This demonstration presents a software-defined radio (SDR) testbed for experimentally evaluating air-to-ground (A2G) communication under dynamic flight conditions. By synchronizing UAV telemetry with orthogonal frequency-division multiplexing (OFDM) measurements, the system enables precise frame-to-telemetry alignment and frame-level bit error rate (BER) analysis. Results show that yaw angle is the dominant flight parameter affecting link reliability. Building on this finding, we demonstrate a yaw-aware adaptive modulation and coding (AMC) scheme that maps angular conditions to the optimum modulation selection. The proposed framework provides a reproducible and practical platform for flight-parameter–aware link adaptation, advancing the robustness and efficiency of future UAV communication systems.

Separation-based pipelines for wireless image transmission suffers a cliff effect at low signal-to-noise ratios. While deep joint source–channel coding may alleviate this drawback, most of the designs ignore readily available side information during decoding. To overcome these drawbacks, we introduce a semantic JSCC scheme in which a convolutional encoder maps an image to a spatial latent vector that traverses through an AWGN channel, and a decoder that is conditioned on class labels and instantaneous SNR values via learnable embeddings. To this end, we trained our model end-to-end with a mixed loss combining L1 fidelity and structural similarity. Our method offers a threefold contributions such as label- and SNR-conditioned decoding, preservation of spatial latent structure, and a simple, compute-efficient objective yielding robust reconstructions under AWGN. Evaluated on STL-10 across a range of SNRs, the proposed method consistently improves both perceptual and distortion metrics over a non-conditioned JSCC baseline. At 5 dB SNR, it improves PSNR value by 4.86 percent and improves LPIPS value by 28 percent. Qualitative reconstructions show reduced over-smoothing and sharper class-relevant details at low SNR. Overall, the results indicate that we provide practical gains especially for compute-constrained noisy wireless links which are used to transmit visual information.

Precise Ultra-Wideband (UWB) localization often relies on tightly synchronized clocks, which limits scalability and increases deployment complexity, especially for real-time, multi-user environments. This constraint is a critical obstacle for lightweight, real-time positioning systems. To address this, we propose three synchronization-free localization models: Centralized-Relay-Based TDoA (i) and Distributed-Relay-Based TDoA (ii), which employ controlled signal relaying and pairwise clock rate estimation to accurately compute the Time Difference of Arrival (TDoA) without the need for global clock synchronization, and Slotted TWR (iii), a collision-free, TDMA-based Two-Way Ranging (TWR) protocol that assigns deterministic retransmission slots at the MAC layer, enabling reliable and scalable localization for many users simultaneously. Experimental evaluation on DWM3001CDK development kits demonstrates that Slotted TWR, in particular, achieves sub-15 cm accuracy and maintains robust performance both within and beyond the anchor region while significantly reducing channel usage compared to conventional TWR.

In today's interconnected world, traditional communication methods have reached the maximum potential set by Shannon's limit. Semantic communication (SemCom) emerged as a response to this situation. It is a visionary strategy that surpasses Shannon's limit, grounded in well-established theoretical foundations, including definitions of semantic noise, semantic encoding/decoding, semantic entropy, semantic channel capacity, and semantic rate-distortion. However, translating these theoretical concepts into practical implementation presents challenges. In this regard, we propose a SemCom-based testbed featuring an encoder-decoder architecture for handling semantic information. This architecture turns theory into a practical system to enhance SemCom strategies. The testbed will aid researchers in developing new protocols, facilitating the adaptation and enhancement of SemCom systems.

UAV swarms have become increasingly valuable due to their adaptability in complex scenarios, yet managing these swarms centrally in harsh environments remains challenging. This paper presents an autonomous and distributed UAV swarm framework designed to improve operational resilience against network disruptions, specifically jamming attacks. The swarm structure utilizes a head UAV orbited by subordinate UAVs that maintain communication through periodic heartbeat signals, monitoring packet delivery ratios (PDR). Adaptive relay ranges, influenced by UAV orbit radius adjustments, significantly enhance communication robustness. The proposed system offers self-healing and jammer avoidance capabilities while maintaining Software Defined Networking (SDN) functionalities that are convenient for operators. Simulation results confirm substantial improvements in latency, jitter, packet delivery ratio, and throughput under jamming conditions.

Unmanned Aerial Vehicle (UAV) swarms enable the rapid deployment of IoT services in dynamic and challenging environments. While these swarms offer flexibility and close proximity to sensing and actuation points, efficiently deploying interdependent services at scale remains a core challenge. Traditional centralized methods struggle to handle the complexity of large UAV networks, leading to increased latency and limited reliability. In this paper, we propose a decentralized approach to service deployment in UAV swarms. Our method relies on local information at each node, allowing UAVs to make their own assignment decisions. Over time, these decisions are iteratively refined as nodes exchange status updates and adapt to network changes. This process avoids the bottlenecks of centralized coordination and enables more responsive resource allocation. Simulation results show that our approach supports the successful deployment of a high number of tasks while maintaining low latency. These findings indicate that decentralized methods with local resource knowledge, improves both scalability and responsiveness in UAV-based IoT systems.

Vehicular ad-hoc networks (VANETs) are characterized by high mobility, dynamic topology changes, and unstable connections, and additionally, they require stringent communication requirements. To make VANET communication more efficient, this poster proposes a Lagrange-based optimized forwarding strategy (LOFS) for information-centric VANETs. The LOFS accounts for several parameters, including link expiration time (LET), link congestion, quality of service (QoS), and traffic conditions to select the next vehicle for Interest and Data packet forwarding. In addition, LOFS mitigates the broadcast storm problem of the existing multicast forwarding strategy, designed for information-centric VANETs.

Real-time healthcare applications demand stringent communication resources to meet the QoS requirements. To meet the application resource demands, this poster proposes a Load and Bandwidth aware Forwarding strategy (LBF-ICN) in Information-Centric Networks (ICN). The LBF-ICN considers router interfaces' pending entries and available bandwidth resources in order to select the next hop for interest forwarding. The LBF-ICN strategy aims to distribute the network load along with bandwidth allocation in interest forwarding.

Coordinated beamforming is one of the crucial improvements for WiFi7. However, to provide sufficient Quality of Service, the corresponding beams have to be aligned accurately. Conventional approaches search for optimal beams in the available codebook to align the beams. However, this introduces considerable delays because of high search space. To address this issue, we utilize Ultra-Wide-Band (UWB) assisted localization to swiftly adjust the beams for users to provide accurate beams. Then by using Received Signal Strength Indicator (RSSI) and state estimation error of the Kalman filter in the position system, we propose a task oriented beamforming alignment system that uses UWB assisted localization only when necessary. Our approach improves beamforming gain by 11.6% compared to conventional beam scanning approaches. On top of this, the proposed solution improves communication effectiveness by 55.6% against naive UWB localization-assisted beam alignment.

Despite the recent advanced safety systems utilizing expensive equipment such as Lidar or high resolution cameras, detecting vulnerable users in the blind-spots is a challenging task especially when LoS obstructed. To address this issue, we propose ultra-wide-band based outdoor localization to detect motorcyclists and cyclists in blind spots as well as a simulation architecture combining vehicle and traffic dynamics with wireless communication models. Our proposed approach offers an affordable solution without necessitating additional hardware by using cellphones of the vulnerable users as beacon sources. Finally, early simulation results show that UWB-based method locates motorcyclists with a similar accuracy when compared to LiDAR in LoS scenarios.

Smart environmental monitoring is crucial for public health and ecological balance as it enables us to monitor and react to environmental hazards. However, effective environmental monitoring can be hindered by the lack of infrastructure and high monetary costs. These challenges are even more pronounced in remote areas, where networking and energy sources are often limited or nonexistent. To address these challenges, we utilize UAVs to form a FANET which can provide effective communication infrastructure suitable for environment monitoring. Moreover, we utilize Edge Intelligence at these UAVs to increase the processing speed and reduce the data size that needs to be transmitted. Our results show that, compared to statically placed gateways, our solution is able to attain similar average age of information for monitoring results while also significantly increasing system capacity.

In the smart emergency detection systems, which automatically detects emergencies and disseminates proper alerts to the citizens and local authorities, accuracy and security is significant concerns. To overcome these concerns, we propose a blockchain-based open emergency alert system, namely EmergencyBlock. Our envisioned system has decentralized structure helps the system’s availability during the crisis situations that cause high load. Nodes in the system have a shared public ledger that holds emergency event data and no other data that may create privacy concerns. Because of the system’s public nature, the blockchain is used to prevent false alerts by protecting the integrity, and also asymmetric encryption is used to protect the confidentiality of the system.

Application domain of Unmanned Aerial Vehicles(UAVs) expands every day. Multiple of these devices can simply be put together to form versatile and powerful Mobile ad hoc Networks (MANETs). Unfortunately, the existing protocols suffer to maintain efficient routes and channel assignments in such dynamic environments, where network topology and channel characteristic are prone to constant changes. In this paper, we propose a cross-layer protocol, utilizing well-known path finding algorithm, D* Lite, to achieve joint optimization of routing and dynamic channel assignment in software-defined aerial networks. Our protocol, CentAir, first uses graph centrality measures to adjust transmit power of the central nodes to both reduce interference rate and congestion around the central nodes. Then, it determines which channel to utilize along the path while taking the estimated collision probabilities into account. Our extensive simulation results show that CentAir provides 132% better channel capacity with the cost of 3% longer paths on e2e routes compared to traditional shortest path routing algorithms with random channel assignments.

Flying Ad Hoc Networks (FANETs) need reliable intra-communication to work effectively during the missions they participate in, especially in contested environments. Furthermore, choosing shortest paths for all e2e links causes over-utilized hot spot nodes in the network. Thus, these hot spots have increased traffic that leads to more collisions around them. Consequently, these central nodes become easy targets for malicious attackers whose aim is to harm the network’s performance as efficiently as possible. To address this, we propose a routing framework (CentAir) that utilizes eigenvector centrality measures of the network nodes to fine-tune their transmission power. Then, it employs D* Lite path-finding algorithm to determine optimal e2e routes under varying network load. As a result, CentAir achieves up to 132% more channel capacity of e2e links compared to shortest path routing algorithms. Additionally, it shows 41% less throughput degradation when jammers are present.

Existing off-the-shelf Wi-Fi technologies lack in terms of providing reliable multihop links for communication-intensive applications. This drawback severely limits the potential of tactical aerial networks, where reliable multihop communication is a must for successful operation. In this paper, we propose a new chain RTS/CTS scheme for multihop communication in Aerial Networks. Our proposed scheme uses the existing IEEE 802.11 Independent Basic Service Set (IBSS) as a stepping stone and extends the capabilities of collision avoidance (CA) mechanism to reserve channels dynamically throughout the multihop communication link. Extensive simulation results, gathered on OMNET++, show 37% throughput improvement in multihop links with 68% increased connection establishment time when compared to existing IEEE 802.11 IBSS with simple channel hopping enhancement.

As the current detection solutions of distributed denial of service attacks (DDoS) need additional infrastructures to handle high aggregate data rates, they are not suitable for sensor networks or the internet of things. Besides, the security architecture of software-defined sensor networks needs to pay attention to the vulnerabilities of both software-defined networks and sensor networks. In this paper, we propose a network-aware automated machine learning (AutoML) framework, which detects DDoS attacks in software-defined sensor networks. Our framework selects an ideal machine learning algorithm to detect DDoS attacks in network-constrained environments, using the metrics such as variable traffic load, heterogeneous traffic rate, and detection time while preventing over-fitting. Our contributions are two-fold: (i) we first investigate the trade-off between the efficiency of ML algorithms and network/traffic state in the scope of DDoS detection. (ii) we design and implement a software architecture containing open-source network tools, with the deployment of multiple ML algorithms. Lastly, we show that under the denial of service attacks, our framework ensures the traffic packets are still delivered within the network with additional delays.

Wireless RF energy transfer for indoor sensors is an emerging paradigm ensuring continuous operation without battery limitations. However, high power radiation within ISM band interferes with packet reception for existing WiFi devices. The paper proposes the first effort in merging RF energy transfer within a standards compliant 802.11 protocol, realizing practical and WiFi-friendly Energy Delivery with Mobile Transmitters (WiFED Mobile). WiFED Mobile architecture is composed of a centralized controller coordinating the actions of multiple energy transmitters (ETs), and deployed sensors that periodically requires charging. The paper first describes 802.11 supported protocol features that can be exploited by sensors to request energy and for ETs to participate in energy transfer. Second, it devises a controller-driven bipartite matching algorithm, assigning appropriate number of ETs to sensors for efficient energy delivery. Thirdly, it detects outlier sensors (OS), which have limited power reception from static ETs and utilizes mobile ETs (METs) to satisfy their charging cycles. The proposed in-band and protocol supported coexistence in WiFED Mobile is validated via simulations and partly in a software defined radio testbed, showing that METs reduce latency by 42% and improve throughput by 83% in scenarios where using only static ETs fails to satisfy charging cycles of OS.

Unmanned aerial systems (UASs) allow easy deployment, three-dimensional maneuverability and high reconfigurability, as they sustain communication network in the absence of pre-installed infrastructure. The proposed FOg Computing in UAS Software-defined mesh network (FOCUS) paradigm aims to realize an implementable network design that considers practical issues of aerial connectivity and computation. It allocates UASs to the tasks of data forwarding and in-network fog computing while maximizing number of ground-users in UAS coverage. FOCUS improves efficient utilization of network resources by introducing on-board computation and innovates on top of software-defined networking stack by integrating the capabilities of network and ground controllers to enable simultaneous orchestration of both UASs and communication flows. There are three main contributions of the paper: First, a SDN-based architecture is designed enabling autonomous configuration of computation and communication as well as managing multi-hop aerial links. Second, a global optimization problem to achieve optimal forwarding and computational allocation is formulated using Open Jackson Network model and solved via a heuristic approach with well defined complexity. Third, FOCUS framework is implemented on a small-scale testbed of Intel®Aero UASs performing image analysis with a full software stack. Experiments reveal at least 32% latency improvement in computation service time compared to traditional centralized computation at the end-server or greedy task allocation schemes within the network.

We propose AirBeam, the first complete algorithmic framework and systems implementation of distributed air-to-ground beamforming on a fleet of UAVs. AirBeam synchronizes software defined radios (SDRs) mounted on each UAV and assigns beamforming weights to ensure high levels of directivity. We show through an exhaustive set of the experimental studies on UAVs why this problem is difficult given the continuous hovering-related fluctuations, the need to ensure timely feedback from the ground receiver due to the channel coherence time, and the size, weight, power and cost (SWaP-C) constraints for UAVs. AirBeam addresses these challenges through: (i) a channel state estimation method using Gold sequences that is used for setting the suitable beamforming weights, (ii) adaptively starting transmission to synchronize the action of the distributed radios, (iii) a channel state feedback process that exploits statistical knowledge of hovering characteristics. Finally, AirBeam provides insights from a systems integration viewpoint, with reconfigurable B210 SDRs mounted on a fleet of DJI M100 UAVs, using GnuRadio running on an embedded computing host.

Unmanned Aerial Vehicular (UAV) networks extend wireless access for devices without infrastructure coverage, and also help establish a connectivity backbone during military reconnaissance and disaster events. This paper focuses on the design of a resilient end-to-end connectivity paradigm under unique architectural and scenario assumptions. First, the UAVs themselves are equipped with multiple interfaces that use standardized protocols, with associated variation in data throughout, range, and bit error rates. Second, there may be adversarial agents seeking to disrupt connectivity through targeted jamming in 3D spaces. Third, we assume an overlay software defined control plane, where the UAVs function as software switches, able to execute forwarding commands and determine preferred routes under controller directives. Our proposed approach devises metrics that influence the choice of the wireless interface and weights edges formed between UAV pairs. Further, it also uses a multi-layer graph model and creates maximally separated paths in 3D space to ensure resiliency to jamming. Simulation results conducted for urban scenarios reveal 34% improvement in enhanced resiliency for end-to-end outages by trading off 12% increase in latency over competing approaches.

The deployment of Self-Organizing Networks (SONs) based architectures has emerged as one of the key points in the 3GPP LTE-Advanced Standard, which aims to embed auto-management skills into the next generation mobile networks. However, the high traffic demands and the increased number of nomadic users have led dense eNodeB coverage, thus challenging the SON management in terms of energy efficiency. Considering these crucial SON challenges, we propose a novel adaptive network coverage model for energy-efficient SONs using a special spatial optimization method. This novel method is based on the Voronoi diagram optimization to provide the minimum number of active eNodeBs for high energy saving. The proposed model mathematically analyzes all the operating eNodeBs deployed in a specific SON area in terms of the utilization, by identifying them by a two-parameter function. These are the spatial coordinates and the utilization of the eNodeB. This eNodeB-specific mathematical model leads to find the redundant eNodeBs with less utilization, deactivate them and rearrange the coverage area with the remaining active eNodeBs using the Voronoi specific optimization. This optimization is solved by a novel heuristic with the aid of a parameter called assignment factor, in order to maximize the utilization for the remaining active eNodeBs in the green SON architecture. This spatial optimization based algorithm aims to adaptively deploy energy-effective cell coverage. The thorough evaluation results prove the generic energy-efficiency of the proposed adaptive coverage algorithm while maintaining the ENodeB utilization above the satisfying QoS levels.