This paper focuses on energy savings in downlink operation of cell-free massive MIMO (CF mMIMO) networks under dynamic traffic conditions. We propose a multi-agent deep reinforcement learning (MADRL) algorithm that enables each access point (AP) to autonomously control antenna reconfiguration and advanced sleep mode (ASM) selection. After the training process, the proposed framework operates in a fully distributed manner, eliminating the need for centralized control and allowing each AP to dynamically adjust to real-time traffic fluctuations. Simulation results show that the proposed algorithm reduces power consumption (PC) by 56.23% compared to systems without any energy-saving scheme and by 30.12% relative to a non-learning mechanism that only utilizes the lightest sleep mode, with only a slight increase in drop ratio. Moreover, compared to the widely used deep Q-network (DQN) algorithm, it achieves a similar PC level but with a significantly lower drop ratio.

In recent years, drones, or unmanned aerial vehicles (UAVs), have become widely used across various applications, from aerial photography and videography to the delivery of packages and medical supplies. However, their increasing presence has raised concerns about physical safety and privacy, highlighting the need for effective drone detection and monitoring solutions. To address this, we utilize the fact that most commercial drones use the Zadoff-Chu (ZC) sequence as the synchronization sequence in their communications, making it a useful feature for detection. Yet, detecting the ZC sequence blindly is challenging, as the transmitter's frequency is unknown to the receiver. While existing studies on ZC sequence detection with different frequency offsets focus largely on Long Term Evolution (LTE) scenarios, the ZC sequence structure and length used by drones differ, leading to unique detection challenges. In this paper, we analyze the autocorrelation properties of the specific ZC sequence used by drones under various center frequency offsets. We further propose a blind detection and identification algorithm that can detect and identify multiple drones utilizing ZC sequences in their video transmission protocols and autocorrelation properties. We study the performance of the proposed algorithm with extensive simulations and field tests. Even in low signal-to-noise ratio (SNR) conditions, with an SNR as low as -14 dB, our algorithm achieves a detection rate exceeding 99 %.

The integration of unmanned aerial vehicles (UAVs) into cellular networks presents significant mobility management challenges, primarily due to frequent handovers caused by probabilistic line-of-sight conditions with multiple ground base stations (BSs). To tackle these challenges, reinforcement learning (RL)-based methods, particularly deep Q-networks (DQN), have been employed to optimize handover decisions dynamically. However, a major drawback of these learning-based approaches is their black box nature, which limits interpretability in the decision making process. This paper introduces an explainable AI (XAI) framework that incorporates Shapley Additive Explanations (SHAP) to provide deeper insights into how various state parameters influence handover decisions in a DQN-based mobility management system. By quantifying the impact of key features such as reference signal received power (RSRP), reference signal received quality (RSRQ), buffer status, and UAV position, our approach enhances the interpretability and reliability of RL-based handover solutions. To validate and compare our framework, we utilize real-world network performance data collected from UAV flight trials. Simulation results show that our method provides intuitive explanations for policy decisions, effectively bridging the gap between AI-driven models and human decision-makers.

Neural networks (NNs) are pivotal in enhancing data processing tasks such as classification, generation, and restoration. A crucial consideration in these applications is the signal-to-noise ratio (SNR), which serves as a measure of the quality of the data. In this paper, we hypothesize that optimizing NNs in some tasks can be more effective when all samples in the dataset are clustered based on quantized SNR levels regarding the statistical similarity between training/test dataset. Hence, we introduce two novel techniques, i.e., 1) a linear algebraic method with a single-shot data sample and 2) an NN-based method with few-shot data samples, for estimating the SNR of a sparse signal. The proposed techniques are based on the mathematical fact that the dominant singular values contain the information of a signal space when signals are Hankelized in matrix form. Both algorithms achieve over 93% clustering accuracy, and almost 100% accuracy in high SNR scenarios and increased signal length. Furthermore, we provide an example of signal denoising as practical validation of the benefits of these clustering results for optimizing NN in a task. The proposed approaches show a superior denoising performance while requiring an extremely small training dataset compared to conventional methods, which can be interpreted as an improvement in the learnability of the NN.

This paper addresses the problem of providing internet connectivity to aircraft flying above the ocean without using satellite connectivity given the lack of ground network infrastructure in the relevant oceanic areas. Is it possible to guarantee a minimum flow rate to each aircraft flying over an ocean by forming an aeronautical ad hoc network and connecting that network to internet via a set of limited number of ground base stations at the coast as anchor points? We formulated the problem as mixed-integer-linear programming (MILP) to maximize the number of aircraft with flow data rate above a certain threshold. Since this multi-commodity flow problem is at least NP-complete, we propose a two-phase heuristic algorithm to efficiently form topology and assign flows to each aircraft by maximizing the minimum flow. The performance of the heuristic algorithm is evaluated over the North Atlantic Corridor, heuristic performs only 8% less than the optimal result with low densities. In high network densities, the connectivity percentage changes from 70% to 40% under 75 Mbps data rate threshold. Furthermore, the connectivity percentage is investigated for different network parameters such as altitude and compared to upper and lower bounds and a baseline algorithm.

Mobility management for terrestrial users is mostly concerned with avoiding radio link failure for the edge users where the cell boundaries are defined. The problem becomes interesting for an aerial user experiencing fragmented coverage in the sky and line-of-sight conditions with multiple ground base stations (BSs). For aerial users, mobility management is not only concerned with avoiding link failures but also avoiding unnecessary handovers while maintaining extended service availability, especially in up-link communication. The line of sight conditions from an Unmanned Aerial Vehicle (UAV) to multiple neighboring BSs make it more prone to frequent handovers, leading to control packet overheads and delays in the communication service. Depending on the use cases, UAVs require a certain level of service availability, which makes their mobility management a critical task. The current mobility robustness optimization (MRO) procedure that adaptively manages handover parameters to avoid unnecessary handovers is optimized only for terrestrial users. It needs to be updated to capture the unique mobility challenges of aerial users. In this work, we propose two approaches to accomplish this: 1) A model based service availability-aware MRO where handover control parameters, such as handover margin and time to trigger are tuned to maintain high service availability with a minimum number of handovers, and, 2) A deep Q-network based model free approach for decreasing unnecessary handovers while maintaining high service availability. Simulation results demonstrate that both the proposed algorithms converge promptly and increase the service availability by more than 40% while the number of handovers is reduced by more than 50% as compared to traditional approaches.

Aerial vehicles (AVs) such as electric vertical take-off and landing (eVTOL) aircraft make aerial passenger transportation a reality in urban environments. However, their communication connectivity is still under research to realize their safe and full-scale operation. This paper envisages a multi-connectivity (MC) enabled aerial network to provide ubiquitous and reliable service to AVs. Vertical heterogeneous networks with direct air-to-ground (DA2G) and air-to-air (A2A) communication, high altitude platforms (HAPs), and low Earth orbit (LEO) satellites are considered. We evaluate the end-to-end (E2E) multi-hop reliability and network availability of the downlink of AVs for remote piloting scenarios, and control/telemetry traffic. Command and control (C2) connectivity service requires ultra-reliable and low-latency communication (URLLC), therefore we analyse E2E reliability and latency under the finite blocklength (FBL) regime. We explore how different MC options satisfy the demanding E2E connectivity requirements taking into account antenna radiation patterns and unreliable backhaul links. Since providing seamless connectivity to AVs is very challenging due to the line-of-sight (LoS) interference and reduced gains of downtilt ground base station (BS) antennas, we use coordinated multi-point (CoMP) among ground BSs to alleviate the inter-cell interference. Furthermore, we solve an optimization problem to select the best MC path under the quality of service (QoS) constraints. We maximize spectral efficiency (SE) to specify the optimum MC path with the minimum number of required links. Based on the simulation results, we find out that even with very efficient interference mitigation, MC is the key enabler for safe remote piloting operations.

Aerial base stations (ABSs) have emerged as a promising solution to meet the high traffic demands of future wireless networks. Nevertheless, their practical implementation requires efficient utilization of limited payload and onboard energy. Understanding the power consumption streams, such as mechanical and communication power, and their relationship to the payload is crucial for analyzing its feasibility. Specifically, we focus on rotary-wing drones (RWDs), fixed-wing drones (FWDs), and high-altitude platforms (HAPs), analyzing their energy consumption models and key performance metrics such as power consumption, energy harvested-to-consumption ratio, and service time with varying wingspans, battery capacities, and regions. Our findings indicate that FWDs have longer service times and HAPs have energy harvested-to-consumption ratios greater than one, indicating theoretically infinite service time, especially when deployed in near-equator regions or have a large wingspan. Additionally, we investigate the case study of RWD-BS deployment, assessing aerial network dimensioning aspects such as ABS coverage radius based on altitude, environment, and frequency of operation. Our findings provide valuable insights for researchers and telecom operators, facilitating effective cost planning by determining the number of ABSs and backup batteries required for uninterrupted operations.

The projected number of Internet of Things (IoT) sensors makes battery maintenance a challenging task. Although battery-less IoT is technologically viable, the sensors should be somehow energized, either locally or remotely. Unmanned aerial vehicles (UAVs) can respond to this quest via wireless power transfer (WPT). However, to achieve energy neutrality across the IoT networks and thus mitigate the maintenance issues, the UAVs providing energy and connectivity to IoT sensors must be supplied by recharging stations having multi-source energy harvesting (EH) capability. Yet, as these sensors rely solely on UAV-transferred power, the absence of UAVs causes sensor outages and hence loss of coverage when they visit recharging stations for battery replenishment. Hence, besides the UAV parameters (e.g., battery size and velocity), recharging duration and station density must be carefully determined to avoid these outages. To address that, this paper uses stochastic geometry to derive the coverage probability of UAV-powered sensors. Our analysis sheds light on the fundamental trade-offs and design guidelines for energy-neutral IoT networks with recharging stations in regard to the regulatory organization limitations, practical rectenna and UAV models, and the minimum power requirements of sensors.

The ubiquitous cellular network is a strong candidate for providing UAVs' wireless connectivity. Due to the maneuverability advantage and higher altitude, UAVs could have line-of-sight (LoS) connectivity with more base station (BS) candidates than terrestrial users. However, the LoS connectivity could also enhance the propagation of up-link interference caused by UAVs over co-existing terrestrial users. In addition, UAVs would perform more handovers than terrestrial users when moving due to the extensive overlap in the coverage areas of many BS candidates. The solution is to bypass the overlapping coverage areas by designing the UAVs' trajectory and to reduce interference by optimizing radio resource allocation through handover management. This paper studies the joint optimization of a UAV's trajectory design and handover management to minimize the weighted sum of three key performance indicators (KPIs): delay, up-link interference, and handover numbers. A dueling double deep Q-network (D3QN) based reinforcement learning algorithm is proposed to solve the optimization problem. Results show that the proposed approach can reduce the handover numbers by 90% and the interference by 18% at the cost of a small increment in transmission delay when compared with the benchmark scheme, which controls the UAV to move along the shortest path and perform handover based on received signal strength. Finally, we verify the advantage of introducing trajectory design, which can reduce the interference by 29% and eliminate the handover numbers by 33% when compared to the D3QN-based policy without trajectory design.

In this paper, the indoor dense space (IDS) channel at 28 GHz is characterized through extensive Ray-Tracing (RT) simulations. We consider IDS as a specific type of indoor environment with confined geometry and packed with humans, such as aircraft cabins and train wagons. Based on RT simulations, we characterize path loss, shadow fading, root-mean-square delay spread, Rician K-factor, azimuth/elevation angular spread of arrival/departure considering different RT simulation scenarios of the fuselage geometry, material, and human presence. While the large-scale fading parameters are similar to the state-of-the-art channel models, the small-scale fading parameters demonstrate richer multipath scattering in IDS, resulting in poorer bit error rate performance in comparison to the 3GPP indoor channel model.

Network traffic prediction in cellular networks improves reliability and efficiency of network resource use via proactive network management schemes. To this end, future traffic arrivals are anticipated via machine learning (ML)-based network traffic predictions based on historical network traffic data. Current literature on ML-based network traffic predictions employs warm-up periods, which are the required duration traffic flows are observed to make meaningful predictions. However, most flows are shorter than the warm-up period. This paper proposes a residual neural network (ResNet) architecture for individual network flow predictions, based on a deep-learning approach that removes the required warm-up period seen in other proposed methods. The ResNet architecture demonstrates the ability to accurately predict the magnitude of packet count, size, and duration of flows using only the information available at the arrival of the first packet such as IP addresses and utilized transport-layer protocols. The results indicate that the proposed method is able to predict the order of magnitude of individual flow characteristics with over 80% accuracy, outperforming traditional ML methods such as linear regression and decision trees.

Feasibility of using unlicensed spectrum for ultra reliable low latency communications (URLLC) is still a question for beyond 5G wireless networks. Low latency access to the channel and efficiently sharing spectrum among the multiple users are the main requirements for exploiting unlicensed spectrum for URLLC. Listen before talk and back-off procedures implemented to avoid the collisions in channel access hinder the low latency communication. In this paper, we propose a novel low-latency medium access control (MAC) scheme based on the collision resolution for a pairwise random wireless network. We use geometric sequence decomposition for collision resolution among the competing users. This enables the system to tackle collisions and thus removing the need for carrier sensing and back-off procedures. This saves time in obtaining access to the channel and improves the efficiency of the system. We implement our approach in the synchronized time slotted system and show that it yields significant improvement over existing MAC schemes.

Low Latency Low Loss Scalable Throughput (L4S) is a technology intended to reduce queue delay problems, ensuring low latency to Internet Protocol flows with a high throughput performance. To reach this goal, it relies on Explicit Congestion Notification (ECN), a mechanism that marks packets to signal congestion in the network avoiding packets to be dropped. The congestion signals are managed at the sender and receiver sides thanks to scalable congestion control algorithms. In this paper, the challenges to implement L4S in a 5G network are analyzed. Using a proprietary state-of-the-art network simulator, the L4S marking strategy has been implemented at the Packed Data Convergence Protocol layer. To evaluate the benefits of the implementation, L4S has been adopted to support Augmented Reality (AR) video gaming traffic while using the IETF experimental standard Self-Clocked Rate Adaptation for Multimedia (SCReAM) for the congestion control. The results show that the video gaming traffic experiences lower delay when supported by L4S. Moreover, in all the cases analyzed, L4S provides an average application layer throughput above the minimum requirements of a high-rate latency-critical application, even at high system loads. Furthermore, the packet loss rate has been significantly reduced thanks to L4S. If it is used in a combination with a Delay Based Scheduler (DBS), a packet loss rate very close to zero has been reached.

Sensors currently deployed on board have wired connectivity, which increases weight and maintenance costs for aircraft. Removing cables for wireless communications of sensors on board alleviates the cost, however, the powering of sensors becomes a challenge inside aircraft. Wireless power transfer (WPT) via radio-frequency (RF) signals is an emerging solution to remotely power sensors for battery-less operation with long-lived capacitors. In this article, we design a WPT system for aircraft IoT-type applications, including low data rate inside (LI) sensors by determining the number, location, and tilt angles of WPT transmitters given constraints based on the cabin geometry and duty cycle of the sensors. We formulate a robust optimization problem to address the WPT system design under channel uncertainties. We also derive an equivalent integer linear programming and solve that for an optimal deployment to satisfy the duty cycle requirements of LI sensors. We perform experiments inside the cabin to validate the wireless avionics intracommunications channel model. Our simulations demonstrate the feasibility of 90% robust design with 14 WPT transmitters for duty cycles less than 0.1% while keeping the human radiation exposure below the recommended reference value of 4.57 W/m2.

Cellular connectivity for drones comes with a wide set of challenges as well as opportunities. Communication of cellular-connected drones is influenced by 3-dimensional mobility and line-of-sight channel characteristics which results in higher number of handovers with increasing altitude. Our cell planning simulations in coexistence of aerial and terrestrial users indicate that the severe interference from drones to base stations is a major challenge for uplink communications of terrestrial users. Here, we first present the major challenges in co-existence of terrestrial and drone communications by considering real geographical network data for Stockholm. Then, we derive analytical models for the key performance indicators (KPIs), including communications delay and interference over cellular networks, and formulate the handover and radio resource management (H-RRM) optimization problem. Afterwards, we transform this problem into a machine learning problem, and propose a deep reinforcement learning solution to solve HRRM problem. Finally, using simulation results, we present how the speed and altitude of drones, and the tolerable level of interference, shape the optimal H-RRM policy in the network. Especially, the heat-maps of handover decisions for different altitudes/speeds of drones have been presented, which promote a revision of the legacy handover schemes and boundaries of cells in the sky.

In-flight Internet connectivity is a necessity for aircraft passengers as well as aircraft systems. It is challenging to satisfy required quality of service (QoS) levels for flows within aircraft due to the large number of users and the highly varying air to ground (A2G) link capacities composed of satellite and direct air to ground communication (DA2GC). To represent service quality variations, we propose models for the generated traffic flows from aircraft and variations in A2G links. We present three different forwarding schemes based on priority, delay requirements and history of the dropped flows metrics. Forwarding schemes schedule the flows in real time by choosing either satellite or direct air to ground link depending on the delay and capacity requirements of flows to maximize the number of accepted flows with required QoS guarantees in terms of dropped packets and delay. Also, the effect of local caching is studied to fully satisfy the QoS requirement of flows in simulated flights. We implement the forwarding procedures and caching in ns-3 and test their performance in a current connectivity scenario of 100 Mbps capacity for both the satellite spot and ground base station in a one-hour flight. Our study shows that although the forwarding procedure based on a combination of priority and delay requirement has relatively better performance than the other schemes, which are based on priority only and weighted average of all metrics, in dropped packet percentage and delay, the current connectivity setup is not able to satisfy all QoS requirements. Furthermore, at least 0.9 cache hit rate is required to satisfy all flows for at least 50% of simulated flights.

Localization plays a key role for safe operation of UAVs enabling beyond visual line of sight applications. Compared to GPS based localization, cellular networks can reduce the positioning error and cost since cellular connectivity is becoming a prominent solution as a communication system for UAVs. As a first step towards localization, UAV needs to receive sufficient number of localization signals each having a signal to interference plus noise ratio (SINR) greater than a threshold. On the other hand, three-dimensional mobility of UAVs, altitude dependent channel characteristics between base stations (BSs) and UAVs, its line of sight and non-line of sight conditions, and resulting interference from the neighboring BSs pose challenges to receive usable signals from the required number of BSs. In this paper, we utilize a tractable approach to calculate localizability probability, which is defined as the probability of successfully receiving usable signals from at least a certain number of BSs. Localizability has an impact on overall localization performance regardless of the localization technique to be used. In our simulation study, we investigate the relation between the localizability probability with respect to the number of participating BSs, post-processing SINR requirement, air-to-ground channel characteristics, and network coordination, which are shown to be the most important factors for the localizability performance of UAVs. We observe the localizability performance is better at higher altitudes which indicates that localizability with cellular networks for UAVs is more favorable than for terrestrial users.

Providing broadband in-flight Internet connectivity to aircraft is challenging. Today's options include satellite communications (SC) and direct air-to-ground communication (DA2GC). To overcome data rate, delay and cost limitations of SC and coverage limitations of DA2GC, one can extend DA2GC with air-to-air communication (A2AC) by enabling multi-hop communication. To investigate the A2AC performance, we construct a mixed integer linear programming (MILP) problem of DA2GC and A2AC, jointly considering interference in topology formation and flow assignment. Our objective is to maximize the number of aircraft that can be connected with a given specific minimum data rate threshold. The evaluation is performed for low aircraft density scenarios over the North Atlantic. We show that in the investigated scenarios, over 90 % of aircraft can have at least 50 Mbps, some being up to 1600 kilometers away from the closest base station (BS). Furthermore, we identify antenna capabilities as an important factor for A2AC performance.

Unmanned aerial vehicles (UAVs) need to "see and be seen" by manned aircraft for their safe operation. For this purpose, using available Automatic Dependent Surveillance-Broadcast (ADS-B) for all UAVs will not only saturate the 1090 MHz spectrum but will require an extra hardware. To mitigate these issues, we propose a ground based sense and avoid (GBSAA) system to enable coexistence of low altitude UAVs and ADS-B enabled flying vehicles (FVs), i.e., manned aircraft, for avoiding collisions. UAVs transmit their location information via a cellular technology to cloud, and a GBSAA base station accesses the aggregated information to send it to the ADS-B enabled FVs via ADS-B technology. We performed analytical and simulation studies to investigate the ADS-B message collision probability for different network parameters such as number of UAVs and ADS-B enabled FVs. Our study shows that for a collision probability of 0.1 with the lowest ADS-B update interval, GBSAA system can support approximately 10 times more UAVs than ADS-B only system. The proposed GBSAA approach provides a tremendous potential to integrate UAVs into airspace in a scalable manner.

In this paper, we investigate the potential benefits and challenges of using millimeter wave (mm-Wave) cellular network to carry out Beyond Visual Line of Sight (BVLOS) operations for remote piloting of Unmanned Aerial Vehicles (UAVs). We evaluate the reliability and latency of the wireless link between a UAV and a serving mm-Wave Base Station (BS) in the finite blocklength regime with the ultra reliable low latency communications (URLLC) requirements of 5G. To perform a comprehensive evaluation, we integrate several realistic models including mm-Wave antenna arrays, beamforming, mm-Wave propagation, and LOS probabilities. Our results show that cooperation and coordination among BSs are critical for UAV piloting using mm-Wave technology. We analyze the performance of mm-Wave links under three scenarios representing different levels of interference mitigation. We show that there is a certain range of message size and block length where it is possible to achieve URLLC requirements with packet error probability of 10−5and latency below 1 ms when assuming cooperation.

In this paper, we propose a model for beyond visual line of sight (BVLOS) operation for remote piloting of unmanned aerial vehicles (UAVs), which utilizes different technologies such as mobile edge computing and augmented reality. Ultra reliable low latency communication (URLLC) is a key service of 5G that enables safe BVLOS operation. Since message size of piloting signal is finite and communication channel is altitude dependent, we study reliability and latency under finite blocklength regime for different altitudes. In our numerical study, we find that for message sizes 30 and 50 bits, coded packet size, i.e., blocklength, should be in the range of 200 and 300 bits to enable BVLOS operation. We also found that minimum distance between UAVs to avoid any crash should be around 0.2 m for 15 m/s UAV speed and different altitudes ranging from 1.5 m to 120 m. According to our study, BVLOS operation of UAVs can be realized by URLLC by providing error probability in the vicinity of 10-3, and latency on the order of milliseconds for downlink communication with blocklength of tens to hundred bits.

Sensor nodes, one of the most crucial elements of Internet of Things (IoT), sense the environment and send their observations to a remote Access Point (AP). One drawback of sensor nodes in an IoT setting is their limited battery supply. Hereby, energy harvesting (EH) stands as a promising solution to reduce or even completely eliminate lifetime constraints of sensors with exploitation of available resources. In this paper, we propose an electric-field EH (EFEH) method to enable battery-less execution of sensor-based IoT services for Smart Grid (SG) context. For this purpose, for the first time in the literature, harvestable energy through EFEH method is investigated with a transformer room experimental set-up. Our experiments reveal that 40 mJ of energy can be harvested in a period of 900 sec with the proposed EFEH method. Building on this energy profile, we define a throughput objective function θ for a "harvest-then-transmit" type system model, to shed light on the harvesting- throughput trade-off specific to IoT-assisted SG applications. Numerical results disclose non- trivial relationships between optimal harvesting period T_H, optimal transmission period T_T and critical network parameters such as node-AP hop distance, path loss exponent and minimum reporting frequency requirement.

Internet of Things (IoT) is a perfect candidate to realize efficient observation and management for Smart City concept. This requires deployment of large number of wireless devices. However, replenishing batteries of thousands, maybe millions of devices may be hard or even impossible. In order to solve this problem, Internet of Energy Harvesting Things (IoEHT) is proposed. Although the first studies on IoEHT focused on energy harvesting (EH) as an auxiliary power provisioning method, now completely battery-free and self-sufficient systems are envisioned. Taking advantage of diverse sources that the concept of Smart City offers helps us to fully appreciate the capacity of EH. In this way, we address the primary shortcomings of IoEHT; availability, unreliability, and insufficiency by the Internet of Hybrid EH Things (IoHEHT). In this paper, we survey the various EH opportunities, propose an hybrid EH system, and discuss energy and data management issues for battery-free operation. We mathematically prove advantages of hybrid EH compared to single source harvesting as well. We also point out to hardware requirements and present the open research directions for different network layers specific to IoHEHT for Smart City concept.

Social Internet of Vehicles (SIoV) is a new paradigm that enables social relationships among vehicles via the Internet. People in the vehicles using online social networks (OSNs) can be an integral part of SIoV that enables the collection of data for sensing a physical phenomenon, i.e., social sensing. In this paper, we study the main social sensing mechanism in Facebook, comment thread network (CTN), which is based on the interactions of users through user walls in Facebook for SIoV. After seeing their commuters' contents about an event, users either add comments or like these posts, and Facebook CTN emerges as a social sensing medium in estimation of an event through social consensus. For the first time, this paper investigates the social sensing capability of Facebook CTN, i.e., the accuracy of collective observations for SIoV. The accuracy depends on the user characteristics and the features of the OSN, since perceptions of the users and how they use Facebook may manipulate their observation signals. We analyze the reliability of Facebook CTN for varying user behaviors, user relationships, Facebook features, and network size. The results indicate that the polarized weighting of the observations and the use of less reliable post types in CTN deteriorate the accuracy of the estimate signal, i.e., social consensus. Furthermore, the selection of users is likely to be an important factor in social sensing.

Network size and number of mobile users are ever-increasing with the advancements in cellular network technologies. Hence, this situation makes the network monitoring highly complex. Although there are numerous network tomography approaches, service providers need real-time network monitoring tools to provide better network utilization. In this paper, we propose a crowdsourcing-based real-time network tomography framework. In the proposed framework, channel condition and user data usage are monitored via an application at the mobile terminals, and then the mobile terminals transmit their data to the server. In this way, the network and user behavior can be continuously monitored, and real-time actions can be implemented to improve the network performance. By using the proposed framework, we propose an optimization framework for the amount and reporting frequency of the transmitted data to avoid battery drain at the mobile terminal and network congestion. At the end, we provide simulation results for the proposed optimization framework.

Cognitive radio sensor networks (CRSNs) are event-based systems such that sensor nodes detect events and the event readings of the sensors are collaboratively conveyed in a multi-hop manner through vacant channels from event regions to a sink. Hence, the event-to-sink communication and the dynamic radio environment require a coordination scheme in CRSNs. In this paper, we propose a spectrum-aware clustering protocol to address the event-to-sink communication coordination issue in mobile CRSNs. Our clustering scheme consists of two phases. The first phase is the determination of nodes eligible for clustering, and the second phase is to form clusters among those nodes according to vacant spectrum bands. Clusters are temporary and they are not preserved after the end of events. Furthermore, we find average re-clustering probability, expected cluster coverage area, and find maximum event generation frequency for energy-efficient operation of our protocol. We study performance of our protocol in terms of control and data packet exchange, time steps required for clustering, connectivity of clusters, energy consumed for clustering, and re-clustering ratio due to the mobility. Performance comparison simulations show that our algorithm has better performance in terms of connectivity and energy consumption.

Wireless networked control system (WNCS) is a control system that a wireless network closes the control loop. WNCS estimator, i.e., Kalman filter, estimates the system state according to the observations of sensors. These observations which are from N independent subnetworks are conveyed to the Kalman filter through vacant bands opportunistically with cognitive radio capability of the nodes. We characterize the successful packet delivery probability and study the maximization of energy-efficiency of overall system under the convergence constraint of the Kalman filter by defining an optimization problem. We also find a lower bound on maximum total coverage area. Furthermore, we perform numerical analysis to observe the effects of system parameters such as number of subnetworks, average ON probability of primary users, transmission ranges and densities of sensor nodes and primary users, and false alarm probability.

The integration of wireless communication and control systems revealed wireless networked control systems (WNCSs). One fundamental problem in WNCSs is to have a wide coverage area. For the first time in the literature, we address this problem and we obtain the maximum coverage area by solving an optimization problem. In this paper, we consider a WNCS where the output sensor measurements are transmitted over separate multi-hop wireless ad-hoc subnetworks. The system state is estimated using the Kalman filter. We present the critical arrival probability for a sensor measurement packet such that if the packet arrival probability is larger than the critical value, it is guaranteed that the expected state estimation error covariance is bounded, and hence the WNCS is stable. We find the optimum hop-diameter of a multi-hop wireless ad-hoc subnetwork under the constraints of both the stability of the WNCS and the cost-efficiency of the multi-hop wireless network. Furthermore, under these constraints, we derive the maximum total coverage area of the wireless subnetworks. The numerical analyses show that the maximum total coverage area can be increased by appropriately adjusting the number of sensors, the successful packet transmission probability between relay nodes, and the eigenvalues of the system matrix.

Wireless sensor networks (WSN) with dynamic spectrum access (DSA) capability, namely cognitive radio sensor networks (CRSN), is a promising solution for spectrum scarcity problem. Despite improvement in spectrum utilization by DSA capability, energy-efficient solutions for CRSN are required due to resource-constrained nature of CRSN inherited from WSN. Clustering is an efficient way to decrease energy consumption. Existing clustering approaches for WSN are not applicable in CRSN and existing solutions for cognitive radio networks are not suitable for sensor networks. In this paper, we propose an event-driven clustering protocol which forms temporal cluster for each event in CRSN. Upon detection of an event, we determine eligible nodes for clustering according to local position of nodes between event and sink. Cluster-heads are selected among eligible nodes according to node degree, available channels and distance to the sink in their neighborhood. They select one-hop members for maximizing the number of two-hop neighbors that are accessible by one-hop neighbors through cluster channels to increase connectivity between clusters. Clusters are between event and sink and are no longer available after the end of the event. This avoids energy consumption due to unnecessary cluster formation and maintenance overheads. Performance evaluation reveals that our solution is energy-efficient with a delay due to spontaneous cluster formation.