D2D Device Research Articles

Optimizing resource allocation for cluster D2D-assisted fog computing networks: A three-layer Stackelberg game approach

Resource management in fog computing is a highly challenging issue. Resource constraints of fog devices, the dynamic and diverse nature of workloads such as data-intensive, computationally intensive, and latency-sensitive applications, and the unpredictability of fog computing environments make it more challenging. How to collaboratively schedule edge, and end multi-layer ubiquitous computing resources to break through single node scale and resource bottlenecks to further improve computing performance also needs to be addressed. In this paper, we leverage idle resources on the user side, and study the resource allocation problems in the D2D-assisted fog computing networks. Firstly, we use an improved Louvain algorithm to cluster users and select the D2D device with the highest priority in each cluster as the cluster head to assist fog computing, where cluster heads can act as both servers and relays. Secondly, considering that fog nodes and D2D devices have limited resources and are not obligated to share their resources for free, we propose a price incentive method to construct a three-layer Stackelberg game model that maximizes the benefits of fog nodes and cluster heads while minimizing user costs. Then, we use the Lagrange multiplier method and the Karush–Kuhn–Tucker (KKT) conditions to solve the equilibrium point of the model to achieve our goal. Finally, the simulation results show that our proposed scheme markedly decreases the overall system cost in comparison to the four benchmark schemes. Moreover, in various scenarios the total cost of our scheme consistently remains lower than the other four schemes, affirming the efficiency of the scheme proposed in this paper.

Device Discovery in D2D Communication: Scenarios and Challenges

Device to Device (D2D) communication is expected to be an essential part of 5G cellular networks. D2D communication enables close-proximity devices to establish a direct communication session. D2D communication offers many advantages, such as reduced latency, high data rates, range extension, and cellular offloading. The first step to establishing a D2D session is device discovery; an efficient device discovery will lead to efficient D2D communication. D2D device further needs to manage its mode of communication, perform resource allocation, manage its interference and most importantly control its power to improve the battery life of the device. This work has developed six distinct scenarios in which D2D communication can be initiated, considering their merits, demerits, limitations, and optimization parameters. D2D communication procedures for the considered scenarios have been formulated, based upon the signal flow, containing device discovery, resource allocation, and session teardown. Finally, latency for each scenario has been evaluated, based on propagation and processing delays.

Detection of DDoS attacks in D2D communications using machine learning approach

In device-to-device (D2D) communications, distributed Denial-of-Service (DDoS) attacks can be quite detrimental because it can result in network structure destruction. Towards this end, the research objective of this paper is to identify and prevent DDoS and Denial-of-Service (DoS) attacks (i.e., SYN, Slowloris) in a D2D communication environment. Specifically, by replicating a real-world scenario, we emulate SLowloris attacks in a D2D communication network and generate a D2D Network-specific Slowloris dataset. This dataset along with the CICDDoS2019 dataset was then used to train our proposed Machine learning (ML) model that aids in the detection and prevention of DDoS attacks (Slowloris and SYN) in the considered D2D framework. The whole process of how to construct an emulation network for D2D communication and test it against a variety of attacks and implementations is also demonstrated in the paper. To quantify the detection accuracy in the context of DDoS and DoS attacks, we use various ML algorithms such as Random Forest, Light GBM, XGBoost, and Ada Boost and study their performance with the aid of extensive emulation. The results collected revealed that both Slowloris and CICDDoS2019 datasets achieve greater accuracy with Random Forest. Consequently, the results compel us to develop a technique for combining the identification of DDoS and DoS attacks in binary classification Random Forests with the binary decision. The proposed technique has been evaluated and compared with other related approaches in the open literature demonstrating significant performance in terms of identification and prevention time, processing and memory resources required, and device battery consumption, without affecting the accuracy of the attack identification. Hence, we advocate that our proposed technique can be extremely beneficial in preventing DDoS and DoS attacks in a D2D communication environment, where its lifetime and capabilities are mainly associated with the resources of the D2D device (i.e., CPU, Memory, and battery life).

D2D-Assisted Multi-User Cooperative Partial Offloading in MEC Based on Deep Reinforcement Learning.

Mobile edge computing (MEC) and device-to-device (D2D) communication can alleviate the resource constraints of mobile devices and reduce communication latency. In this paper, we construct a D2D-MEC framework and study the multi-user cooperative partial offloading and computing resource allocation. We maximize the number of devices under the maximum delay constraints of the application and the limited computing resources. In the considered system, each user can offload its tasks to an edge server and a nearby D2D device. We first formulate the optimization problem as an NP-hard problem and then decouple it into two subproblems. The convex optimization method is used to solve the first subproblem, and the second subproblem is defined as a Markov decision process (MDP). A deep reinforcement learning algorithm based on a deep Q network (DQN) is developed to maximize the amount of tasks that the system can compute. Extensive simulation results demonstrate the effectiveness and superiority of the proposed scheme.

Dynamic D2D Communication in 5G/6G Using a Distributed AI Framework

This paper focuses on D2D communication for 5G/6G in dynamic environments where the D2D network topology changes through time due to D2D Devices’ mobility. Specifically, we consider a scenario within the coverage area of a Base Station (BS) serving a number of User Devices (UEs), with variations in speed and direction causing changes in the D2D network topology, either via direct connections or via D2D single-hop or D2D Multi-hop paths. Based on this scenario, we formulate a problem aiming to maximise the total Spectral Efficiency (SE) whilst minimising the total Power Consumption (PC), by selecting the best transmission mode that the D2D devices will operate. In order to address the aforementioned problem, the Distributed Artificial Intelligence Solution (DAIS) plan proposed in our earlier work and designed for static environments is extended to consider the mobility (i.e., speed, direction and indirectly link distance change) of the D2D Device, targeting the dynamic creation of stable and efficient clusters and good backhauling links towards the gateway. The enhanced DAIS performance is comparatively evaluated in terms of SE and PC against selected variations in UE speed and direction, changes in the link Transmission Power (TP), and an increase in the number of Devices in the D2D network. Overall, the results obtained demonstrated superior performance of enhanced DAIS over all the other related investigated approaches (i.e., Distributed Sum Rate (DSR), Single Hop Relay Approach (SHRA), Distributed Random (DR)), in terms of Spectral Efficiency (SE) and Power Consumption (PC). Also, the ability of enhanced DAIS to react and adapt quickly and efficiently to D2D network topology changes, with reduced signalling overhead and control delay in responding to changes, shows that it is a well-poised approach to be used in a Dynamic D2D Environment.

Mountain Rainfall Estimation and BIM Technology Site Safety Management Based on Internet of Things

Mountain rainfall estimation is a major source of information for determining the safety of a geographical (mountainous) area. It can be done easily by using a modeling and simulation application, BIM, which is a building information modeling tool. It helps in transforming the real-time scenarios into the construction and business models. Now, this whole process can be easily realized by the help of an evolving technology known as IoT (Internet of Things). Internet of Things is supposedly going to take over the world by the end of this decade. It will reshape the whole communication architecture. IoT is actually going to be a basis for D2D (Device to Device) communication. Here, the MTC (Machine Type Communications) are going to take place which have almost zero human involvement. Now, in order to overcome the problem that the traditional construction site safety management method is difficult to accurately estimate the rainfall, resulting in poor safety management effect, a mountain rainfall estimation and BIM technology site safety management methods based on Internet of things are proposed. Firstly, based on the Internet of Things data, the limit learning machine method is used to accurately estimate the mountain rainfall. Secondly, based on the rainfall estimation results and combined with BIM technology, the construction site safety and management model is constructed. In the end, experimental verification is carried out. The experimental results show that this method can precisely estimate the rainfall in mountainous areas, and the computational results of safety factor are basically consistent with the actual results, indicating that the safety management effect of this system is good. In this paper, I reveal the complications and drawbacks associated with the ongoing mechanisms used for mountain rainfall estimations and how to overcome them by using the new technology, i.e., Internet of Things.

Evaluation of RF Energy Harvesting by Mobile D2D Nodes Within a Stochastic Field of Base Stations

Radio frequency energy harvesting can prolong the battery life and improve energy efficiency of device-to-device (D2D) communication. In this paper, we analyze the performance of a mobile D2D device powered by EH from the transmissions of underlying cellular base stations (BSs), whose locations are modeled as a homogeneous Poisson point process. We model the movements of D2D nodes via a modified random waypoint model. Log-distance path loss and Rayleigh fading are considered, and EH takes place solely within harvesting zones surrounding each BS and each D2D user harvests energy for a fixed number of charging time slots before attempting to transmit. We derive the probability of a D2D device being within an EH region surrounding BSs after multiple movements, and the probability of being within the fully charged state using a Markov-chain approach taking into account temporal effects. Moreover, the statistics of the harvested energy are characterized, and subsequently, the outage probability of a D2D transmission utilizing the harvested energy is derived. We show that the number of movements required to be within a harvesting region increases significantly when the harvesting threshold power increases, and that the number of harvesting time slots should be selected judiciously.

Joint Relay Selection, Full-Duplex and Device-to-Device Transmission in Wireless Powered NOMA Networks

This paper investigates non-orthogonal multiple access (NOMA), cooperative relaying, and energy harvesting to support device-to-device (D2D) transmission. In particular, we deploy multiple relay nodes and a cell-center D2D device which can operate in full-duplex (FD) or half-duplex (HD) mode to communicate with a cell-edge D2D device. In this context, there are two possible signal transmission paths from the base station (BS) to the far D2D user either through multiple decode-and-forward (DF) relay nodes or through a near D2D user. Consequently, we propose three schemes to support D2D-NOMA systems, namely non-energy harvesting relaying (Non-EHR), energy harvesting relaying (EHR) and quantize-map-forward relaying (QMFR) schemes. For each of the proposed schemes, closed-form expressions of the outage probabilities of both D2D users are derived. Extensive Monte-Carlo simulation results are provided to validate the derived analytical expressions. The study results show that the proposed schemes can improve the outage performance compared to conventional orthogonal multiple access (OMA) schemes. Moreover, it is shown that the Non-EHR scheme achieves the best outage performance among the three considered schemes.

D2D Communications Meet Mobile Edge Computing for Enhanced Computation Capacity in Cellular Networks

The future 5G wireless networks aim to support high-rate data communications and high-speed mobile computing. To achieve this goal, the mobile edge computing (MEC) and device-to-device (D2D) communications have been recently developed, both of which take advantage of the proximity for better performance. In this paper, we integrate the D2D communications with MEC to further improve the computation capacity of the cellular networks, where the task of each device can be offloaded to an edge node and a nearby D2D device. We aim to maximize the number of devices supported by the cellular networks with the constraints of both communication and computation resources. The optimization problem is formulated as a mixed integer non-linear problem, which is not easy to solve in general. To tackle it, we decouple it into two subproblems. The first one minimizes the required edge computation resource for a given D2D pair, while the second one maximizes the number of supported devices via optimal D2D pairing. We prove that the optimal solutions to the two subproblems compose the optimal solution to the original problem. Then, the optimal algorithm to the original problem is developed by solving two subproblems, and some insightful results, such as the optimal transmit power allocation and the task offloading strategy, are also highlighted. Our proposal is finally tested by extensive numerical simulation results, which demonstrate that combining D2D communications with MEC can significantly enhance the computation capacity of the system.

Asynchronous Device Detection for Cognitive Device-to-Device Communications

Dynamic spectrum sharing will facilitate the interference coordination in device-to-device (D2D) communications. In the absence of network level coordination, the timing synchronization among D2D users will be unavailable, leading to inaccurate channel state estimation and device detection, especially in time-varying fading environments. In this paper, we design an asynchronous device detection/discovery framework for cognitive-D2D applications, which acquires timing drifts and dynamical fading channels when directly detecting the existence of a proximity D2D device (e.g. or primary user). To model and analyze this, a new dynamical system model is established, where the unknown timing deviation follows a random process, while the fading channel is governed by a discrete state Markov chain. To cope with the mixed estimation and detection problem, a novel sequential estimation scheme is proposed, using the conceptions of statistic Bayesian inference and random finite set. By tracking the unknown states (i.e. varying time deviations and fading gains) and suppressing the link uncertainty, the proposed scheme can effectively enhance the detection performance. The general framework, as a complimentary to a network-aided case with the coordinated signaling, provides the foundation for development of flexible D2D communications along with proximity-based spectrum sharing.

Trust Management Method of D2D Communication Based on RF Fingerprint Identification

iWith the rapid development and the increasing security threats of D2D communication, the D2D communication security has become more and more important. Device recognition is the foundation of communication security. Traditional device authentication is the cryptographic mechanism and the security protocol that are not enough for the new security threat. RF fingerprint of wireless device has physical characteristics, which is difficult to clone. Therefore, it can be used for D2D device recognition. In this paper, a recognition algorithm of D2D device based on RF fingerprint is proposed. First, Hilbert transform and principal component analysis is used to generate the RF fingerprint of D2D device. Second, support vector machine (SVM) is introduced and CV-SVM is used as the classifier. Finally, simulation results show that the recognition rate is more than 90% under SNR = 5 dB, which proves that the proposed method is effective for D2D device recognition. Therefore, it can be used as the trust management of D2D communication.

Extensive Cooperative Caching in D2D Integrated Cellular Networks

Device-to-device (D2D) communication is a promising supplement to cellular networks, since it provides high rate transmission without changing network infrastructure. For a D2D integrated cellular network, caching popular content (e.g., multimedia files) at a base station (BS) or a D2D device near to a requesting user not only reduces content delivery delay, but also alleviates backhaul traffic load. In order to minimize content delivery delay, in this letter, we propose an extensive cooperative caching (EC-Caching) scheme, which allows inter-BS, inter-device and between-BS-and-device cooperation in content caching. Simulation results demonstrate that the extension in degree of cooperation between the caching nodes contributes significant performance gain in terms of content delivery delay and cache hit rate, especially in populated areas.

Interference-Aware Transmission for D2D Communications in a Cellular Network

Underlay device-to-device (D2D) communication in a cellular network is expected to improve spectrum utilization as well as to enable the use of new proximity-based applications. However, deploying D2D communications in a cellular network poses some challenges in the system design due to the potential interference between the cellular links and the D2D links. In this paper, we consider a scenario where a D2D device equipped with multiple antennas attempts to reuse the downlink resources of an active cellular user. Two strategies are proposed to handle the interference that the D2D link may cause to the cellular link. Interference nulling (IN), the first strategy, completely eliminates the interference, while interference constraining (IC), the second strategy, intends to limit the amount of residual interference such that the signal-to-interference-plus-noise ratio (SINR) for the cellular user is ensured to be higher than a certain threshold. Both methods do not produce additional overhead at the base station (BS), since the D2D side autonomously determines the transmit signals without involvement of the BS. The results of a numerical analysis are then presented to compare the performance of these two strategies under various environments.

재난 무선통신을 위한 D2D 단말탐색 기반 주파수 자원 확보 기술

재난 무선통신을 위한 D2D 단말탐색 기반 주파수 자원 확보 기술

Interference mitigation and D2D parameter estimation for distributed‐control D2D underlay systems

AbstractAlthough centralised‐control schemes for device‐to‐device (D2D) underlay communications can produce satisfactory results by efficiently managing the interference between cellular and D2D transmissions, they can become prohibitively complicated and may require a large amount of feedbacks from devices, because it is important to handle the inter‐cell interference as well as the intra‐cell interference. Such a burden can be alleviated by a distributed‐control mechanism, in which a base station performs the resource management of its cellular devices and each D2D device selects its own resource without a small‐scale control of the cellular network. This paper addresses interference issues in distributed‐control D2D underlay systems and presents an interference mitigation technique based on frequency spreading, in order to meet the target outage probability. This paper also proposes a simple estimation scheme, which can be used for the mode selection and the determination of D2D transmission parameters. Copyright © 2015 John Wiley & Sons, Ltd.