In Internet of Things (IoT) networks, ensuring the timely delivery of information is significantly constrained by the limited energy resources of IoT devices and the signal attenuation experienced in wireless channels. In this paper, we investigate resource management for self-sustaining IoT networks with ambient radio frequency (RF) energy harvesting via a spatio-temporal approach. We consider a hard deadline for packet delivery, and we aim to jointly reduce the age of information (AoI) and the packet drop rate due to the hard deadline for packet delivery or buffer overflow. To achieve that, using tools from deep reinforcement learning (DRL) and stochastic geometry, we propose a joint rate adaptation and power control scheme that accounts for the spatial topology of the network and the temporal attributes at the device level. In particular, stochastic geometry is leveraged to characterize the energy harvesting process and the packet transmission success probability for a given transmit rate and power. Furthermore, the joint rate adaptation and power control policy at the device level is obtained using a deep R-network (DRN), which is a DRL algorithm that utilizes a deep neural network to approximate the R-function (the expected average reward). The performances of the last-come-first-served (LCFS) queuing discipline, the first-come-first-served (FCFS) queuing discipline, and a proposed hybrid queuing discipline are compared. For the proposed hybrid queuing discipline, DRL is used not only for rate adaptation and power control but also for specifying the transmission order of generated packets. The presented numerical results demonstrate that the LCFS queuing discipline improves AoI performance, while the FCFS queuing discipline improves packet drop rate. Also, the proposed hybrid queuing discipline strikes an intricate balance between AoI and packet drop rate, and achieves a good performance in both measures compared to the other queuing disciplines.

Feedback transmissions are used to acknowledge correct packet reception, trigger erroneous packet re-transmissions, and adapt transmission parameters (e.g., rate and power). Despite the feedback paramount role in establishing reliable communication links, the majority of the literature overlooks its impact by assuming genie-aided systems with flawless and instantaneous feedback. However, this idealistic assumption is no longer valid for large-scale Internet of Things (IoT) networks, characterized by energy-constrained devices, susceptible to interference, and serving delay-sensitive applications. Furthermore, feedback-free operation is necessitated for IoT receivers with stringent energy constraints. In this context, this paper explicitly accounts for the impact of feedback in energy-constrained delay-sensitive large-scale IoT networks. We consider a time-slotted system with closed-loop and open-loop rate adaptation schemes, where packets are fragmented to operate at a reliable transmission rate satisfying packet delivery deadlines. In the closed-loop scheme, the delivery of each fragment is acknowledged through an error-prone feedback channel. The open-loop scheme has no feedback mechanism, and hence, a predetermined fragment repetition strategy is employed to improve transmission reliability. Using stochastic geometry and queueing theory, we develop a novel spatiotemporal framework for both schemes to quantify the impact of feedback on network performance in terms of transmission reliability, latency, and energy consumption.

Delivering an immersive experience to virtual reality (VR) users through wireless connectivity offers the freedom to engage from anywhere at any time. Nevertheless, it is challenging to ensure seamless wireless connectivity that delivers real-time and high-quality videos to the VR users. This paper proposes a field of view (FoV) aware caching for mobile edge computing (MEC)-enabled wireless VR network. In particular, the FoV of each VR user is cached/prefetched at the base stations (BSs) based on the caching strategies tailored to each BS. Specifically, decentralized and personalized federated learning (DP-FL) based caching strategies with guarantees are presented. Considering VR systems composed of multiple VR devices and BSs, a DP-FL caching algorithm is implemented at each BS to personalize content delivery for VR users. The utilized DP-FL algorithm guarantees a probably approximately correct (PAC) bound on the conditional average cache hit. Further, to reduce the cost of communicating gradients, one-bit quantization of the stochastic gradient descent (OBSGD) is proposed, and a convergence guarantee of$\mathcal {O}(1/\sqrt {T})$is obtained for the proposed algorithm, where T is the number of iterations. Additionally, to better account for the wireless channel dynamics, the FoVs are grouped into multicast or unicast groups based on the number of requesting VR users. The performance of the proposed DP-FL algorithm is validated through realistic VR head-tracking dataset, and the proposed algorithm is shown to have better performance in terms of average delay and cache hit as compared to baseline algorithms.

Underwater monitoring and exploration benefit from Internet of Underwater Things (IoUT). However, the lifetime of IoUT networks is limited due to batteries that require frequent replacement, which is costly and unfeasible in a hostile environment. To maximize IoUT device lifetimes and reduce system costs, we propose on-demand wake-up radios, activated by wake-up calls from deployed surface buoys via acoustic, optical, and magnetic induction communication. Using stochastic geometry tools, we analyze the wake-up scheme’s performance, deriving analytical solutions for success and false wake-up probabilities. We characterize the scheme’s performance under different design parameters and highlight its benefits.

This study presents a framework to analyze the performance of uplink localization with reconfigurable intelligent surfaces (RISs) in large-scale cellular networks. First, we propose a novel RIS-aided uplink localization algorithm, where the received signal strength (RSS) is observed at the base station (BS) for various pre-defined phase shift patterns of the RIS, i.e., a codebook of beams. We present a maximum likelihood estimator (MLE) and evaluate its performance by comparing it to the position error bound (PEB), defined as the square root of the Cramér-Rae lower bound (CRLB). Then, to analyze the localization performance on a large scale, we employ stochastic geometry tools, allowing the derivation of a tractable expression for the marginal PEB distribution. The obtained results demon-strate that the proposed algorithm converges to the CRLB for a narrow search grid, in a high SNR regime. Furthermore, higher BS density, number of RIS elements, and RIS element size are shown to enhance localization precision.

The ubiquitous deployment of Internet of Things (IoT) and the ever-evolving IoT services seek fully autonomous devices with no energy limitations. To fulfill this demand, we investigate the usage of unmanned aerial vehicles (UAVs) to overcome the limited battery constraint of IoT deployments in hard-to-reach locations. Specifically, we present a UAV-enabled wake-up radio (WuR) and data collection (U-WuRIoT) solution for future IoT networks. The proposed solution leverages UAVs to wake-up IoT devices from an ultralow power sleep mode by transmitting WuR signals. Upon successful wake-up, the devices use their batteries to transmit the collected data to the UAV. In this article, we present an overview of U-WuRIoT and its applications and discuss the challenges and enabling technologies toward realizing it. Candidate enablers, such as advances on wake-up receivers and UAV transmitters’ hardware, combined energy harvesting and WuR, new spectrum opportunities, energy beamforming, channel state information (CSI)-limited schemes, and UAV trajectory optimization, are outlined. A realistic experimental testbed, using a fully operational prototype implemented via off-the-shelf components, is constructed to validate the applicability of U-WuRIoT and its benefits compared to traditional duty cycling (DCY) solutions. Furthermore, a theoretical study is conducted to extrapolate the performance of U-WuRIoT in large-scale deployments. The obtained experimental and theoretical results demonstrate that U-WuRIoT can extend the lifetime of the IoT device up to three times the lifetime when DCY is applied and can reduce the false alarm rate to less than 10%. Finally, key research directions toward implementing U-WuRIoT in the 6G era are identified.

Satellite networks are playing an important role in realizing global seamless connectivity in beyond 5G and 6G wireless networks. In this paper, we develop a comprehensive analytical framework to assess the performance of hybrid terrestrial/satellite networks in providing rural connectivity. We assume that the terrestrial base stations are equipped with multiple-input-multiple-output (MIMO) technologies and that the user has the option to associate with a base station or a satellite to be served. Using tools from stochastic geometry, we derive tractable expressions for the coverage probability and average data rate and prove the accuracy of the derived expressions through Monte Carlo simulations. The obtained results capture the impact of the satellite constellation size, the terrestrial base station density, and the MIMO configuration parameters.

The recent breakthroughs in electronic and photonic technologies enabled the design and implementation of intelligent reflective surfaces (IRSs) to manipulate electromagnetic waves and control the wireless environment. A promising application of IRSs is their integration with Terahertz (THz) communications. IRSs can cope with the blockage sensitivity of THz propagation by providing alternative line-of-sight (LoS) links to user equipment (UEs) which are initially blocked. However, deploying more IRSs may degrade the network performance as it leads to non-negligible interference levels. In this paper, we use tools from stochastic geometry to investigate the coverage probability of a downlink (DL) indoor THz network assisted by IRSs, which are added to a subset of the existing blockages. The numerical results reveal that there is an optimal density of IRSs that should be deployed to maximize the coverage of UEs in THz networks.

The cooperation between unmanned aerial vehicles (UAVs) and ground mobile-edge computing (MEC) servers in processing tasks is becoming one of the main research trends of MEC networks. Despite the advantages of UAV-assisted MEC, it is restricted by the limited battery capacity and sensitive energy consumption of UAVs. Unlike the previous works where UAVs are allowed to either process tasks locally or offload them to ground MEC servers, in this article, we propose a multihop task routing solution for Internet of Things (IoT) networks in which a UAV can also relay to another UAV with better connection to a ground MEC server. Furthermore, the UAV can make benefit of existing intelligent reflective surfaces (IRSs) to further improve task offloading and reduce energy consumption. We show that the problem of minimizing the total energy of UAVs is NP-hard, and we propose a graph-based heuristic solution to solve it. Simulation results show that the proposed graph-based solution outperforms the traditional no UAV–UAV relaying scheme, especially when IRSs are deployed. Furthermore, a convolutional neural network (CNN) is devised to reduce the delay of finding the decisions for the UAVs at the centralized coordinator. Simulations show that the CNN achieves very close energy consumption performance and a remarkable reduction in execution time compared to the graph-based heuristic solution.

Wireless communications over Terahertz (THz)-band frequencies are vital enablers of ultra-high rate applications and services in sixth-generation (6G) networks. However, THz communications suffer from poor coverage because of inherent THz features such as high penetration losses, significant molecular absorption, and severe path loss. To surmount these critical challenges and fully exploit the THz band, we explore a coexisting radio frequency (RF) and THz finite indoor network in which THz small cells are deployed to provide high data rates, and RF macrocells are deployed to satisfy coverage requirements. Using stochastic geometry tools, we assess the performance of coexisting RF and THz networks and derive tractable analytical expressions for the coverage probability and average achievable rate. The analytical results are validated with Monte-Carlo simulations. Several insights are devised for accurate tuning and optimization of THz system parameters, including the THz bias, and the fraction of THz access points (APs) to deploy. The obtained results recognize a clear coverage/rate trade-off where a high fraction of THz AP improves the rate significantly but may degrade the coverage performance. Furthermore, the location of a user in the finite area highly affects the fraction of THz APs that optimizes its quality of service.

Internet of things (IoT) presupposes a massive number of low-complexity wireless devices, placed in hardly accessible locations and often powered by batteries with limited size and capacity. To extend the lifetime of these devices, wake-up radio (WuR) techniques were proposed. In the literature, WuR solutions have been evaluated with single-antenna base stations (BSs). In this paper, we evaluate the benefits of adding multiple antennas at BSs to transmit precoded WuR signals. The considered precoded schemes provide better spatial selectivity by focusing the power of the transmitted WuR signal on the targeted devices. Monte-Carlo simulations are used to assess the performance of the system in terms of successful wake-up probability of the WuR receiver. Numerical results show that, unlike data transmission scenarios, complex precoders as multi-cell minimum mean-squared error (M-MMSE), are surpassed by the simpler maximum ratio (MR) precoder for WuR.

This article proposes a novel ultra-low power, tunable, and asynchronous baseband architecture for joint radio-frequency (RF) wakeup and charging receivers. The designed system switches between the wakeup and charging operations based on the type of the received RF signal. To our knowledge, the proposed system is the first of a kind that introduces multiple power states to reduce the energy consumption by sequentially activating minimal components required for RF wakeup or charging. The fabricated prototype, using off-the-shelf components, features a sensitivity of −40 dBm, a bit rate of 500 bps, and a current consumption of 225 nA at a bias voltage of 2.6 V in the listening state. Current consumption is estimated at 3.225 and$13.725~\mu \text{A}$while processing the preamble and bit sequence, respectively. The address detector is powered OFF during charging to reduce the system’s current consumption to 150 nA. Our experimental results show that shutting down the address detector during charging reduces charging time and allows charging from received power levels that are as low as −6 dBm. We demonstrate that operating the detector with multiple power modes reduces its current consumption and enhances its noise immunity when compared to conventional address detectors with two power modes of operation.

To best provision the wireless data deluge, service providers are increasingly considering the use of Unmanned aerial vehicles (UAVs) for enhancing wireless connectivity. UAVs are especially important in case of disasters and accidents which may cripple terrestrial networks. In order to maintain the communication of UAVs with the core network, it becomes particularly important to connect UAVs to terrestrial base stations (BSs) via wireless backhaul links. In this work, we use stochastic geometry to study the impact of millimeter-wave (mmWave) backhauling of UAVs in a hybrid aerial-terrestrial cellular network, where the UAVs are added to assist terrestrial BSs in delivering service to users (UEs). In the proposed model, the UE can associate with either a terrestrial BS or a UAV connected to a BS to get backhaul support. The performance of the model is evaluated in terms of coverage probability and validated against intensive simulations. The obtained results unveil that the quality of the UAVs backhaul link has a significant role in improving the UEs experience. The results further illustrate the impact of the different UAVs heights regimes on the coverage probability.

In today's wireless networks, interference serves as a harmful factor that can notably degrade the overall performance. Nevertheless, interference can also be beneficial when utilized as an additional power source for radio frequency (RF) based energy harvesting in order to charge Internet of Things (IoT) devices. In this paper, we quantify the positive impact of interference for energy harvesting using tools from stochastic geometry; in particular, we derive an expression for a suitable success probability metric and validate its accuracy using Monte-Carlo simulations as a function of various system parameters. Moreover, we design, implement and evaluate an experimental test bed for RF-based energy harvesting using WiFi access points (APs) in order to demonstrate the practical performance gains resulting from interference as a function of the number of access points, their relative locations, and the receivers antenna configuration. The obtained experimental measurement results corroborate the insights extracted from the analytical derivations.

We use stochastic geometry to analyze the performance of an energy-efficient joint downlink/uplink (UL) radio-frequency (RF) wake-up solution for Internet of Things (IoT) devices over cellular networks. When the IoT device has no data to transmit, it turns its main circuitry completely OFF and switches to a deep sleep mode. The transition back to the active mode is only achieved upon receiving enough power at the device's front end. After wake up, the device initiates regular UL communication with its serving base station (BS). The device experiences a successful wake-up event when the total received power includes a wake-up signal transmitted from its serving BS, and the UL signal-to-interference-and-noise ratio (SINR) is above a predefined threshold. On the other hand, the device experiences a false wake-up event when the wake up is due to received power from neighboring BSs excluding the serving BS. We derive lower and upper bounds for the success and false wake-up probabilities in addition to closed-form expression for the UL SINR coverage probability after successful wake up. We present performance results as a function of various key design parameters and highlight the effectiveness and tradeoffs of RF wake up for IoT devices.

In the future, all devices that benefit from an Internet connection will be connected. Internet of Things technologies are key enablers of this vision by moving beyond basic connectivity machine-to-machine (M2M) communications brings to more intelligent interconnection of physical things on a massive scale. This anticipated growth is expected to challenge the planning and operation of cellular networks due to new diverse traffic models and high signaling loads. In this paper, we conduct a detailed experimental study using state-of-the-art drive testing equipment in order to measure, quantify, and analyze the signaling overhead of two classes of M2M services that resemble smart metering and vehicular applications. Two practical signaling reduction techniques are proposed and analyzed, with focus on aggregation as an efficient approach to overcome the resulting surge in signaling load. We complement the experimental results with an analytical evaluation to quantify the tradeoffs between M2M data transmission delay and the level of aggregation. Moreover, we present a novel case study to assess the potential negative impact of M2M signaling traffic on network planning and operation in 4G cellular networks.

The severe power limitations of machine-to-machine (M2M) devices challenge their access connectivity and reliable communication over cellular networks. In this work, we propose a new solution to reduce the power consumption of M2M over cellular by minimizing the power dissipated during inactive intervals. When the M2M device has no data to transmit, it will turn its main circuitry off and switch to a new deep sleep mode. The transition back to the active mode is only achieved upon receiving a radio frequency (RF) wake-up signal from the device's serving base station (BS). We use stochastic geometry to analyze the performance of the proposed wake-up solution. In the proposed model, the M2M device leaves the deep sleep mode when it receives enough power to be activated. The device experiences a successful wake-up event when the total received power includes a wake-up signal transmitted from its serving base station. On the other hand, the device experiences a false wake-up event when it wakes up due to received power from neighboring base station excluding the serving base station. The performance of the proposed model is evaluated in terms of the probabilities of these two events. We use Poisson point processes (PPPs) to derive tractable expressions for the performance metrics, and we present insights for network design and optimization.