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Facing environmental pollution and resource waste, the green building becomes more and more popular. This study analyses the advantages and future trends of green buildings from the green building materials, framework, and design. Green buildings were proposed in the 20th century and slowly developed into the dominant architectural model today. The application of green building concepts in architecture focuses on three components of green materials, green frames, and green design. Green materials are the basis for the implementation of the green building concept, more and more high-performance, environmentally friendly green materials have been developed, effectively reducing environmental pollution and harm to the human body. The green framework is an integral part of the implementation of the green building concept, and the green framework composed of green materials can effectively use natural energy to replace or convert it into advanced energy, effectively reducing energy consumption and environmental pollution. Green design is the specific performance of the combination of the green building concept and the building, both improving building beauty and the environmental quality. It indicates that the concept of the green building makes better use of energy to create personalized buildings that are harmless to the environment and better use energy, providing residents with a more comfortable environment.

A variety of novel mobile applications are developed to attract the interests of potential users in the emerging 5G-enabled vehicular networks. Although computation offloading and task scheduling have been widely investigated, it is rather challenging to decide the optimal offloading ratio and perform adaptive task scheduling in high-dynamic networks. Furthermore, the scheduling policy made by the network operator may be violated, since vehicular users are rational and selfish to maximize their own profits. By considering the incentive compatibility and individual rationality of vehicular users, we present POETS, an efficient partial computation offloading and adaptive task scheduling algorithm to maximize the overall system-wide profit. Specially, a two-sided matching algorithm is first proposed to derive the optimal transmission scheduling discipline. After that, the offloading ratio of vehicular users can be obtained through convex optimization, without any information of other users. Furthermore, a non-cooperative game is constructed to derive the payoff of vehicular users that can reach the equilibrium between users and the network operator. Theoretical analyses and performance evaluations based on real-world traces of taxies demonstrate the effectiveness of our proposed solution.

The paradigm of Internet of Things (IoT) has attracted much attention in both academia and industry areas in the past decades. Recently, the integration of IoT and social networks is advocated to promote further development. This article focuses on the applications, solutions, and challenges of Social Internet of Things (SIoT) over collaborative edge computing, which exploits the advantages of both mobile edge computing and social relationships among SIoT users. First, applications of collaborative offloading, caching, and streaming data processing are illustrated, followed by representative social-aware solutions, including auction, coalition game, and federated learning. Finally, several research challenges are described. The main contributions of this article are as follows: 1) we specify the role of social ties in traditional applications of IoT and its impacts on individual's selection; 2) we elaborate the reasons why the presented three approaches can be applied in SIoT; and 3) the discussed challenges can contribute to the future development of secure, robust, and intelligent SIoT frameworks.

Due to line-of-sight communication links and distributed deployment, Unmanned Aerial Vehicles (UAVs) have attracted substantial interest in agile Mobile Edge Computing (MEC) service provision. In this paper, by clustering multiple users into independent communities based on their geographic locations, we design a 5G-enabled UAV-to-community offloading system. A system throughput maximization problem is formulated, subjected to the transmission rate, atomicity of tasks and speed of UAVs. By relaxing the transmission rate constraint, the mixed integer non-linear program is transformed into two subproblems. We first develop an average throughput maximization-based auction algorithm to determine the trajectory of UAVs, where a community-based latency approximation algorithm is developed to regulate the designed auction bidding. Then, a dynamic task admission algorithm is proposed to solve the task scheduling subproblem within one community. Performance analyses demonstrate that our designed auction bidding can guarantee user truthfulness, and can be fulfilled in polynomial time. Extensive simulations based on real-world data in health monitoring and online YouTube video services show that our proposed algorithm is able to maximize the system throughput while guaranteeing the fraction of served users.

The recent COVID-19 pandemic has brought significant challenges to the traditional healthcare industry. A novel eHealth system is required to cope with infectious and chronic diseases. This article proposes an online eHealth system to monitor patients and provides edge computing services based on the Internet of Unmanned Aerial Vehicles (UAVs). In order to minimize the health monitoring latency and guarantee the resource utilization efficiency of UAVs, the Lyapunov optimization method is utilized to decompose the long-term optimization problem into a series of instantaneous optimization problems. Then the decomposed sub-problem is formulated as a minimum cost maximum flow problem by constructing a bipartite graph that maps medical analysis requests to target UAVs. Extensive experimental results demonstrate the effectiveness of our system. Finally, system analyses illustrate that the robustness and scalability of our eHealth system is favorable, and our system can provide agile edge computing services for dispersed patients.

The explosive growth of mobile devices promotes the prosperity of novel mobile applications, which can be realized by service offloading with the assistance of edge computing servers. However, due to limited computation and storage capabilities of a single server, long service latency hinders the continuous development of service offloading in mobile networks. By supporting multi-server cooperation, Pervasive Edge Computing (PEC) is promising to enable service migration in highly dynamic mobile networks. With the objective of maximizing the system utility, we formulate the optimization problem by jointly considering the constraints of server storage capability and service execution latency. To enable dynamic service placement, we first utilize Lyapunov optimization method to decompose the long-term optimization problem into a series of instant optimization problems. Then, a sample average approximation-based stochastic algorithm is proposed to approximate the future expected system utility. Afterwards, a distributed Markov approximation algorithm is utilized to determine the service placement configurations. Through theoretical analysis, the time complexity of our proposed algorithm is linear to the number of users, and the backlog queue of PEC servers is stable. Performance evaluations are conducted based on both synthetic and real trace-driven scenarios, with numerical results demonstrating the effectiveness of our proposed algorithm from various aspects.

The prompt evolution of Internet of Medical Things (IoMT) promotes pervasive in-home health monitoring networks. However, excessive requirements of patients result in insufficient spectrum resources and communication overload. Mobile Edge Computing (MEC) enabled 5G health monitoring is conceived as a favorable paradigm to tackle such an obstacle. In this paper, we construct a cost-efficient in-home health monitoring system for IoMT by dividing it into two sub-networks, i.e., intra-Wireless Body Area Networks (WBANs) and beyond-WBANs. Highlighting the characteristics of IoMT, the cost of patients depends on medical criticality, Age of Information (AoI) and energy consumption. For intra-WBANs, a cooperative game is formulated to allocate the wireless channel resources. While for beyond-WBANs, considering the individual rationality and potential selfishness, a decentralized non-cooperative game is proposed to minimize the system-wide cost in IoMT. We prove that the proposed algorithm can reach a Nash equilibrium. In addition, the upper bound of the algorithm time complexity and the number of patients benefiting from MEC is theoretically derived. Performance evaluations demonstrate the effectiveness of our proposed algorithm with respect to the system-wide cost and the number of patients benefiting from MEC.

The emerging 5G-enabled vehicular networks can satisfy various requirements of vehicles by traffic offloading. However, limited cellular spectrum and energy supplies restrict the development of 5G-enabled applications in vehicular networks. In this article, we construct an intelligent offloading framework for 5G-enabled vehicular networks, by jointly utilizing licensed cellular spectrum and unlicensed channels. A cost minimization problem is formulated by considering the latency constraint of users and is further decomposed into two subproblems due to its complexity. For the first subproblem, a two-sided matching algorithm is proposed to schedule the unlicensed spectrum. Then, a deep-reinforcement-learning-based method is investigated for the second one, where the system state is simplified to realize distributed traffic offloading. Real-world traces of taxies are leveraged to illustrate the effectiveness of our solution.

The emerging vehicular services call for updated communication and computing platforms. Fog computing, whose infrastructure is deployed in close proximity to terminals, extends the facilities of cloud computing. However, due to the limitation of vehicular fog nodes, it is challenging to satisfy the quality of experiences of users, calling for intelligent networks with updated computing abilities. This paper constructs a three-layer offloading framework in intelligent Internet of Vehicles (IoV) to minimize the overall energy consumption while satisfying the delay constraint of users. Due to its high computational complexity, the formulated problem is decomposed into two parts: 1) flow redirection and 2) offloading decision. After that, a deep reinforcement learning-based scheme is put forward to solve the optimization problem. Performance evaluations based on real-world traces of taxis in Shanghai, China, demonstrate the effectiveness of our methods, where average energy consumption can be decreased by around 60% compared with the baseline algorithm.

The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users’ traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users’ Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.

Mobile Edge Computing (MEC) is promising to alleviate the computation and storage burdens for terminals in wireless networks. The huge energy consumption of MEC servers challenges the establishment of smart cities and their service time powered by rechargeable batteries. In addition, Orthogonal Multiple Access (OMA) technique cannot utilize limited spectrum resources fully and efficiently. Therefore, Non-Orthogonal Multiple Access (NOMA)-based energy-efficient task scheduling among MEC servers for delay-constraint mobile applications is important, especially in highly-dynamic vehicular edge computing networks. The various movement patterns of vehicles lead to unbalanced offloading requirements and different load pressure for MEC servers. Self-Imitation Learning (SIL)-based Deep Reinforcement Learning (DRL) has emerged as a promising machine learning technique to break through obstacles in various research fields, especially in time-varying networks. In this paper, we first introduce related MEC technologies in vehicular networks. Then, we propose an energy-efficient approach for task scheduling in vehicular edge computing networks based on DRL, with the purpose of both guaranteeing the task latency requirement for multiple users and minimizing total energy consumption of MEC servers. Numerical results demonstrate that the proposed algorithm outperforms other methods.