Cognitive Mesh Research Articles

Embodied cognition and its pathologies: The dynamics of institutional failure on wickedly hard problems

The physical and the cognitive mesh and clash in a dynamic syncretism that includes, not only the overwhelming influences of culture and path-dependent historical trajectory, but the inevitable burdens of Clausewitzian fog and friction. Such factors sculpt conflict at all scales and levels of organization. We show how institutional cognition – always and inevitably embodied – even when ‘enhanced’ by artificial intelligence or related machineries, must suffer significantly increased probability of failure under conditions of fog and friction, particularly for ‘wickedly hard’ tactical, operational, and strategic problems not amenable to straightforward, if difficult, engineering solutions. Perhaps not surprisingly, problems for which ‘big data’ optimization fails, particularly at the strategic level, enter realms of subtlety parallel to those that confound the understanding and representation of individual human consciousness. In particular, we develop probability models of cognitive failure on wickedly hard problems that may be convertible to statistical tools for empirical study and limited control of the phenomena.

Distributed energy‐efficient channel assignment in cognitive mesh network for IoT systems

AbstractInternet of Things (IoT) has attracted a lot of attention to solve the real‐world problem to improve quality of life. Cognitive radio mesh networks (CRMNs) can provide connectivity to a large number of IoT nodes and have the potential to self‐organize and reconfigure to make efficient use of spectrum resources. The growing concern regarding energy consumption in communication infrastructure and low power IoT nodes has led researchers to regard minimizing of energy consumption. In this paper, we propose a distributed energy‐efficient channel assignment in CRMNs for the IoT systems. We formulate an optimization problem for channel assignment and sharing in CRMNs for IoT systems with an objective to maximize energy‐efficiency of the network. We consider constraints on both intra and inter‐link interference. We then propose two distributed approaches to solve the channel assignment problem: (1) greedy approach and (2) game theoretic approach while considering the scalability and self‐organization requirements of the CRMNs for the IoT systems. We then present extensive simulation results to demonstrate the performance of proposed schemes.

Economic-Robust Transmission Opportunity Auction for D2D Communications in Cognitive Mesh Assisted Cellular Networks

Device-to-device (D2D) communications can potentially alleviate cellular network congestion by utilizing local available links, and have attracted intensive attention recently. Cognitive radio (CR) allows users to opportunistically access unused licensed spectrums. It thus serves as a great candidate technology for D2D communications, but has not been widely employed in cellular networks due to hardware development limitations. In this paper, we propose a new architecture, called cognitive mesh assisted cellular network (CMCN), in which several secondary service providers (SSPs) deploy CR routers to facilitate D2D communications among wireless users. To address the competition among the SSPs, we further construct a secondary spectrum auction market. Although a few works have studied spectrum auctions, most of them are designed for single-hop communications, and it is usually not clear whom a winning user communicates with. Uncertain spectrum availability is not considered in previous schemes either. In this paper, we propose a transmission opportunity auction scheme, called TOA, which can address these problems. Extensive simulations are conducted to validate the efficiency of the CMCN architecture and that of the TOA scheme.

Traffic engineering in cognitive mesh networks: Joint link-channel selection and power allocation

In this paper, we investigate traffic engineering (TE) methods in Cognitive Radio Wireless Mesh Networks (CR-WMNs) through joint selection of link-channel pairs at each forwarding router and allocating powers on those so as to boost up overall network throughput. We first present an optimization framework, Centralized Optimal Traffic Engineering (COTE), aiming at maximizing aggregated network throughput by selecting optimal set of link-channel pairs, power allocation over those and fair traffic splitting. Further, a centralized Suboptimal Traffic Engineering (SOTE) solution is proposed by employing Lagrangian dual decomposition on the COTE problem, to ensure a resolution in polynomial time. Finally, a Distributed Greedy Traffic Engineering (DGTE) method is proposed to ensure fast convergence to the dynamic changing network behavior and to improve scalability. Extensive simulation results are presented to demonstrate the effectiveness of our proposed TE mechanisms compared to the state-of-the-art works.

Toward network function virtualization for cognitive wireless mesh networks: a TCP case study

Nowadays, cognitive radio (CR) is considered the key technology to deploy wireless nodes that are able to adapt their transmission parameters based on the characteristics of the environment. In our previous works, we leveraged the reconfigurability properties offered by the CR technology to implement Cognitive Wireless Mesh Networks (CMNs) operating on the 2.4-GHz industrial, scientific, and medical (ISM) band. Urban-X, a new architecture for Multi-radio Cognitive Mesh Networks, self-configures and self-adapts to the dynamic interference conditions experienced on the Wi-Fi channels in dense urban environments. Urban-X estimates traffic load of existing users outside the mesh in each channel and utilizes less occupied channels opportunistically. In such dynamic environment, a transport layer protocol can suffer from variation in delay and available bandwidth due to spectrum sensing and mobility. Several TCP solutions for the CWMNs have been proposed but they could not be employed for implementation due to complexity and inefficiency. Instead, we adopt network function virtualization (NFV) technique which allows to instantiate virtualized network functions (VNFs) dynamically on demands. For this, we propose overall architecture of NFV framework for the CMNs and apply TCP accelerators (ACC) VNFs to improve TCP throughput as a case study. Simulation results show TCP-ACC VNF can achieve a notable performance enhancement compared to previous TCPs for the CMNs.

Genetic Algorithm based Resource Management for Cognitive Mesh Networks with Real-time and Non-real-time Services

Quality-of-service (QoS) provisioning for a cognitive mesh network (CMN) with heterogeneous services has become a challenging area of research in recent days. Considering both real-time (RT) and non-real-time (NRT) traffic in a multihop CMN, [1] studied cross-layer resource management, including joint access control, route selection, and resource allocation. Due to the complexity of the formulated resource allocation problems, which are mixed-integer non-linear programming, a low-complexity yet efficient algorithm was proposed there to approximately solve the formulated optimization problems. In contrast, in this work, we present an application of genetic algorithm (GA) to re-address the hard resource allocation problems studied in [1]. Novel initialization, selection, crossover, and mutation operations are designed such that solutions with enough randomness can be generated and converge with as less number of attempts as possible, thus improving the efficiency of the algorithm effectively. Simulation results show the effectiveness of the newly proposed GA-based algorithm. Furthermore, by comparing the performance of the newly proposed algorithm with the one proposed in [1], more insights have been obtained in terms of the tradeoff among QoS provisioning for RT traffic, throughput maximization for NRT traffic, and time complexity of an algorithm for resource allocation in a multihop network such as CMN.

A New Routing Selection Algorithm in Cognitive Mesh Networks

In cognitive networks, the existing RASR routing algorithm does not consider channel usage ratio of the PU and results in an unstable path problem. To address these issues, a new Routing Selection Algorithm in Cognitive mesh networks (RSAC) is proposed in this paper. RSAC is designed by piggybacking the information of link stability factor which is the sum of the channel usage ratio of secondary users and choosing routes based on it. It enhances the route stability, improving the success ratio of data packets transmission, reducing the average end-to-end delay. Theoretical analysis and simulation results show that RSAC outperforms the RASR routing algorithm in terms of the success ratio of data packets transmission, the average end-to-end delay.

Spectrum trading for routing in a multi service cognitive mesh network

Cognitive networks CNs enable providing service to unlicensed users secondary users, SUs by trading the unused spectrum. In our work, SUs compete with each other for accessing the unused spectrum. PUs rent the surplus spectrum to get some reward. The paper proposes a path discovery based on the cost of spectrum. Minimising the cost of spectrum renting is the key objective for SUs. In our work, the proposed adaptive trading approach for the PU is based on the gain maximising. In this spectrum market, the PU has to decide what price it should charge SUs. The PU changes the price of the spectrum over time based on the demand of spectrum and the competition with other service providers. On the other hand, SUs rent spectrum based on the cost of spectrum. The effectiveness of our routing algorithm in terms of cost minimisation is verified through extensive simulations.

Coordination problem in cognitive wireless mesh networks

Due to their potential to create and extend pervasive communication applications to cognitive environments with distributed control, the emerging technology of cognitive wireless mesh networks is gaining significant attention from a growing research community. However, the major challenge in cognitive networks is the adaptation to time and space variability of the available resources, namely chunks of the frequency spectrum called channels. In particular, this problem is exacerbated in cognitive mesh networks because there exists no direct communication among devices which thereby cannot establish a global (common) control channel to coordinate the entire network. Instead, only local control channels that vary depending on the time instant and location, can be established to coordinate cognitive devices among themselves. This paper first analyzes the underlying challenges and existing approaches to address the absence of a static and global control channel, and then propose a novel Control channel formation protocol, called Connor. Our protocol Connor is a fully distributed coordination scheme where cognitive mesh devices self-organize into clusters based on the similarity of available channels and on topological constraints. Compared with the existing clustering algorithms, which requires synchronization, the proposed Connor performs better in most cases without imposing synchronization.

Cross‐layer scheduling for multi‐users in cognitive multi‐radio mesh networks

ABSTRACTA wireless mesh network has been popularly researched as a wireless backbone for Internet access. However, the deployment of wireless mesh networks in unlicensed bands of urban areas is challenging because of interference from external users such as residential access points. We have proposed Urban‐X, which is a first attempt towards multi‐radio cognitive mesh networks in industrial, scientific, and medical bands. Urban‐X first controls network topology with a distributed channel assignment to avoid interference in large timescale. In such a topology, we develop a new link‐layer transmission‐scheduling algorithm together with source rate control as a small‐timescale approach, which exploits receiver diversity when receivers of multi‐flows can have different channel conditions because of varying interference. For this purpose, mesh nodes probe the channel condition of received mesh nodes using group Request to Send and group Clear to Send. In this study, we establish a mathematical Urban‐X model in a cross‐layer architecture, adopting a well‐known network utility maximization framework. We demonstrate the feasibility of our idea using a simulation on the model. Simulation results show improved network throughput from exploiting receiver diversity and distributed channel assignment under varying external user interference. Copyright © 2012 John Wiley & Sons, Ltd.

Performance Comparison of Two Channel Allocation Strategies in Cellular Networks

Channel allocation plays an important role in increasing the throughput and assuring fairness to users not only in traditional cellular networks but also in cognitive radio networks and mesh networks. In this paper, we study the effect of pre-allocating channels to cells with respect to a distributed and fault-tolerant channel allocation algorithm. Results from our extensive performance evaluation indicate that distributed channel allocation algorithms that pre-allocate all channels to cells can have lower call failure rate, call blocking rate, and handoff drop rate compared to algorithms that partially pre-allocate channels or does not pre-allocate channels.

Performance Modelling and Analysis of Cognitive Mesh Networks

A new analytical model is proposed to investigate the delay and throughput in cognitive mesh networks. The validity of the model is demonstrated via extensive simulation experiments. The model is then used to evaluate the effects of the number of licensed channels and channel utilisation on the network performance.

Differentiated service provisioning in the MAC layer of cognitive radio mesh networks

MAC protocols utilising the enhanced distributed channel access in IEEE 802.11-2007 can provide differentiated QoS in wireless networks. However, the performance of high priority traffic can be seriously degraded in the presence of strong noise over the wireless channels. Schemes utilising adaptive modulation and coding technique have also been proposed for QoS provisioning. They can provide limited protection in the presence of noise but are ineffective in a high noise scenario. Although multiple non-overlapped channels exist in the 2.4 GHz and 5 GHz spectrum, most IEEE 802.11-based multi-hop ad hoc networks today use only a single channel at anytime and cannot fully exploit the aggregate bandwidth available in the radio spectrum. By identifying vacant channels through the use of cognitive radios technique, the noise problem can be mitigated by distributing network traffic across multiple vacant channels to reduce the node density per transmission channel. In this paper, we propose the MAC-layer QoS provisioning protocol which combines adaptive modulation and coding with dynamic spectrum access. Simulation results demonstrate that MQPP can achieve better performance in terms of lower delay and higher throughput.

Joint Detection and User Selection Algorithms inCognitive Wireless Communication System

Cognitive wireless networks usually utilize large numbers of cognitive nodes to collect cognitive information of the authorized frequency bands and do automatic configuration to finish spectrum sharing processes. How to realize joint detection and choose appropriate cognitive nodes to achieve opportunity type spectrum access should be considered in a cognitive wireless communication system. In this paper, we present a joint detection algorithm to finish cognitive information collection and one user selection scheme to minimize co-channel interference for the downlink of a cognitive mesh network with the coexistence of many primary nodes and cognitive nodes. In the joint detection strategy, the central node controls more than one cognitive user to jointly detect the primary channel. While the effective user selection algorithm is proposed to choose a group of cognitive nodes while maximizing the sum rate of primary nodes and satisfying the co-channel interference constraint from the cognitive nodes to primary nodes, meanwhile, the total transmitted power constraint for primary access point (PAP) as well as the Signal to Interference and Noise Ratio of the cognitive nodes is bigger than a given threshold γ . Simulation results show that our suboptimal algorithm is able to achieve high sum rate throughput with low complexity.

Profit optimization in multi-service cognitive mesh network using machine learning

Cognitive technology enables licensed users (primary users, PUs) to trade the surplus spectrum and to transfer temporarily spectrum usage right to the unlicensed users (secondary users, SUs) to get some reward. The rented spectrum is used to establish secondary network. However, the rented spectrum size influences the quality of service (QoS) for the PU and the gained rewards. Therefore, the PU needs a resource management scheme that helps it to allocate optimally a given amount of the offered spectrum among multiple service classes and to adapt to changes in the network conditions. The PU should support different classes of SUs that pay different prices for their usage of spectrum. We propose a novel approach to maximize a PU reward and to maintain QoS for the PUs and for the different classes of SUs. These complex contradicting objectives are embedded in our reinforcement learning (RL) model that is developed to derive resource adaptations to changing network conditions, so that PUs' profit can continuously be maximized. Available spectrum is managed by the PU that executes the optimal control policy, which is extracted using RL. Performance evaluation of the proposed RL solution shows that the scheme is able to adapt to different conditions and to guarantee the required QoS for PUs and to maintain the QoS for a multiple classes of SUs, while maximizing PUs profits. The results have shown that cognitive mesh network can support additional SUs traffic while still ensuring PUs QoS. In our model, PUs exchange channels based on the spectrum demand and traffic load. The solution is extended to the case in which there are multiple PUs in the network where a new distributed algorithm is proposed to dynamically manage spectrum allocation among PUs.

SASMS: Spectrum Allocation Scheme under Multi-transceiver Structure in CogMesh Network

A novel spectrum allocation scheme in a centralized scenario with the support of multi-transceiver structure of CMR (cognitive mesh router) in CogMesh network is proposed. Traditional MR (mesh router) in WMN (wireless mesh network) is no longer met the demand of the development of wireless communication in the field of dynamically utilizing vacant spectrum holes. Therefore, MR is reconstructed into CMR with a multi-transceiver structure. CMR inherits some good merits from MR and add some special function, such as learning, aggregation, allocation and etc to better adapt to dynamic spectrum scenario. In the paper, CMR can use the proposed SASMS to allocate vacant spectrum bands for SMCs (secondary mesh client), which access the CMR with one hop. Besides, the scheme is designed according to the different spectrum scenario and constraint by the average minimum back-off delay of each SMC to guarantee the relatively small end-to-end delay and the equilibrium of each transceiver. From the simulation, the outstanding performance gain will be got in the term of decreasing SMC's back-off delay through a theoretical approach and the Qualnet.

Cognitive Mesh Network Under Interference from Primary User

In a commonly accepted usage scenario, a cognitive radio appears as a secondary user of certain spectrum which is licensed to another, primary system. A prominent example of cognitive system is a m...

Cognitive Wireless Mesh Networks with Dynamic Spectrum Access

Wireless Mesh Networks (WMNs) are envisaged to extend Internet access and other networking services in personal, local, campus, and metropolitan areas. Mesh routers (MR) form the connectivity backbone while performing the dual tasks of packet forwarding as well as providing network access to the mesh clients. However, the performance of such networks is limited by traffic congestion, as only limited bandwidth is available for supporting the large number of nodes in close proximity. This problem can be alleviated by the cognitive radio paradigm that aims at devising spectrum sensing and management techniques, thereby allowing radios to intelligently locate and use frequencies other than those in the 2.4 GHz ISM band. These promising technologies are integrated in our proposed Cognitive Mesh NETwork (COMNET) algorithmic framework, thus realizing an intelligent frequency-shifting self-managed mesh network. The contribution of this paper is threefold: (1) A new approach for spectrum sensing is devised without any change to the working of existing de facto mesh protocols. (2) An analytical model is proposed that allows MRs to estimate the power in a given channel and location due to neighboring wireless LAN traffic, thus creating a virtual map in space and frequency domains. (3) These models are used to formulate the task of channel assignment within the mesh network as an optimization problem, which is solved in a decentralized manner. Our analytical models are validated through simulation study, and results reveal the benefits of load sharing by adopting unused frequencies for WMN traffic.