Cognitive Radio Based Smart Grid Research Articles

Fuzzy-based reliable and secure cooperative spectrum sensing for the smart grid

Cognitive radio is a promising technology to solve the spectrum scarcity problem caused by inefficient utilization of radio spectrum bands. It allows secondary users to opportunistically access the underutilized spectrum bands assigned to licensed primary users. The local individual spectrum detection is inefficient, and cooperative spectrum sensing is employed to enhance spectrum detection accuracy. However, cooperative spectrum sensing opens up opportunities for new types of security attacks related to the cognitive cycle. One of these attacks is the spectrum sensing data falsification attack, where malicious secondary users send falsified sensing reports about spectrum availability to mislead the fusion center. This internal attack cannot be prevented using traditional cryptography mechanisms. To the best of our knowledge, none of the previous work has considered both unreliable communication environments and the spectrum sensing data falsification attack for cognitive radio based smart grid applications. This paper proposes a fuzzy inference system based on four conflicting descriptors. An attack model is formulated to determine the probability of detection for both honest and malicious secondary users. It considers four independent malicious secondary users’ attacking strategies of always yes, always no, random, and opposite attacks. The performance of the proposed fuzzy fusion system is simulated and compared with the conventional fusion rules of AND, OR, Majority, and the reliable fuzzy fusion that does not consider the secondary user’s sensing reputation. The results indicate that incorporating sensing reputation in the fusion center has enhanced the accuracy of spectrum detection and have prevented malicious secondary users from participating in the spectrum detection fusion

An Energy Efficient Network Architecture and Spectrum Sharing Technique for Cognitive Radio Based Smart Grid Communications

With the introduction of an automated electric power system known as smart grid, issues relating to power quality, shortages and inefficiency have been solved. Two vital foundations necessary for the sustainability and the stability of Smart Grids (SG) are a resourceful and reliable communication between users in a smart grid network and an energy efficient Smart Grid Communication Network (SGCN) architecture. On the other hand, Cognitive radios (CR) are equipped with the capability of exploiting unused licensed spectrum resources for effective spectrum utilization. The objective of this paper is to integrate cognitive radio into smart grid communication network in order to make the network a lot smarter and provide spectrum sharing solutions in SGCN. In this paper, an energy efficient SGCN architecture that is layered into the Home Area Network (HAN), Neighbourhood Area Network (HAN) and the Wide Area Network (WAN) has been proposed. Furthermore, in order to achieve a fair spectrum sharing amongst users in the network, CR users in the HANs estimate the need of its neighbouring users to transmit data. Then it uses this information to predict the vacant channels that will be utilized by any node in the network using a Markov chain process. In the NANs, a model is also proposed where every user sensor node access the spectrum based on the buffer occupancy estimates of its neighboring nodes. Simulation results show that there is a higher throughput in the network and also an increase in the quantity of data packets successfully transmitted using the proposed model.

SCAN-CogRSG: Secure Channel Allocation by Dynamic Cluster Switching for Cognitive Radio Enabled Smart Grid Communications

ABSTRACTSmart grid (SG) communication requires multilevel communication among multiple layers. To handle this paramount spectrum requirement, cognitive radio network (CRN) is one of the promising solutions. However, the integration of CRN for SG communications introduces new challenges in spectrum allocation. Still, achieving high spectrum utilization is restricted by security vulnerability and poor algorithm design. This paper resolves all these issues to achieve the objective of spectrum utilization efficiency. A novel dynamic cluster switching approach is designed for secure channel allocation for cognitive radio-based smart grid (SCAN-CogRSG) communications. Strong authentication is enabled for secondary users (SUs) by Binate physically unclonable function (Bi-PUF) authentication mechanism. The key idea behind Bi-PUF mechanism is to prevent spectrum resources from unauthorized access. The formed neighborhood area network (NAN) clusters are dynamically switched to distribute uniform spectrum availability. For dynamic switching, a novel Topology aware fuzzy-reinforcement learning (TA-FuReil) algorithm is presented. For an effectual channel allocation, feature-based K-means (FK-means) algorithm and tri-objective cuttlefish optimization (TriO-CFO) algorithm are proposed. The formulated objectives are interference minimization, power minimization, and data rate maximization. In the case of priority situations, the one-to-many matching-based TriO-CFO (OMTriO-CFO) algorithm is utilized. The proposed SCAN-CogSG is modeled in NS-3 and validated for performance evaluation. The observed results show betterment in throughput, retransmission probability, latency, and authentication time.

Joint power and channel allocation scheme for IEEE 802.11af based smart grid communication network

To reap the full potentials of Smart grid (SG), a reliable, resourceful, efficient and cost-effective communication network is inevitable. The crucial need to transmit a significant amount of smart grid application data in an efficient spectral manner makes cognitive radio (CR) technology most suitable for SG environment. Identically, TV white space (TVWS) is the most expectant candidate for CR based smart grid communication network (CRSGCN). In this paper, we are investigating the problem of joint power and channel allocation (JPCA), which is among the most important and widely explored area in cognitive radio domain. We first model a typical scenario in a neighborhood area network (NAN) communication using IEEE 802.11af via open loop regulatory framework for TVWS. A mathematical model is formulated to jointly address power and channel allocation considering practical constraints for two real-world scenarios of fairness-based and priority-based allocation. Next, an efficient power allocation (PA) scheme is presented, meeting quality-of-service (QoS) requirements, followed by channel allocation (CA) scheme based on cuckoo search algorithm (CSA). The performance of the proposed solutions is analyzed using exhaustive simulations to optimize power consumption, fairness and user rewards. The presented results in the form of graph and numerical comparisons indicate the effectiveness of our allocation algorithm to achieve the desired objectives.

QoS-based dynamic channel selection algorithm for cognitive radio based smart grid communication network

Smart Grid (SG) operation requires multi-way communication among power generation, transmission, distribution, and consumer facilities. The complete SG architecture involves multiple layers of networks stretching from energy control center to consumer facilities and multiple applications with diverse Quality-of-Service (QoS) requirements. To meet these requirements, Cognitive Radio (CR) which offers efficient spectrum utilization and sharing, has been considered as a favorable solution. Due to diverse requirements of latency, data rate and reliability, it is a great challenge to support various SG applications under spectrum sensing uncertainties, Signal-to-Noise Ratio (SNR) variations and availability of desired channel bandwidth. In this paper, we propose a dynamic channel selection algorithm based on Fuzzy Inference System (FIS) capable of selecting the most appropriate available idle channel with desired bandwidth, minimum required SNR and probability of miss detection Pmd. The proposed algorithm utilizes various modes of IEEE Standard 802.22, Wireless Regional Area Network (WRAN), at the physical layer. In case of unavailability of idle channels with required SNR and/or Pmd, we propose a novel approach to select the channel with closest possible parameters aiming to reduce the re-transmission probability and thus achieve the desired throughput, latency and reliability requirements. The performance is compared with existing algorithms to demonstrate the effectiveness and superiority of the proposed algorithm.

Data broadcasting strategies for cognitive radio based AMI networks

Communication systems play an important role in smart grid (SG). Advanced Metering Infrastructure (AMI) is hybrid architecture in smart grid comprising of smart meters and gateways. With AMI network, consumers can achieve demand side management, real time pricing, load scheduling, and upgrading of software updates through gateways to a number of smart meters without the need to visit every place. Information exchange can occur in the form of meter readings from meters to utility, from meters to AMI and from AMI to utility. Broadcasting is one possible solution in these scenarios for information exchange and cognitive radio networks (CRNs) are one possible solution for communication that provides large data transmission utilizing available spectrum resources from licensed and unlicensed bands. Broadcasting is challenging in CRNs due to licensed/primary user (PU) activity, availability of multiple channels, and channel diversity. Therefore, PU protection must be provided at all cost and data transmission should be quick and reliable for a smooth operation. In this paper, we propose two novel reliable schemes, i.e., probability-based broadcasting scheme for CR-based smart grid and area-based broadcasting scheme for CR-based smart grid specifically designed for AMI networks. Our schemes provide reliable data to smart meters with sufficient PU protection. We have compared our schemes with two flooding techniques, i.e., key policy attribute based encryption (Fadlullah et al. in IEEE Commun Mag 50:150–156, 2012) and key management scheme (Liu et al. in IEEE Trans Ind Electron 60(10):4746–4756, 2013). Simulation in NS-2 validates our schemes as a viable solution in cognitive radio-based smart grid systems.

Clustering-Based Channel Allocation Scheme for Neighborhood Area Network in a Cognitive Radio Based Smart Grid Communication

Cognitive radio ((CR))-based standards are apt to cater the diverse communication requirements for the humongous volume of data generated by Smart Grid applications. Although CR technology is one of the most promising techniques to increase the spectral efficiency of any wireless communication network, efficient spectrum allocation among secondary user (SUs) in different application scenarios remains an intriguing area for researchers. In this paper, we propose a clustering-based approach to deal with channel allocation ((CA)) problem among SUs considering practical constraints in SG environment. We first present a simple CR based SG communication network architecture by dividing the service area into groups of SUs called neighborhood area network clusters, depending upon the distance of Smart Meters from data concentrator unit. Then we formulate a multiple constraint NP-hard CA problem using interference avoidance strategy by considering two practical scenarios: fairness-based allocation and priority-based allocation. We then propose our CA algorithm based on cat swarm optimization to eliminate the severe integer constraints of the problem under consideration. We simulate the two above-mentioned practical scenarios to measure a number of allocations per SU, Jain’s Fairness Index and per user average rewards. Moreover, conventional average user rewards are compared for a varying number of channels, SUs, and rounds to evaluate the performance of proposed CA scheme. The results indicate that our proposed CA algorithm works well, for both fairness-based and priority-based cases.

Cognitive Radio-Based Smart Grid Traffic Scheduling With Binary Exponential Backoff

This paper develops the traffic models of smart grid electronic data ( ${E}$ data) and multimedia video over cognitive radio (CR). Unlike the traditional “Poisson” arrival model, each arrival monitoring stream follows fixed time triggered Gaussian distribution which approximates to the reality, and the video data is classified as key frame data with higher priority than nonkey frame data to reduce communication burden. To enhance the delivery probability of ${E}$ data and multimedia data, we adopt a buffer mechanism to store the “sending fail” data and try to resend them together with new coming data by using the new data’s sending opportunity. To avoid buffer overflow, the unsent data should be compressed and some should be removed, and the new coming data rate should be reduced to alleviate the congestion of the CR communication network. In this paper, we propose a new binary exponential backoff (NBEB) algorithm to “compress” the unsent data which can keep key information but recover the electronic tendency as much as possible. With NBEB, the new coming data can be temporally selected and thrown into the buffer and more new data can be put in the buffer. The algorithm can reduce the arrival traffic rate exponentially related with the sending failure times. The results show that NBEB can significantly decrease the blocking/dropping probability, increase the communication success probability, and improve the communication performance.

Dynamic resource allocation for cognitive radio based smart grid communication networks

Dynamic resource allocation for cognitive radio based smart grid communication networks

Performance evaluation of cognitive radio in advanced metering infrastructure communication

Smart grid is an intelligent electricity grid system. A reliable two-way communication system is required to transmit both critical and non-critical smart grid data. However, it is difficult to locate a huge chunk of dedicated spectrum for smart grid communications. Hence, cognitive radio based communication is applied. Cognitive radio allows smart grid users to access licensed spectrums opportunistically with the constraint of not causing harmful interference to licensed users. In this paper, a cognitive radio based smart grid communication framework is proposed. Smart grid framework consists of Home Area Network (HAN) and Advanced Metering Infrastructure (AMI), while AMI is made up of Neighborhood Area Network (NAN) and Wide Area Network (WAN). In this paper, the authors only report the findings for AMI communication. AMI is smart grid domain that comprises smart meters, data aggregator unit, and billing center. Meter data are collected by smart meters and transmitted to data aggregator unit by using cognitive 802.11 technique; data aggregator unit then relays the data to billing center using cognitive WiMAX and TV white space. The performance of cognitive radio in AMI communication is investigated using Network Simulator 2. Simulation results show that cognitive radio improves the latency and throughput performances of AMI. Besides, cognitive radio also improves spectrum utilization efficiency of WiMAX band from 5.92% to 9.24% and duty cycle of TV band from 6.6% to 10.77%.

Trust based reliable transmissions strategies for smart home energy consumption management in cognitive radio based smart grid

Reliability of smart home energy management (SHEM) is enhanced due to the use of opportunistic cognitive radio based communications for reliable transmission of peak period energy utility data. Cognitive radio networks are apt communication solutions capable of offering reliable opportunistic data transmissions through spectrum reuse. Reliable opportunistic data transmissions ensure strategic decision making and to execute critical control operations for sustainable energy utilizations. Accurate spectrum sensing and efficient spectrum sharing are vital to aspects of reliable peak energy consumption data transmission. Cooperative spectrum sensing has been proposed as a more reliable method for gaining accurate spectrum availability detection. Trustworthiness of the local decisions of cooperating users is vital for the accuracy of the final spectrum availability decision. However, measures which can assess the trustworthiness of cooperating users are given less attention. In this paper, we describe a novel multi-attribute trust based framework to facilitate reliable spectrum sensing and priority based spectrum access allocation to enhance delay sensitive data transmissions. We have evaluated our solutions using extensive simulation experiments. Furthermore, we comparatively analyzed the reliability of the proposed user selection method for known spectrum sensing data falsification (SSDF) attack behaviors to accurately identify the non-malicious users. As evidenced by the results proposed novel multi-attribute trust based transmission strategies offer greater reliability in ensuring timely availability of peak period energy utility data for SHEM.

Optimisation of sensing time and transmission time in cognitive radio-based smart grid networks

ABSTRACTCognitive radio (CR)-based smart grid (SG) networks have been widely recognised as emerging communication paradigms in power grids. However, a sufficient spectrum resource and reliability are two major challenges for real-time applications in CR-based SG networks. In this article, we study the traffic data collection problem. Based on the two-stage power pricing model, the power price is associated with the efficient received traffic data in a metre data management system (MDMS). In order to minimise the system power price, a wideband hybrid access strategy is proposed and analysed, to share the spectrum between the SG nodes and CR networks. The sensing time and transmission time are jointly optimised, while both the interference to primary users and the spectrum opportunity loss of secondary users are considered. Two algorithms are proposed to solve the joint optimisation problem. Simulation results show that the proposed joint optimisation algorithms outperform the fixed parameters (sensing time and transmission time) algorithms, and the power cost is reduced efficiently.

Spectrum survey for reliable communications of cognitive radio based smart grid network

The smart grid (SG) system is expected to involve huge amount of data with different levels of priorities to different applications or users. The traditional grid which tend to deploy propriety networks with limited coverage and bandwidth, is not sufficient to support large scale SG network. Cognitive radio (CR) is a promising communication platform for SG network by utilizing potentially all available spectrum resources, subject to interference constraint. In order to develop a reliable communication framework for CR based SG network, thorough investigations on the current radio spectrum are required. This paper presents the spectrum utilization in Malaysia, specifically in the UHF/VHF bands, cellular (GSM 900, GSM 1800 and 3G), WiMAX, ISM and LTE band. The goal is to determine the potential spectrum that can be exploit by the CR users in the SG network. Measurements was conducted for 24 hours to quantify the average spectrum usage and the amount of available bandwidth. The findings in this paper are important to provide insight of actual spectrum utilization prior to developing a reliable communication platform for CR based SG network.

Cognitive Radio Application for Smart Grid

The communication infrastructure between energy generation, transmission, distribution and utilization will require multi-way communications, interoperability between the advanced and existing system, and end-to-end reliable and secure communications with low-latencies. Revolutionary communication architecture is required for effective operation and control of smart grid, and cognitive radio based communication architecture can provide a unique solution. By leveraging cognitive radio technology, the suggested communications infrastructure promises to utilize potentially all available spectrum resources efficiently in the smart grid. The radio agility allows the smart grid devices to sense the unused spectrum opportunities in the surroundings and utilize them subject to interference constraints. Dynamic spectrum access enabled by cognitive radio technology can be adopted by the smart grid to exploit the underutilized frequencies in an opportunistic manner. As a result, the flexibility, efficiency, and reliability can be significantly enhanced in a cognitive radio based smart grid network.