This paper focuses on enhancing the energy efficiency (EE) of a cooperative network that features a miniature unmanned aerial vehicle (UAV) operating at terahertz (THz) frequencies and equipped with holographic surfaces to improve network performance. Unlike traditional reconfigurable intelligent surfaces (RIS), which serve as passive relays for signal reflection, this work introduces a novel concept: energy harvesting (EH) using reconfigurable holographic surfaces (RHS). These surfaces provide more powerful and focused energy delivery during wireless power transfer than RIS and are mounted on the miniature UAV. In this system, a source node enables the UAV to simultaneously receive both information and energy signals, with the harvested energy powering data transmission to a specific destination. The EE optimization problem involves adjusting non-orthogonal multiple access (NOMA) power coefficients and the UAV’s flight path while accounting for the unique characteristics of the THz channel. The problem is solved in two stages to maximize EE and meet a target transmission rate. The UAV trajectory is optimized using a successive convex approximation (SCA) method, followed by the adjustment of NOMA power coefficients through a quadratic transform technique. Simulation results demonstrate the effectiveness of the proposed algorithm, showing significant improvements over baseline methods.

The increasing miniaturization of Unmanned Aerial Vehicles (UAVs) has expanded their deployment potential to indoor and hard-to-reach areas. However, this trend introduces distinct challenges, particularly in terms of flight dynamics and power consumption, which limit the UAVs’ autonomy and mission capabilities. This paper presents a novel approach to overcoming these limitations by integrating Neural 3D Reconstruction (N3DR) with small UAV systems for fine-grained 3-Dimensional (3D) digital reconstruction of small static objects. Specifically, we design, implement, and evaluate an N3DR-based pipeline that leverages advanced models, i.e., Instant-ngp, Nerfacto, and Splatfacto, to improve the quality of 3D reconstructions using images of the object captured by a fleet of small UAVs. We assess the performance of the considered models using various imagery and pointcloud metrics, comparing them against the baseline Structure from Motion (SfM) algorithm. The experimental results demonstrate that the N3DR-enhanced pipeline significantly improves reconstruction quality, making it feasible for small UAVs to support high-precision 3D mapping and anomaly detection in constrained environments. In more general terms, our results highlight the potential of N3DR in advancing the capabilities of miniaturized UAV systems.

Localization of in-body devices is beneficial for Gastrointestinal (GI) diagnosis and targeted treatment. Traditional methods such as imaging and endoscopy are invasive and limited in resolution, highlighting the need for innovative alternatives. This study presents an experimental framework for Radio Frequency (RF)-backscatter-based in-body localization, inspired by the ReMix approach, and evaluates its performance in real-world conditions. The experimental setup includes an in-body backscatter device and various off-body antenna configurations to investigate harmonic generation and reception in air, chicken and pork tissues. The results indicate that optimal backscatter device positioning, antenna selection, and gain settings significantly impact performance, with denser biological tissues leading to greater attenuation. The study also highlights challenges such as external interference and plastic enclosures affecting propagation. The findings emphasize the importance of interference mitigation and refined propagation models to enhance performance.

Rapid and accurate localization of individuals during search-and-rescue (SAR) missions is essential for reducing casualties in emergencies. Traditional methods often struggle in disaster scenarios, where obstacles like debris or dense foliage hinder performance, and reliance on user-side actions proves impractical for mission-critical operations. This paper presents a novel approach that leverages a 5G-new radio (NR)-based unmanned aerial vehicle (UAV) localization framework, integrating hybrid techniques with adaptive clustering strategies to localize user equipments (UEs). Unlike traditional methods, our system dynamically adjusts its trajectory to balance latency and accuracy, achieving UEs positioning accuracy within tens of centimeters during simulation tests. By integrating 5G-NR technology into UAV-based localization, our approach provides a robust and scalable solution for mission-critical SAR operations, significantly enhancing the latency and reliability of locating individuals in emergency situations.

Achieving high-quality wireless interactive Extended Reality (XR) will require multi-gigabit throughput at extremely low latency. The Millimeter-Wave (mmWave) frequency bands, between 24 and 300 GHz, can achieve such extreme performance. However, maintaining a consistently high Quality of Experience with highly mobile users is challenging, as mmWave communications are inherently directional. In this work, we present and evaluate an end-to-end approach to such a mmWave-based mobile XR system. We perform a highly realistic simulation of the system, incorporating accurate XR data traffic, detailed mmWave propagation models and actual user motion. We evaluate the impact of the beamforming strategy and frequency on the overall performance. In addition, we provide the first system-level evaluation of the CoVRage algorithm, a proactive and spatially aware user-side beamforming approach designed specifically for highly mobile XR environments.

Next-generation Virtual Reality (VR) technology enables full-user immersion and support for multiuser Virtual Experiences (VEs). Given the low-cost and passive nature of intelligent reflecting surfaces (IRSs), this paper investigates the optimal design of a multi-user IRS-assisted VR network, where an IRS is optimally deployed in a confined space as a function of VR fully-immersed users' trajectory. In particular, we consider sum-rate maximization of all VR users and optimize the Access Point's (AP) active beamforming, and the IRS's placement, phase shifts, and radiation patterns in a confined indoor environment operating in millimeter Wave (mmWave) frequencies. We introduce the Alternating Optimization (AO) algorithm, decompose the problem into distinct sub-problems, and solve each problem optimally. That is, maximum-ratio transmission (MRT) is applied for optimal beamforming at the AP, optimal closed-from IRS phase shifts are determined using quadratic transformation, global optimization is conducted to determine the ideal locations for the IRS elements, and the monotonic optimal radiation pattern has been analyzed. Our findings highlight that strategically allocating the IRS's resources at optimal physical locations enhances signal stability and maximizes per-user throughput.

Contemporary research advances in nanotechnology and material science are rooted in the emergence of nanodevices as a versatile tool that harmonizes sensing, computing, wireless communication, data storage, and energy harvesting. These devices hold promise in precision medicine, offering novel pathways for disease diagnostics, treatment, and monitoring within the bloodstreams. Ensuring precise localization of events of diagnostic interest, which underpins the concept of flow-guided in-body nanoscale localization, would intuitively provide an added diagnostic value to the detected events. Raw data generated by the nanodevices is pivotal for this localization and consist of an event detection indicator and the time elapsed since the last passage of a nanodevice through the heart. The communication and energy constraints of the nanodevices lead to intermittent operation and unreliable communication, intrinsically affecting this data. This posits a need for comprehensively modelling the features of this data. These imperfections also have profound implications for the viability of existing flow-guided localization approaches, which are ill-prepared to address the intricacies of the environment. Our first contribution lies in an analytical model of raw data for flow-guided localization, dissecting how communication and energy capabilities influence the nanodevices’ data output. This model acts as a vital bridge, reconciling idealized assumptions with practical challenges of flow-guided localization. Toward addressing these practical challenges, we also present an integration of Graph Neural Networks (GNNs) into the flow-guided localization paradigm. GNNs, reinforced by the adaptability and resilience of Heterogeneous Graph Transformers (HGTs), excel in capturing complex dynamic interactions inherent to the localization of events sensed by the nanodevices. Our results highlight the potential of GNNs not only to enhance localization accuracy but also extend coverage to encompass the entire bloodstream.

Wireless communications at the chip scale emerge as a interesting complement to traditional wire-based approaches thanks to their low latency, inherent broadcast nature, and capacity to bypass pin constraints. However, as current trends push towards massive and bandwidth-hungry processor architectures, there is a need for wireless chip-scale networks that exploit and share as many channels as possible. In this context, this work addresses the issue of channel sharing by exploring the design space of multi-channel Medium Access Control (MAC) protocols for chip-scale networks. Distinct channel assignment strategies for both random access and token passing are presented and evaluated under realistic traffic patterns. It is shown that, even with the improvements enabled by the multiple channels, both protocols maintain their intrinsic advantages and disadvantages.

Recent advances in robotics and nanotechnology resulted in a set of miniaturized Unmanned Aerial Vehicles (UAVs). Such small UAVs are envisioned to operate in hard-to-reach areas for enabling applications such as structural monitoring or content capturing. Towards showcasing this vision, we demonstrate a small UAV-supported setup for real-time autonomous generation of 3-Dimensional (3D) representations of static objects. In the setup, a small UAV (i.e., CrazyFlie 2.1) is envisioned to visit a set of locations, acting as a carrier and power source of a camera sensor. At each location, the sensor is expected to take a picture of the object and report it to the station. The station implements a pipeline for 3D reconstruction based on the pictures taken by the UAV.

Terahertz (THz) communications are envisioned as a key technology of next-generation wireless systems due to its ultra-broad bandwidth. One step forward, THz integrated sensing and communication (ISAC) system can realize both unprecedented data rates and millimeter-level accurate sensing. However, THz ISAC meets stringent challenges on waveform and receiver design to fully exploit the peculiarities of THz channel and transceivers. In this work, a sensing integrated discrete Fourier transform spread orthogonal frequency division multiplexing (SI-DFT-s-OFDM) system is proposed for THz ISAC, which can provide lower peak-to-average power ratio than OFDM and is adaptive to flexible delay spread of the THz channel. Without compromising communication capabilities, the proposed SI-DFT-s-OFDM realizes millimeter-level range estimation and decimeter-per-second-level velocity estimation accuracy. In addition, the bit error rate (BER) performance is improved by 5 dB gain at the 10°3 BER level compared with OFDM. At the receiver, a deep learning based ISAC receiver with two neural networks is developed to recover transmitted data and estimate target range and velocity, while mitigating the imperfections and non-linearities of THz systems. Extensive simulation results demonstrate that the proposed deep learning methods can realize mutually enhanced performance for communication and sensing, and is robust against Doppler effects, phase noise and multi-target estimation.

Contemporary Virtual Reality (VR) setups often include an external source delivering content to a Head-Mounted Display (HMD). “Cutting the wire” in such setups and going truly wireless will require a wireless network capable of delivering enormous amounts of video data at an extremely low latency. The massive bandwidth of higher frequencies, such as the millimeter-wave (mmWave) band, can meet these requirements. Due to high attenuation and path loss in the mmWave frequencies, beamforming is essential. In wireless VR, where the antenna is integrated into the HMD, any head rotation also changes the antenna’s orientation. As such, beamforming must adapt, in real-time, to the user’s head rotations. An HMD’s built-in sensors providing accurate orientation estimates may facilitate such rapid beamforming. In this work, we present coVRage, a receive-side beamforming solution tailored for VR HMDs. Using built-in orientation prediction present on modern HMDs, the algorithm estimates how the Angle of Arrival (AoA) at the HMD will change in the near future, and covers this AoA trajectory with a dynamically shaped oblong beam, synthesized using sub-arrays. We show that this solution can cover these trajectories with consistently high gain, even in light of temporally or spatially inaccurate orientational data.

Full-immersive multiuser Virtual Reality (VR) envisions supporting unconstrained mobility of the users in the virtual worlds, while at the same time constraining their physical movements inside VR setups through redirected walking. For enabling delivery of high data rate video content in real-time, the supporting wireless networks will leverage highly directional communication links that will “track” the users for maintaining the Line-of-Sight (LoS) connectivity. Recurrent Neural Networks (RNNs) and in particular Long Short-Term Memory (LSTM) networks have historically presented themselves as a suitable candidate for near-term movement trajectory prediction for natural human mobility, and have also recently been shown as applicable in predicting VR users' mobility under the constraints of redirected walking. In this work, we extend these initial findings by showing that Gated Recurrent Unit (GRU) networks, another candidate from the RNN family, generally outperform the traditionally utilized LSTMs. Second, we show that context from a virtual world can enhance the accuracy of the prediction if used as an additional input feature in comparison to the more traditional utilization of solely the historical physical movements of the VR users. Finally, we show that the prediction system trained on a static number of coexisting VR users be scaled to a multi-user system without significant accuracy degradation.

The$6^{th}$generation of wireless communication (6G) is envisioned to give rise to various technologies for improving the end-to-end communication performance, where the commu-nication is envisioned to utilize wireless signals in the millimeter wave (mmWave) frequencies and above. Among others, these technologies comprise Intelligent Reflective Surfaces (IRSs) and Mobile Relays (MRs), whose envisaged roles include mitigating the negative effects of Non-Line-of-Sight (NLoS) connectivity, in particular at mm Wave and higher frequencies. The core idea behind these technologies is to use cooperative networking where the source sends a signal to a repeater, in this case the IRS or the MR, which is upon reception forwarded to the destination. When comparing the two technologies, it is important to realize that the IRSs are primarily envisioned to be static entities attached to various objects in the environment such as walls and furniture. In contrast, the MRs will feature a higher degree of freedom, as they will be able to position themselves seamlessly in the environment. Based on the above assumptions, we derive an approach for determining the optimal position of the IRS and MR in indoor environments, i.e., the one that maximizes the end-to-end link quality between the source and the destination. We follow by capturing the communication quality indicators for both IRS- and MR-supported NLoS avoidance in indoor mm Wave communication in a number of scenarios. Our results show that, from the end-to-end link quality perspective, the MRs generally outperform the IRSs, suggesting their utilization potential for throughput-optimized NLoS avoidance scenarios.

Low-Power Wide-Area Network (LPWAN) multi-Radio Access Technology (RAT) devices combine features of different low-power network technologies to flexibly manage heterogeneous requirements stemming from various Internet of Things (IoT) applications. IEEE 802.11ah is a novel technology that is envisioned to provide a “bridge” between Wi-Fi and s, thus it has been often considered as one of the supporting technologies in multi-RAT devices. In such multi- RAT scenarios, network discovery and handover procedures need to be utilized for determining the availability of a given technology and managing if the connection to the technology should be initiated. However, traditional discovery and handover procedures, such as beacon listening, have to be performed periodically and, therefore, consume substantial amount of energy, making them unsuitable for battery-powered IoT devices. To address this issue, we present a mechanism that is able to make more optimal discovery and handover decisions by leveraging the physical location information of the multi- RAT devices. We demonstrate how this approach is feasible in performing energy efficient handovers between a technology (NB-IoT) and IEEE 802.11ah based on estimated location. We do that by showing that the location-based procedure substantially reduces the energy consumption of the mobile device compared to the traditional discovery based on periodical listening for beacons, while maintaining comparable duration of the device’s association to IEEE 802.11ah. Moreover, we evaluate the energy and delay overheads caused by the localization service, showing only slight effects on the performance of the mechanism.

Full-immersive multiuser Virtual Reality (VR) setups envision supporting seamless mobility of the VR users in the virtual worlds, while simultaneously constraining them inside shared physical spaces through redirected walking. For enabling high data rate and low latency delivery of video content in such setups, the supporting wireless networks will have to utilize highly directional communication links, where these links will ideally have to “track” the mobile VR users for maintaining the Line-of-Sight (LoS) connectivity. The design decisions about the mobility patterns of the VR users in the virtual worlds will thus have a substantial effect on the mobility of these users in the physical environments, and therefore also on performance of the underlying networks. Hence, there is a need for a tool that can provide a mapping between design decisions about the users' mobility in the virtual words, and their effects on the mobility in constrained physical setups. To address this issue, we have developed and in this paper present a simulator for enabling this functionality. Given a set of VR users with their virtual movement trajectories, the outline of the physical deployment environment, and a redirected walking algorithm for avoiding physical collisions, the simulator is able to derive the physical movements of the users. Based on the derived physical movements, the simulator can capture a set of performance metrics characterizing the number of perceivable resets and the distances between such resets for each user. The simulator is also able to indicate the predictability of the physical movement trajectories, which can serve as an indication of the complexity of supporting a given virtual movement pattern by the underlying networks.

Terahertz (THz) communications are envisioned as a key technology of next-generation wireless systems due to its ultra-broad bandwidth. By integrating communication and sensing, the the THz joint communication and sensing (JCS) system can realize both unprecedented data rates and ubiquitously accurate sensing. However, THz JCS meets stringent challenges due to the peculiarities of THz channel and devices. In this work, a sensing integrated discrete Fourier transform spread orthogonal frequency division multiplexing (SI-DFT-s-OFDM) system is proposed for THz JCS. The proposed system is able to provide lower peak-to-average power ratio than OFDM. Without compromising communication capabilities, SI-DFT-s-OFDM can realize millimeter-level range estimation accuracy and improve the velocity estimation accuracy by 10 times than OFDM. Finally, SI-DFT-s-OFDM achieves 18% improvement of data rate over DFT-s-OFDM.

Recently researchers have been attracted by the joint communication and sensing (JCS) techniques, due to their diverse benefits in communication-sensing applications. By sharing the spectrum and hardware components, the advantages of JCS systems include enhanced spectrum efficiency and reduced device costs. Following the trend of scaling up the carrier frequencies for 5G and beyond, Terahertz (THz) band is envisioned as a promising chunk of spectrum featuring multi-GHz bandwidth windows. However, despite the great promise, the waveform design for THz JCS is still not explored enough. In this work, the design guidelines for THz JCS waveform are presented. With the help of these guidelines, a non-uniform multi-wideband orthogonal frequency division multiplexing (NMW-OFDM) THz system is proposed to overcome the limitations of OFDM based JCS. The proposed NMW-OFDM system is able to realize sub-millimeter-level accuracy of the range estimation by using a multi-stage sensing algorithm, which is three orders of magnitude improvement compared to the OFDM-based JCS system. In addition, the accuracy of velocity estimation can be enhanced by 10 times within a short frame time. Finally, the system can achieve unprecedented communication data rate of 100 Gbps without sacrificing the maximum detectable distance.

Flow-guided electromagnetic nanonetworks will enable innovative medical applications for monitoring, information gathering, and data transmission inside the human body. These nanonetworks will have to operate under extreme computational and powering-related constraints, and in very hostile environments inside human vascular systems. Under these circumstances, successful transmissions between in-body nanonodes and an on-body nanorouter rarely occur, thus requiring new approaches to improve the network throughput in this scenario. Along this view, in classical flow-guided nanonetworks the nanonodes are envisioned to transmit packets if they have enough energy for the transmission, regardless of their vicinity to the nanorouter. In this paper, we propose a nanorouter awareness model that can provide significant throughput gains compared to the baseline based on blind transmissions, facilitating the roll-out of nanocommunication-supported medical applications.

Recent developments in nanotechnology herald nanometer-sized devices expected to bring light to a number of groundbreaking applications. Communication with and among nanodevices will be needed for unlocking the full potential of such applications. As the traditional communication approaches cannot be directly applied in nanocommunication, several alternative paradigms have emerged. Among them, electromagnetic nanocommunication in the terahertz (THz) frequency band is particularly promising, mainly due to the breakthrough of novel materials such as graphene. For this reason, numerous research efforts are nowadays targeting THz band nanocommunication and consequently nanonetworking. As it is expected that these trends will continue in the future, we see it beneficial to summarize the current status in these research domains. In this survey, we therefore aim to provide an overview of the current THz nanocommunication and nanonetworking research. Specifically, we discuss the applications envisioned to be supported by nanonetworks operating in the THz band, together with the requirements such applications pose on the underlying nanonetworks. Subsequently, we provide an overview of the current contributions on the different layers of the protocol stack, as well as the available channel models and experimentation tools. Finally, we identify a number of open research challenges and outline several future research directions.

Location information is among the main enablers of context-aware applications and wireless networks. Practical localization services are able to generate location estimates that are generally erroneous. To maximize its usability and benefits, each location estimate should be leveraged jointly with the corresponding estimate of its localization error. Hence, we propose an Artificial Neural Network (ANN)-based method for the estimation of individual localization errors. We do that for fingerprinting, one of the most prominent localization solutions for GPS-constrained environments. First, we provide insights on how to optimally hyperparameterize the proposed method. We do that by exploring its hyperparameter' space in order to find its close-to-optimal hyperparameterization for different environments and fingerprinting technologies. We believe the provided insights can serve to reduce the overhead of deploying the method in new environments. Second, we demonstrate that the method, when hyperparameterized according to the provided insights, substantially outperforms the current state-of-the-art. The improvement is more than 25% in the best case scenario.

IEEE 802.11ah is a novel sub-GHz license-free WiFi technology that is of great interest for Smart Cities, primarily due to its long range, low energy consumption, reliability, and ubiquitousness. To guarantee a long lifetime of battery-powered IEEE 802.11ah Mobile Terminals (MTs), network discovery based on beacon listening or active probing should ideally be avoided. Alternative discovery approaches heavily rely on crowd-sourcing, which in turn relies on propagation modeling. Due to the novelty of IEEE 802.11ah, the accuracy of the available propagation models is currently all but clear, and this paper makes one of the first steps in bridging this gap. Specifically, for a number of Smart City-relevant environments we experimentally evaluate the accuracy of an exhaustive set of propagation models by comparing their outputs against real measurements obtained using IEEE 802.11ah-compatible hardware. Our results are encouraging, showing that the existing models are indeed suitable for IEEE 802.11ah. However, they also indicate that diverse models perform best in distinct types of deployment environments, suggesting the need for an environment-tailored design of IEEE 802.11ah-based systems that utilize propagation modeling.

The need for cables with high-fidelity Virtual Reality (VR) headsets remains a stumbling block on the path towards interactive multi-user VR. Due to strict latency constraints, designing fully wireless headsets is challenging, with the few commercially available solutions being expensive. These solutions use proprietary millimeter wave (mmWave) communications technologies, as extremely high frequencies are needed to meet the throughput and latency requirements of VR applications. In this work, we investigate whether such a system could be built using specification-compliant IEEE 802.11ad hardware, which would significantly reduce the cost of wireless mmWave VR solutions. We present a theoretical framework to calculate attainable live VR video bitrates for different IEEE 802.11ad channel access methods, using 1 or more head-mounted displays connected to a single Access Point (AP). Using the ns-3 simulator, we validate our theoretical framework, and demonstrate that a properly configured IEEE 802.11ad AP can support at least 8 headsets receiving a 4K video stream for each eye, with transmission latency under 1 millisecond.

The performance of wireless networking approaches degrades under user's mobility. To objectively and reliably establish their performance under mobility, one has to guarantee highly repeatable experimentation with minimized external influences, which is currently a burdensome manual process for 3-dimensional (3D) environments. To address this issue, we propose a drone-based testbed for automated, autonomous, and repeatable experimentation with mobile wireless infrastructures in heterogeneous 3D environments. The developed testbed can be easily deployed in various environments, allows for simple integration of a new System Under Test (SUT), and guarantees the absence of interference with the SUT.

Multi-Radio Access Technology (RAT) IoT devices are able to combine the high coverage of Low-Power Wide-Area (LPWA) technologies with the higher data-rates of shorter range technologies such as IEEE 802.11ah. In such scenarios, a discovery procedure has to be used for detecting the availability of a IEEE 802.11ah network. Currently, these procedures consume substantial energy, as the discovery has to be periodically performed, even if the IEEE 802.11ah technology is not available, which is undesirable for low-power Internet of Things (IoT) devices. We propose using the device's location information for making more optimized discovery and handover decisions. We demonstrate the feasibility of this approach in performing energy efficient handovers between various LPWA technologies and IEEE 802.11ah based on estimated location. We carry out our evaluation in terms of the energy consumption of the procedure and the duration of the device's association to IEEE 802.11ah. We show that the location-based procedure substantially reduces the energy consumption of the mobile device compared to the traditional discovery based on periodical listening for beacons.

Widespread adoption of indoor positioning systems based on WiFi fingerprinting is at present hindered by the large efforts required for measurements collection during the offline phase. Two approaches were recently proposed to address such an issue: crowdsourcing and RSS radiomap prediction, based on either interpolation or propagation channel model fitting from a small set of measurements. RSS prediction promises better positioning accuracy when compared to crowdsourcing, but no systematic analysis of the impact of system parameters on positioning accuracy is available. This paper fills this gap by introducing ViFi, an indoor positioning system that relies on RSS prediction based on Multi-Wall Multi-Floor (MWMF) propagation model to generate a discrete RSS radiomap (virtual fingerprints). Extensive experimental results, obtained in multiple independent testbeds, show that ViFi outperforms virtual fingerprinting systems adopting simpler propagation models in terms of accuracy, and allows a seven-fold reduction in the number of measurements to be collected, while achieving the same accuracy of a traditional fingerprinting system deployed in the same environment. Finally, a set of guidelines for the implementation of ViFi in a generic environment, that saves the effort of collecting additional measurements for system testing and fine tuning, is proposed.

A variety of nanoscale applications require downlink and broadcast-based transmission of control packets from a powered transmitter to energy-harvesting nanonodes with constrained storage capacity. The nanonode's communication system is anticipated to be a bottleneck for such nanonetworks, hence an accurate modeling of its energy consumption is needed. Currently, TS-OOK is a prevailing scheme for nanocommunication in the terahertz (THz) frequencies, with short pulses representing logical "1"s and silences logical "0"s. In the energy modeling of this scheme, certain energy consumptions are attributed to the transmission and reception of the pulses. However, traditional communication systems teach us that the idling energy consumption should not be neglected. Hence, we provide an energy consumption model for TS-OOK-based nanocommunication systems that accounts for the energy consumed in idling, in addition to the transmission and reception-originated consumptions. We demonstrate that, for a meaningful performance of the considered nanonetwork, the nanonode's idling energy consumption has to be at least nine orders of magnitude lower than the corresponding energy consumption in reception. To increase the tolerable idling energy, we propose a new energy lifecycle for the receiving nanonodes. Assuming frequent packet repetitions on the transmit side, the proposed lifecycle utilizes periodic short wake-ups of the receiving nanonodes. We show that, when the proposed lifecycle is utilized, up to three orders of magnitude higher idling energy consumption can be tolerated compared to the baseline.

Location information is a valuable source of context that can be utilized by end-user applications and wireless networks to optimize their performance and usability. When used, location information should ideally be considered jointly with the estimate of its accuracy. Most of the current approaches for estimating the accuracy rely on performing a static performance benchmark of a localization solution in a deployment environment, which fails to capture the dynamic nature of the environment. We address this problem for fingerprinting-based localization by grounding the estimation of localization errors on the low-level features, i.e. the RSS values from APs used in fingerprinting. We use these low-level features measured at different locations in an environment, as well as their respective localization errors, to train different regression models, allowing us to predict the localization errors at new locations, given new observed values of the low-level features at these locations. Our evaluation results show substantially better performance of the proposed regression-based estimation of localization errors compared to static performance benchmarks.

Low-power wide-area network (LPWAN) multi-radio access technology (RAT) devices promise enabling Internet of Things (IoT) use-cases that simultaneously require high coverage and data rates, and low energy consumption. For such devices, active probing is usually used for discovering if communication between a mobile terminal (MT) and a base station (BS) or a handover of the MT across LPWAN technologies should be initiated. Because of continuous probing, this procedure increases signaling overhead and energy consumption of the MT. Assuming that the location information of the MT is required for enabling an IoT use-case, this information can potentially also be used for enhancing the discovery and vertical handover procedures in heterogeneous LPWANs. Hence, we propose a location-based mechanism for making discovery and handover decisions in outdoor LPWANs. We do that under the assumption that the location of the MT can be estimated with a certain level of localization errors, while the perfectly accurate location information of the BSs are known to the MT. The mechanism grounds the decisions on the expected SNR between the MT and the BS, which removes the need for continuous probing. If the location information of the MT can be estimated with GPS-like accuracy, we demonstrate that the mechanism can achieve more than 90% correct discovery decisions. We also show that the mechanism is highly accurate in determining if a handover between technologies should be initiated. For an order of magnitude less accurate location information (e.g., for SigFox-based fingerprinting), we show that the mechanism can still make reasonable discovery decisions.

Aggregation of location estimates from multiple services for provisioning of location information enhances the accuracy and robustness of the final location information. Recent Internet of Things (IoT)-based localization service architectures therefore envision a "manager" for selecting and invoking provisioning services and, in the later step, aggregating the received information and providing it to location-based applications. The selection of provisioning services should take into account the accuracy and latency requirements from the applications and accuracy, latency, and power consumption characteristics of provisioning services. However, it is yet unclear how such selection should be made. We propose two algorithms for the selection of provisioning services aiming at meeting latency and subsequently accuracy requirements from the applications, one subject to minimizing per-request power consumption, while the other subject to a per-time bucket power minimization. In the considered examples, we show that the per-time bucket optimization achieves around 25% better performance in terms of power consumption, while trading-off accuracy satisfaction.

With the abundance of mobile connected devices and coexisting networks, localization solutions are inevitably subject to interference. In this paper, effects of interference on the performance of Fingerprinting Localization Algorithms (FPS) are studied both theoretically and through experimentation. The previously introduced theoretical framework based on Hypothesis Testing (HT) problem is employed to characterize the performance of FPS and provide guidelines for combating negative impact of interference. In particular, it is shown that background interference interestingly can improve the performance of FPS, while the interference in the measurement phase incurs an error by pushing the reported location closer to the anchors. Moreover, the anchors provide the highest interference robustness for locations in their proximity. These results are further verified through simulations and experimentation in realistic setups.

Location information of mobile devices is a foundational input to location-based services and a valuable source of context information in wireless networks. To maximize the value, we need location information that is accurate, robust, and promptly and seamlessly available. Unfortunately, individual localization services seldom satisfy all these requirements. For achieving that vision, a set of challenges has to be addressed, pertaining to handover, fusion, and integration of different sources of location information. Current approaches for integration of individual localization services are either not specific enough or are limited in scope and lack flexibility. In the following, we provide a detailed design and a prototypical implementation of the Standardized Localization Service (SLSR), a middleware architecture for achieving those goals. We instantiate the service in an office environment and perform exhaustive performance benchmarking in a testbed specifically designed for supporting such experimentation. Our results characterize the effects of different functional components envisioned in the SLSR on its performance. Our results also quantify the accuracy benefits of fusion of representative sources of location information.

Individual localization services seldom satisfy the requirements for accurate, robust, punctual, and seamless location information. To enhance the provisioning, a set of challenges has to be addressed, pertaining to handover, fusion, and integration of individual localization services. In the following, we advertise the Standardized Localization Service (SLSR), a middleware architecture for achieving those goals. We instantiate the SLSR in a office-like indoor environment and evaluate its performance in a specifically designed testbed for evaluation of indoor localization approaches. Our results typify the contributions of different functional components envisioned in the SLSR on its overall performance.

Despite the popularity of Fingerprinting Localization Algorithms (FPS), general theoretical frameworks for their performance studies have rarely been discussed in the literature. In this work, after setting up an abstract model for the FPS, we show that a fingerprinting-based localization problem can be cast as a Hypothesis Testing (HT) problem and therefore various results from the HT literature can be used to provide insights, guidelines, and performance bounds for the FPS. This includes the scaling limits of error probability in terms of the number of measurements and the precise characterization of localization error. The provided results hold for the general FPS. Additionally, Received Signal Strength (RSS)-based fingerprinting algorithms are particularly considered from the theoretical viewpoint due to their widespread practical usage. Simulations and experimental results characterize numerically the findings of the theoretical framework and demonstrate its consistency with realistic localization scenarios.

Device-to-Device (D2D) communication has a high potential in reducing the amount of network traffic and improving the latency and energy efficiency of communication. Currently, D2D link establishment decisions are based on active probing between devices that wish to establish a D2D link. The main drawback of such approaches is a large overhead as during active probing no data communication can take place. We leverage physical locations of the devices that wish to establish a D2D link in order to estimate the probability of success in establishing the link before making an attempt to communicate. The probability of success is given as a closed form equation that takes into account the imperfections of location information of the devices and intrinsic randomness of wireless environments. We experimentally evaluate the proposed location-based decision- making mechanism for D2D link establishment in a complex office-like indoor environment. We show that setting the Signal-to-Noise Ratio (SNR) threshold of the proposed mechanism to a value that is 5 dB higher than the nominal SNR required for communication results in reliable link establishment with false positive rate of less than 2%. Furthermore, we show a relatively small loss of link establishment potential due to an increase in the inaccuracies of location information.

The interest for RF-based indoor localization, and in particular for WiFi RSSI-based fingerprinting, is growing at a rapid pace. This is despite the existence of a trade-off between the accuracy of location estimation and the density of a laborious and time consuming survey for collecting training fingerprints. A generally accepted concept of increasing the density of a training dataset, without an increase in the amount of physical labor and time needed for surveying an environment for additional fingerprints, is to leverage a propagation model for the generation of virtual training fingerprints. This process, however, burdens the user with an overhead in terms of implementing a propagation model, defining locations of virtual training fingerprints, generating virtual fingerprints, and storing the generated fingerprints in a training database. To address this issue, we propose the Enriched Training Database (ETD), a web-service that enables storage and management of training fingerprints, with an additional “enriching” functionality. The user can leverage the enriching functionality to automatically generate virtual training fingerprints based on propagation modeling in the virtual training points. We further propose a novel method for defining locations of virtual training fingerprints based on modified Voronoi diagrams, which removes the burden of defining virtual training points manually and which automatically “covers” the regions without sufficient density of training fingerprints. The evaluation in our testbed shows that the use of automated generation of virtual training fingerprints in ETD results in more than 25% increase in point accuracy and 15% in room-level accuracy of fingerprinting.

Localization services available on today's mobile devices are proprietary and leverage a limited set of sources of location information. Integration of new location estimation methods is therefore cumbersome, requiring adaptation to the specific interfaces of the proprietary location service. In addition, location-based applications are tightly interwoven with the location service that is typically provided by the operating system, hence these applications require significant restructuring to be able to run with another location service. To address these problems, we propose a modular localization service architecture that consists of location-based applications, an integrated location service enabling a fusion of so-called elementary location services, and resources for generating location information. A unified style of interaction among these components is enabled by a set of well-defined Application Programming Interfaces (APIs). The practicability and advantages of the proposed design is demonstrated by outlining how the APIs can be realized using modern types of component interactions.

This demonstration presents a user-friendly web-based platform that supports intuitive remote benchmarking of different indoor localization solutions. It reduces the barriers for experimental evaluation and fair comparison of their performance across a set of testing environments. The platform is aimed at addressing the limitations in the current praxis of publishing indoor localization research evaluated only in local, potentially biased environments. To this end, it provides a holistic support for the benchmarking process offering: (i) multiple pre-collected raw data-traces from different RF technologies, (ii) high-level interface to control remote wireless testbed facilities, and (iii) a set of tools for creating, storing, comparing and visualizing the performance results of multiple indoor localization solutions.

mmWave (millimeter-Wave) is a very promising technology for the future wireless communication. To mitigate its high attenuation characteristics, mmWave communication frequently employs directional beamforming for both transmission and reception. Localization commonly takes advantage of directionality in RF frequencies in urban and indoor environments. In this paper, we use lessons learned from classical RF-based localization for discussing a set of feasible localization approaches in the context of mmWave bands. We further map the requirements of each discussed localization approach to design requirements for future mmWave devices and assess the expected accuracy of such approaches for a set of realistic scenarios. Our results show that mmWave-based localization is promising in both its availability and accuracy, even in the presence of a limited number of localization anchor nodes.

This paper describes a major effort to collect smartphone data useful for indoor localization. Data has been collected with four Android phones according to numerous scenarios in each of four large buildings. The data includes time-stamped traces of various environmental, position, and motion sensors available on a smartphone, Wi-Fi and cellular signal strengths, and GPS fixes, whenever available. Quantitative evidence is presented to validate the collected data and attest to its quality. This unique, extensive data repository is made available to the R&D community through the PerfLoc web portal to facilitate development of smartphone indoor localization apps and to enable performance evaluation of such apps according to the ISO/IEC 18305 international standard in the near future.

Range-based IEEE 802.15.4 localization systems currently require relatively high anchor density for indoor deployments. It can therefore be beneficial to use external sources of transmission as additional anchors. We present methods for using WiFi beacons to improve localization accuracy of a range-based IEEE 802.15.4 localization system in cases where only two IEEE 802.15.4 anchor nodes are available. We do this by identifying WiFi beacons from RSSI traces that we dynamically sample online, and applying fingerprinting and range-based methods using the RSSI values of the identified beacons. However, because the data of the WiFi traffic is not decodable by the IEEE 802.15.4 devices, these RSSI measurements lack identifiers that can associate them to specific WiFi Access Points (APs). Therefore, novel methods are required for both fingerprinting and range-based approaches to allow for these additional WiFi APs to be used as anchors. We show by using real-world measurements that our beacon identification method gives a false-positive rate of only 3%, and that if the range measurements to the IEEE 802.15.4 anchors are relatively accurate, with a standard deviation of 1 and 3 m, a localization accuracy improvement of 47% and 24% can be gained, respectively.

We study the effect of interference on localization algorithms through the study of the interference effect on signal features that are used for localization. Particularly, the effect of interference on packet-based Received Signal Strength Indicator (RSSI), reported by IEEE 802.11 and IEEE 802.15.4 technologies, and on Time of Flight (ToF), reported by IEEE 802.15.4 technology, is studied using both theoretical discussions and experimental verifications. As for the RSSI values, using an information theoretic formulation, we distinguish three operational regimes and we show that the RSSI values, in dBm, remain unchanged in the noise-limited regime, increase almost linearly with interference power in dBm in the interference- limited regime and cannot be obtained due to packet loss in the collision regime. The maximum observable RSSI variation is dependent on the transmission rate and Signal to Noise Ratio (SNR). We also show that ToF is, interestingly, decreased under interference which is caused in the symbol synchronization procedure at the receiver. After providing the experimental results, we discuss how the localization algorithms are affected by interference.

Despite their popularity, the current praxis of comparative experimental evaluation of fingerprinting-based localization algorithms is lacking rigor, with studies typically following an ad-hoc evaluation process and focusing on black-box comparison of complete algorithms. In this paper we present a systematic benchmarking methodology that is focused on gaining finegrained insight about the relative contributions of the individual phases of the fingerprinting-based localization algorithms to their overall performance. To this end, we decompose the localization algorithms in common phases (collection of raw measurements, creation of fingerprints, pattern matching and post-processing) and systematically asses the performance of different procedures that can be applied in each of these phases. We illustrate the application of the proposed methodology using a comprehensive experimental case-study of 3 WiFi fingerprinting algorithms with 4 raw RSSI collection procedures, 3 fingerprint creation and pattern matching procedures, 4 different post-processing procedures in 3 testbeds and 4 evaluation scenarios, resulting in 36 individual experiments. The results demonstrate that in the evaluated scenarios, a lower number of WiFi APs and rather simple fingerprint creation and pattern matching can achieve better performance in terms of location accuracy than more sophisticated alternatives. The results also show that postprocessing steps like k-Nearest Neighbours (kNN) procedure are indeed effective in reducing the localization error variability and extremes, thus increasing the stability of the location estimation.