Distributed Resource Allocation Algorithm Research Articles

Resilient algorithm for distributed resource allocation under false data injection attacks

AbstractFor a first‐order nonlinear multi‐agent system who is subject to false data injection (FDI) attacks on agents' actuators and sensors, agents execute a distributed resource allocation algorithm according to the compromised control inputs and interactive information such that the multi‐agent system is unstable and agents' decisions deviate from the optimal resource allocation. At first, we analyze the robustness of the distributed resource allocation algorithm under the FDI attacks. Then, a resilient distributed algorithm is proposed to solve the distributed resource allocation problem by resisting the adverse effect of the attacks. In detail, the unknown nonlinear term and the false data injected in agents are considered as extended states that can be estimated by extended state observers. The estimation is used in the feedback control to suppress the effect of the FDI attacks. As a result, the designed resilient algorithm ensures that agents' decisions converge to the optimal allocation without requiring any information about the nature of the attacks. An example is given to illustrate the results.

Distributed resource allocation model with presence of multiple jammer for underwater wireless sensor networks

Underwater-wireless sensor network (WSN) are prone to the jamming attacks; mainly in case of reactive jamming. Reactive jamming has emerged as one of the critical security threat for underwater-WSN; this occurs due to the reactive jammer capabilities of controlling and regulating jamming duration. Further reactive jammer possesses low detection probability and high vulnerability; moreover the existing model has been designed in consideration with terrestrial-WSN. Hence these models possesses limited capabilities of detecting the jamming and distinguish among uncorrupted and corrupted packets; also it fails to adapt with the dynamic environment. Furthermore co-operative mechanism of jamming model is presented for utilizing the resources in efficient way; however only few existing work has been carried out through the co-operative jamming detection; especially under presence of multiple jammer nodes. For overcoming research issues this paper presents distributed resource allocation (DRA) model adopting cross layer architecture under presence of multiple jammer. DRA algorithm is designed for allocating resource to jammer user in optimal manner. Experiment outcome shows the proposed DRA model achieves much better detection rate considering multi-jammer environment. Thus aid in achieving much better detection accuracy, packet drop, packet transmission and resource utilization performance.

A distributed extremum seeking based resource allocation algorithm over switching networks

AbstractIn this article, a distributed resource allocation problem for second‐order multi‐agent systems over undirected switching topologies is investigated. For the distributed resource allocation problem, most of the existing algorithms depend on the gradient of cost function with specific expression, which may be unknown to agents. To deal with unknown cost functions, the extremum seeking control is introduced in the designed distributed resource allocation algorithm to estimate the gradient of the cost function only by the value of the cost. Besides, an estimator is proposed to estimate the optimal Lagrange multiplier associated with the equality constraint. The communication graph is supposed to be switched among a set of undirected subgraphs, which may be connected or unconnected. For undirected and connected switching graphs, the average dwell‐time constraint is used to describe the switching frequency. For the undirected graphs, containing connected and unconnected ones, both the average dwell‐time and the time‐ratio constraints are used to model the switching signal. For the two cases, the semi‐globally practically asymptotical stability of the designed algorithm is established. The obtained results are verified by an example.

Joint Deep Reinforcement Learning and Unsupervised Learning for Channel Selection and Power Control in D2D Networks.

Device-to-device (D2D) technology enables direct communication between devices, which can effectively solve the problem of insufficient spectrum resources in 5G communication technology. Since the channels are shared among multiple D2D user pairs, it may lead to serious interference between D2D user pairs. In order to reduce interference, effectively increase network capacity, and improve wireless spectrum utilization, this paper proposed a distributed resource allocation algorithm with the joint of a deep Q network (DQN) and an unsupervised learning network. Firstly, a DQN algorithm was constructed to solve the channel allocation in the dynamic and unknown environment in a distributed manner. Then, a deep power control neural network with the unsupervised learning strategy was constructed to output an optimized channel power control scheme to maximize the spectrum transmit sum-rate through the corresponding constraint processing. As opposed to traditional centralized approaches that require the collection of instantaneous global network information, the algorithm proposed in this paper used each transmitter as a learning agent to make channel selection and power control through a small amount of state information collected locally. The simulation results showed that the proposed algorithm was more effective in increasing the convergence speed and maximizing the transmit sum-rate than other traditional centralized and distributed algorithms.

A Distributed Resource Allocation Algorithm for the General Linear Multiagent Systems

We study the distributed resource allocation problem for heterogeneous multiagent systems over an undirected graph, an essential issue in multiagent system coordinated control and complex network system control. The decision variable is subject to global equality and local convex set constraints, and the objective is smooth and convex. It aims to minimize the global objective function by exchanging neighboring information between agents. An adaptive distributed algorithm is designed using the distance function-based exact penalty function method. A state- and time-based triggering condition is designed to avoid continuous communication and reduce the communication burden. From a random initial state, the proposed algorithm asymptotically converges to the optimal value by means of LaSalle’s invariance principle. Finally, examples are provided to demonstrate the effectiveness of our algorithm.

Optimal resource allocation for high‐order nonlinear uncertain multi‐agent systems

AbstractThis article investigates the optimal resource allocation of nonlinear high‐order multi‐agent systems with coupling uncertainties. The objective is to design an output‐feedback distributed algorithm for each agent to optimize the global cost function under network constraints. The global cost function is the sum of local cost functions. The available information to design the algorithm for each agent is only its own output and cost function, the outputs of its neighbors. To address the key technical challenge arising from the uncertainties and nonlinearities of network systems, a novel distributed resource allocation algorithm is proposed using active disturbance rejection strategy. The simulation results in the economic dispatch of a smart grid validate the effectiveness of the proposed method.

Distributed algorithm design for constrained resource allocation problems with high-order multi-agent systems

This paper studies distributed resource allocation problems of multi-agent systems, where the decisions of agents are subject to inequality network resource constraints and local inequality constraints. Compared with well-known resource allocation problems, our problem considers the high-order dynamics of agents. Without involving the control of high-order dynamics, existing distributed resource allocation algorithms cannot deal with our problem. Meanwhile, the high-order dynamics together with the inequality constraints makes it difficult to design and analyze distributed resource allocation algorithms, because the outputs of agents cannot be decided by their inputs directly and the optimal decisions of agents must satisfy the inequality constraints. In order to control the high-order agents to accomplish the resource allocation tasks autonomously, a distributed algorithm is designed by state feedback, gradient descent and primal–dual methods. Moreover, the convergence of the algorithm is analyzed by convex analysis and Lyapunov stability theory. With the algorithm, the high-order agents converge to the optimal allocation. Finally, numerical simulations verify the effectiveness of the algorithm.

Distributed resource allocation algorithm for second-order multi-agent systems with external disturbances

This paper focuses on the distributed resource allocation problem of a second-order multi-agent system with external disturbances. The global network resource equality constraint is considered. A distributed algorithm with an exponential convergence rate is proposed based on an internal model for a known bounded disturbance. By using the active disturbance approach to design a reduced-order extended state observer, the proposed algorithm is modified to reject an unknown external disturbance. Moreover, exponential convergence is achieved through the Lyapunov theory when the disturbance is constant. When the differential of the disturbance is bounded, by adjusting the control parameter of the observer, the proposed algorithm converges to an arbitrarily small neighbourhood of the optimal solution. Finally, our simulation results demonstrate the effectiveness of our proposed algorithm.

Distributed Fixed-Time Resource Allocation Algorithm for the General Linear Multi-Agent Systems

This brief focuses on the distributed resource allocation problem (RAP) for a general linear heterogeneous multi-agent system (MAS), in which each agent adopts different structure parameters. By means of multi-agent consensus approach and symbolic-function-based fixed-time control theory, an initialization-free distributed resource allocation algorithm is designed. Moreover, the equality constraint is solved based on the proportional-integral (PI) control idea and the output of all agents tracks to the optimal solution within fixed-time. Finally, we reveal the effectiveness and fast convergence performance of our proposed algorithm by a simulation example.

Distributed Resource Allocation Optimization for User-Centric Cell-Free MIMO Networks

We develop two distributed downlink resource allocation algorithms for user-centric, cell-free, spatially-distributed, multiple-input multiple-output (MIMO) networks. In such networks, each user is served by a subset of nearby transmitters that we call distributed units or DUs. The operation of the DUs in a region is controlled by a central unit (CU). Our first scheme is implemented at the DUs, while the second is implemented at the CUs controlling these DUs. We define a hybrid quality of service metric that enables distributed optimization of system resources in a proportional fair manner. Specifically, each of our algorithms performs user scheduling, beamforming, and power control while accounting for channel estimation errors. Importantly, our algorithm does not require information exchange amongst DUs (CUs) for the DU-distributed (CU-distributed) system, while also smoothly converging. Our results show that our CU-distributed system provides 1.3- to 1.8-fold network throughput compared to the DU-distributed system, with minor increases in complexity and front-haul load - and substantial gains over benchmark schemes like local zero-forcing. We also analyze the trade-offs provided by the CU-distributed system, hence highlighting the significance of deploying multiple CUs in user-centric cell-free networks.

Managing Heterogeneous and Time-Sensitive IoT Applications through Collaborative and Energy-Aware Resource Allocation

In the Internet of Things (IoT) environment, the computing resources available in the cloud are often unable to meet the latency constraints of time critical applications due to the large distance between the cloud and data sources (IoT devices). The adoption of edge computing can help the cloud deliver services that meet time critical application requirements. However, it is challenging to meet the IoT application demands while using the resources smartly to reduce energy consumption at the edge of the network. In this context, we propose a fully distributed resource allocation algorithm for the IoT-edge-cloud environment, which (i) increases the infrastructure resource usage by promoting the collaboration between edge nodes, (ii) supports the heterogeneity and generic requirements of applications, and (iii) reduces the application latency and increases the energy efficiency of the edge. We compare our algorithm with a non-collaborative vertical offloading and with a horizontal approach based on edge collaboration. Results of simulations showed that the proposed algorithm is able to reduce 49.95% of the IoT application request end-to-end latency, increase 95.35% of the edge node utilization, and enhance the energy efficiency in terms of the edge node power consumption by 92.63% in comparison to the best performances of vertical and collaboration approaches.

Distributed Resource Allocation Algorithm for General Linear Multiagent Systems

We focus on the optimal resource allocation problems with global equality constraints and convex function inequality constraints over heterogeneous linear multi-agent systems. The resource allocation problem aims to minimize the total objective function through neighboring information exchange. First, based on the state variable information, Karush-Kuhn-Tucker(KKT) conditions, and proportional-integral control concept, we propose an initialization-free distributed optimization algorithm, where each agent is driven by the gradient(subgradient) of its local objective function and local constraint convex function. In addition, the penalty factor control parameter is changed adaptively. Next, we propose an output-based distributed optimization algorithm that uses a Luenberger observer when the state variable is not accessible. Based on Lyapunov stability, it is proved that the proposed algorithms converge to the optimal solution of the resource allocation problem. Finally, simulation examples are used to demonstrate the effectiveness of the proposed algorithms.

Federated Imitation Learning: A Cross-Domain Knowledge Sharing Framework for Traffic Scheduling in 6G Ubiquitous IoT

The ubiquitous Internet of Things (IoT) system is a key component of future 6G networks to realize a fully connected world. Extensive efforts have been made to provide on-demand traffic scheduling in IoT networks through machine learning algorithms. However, the current learning approaches are hindered by the heterogeneous information in ubiquitous IoT systems since the data are collected from different domains (e.g., space, air, ground, and ocean). To uncover the complete picture of ubiquitous IoT, this article presents a novel federated imitation learning framework for traffic prediction without compromising privacy. This framework contains a knowledge-sharing module to imitate the status of cross-domain models. After that, we design a distributed resource allocation algorithm, where the IoT devices cooperatively make association decisions using matching theory. Simulation results reveal that our proposed approach outperforms state-of-the-art federated transfer learning and achieves desirable traffic scheduling performance in a cross-domain environment.

Distributed Resource Allocation for General Energy Efficiency Maximization in Offshore Maritime Device-to-Device Communication

In maritime scenario, the communication service, if any, is likely to be expensive due to the high-cost satellite transmissions or the very limited offshore coverage of terrestrial networks. Thus, how to achieve efficient radio resource management is one of the critical issues in maritime communication. In this letter, by exploiting the device-to-device (D2D) mechanism, we identify a general energy efficiency (GEE) optimization problem, which takes into account the physical link performance and the users’ pairing willingness (PW) quantified by a modified Jaccard coefficient metric. Then, we divide the GEE optimization problem into two sub-problems of user pairing and power allocation, and solve them by a distributed pairing method and by a new distributed resource allocation scheme, respectively. Simulation results demonstrate significant performance gains achieved by the proposed GEE maximization-based distributed D2D resource allocation (GEEM-DD2D-RA) algorithm over existing methods.

Distributed Joint Optimization of Beamforming and PowerAllocation for Maximizing the Energy Efficiency of CognitiveHeterogeneous Networks.

This paper investigated an energy-efficient beamforming and power allocation strategy for cognitive heterogeneous networks with multiple-input-single-output interference channels. To maximize the sum energy efficiency of secondary users (SUs) while keeping the interference to primary networks under a predetermined threshold, I propose a distributed resource allocation algorithm using dual methods, in which each SU updates its beamforming vector and transmit power iteratively without any information sharing until convergence. The simulation results verify that the performance of the proposed scheme is comparable to that of the optimal scheme but with a much shorter computation time.

Distributed Resource Allocation for Green HetNets with Renewable Energy Resources

In fifth generation (5G) systems, green heterogeneous network (HetNet) is capable of achieving energy efficiency by densely deploying renewable-powered small cells. However, the small cells may suffer performance degradation due to the limited backhaul from macro base station (BS) and renewable intermittency. In this paper, we introduce a distributed HetNet architecture in which the renewable-powered small cell BSs collaboratively exchange information and allocate the spectrum and power resources by themselves. Considering the uncertainty of the available spectrum, renewable energy supply and traffic loads, a stochastic optimization problem is formulated to maximize the energy efficiency for distributed small cell BSs. A distributed resource allocation algorithm is proposed to obtain the optimal spectrum and power allocating strategies for each small cell. Finally, the numerical results demonstrate the effectiveness of the proposed algorithm.

Distributed Algorithm Design for Resource Allocation Problems of High-Order Multiagent Systems

A distributed resource allocation problem of high-order systems is studied in this article, where the decisions of agents are constrained by network resources. In contrast to well-known resource allocation problems, this problem involves the high-order dynamics of agents, which results in the ineffectiveness of existing distributed resource allocation algorithms. In order to optimally allocate the network resource, a distributed algorithm is developed via state feedback and gradient descent. Moreover, the convergence of the algorithm is analyzed with the help of convex analysis and Lyapunov stability theory. By the algorithm, the high-order agents globally exponentially converge to the optimal solution. Finally, the algorithm is illustrated by numerical examples.

DRAGON: A Dynamic Distributed Resource Allocation Algorithm for Wireless Networks

We propose a dynamic distributed resource allocation algorithm for wireless networks, where the resources allocated to adjacent nodes (vertices) must be different and the topology of the network (graph) may change. Time is divided into rounds. In each round, a maximal independent set (MIS) is found, and the vertices in the MIS will use the resource in the next round. Therefore, the resource reuse equals the expected value of sizes of MISs, and we present two lower bounds for it. The number of messages transmitted by each vertex included/excluded in the MIS is one/zero, and messages are 1-bit. Therefore, the independence number of the graph is an upper bound for the number of bits transmitted in a round.

Distributed algorithm for resource allocation problems under persistent attacks

This paper explores the ability of a distributed resource allocation algorithm under attacks to identify the optimal solution in the framework of hybrid dynamical systems. Since the attacks can result in time-varying topologies, differential inclusions are used to model attack modes and an average dwell-time automaton and time-ratio monitor are employed to constrain attacks. To address the effect induced by such attacks, a switched algorithm is considered. Then, to analyze stability, the switched algorithm is first modeled as a hybrid system and then a Lyapunov function is constructed to prove that the distributed algorithm under attacks is able to converge to the optimal solution of the resource allocation problem. Moreover, based on automaton and monitor constraints, an inequality condition is provided to guarantee exponential stability of the optimal solution. Finally, an example is presented to verify the theoretical result.

An Overlapping Coalition Formation Game Based Multicast Scheme in Backhaul-Limited Small Cell Networks

This paper studies the resource allocation for multicast transmission in wireless small cell networks, which allocates appropriate Modulation and Coding Scheme (MCS) on each physical Resource Block (RB). This problem is not trivial due to the impact of interference and the limited wireless backhaul. We first formulate our concerned problem with the objective of maximizing the network throughput. Afterwards, we utilize framed overlapping coalition formulation game theory to design a distributed resource allocation algorithm. We further analyze the stability and convergence of the proposed algorithm. Finally, we conduct extensive simulations to demonstrate that the limited wireless backhaul capacity is an important factor when identifying the optimal multicast resource allocation solution, and our proposed algorithm can significantly improve the multicast throughput.