DYNAMIC SMART GRID COMMUNICATION PARAMETERS BASED COGNITIVE RADIO NETWORK

The demand for more spectrums in a smart grid communication network is a significant challenge in originally scarce spectrum resources. Cognitive radio (CR) is a powerful technique for solving the spectrum scarcity problem by adapting the transmission parameters according to predefined objectives in an active wireless communication network. This paper presents a cognitive radio decision engine that dynamically selects optimal radio transmission parameters for wireless home area networks (HAN) of smart grid applications via the multi-objective differential evolution (MODE) optimization method. The proposed system helps to drive optimal communication parameters to realize power saving, maximum throughput and minimum bit error rate communication modes. A differential evolution algorithm is used to select the optimal transmission parameters for given communication modes based on a fitness function that combines multiple objectives based on appropriate weights. Simulation results highlight the superiority of the proposed system in terms of accuracy and convergence as compared with other evolution algorithms (genetic optimization, particle swarm optimization, and ant colony optimization) for different communication modes (power saving mode, high throughput mode, emergency communication mode, and balanced mode)

Secure and reliable surveillance over cognitive radio sensor networks in smart grid

In view of recent attacks on smart grid surveillance is of vital importance to enforce surveillance based disaster recovery management operations to ensure seamless energy generation and distribution. The reliability of disaster recovery management depends on availability and privacy preservation of surveillance data. In this paper we propose a reliable privacy preserving smart grid surveillance architecture over cognitive radio sensor networks. Cognitive radio sensor networks are capable of facilitating reliable communications through opportunistic spectrum sensing capabilities as opposed to fixed radio terminal networks based surveillance architectures. The main privacy preserving feature is a novel energy aware physical unclonable function (PUF) based cryptographic key generation method. The proposed solution determines the encryption key length depending on the remaining energy reserve to facilitate data transmission over an expected period of time with minimum channel interferences. Based on the experimental evaluation, the PUF pattern matching based key generation is viable for 32 bits pattern length over a cognitive radio sensor with optimum power utilization and with a probability of reproducibility of a bit pattern (i-p)=0. We have also performed experiments to validate the reliability model using real-world data. In conclusion, our proposed cognitive radio sensor based solution provide more pragmatic insights in reliability assurances for surveillance in smart grid

Reliable and secure low energy sensed spectrum communication for time critical cloud computing applications

Reliability and security of data transmission and access are of paramount importance to enhance the dependability of time critical remote monitoring systems (e.g. tele-monitoring patients, surveillance of smart grid components). Potential failures for data transmissions include wireless channel unavailability and delays due to the interruptions. Reliable data transmission demands seamless channel availability with minimum delays in spite of interruptions (e.g. fading, denial-of-service attacks). Secure data transmissions require sensed data to be transmitted over unreliable wireless channels with sucient security using suitable encryption techniques. The transmitted data are stored in secure cloud repositories. Potential failures for data access include unsuccessful user authentications due to mis-management of digital identities and insucient permissions to authorize situation specic data access requests. Reliable and secure data access requires robust user authentication and context-dependent authorization to fulll situation specic data utility needs in cloud repositories. The work herein seeks to enhance the dependability of time critical remote monitoring applications, by reducing these failure conditions which may degrade the reliability and security of data transmission or access. As a result of an extensive literature survey, in order to achieve the above said security and reliability, the following areas have been selected for further investigations. The enhancement of opportunistic transmissions in cognitive radio networks to provide greater channel availability as opposed to xed spectrum allocations in conventional wireless networks. Delay sensitive channel access methods to ensure seamless connectivity in spite of multiple interruptions in cognitive radio networks. Energy ecient encryption and route selection mechanisms to enhance both secure and reliable data transmissions. Trustworthy digital identity management in cloud platforms which can facilitate ecient user authentication to ensure reliable access to the sensed remote monitoring data. Context-aware authorizations to reliably handle the exible situation specic data access requests. Main contributions of this thesis include a novel trust metric to select non-malicious cooperative spectrum sensing users to reliably detect vacant channels, a reliable delaysensitive cognitive radio spectrum hand-o management method for seamless connectivity and an energy-aware physical unclonable function based encryption key size selection method for secure data transmission. Furthermore, a trust based identity provider selection method for user authentications and a reliable context-aware situation specic authorization method are developed for more reliable and secure date access in cloud repositories. In conclusion, these contributions can holistically contribute to mitigate the above mentioned failure conditions to achieve the intended dependability of the timecritical remote monitoring applications