$\mathsf{REboost}$ : Improving Throughput in Wireless Networks Using Redundancy Elimination

A Comprehensive Review of Recent Advances in Smart Grids: A Sustainable Future with Renewable Energy Resources

“…The acquired data may even suffer from redundancy and repetition, which require more storage capacity and add more cost. Hence, the redundant data need to be identified and eliminated [253][254][255]. The authors of Reference [256] presumed that the data collected from smart grids facilities are equivalent to the data generated from media platforms.…”

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

“…The work [270] adopted an energy-efficient architecture for Industrial Internet of Things (IIoT) with a sense entities domain, RESTful service hosted networks, a cloud server, and user applications to balance the traffic load and support a longer lifetime of the whole system. The paper [271] mainly studied two important issues: 1) performing cyber-physical systems (CPS) communications over cellular [196] Secure data placement on cloud i) ensures users' data protection on clouds; ii) does not require complex signatures and keys; iii) provides data privacy at users' side i) data might need to be encrypted/decrypted before transferring to clouds; ii) can have a long data retrieval time; iii) not suitable for making real-time network decisions; iv) not very robust as attackers can guess data's locations; v) service providers can have access to users' data; vi) complicated data management and access [195] [197] [199] [202] [223] [215] Cryptographic Solutions i) ensures information protection, data privacy and integrity; ii) works in conjunction with other security solutions for more robust security i) costly in terms of computational overhead; ii) can have a large power consumption; iii) increases latency; iv) inefficient for many CPS devices with limited computational capabilities; v) works only for fixed malicious behaviors Data Access Permission Restrictions [50] [204] [224] [225] Advanced security controls i) selective access control; ii) can be used in conjunction with cryptography for a more robust security; iii) good for real-time data streams; iv) provides additional layer of protection i) difficult to access for legitimate users; ii) needs to be combined with other solutions; iii) not sufficient by itself to protect users' data; iv) might increase latency from accessing the data [24], [56], [128], [145], [233] -Minimize the number of relay transmissions [233] -Data reduction techniques(aggregation, compression, network coding) [57], [58], [59], [60], [61]- [63], [184], [234], [235], [236], [237], [238] Power Management -Smart Grids -Gr...…”

“…This coding technique was shown to reduce the volume of communications by 62%, along with other advantages such as improving the utilization of system resources from disk utilization, queue size, and the number of bits transmitted during shuffle phase of Hadoop (200%) [234]. Other approaches to reduce the burden on data centers from the information exchange include traffic flow prediction to reduce network transfers [184], [235], and redundancy elimination scheme to remove redundant information data exchange, among others [236]- [238].…”

“…Processing [30] [258], [259], [260], [261] [57], [58], [59], [60], [61]- [63], [184], [234], [235], [236], [237], [238] [99], [100] [108], [257] Energy-efficient orchestrators Checkpointing aided parallel execution (CAPE) Reducing the amount of exchanged data Cloudlets Virtual machine placement for energy efficiency in [300] with four main elements: 1) data resource where smart grids data is generated by different devices, companies and systems with complex correlated relationship among them, 2) data storage where only meaningful information is stored and processed with in stream mode or batch mode, 3) data analysis using demand-side management or load forecasting for purposes of categorizing the total demand response in a specific region, and 4) data transmission which bridges the previous elements together. Using this architecture with energy scheduling scheme based on game theory and a generic algorithm-based optimization to obtain the optimal deployment of energy storage devices for each customer, the results show a significant reduction in total costs of consumers over long term.…”

“…For security threats that occur in short time, a security situational awareness technique was proposed in [299], which uses fuzzy cluster analysis based on game theory and reinforcement learning to enhance security in smart grids. A big data computing architecture for smart grids was proposed [239], [240], [241] [262], [263], [264], [242] Minimizing number of relay transmissions Removing redundant transmission links Data compression techniques Computing [29], [246], [247], [248] [56], [80], [249], [250], [251], [252], [253], [254], [255], [256] Dynamic voltage and frequency scaling Traffic engineering Processing [30] [258], [259], [260], [261] [57], [58], [59], [60], [61]- [63], [184], [234], [235], [236], [237], [238] [99], [100] [108], [257] Energy-efficient orchestrators Checkpointing aided parallel execution (CAPE) Reducing the amount of exchanged data Cloudlets Virtual machine placement for energy efficiency in [300] with four main elements: 1) data resource where smart grids data is generated by different devices, companies and systems with complex correlated relationship among them, 2) data storage where only meaningful information is stored and processed with in stream mode or batch mode, 3) data analysis using demand-side management or load forecasting for purposes of categorizing the total dema...…”