Showing papers on "Cognitive network published in 2021"

Joint Buffer-Aided Hybrid-Duplex Relay Selection and Power Allocation for Secure Cognitive Networks With Double Deep Q-Network

Abstract: This paper applies the reinforcement learning in the joint relay selection and power allocation in the secure cognitive radio (CR) relay network, where the data buffers and full-duplex jamming are applied at the relay nodes. Two cases are considered: maximizing the throughput with the delay and secrecy constraints, and maximizing the secrecy rate with the delay constraint, respectively. In both cases, the optimization relies on the buffer states, the interference to/from the primary user, and the constraints on the delay and/or secrecy. This makes it mathematically intractable to apply the traditional optimization methods. In this paper, the double deep Q-network (DDQN) is used to solve the above two optimization problems. We also apply the a-priori information in the CR network to improve the DDQN learning convergence. Simulation results show that the proposed scheme outperforms the traditional algorithm significantly.

Is the Mind a Network? Maps, Vehicles, and Skyhooks in Cognitive Network Science.

Abstract: Cognitive researchers often carve cognition up into structures and processes. Cognitive processes operate on structures, like vehicles driving over a map. Language alongside semantic and episodic memory are proposed to have structure, as are perceptual systems. Over these structures, processes operate to construct memory and solve problems by retrieving and manipulating information. Network science offers an approach to representing cognitive structures and has made tremendous inroads into understanding the nature of cognitive structure and process. But is the mind a network? If so, what kind? In this article, we briefly review the main metaphors, assumptions, and pitfalls prevalent in cognitive network science (maps and vehicles; one network/process to rule them all), highlight the need for new metaphors that elaborate on the map-and-vehicle framework (wormholes, skyhooks, and generators), and present open questions in studying the mind as a network (the challenge of capturing network change, what should the edges of cognitive networks be made of, and aggregated vs. individual-based networks). One critical lesson of this exercise is that the richness of the mind as network approach makes it a powerful tool in its own right; it has helped to make our assumptions more visible, generating new and fascinating questions, and enriching the prospects for future research. A second lesson is that the mind as a network-though useful-is incomplete. The mind is not a network, but it may contain them.

Cognitive Network Science for Understanding Online Social Cognitions: A Brief Review.

Abstract: Social media are digitalizing massive amounts of users' cognitions in terms of timelines and emotional content. Such Big Data opens unprecedented opportunities for investigating cognitive phenomena like perception, personality, and information diffusion but requires suitable interpretable frameworks. Since social media data come from users' minds, worthy candidates for this challenge are cognitive networks, models of cognition giving structure to mental conceptual associations. This work outlines how cognitive network science can open new, quantitative ways for understanding cognition through online media like: (i) reconstructing how users semantically and emotionally frame events with contextual knowledge unavailable to machine learning, (ii) investigating conceptual salience/prominence through knowledge structure in social discourse; (iii) studying users' personality traits like openness-to-experience, curiosity, and creativity through language in posts; (iv) bridging cognitive/emotional content and social dynamics via multilayer networks comparing the mindsets of influencers and followers. These advancements combine cognitive-, network- and computer science to understand cognitive mechanisms in both digital and real-world settings but come with limitations concerning representativeness, individual variability, and data integration. Such aspects are discussed along with the ethical implications of manipulating sociocognitive data. In the future, reading cognitions through networks and social media can expose cognitive biases amplified by online platforms and relevantly inform policy-making, education, and markets about complex cognitive trends.

A Critical Review of Network-Based and Distributional Approaches to Semantic Memory Structure and Processes.

Abstract: Some of the earliest work on understanding how concepts are organized in memory used a network-based approach, where words or concepts are represented as nodes, and relationships between words are represented by links between nodes. Over the past two decades, advances in network science and graph theoretical methods have led to the development of computational semantic networks. This review provides a modern perspective on how computational semantic networks have proven to be useful tools to investigate the structure of semantic memory as well as search and retrieval processes within semantic memory, to ultimately model performance in a wide variety of cognitive tasks. Regarding representation, the review focuses on the distinctions and similarities between network-based (based on behavioral norms) approaches and more recent distributional (based on natural language corpora) semantic models, and the potential overlap between the two approaches. Capturing the type of relation between concepts appears to be particularly important in this modeling endeavor. Regarding processes, the review focuses on random walk models and the degree to which retrieval processes demand attention in pursuit of given task goals, which dovetails with the type of relation retrieved during tasks. Ultimately, this review provides a critical assessment of how the network perspective can be reconciled with distributional and machine-learning-based perspectives to meaning representation, and describes how cognitive network science provides a useful conceptual toolkit to probe both the structure and retrieval processes within semantic memory.

Construction and Supervised Learning of Long-Term Grey Cognitive Networks

Abstract: Modeling a real-world system by means of a neural model involves numerous challenges that range from formulating transparent knowledge representations to obtaining reliable simulation errors. However, that knowledge is often difficult to formalize in a precise way using crisp numbers. In this paper, we present the long-term grey cognitive networks which expands the recently proposed long-term cognitive networks (LTCNs) with grey numbers. One advantage of our neural system is that it allows embedding knowledge into the network using weights and constricted neurons. In addition, we propose two procedures to construct the network in situations where only historical data are available, and a regularization method that is coupled with a nonsynaptic backpropagation algorithm. The results have shown that our proposal outperforms the LTCN model and other state-of-the-art methods in terms of accuracy.

Interference Characterization in Underlay Cognitive Networks With Intra-Network and Inter-Network Dependence

Abstract: Interference modeling in cognitive radio network is important to ensure adequate coverage in the network. A reliable interference model, however, depends on accurately characterizing the distribution of users. In this paper, the dependence between primary and secondary networks is examined in order to capture more system parameters related to system characterization. Hence, two cases are considered – primary user (PU) interference control and PU with secondary user (SU) interference control mechanisms. Under PU interference control, distributions of PUs follow the Matern hard core process while the distribution of SUs follow the Poisson hole process (PHP). However, under PU with SU interference control, the distribution of active SUs follow a modified PHP. Bound and approximate expressions were derived for coverage probability at both primary and secondary networks, while simple yet accurate expressions were obtained to depict the number of simultaneous active users supported for the two cases. The tight closeness between the bound and the approximate expressions shows the reliability of the presented theoretical analysis. Furthermore, the bipolar network model assumption was relaxed while the case of independence assumption among users was also considered. Numerical result showed close tightness when the bipolar network model assumption was relaxed while the independence assumption was shown to overestimate users’ coverage probability.

From Cognitive to Intelligent Secondary Cooperative Networks for the Future Internet: Design, Advances, and Challenges

Abstract: Cognitive Radio (CR) technology was first introduced to solve the problem of radio spectrum under-utilization. A cognitive radio network consists of smart radio devices that have the ability to sense radio environment variables and take actions accordingly. To realize their full potential and to become fully cognitive, the CR nodes need to be equipped with learning and reasoning capabilities. Machine learning has been one of the enabling vehicles for intelligent CR networks. Inspired by the cognition cycle of a CR node, over the past years there has been an ever growing interest in using machine learning techniques to enhance the performance of CR networks. In this article, an overview of the various learning techniques currently used in the literature of CR networks is given. We focus on feature classification and clustering algorithms, and their application in cooperative CR networks. We outline the steps to establishing a learning-based cooperative secondary network, highlighting factors that impact detection performance. Additionally, current state-of-the-art learning-based applications in Cognitive Internet of Things (CIoT) are presented. Finally, the key challenges and future directions of intelligent cognitive networks are discussed.

Deep reinforcement learning for resource allocation with network slicing in cognitive radio network

Abstract: With the development of wireless communication technology, the requirement for data rate is growing rapidly. Mobile communication system faces the problem of shortage of spectrum resources. Cognitive radio technology allows secondary users to use the frequencies authorized to the primary user with the permission of the primary user, which can effectively improve the utilization of spectrum resources. In this article, we establish a cognitive network model based on underlay model and propose a cognitive network resource allocation algorithm based on DDQN (Double Deep Q Network). The algorithm jointly optimizes the spectrum efficiency of the cognitive network and QoE (Quality of Experience) of cognitive users through channel selection and power control of the cognitive users. Simulation results show that proposed algorithm can effectively improve the spectral efficiency and QoE. Compared with Q-learning and DQN, this algorithm can converge faster and obtain higher spectral efficiency and QoE. The algorithm shows a more stable and efficient performance.

Dynamic Spectrum Sensing Under Crash and Byzantine Failure Environments for Distributed Convergence in Cognitive Radio Networks

Abstract: In cognitive networks, efficient spectrum sensing is of great importance for communication of unlicensed secondary users (SU) without interfering with the communication of licensed primary users (PU). Such spectrum sensing requires robust and reliable communication between the SUs to sense the spectrum efficiently under different network circumstances and to make a quick decision for the data transmission. In this paper, we are proposing a decentralized cooperative algorithm for efficient sensing of spectrum in the networked cognitive radios. The proposed algorithm is investigated under crash and Byzantine failure environments to study their behavior and efficiency for consensus. Energy detector module is modeled for each cooperating SU in cognitive radio network for sensing the presence of PU in a dedicated spectrum. Moreover, SU is modeled as agents connected through undirected graphs to simulate communication among them related to the spectrum availability. Multiple simulation scenarios, based on autonomous SU using the proposed distributed consensus algorithm are presented to demonstrate the theoretical development of proposed algorithm to be visualized in real scenarios. The simulation results reveal that the proposed method provides a significant improvement in convergence rate, reliability, and in terms of various key performance indicators.

Intercept Probability Analysis of Cooperative Cognitive Networks Using Fountain Codes and Cooperative Jamming

Abstract: This paper evaluates intercept probability (IP) of a cooperative cognitive radio network. Using Fountain codes, a secondary source continuously generates encoded packets, and sends them to secondary destination and relay nodes that attempt to receive a sufficient number of the encoded packets for recovering the source data. If the relay can sufficiently collect the packets before the destination, it replaces the source to transmit the encoded packets to the destination. Also in the secondary network, a passive eavesdropper attempts to illegally receive the packets sent by the source and relay nodes, and if it can accumulate enough encoded packets, the source data is intercepted. To enhance secrecy performance, in terms of IP, a cooperative jammer is used to transmit noises on the eavesdropper. We also propose a simple transmit power allocation method for the secondary transmitters such as source, relay and jammer so that outage performance of a primary network is not harmful. We derive an exact closed-form expression of IP over Rayleigh fading channel, and verify it by performing Monte-Carlo simulations.

Wireless sensor network–based delay minimization framework for IoT applications

Abstract: In IoT, the major challenge is processing huge amount of data from different types of sensors and to achieve a reliable data transmission in the sensor network This makes it a necessity in enhancing Quality of Service, to acquire real-time service with assured quality The major problem faced in the sensor network is delay, as more time is required to set up a connection with limited spectrum for maintaining numerous state information per connection Finding the optimal route with efficient bandwidth is not ideal using an existing routing algorithm, both in ad hoc and cognitive network As a result, a protocol is proposed in this paper to minimize the delay and maximize the effective spectrum allocation The proposed algorithm is implemented in real-time traffic monitoring application using network simulator to estimate the quality of service The performance of the proposed system is compared with the existing systems in terms of throughput and delay The delay decreases by 3% approximately when compared with the existing techniques

An Autocatalytic Network Model of Conceptual Change.

Abstract: In reflexively autocatalytic foodset (RAF)-generated networks, nodes are not only passive transmitters of activation, but they also actively galvanize, or "catalyze" the synthesis of novel ("foodset-derived") nodes from existing ones (the "foodset") Thus, RAFs are uniquely suited to modeling how new structure grows out of currently available structure, and analyzing phase transitions in potentially very large networks RAFs have been used to model the origins of evolutionary processes, both biological (the origin of life) and cultural (the origin of cumulative innovation), and may potentially provide an overarching framework that integrates evolutionary and developmental approaches to cognition Applied to cognition, the foodset consists of information obtained through social learning or individual learning of pre-existing information, and foodset-derived items arise through mental operations resulting in new information Thus, mental representations are not only propagators of spreading activation, but they also trigger the derivation of new mental representations To illustrate the application of RAF networks in cognitive science, we develop a step-by-step process model of conceptual change (ie, the process by which a child becomes an active participant in cultural evolution), focusing on childrens' mental models of the shape of the Earth Using results from (Vosniadou & Brewer, 1992), we model different trajectories from the flat Earth model to the spherical Earth model, as well as the impact of other factors, such as pretend play, on cognitive development As RAFs increase in size and number, they begin to merge, bridging previously compartmentalized knowledge, and get subsumed by a giant RAF (the maxRAF) that constrains and enables the scaffolding of new conceptual structure At this point, the cognitive network becomes self-sustaining and self-organizing The child can reliably frame new knowledge and experiences in terms of previous ones, and engage in recursive representational redescription and abstract thought We suggest that individual differences in the reactivity of mental representations, that is, their proclivity to trigger conceptual change, culminate in different cognitive networks and concomitant learning trajectories

A Graph Theory Approach to Clarifying Aging and Disease Related Changes in Cognitive Networks

Abstract: In accordance with the physiological networks that underlie it, human cognition is characterized by both the segregation and interdependence of a number of cognitive domains. Cognition itself, therefore, can be conceptualized as a network of functions. A network approach to cognition has previously revealed topological differences in cognitive profiles between healthy and disease populations. The present study, therefore, used graph theory to determine variation in cognitive profiles across healthy aging and cognitive impairment. A comprehensive neuropsychological test battery was administered to 415 participants. This included three groups of healthy adults aged 18-39 (n = 75), 40-64 (n = 75), and 65 and over (n = 70) and three patient groups with either amnestic (n = 75) or non-amnestic (n = 60) mild cognitive impairment or Alzheimer's type dementia (n = 60). For each group, cognitive networks were created reflective of test-to-test covariance, in which nodes represented cognitive tests and edges reflected statistical inter-nodal significance (p < 0.05). Network metrics were derived using the Brain Connectivity Toolbox. Network-wide clustering, local efficiency and global efficiency of nodes showed linear differences across the stages of aging, being significantly higher among older adults when compared with younger groups. Among patients, these metrics were significantly higher again when compared with healthy older controls. Conversely, average betweenness centralities were highest in middle-aged participants and lower among older adults and patients. In particular, compared with controls, patients demonstrated a distinct lack of centrality in the domains of semantic processing and abstract reasoning. Network composition in the amnestic mild cognitive impairment group was similar to the network of Alzheimer's dementia patients. Using graph theoretical methods, this study demonstrates that the composition of cognitive networks may be measurably altered by the aging process and differentially impacted by pathological cognitive impairment. Network alterations characteristic of Alzheimer's disease in particular may occur early and be distinct from alterations associated with differing types of cognitive impairment. A shift in centrality between domains may be particularly relevant in identifying cognitive profiles indicative of underlying disease. Such techniques may contribute to the future development of more sophisticated diagnostic tools for neurodegenerative disease.

The Value of Statistical Learning to Cognitive Network Science

Abstract: To study the human mind is to consider the nature of associations-how are they learned, what are their constituent parts, and how can they be severed or adjusted? The manipulation of associations stands as a pillar of statistical learning (SL) research, which strongly suggests that processes as diverse as word segmentation, learning of grammatical patterns, and event perception can be explained by the learner's sensitivity to simple temporal dependencies (among other regularities). Used to determine the edges of a network, associations are similarly crucial to consider when quantifying the graph-theoretical properties of various cognitive systems. With this point of convergence in mind, the present work reaffirms the unique value of network science in illuminating the broad-level architectures of complex cognitive systems. However, I also describe how insights from the SL literature, coupled with insights from psycholinguistics more broadly, offer a strong theoretical backbone upon which we can develop and study networks that reflect, as closely as possible, the psychological realities of learning.

Efficient spectrum sensing framework for cognitive networks

Abstract: Cognitive network is a modern networking technology that utilizes both the radio spectrum and the wireless station. The resources can be effectively utilized based on availability of knowledge gathered from the past experiences. By using cognitive radio technology, the new network is constructed to every node. Wireless devices' necessity is being increased nowadays, which leads to the demand for spectrum. The problems are high fluctuations of the different spectrum bands and the disconnections of the network. The cognitive radio network is proposed to be an optimal solution for both the spectrum scarcity and the spectrum inefficiency problems. Using the cognitive radio network, the wireless channel is shared with the primary users. The cognitive radio network provides high bandwidth for the mobile users by using specialized heterogeneous wireless architecture. These challenges are overcome by CR networks that define the concept of spectrum sensing. While using the spectrum, the performance of the network should not degrade. Hence, the framework planned will able to do this situation.

Advanced spatial network metrics for cognitive management of 5G networks

Abstract: The emerging fifth-generation (5G) mobile networks are empowered by softwarization and programmability, leading to the huge potentials of unprecedented flexibility and capability in cognitive network management such as self-reconfiguration and self-optimization. To help unlock such potentials, this paper proposes a novel framework that is able to monitor and calculate 5G network topological information in terms of advanced spatial metrics. These metrics, together with enabling and optimization algorithms, are purposely designed to address the complexity of 5G network topologies introduced by network virtualization and infrastructure sharing among operators (multi-tenancy). Consequently, this new framework, centred on a topology monitoring agent (TMA), enables on-demand 5G networks’ spatial knowledge and topological awareness required by 5G cognitive network management in making smart decisions in various autonomous network management tasks including but not limited to virtual network function placement strategies. The paper describes several technical use cases enabled by the proposed framework, including proactive cache allocation, computation offloading, node overloading alerting, and load balancing. Finally, a realistic 5G testbed is deployed with the central component TMA, together with the new spatial metrics and associated algorithms, implemented. Experimental results empirically validate the proposed approach and demonstrate the scalability and performance of the TMA component.

Robust Spectrum Sensing Detector Based on MIMO Cognitive Radios with Non-Perfect Channel Gain

Abstract: The spectrum has increasingly become occupied by various wireless technologies. For this reason, the spectrum has become a scarce resource. In prior work, the authors have addressed the spectrum sensing problem by using multi-input and multi-output (MIMO) in cognitive radio systems. We considered the detection and estimation framework for MIMO cognitive network where the noise covariance matrix is unknown with perfect channel state information. In this study, we propose a generalized likelihood ratio test (GLRT) for the spectrum sensing problem in cognitive radio where the noise covariance matrix is unknown with non-perfect channel state information. Two scenarios are examined in this study: (i) in the first scenario, the sub-optimal solution of the worst case of the system’s performance is considered; (ii) in the second scenario, we present a robust detector for the MIMO spectrum sensing problem. For both scenarios, the Bayesian approach with a generalized likelihood ratio test based on the binary hypothesis problem is used. From the results, it can be seen that our approach provides the best performance in the spectrum sensing problem under specified assumptions. The simulation results also demonstrate that our approach significantly outperforms other state-of-the-art spectrum sensing detectors when the channel uncertainty is addressed.

Trust Optimization for Byzantine Attacks in Cognitive Networks

Abstract: Cognitive Radio Network (CRN) is an emerging technology that provides ability of adapting operating parameters to wireless devices in order to overcome spectrum scarcity problems. However, involvement of numerous pervasive smart wireless devices introduces many security threats in CRN. The research work specifically highlights the different kinds of attacks that occur in cognitive networks. The algorithm is designed which focuses on vulnerabilities that occur in any single or an individual node. The proposed algorithm will take many factors into consideration like integer programming, trust and Byzantine failure model. Though there are various security parameters especially the five major attributes like integrity, confidentiality, availability, authorization, access control and nonrepudiation, which should be considered for developing any alogotithm. Nevertheless, certain security parameters are kept into consideration, which are embedded into algorithm to make it of its unique kind.

Energy Efficiency Analysis in Spectrum Sensing Cognitive Radio Network

Abstract: The spectrum scarcity problem is addressed by number solutions by various researchers in cognitive network field. The dynamic spectrum allocation using cooperative spectrum sensing is required to analyze with respect to errors present in detection due to fixed threshold. The spectrum allocation on the basis of demand may involve the priority based requests for spectrum allocation. This paper contributes in terms of efficient energy sensing using dynamic threshold strategy. The principle of sensing is optimized using energy sensing using effects of Marcum q function for the PU presence sensing. The effective detection analysis is performed along with energy consumption which shows effective efficiency during sensing work.

Optimal cooperative spectrum sensing for 5G cognitive networks using evolutionary algorithms

Abstract: Wireless communication technology is used in various applications and therefore the availability of wireless spectrum is a serious concern. The number of cellular users is increasing rapidly. The 5G network will be able to cater to the requirements of the increasing users. However, the spectrum efficiency needs to be improved. Cooperative spectrum sensing is being widely used by cognitive radios for utilizing the available spectrum in an efficient manner. Evolutionary Algorithm based optimization methods are used in various applications and have proved to be very efficient. These algorithms can also be used for optimizing the cooperative spectrum sensing in cognitive radios. In this paper, two methods are proposed for optimal Cooperative Spectrum Sensing for 5G cognitive networks. The optimization algorithms are designed using whale optimization algorithm (WOA) and Particle Swarm Optimization (PSO). The objective is to increase the probability of detection by optimizing the ‘weighting vector’. In the first method, WOA is used for cooperative spectrum sensing optimization in cognitive radios. In the second method, WOA method is improved using the PSO algorithm. A hybridized WOA-PSO algorithm is proposed to further improve the probability of detection. The results obtained are compared with other existing algorithms. The proposed methods perform better than the existing methods.

Solving Channel Assignment Problem in Cognitive Radio Networks with Immune Plasma Algorithm

Abstract: The new coronavirus or COVID-19 pandemic has focused researchers from various disciplines including computer sciences on existing diagnosis and treatment methods. As a result of this increasing interest, Immune Plasma algorithm (IP algorithm or IPA) that is a new meta-heuristic referencing a treatment method called immune or convalescent plasma has been introduced recently. In this study, IP algorithm was modified by considering the channel assignment problem on cognitive networks and its performance was investigated on solving mentioned problem. Moreover, the results of the IPA based technique were compared with the results of the Brute force search. Comparative studies showed that IP algorithm is capable of obtaining better solutions than the Brute force search.

Interference mitigation with spectrum sharing and aggregation for SDN based cognitive networks

Abstract: The Cognitive Radio based Software Defined Network (CR-SDN) integrated with 5G technology provides a promising solution for improving wireless broadband coverage. Cognitive radio technology allows users to communicate with an unlicensed spectrum when it is empty. The proposed Cognitive Radio based Software Defined Networking architecture (CR-SDN) allows Wireless Fidelity (Wi-Fi) and Long Term Evolution (LTE) users to access the Television White Space (TVWS) network whenever there is no vacant band available in Wi-Fi and LTE networks. Wi-Fi and there is a possibility whenever the LTE band interference between two users communicating in the same frequency band simultaneously. This interference should be mitigated, and the spectrum should be shared efficiently among the available users. In this paper, the proposed Interference Mitigation with Spectrum Sharing and Aggregation (IMSSA) technique mitigates the interference and shares the spectrum effectively among licensed and unlicensed users. This technique also aggregates the vacant spectrum within the licensed band (TV band and LTE band) and shares the aggregated band among LTE and Wi-Fi users for achieving a higher data rate. Adaptive Q Learning also made based on the spectrum management adaptive spectrum management, spectrum and a frequency band selection scheme used include dynamic access. This offers high spectrum efficiency and aggregation efficiency for ensuring network performance. The mobility management framework which is adopted in the proposed algorithm undertakes an efficient handover process whenever link failure occurs. In this proposed method, to evaluate the performance following parameter there are transmission rate, primary user analysis and transmission power.

ML-based Handover Prediction and AP Selection in Cognitive Wi-Fi Networks

Abstract: Device mobility in dense Wi-Fi networks offers several challenges. Two well-known problems related to device mobility are handover prediction and access point selection. Due to the complex nature of the radio environment, analytical models may not characterize the wireless channel, which makes the solution of these problems very difficult. Recently, cognitive network architectures using sophisticated learning techniques are increasingly being applied to such problems. In this paper, we propose a data-driven machine learning (ML) schemes to efficiently solve these problems in WLAN networks. The proposed schemes are evaluated and results are compared with traditional approaches to the aforementioned problems. The results report significant improvement in network performance by applying the proposed schemes. For instance, the proposed scheme for handover prediction outperforms traditional methods i.e. RSS method and traveling distance method by reducing the number of unnecessary handovers by 60% and 50% respectively. Similarly, in AP selection, the proposed scheme outperforms the SSF and LLF algorithms by achieving higher throughput gains upto 9.2% and 8% respectively.

Network controllability mediates the relationship between rigid structure and flexible dynamics

Abstract: Precisely how the anatomical structure of the brain supports a wide range of complex functions remains a question of marked importance in both basic and clinical neuroscience. Progress has been hampered by the lack of theoretical frameworks explaining how a structural network of relatively rigid inter-areal connections can produce a diverse repertoire of functional neural dynamics. Here, we address this gap by positing that the brain’s structural network architecture determines the set of accessible functional connectivity patterns according to predictions of network control theory. In a large developmental cohort of 823 youths aged 8 to 23 years, we found that the flexibility of a brain region’s functional connectivity was positively correlated with the proportion of its structural links extending to different cognitive systems. Notably, this relationship was mediated by nodes’ boundary controllability, suggesting that a region’s strategic location on the boundaries of modules may underpin the capacity to integrate information across different cognitive processes. Broadly, our study provides a mechanistic framework that illustrates how temporal flexibility observed in functional networks may be mediated by the controllability of the underlying structural connectivity. AUTHOR SUMMARY Precisely how the relatively rigid white matter wiring of the human brain gives rise to a diverse repertoire of functional neural dynamics is not well understood. In this work, we combined tools from network science and control theory to address this question. Capitalizing on a large developmental cohort, we demonstrated that the ability of a brain region to flexibly change its functional module allegiance over time (i.e., its modular flexibility), was positively correlated with its proportion of anatomical edges projecting to multiple cognitive networks (i.e., its structural participation coefficient). Moreover, this relationship was strongly mediated by the region’s boundary controllability, a metric capturing its capacity to integrate information across multiple cognitive domains.