Modern network-centric operations increasingly rely on multi-layer Kill Webs (KWs), enabling redundant and non-linear sensing-to-strike pathways while introducing a combinatorial equipment selection problem under uncertainty and resource constraints. This paper formulates a multi-layer KW equipment combination selection as a sequential decision-making problem by explicitly modeling heterogeneous equipment capabilities, resource constraints, and the network topology. To address this problem, we developed an RL learning-based optimization framework, where a multi-objective reward function integrates normalized relevance, operational risk, and timeliness, with a penalty mechanism for infeasible or incomplete kill-chain closure. Based on the jointly captured state information (e.g., network structure, equipment attributes, target characteristics, and resource availability), an Actor-Critic (AC) algorithm is developed to learn adaptive equipment combination selection across different operational stages using temporal-difference advantage estimation and entropy regularization. Simulation results under diverse battlefield scenarios demonstrate that the proposed framework consistently outperforms Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Particle Swarm Optimization (PSO), achieving at least a 19.6% improvement in overall operational effectiveness while maintaining low decision latency.

A new concept of center processing unit (CPU)-integrated simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is proposed, namely computing STAR-RIS. Computation-intensive and delay-sensitive tasks from mobile users can be partially processed at the computing STAR-RIS. We aim to minimize the energy consumption of users and the computing STAR-RIS, and formulate a joint task offloading and transmission resource allocation problem. The solution of this problem is affected by the offloading decision and the amplitude and phase-shift of the computing STAR-RIS. To solve the non-convex problem, we decompose it into two subproblems: 1) For the task offloading subproblem, the offloading decision is optimized utilizing the Karush-Kuhn- Tucker (KKT) conditions; and 2) For the transmission resource allocation subproblem, the transmission-reflection coefficient matrix are optimized via successive convex approximation (SCA). Simulation results show that our proposed algorithm can converge faster and have lower energy consumption than the conventional STAR-RIS.

With the advancement of mobile communication technology, there has been a marked increase in the demand for personalized and ubiquitous Internet of Things (IoT) services, raising the expectations for network Quality of Service (QoS) and Quality of Experience (QoE). Existing popularity-prediction-based content caching policies improve QoS and QoE by precaching contents at the network edge, but jointly optimizing multiple network metrics remains a challenge. To address this challenge, we propose a many-objective optimization-based popularity prediction for cooperative caching (MaOPPC-Caching) framework for cloud–edge–end collaborative IoT networks. This framework simultaneously optimizes prediction accuracy, delay, offloaded traffic, and load balance. We integrate three prediction algorithms to forecast content popularity and present a horizontal and vertical collaborative caching decision strategy to generate caching forms based on the predicted results. Then, the many-objective evolutionary algorithm (MaOEA) is employed to optimize the combined proportions to take full advantage of hidden preferences and popularity characteristics of both users and items. To promote the convergence of the framework, we present a knowledge mining-based MaOEA (KMaOEA) to incorporate knowledge mining into the optimization process. Simulation results show that the proposed MaOPPC-Caching framework outperforms existing prediction algorithms in terms of four evaluation indicators. Furthermore, KMaOEA shows a significant advantage over NSGA-III in load balance, as indicated by a Mann–Whitney rank sum test with a$p$-value of 0.040.

The development of intelligent transportation systems (ITS) has attracted significant attention to connected and autonomous vehicles (CAVs). It is urgent to investigate multi-CAV intelligent cruise control solutions in mixed traffic environments. In addition, the impact of platoon dynamics and time delays, induced by shared wireless communications, data processing, and actuation cannot be ignored. This article investigates the development of a multi-agent deep reinforcement learning (MADRL) controller tailored for CAVs operating within mixed and dynamic traffic scenarios that involve time delays. Firstly, the error dynamics in the discrete-time domain for each subplatoon is derived by considering the time-varying delays and leading vehicle states, and then the optimal CAV cruise control problem is formulated. Subsequently, the partially observable Markov game (POMG) is used to construct the multi-agent environment, and then a centralized training decentralized execution (CTDE) algorithm framework is proposed based on the multi-agent deep deterministic policy gradient (MADDPG) method. Finally, the computational complexity and the influence of delay are analyzed. The simulation results illustrate the effectiveness of the proposed intelligent algorithm.

To satisfy the requirements of content distribution in computation-intensive and delay-sensitive services, this paper presents a novel joint task offloading and content caching (JTOCC) scheme in multi-cell multi-carrier non-orthogonal multiple-access (MCMC-NOMA)-assisted cloud-edge-terminal cooperation networks. Based on queuing theory, we formulate a delay minimization model that aggregates users’ requests to reduce repeated content delivery. To minimize network latency, the model is decomposed into three subproblems: task offloading, user clustering and communication resource allocation, and cache state updating. In each slot, the task offloading subproblem is solved utilizing deep reinforcement learning (DRL) under a resource-constrained cloud-edge-terminal setting. During a transition between slots, mobile terminals are grouped using K-means-based user clustering, and the allocations of the subchannels and transmit power are optimized utilizing matching theory and successive convex approximation (SCA), respectively. Contents cached at the network nodes are updated, according to long-short-term memory (LSTM)-based predicted popularity. Simulations show that the proposed JTOCC model achieves lower-delay content distribution than its existing counterparts in cloud-edge-terminal cooperation environments, and converges fast in heterogeneous networks.

A simultaneously transmitting and reflecting surface (STARS) assisted multi-user downlink multiple-input signal-output (MISO) multi-cellular edge caching system is investigated. The deployment of STARS enhances the coverage of base stations (BSs), particularly at cellular boundaries. However, this advancement introduces a complex user association issue that necessitates the consideration of both caching state and channel state information (CSI). In this paper, we formulate a joint optimization problem involving content caching, user association, active beamforming at BS, and passive beamforming at STARS for minimizing long-term power consumption. We propose two algorithms for the formulated problem: 1) A two time-scale cooperative twin delayed deep deterministic policy gradients (TD3). Considering the distinct time scales of the pushing and delivering phases in edge caching, the Markov decision process (MDP) models of dual time scales are constructed and two deep reinforcement learning (DRL) agents work together to jointly address the optimization problem. 2) A bio-inspired DRL framework, especially, a particle swarm optimization (PSO)-inspired TD3 algorithm is introduced in detail. Inspired by the behavior of the biological population in nature, this algorithm regards agents as individuals and enables the concurrent training of multiple agents while they interact with global information via a biological population information interaction mode, thereby enhancing the performance of power optimization. The numerical results demonstrate that the STARS-assisted multi-cellular edge caching system has advantages over traditional cellular systems, especially in scenarios where the number of mobile users and Zipf skewness factor is large. Moreover, the proposed two time-scale cooperative TD3 and PSO-inspired TD3 algorithms are superior in reducing network power consumption than conventional TD3.

A simultaneously transmitting and reflecting surface (STARS) enabled edge caching system is proposed for reducing backhaul traffic and ensuring the quality of service. A novel Caching-at-STARS structure, where a dedicated smart controller and cache memory are installed at the STARS, is proposed to satisfy user demands with fewer hops and desired channel conditions. Then, a joint caching replacement and information-centric hybrid beamforming optimization problem is formulated for minimizing the network power consumption. As long-term decision processes, the optimization problems based on independent and coupled phase-shift models of Caching-at-STARS contain both continuous and discrete decision variables, and are suitable for solving with deep reinforcement learning (DRL) algorithm. For the independent phase-shift Caching-at-STARS model, we develop a frequency-aware based twin delayed deep deterministic policy gradient (FA-TD3) algorithm that leverages user historical request information to serialize high-dimensional caching replacement decision variables. For the coupled phase-shift Caching-at-STARS model, we conceive a cooperative TD3 & deep-Q network (TD3-DQN) algorithm comprised of FA-TD3 and DQN agents to decide on continuous and discrete variables respectively by observing the network external and internal environment. The numerical results demonstrate that: 1) The Caching-at-STARS-enabled edge caching system has advantages over traditional edge caching, especially in scenarios where Zipf skewness factors or cache capacity is large; 2) Caching-at-STARS outperforms the RIS-assisted edge caching systems; 3) The proposed FA-TD3 and cooperative TD3-DQN algorithms are superior in reducing network power consumption than conventional TD3.

To satisfy the differentiated service requirements of delay-sensitive and computing-intensive tasks, it is urgent to efficiently allocate limited network resources to improve content distribution in cloud-edge environments. In this paper, we proposes a task partition-based intelligent offloading scheme to optimize resource allocation in cache-assisted cloud-edge cooperation environments. Specifically, we formulate the task partition-based optimal computation offloading problem as a latency minimization model in the cache-aided cloud-edge collaboration system. A new deep reinforcement learning (DRL) algorithm is designed to make optimal subtask offloading and resource allocation decisions based on current network state information, improving resource utilization and network delay. Simulation results demonstrate that the proposed model achieves lower-latency content delivery than the existing popular models in cache-enabled cloud-edge cooperation networks, and fast converges.

To satisfy differentiated service requirements of delay-sensitive and computing-intensive tasks in satellite communication networks, we propose a cache-assisted low-latency content distribution scheme in multi-cell multi-carrier nonorthogonal multiple access (MCMC-NOMA)-based satellite-edge-terminal cooperation environments. In the paper, a delay minimization problem is formulated to achieve optimal content distribution by jointly optimizing the allocation of subchannels and transmit power, which is decomposed and solved in each slot by using many-to-one matching and successive convex approximation, respectively. Simulation results show that the proposed model significantly improve network latency and content distribution in comparison to its existing counterparts in cloud-edge-terminal cooperation networks.

A novel Caching-at-STARS structure, where dedicated smart controller and cache memory are installed at the STARS, is proposed to satisfy user demands with fewer hops and desired channel condition. Then, a joint caching replacement and information-centric hybrid beamforming optimization problem is formulated for minimizing the network power consumption. We conceive a cooperative twin delayed deep deterministic policy gradient & deep-Q network (TD3-DQN) algorithm comprised by TD3 and DQN agents to decide on continuous and discrete variables respectively by observing the network external and internal environment. The numerical results demonstrate that: 1) The Caching-at-STARS-enabled edge caching system has advantages over traditional edge caching, especially in scenarios where Zipf skewness factors or cache capacity is large; 2) STARS outperforms RIS significantly in edge caching systems; 3) The proposed cooperative TD3-DQN algorithms is superior in reducing network power consumption than conventional TD3.

With the widespread utilization of intelligent devices, massive mobile users' needs for rich multimedia services bring serious challenges in the aspects of network traffic, energy consumption and carbon emission. How to realize green content distribution by optimizing resource allocation is an urgent problem to solve in complex and dynamic networks. In this paper, we design a cross-layer cooperative scheme to promote energy efficiency in non-orthogonal multiple access (NOMA)-assisted cloud-edge-side environments. To be specific, we formulate the joint optimization issue of computation, caching and communication resources as an energy minimization model while considering request aggregation. Next, we propose a new deep reinforcement learning (DRL)-based task offloading strategy to minimize energy consumption by making optimal resource allocation decisions according to content request history and resource availability. Simulation results show that the proposed solution has better performance than current typical strategies in cloud-edge-end collaboration environments.

Aiming at providing communication services to sparsely-populated areas and easing the burden of the existing ground network, non-terrestrial networks (NTN) have been under the exploration of 3GPP. In this present research, a novel design of the initial access procedure is proposed on the premise that both the satellite base station (SBS) and the terrestrial base station (TBS) are capable of leveraging large antenna arrays to perform beamforming at both transmission and receiving ends. In the proposed architecture, a group of user equipment (UE) can share the same resource for communication with other groups of UE within the same satellite cell, in light of the spatial orthogonality this designed system promises. Analysis and simulations not only reveal that in this architecture, a desirable signal-to-interference-plus-noise ratio (SINR) can be obtained if a proper searching beam offset is chosen, but also infer a 99% preamble detection rate can be reached even within a very low SINR range in single beam scenario.

With the continuous development of the 5th Generation Mobile Communication Technology (5G), various forms and demands of the Internet of Things (IoT) business have emerged. There are high requirements about the latency and reliability in some IoT services, especially for the ultra reliable low latency Communication (URLLC) services of IoT. One important factor for URLLC is time-frequency resource allocation. In this paper, a joint delay and channel quality based proportional fair scheduling (JDCPF) algorithm is proposed, which considers scheduling delay and channel conditions to calculate scheduling priorities for different IoT services with different communication requirements. Moreover, an adjustment factor is introduced based on the latency sensitivity of different services. According to the provided simulation results, the proposed JDCPF algorithm effectively could help to reduce the scheduling latency for latency-sensitive services.

In this paper, we develop a unified dynamic intelligent reflecting surface (IRS) beamforming framework to boost the sum computation rate of an IRS-aided mobile edge computing (MEC) system, where each device follows a binary offloading policy. Specifically, the task of each device has to be either executed locally or offloaded to MEC servers as a whole with the aid of given number of IRS beamforming vectors available. By flexibly controlling the number of times for IRS reconfiguring phase-shifts, the system can achieve a balance between the performance and associated signalling overhead. We aim to maximize the sum computation rate by jointly optimizing the computational mode selection for each device, offloading time allocation, and IRS beamforming vectors across time. Since the resulting optimization problem is non-convex and NP-hard, there are generally no standard methods to solve it optimally. To tackle this problem, we first propose a penalty-based successive convex approximation algorithm, where all the associated variables in the inner-layer iterations are optimized simultaneously and the obtained solution is guaranteed to be locally optimal. Then, we further derive the offloading activation condition for each device by deeply exploiting the intrinsic structure of the original optimization problem. According to the offloading activation condition, a low-complexity algorithm based on the successive refinement method is proposed to obtain high-quality suboptimal solutions, which are more appealing for practical systems with a large number of devices and IRS elements. Moreover, the optimal condition for the proposed low-complexity algorithm is revealed. The effectiveness of the proposed algorithms is demonstrated through numerical examples. In addition, the results illustrate the practical significance of the IRS in MEC systems for achieving coverage extension and supporting multiple energy-limited devices for task offloading, and also unveil the fundamental performance-cost tradeoff embedded in the proposed dynamic IRS beamforming framework.

A novel random access (RA) preamble format is proposed in this paper to support fifth generation new ratio (5G NR) enabled satellite system, which is a low earth orbiting (LEO) based non-terrestrial network (NTN). Considering a fact that traditional design of RA preamble can not meet the link budget due to a long distance between the satellite and terminal on the earth, and also will cause a wrong or failure detection of PRACH, or wrong timing estimation for uplink synchronization. The frequency offset under the large relative moving speed between the satellite and the terminal will also increase the failure detection of PRACH (physical random access channel). Therefore, a novel RA preamble format, i.e., a Zadoff-Chu (ZC) sequence with multiple lengths, are designed. To reduce the ambiguous estimation of RA preamble, a symmetric transmission of the proposed preamble is analyzed. Further, two detection algorithms (Algorithm 1 and Algorithm 2) are proposed to detect PRACH. Simulation results validate that the proposed RA preamble can meet the LEO based NTN performance requirements. According to simulation results, it can be proved that Algorithm 2 is more robust, considering timing error and frequency offset.

In this paper, we consider a downlink multiple-input single-output non-orthogonal multiple access (MISO-NOMA) network with several legitimate users and a eavesdropper using successive interference cancellation (SIC). The purpose of this paper is to maximize the secrecy performance of the MISO-NOMA network by designing the transmit power between the legitimate users and the artificial jamming. Explicitly, the secrecy sum rate of the MISO-NOMA network is to be maximized by optimizing the transmit beamforming vectors and the artificial jamming vector, subject to the required quality of service of each legitimate user, the artificial jamming beamforming design constraint and the SIC decoding condition. Due to the non-convexity of the optimization problem, we reformulate the original problem into an equivalent optimization problem and then provide a successive convex approximation based iterative algorithm for solving it. Simulation results demonstrate that the proposed optimization scheme outperforms the existing schemes.

With the rapid development of wireless communication technologies, the emerging multimedia applications make mobile Internet traffic grow explosively while putting forward higher service requirements for the next-generation wireless networks. Therefore, how to achieve low-latency content transmission by effectively allocating heterogeneous network resources to improve the network quality of service and end-user quality of experience is a key issue to be solved urgently in the current Internet. In this article, we propose a deep reinforcement learning (DRL)-based resource allocation scheme to improve content distribution in a layered fog radio access network (FRAN). We formulate the optimal resource allocation problem as a minimal delay model, where in-network caching is deployed and the same content requests from mobile users can be aggregated in the queue of each base station. To cope with the increasing user requests and overcome capacity constraints of the FRAN, moreover, a cloud–edge cooperation offloading scheme is utilized in our model, where the integrated allocation of caching, computing, and communication resources and joint optimization between in-network caching and routing are considered to promote resource utilization and content delivery. In our solution, a new DRL policy is designed to make cross-layer cooperative caching and routing decisions for the arriving content requests according to request history information and available network resources in the system. Simulation results demonstrate that our proposed model can performs much better than the existing cloud–edge cooperation schemes in the FRAN.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) offers a promising technique to fulfil the high data demand and connectivity of the Internet-of-Things (IoT) and 5G communications because it owns valuable and unknown spectrum resources. Using massive antennas with recently introduced drone-enabled aerial computing platforms, named unmanned aerial vehicles (UAVs), can cast high energy consumption if fully-digital precoding is employed at the UAVs. Using hybrid precoding at a UAV can reduce hardware complexity and energy consumption but is challenging with a need for joint optimization of three precoding matrices at the UAV (sixth-order polynomial objective function). In this paper, we propose to decompose the original UAV hybrid precoding challenge into three subproblems and develop a coordinated descent optimization (CDO) algorithm to solve the three problems recursively. In addition, the convergence and complexity of this new technique are analyzed. Numerical studies indicate the improved effectiveness of the proposed solution over existing solutions.

In this paper, the optimal navigation control design for biomedical untethered microrobot is comprehensively investigated in discrete-time domain with stochastic network-induced delays. First, the error dynamics of the microrobot tracking location and velocity are analyzed based on the 3D-based microrobot navigation modeling. Then, the optimal navigation optimization problem is formulated to regulate the microrobot to achieve the target reference trajectory, and a two-step control algorithm is proposed by using a backward recursion method. In particular, for each sampling interval, the optimal control gain is iteratively derived off-line and the control strategy can be calculated on-line in a real-time fashion.

Wireless sensor actuator network (WSAN) emerges as a potential technology with the capacities of self-organizing communication and feedback control. In this paper, we present a novel collaborative optimization algorithm of plant control and system cost toward WSANs taking time delay into account. First, the WSAN model is formulated as a linear system with multipath network structure. In order to provide effective control and reduce the usage of system resource, the quadratic cost function is introduced as the collaborative optimization problem in discrete-time domain. Then, a two-phase design is proposed to derive the design of optimal control for each given path in terms of a backward recursion. In addition, the best transmission path selection is obtained depending on minimal system power consumption. Finally, numerical simulations are utilized to show the effectiveness of the proposed algorithm in both traditional control system and load frequency control in the power grid application.

With the rapid developments of wireless communication and increasing number of connected vehicles, Vehicular Ad Hoc Networks (VANETs) enable cyberinteractions in the physical transportation system. Future networks require real-time control capability to support delay-sensitive application such as connected autonomous vehicles. In recent years, fog computing becomes an emerging technology to deal with the insufficiency in traditional cloud computing. In this paper, a fog-based distributed network control design is proposed toward connected and automated vehicle application. The proposed architecture combines VANETs with the new fog paradigm to enhance the connectivity and collaboration among distributed vehicles. A case study of connected cruise control (CCC) is introduced to demonstrate the efficiency of the proposed architecture and control design. Finally, we discuss some future research directions and open issues to be addressed.

As mobile data rapidly grows, power efficiency problem becomes an increasing concern in wireless networks. To efficiently reduce energy consumption, nowadays researchers attempt to introduce the thought of "edge cache" into Internet. However, the power efficiency problem in the existing solutions is mainly researched under the background of the access networks and lack of in-depth analysis from the perspective of the whole network. Therefore, we design a new power minimization mechanism for content distribution applications by deploying edge caches in wireless network scenarios. Then, we theoretically analyze the optimal power efficiency problem to realize efficient content distribution by simultaneously taking into account the effects of edge cache size, popularity distribution of network contents, network topology, and the number of different contents. Simulation process indicates that the designed model can significantly reduce power consumption in comparison to traditional Internet solutions without deploying edge caches at the edge of wireless networks.

In this paper, an optimal control algorithm for connected vehicle systems is proposed in order to ensure the vehicular platoon stable as well as reduce the transmission power consumptions in the presence of the network-induced delays. First, the vehicle dynamic modeling and power consumption analysis are addressed based on a typical 3-vehicle platoon. With the objective of minimizing the deviations of vehicle's headway and velocity as well as reducing power consumption, an optimization problem is formulated using a quadratic cost function. Then, the design of the optimal control strategy with minimum power consumption for connected vehicle systems can be divided into two steps: first the minimal hop routings for human- driven vehicles are obtained based on the network topology, and then the optimal control strategy is derived based on the determined transmission routing.

Wireless sensor and actuator networks (WSANs), as the promising technologies to realize efficient and energy-saving control, recently have been one of the main research focuses in academic fields as well as in control applications. In this paper, considering the network-induced delays, the joint design of optimal control strategy and power consumption for WSANs is addressed. First, the WSAN system with multiple transmission paths is modeled as a linear system and the joint optimization problem is formulated by using a quadratic cost function. Then, a two-step control scheme is presented to realize the joint design of control law and power consumption. In particular, the optimal control strategy for each given path is iteratively derived, and then the optimal transmission path is selected with the minimum system cost. Finally, numerical simulations in both generic control systems and power grid systems demonstrate the effectiveness of the proposed control scheme.

In most cases, the batteries of sensor nodes in the Internet of Things (IoT) are usually constrained by size and weight, and are difficult to recharge or replace. In traditional wireless sensor networks, data is transmitted in a multi-hop manner, which may cause the high data transmission delay and unbalanced traffic load. In this paper, an Unmanned Aerial Vehicle (UAV)-assisted IoT architecture is introduced, in which UAV is utilized to achieve low-latency and seamless-coverage acquisition of the sensing data. Furthermore, based on the recent advances on deep reinforcement learning algorithms, considering both data delay requirements and network energy consumption, a real-time flight path planning scheme of the UAV in the dynamic IoT sensor networks has been proposed based on dueling deep Q-network (DQN). Besides, the grid-based method is used to handle the network state modeling, which effectively reduces the complexity of the proposed scheme. Simulation results show that the proposed scheme significantly improves the network performance.

Considering the distributed controllers, this paper studies the optimal control law for the wireless sensor and actuator network (WSAN) with stochastic network-induced delays. First, the structure of the WSAN including multiple controllers is presented, and the network stochastic properties such as the network delay and plant noise are analyzed. Then, the optimization problem to minimize the total cost in order to keep the system stability is formulated, and the optimal state estimation control strategy is derived using the Kalman filter approach and the non-cooperative game. Finally, the proposed algorithm is validated by the simulation experiments of a stable control system and the load frequency control system.

Summary In order to suppress the interference in local area networks, this paper presents a Wireless Local Area Networks (WLAN) interference self‐optimization method based on a Self‐Organizing Feature Map (SOM) neural network model. This method trains the model by using original data sets as the initial vector set and using the whole Signal to Interference plus Noise Ratio (SINR) vector generated by the change of one Wireless Access Point (AP) channel as the basic feature. After the training, the SOM neural network can quickly locate the fault AP and optimize the network according to the changes of the network environment. Simulation results reveal that the proposed scheme can efficiently locate the AP where interference happens and optimize the interference with an improved user experience. Copyright © 2016 John Wiley & Sons, Ltd.

In Content-Centric Networking (CCN), most existing works do not consider energy savings by turning off network devices in CCN. In this paper, we systematically analyze the energy efficiency problem in CCN by turning off the content routers and network links. We formulate the energy consumption issue as a Mixed Integer Linear Programming (MILP) model, and propose a centralized solution via spanning tree heuristic and a fully distributed consensus optimization algorithm via the alternating direction method of multipliers (ADMM) to solve the problem for CCN. By duplicating flow variables, the energy consumption problem decomposes into node specific subproblems with local variables. These variables are iteratively driven into consensus via the ADMM. Simulation results reveal that the proposed distributed algorithm is amenable to energy-efficient implementation, due to smaller amount of local information exchange at each iteration. Moreover, the proposed algorithm can converge to final status in a significantly smaller number of iterations compared to the method based on dual decomposition. In addition, our algorithm scales better to large networks and it does not require intensive finetuning of the step size.

Due to the in-network caching capability, Content-Centric Networking (CCN) has emerged as one of the most promising architectures for the diffusion of contents over the Internet. Most existing works on CCN focus on network resource utilization, and the energy efficiency aspect is largely ignored. In this paper, we formulate the energy consumption issue as a Mixed Integer Linear Programming (MILP) problem, and propose a centralized solution via spanning tree heuristic and a fully distributed energy consumption optimization algorithm via dual decomposition (DD) to solve the problem for CCN. The dual decomposition method transforms the centralized energy consumption optimization problem into the router status, link status, and link flow subproblems. Simulation results reveal that the proposed scheme exhibits a fast convergence speed, and achieves superior energy efficiency compared to other widely used schemes in CCN.