed in this paper and experiments conducted to validate the function and correctness of the proposed framework., wisenet, resumenet

In Delay Tolerant Networks (DTNs) as an emerging content dissemination platform, mobile nodes opportunistically exchange content as they meet, with the intent of disseminating content among nodes that share common interests. During a meeting, nodes can exchange both content of direct interest to themselves as well as content that is of interest to a larger set of nodes that may be encountered in the future. The utility of DTN is governed by the content exchange opportunity (the amount of content that can be exchanged during a meeting) as well as the selection of content to be exchanged in order to maximise the interest nodes will have in information they are exposed to. Considering that there is a cost associated with the content exchange (e.g. battery usage, buffer occupancy or consumed transmission opportunity) the aim for nodes participating in content dissemination should be to maximise their payoff. In this paper, we contribute a generic framework for describing the characteristics of content exchange among participating nodes in a network. We incorporate a distributed information popularity measurement and the pairwise interaction of nodes modelled as a bargaining problem. The outcome of this process is the fair split up of dwelling time as a network resource and the selection of which content objects to exchange in order to maximise the nodes’ payoff. The framework is generally intended to be used as a capstone for investigation of content dissemination properties and various content exchange strategies in a DTN, a topic addressed in this paper and experiments conducted to validate the function and correctness of the proposed framework., wisenet, resumenet

In Delay Tolerant Networks (DTNs) as an emerging content dissemination platform, mobile nodes opportunistically exchange content as they meet, with the intent of disseminating content among nodes that share common interests. During a meeting, nodes can exchange both content of direct interest to themselves as well as content that is of interest to a larger set of nodes that may be encountered in the future. The utility of DTN is governed by the content exchange opportunity (the amount of content that can be exchanged during a meeting) as well as the selection of content to be exchanged in order to maximise the interest nodes will have in information they are exposed to. Considering that there is a cost associated with the content exchange (e.g. battery usage, buffer occupancy or consumed transmission opportunity) the aim for nodes participating in content dissemination should be to maximise their payoff. In this paper, we contribute a generic framework for describing the characteristics of content exchange among participating nodes in a network. We incorporate a distributed information popularity measurement and the pairwise interaction of nodes modelled as a bargaining problem. The outcome of this process is the fair split up of dwelling time as a network resource and the selection of which content objects to exchange in order to maximise the nodes’ payoff. The framework is generally intended to be used as a capstone for investigation of content dissemination properties and various content exchange strategies in a DTN, a topic addressed in this paper and experiments conducted to validate the function and correctness of the proposed framework., wisenet, resumenet