The majority of work in cognitive radio networks have focused on single-hop networks with mainly challenges at the physical and MAC layers. Recently, multi-hop secondary networks have gained attention as a promising design to leverage the full potential of cognitive radio networks. One of the main features of routing protocols in multi-hop networks is the routing metric used to select the best route for forwarding packets. In this paper, we survey the state-of-the-art routing metrics for cognitive radio networks. We start by listing the challenges that have to be addressed in designing a good routing metric for cognitive radio networks. We then provide a taxonomy of the different metrics and a survey of the way they have been used in different routing protocols. Then we present a case study to compare different classes of metrics. After that, we discuss how to combine individual routing metrics to obtain a global one. We end the paper with a discussion of the open issues in the design of future metrics for cognitive radio networks.

Routing Metrics of Cognitive Radio Networks: A Survey

Cognitive communication model perform the investigation and surveillance of spectrum in cognitive radio networks abetment in advertent primary users (PUs) and in turn help in allocation of transmission space for secondary users (SUs). In effective performance of regulation of wireless channel handover strategy in cognitive computing systems, new computing models are desired in operating set of tasks to process business model, and interact naturally with humans or machine rather being programmed. Cognitive wireless network are trained via artificial intelligence (AI) and machine learning (ML) algorithms for dynamic processing of spectrum handovers. They assist human experts in making enhanced decisions by penetrating into the complexity of the handovers. This paper focuses on learning and reasoning features of cognitive radio (CR) by analyzing primary user (PU) and secondary user (SU) data communication using home location register (HLR) and visitor location register (VLR) database respectively. The SpecPSO is proposed for optimizing handovers using supervised machine learning technique for performing dynamic handover by adapting to the environment and make smart decisions compared to the traditional cooperative spectrum sensing (CSS) techniques.

Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers

Cooperative co-evolution (CC) is an evolutionary computation framework that can be used to solve high-dimensional optimization problems via a “divide-and-conquer” mechanism. However, the main challenge when using this framework lies in problem decomposition. That is, deciding how to allocate decision variables to a particular subproblem, especially interacting decision variables. Existing decomposition methods are typically computationally expensive. In this paper, we propose a new decomposition method, which we call recursive differential grouping (RDG), by considering the interaction between decision variables based on nonlinearity detection. RDG recursively examines the interaction between a selected decision variable and the remaining variables, placing all interacting decision variables into the same subproblem. We use analytical methods to show that RDG can be used to efficiently decompose a problem, without explicitly examining all pairwise variable interactions. We evaluated the efficacy of the RDG method using large scale benchmark optimization problems. Numerical simulation experiments showed that RDG greatly improved the efficiency of problem decomposition in terms of time complexity. Significantly, when RDG was embedded in a CC framework, the optimization results were better than results from seven other decomposition methods.

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A Recursive Decomposition Method for Large Scale Continuous Optimization

Spectrum handoff in cognitive radio networks

Humans with good health condition is some more difficult in today’s life, because of changing food habit and environment. So we need awareness about the health condition to the survival. The health-support systems faces significant challenges like lack of adequate medical information, preventable errors, data threat, misdiagnosis, and delayed transmission. To overcome this problem, here we proposed wearable sensor which is connected to Internet of things (IoT) based big data i.e. data mining analysis in healthcare. Moreover, here we design Generalize approximate Reasoning base Intelligence Control (GARIC) with regression rules to gather the information about the patient from the IoT. Finally, Train the data to the Artificial intelligence (AI) with the use of deep learning mechanism Boltzmann belief network. Subsequently Regularization _ Genome wide association study (GWAS) is used to predict the diseases. Thus, if the people has affected by some diseases they will get warning by SMS, emails. Etc., after that they got some treatments and advisory from the doctors.

IOT based wearable sensor for diseases prediction and symptom analysis in healthcare sector

A path planning approach for crowd evacuation in buildings based on improved artificial bee colony algorithm

Cognitive Radio (CR) emerges as a promising solution to current unbalanced spectrum utilization. The cognitive ad hoc network can take advantage of dynamic spectrum access and spectrum diversity over wide spectrum. It could achieve higher network capacity compared to traditional ad hoc networks, thus supporting bandwidth-demanding applications. A cognitive radio operates over wide spectrum with unpredictable channel availability. Moreover, the transmission opportunity of a cognitive node is not guaranteed due to the presence of primary users (PUs). These two unique features define new routing problems in cognitive ad hoc networks. To better characterize the unique features of cognitive radio networks, we propose new routing metrics, including accumulated spectrum temperature, highest spectrum temperature, and mixed spectrum temperature to account for the time-varying spectrum availability. The proposed metrics favor the "coolest'' path, or the path with the most balanced and/or the lowest spectrum utilization by the primary users. We also study the computational complexity of the routing algorithm in cognitive ad hoc networks. Experiment results on our USRP-2 testbed show that the proposed metrics are capable of capturing the fluctuation of spectrum availability and suitable for cognitive ad hoc networks.

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Coolest Path: Spectrum Mobility Aware Routing Metrics in Cognitive Ad Hoc Networks

The current evacuation model does not consider the impact of emotion and personality on crowd evacuation. Thus, there is large difference between evacuation results and the real-life behavior of the crowd. In order to generate more realistic crowd evacuation results, we present a method of emotion contagion for crowd evacuation. First, we combine OCEAN (Openness, Extroversion, Agreeableness, Neuroticism, Conscientiousness) model and SIS (Susceptible Infected Susceptible) model to construct the P-SIS (Personalized SIS) emotional contagion model. The P-SIS model shows the diversity of individuals in crowd effectively. Second, we couple the P-SIS model with the social force model to simulate emotional contagion on crowd evacuation. Finally, the photo-realistic rendering method is employed to obtain the animation of crowd evacuation. Experimental results show that our method can simulate crowd evacuation realistically and has guiding significance for crowd evacuation in the emergency circumstances.

A method of emotion contagion for crowd evacuation

In a crisis situation, negative emotions often spread among the crowd, and they have adverse impacts on human decisions, resulting in stampedes and crushes. Safety officers are often dispatched to scenes of emergencies because their positive emotions can calm the crowd down and avoid serious accidents. However, how to utilize the positive emotional contagion to maximize the “calm-down” effect remains a challenging problem in crowd evacuation. In this paper, we present an approach for optimizing positive emotional contagion in crowd evacuation. First, a computational model of positive emotional contagion is proposed to describe how safety officers calm a crowd down. To capture important influential factors for positive emotional contagion, such as the trust relationships among the individuals involved in a crisis situation and the variations of emotional contagion speed, we construct a trust-based emotional contagion network (Trust-ECN) and a heterogeneous emotional contagion speed computation model (HECS-CM). Based on these models, the emotional contagion process can be analyzed in a parametric way, and the infection probability for each individual in a given time window can be computed analytically with a continuous-time Markov chain (CTMC). Second, a maximization problem of emotional contagion is formulated. Since this optimization problem is NP-hard, an artificial bee colony optimized emotional contagion (ABCEC) algorithm is used to solve for the optimal positions of safety officers. We demonstrate the effectiveness of our method on both synthetic and real-world data at different scales. Finally, we implement a crowd simulation system to visualize the results of our theoretical analysis in a graphical manner. The proposed method can provide guidance for emergency response management.

Strategies to Utilize the Positive Emotional Contagion Optimally in Crowd Evacuation

The continuous growth of interconnected objects in the Internet of Things (IoT) raises a challenge to the wireless communication technology. Cognitive radio could make full use of the dynamic spectrum access and spectrum diversity over wide spectrum to alleviate the spectrum scarcity problem and satisfy the enormous connectivity demands in IoT, which has garnered significant attention over the last few years. This paper addresses the spectrum allocation problem with respect to both spectrum utilization and network throughput in the cognitive-radio-based IoT. On the one side, each link in a transmission path intends to improve the transmission performance on the assigned spectrum channel to maximize the end-to-end throughput. On the other side, these links share the same spectrum channel to concurrently transmit as much as possible to achieve the maximum spectrum utilization. In order to solve the problem, we propose a concurrent transmission model in the network which reveals the constraints of mutual interference and resource competition in links concurrent transmissions. Based on this model, we formulate the spectrum allocation plan for links as the chromosome (solution) in genetic algorithms. Then, we apply the nondominated sorting genetic algorithm-II to solve the multiobjective spectrum allocation problem. Simulation results validate that the proposed strategy can search the optimal solutions efficiently and satisfy the requirements of spectrum allocation in various cases.

Dianjie Lu

Papers

A path planning approach for crowd evacuation in buildings based on improved artificial bee colony algorithm

Coolest Path: Spectrum Mobility Aware Routing Metrics in Cognitive Ad Hoc Networks

A method of emotion contagion for crowd evacuation

Strategies to Utilize the Positive Emotional Contagion Optimally in Crowd Evacuation

An Effective Multi-Objective Optimization Algorithm for Spectrum Allocations in the Cognitive-Radio-Based Internet of Things