Smart contracts, as an added functionality to blockchain, have received increased attention recently. They are executable programs whose instance and state are stored in blockchain. Hence, smart contracts and blockchain enable a trustable, trackable, and irreversible protocol without the need for trusted third parties which generally constitute a single point of failure. If a user creates and distributes a smart contract, others will be able to interact with it while the underlying blockchain ensures a trustable execution. In this paper, we aim to introduce state-of-the-art technologies of the smart contract protocol. We firstly introduce the history of blockchain and smart contracts followed by their step-by-step operations. Then, we introduce the survey results which are classified into four categories based on their purposes: cryptography, access management, social application, and smart contract structure. By presenting the most recent knowledge, this paper will contribute to the advances and proliferation of smart contracts.

Traffic classification is widely used in various network functions such as software-defined networking and network intrusion detection systems. Many traffic classification methods have been proposed for classifying encrypted traffic by utilizing a deep learning model without inspecting the packet payload. However, they have an important challenge in that the mechanism of deep learning is inexplicable. A malfunction of the deep learning model may occur if the training dataset includes malicious or erroneous data. Explainable artificial intelligence (XAI) can give some insight for improving the deep learning model by explaining the cause of the malfunction. In this paper, we propose a method for explaining the working mechanism of deep-learning-based traffic classification as a method of XAI based on a genetic algorithm. We describe the mechanism of the deep-learning-based traffic classifier by quantifying the importance of each feature. In addition, we leverage the genetic algorithm to generate a feature selection mask that selects important features in the entire feature set. To demonstrate the proposed explanation method, we implemented a deep-learning-based traffic classifier with an accuracy of approximately 97.24%. In addition, we present the importance of each feature derived from the proposed explanation method by defining the dominance rate.

Device-to-Device (D2D) communication technology has become a key factor in wireless sensor networks to form autonomous communication links among sensor nodes. Many research results for D2D have been presented to resolve different technical issues of D2D. Nevertheless, the previous works have not resolved the shortage of data rate and limited coverage of wireless sensor networks. Due to bandwidth shortages and limited communication coverage, 3rd Generation Partnership Project (3GPP) has introduced a new Device-to-Device (D2D) communication technique underlying cellular networks, which can improve spectral efficiencies by enabling the direct communication of devices in proximity without passing through enhanced-NodeB (eNB). However, to enable D2D communication in a cellular network presents a challenge with regard to radio resource management since D2D links reuse the uplink radio resources of cellular users and it can cause interference to the receiving channels of D2D user equipment (DUE). In this paper, a hybrid mechanism is proposed that uses Fractional Frequency Reuse (FFR) and Almost Blank Sub-frame (ABS) schemes to handle inter-cell interference caused by cellular user equipments (CUEs) to D2D receivers (DUE-Rxs), reusing the same resources at the cell edge area. In our case, DUE-Rxs are considered as victim nodes and CUEs as aggressor nodes, since our primary target is to minimize inter-cell interference in order to increase the signal to interference and noise ratio (SINR) of the target DUE-Rx at the cell edge area. The numerical results show that the interference level of the target D2D receiver (DUE-Rx) decreases significantly compared to the conventional FFR at the cell edge. In addition, the system throughput of the proposed scheme can be increased up to 60% compared to the conventional FFR.

While NOMA-V2V (non-orthogonal multiple accesscan-vehicle-to-vehicle) effectively achieve massive connectivity requirements in 5G network systems, minimizing communication latency is a very crucial challenge. To address the latency problem, we propose a channel allocation method called hyper-fraction, which divides the road into many zones and allocates a channel to each zone. Then, a vehicle located within the corresponding zone uses the channel allocated to the zone. Hyper-fraction will allow the system to minimize communication latency between a user equipment (UE) and a base station (BS) caused by scheduling processes and consequentially reduce the overall latency of the system. In the simulation, a novel concept of genetic algorithm (GA) is utilized, called GA with continuous pool. It is an approach to enable conventional GA to solve optimization problems for continuous situations within much less computation, especially in situations where the elements in the system keep moving such as vehicular networks. As a result, GA with continuous pool is proven to be an effective heuristic method to improve throughput rate, as well as hyper-fraction improving the latency of NOMA V2V and vehicle-to-infrastructure (V2I) systems.