Cognitive Sensor Networks Research Articles

Throughput Improvement in Energy-Efficient Cooperative Spectrum Sensing Based on Sensor Selection

Cooperative spectrum sensing in cognitive sensor networks has been shown to be very effective in improving the sensing performance. Sensors in these networks are assumed to be power re-source constrained. We propose an energy efficient cooperative spectrum sensing, in which all sensors are not participated in spectrum sensing and some nodes send their data to a fusion center only in order to improve the throughput of the network. We determine the nodes which sense the spectrum and the nodes which only contribute in data transmitting. The constraints on our problem are the global probability of detection, global probability of false alarm and the minimum required throughput. Based on the standard optimization techniques, the optimal conditions are obtained and a closed-form equation is expressed to determine the priority of nodes for spectrum sensing and data transmitting. Simulation results show that our algorithm leads to less energy consumption and improves the throughput in different conditions.

A Multiobjective Evolutionary Algorithm for Energy-Efficient Cooperative Spectrum Sensing in Cognitive Radio Sensor Network

Cognitive radio has emerged as a promising solution to address the problems posed by coming spectrum scarcity for the inherently resource-constrained sensor networks. Reliability and energy consumption are key objectives for spectrum sensing in cognitive sensor networks. In this paper, a fast differential evolution algorithm is proposed to optimize the energy consumption and spectrum sensing performance jointly. By constructing a comprehensive performance metric, the joint optimization is transferred to a multiobjective optimal problem, in which the sleeping schedule and censoring mechanism are taken into consideration. The main objective of the proposed algorithm is to minimize the network energy consumption subjected to constraints on the detection performance by optimally deriving the censoring and sleeping probabilities. To accelerate the convergence speed and maintain the diversity, the algorithm utilizes the advantages of opposite-based learning for generating the initial population and a tournament scheme in mutation step. In the crossover step, a control parameters dynamic adjustment scheme is applied to make a trade-off between exploration and exploitation. Finally, a selection mechanism is introduced for generating a well-distributed Pareto optimal front. The simulation results show that the proposed algorithm can reduce the average energy consumption of cognitive sensor node, while improving the global probability of spectrum sensing.

A novel multistage decision fusion for cognitive sensor networks using AND and OR rules

We propose a centralized radix-2 multistage decision fusion strategy comprising simple AND and OR rules for cooperative spectrum sensing in cognitive sensor networks. Earlier works on centralized decision fusion show the half-voting and majority rules to be optimum in many spectrum sensing scenarios in terms of minimizing the decision error (or equivalently maximizing the probability of correct decision). We consider a commonly occurring case in spectrum sensing in which the detection probability of a cognitive radio enabled sensor node is greater than its false-alarm probability. For this case, we consider five scenarios and demonstrate that the proposed method either performs better than half-voting and majority rules or exhibits a comparable performance. In this context, we also establish a criterion to make a choice between the AND and OR rules and compute the optimum number of nodes participating in cooperative spectrum sensing for these rules to maximize the correct decision probability.

Sensor Selection and Optimal Energy Detection Threshold for Efficient Cooperative Spectrum Sensing

In this paper, an energy-efficient scheme is proposed for cooperative spectrum sensing in cognitive sensor networks. In our scheme, we introduce a technique to select the sensing nodes and to set energy detection threshold so that energy saving can be accomplished in the nodes. Our objective is to minimize the energy consumed in distributed sensing subject to constraints on global probability of detection and probability of false alarm by determining the detection threshold and selection of the sensing nodes. The energy detector is applied to detect the primary-user activity for the sake of simplicity. At first, it is assumed that the instantaneous signal-to-noise ratio (SNR) for each node is known. Then, the optimal conditions are obtained, and a closed-form equation is expressed to determine the priority of nodes for spectrum sensing, as well as the optimum detection threshold. This problem is also solved when the average SNRs of sensors are available according to real situations. To achieve more energy savings, the problem of joint sensing node selection, detection threshold, and decision node selection is analyzed, and an efficient solution is extracted based on the convex optimization framework. Simulation results show that the proposed algorithms lead to significant energy savings in cognitive sensor networks.

Adaptive compressive sensing based sample scheduling mechanism for wireless sensor networks

Sample scheduling is a crucial issue in wireless sensor networks (WSNs). The design objectives of efficient sample scheduling are in general two-folds: to achieve a low sample rate and also high sensing quality. Recently, compressive sensing (CS) has been regarded as an effective paradigm for achieving high sensing quality at a low sample rate. However, most existing work in the area of CS for WSNs use fixed sample rates, which may make sensor nodes in a WSN unable to capture significant changes of target phenomenon, unless the sample rate is sufficiently high, and thus degrades the sensing quality. In this paper, to pursue high sensing quality at low sample rate, we propose an adaptive CS based sample scheduling mechanism (ACS) for WSNs. ACS estimates the minimum required sample rate subject to given sensing quality on a per-sampling-window basis and accordingly adjusts sensors’ sample rates. ACS can be useful in many applications such as environment monitoring, and spectrum sensing in cognitive sensor networks. Extensive trace-driven experiments are conducted and the numerical results show that ACS can obtain high sensing quality at low sample rate.

Energy-efficient power allocation in cognitive sensor networks: a coupled constraint game approach

In this paper, we investigate power allocation in cognitive sensor networks, where cognitive users (cognitive enabled sensor nodes) opportunistically share the common spectrum with primary users (licensed devices). Consider that sensor nodes are self-interested to maximize their own utilities, we formulate the energy-efficient power allocation problem as a non-cooperative coupled constraint game, by taking the interference temperature into account. An energy efficiency-oriented utility function is defined as a new metric to evaluate the performance of power allocation. Firstly, we prove that there exist Nash equilibriums in the proposed game. Then, we prove that the power allocation game is a super-modular game under some conditions. Finally, we design centralized and distributed Game-based Efficiency-oriented Power Allocation algorithms (i.e., centralized GEPA and distributed GEPA) to obtain the Nash equilibriums. Extensive simulations are conducted to demonstrate that the proposed power allocation algorithms can achieve satisfactory performance in terms of energy efficiency, convergence speed and fairness in cognitive sensor networks.

CRB-MAC: A Receiver-Based MAC Protocol for Cognitive Radio Equipped Smart Grid Sensor Networks

Wireless sensor networks (WSNs) have been widely recognized as a promising solution for enhancing various aspects of electric power grid and realizing the vision of smart grid. However, the challenging wireless environment in smart grid creates a number of challenges for WSNs, as a result of which energy efficiency and reliability become critically important. On the other hand, cognitive radio (CR) technology is expected to play a vital role in smart grid networks. The CR equipped sensor networks [or cognitive sensor networks (CSNs)] can effectively address the unique challenges of WSNs in smart grid. In this paper, we aim to design an energy efficient and reliable medium access control (MAC) protocol for CSNs. In this regard, we propose CRB-MAC which is a receiver-based MAC protocol for CSNs. The CRB-MAC uses preamble sampling and opportunistic forwarding techniques for providing high energy efficiency and reliability. In addition, CRB-MAC explicitly accounts for the peculiarities of a CR environment. Analytical and simulation results demonstrate the effectiveness of CRB-MAC as a viable solution for CSNs.

Lifetime Maximization in Cognitive Sensor Networks Based on the Node Selection

This paper proposes two methods for maximizing the lifetime of the cognitive sensor networks by focusing on node selection criteria for spectrum sensing in such networks. We maximize the network lifetime with constraints on the detection performance by determining the sensors, which sense the spectrum. This is a NP-complete problem and cannot be solved by standard methods. Therefore, we relax it to a more tractable form. Based on convex optimization framework, the optimal conditions are obtained and the sensors, which sense the spectrum, are determined. Simulation results show that our proposed algorithm is very efficient in terms of network lifetime maximization.

Energy‐aware secondary user selection in cognitive sensor networks

In cognitive radio, accurate spectrum sensing is essential to optimally use the available spectrum opportunities. On the other hand, energy is a scarce resource especially in cognitive sensor networks. In this study, the authors combine both these conflicting requirements and propose an energy-aware secondary user selection algorithm for cognitive sensor networks. First, an optimisation problem is solved to obtain the minimum required number of cognitive users, whereas satisfying the system requirements. Second, the most eligible cognitive users are identified through a probability-based approach. They study two extreme cases by focusing on either energy or accuracy parameters. By numerical analysis, it is shown that the accuracy benchmark is increased by as much as 39% by only considering the sensing accuracy, and the energy benchmark is reduced by as low as 76% by only considering the remaining level of energy. In addition, they conduct computer simulation and compare the network's lifetime at several sensing accuracy thresholds. It is elaborated that greater sensing accuracy thresholds lead to longer network lifetime. Finally, the effects of several fusion rules on the proposed method are studied through simulation and numerical analyses. It is discussed that the Majority rule has the best performance among the examined rules.

Efficient Power and Channel Allocation Strategies in Cooperative Potential Games for Cognitive Radio Sensor Networks

In wireless cognitive sensor networks, natural antagonism arises among unlicensed users when nodes opportunistically compete for unused frequency bands and the operations are seriously hampered by acute scarcity of resources. The transmitted power, which is inherently pertinent to the signal-to-interference-plus-noise ratio (SINR), cognition methodology, and lack of central management, must be preserved for longer network lifetime. In the midst of this struggle to acquire desired frequency band, where the performance of the entire network is dependent upon the behavior and etiquette exhibited by individual nodes, it is pivotal to introduce an effective cooperation mechanism in order to improve the vital network parameters. In this paper, we employ the concepts of game theory to develop an efficient and sustainable cooperation mechanism for efficient cognition and improved spectrum utilization. The nodes exhibit autochthonous pattern in opting for spectrum choices, which results in acceptable level of cooperation and consequently improvement in spectrum utilization. In order to achieve this global benefit, the users are motivated to carefully analyze the impact of their own choice in selecting a channel for transmission and its peers.

Self-Organized Cognitive Sensor Networks: Distributed Channel Assignment for Pervasive Sensing

We study the channel assignment strategy in multichannel wireless sensor networks (WSNs) where macrocells and sensor nodes are overlaid. The WSNs dynamically access the licensed spectrum owned by the macrocells to provide pervasive sensing services. We formulate the channel assignment problem as a potential game which has at least one pure strategy Nash equilibrium (NE). To achieve the NE, we propose a stochastic learning-based algorithm which does not require the information of other players’ actions and the time-varying channel. Cluster heads as players in the game act as self-organized learning automata and adjust assignment strategies based on their own action-reward history. The convergence property of the proposed algorithm toward pure strategy NE points is shown theoretically and verified numerically. Simulation results demonstrate that the learning algorithm yields a 26% sensor node capacity improvement as compared to the random selection, and incurs less than 10% capacity loss compared to the exhaustive search.

Spectrum Sensing in Opportunity-Heterogeneous Cognitive Sensor Networks: How to Cooperate?

Cognitive sensor network (CSN) is a promising paradigm to address the spectrum scarcity problem in traditional wireless sensor networks. Reliable spectrum sensing is essential to enable the normal operation of a CSN. Existing researches showed that by exploiting spatial diversity, cooperative sensing can greatly improve the detection performance over non-cooperative sensing in opportunity-homogeneous environment. At a given time, cognitive sensors at different locations, however, may experience heterogeneous spectrum opportunities making the cooperation among cognitive sensors intractable. In this paper, we show the limitations and drawbacks of merely using temporal-domain detection performance metrics and introduce novel spatio-temporal detection performance metrics to guide the design of joint spatio-temporal spectrum sensing. An efficient one-bit hard decision based three-phase (i.e., a global cooperation phase, a local cooperation phase, and a joint decision phase) spatio-temporal sensing algorithm is proposed and numerical results demonstrates the effectiveness of the proposed algorithm.

Application‐specific spectrum sensing method for cognitive sensor networks

The authors address an important aspect of spectrum sensing that has been often overlooked in the cognitive radio (CR) research. Although CR is supposed to be aware of its surrounding, most existing articles do not consider the characteristics of secondary users in the optimisation of sensing period. In this study, based on a continuous-time Markov chain model for cognitive sensor networks and energy detection method, the authors propose an application-specific spectrum sensing method that obtains the optimal sensing period according to the characteristics of both ‘primary and secondary’ users (hybrid scheme). The authors define and analytically derive two parameters, the interference ratio and the undetected opportunity ratio, and analytically find the optimum sensing period. Numerical and simulation results indicate that our proposed method is able to provide an optimal sensing period, that is customised for different cognitive networks. The proposed method significantly increases the system throughput by up to 11% and reduces the network's power consumption by as low as 33%. Finally, the trade-off between the throughput maximisation and power consumption minimisation is discussed.

A Novel Sensing Nodes and Decision Node Selection Method for Energy Efficiency of Cooperative Spectrum Sensing in Cognitive Sensor Networks

In this paper, we address the problem of sensor selection for energy efficient spectrum sensing in cognitive sensor networks. We consider minimizing energy consumption and improving spectrum sensing performance simultaneously. For this purpose, we employ the energy detector for spectrum sensing and formulate the problem of sensor selection in order to achieve energy efficiency in spectrum sensing while reducing complexity. Due to the NP-complete nature of the problem, we simplify the problem to a more tractable form through mapping assignment indices from integer to the real domain. Based on the standard optimization techniques, the optimal conditions are obtained and a closed-form equation is expressed to determine the priority of nodes for spectrum sensing. In the next step, to save more energy, the decision node (DN) selection procedure is proposed to address the problem of direct transmissions to fusion center. Then, the problem of joint sensing node selection and DN selection is analyzed and an efficient solution is extracted based on the convex optimization framework. The novelty of the proposed work is to address the selection of the best sensing nodes while minimizing energy consumption. Simulation results show that significant energy is saved due to the proposed schemes in different scenarios.

Joint Sensor-Node Selection and Channel Allocation Scheme for Cognitive Radio Sensor Networks

The conventional wireless sensor networks (WSNs) operating on the license-free ISM band are now experiencing large interference from other ISM band based wireless devices. The cognitive radio sensor network (CRSN) can solve this issue of interference and it can provide better services within the domain of the WSNs. In this paper, we propose a novel joint sensor node selection and channel allocation scheme to improve the performance of the CRSNs. We consider the cluster oriented sensor network and form clusters using the K-means clustering algorithm. The node selection scheme is formulated using the knapsack problem where each cluster head selects the optimal number of sensor nodes (SNs). Later on, the Hungarian algorithm is employed to allocate the best channels among SNs for their reporting process. Simulation results show that our scheme outperforms the existing schemes in terms of prolonging the network life, selecting the more reliable SNs and allocating the optimal channels among SNs for their data transmission during the reporting process.

Cooperative Spectrum Sensing and Localization in Cognitive Radio Systems Using Compressed Sensing

We propose to fuse two main enabling features in cognitive radio systems (CRS): spectrum sensing and location awareness in a single compressed sensing based formalism. In this way, we exploit sparse characteristics of primary units to be detected, both in terms of spectrum used and location occupied. The compressed sensing approach also allows to overcome hardware limitations, in terms of the incapacity to acquire measurements and signals at the Nyquist rate when the spectrum to be scanned is large. Simulation results for realistic network topologies and different compressed sensing reconstruction algorithms testify to the performance and the feasibility of the proposed technique to enable in a single formalism the two main features of cognitive sensor networks.

A Bayesian Game Analysis of Byzantine Attacks for Wireless Cognitive Sensor Networks in Smart Grids

In this paper, we propose a revolutionary communication paradigm—Wireless Cognitive Sensor Network (WCSN) — to solve these problems. We analyze the incentives to introduce WCSN in smart grids and review the main research work with respect to Byzantine attacks in wireless cognitive sensor network. Furthermore, we provide some preliminary discussions on Bayesian game and wireless cognitive sensor network. More importantly, we propose a Bayesian game model for the Byzantine attacks of wireless cognitive sensor network in the smart grid and present a detailed equilibrium analysis of the interactions between malicious secondary users and honest secondary users. Analysis results demonstrate that rather than focus on penalties, regulators would be wise to ensure that the gain from challenging is significant and the cost of challenging is small, as this reduces the probability of sending false readings.

A Multi-Layer Security Architecture for Wireless Cognitive Sensor Networks in Smart Grids

Wireless sensor networks have been extensively deployed in the electric power systems for sensing, transmission and control, which provide more opportunities for wireless low power radios to operate. As the next generation electricity system, the smart grid should possess the capability to transmit reliable and real-time information to the control centers of the utilities. In order to solve the issues such as heterogeneous coexistence, spectrum scarcity, tremendous data processing and Security guarantees, revolutionary communication architecture is urgently demanded. In this paper, after analyzing the hierarchical structure of smart grid and illustrating the principle of WCSN in smart grids, we propose the security architecture of wireless sensor networks based on cognitive radio for smart grids, which can be used as a reference to design and develop the Wireless Cognitive Sensor Network (WCSN) security schemes in the electric power systems.

Propagation of Spurious Data for Wireless Cognitive Sensor Network in the Smart Grid: A Social Network Method

The nature of white space utilization, tremendous data processing, heterogeneous coexistence and security guarantees in Wireless Cognitive Sensor Network (WCSN) brings significant advantages over traditional Wireless Sensor Network (WSN) used in the smart grid. However, one of the main security threats to WCSN in the smart grid is the transmission of spurious data by malicious secondary users, which can induce the control center to make a wrong spectrum allocation and power dispatching decisions. In this paper, after analyzing the reference structure of smart grid and the feasibility of applying the epidemic theory into WCSN, we propose the UDG model of WCSN in the smart grid. Based on the epidemic theory in social network, we model and analyze the spurious data propagation process and identify key factors determining potential epidemic outbreaks in WCSN. In conclusion, we validate the feasibility of SIS model and perform investigations on the system dynamics.

Spectrum-aware and cognitive sensor networks for smart grid applications

Recently, wireless sensor networks have been considered as an opportunity to realize reliable and low-cost remote monitoring systems for smart grid. However, interference due to nonlinear electric power equipment and fading as a result of obstacles in various smart grid environments from generation to end-user sides make realization of reliable and energy-efficient communication a challenging task for WSNs in smart grid. In this article, spectrum-aware and cognitive sensor networks (SCSNs) are proposed to overcome spatio-temporally varying spectrum characteristics and harsh environmental conditions for WSN-based smart grid applications. Specifically, potential advantages, application areas, and protocol design principles of SCSN are introduced. The existing communication protocols and algorithms devised for dynamic spectrum management networks and WSNs are discussed along with the open research issues for the fulfillment of SCSNs. A case study is also presented to reveal the reliable transport performance in SCSNs for different smart grid environments. Lastly, different energy harvesting techniques for SCSN-based smart grid applications are reviewed. Here, our goal is to envision potentials of SCSNs for reliable and low-cost remote monitoring solutions for smart grid.