Second-generation blockchains like Ethereum allow users to execute smart contracts. Usually, blockchains charge gas fees for deploying and invocating smart contracts. These costs can be significant and even render some use cases non-economical. Therefore, optimizing smart contracts regarding gas costs is a significant achievement, and several approaches have already been presented. However, existing methods of gas cost minimization are often based on rule-based code optimization techniques, which can perform only a subset of possible optimizations and cannot detect outlying and uncommon code patterns.Therefore, this paper discusses using machine learning methods to detect a more cost-efficient version of a Solidity smart contract. This approach trains a Siamese neural network to detect the similarity between a contract and its optimized version, providing the basis for informing the user about existing optimizing patterns. We evaluate our approach using a repository of 30,432 Solidity smart contracts.

This work considers a cognitive radio (CR) network consisting of a set of CR transmit-receive node pairs, one fusion center (FC), multiple primary user emulation attack (PUEA) nodes, an eavesdropper ($E_{av}$) node and a set of friendly jammers used for protection of CR data transmission from eavesdropping. At the initial time slot of the frame, simultaneous energy harvesting (EH) and spectrum sensing are done through power splitting (PS) mode. CR transmit nodes then amplify and forward the sensed samples of both the primary user (PU) and the PUEA to the FC for cooperative spectrum sensing (CSS). Based on the CSS decision, CR transmit nodes either perform EH over the entire duration or make an opportunistic data transmission in time division mode. The closed form expressions of the optimal sensing duration, power allocation factor and transmit power for each secondary user (SU) are found. The sum secondary throughput of the network is maximized under the constraints of meeting the sensing reliability of the PU, individual energy causality for each SU and the best selected jammer, interference at the PU receiver, individual secondary and secrecy outage probability. Simulation results show a performance gain on the maximum value of the sum secondary throughput by$\sim$17.56 and$\sim$49.69 percent over the existing works.

Recent progress in cognitive radio networks (CRNs) promises to meet device‐to‐device communication requirements for spectrum utilisation and power control to support billions of machines/devices to be connected worldwide. The architecture of the CRN must maintain a high data rate (throughput) at low power consumption, which requires both spectrum efficient and energy efficient system design. To this aim, the proposed work adopts a CRN model, which operates in an interweave mode that allows spectrum sensing followed by opportunistic secondary user (SU) data transmission over the unused bandwidth of the primary user (PU) in a non‐overlapping frame structure. Closed form expressions of the optimal spectrum sensing duration, bandwidth, and power allocation of each secondary node are obtained to maximise the sum throughput of the overall CRN while maintaining the constraints of sensing reliability of PU, individual SU transmission outage probability, permissible interference, and residual bandwidth. Numerical results highlight that the proposed scheme maximises the sum network throughput by and over the other existing techniques.

Cooperation among the sensing nodes in cognitive radio (CR) networks improves spectrum sensing (SS) reliability. However, determining the efficient set of secondary users (SUs) to be involved in cooperative SS (CSS) becomes challenging in vehicular networks (VNs) due to the mobility of primary user (PU) and/or SU nodes. This work explores an energy detection‐based CSS scheme in CR enabled VNs (CR‐VNs) considering the uniform velocity motion of PU and SU nodes. A distance‐dependent distribution function is developed first to find the probability of a SU resides in a specific coverage of the PU transmission zone. This distribution function is then used in deriving the expression of the probability of detection and probability of false alarm using majority rule at the fusion centre. The efficacy of the mathematical analysis is studied through a large set of simulation results that highlight the enhanced SS performance in terms of the probability of detection by , and at uniform velocity over the existing works in CR‐VN.

This work considers an energy harvesting (EH) based two‐hop relay assisted cognitive radio system that consists of a secondary transmitter, a relay node and a cooperative jammer operated in an underlay mode with a primary transmitter–receiver pair. An optimisation problem is formulated to minimise the secrecy outage probability under the constraints of simultaneously meeting the energy causality of the secondary transmit nodes, interference to the primary receiver and secondary outage probability. An analytical expression of the secrecy outage probability is developed followed by deriving the closed‐form expressions to determine the optimal transmit power of the source, the relay node and the fraction of the time slot for EH. Simulation results show that the minimum value of the secrecy outage probability is and less over the existing works.

Recent advancement in wireless communications demands high data rate transmission at low power consumption. To this aim, the present work proposes an energy harvesting based cognitive radio (CR) system operating in time slotted mode. Energy harvesting and spectrum sensing (SS) are done simultaneously by the CR user through power splitting mode. On the other hand, during the transmission time slot, the CR user either harvests energy or transmits its own data based on SS decision result. An optimization problem is developed to maximize the residual energy under the constraints of sensing reliability of the PU and spectrum efficiency (SE) target rate of the CR. The concavity of the objective function is shown which ensures a global solution to the problem. Closed form expressions for the sensing time and CR transmit power are found and the results are validated by simulations.