Large Language Models (LLMs) can revolutionize how we deploy and operate Open Radio Access Networks (0-RAN) by enhancing network analytics, anomaly detection, and code generation and significantly increasing the efficiency and reliability of a plethora of 0- RAN tasks. In this paper, we present ORAN-Bench-13K, the first comprehensive benchmark designed to evaluate the performance of Large Language Models (LLMs) within the context of O-RAN. Our benchmark consists of 13,952 meticulously curated multiple-choice questions generated from 116 O-RAN specification documents. We leverage a novel three- stage LLM framework, and the questions are categorized into three distinct difficulties to cover a wide spectrum of 0 RAN- related knowledge. We thoroughly evaluate the performance of several state-of-the-art LLMs, including Gemini, Chat-GPT, and Mistral. Additionally, we propose ORANSight, a Retrieval- Augmented Generation (RAG)-based pipeline that demonstrates superior performance on ORAN-Bench-13K compared to other tested closed-source models. Our findings indicate that current popular LLM models are not proficient in O-RAN, highlighting the need for specialized models. We observed a noticeable performance improvement when incorporating the RAG-based ORANSight pipeline, with a Macro Accuracy of 0.784 and a Weighted Accuracy of 0.776, which was on average 21.55% and 22.59% better than the other tested LLMs.

The advancement of 5G and NextG networks through Open Radio Access Network (O-RAN) architecture marks a transformative shift towards more virtualized, modular, and disaggregated configurations. A critical component within this O-RAN architecture is the RAN Intelligent Controller (RIC), which facilitates the management and control of the RAN through sophisticated machine learning-driven software microservices known as xApps. These xApps rely on accessing a diverse range of sensitive data from RAN and User Equipment (UE), stored in the near Real-Time RIC (Near-RT RIC) database. The inherent nature of this shared, multi-vendor, and open environment significantly raises the risk of unauthorized sensitive RAN/UE data exposure. In response to these privacy concerns, this paper proposes a privacy-preserving zero-trust RIC (dubbed as, ZT-RIC) framework that preserves RAN/UE data privacy within the RIC platform (i.e., shared RIC database, xA$p$p, and E2 interface). The underlying idea is to employ a computationally efficient cryptographic technique called Inner Product Functional Encryption (IPFE) to encrypt the RAN/UE data at the base station, thus, preventing data leaks over the E2 interface and shared RIC database. Furthermore, ZT-RIC customizes the xAp$p$'s inference model by leveraging the inner product operations on encrypted data supported by IPFE to enable xAp$p$to make accurate inferences without data exposure. For evaluation purposes, we leverage a state-of-the-art InterClass xApp, which utilizes RAN key performance metrics (KPMs) to identify jamming signals within the wireless network. Prototyping on an LTE/5G O-RAN testbed demonstrates that ZT-RIC not only ensures data privacy/confidentiality but also guarantees a desired model accuracy of 97.9% in detecting jamming signals as well as meeting stringent sub-second timing requirement with a round-trip time (RTT) of 0.527 seconds.

Recent advancements in artificial intelligence (AI) and edge computing have accelerated the development of machine-centric applications (MCAs), such as smart surveillance systems. In these applications, video cameras and sensors offload inference tasks like license plate recognition and vehicle tracking to remote servers due to local computing and energy constraints. However, legacy network solutions, designed primarily for human-centric applications, struggle to reliably support these MCAs, which demand heterogeneous and fluctuating quality of service (QoS) (due to diverse application inference tasks), further challenged by dynamic wireless network conditions and limited spectrum resources. To tackle these challenges, we propose an Application Context-aware Cross-layer Optimization and Resource Design (ACCORD) framework. This innovative framework anticipates the evolving demands of MCAs in real time, quickly adapting to provide customized QoS and optimal performance, even for the most dynamic and unpredictable MCAs. This also leads to improved network resource management and spectrum utilization. ACCORD operates as a closed feedback-loop system between the application client and network and consists of two key components: (1) Building Application Context: It focuses on understanding the specific context of MCA requirements. Contextual factors include device capabilities, user behavior (e.g., mobility speed), and network channel conditions, and (2) Cross-layer Network Parameter Configuration: Utilizing a deep reinforcement learning (DRL) approach, this component leverages the contextual information to optimize network configuration parameters across various layers, including physical (PHY), medium access control (MAC), and radio link control (RLC), as well as the application layer, to meet the desired QoS requirement in realtime. Extensive evaluation with the 3GPP-compliant MATLAB 5G toolbox demonstrates the practicality and effectiveness of our proposed ACCORD framework.

The advent of Open Radio Access Networks (O-RAN) has transformed the telecommunications industry by promoting interoperability, vendor diversity, and rapid innovation. However, its disaggregated architecture introduces complex testing challenges, particularly in validating multi-vendor components against O-RAN ALLIANCE and 3GPP specifications. Existing frameworks, such as those provided by Open Testing and Integration Centres (OTICs), rely heavily on manual processes, are fragmented and prone to human error, leading to inconsistency and scalability issues. To address these limitations, we present AI5GTest -- an AI-powered, specification-aware testing framework designed to automate the validation of O-RAN components. AI5GTest leverages a cooperative Large Language Models (LLM) framework consisting of Gen-LLM, Val-LLM, and Debug-LLM. Gen-LLM automatically generates expected procedural flows for test cases based on 3GPP and O-RAN specifications, while Val-LLM cross-references signaling messages against these flows to validate compliance and detect deviations. If anomalies arise, Debug-LLM performs root cause analysis, providing insight to the failure cause. To enhance transparency and trustworthiness, AI5GTest incorporates a human-in-the-loop mechanism, where the Gen-LLM presents top-k relevant official specifications to the tester for approval before proceeding with validation. Evaluated using a range of test cases obtained from O-RAN TIFG and WG5-IOT test specifications, AI5GTest demonstrates a significant reduction in overall test execution time compared to traditional manual methods, while maintaining high validation accuracy.

The transition to Open Radio Access Networks (O-RAN) introduces testing challenges due to multi-vendor interoperability requirements, with existing manual frameworks being error-prone and unscalable. To address this, we propose AI5GTest, an AI-driven framework that automates O-RAN component testing using cooperative Large Language Models (LLMs). Gen-LLM generates test flows from O-RAN/3GPP specifications, Val-LLM validates signaling compliance, and Debug-LLM diagnoses failures. A human-in-the-loop mechanism ensures transparency by verifying specifications before validation. Evaluated on 24 test cases with the srsRAN 5G stack, AI5GTest reduces test execution time significantly compared to manual methods while maintaining high accuracy, demonstrating scalable, trustworthy automation for O-RAN ecosystems.

While the disaggregated architecture of the industry-driven Open Radio Access Network (O-RAN) promises to foster vendor competition, accelerate innovation, and reduce cost for 5G/6G cellular network deployments, it also exposes the cellular network to various new cybersecurity and privacy vulnerabilities. This demo paper highlights one such new potential cybersecurity vulnerability in the Radio Unit (RU) of O-RAN networks, where an adversary can infer RU activity by analyzing electromagnetic side-channel emissions. We present a custom-built, open-source cellular O-RAN testbed equipped with EM measurement capabilities that enables direct observation of the FPGA-based RU during operation. By capturing EM emissions from the RU, we extract side-channel traces that reveal the underlying RU activity. These traces are then analyzed using a Random Forest-based machine learning classifier, which accurately distinguishes between different RU activity patterns. Our preliminary findings demonstrate the feasibility of inferring RU-level operations via passive EM observation, highlighting a previously unexplored security threat in O-RAN systems. All code and experimental artifacts are made publicly available at https://github.com/SPIRE-GMU/NextGRadio_Sidechanel.

The advent of 5G positioning techniques by 3GPP has unlocked possibilities for applications in public safety, vehicular systems, and location-based services. However, these applications demand accurate and reliable positioning performance, which has led to the proposal of newer positioning techniques. To further advance the research on these techniques, in this paper, we develop a 3GPP-compliant 5G positioning testbed, incorporating gNodeBs (gNBs) and User Equipment (UE). The testbed uses New Radio (NR) Positioning Reference Signals (PRS) transmitted by the gNB to generate Time of Arrival (TOA) estimates at the UE. We mathematically model the inter-gNB and UE-gNB time offsets affecting the TOA estimates and examine their impact on positioning performance. Additionally, we propose a calibration method for estimating these time offsets. Furthermore, we investigate the environmental impact on the TOA estimates. Our findings are based on our mathematical model and supported by experimental results.

This work presents SPARC (Spatio-Temporal Adaptive Resource Control), a novel approach for multi-site spectrum management in NextG cellular networks. SPARC addresses the challenge of limited licensed spectrum in dynamic environments. We leverage the O-RAN architecture to develop a multi-timescale RAN Intelligent Controller (RIC) framework, featuring an xApp for near-real-time interference detection and localization, and a μApp for real-time intelligent resource allocation. By utilizing base stations as spectrum sensors, SPARC enables efficient and fine-grained dynamic resource allocation across multiple sites, enhancing signal-to-noise ratio (SNR) by up to 7dB, spectral efficiency by up to 15%, and overall system throughput by up to 20%.

Automated spectrum monitoring necessitates the accurate detection of low probability of intercept (LPI) radio frequency (RF) anomaly signals to identify unwanted interference in wireless networks. However, detecting these unforeseen low-power RF signals is fundamentally challenging due to the scarcity of labeled RF anomaly data. In this paper, we introduce WANDA (Wireless ANomaly Detection Algorithm), an automated framework designed to detect LPI RF anomaly signals in low signal-to-interference ratio (SIR) environments without relying on labeled data. WANDA operates through a two-step process: (i) Information extraction, where a convolutional neural network (CNN) utilizing soft Hirschfeld-Gebelein-Rényi correlation (HGR) as the loss function extracts informative features from RF spectrograms; and (ii) Anomaly detection, where the extracted features are applied to a one-class support vector machine (SVM) classifier to infer RF anomalies. To validate the effectiveness of WANDA, we present a case study focused on detecting unknown Bluetooth signals within the WiFi spectrum using a practical dataset. Experimental results demonstrate that WANDA outperforms other methods in detecting anomaly signals across a range of SIR values (-10 dB to 20 dB).

While the open architecture, open interfaces, and integration of intelligence within Open Radio Access Network technology hold the promise of transforming 5G and 6G networks, they also introduce cybersecurity vulnerabilities that hinder its widespread adoption. In this paper, we conduct a thorough system-level investigation of cyber threats, with a specific focus on machine learning (ML) intelligence components known as xApps within the O-RAN's near-real-time RAN Intelligent Controller (near-RT RIC) platform. Our study begins by developing a malicious xApp designed to execute adversarial attacks on two types of test data - spectrograms and key performance metrics (KPMs), stored in the RIC database within the near-RT RIC. To mitigate these threats, we utilize a distillation technique that involves training a teacher model at a high softmax temperature and transferring its knowledge to a student model trained at a lower softmax temperature, which is deployed as the robust ML model within xApp. We prototype an over-the-air LTE/5G O-RAN testbed to assess the impact of these attacks and the effectiveness of the distillation defense technique by leveraging an ML-based Interference Classification (InterClass) xApp as an example. We examine two versions of InterClass xApp under distinct scenarios, one based on Convolutional Neural Networks (CNNs) and another based on Deep Neural Networks (DNNs) using spectrograms and KPMs as input data respectively. Our findings reveal up to 100% and 96.3% degradation in the accuracy of both the CNN and DNN models respectively resulting in a significant decline in network performance under considered adversarial attacks. Under the strict latency constraints of the near-RT RIC closed control loop, our analysis shows that the distillation technique outperforms classical adversarial training by achieving an accuracy of up to 98.3% for mitigating such attacks.

Open RAN (O-RAN) has the potential for revolutionizing not only cellular communication but also spectrum sensing by carefully controlling uplink/downlink traffic in shared spectrum bands. In this paper, we present the design ofSenseORAN, which detects the presence of radar pulses within the Citizens Broadband Radio Service (CBRS) band. SenseORAN is especially useful for scenarios where these pulses (highest priority) are fully overlapping with interfering LTE signals (secondary priority licensee), requiring immediate detection of such an occurrence. This design paradigm of re-using existing cellular infrastructure with ORAN-compliant sensing and communication slices can potentially eliminate the need for dedicated spectrum sensors along the coastline as well as severe restrictions on the transmit power for the LTE operators that are enforced today. Our approach involves a machine learning module deployed as aRadar Detection xAppat the near-Real-Time (near-RT) Radio Access Network (RAN) Intelligent Controller, i.e., near-RT RIC. The base station or gNB (i) uses the you-only-look-once (YOLO)-based machine learning framework that is modified to detect radar signals present within spectrograms generated from I/Q samples collected during the regular uplink cellular operation, and (ii) maintains a list of ‘occupied’ channels in the 3.5 GHz CBRS band that indicate radar presence. Our design is validated with (i) an over the air collected dataset composed of Type 1 radar and standard-compliant LTE waveforms, and (ii) an experimental testbed of SDRs running a complete Open RAN stack with a near-RT RIC implementation integrated with our YOLO-based xApp. We show radar detection accuracy of 100% under SINR conditions ≥ 12 dB after combining 7 spectrograms into a single decision. Furthermore, using testbed results, we demonstrate that the gNB can be reconfigured to avoid radar interference within 866 ms, which represents a reduction of 85.5% over the 60 s response time mandated for pausing cellular operation in detecting radar presence in the CBRS band today.

Open Radio Access Network (O-RAN) is considered as a major step in the evolution of next-generation cellular networks given its support for open interfaces and utilization of artificial intelligence (AI) into the deployment, operation, and maintenance of RAN. However, due to the openness of the O-RAN architecture, such AI models are inherently vulnerable to various adversarial machine learning (ML) attacks, i.e., adversarial attacks which correspond to slight manipulation of the input to the ML model. In this work, we showcase the vulnerability of an example ML model used in O-RAN, and experimentally deploy it in the near-real time (near-RT) RAN intelligent controller (RIC). Our ML-based interference classifier xAp$p$(extensible application in near-RT RIC) tries to classify the type of interference to mitigate the interference effect on the O-RAN system. We demonstrate the first-ever scenario of how such an xApp can be impacted through an adversarial attack by manipulating the data stored in a shared database inside the near-RT RIC. Through a rigorous performance analysis deployed on a laboratory O-RAN testbed, we evaluate the performance in terms of capacity and the prediction accuracy of the interference classifier xApp using both clean and perturbed data. We show that even small adversarial attacks can significantly decrease the accuracy of ML application in near-RT RIC, which can directly impact the performance of the entire O-RAN deployment.

With the increasing diversity in the requirement of wireless services with guaranteed Quality of Service (QoS), radio access network (RAN) slicing becomes an important aspect in implementation of next-generation wireless systems (5G). RAN slicing involves the division of network resources into many logical segments where each segment has specific QoS and can serve users of the mobile virtual network operator (MVNO) with these requirements. This allows the network operator (NO) to provide service to multiple MVNOs each with different service requirements. Efficient allocation of the available resources to slices becomes vital in determining the number of users and therefore, the number of MVNOs that a NO can support. In this work, we study the problem of the modulation and coding scheme (MCS)-aware RAN slicing (MaRS) in the context of a wireless system having MVNOs which have users with minimum data rate requirement. Channel quality indicator (CQI) report sent from each user in the network determines the MCS selected, which in turn determines the achievable data rate. But the channel conditions might not remain the same for the entire duration of a user being served. For this reason, we consider the channel conditions to be dynamic where the choice of the MCS level varies at each time instant. We model the MaRS problem as a NonLinear Programming problem and show that it is NP-Hard. Next, we propose a solution based on the greedy algorithm paradigm. We then develop an upper performance bound for this problem and finally evaluate the performance of the proposed solution by comparing it against the upper bound under various channel and network configurations.

Multi unmanned aerial vehicle (UAV) network is a promising solution to providing wireless coverage to ground users in challenging rural areas (such as Internet of Things (IoT) devices in farmlands), where the traditional cellular networks are sparse or unavailable. A key challenge in such networks is the 3-D placement of all UAV base stations (BSs) such that the formed multi-UAV network: 1) utilizes a minimum number of UAVs while ensuring—2) backhaul connectivity directly (or via other UAVs) to the nearby terrestrial BS; and 3) wireless coverage to all ground users in the area of operation. This joint backhaul-and-coverage-aware drone deployment (BoaRD) problem is largely unaddressed in the literature and, thus, is the focus of this article. We first formulate the BoaRD problem as integer linear programming (ILP). However, the problem is NP-hard and, therefore, we propose a low complexity algorithm with a provable performance guarantee to solve the problem efficiently. Our simulation study shows that the Proposed algorithm performs very close to that of the Optimal algorithm (solved using ILP solver) for smaller scenarios, where the area size and the number of users are relatively small. For larger scenarios, where the area size and the number of users are relatively large, the proposed algorithm greatly outperforms the baseline approaches—Backhaul-aware Greedy and random algorithm, respectively, by up to 17% and 95% in utilizing fewer UAVs while ensuring 100% ground-user coverage and backhaul connectivity for all deployed UAVs across all considered simulation setting.

Unmanned Aerial Vehicles (UAVs) can be utilized as aerial base stations to establish wireless communication networks in various challenging scenarios, such as emergency disaster areas and rural areas. Under large regions, the aerial communication networks would require UAVs to form wireless (backhaul) links among each other to provide end-to-end wireless services between two or more ground users (via one or more UAVs). Such UAV backhauling in aerial communication networks may be severely compromised if one or more UAVs are knocked off during the time of operation – it may be due to UAV hardware/software faults, limited battery, malicious attacks, etc. Deep reinforcement learning (DRL) has emerged as a powerful tool for learning tasks with large state and continuous action spaces. In this paper, we leverage emerging DRL to achieve reliable backhauling in an aerial communication network that remains functional and supports end-to-end wireless services even under various random and/or targeted UAV node failures. The proposed method (i) maximizes the reliability of UAV backhauling with joint consideration for communication coverage, (ii) learns the complex environment and its dynamics, and (iii) makes 3D positioning decisions for each UAV under the guidance of two deep neural networks. Our performance evaluation reveals that the proposed DRL approach outperforms the baseline method in terms of wireless coverage and network reliability against UAV failures.

Emerging 802.11p vehicle-to-vehicle (V2V) networks rely on periodic Basic Safety Messages (BSMs) to disseminate time-sensitive safety-critical information, such as vehicle position, speed, and heading - that enables several safety applications and has the potential to improve on-road safety. Due to mobility, lack of global-knowledge and limited communication resources, designing an optimal BSM broadcast rate-control protocol is challenging. Recently, minimizing Age of Information (AoI) has gained momentum in designing BSM broadcast rate-control protocols. In this paper, we show that minimizing AoI solely does not always improve the safety of V2V networks. Specifically, we propose a novel metric, termed Trackability-aware Age of Information TAoI, that in addition to AoI, takes into account the self risk assessment of vehicles, quantified in terms of self tracking error (self-TE) - which provides an indication of collision risk posed by the vehicle. Self-TE is defined as the difference between the actual location of a certain vehicle and its self-estimated location. Our extensive experiments, based on realistic SUMO traffic traces on top of ns-3 simulator, demonstrate that TAoI based rate-protocol significantly outperforms baseline AoI based rate protocol and default 10 Hz broadcast rate in terms of safety performance, i.e., collision risk, in all considered V2V settings.

Due to ease-of-deployment, autonomous control and low cost, unmanned aerial vehicles (UAVs), as fixed aerial base stations, are increasingly being used as relays to collect time-sensitive information (i.e., status updates) from IoT devices and deliver it to the nearby terrestrial base station (TBS), where the information gets processed. In order to ensure timely delivery of information to the TBS (from all IoT devices), optimal scheduling of time-sensitive information over two hop UAV-relayed IoT networks (i.e., IoT device to the UAV [hop 1], and UAV to the TBS [hop 2]) becomes a critical challenge. To address this, we propose scheduling policies for Age of Information (AoI) minimization in such two-hop UAV-relayed IoT networks. To this end, we present a low-complexity MAF-MAD scheduler, that employs Maximum AoI First (MAF) policy for sampling of IoT devices at UAV (hop 1) and Maximum AoI Difference (MAD) policy for updating sampled packets from UAV to the TBS (hop 2). We show that MAF-MAD is the optimal scheduler under ideal conditions, i.e., error-free channels and generate-at-will traffic generation at IoT devices. On the contrary, for realistic conditions, we propose a Deep-Q-Networks (DQN) based scheduler. Our simulation results show that DQN-based scheduler outperforms MAF-MAD scheduler and three other baseline schedulers, i.e., Maximal AoI First (MAF), Round Robin (RR) and Random, employed at both hops under general conditions when the network is small (with 10’s of IoT devices). However, it does not scale well with network size whereas MAF-MAD outperforms all other schedulers under all considered scenarios for larger networks.

Decentralized vehicle-to-everything (V2X) networks (i.e., Mode-4 C-V2X and Mode 2a NR-V2X), rely on periodic Basic Safety Messages (BSMs) to disseminate time-sensitive information (e.g., vehicle position) and has the potential to improve on-road safety. For BSM scheduling, decentralized V2X networks utilize sensing-based semi-persistent scheduling (SPS), where vehicles sense radio resources and select suitable resources for BSM transmissions at prespecified periodic intervals termed as Resource Reservation Interval (RRI). In this paper, we show that such a BSM scheduling (with a fixed RRI) suffers from severe under- and over-utilization of radio resources under varying vehicle traffic scenarios; which severely compromises timely dissemination of BSMs, which in turn leads to increased collision risks. To address this, we extend SPS to accommodate an adaptive RRI, termed as SPS++. Specifically, SPS++ allows each vehicle - (i) to dynamically adjust RRI based on the channel resource availability (by accounting for various vehicle traffic scenarios), and then, (ii) select suitable transmission opportunities for timely BSM transmissions at the chosen RRI. Our experiments based on Mode-4 C-V2X standard implemented using the ns-3 simulator show that SPS++ outperforms SPS by at least 50% in terms of improved on-road safety performance, in all considered simulation scenarios.

Unmanned Aerial Vehicles (UAVs), as aerial base stations, is a promising solution for providing end-to-end wireless communications to ground users, thanks to its positioning, flexibility, and autonomy. However, to provide end-to-end wireless communication services, all UAVs must ensure a reliable multi-UAV network topology, even when one or more UAVs are knocked off the network due to hardware/software faults, unreliable wireless connections, etc. Hence, how to design a reliable Multi-UAV network with a minimum number of UAVs becomes a key design issue, which is largely unaddressed in the literature. In this paper, we propose exploring biological robustness to design a reliable MuLtI-UAV NetworK, termed, bio-LINK, which is resilient against the UAV node failures and thus, ensures reliable end-to-end communication services to ground users. We first formulate the above bio-LINK problem as an integer linear programming (ILP) optimization problem and show it is NP-Hard. Next, we propose a polynomial-time heuristic that employs an iterative UAV positioning inspired by Markov Chain Monte Carlo (MCMC) random sampling approach. Our extensive simulation study shows that the proposed algorithm outperforms three baseline algorithms in terms of several considered robustness metrics (e.g., motif count, network efficiency, etc.) and ground user coverage, notwithstanding the random and targeted failure of UAV nodes. When compared with a baseline algorithm with the same number of UAVs, the proposed algorithm retains the motif count by 5-6 folds and improves network efficiency by 39 - 95% and ground user coverage by 2-18%.

As several new spectrum bands are opening up for shared use, a new paradigm of Diverse Band-aware Dynamic Spectrum Access (d-DSA) has emerged. d-DSA equips a secondary device with software defined radios (SDRs) and utilize whitespaces (or idle channels) in multiple bands, including but not limited to TV, LTE, Citizen Broadband Radio Service (CBRS), unlicensed ISM. In this paper, we propose a decentralized, online multi-agent reinforcement learning based cross-layer BAnd selection and Routing Design (BARD) for such d-DSA networks. BARD not only harnesses whitespaces in multiple spectrum bands, but also accounts for unique electro-magnetic characteristics of those bands to maximize the desired quality of service (QoS) requirements of heterogeneous message packets; while also ensuring no harmful interference to the primary users in the utilized band. Our extensive experiments demonstrate that BARD outperforms the baseline dDSAaR algorithm in terms of message delivery ratio, however, at a relatively higher network latency, for varying number of primary and secondary users. Furthermore, BARD greatly outperforms its single-band DSA variants in terms of both the metrics in all considered scenarios.

Vehicle-to-Vehicle (V2V) communication networks enable safety applications via periodic broadcast of Basic Safety Messages (BSMs) or safety beacons. Beacons include time-critical information such as sender vehicle's location, speed and direction. The vehicle density may be very high in certain scenarios and such V2V networks suffer from channel congestion and undesirable level of packet collisions; which in turn may seriously jeopardize safety application reliability and cause collision risky situations. In this work, we perform experimental analysis of safety application reliability (in terms of collision risks), and conclude that there exists a unique beacon rate for which the safety performance is maximized, and this rate is unique for varying vehicle densities. The collision risk of a certain vehicle is computed using a simple kinematics-based model, and is based on tracking error, defined as the difference between vehicle's actual position and the perceived location of that vehicle by its neighbors (via most-recent beacons). Furthermore, we analyze the interconnection between the collision risk and two well-known network performance metrics, Age of Information (AoI) and throughput. Our experimentation shows that AoI has a strong correlation with the collision risk and AoI-optimal beacon rate is similar to the safety-optimal beacon rate, irrespective of the vehicle densities, queuing sizes and disciplines. Whereas throughput works well only under higher vehicle densities.

A major challenge in mobile crowdsensing applications is the generation of false (or spam) contributions resulting from selfish and malicious behaviors of users, or wrong perception of an event. Such false contributions induce loss of revenue owing to undue incentivization, and also affect the operational reliability of the applications. To counter these problems, we propose an event-trust and user-reputation model, called QnQ, to segregate different user classes such as honest, selfish, or malicious. The resultant user reputation scores, are based on both `quality' (accuracy of contribution) and `quantity' (degree of participation) of their contributions. Specifically, QnQ exploits a rating feedback mechanism for evaluating an event-specific expected truthfulness, which is then transformed into a robust quality of information (QoI) metric to weaken various effects of selfish and malicious user behaviors. Eventually, the QoIs of various events in which a user has participated are aggregated to compute his reputation score, which in turn is used to judiciously disburse user incentives with a goal to reduce the incentive losses of the CS application provider. Subsequently, inspired by cumulative prospect theory (CPT), we propose a risk tolerance and reputation aware trustworthy decision making scheme to determine whether an event should be published or not, thus improving the operational reliability of the application. To evaluate QnQ experimentally, we consider a vehicular crowdsensing application as a proof-of-concept. We compare QoI performance achieved by our model with Jøsang's belief model, reputation scoring with Dempster-Shafer based reputation model, and operational (decision) accuracy with expected utility theory. Experimental results demonstrate that QnQ is able to better capture subtle differences in user behaviors based on both quality and quantity, reduces incentive losses, and significantly improves operational accuracy in presence of rogue contributions.

According to the Smart City Council, an adequate telecommunications infrastructure is vital for the success of businesses, industries as well as residents of Smart cities. However, currently available standard and cellular technologies, such as 3/4G, GSM (Global System for Mobile Communications) and LTE (Long-Term Evolution), are rapidly reaching their limit mainly due to increased traffic demand. Such limitations are only going to worsen in the next years, due to the advent of Internet of Things technologies that are expected to interconnect billions of devices to the Internet. In this paper, we propose a novel network architecture that supports several delay-tolerant (non-real-time) Smart city applications and services (e.g., gathering air pollution information), and therefore, a promising approach to address the burdening of increased traffic demand to Smart city's legacy standard and cellular communication infrastructure. The proposed architecture is based on an innovative diverse band-aware Dynamic Spectrum Access (d-DSA) paradigm, that allows a certain wireless device to opportunistically access idle channels in multiple licensed/unlicensed spectrum bands. d-DSA radio devices are mounted on Smart city's urban vehicles (e.g., taxis) that act as mobile routers to gather, carry, and forward various types of data traffic. This results in a time-varying and unpredictable delay-tolerant network (DTN) where each node can access whitespace channels and transmit in multiple spectrum bands. Given lack of research in efficient routing schemes for such d-DSA DTN networks, we propose a distributed and lightweight d-DSA aware Geographical Routing (dDSA-GR) protocol, that utilizes a weighted linear metric for selecting a suitable spectrum band, and classic georouting principle for choosing next hop node in the path route between any node pair in d-DSA DTNs. Results on realistic traces based on the map of Lexington, KY, USA, show that our dDSA-GR routing protocol outperforms baseline approaches in terms of network delay, message delivery ratio, and energy efficiency, under all considered scenarios.

This paper presents a novel network architecture, termed neXt-generation CHAllenged NeTwork (X-CHANT), for improving connectivity in rural environments. The underlying idea is to deploy diverse Dynamic Spectrum Access (d-DSA) radio devices on the public transportation vehicles, such as buses. This results in a d-DSA enabled delay-tolerant network in which the devices can operate in various (un)licensed bands (e.g., TV, LTE, ISM, CBRS), if available. Given the lack of research in efficient routing for such time-varying d-DSA enabled networks, we propose a novel diverse DSA aware routing (dDSAaR) protocol that jointly exploits various (un)licensed bands besides the time-varying yet sufficiently predictable mobility of public transportation vehicles. We compare X-CHANT, utilizing dDSAaR, to the conventional non-DSA/DSA architectures, utilizing a standard (single band) routing protocol (e.g., Epidemic). We use real bus mobility traces collected at the University of Massachusetts, Amherst campus. Results show that X-CHANT achieves better message delivery, negligible message overhead, and better energy expenditure, at the expense of a slight increase in delay. Never-theless, the delay improves with higher predictable mobility.

In the aftermath of large-scale disasters, such as earthquakes or hurricanes, existing communication infrastructures are often critically impaired, preventing timely information exchange between the survivors, responders, and the coordination center. Smart devices, movable base stations, easily deployable WiFi routers, and unimpaired communication towers can be used to set up temporary networks, called disaster response networks (DRNs). However, such networks are challenged by rapid energy depletion of smart devices as well as component failures. To address these issues, in this paper we propose a novel energy-efficient yet robust DRN topology, termed Bio-DRN, that mimics the inherent robustness of a biological network of living organisms, called gene regulatory network (GRN). Specifically, the Bio-DRN is a subgraph of the DRN topology generated by one-to-one mapping between the structurally similar genes and DRN components, i.e., survivors, points of interest like shelter points, and the coordination center. We first formulate the construction of Bio-DRN topology as an integer linear programming optimization problem, and show that it is NP-hard. Then, we present a sub-optimal heuristic that constructs the Bio-DRN topology as a common subgraph of both GRN and DRN topologies. Our experimental study on a real disaster prone region in Bhaktapur, Nepal, shows that Bio-DRN preserves the topological properties of GRN, such as low graph density and motif abundance, and achieves both energy efficiency and network robustness, while ensuring timely message delivery.

Smart and connected communities (SCCs) are emerging as a novel paradigm that allows the community residents to be connected with surrounding environments through smart technologies. However, there remain important challenges to fully exploit the potential of SCCs in improving societal well-being and prosperity. In particular, there is a need for designing green communication systems that are also capable of providing high quality of service (QoS) to distribute and collect information to and from SCCs. However, simultaneously satisfying both of these criteria is difficult due to varying demands posed by heterogeneous sensing modalities, lack of dedicated infrastructure in rural/sub-urban areas, and certain sustainability constraints. While low-power short-range technologies often fail to achieve high QoS, using 3G or 4G technologies (LTE, LTE-A, GSM) for SCCs will eventually face spectrum scarcity and cross technology interference. In recent times, Dynamic spectrum access (DSA) has been proposed as a solution to overcome policy constraints and improve spectrum scarcity by spectrum sharing. In this article, we show that harnessing DSA in the context of SCCs can also achieve notable benefits in terms of energy efficiency and sustainability. Specifically, we propose a novel architecture for designing sustainable SCCs using a small-scale DSA-enabled overlay network that improves end-to-end energy efficiency of the network while guaranteeing QoS. We also propose a dynamic spectrum band selection approach that intelligently matches any message requirement to a suitable band type by exploiting distinct electro-magnetic characteristics of various bands. Since data generated in SCCs are typically valuable only when delivered within a certain hard (or soft ) deadline, we formulate a linear optimization problem for determining the most energy-efficient path that ensures a delivery time within the hard deadline. After proving that such a problem is NP-Hard, we propose an exact pseudo-polynomial time dynamic programming algorithm to solve it followed by a polynomial time greedy heuristic. Additionally, we formulate a non-linear optimization problem to find the optimal path when the message delivery time is defined as a soft deadline and extend our greedy heuristic to handle soft deadlines. Compared to the homogeneous band access approaches that opportunistically access free channels within a given spectrum band, our extensive simulation study shows that the proposed dynamic multi-band selection approach significantly improves the achievable energy efficiency while meeting various hard and soft deadlines.

Large scale disasters require prompt rescue and relief operations to restrict further casualties. To carry out such operations, it is essential to have a communication infrastructure between survivors and responders, which is often impaired due to the disaster. Off-the-shelf wireless devices such as smartphones, PDAs and Laptops offer an effective solution towards the establishment of makeshift communication infrastructure. However, in the absence of bonafide power sources, it becomes imperative to judiciously utilize energy (battery power) of such devices such that the network is functional until primary infrastructure is restored. This paper proposes a novel approach, called Cluster based Topological Routing (CTR) that prolongs the longevity of the network by exploiting the natural gathering of survivors in shelter points. In particular, the clustering algorithm identifies such survivor groups combined with a data forwarding approach, to minimize the number of data transmissions yet guaranteeing the required packet delivery and network latency. Our extensive simulation study shows that CTR yields twice the network lifetime than existing routing approaches in disaster response networks, while ensuring comparable packet delivery and network latency.