Cognitive Sensor Networks Research Articles

Green Energy Powered Cognitive Sensor Network With Cooperative Sensing

Green energy powered cognitive radio is proposed as a promising method to improve the energy efficiency (EE) and spectrum efficiency (SE). We consider the green power farms that harvest energy from solar and wind, which is used for the primary transmitter and the cognitive sensor network (CSN). The primary transmitter has priority to utilize the spectrum and the energy, and then the energy powered for the CSN may not be sufficient. Both the energy management and energy harvesting process will affect the throughput of the cognitive sensors (CSs). In this paper, we aim to design the system parameters in the CSN (including the sensing threshold, the sensing time, the final decision threshold in the fusion center, and the number of cooperating CSs) that can improve the utilization efficiency of the harvested energy and maximize the CSs’ throughput. Since the parameters are intertwined with the energy causality constraint and the average throughput, we decouple the influence of final decision threshold on the throughput from the influence of the sensing time and sensing threshold. The optimization problem is divided into two sub-problems. Algorithm 1 and Algorithm 2 are proposed to solve sub-problem 1 and sub-problem 2, respectively. The simulation results show that the proposed scheme can improve the average throughput significantly, and is able to achieve the best tradeoff between SE and EE.

Non-Gradient Based PDF Approximation for Sensor Selection in Cognitive Sensor Networks

Energy consumption in detection is a key objective for cognitive sensor network. Therefore, measuring the energy consumption is an important issue for efficient spectrum sensing. In order to compute the consumed energy at sensor nodes, their energy probability density function (PDF) is often required. In this article, the authors study the problem of spectrum sensing in cognitive networks and focus on strategies that can substantially affect the energy efficiency and complexity of such algorithms. In particular, they consider an energy detection mechanism in cooperative spectrum sensing where the knowledge of the energy PDF is the key. Since in practice the true value of such a PDF is unavailable, the authors propose to use non-gradient based optimization algorithms to find the parameters of approximated PDF function. In the proposed method, the corresponding PDF parameters are computed iteratively using Genetic and PSO algorithms. The numerical results show that the proposed technique outperforms prior methods.

Applying Case-Based Reasoning to Tactical Cognitive Sensor Networks for Dynamic Frequency Allocation.

In this paper, a cognitive radio engine platform is proposed for exploiting available frequency channels for a tactical wireless sensor network while aiming to protect incumbent communication devices, known as the primary user (PU), from undesired harmful interference. In the field of tactical communication networks, there is an urgent need to identify available frequencies for opportunistic and dynamic access to channels on which the PU is active. This paper introduces a cognitive engine platform for determining the available channels on the basis of a case-based reasoning technique deployable as a core functionality on a cognitive radio engine to enable dynamic spectrum access (DSA) with high fidelity. To this end, a plausible learning engine to characterize the channel usage pattern is introduced to extract the best channel candidate for the tactical cognitive radio node (TCRN). The performance of the proposed cognitive engine was verified by simulation tests that confirmed the reliability of the functional aspect, which includes the learning engine, as well as the case-based reasoning engine. Moreover, the efficacy of the TCRN with regard to the avoidance of collision with the PU operation, considered the etiquette secondary user (SU), was demonstrated.

Optimal Time Assignment Policy for Maximizing Throughput in Cognitive Sensor Network with Energy Harvesting.

A cognitive sensor network with energy harvesting (EH-CSN) is a promising paradigm to address the issues both in spectrum efficiency and in energy efficiency. The cognitive sensors (CSs) equipped with energy harvesting devices are assumed to operate in a harvesting-sensing-transmission mode and permitted to access the idle licensed frequency bands without causing any harmful jamming to the primary user. By identifying the time fractions of harvesting, sensing, and transmission, we can discuss some design considerations for the EH-CSN. In the meantime, considering the possibility that the primary user may reoccupy the idle channel during the CS’s data transmission duration, we formulate an optimization problem to maximize the average throughput of EH-CSN under a collision constraint and an energy constraint. After deriving the lower and upper bounds of the time fraction for energy harvesting, the uniqueness and existence of the optimal time fraction set have been proved. Finally, our theoretical analysis is also verified through numerical simulations.

Sensing Time Optimisation and Sensor Selection in Multi‐channel Multi‐antenna Wireless Cognitive Sensor Networks

Energy efficiency of cooperative spectrum sensing is a critical issue in wireless cognitive sensor networks (CSNs). However, sensor nodes consume high energy to support cognitive radio (CR) functionalities such as channel sensing. In this study, the energy-efficient cooperative spectrum sensing in multi-channel multi-antenna CSNs is investigated. First, a multi-channel CSN is considered to obtain sensing sensors and sensing time for each band in a CR system. The criteria is to minimise energy consumption of cooperative spectrum sensing under the global probabilities of detection and false alarm constraints. The authors employ a convex optimisation method to obtain optimal sensing time and sensing nodes for each band. Next, their analysis is extended into multi-antenna sensors, which is a promising solution instead of using more sensors. In multi-antenna cooperative spectrum sensing, diversity helps sensors behave virtually the same as multi-sensors systems. Then, the problem of sensor selection and also setting the sensing time for each band is solved based on the convex optimisation framework. Simulation results highlight that in their proposed algorithms a significant energy is saved compared with the baseline methods with equal sensing time in all bands.

Analysis of Information Delivery Dynamics in Cognitive Sensor Networks Using Epidemic Models

To fully empower sensor networks with cognitive Internet of Things (IoT) technology, efficient medium access control protocols that enable the coexistence of cognitive sensor networks with current wireless infrastructure are as essential as the cognitive power in data fusion and processing due to shared wireless spectrum. Cognitive radio (CR) is introduced to increase spectrum efficiency and support such an endeavor, which thereby becomes a promising building block toward facilitating cognitive IoT. In this paper, primary users (PUs) refer to devices in existing wireless infrastructure, and secondary users (SUs) refer to cognitive sensors. For interference control between PUs and SUs, SUs adopt dynamic spectrum access and power adjustment to ensure sufficient operation of PUs, which inevitably leads to increasing latency and poses new challenges on the reliability of IoT communications. To guarantee operations of primary systems while simultaneously optimizing system performance in CR ad hoc networks (CRAHNs), this paper proposes interference-aware flooding schemes exploiting global timeout and vaccine recovery schemes to control the heavy buffer occupancy induced by packet replications. The information delivery dynamics of SUs under the proposed interference-aware recovery-assisted flooding schemes is analyzed via epidemic models and stochastic geometry from a macroscopic view of the entire system. The simulation results show that our model can efficiently capture the complicated data delivery dynamics in CRAHNs in terms of end-to-end transmission reliability and buffer occupancy. This paper sheds new light on analysis of recovery-assisted flooding schemes in CRAHNs and provides performance evaluation of cognitive IoT services built upon CRAHNs.

An Online Learning Method to Maximize Energy Efficiency of Cognitive Sensor Networks

In this letter, an online reinforcement learning method based on particle swarm optimization (PSO) is proposed to maximize transmission energy efficiency of cognitive radio sensor networks by regularizing packet lengths. The idea is to simulate a set of artificial channels to find a hypothetical channel that behaves almost equivalently to the actual channel by minimizing a properly developed loss function. This method eliminates the need for a separate offline channel estimation. The results show an improvement of, respectively, 40% and 20% in the energy efficiency of the proposed method compared with the constant packet length and MLE-based offline channel estimation. Also, the PSO-based optimization method outperforms similar evolutionary methods. This framework is general and can be used to optimize a desired parameter in cognitive radio networks.

Maximizing the lifetime of energy‐constrained cooperative spectrum sensing sensor network

SummaryAdding the cognitive capability to wireless sensor networks allows the sensors to monitor the spectrum and identify the spectrum holes to operate in different frequencies according to the radio environment which result in better spectrum utilization. Spectrum sensing is a main component in any cognitive radio network. In this paper, we propose a new cooperative sensing scheme based on energy detection for cognitive sensor networks which is constrained by the energy limitation of the wireless sensor elements. The proposed scheme minimizes the sensing energy for individual sensor and carefully selects the suitable participant sensors in each cooperative sensing process. This results in maximizing the lifetime of energy‐constrained wireless sensor networks. The proposed scheme also takes into consideration the constraints on the detection accuracy. The simulation results show that the proposed scheme prolonged the lifetime of the cognitive network, makes efficient usage of available spectrum by secondary users, and satisfy the target detection performance.

Joint resource optimization for DF relaying SWIPT based cognitive sensor networks

Spectrum and energy are two important resources for wireless sensor networks (WSNs). Cognitive Radio (CR) can improve the efficiency of spectrum utilization by allowing secondary users (SUs) access to the licensed bands as long as they do not significantly affect the performance of primary users (PUs). Simultaneous wireless information and power transfer (SWIPT) can harvest energy and decode information simultaneously from radio-frequency (RF) signal, which can prolong the life time of energy-constraint networks (e.g., WSNs). In this paper, we propose a joint resource optimization scheme in respect of transmission power and power splitting ratio for decode-and-forward (DF) relaying SWIPT based cognitive sensor networks (CSNs), where relay sensor node (RSN) harvests energy from the signal of source sensor node (SSN) by using power splitting (PS) receiver architecture and utilize the harvested energy to help forward SSNs information. We maximize the throughput of SUs while guaranteeing the interference to PUs caused by SUs is smaller than the interference threshold. Simulation results demonstrate the performance of the joint resource optimization scheme.

Joint Resource Allocation for Wireless Energy Harvesting Enabled Cognitive Sensor Networks

In this paper, we consider a cognitive relaying network, where the cognitive sensor transmitter (CST) is capable of harvesting energy from the radio frequency signals with a power splitting scheme. Specifically, the primary transmitter (PT) broadcasts its signal to primary receiver (PR) and CST in the first transmission slot. After receiving PT’s signal, CST splits the received signal into two parts, one for information decoding and the other for energy harvesting. In the second transmission slot, CST allocates a part of the accessed bandwidth to forward PT’s signal to PR with amplify-and-forward or decode-and-forward relaying protocols by using the harvested energy in the first transmission slot. As a reward, CST can utilize the remained bandwidth to transmit its own signal to the cognitive sensor receiver. The main object is to maximize cognitive network transmission rate by jointly optimizing the power splitting ratio and bandwidth subject to the primary transmission rate constraint. Simulation results are presented to illustrate the performance improvement of both primary and cognitive systems.

Cluster-Based Energy Efficient Collaborative Spectrum Sensing for Cognitive Sensor Network

The high energy efficiency for cognitive sensor network (CSN) has been widely studied for supporting various applications in military and civil fields. By identifying time fraction, local detection threshold, and the number of cognitive sensors, we discuss some design considerations for a cluster-based collaborative spectrum sensing scheme. We formulate the optimization problem to maximize energy efficiency of CSN under a collision constraint and false alarm probability constraint. In order to overcome the problem of finding the optimal detection threshold when k-out-of-M fusion rule is used by the sink node (SN), we design a fictitious cognitive sensor (FCS), which has the same sensing performance as SN. The process for finding the optimal local detection threshold can be converted into the process for searching an optimal received SNR of FCS. Theoretical analysis shows that there exists a unique time fraction to maximize the energy efficiency. Finally, our theoretical analysis is verified through numeric simulations.

Sensor selection for extending lifetime of multi-channel cooperative sensing in cognitive sensor networks

Wireless cognitive sensor networks (WCSNs) are composed of tiny, low-power and low-cost sensors. An important function of these networks is to sense the channels in order to find the spectrum holes. Sensors cannot sense more than one channel simultaneously because they do not have high-speed Analog-to-Digital-Converters (ADCs) which need high-power batteries. Therefore, it is a challenging issue to select cooperative sensors to sense different channels so that all the channels can be sensed simultaneously. On the other hand, lifetime extending in a WCSN is an important issue; it has attracted the most attention recently. In this paper, a tuneable receiver is used for sensors so that they can sense different channels in different sensing periods. To reduce energy consumption, node selection is proposed so that only sufficient nodes perform sensing the channels while the sensing quality constraints are met. A subset of nodes to sense every channel is selected in a way that the residual energy of sensors is balanced and the network lifetime is maximized. The problem can be solved based on the convex optimization framework. Closed form priorities of sensors to sense different channels are determined based on their residual energy, distance and detection probability. Then efficient dynamic sensor selection algorithms are proposed for multi-channel CSS. Simulation results show the advantages of the proposed algorithms in finding the efficient answer. In addition, in terms of network lifetime, success percentage, and energy consumption, the proposed algorithms are compared with the other sensor selection methods.

Joint Resource Optimization for Cognitive Sensor Networks with SWIPT-Enabled Relay.

Energy-constrained wireless networks, such as wireless sensor networks (WSNs), are usually powered by fixed energy supplies (e.g., batteries), which limits the operation time of networks. Simultaneous wireless information and power transfer (SWIPT) is a promising technique to prolong the lifetime of energy-constrained wireless networks. This paper investigates the performance of an underlay cognitive sensor network (CSN) with SWIPT-enabled relay node. In the CSN, the amplify-and-forward (AF) relay sensor node harvests energy from the ambient radio-frequency (RF) signals using power splitting-based relaying (PSR) protocol. Then, it helps forward the signal of source sensor node (SSN) to the destination sensor node (DSN) by using the harvested energy. We study the joint resource optimization including the transmit power and power splitting ratio to maximize CSN’s achievable rate with the constraint that the interference caused by the CSN to the primary users (PUs) is within the permissible threshold. Simulation results show that the performance of our proposed joint resource optimization can be significantly improved.

Joint Resource Allocation of Spectrum Sensing and Energy Harvesting in an Energy-Harvesting-Based Cognitive Sensor Network.

The cognitive sensor (CS) can transmit data to the control center in the same spectrum that is licensed to the primary user (PU) when the absence of the PU is detected by spectrum sensing. However, the battery energy of the CS is limited due to its small size, deployment in atrocious environments and long-term working. In this paper, an energy-harvesting-based CS is described, which senses the PU together with collecting the radio frequency energy to supply data transmission. In order to improve the transmission performance of the CS, we have proposed the joint resource allocation of spectrum sensing and energy harvesting in the cases of a single energy-harvesting-based CS and an energy-harvesting-based cognitive sensor network (CSN), respectively. Based on the proposed frame structure, we have formulated the resource allocation as a class of joint optimization problems, which seek to maximize the transmission rate of the CS by jointly optimizing sensing time, harvesting time and the numbers of sensing nodes and harvesting nodes. Using the half searching method and the alternating direction optimization, we have achieved the sub-optimal solution by converting the joint optimization problem into several convex sub-optimization problems. The simulation results have indicated the predominance of the proposed energy-harvesting-based CS and CSN models.

Energy-Efficiency Maximization for Cooperative Spectrum Sensing in Cognitive Sensor Networks

Spectrum sensing is the prerequisite of opportunistic spectrum access in cognitive sensor networks (CSNs) as its reliability determines the success of transmission. However, spectrum sensing is an energy-consuming operation that needs to be minimized for CSNs due to resource limitations. This paper considers the case where the cognitive sensors cooperatively sense a licensed channel by using the CoMAC-based cooperative spectrum sensing (CSS) scheme to determine the presence of primary users. Energy efficiency (EE), defined as the ratio of the average throughput to the average energy consumption, is a very important performance metric for CSNs. We formulate an EE-maximization problem for CSS in CSNs subject to the constraint on the detection performance. In order to address the non-convex and non-separable nature of the formulated problem, we first find the optimal expression for the detection threshold and then propose an iterative solution algorithm to obtain an efficient pair of sensing time and the length of the modulated symbol sequence. Simulations demonstrate the convergence and optimality of the proposed algorithm. It is also observed in simulations that the combination of the CoMAC-based CSS scheme and the proposed algorithm yields much higher EE than conventional CSS schemes while guaranteeing the same detection performance.

Spectrum-Availability Based Routing for Cognitive Sensor Networks

With the occurrence of Internet of Things (IoT) era, the proliferation of sensors coupled with the increasing usage of wireless spectrums especially the ISM band makes it difficult to deploy real-life IoT. Currently, the cognitive radio technology enables sensors transmit data packets over the licensed spectrum bands as well as the free ISM bands. The dynamic spectrum access technology enables secondary users (SUs) access wireless channel bands that are originally licensed to primary users. Due to the high dynamic of spectrum availability, it is challenging to design an efficient routing approach for SUs in cognitive sensor networks. We estimate the spectrum availability and spectrum quality from the view of both the global statistical spectrum usage and the local instant spectrum status, and then introduce novel routing metrics to consider the estimation. In our novel routing metrics, one retransmission is allowed to restrict the number of rerouting and then increase the routing performance. Then, the related two routing algorithms according to the proposed routing metrics are designed. Finally, our routing algorithms in extensive simulations are implemented to evaluate the routing performance, and we find that the proposed algorithms achieve a significant performance improvement compared with the reference algorithm.

Multibit-Quantization-Based Collaborative Spectrum Sensing Scheme for Cognitive Sensor Networks

In order to overcome the bandwidth limitations of common control channels, we introduce a multibit quantization scheme to preprocess raw measurements in a collaborative spectrum sensing (CoSS) scheme. After quantifying the raw measurement, the cognitive sensors (CSs) send the multibit sensing information to a sink node (SN), which makes a final decision by using a soft decision fusion rule. In order to better understand the effects of quantization parameters on collaborative spectrum sensing in a cognitive sensor network (CSN), we discuss the following two problems: the average error probability minimum problem (AEPMP) and throughput maximum problem. By identifying the number of quantization bits, number of CSs, and global decision threshold, we can discuss some design considerations for the multibit-quantization-based CoSS scheme. A closed-form expression for the optimal global decision threshold is derived in the process of analyzing the AEPMP. Furthermore, an iterative algorithm with low complexity is proposed to find the optimal parameters for maximizing the throughput of a CSN under a target detection probability constraint. Finally, we investigate the impact of optimized parameters on the system performance of a CSN. Our theoretical analysis is verified through numerical simulations.

A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks.

Cognitive radio sensor networks are one of the kinds of application where cognitive techniques can be adopted and have many potential applications, challenges and future research trends. According to the research surveys, dynamic spectrum access is an important and necessary technology for future cognitive sensor networks. Traditional methods of dynamic spectrum access are based on spectrum holes and they have some drawbacks, such as low accessibility and high interruptibility, which negatively affect the transmission performance of the sensor networks. To address this problem, in this paper a new initialization mechanism is proposed to establish a communication link and set up a sensor network without adopting spectrum holes to convey control information. Specifically, firstly a transmission channel model for analyzing the maximum accessible capacity for three different polices in a fading environment is discussed. Secondly, a hybrid spectrum access algorithm based on a reinforcement learning model is proposed for the power allocation problem of both the transmission channel and the control channel. Finally, extensive simulations have been conducted and simulation results show that this new algorithm provides a significant improvement in terms of the tradeoff between the control channel reliability and the efficiency of the transmission channel.

A Novel Wireless Power Transfer-Based Weighed Clustering Cooperative Spectrum Sensing Method for Cognitive Sensor Networks.

In a cognitive sensor network (CSN), the wastage of sensing time and energy is a challenge to cooperative spectrum sensing, when the number of cooperative cognitive nodes (CNs) becomes very large. In this paper, a novel wireless power transfer (WPT)-based weighed clustering cooperative spectrum sensing model is proposed, which divides all the CNs into several clusters, and then selects the most favorable CNs as the cluster heads and allows the common CNs to transfer the received radio frequency (RF) energy of the primary node (PN) to the cluster heads, in order to supply the electrical energy needed for sensing and cooperation. A joint resource optimization is formulated to maximize the spectrum access probability of the CSN, through jointly allocating sensing time and clustering number. According to the resource optimization results, a clustering algorithm is proposed. The simulation results have shown that compared to the traditional model, the cluster heads of the proposed model can achieve more transmission power and there exists optimal sensing time and clustering number to maximize the spectrum access probability.

Convergence Research Directions in Cognitive Sensor Networks for Elderly Housing Design

As part of resolving the issue related to super-aging society, various information systems for the elderly care have been developed utilizing cognitive sensor networks. However, many sensor network-based systems cognizing radios, resources, environments, and so forth are not practically feasible and useful for the elderly themselves. Therefore, this paper tries to propose novel research directions for designing smart residential environment for the elderly in terms of converging information technologies including cognitive sensor networks and architectural design. In this paper, we clarify the definition of smart home from the literatures and briefly surveys cognitive sensor network-based systems for smart elderly housing. After that, this paper proposes research directions for cognitive wireless sensor networks not only for the elderly smart home services, but also for cooperating with architecture technologies. The proposed directions are to utilize cognitive radio technologies, to classify sensor network technologies according to the types of the elderly, to expand sensor networks services for the elderly housing to the wider area such as blocking unnecessary energy loss, to embed sensor network modules into BIM system, and to research sensor networks fit to modular construction.