D2D Offloading Research Articles

Hyper-graph matching D2D offloading scheme for enhanced computation and communication capacity

As the Internet of Things(IoT) and its intelligent applications continue to proliferate, forthcoming 6G networks will confront the dual challenge of heightened communication and computing capacity demands. To address this, D2D collaborative computing is being explored. However, the current D2D collaborative computing ignores the integrity of computing and communication. For a single-task device, offloading operations intertwine computing and communication, internal coupling causes due to parallel executed between local and D2D offloading. In addition, external coupling arises among devices competing for limited radio and computing resources. Worse, internal coupling and external coupling interact, exacerbating the situation. To address these challenges, a novel D2D offloading framework is proposed based on hyper-graph matching in this paper. Our goal is to minimize both delay and energy costs while ensuring service quality for all users by jointly optimizing task scheduling, offload policies and resource allocation. The original problem is formulated as a nonlinear integer programming problem. Then, by three-stage strategy optimization decomposition, it is separated into several sub-problems. In the first stage, a polynomial-time algorithm has been developed to optimize the task offloading ratio, taking into account both its upper and lower bounds. In the second stage, a geometric programming algorithm has been created to address power allocation. In the third stage, a three-dimensional hyper-graph matching model is employed to derive the optimal offloading and channel allocation policies. This is based on analyzing the conflict graph and applying the claw theorem. Simulation results demonstrate that the proposed scheme outperforms other algorithms by approximately 12%, 20%, 28%, 40%, respectively. Moreover, it enhances both spectral efficiency and computational efficiency.

Mean Field Graph Based D2D Collaboration and Offloading Pricing in Mobile Edge Computing

Mean Field Graph Based D2D Collaboration and Offloading Pricing in Mobile Edge Computing

Joint computation offloading and resource allocation strategy for D2D-assisted and NOMA-empowered MEC systems

Multi-access edge computing (MEC) emerged as a promising network paradigm that provides computation, storage and networking features within the edge of the pervasive mobile radio access network. This paper jointly considers computation offloading and resource allocation problem in device-to-device (D2D)-assisted and non-orthogonal multiple access (NOMA)-empowered MEC systems, where each mobile device (MD) is allowed to execute its task in one of the three ways, i.e., local computing, MEC offloading or D2D offloading. We invoke orthogonal multiple access (OMA) and NOMA schemes for MDs that select D2D offloading mode, allowing them to assign tasks to their peers using OMA or NOMA. The original problem is formulated as an overall energy consumption minimization problem, which proves to be NP-hard, making it intractable to solve optimally. We start from a simple case, OMA case and transform the original problem into two sub-problems, i.e., resource allocation sub-problem and computation offloading sub-problem and propose two heuristic algorithms to obtain the sub-optimal solutions of both sub-problems. Then, for the MDs selecting D2D offloading mode, we conduct user pairing and apply the NOMA scheme. Finally, simulation results demonstrate the efficiency of the proposed scheme when compared with the related schemes.

SVM-Based Task Admission Control and Computation Offloading Using Lyapunov Optimization in Heterogeneous MEC Network

Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to run complex applications. This can be realized through efficient computing data offloading and yet enhanced while simultaneously using multiple wireless interfaces for D2D, MEC and cloud offloading. In this work, we propose user-centric real-time computation task offloading and resource allocation strategies aiming at minimizing energy consumption and monetary cost while maximizing the number of completed tasks. We develop dynamic partial offloading solutions using the Lyapunov drift-plus-penalty optimization approach. Moreover, we propose a task admission solution based on support vector machines (SVM) to assess the potential of a task to be completed within its deadline, and accordingly, decide whether to drop from or add it to the user’s queue for processing. Results demonstrate high performance gains of the proposed solution that employs SVM-based task admission and Lyapunov-based computation offloading strategies. Significant increase in number of completed tasks, energy savings, and cost reductions are resulted as compared to alternative baseline approaches.

Outage Constrained Design in NOMA-Based D2D Offloading Systems

Non-orthogonal multiple access (NOMA) is a new multiple access method that has been considered in 5G cellular communications in recent years, and can provide better throughput than traditional orthogonal multiple access (OMA) to save communication bandwidth. Device-to-device (D2D) communication, as a key technology of 5G, can reuse network resources to improve the spectrum utilization of the entire communication network. Combining NOMA technology with D2D is an effective solution to improve mobile edge computing (MEC) communication throughput and user access density. Considering the estimation error of channel, we investigate the power of the transmit nodes optimization problem of NOMA-based D2D networks under the rates outage probability (OP) constraints of all single users. Specifically, under the channel statistical error model, the total system transmit power is minimized with the rate OP constraint of a single device. Unfortunately, the problem presented is thorny and non-convex. After equivalent transformation of the rate OP constraints by the Bernstein inequality, an algorithm based on semi-definite relaxation (SDR) can efficiently solve this challenging non-convex problem. Numerical results show that the channel estimation error increases the power consumption of the system. We also compare NOMA with the OMA mode, and the numerical results show that the D2D offloading systems based on NOMA are superior to OMA.

Incentive-Driven Deep Reinforcement Learning for Content Caching and D2D Offloading

Offloading cellular traffic via Device-to-Device communication (or D2D offloading) has been proved to be an effective way to ease the traffic burden of cellular networks. However, mobile nodes may not be willing to take part in D2D offloading without proper financial incentives since the data offloading process will incur a lot of resource consumption. Therefore, it is imminent to exploit effective incentive mechanisms to motivate nodes to participate in D2D offloading. Furthermore, the design of the content caching strategy is also crucial to the performance of D2D offloading. In this paper, considering these issues, a novel Incentive-driven and Deep Q Network (DQN) based Method, named IDQNM is proposed, in which the reverse auction is employed as the incentive mechanism. Then, the incentive-driven D2D offloading and content caching process is modeled as Integer Non-Linear Programming (INLP), aiming to maximize the saving cost of the Content Service Provider (CSP). To solve the optimization problem, the content caching method based on a Deep Reinforcement Learning (DRL) algorithm, named DQN is proposed to get the approximate optimal solution, and a standard Vickrey-Clarke-Groves (VCG)-based payment rule is proposed to compensate for mobile nodes' cost. Extensive real trace-driven simulation results demonstrate that the proposed IDQNM greatly outperforms other baseline methods in terms of the CSP's saving cost and the offloading rate in different scenarios.

Multi-MEC server multi-user resource allocation in heterogeneous network

Aiming at the coexistence of large and small cells in heterogeneous cellular networks, the difference of computing resources between mobile equipment and base stations, and the diversity of service quality demands of users, a resource allocation algorithm based on matching game theory was proposed. We assume that some users have computing needs and can perform MEC offloading or D2D offloading. We define cost performance as the ratio of the data rate per user to its price cost, and resource allocation as the cost performance maximization problem. Considering that the formulation problem is a mixed integer nonlinear problem and it is difficult to obtain the optimal solution, we design an improved Gale-Shapley algorithm based on matching game theory to obtain the feasible solution of the problem. Simulation results show that the proposed multi-MEC server multi-user resource allocation strategy can effectively reduce user service delay and improve system performance in heterogeneous cellular networks.

Cache Partitioning and Caching Strategies for Device-to-Device Caching Systems

The amount of traffic in wireless networks is increasing exponentially, and this problem can be mitigated using device-to-device (D2D) caching technology, which installs a cache on a mobile end device. Devices can reduce the cell load through self-offloading via content in their own cache and D2D offloading using content in others' caches. However, especially in the early stage of D2D caching systems, a limited number of devices with a small storage might be used, and it is required to develop a caching scheme with excellent performance despite the small cache size. Regarding content popularity, which is common to most users, the preference probability values are not concentrated on some pieces of content, making it difficult to achieve satisfactory performance using a small cache. On the other hand, when considering individual users, content preferences may contain large values for specific content based on individual characteristics. In addition, the performance can be improved by considering short-term content preferences that reflect changes in content preferences over time or newly created content during peak hours. In this article, the hit ratio is divided into six parts considering self- and D2D offloading, common and individual user preferences, and little and large temporal changes in content preferences during peak hours. We also conceptually divide the cache of a helper into six areas in relation to the six parts of the hit ratio, and discuss cache partitioning and proactive caching strategies according to the environment.

Differential Security Game in Heterogeneous Device-to-Device Offloading Network Under Epidemic Risks

Cooperative computation among peer mobile devices via device-to-device (D2D) links, a.k.a. D2D offloading, is a promising technology to enhance mobile computing performance and reduce core wireless network traffic. However, D2D offloading also creates new security risks as malware can relatively easily compromise mobile devices participating in D2D offloading and propagate across the entire network. In this article, we build an epidemic model to understand the malware propagation process in the D2D offloading-enabled mobile network where devices have heterogeneous computation demand and new devices can enter the system over time. This model also allows mobile devices to intentionally enter a “non-cooperative” state as a preventive defense strategy to thwart malware propagation. We prove a thresholding result of the malware propagation similar to that in classic epidemic models under given static defender (i.e. the network operator) and attacker strategies. We further model the strategic interaction between the defender and the attacker as a zero-sum differential game. The existence of a saddle-point equilibrium is proved, and the optimal dynamic defense and attack strategies are derived based on the Pontryagin's maximum principle, which are proven to be a bang-bang control strategy. Simulation results validate the proposed model and show that the dynamic optimal strategies significantly improve the system utility compared with baseline strategies.

A Socially-Aware Hybrid Computation Offloading Framework for Multi-Access Edge Computing

Computation offloading manages resource-intensive and mobile collaborative applications (MCA) on mobile devices where much processing is replicated with multiple users in the same environment. In this article, we propose a novel hybrid multicast-based task execution framework for multi-access edge computing (MEC), where a crowd of mobile devices at the network edge leverage network-assisted device-to-device (D2D) collaboration for wireless distributed computing (MDC) and outcome sharing. The framework is socially aware in order to build effective D2D links. A key objective of this framework is to achieve an energy-efficient task assignment policy for mobile users. Specifically, we first introduce the socially aware hybrid computation offloading (SAHCO) system model, which combines of MEC offloading and D2D offloading in detail. Then, we formulate the energy-efficient task assignment problem by taking into account the necessary constraints. We next propose a Monte Carlo Tree Search based algorithm, named, TA-MCTS for the task assignment problem. Simulation results show that compared to four alternative benchmark solutions in literature, our proposal can reduce energy consumption up to 45.37 percent.

Task Execution Cost Minimization-Based Joint Computation Offloading and Resource Allocation for Cellular D2D MEC Systems

The rapid proliferation of new applications poses great challenges to the computation capability of mobile devices. To tackle this problem, computation offloading was proposed as a promising paradigm. In this paper, we consider a cellular device-to-device (D2D) mobile edge computing (MEC) system consisting of one base station (BS) deployed with an MEC server and users. We assume that a portion of users have task computation requirements and may perform local computing, MEC offloading, or D2D offloading. Further assuming that tasks can be partitioned into small-sized data and can be executed simultaneously via various computation modes, we jointly study computation offloading and resource allocation problem. To achieve efficient information interaction and task management, we first propose a joint task management architecture. Defining task execution cost as the weighted sum of task execution latency and energy consumption, we then formulate the joint optimization problem as a task execution cost minimization problem. As the formulated problem is a mixed integer nonlinear problem, which cannot be solved conveniently, we propose a heuristic algorithm that successively solves computation offloading subproblem and resource allocation subproblem by Kuhn-Munkres algorithm and Lagrangian dual method, respectively. Numerical results demonstrate the effectiveness of the proposed algorithm.

Joint Communication and Computation Optimization for Wireless Powered Mobile Edge Computing with D2D Offloading

This paper studies a wireless powered mobile edge computing (MEC) system with device-to-device (D2D)-enabled task offloading. In this system, a set of distributed multi-antenna energy transmitters (ETs) use collaborative energy beamforming to wirelessly charge multiple users. By using the harvested energy, the actively computing user nodes can offload their computation tasks to nearby idle users (as helper nodes) via D2D communication links for self-sustainable remote computing. We consider the frequency division multiple access (FDMA) protocol, such that the D2D communications of different user-helper pairs are implemented over orthogonal frequency bands. Furthermore, we focus on a particular time block for task execution, which is divided into three slots for computation task offloading, remote computing, and result downloading, respectively, at different user-helper pairs. Under this setup, we jointly optimize the collaborative energy beamforming at ETs, the communication and computation resource allocation at users and helpers, and the user-helper pairing, so as to maximize the sum computation rate (i.e., the number of task input-bits executed over this block) of the users, subject to individual energy neutrality constraints at both users and helpers. First, we consider the computation rate maximization problem under any given user-helper pairs, for which an efficient solution is proposed by using the techniques of alternating optimization and convex optimization. Next, we develop the optimal user-helper pairing scheme based on exhaustive search and a low-complexity scheme based on greedy selection. Numerical results show that the proposed design significantly improves the sum computation rate at users, as compared to benchmark schemes without such joint optimization.

D2D data offloading in vehicular environments with optimal delivery time selection

Within the framework of a Device-to-Device (D2D) data offloading system for cellular networks, we propose a Content Delivery Management System (CDMS) in which the instant for transmitting a content to a requesting node, through a D2D communication, is selected to minimize the energy consumption required for the transmission. The proposed system is particularly fit to highly dynamic scenarios, such as vehicular ones, where the network topology changes at a rate which is comparable with the order of magnitude of the delay tolerance. We present an analytical framework able to predict the system performance, in terms of energy consumption, considering concurrent requests for contents with different popularity. We validate the analytical model through simulations, and provide a thorough performance evaluation of the proposed CDMS, in terms of energy consumption and spectrum use. The proposed CDMS can be configured in two ways: (i) either use I2D transmissions only as a last resort, if it is not possible to serve request through D2D offloading within the limit of the delay tolerance; or (ii) include the possibility for the Base Stations (BSs) to transmit contents even before the deadline, if this is found to be more energy efficient from the point of view of the overall energy consumption, renouncing a portion of the offloading opportunities. The proposed scheme is benchmarked against a plain classic cellular scheme, a cellular (I2D only) scheme with optimal Infrastructure-to-Device (I2D) transmission scheduling for delay-tolerant applications, and a D2D data offloading scheme (proposed in previous works) in which the D2D transmission is performed as soon as a close-by device with the required content is found (i.e., without optimizing the transmission instant). We implemented a quite detailed simulator complete of Radio Resource Management (RRM) over the MAC layer of a multi carrier system, in a multi-cell scenario. Our results show that the proposed CDMS can induce a reduction of an order of magnitude, i.e. 90% or higher, of the energy consumed by the devices to implement D2D offloading, with respect to the benchmark D2D offloading scheme. At the same time, we observed a reduction of the energy consumption of the overall system in the order of 30%, under conservative assumptions. Finally, the system induces a remarkable degree of spatial frequency reuse, thus reducing the overall spectrum occupation.

ERP: Edge Resource Pooling for Data Stream Mobile Computing

Recently, the explosion of resource-hungry and delay-sensitive Internet-of-Things (IoT) applications as exemplified by wearable appliances, video surveillance, and connected vehicles have posed great challenges on the underlying IoT devices which typically have limited computation resource. In response, computation offloading is envisioned as a promising approach to augmenting capability of IoT devices. Toward real-time and efficient computation offloading, in this paper we propose a novel edge resource pooling framework, in which a massive crowd of devices at the network edge exploit device-to-device (D2D) collaboration for pooling and sharing computation resource with each other. Specifically, we first formulate the utility maximization problem under both computation and communication constraints as a mixed-integer linear programming problem, which is further proven to be NP-hard. To address this challenge, we propose a greedy heuristic based on the classical maximum network flow problem, and thus to schedule the task offloading in a cost-efficient manner. By considering the case that a centralized controller (e.g., a network operator) is not available, a decentralized task offloading scheme is further proposed, in which IoT devices communicate and determine D2D offloading strategy locally. Rigorous theoretical analysis and extensive evaluations demonstrate the effectiveness of the proposed algorithms.

Designing Security-Aware Incentives for Computation Offloading via Device-to-Device Communication

Computation offloading via device-to-device (D2D) communication, or D2D offloading, can enhance mobile computing performance by exploiting spare computing resources of nearby user devices. The success of D2D offloading relies on user participation in collaborative service provisioning, which incurs extra costs to users providing the service, thus mandating an incentive mechanism that can compensate for these costs. Although incentive mechanism design has been intensively studied in the literature, this paper considers a much more challenging yet less investigated problem in which selfish users are also facing interdependent security risks, such as infectious proximity-based attacks. Security cost is significantly different in nature from conventional service provisioning costs such as energy consumption because security risks often depend on the collective behavior of all users. To this end, we build a novel mathematical framework by leveraging the combined power of game theory and epidemic theory to investigate the interplay between user incentives and interdependent security risks in D2D offloading, thereby enabling the design of security-aware incentive mechanisms. Our analysis discovers an interesting “less is more” phenomenon: although giving users more incentives promotes more participation, it may harm the network operator’s utility. This is because too much participation may foster persistent security risks, and as a result, the effective participation level does not improve.

Discount Interference Pricing Mechanism for Data Offloading in D2D Communications

Offloading in device-to-device (D2D) communications is recognized as a promising solution for reducing the traffic load of cellular networks. However, in underlay D2D communications, the cellular users (CUEs) suffer from severe cross-tier interference from D2D users. To keep this interference under a certain limit, the cellular base station (CBS) controls the transmit power of D2D users by pricing the interference power generated at it on shared subchannels. Moreover, D2D users can help the CBS by sharing its content with nearby CUEs, after receiving monetary incentives from CBS. In this letter, we propose a discount interference pricing scheme for D2D offloading. In the proposed framework, the CBS encourages some users, acting as D2D transmitters to provide service to CUEs, thereby improving its overall economic efficiency. In exchange, the CBS provides D2D users a discount on the interference price. We utilize a two-stage non-cooperative game to analytically model this scenario. Simulations results affirm that both CBS and D2D users can benefit from such cooperation and significantly increase their utility by employing the proposed discount interference pricing mechanism.

A proactive scalable approach for reliable cluster formation in wireless networks with D2D offloading

With the current exponential growth in traffic and service demands, device-to-device (D2D) cooperation is identified as a major mechanism to enable 5G networks to effectively and efficiently augment network resources. The effectiveness of D2D cooperation depends on a wide range of decision making processes that include cluster formation, resource allocation, in addition to connection and mobility management. Irrespective of the D2D cooperation scenario whether in sensor, ad hoc, or cellular networks, the literature normally assumes that devices selected as relays or data sources are reliable; this means that they will maintain the connection till the communication session ends. Yet, this assumption is challenged in practice since devices’ batteries can be depleted (e.g., sensors in an IoT network) and devices can move leading to connection termination (e.g., mobile users in a WiFi network or cars in a vehicular ad hoc network). To this end, we address the problem of reliable D2D cooperation in wireless networks by proposing a novel approach that is proactive by utilizing reliability metrics in the decision making process, and scalable by having low implementation complexity suitable for dense networks. These differentiating factors are shown to enhance the overall network reliability compared to standard techniques and to facilitate dynamic operation which is essential for practical implementation. Performance is evaluated using extensive simulations in addition to test bed experimental demonstration in order to quantify gains and extract insights on a range of existing design tradeoffs.

Empowering Device-to-Device Networks with Cross-Link Interference Management

Device-to-device (D2D) communications is an emerging service model that is currently under standardization by 3GPP. While D2D offloading has a great potential to relieve increasingly congested cellular networks, its benefits, however, come at a cost, namely interference. Most of the prevailing D2D designs conservatively avoid interference via either spectrum resource allocation or power control. These designs, however, do not exploit spatial degrees of freedom (DoF), which are inherently supported by multi-antenna devices. In this work, we present $\sf {MD2D}$ , a multiuser D2D system that embraces concurrent D2D transmissions, while leveraging MIMO techniques to actively eliminate interference across D2D pairs. $\sf{MD2D}$ has a systematic methodology that checks whether the antenna combination in a D2D network is capable of eliminating cross-pair interference and, thereby, ensuring interference-free concurrent transmissions. If the interference can be eliminated, then $\sf{MD2D}$ applies a bucket-based DoF assignment algorithm to determine an effective antenna usage configuration that handles the interference. We evaluate our design via testbed experiments and large-scale simulations. The results show that, as compared to the traditional interference avoidance scheme, $\sf{MD2D}$ improves the throughput by 87.39 and 218.84 percent in a three-pair testbed and in large-scale simulations, respectively.

Operator’s Economy of Device-to-Device Offloading in Underlaying Cellular Networks

Offloading onto device-to-device (D2D) communications is considered a promising technology to alleviate traffic load of cellular networks. However, the success of D2D communications relies on user’s willingness to share contents and operator’s incentives to tolerate interference in networks, which has been overlooked in most literature. We propose an incentive framework of D2D offloading, where the operator encourages some users acting as D2D transmitters to broadcast their popular contents to nearby region to improve operators, overall economic efficiency. A two-stage Stackelberg game is employed to analytically model such interaction: The operator (leader) determines the incentive price to maximize its interests and D2D transmitters (followers) choose appropriate traffic volume to be offloaded. The transmit powers at the base station and D2D transmitters are simultaneously characterized by considering the intra-tier and inter-tier interferences in complex cellular networks. Simulations confirm that the cellular operator can fully utilize the spectrum and significantly increase its profit by incorporating this D2D offloading incentive mechanism.

Joint storage assignment for D2D offloading systems

D2D offloading reduces the load of cellular network by asking mobile nodes to download content directly from storage of neighboring helpers via short range links. In this paper, we introduce a novel storage assignment scheme that can enhance storage utilization for D2D networks that have different types of storage nodes. Unlike traditional D2D systems that only use storage as static content cache, our scheme uses on-demand relaying to enhance storage utilization. Our on-demand relaying scheme replicates rare content when it is requested. Therefore, the proposed scheme can greatly increase the amount of content supported by the offloading system. We develop a convex optimization based algorithm to find the optimal storage assignment tradeoff between static caching and on-demand relaying. Numerical results and real-world trace-driven simulations show that our algorithm can achieve 30% reduction in offloading failure rate compared to static schemes.