Recognizing human physical activities from streaming smartphone sensor readings is essential for the successful realization of a smart environment. Physical activity recognition is one of the active research topics to provide users the adaptive services using smart devices. Existing physical activity recognition methods lack in providing fast and accurate recognition of activities. This paper proposes an approach to recognize physical activities using only2-axes of the smartphone accelerometer sensor. It also investigates the effectiveness and contribution of each axis of the accelerometer in the recognition of physical activities. To implement our approach, data of daily life activities are collected labeled using the accelerometer from 12 participants. Furthermore, three machine learning classifiers are implemented to train the model on the collected dataset and in predicting the activities. Our proposed approach provides more promising results compared to the existing techniques and presents a strong rationale behind the effectiveness and contribution of each axis of an accelerometer for activity recognition. To ensure the reliability of the model, we evaluate the proposed approach and observations on standard publicly available dataset WISDM also and provide a comparative analysis with state-of-the-art studies. The proposed approach achieved 93% weighted accuracy with Multilayer Perceptron (MLP) classifier, which is almost 13% higher than the existing methods.

https://www.mdpi.com/1424-8220/20/8/2216/pdf

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition.

Network function virtualization (NFV) in 6G can use standard virtualization techniques to enable network functions via software. Resource scheduling is one of the key research areas of NFV in 6G and is mainly used to deploy service function chains (SFCs) in substrate networks. However, determining how to utilize network resources efficiently has always been a difficult problem in SFC deployment. This article focuses on how to efficiently provision online SFC requests in NFV with 6G. We first establish a mathematical model for the problem of online SFC provisioning. Then, we propose an efficient online service function chain deployment (OSFCD) algorithm that selects the path to deploy that is close to the SFC length. Finally, we compare our proposed algorithm with three other existing algorithms by simulation experiments. The experimental results show that the OSFCD algorithm optimizes multiple performance indicators of online SFC deployment.

Dynamic Network Function Provisioning to Enable Network in Box for Industrial Applications

Recently, a new technology topic has been known as the Internet of Things (IoT), where all devices like smartphones, smart TVs, medical and healthcare ones, and home appliances have been applied for data generating. Due to the variety of services, the numerous service composition problems, mostly related to the Quality-of-Service (QoS) parameters, are recognized in the IoT domain. Since this issue is an NP-hard obstacle, different metaheuristic approaches have been utilized up until now to solve it. Many varieties of services can be brought into the IoT, depending on users’ demands. In this research, we have proposed an effective way based on a hidden Markov model (HMM) and an ant colony optimization (ACO) to answer the service composition issue by enhancing the QoS. The HMM has been trained to predict QoS. The emission and transition matrices have been improved using the Viterbi algorithm. We have executed the QoS estimation using the ACO algorithm and found a suitable path. The outcomes have illustrated the efficacy of the introduced method regarding availability, response time, cost, reliability, and energy consumption compared to the previous methods.

A QoS-Aware Service Composition Mechanism in the Internet of Things Using a Hidden-Markov-Model-Based Optimization Algorithm

Network function virtualization (NFV) and mobile-edge computing (MEC) have been introduced by Internet service providers (ISPs) to deal with various challenges, which hinder them from satisfying ambitious quality of experience demands of the Internet-of-Things (IoT) applications. In NFV/MEC-enabled IoT networks, any IoT service can be expressed as a service function chain (SFC) consisting of several strictly ordered virtual network functions (VNFs), which can be geographically placed onto edge clouds close to IoT terminals. However, regarding the large number of IoT terminals and constant dynamics of IoT networks, determining the placement of VNFs and routing service paths that optimize the end-to-end delays in the hybrid edge clouds is a challenging problem. This problem is also called SFC dynamic orchestration problem (SFC-DOP). To address the SFC-DOP, we are motivated to creatively present an SFC dynamic orchestration framework for IoT deep reinforcement learning (DRL). Also, a DRL-based algorithm for SFC-DOP with the actor–critic and the deterministic policy gradient scheme is provided, which can efficiently deal with the SFC-DOP in IoT networks. The obtained experimental results show the outstanding performance of our proposal compared with the current benchmarks.

Dynamic Service Function Chain Orchestration for NFV/MEC-Enabled IoT Networks: A Deep Reinforcement Learning Approach

Network slicing has become a fundamental property for next-generation networks, especially because an inherent part of 5G standardisation is the ability for service providers to migrate some or all of their network services to a virtual network infrastructure, thereby reducing both capital and operational costs. With network function virtualisation (NFV), network functions (NFs) such as firewalls, traffic load balancers, content filters, and intrusion detection systems (IDS) are either instantiated on virtual machines (VMs) or lightweight containers, often chained together to create a service function chain (SFC). In this work, we review the state-of-the-art NFV and SFC implementation frameworks and present a taxonomy of the current proposals. Our taxonomy comprises three major categories based on the primary objectives of each of the surveyed frameworks: (1) resource allocation and service orchestration, (2) performance tuning, and (3) resilience and fault recovery. We also identify some key open research challenges that require further exploration by the research community to achieve scalable, resilient, and high-performance NFV/SFC deployments in next-generation networks.

/pdf/network-function-virtualization-and-service-function-1ej4nbnl.pdf

Network Function Virtualization and Service Function Chaining Frameworks: A Comprehensive Review of Requirements, Objectives, Implementations, and Open Research Challenges

The efficient deployment of virtual network functions (VNFs) for network service provisioning is key for achieving network function virtualization (NFV); however, most existing studies address only offline or one-off deployments of service function chains (SFCs) while neglecting the dynamic (i.e., online) deployment and expansion requirements. In particular, many methods of energy/resource cost reduction are achieved by merging VNFs. However, the energy waste and device wear for large-scale collections of servers (e.g., cloud networks and data centers) caused by sporadic request updating are ignored. To solve these problems, we propose an energy-aware routing and adaptive delayed shutdown (EAR-ADS) algorithm for dynamic SFC deployment, which includes the following features: 1) energy-aware routing (EAR): by considering a practical deployment environment, a flexible solution is developed based on reusing open servers and selecting paths with the aims of balancing energy and resources and minimizing the total cost and 2) adaptive delayed shutdown (ADS): the delayed shutdown time of the servers can be flexibly adjusted in accordance with the usage of each device in each time slot, thus eliminating the no-load wait time of the servers and frequent on/off switching. Therefore, the EAR-ADS can achieve dual-energy savings by both decreasing the number of open servers and reducing the idle/switching energy consumption of these servers. The simulation results show that EAR-ADS not only minimizes the cost of energy and resources but also achieves an excellent success rate and stability. Moreover, EAR-ADS is efficient compared with an improved Markov algorithm (SAMA), reducing the average deployment time by more than a factor of 40.

Energy-Efficient Provisioning for Service Function Chains to Support Delay-Sensitive Applications in Network Function Virtualization

The invention discloses an SFC dynamic deployment method with flow consciousness and energy perception, which comprises the following steps: S1, detecting the current physical network state; S2: traversing the VNF of the SFC; 3, selecting a set of that powered-on deployment; S4, executing BL-BFS algorithm, obtaining the shortest path union; 5, selecting an unpowered deployment set; S6, executing BL-BFS algorithm to get the shortest path; 7, selecting a set of that powered-on deployments; S8, executing BL-BFS algorithm to get the shortest path; 9, selecting an unpowered deployment set; S10, executing BL-BFS algorithm to get the shortest path; S11, outputting the deployment scheme composed of the shortest path; S12, checking and releasing the physical resources of the bottom layer occupied by the SR which reaches the online working time, and realizing the dynamic deployment of the SFC; The invention solves the problems of low practicability, low utilization rate of network resources andlow deployment efficiency existing in the prior art

A dynamic deployment method of SFC with flow awareness and energy perception

The present invention discloses an adaptive delay shutdown method of a server based on a cloud network. The method comprises the following steps of: S1, starting a node server and updating attributes;S2, initializing no-load delay time; S3, traversing all the SRs in a work state; S4, traversing the nodes of the SRs, and determining whether that the nodes are in a no-load state or not; S5, determining whether no-load delay time is reached or not; S6, closing the current node server to update the node attributes; S7, updating the no-load delay time, the no-load waiting time and the no-load energy consumption; S8, recovering the delay time to an initial value, and updating the node work time and the work energy consumption when the nodes work; S9, returning the consumption energy and the delay cost; S10, determining whether there is the next node or not; and S11, determining whether there is the next SR or not, if not, returning total consumption energy and total delay cost to achieve delay shutdown. The problems are solved that the energy consumption is large and the machine wearing is large in the prior art.

Adaptive delay shutdown method of server based on cloud network

The invention discloses a rapid migration method of a mobile application in a cloud network. For the primary purpose of improving quality of service of a user and realizing seamless service migration,migration time of an SFC (Service Function Chain) is reduced to the greatest extent. In order to ensure service migration success and rationality, cost such as migration operation cost and resource consumption is taken into comprehensive consideration. The method provided by the invention is efficient and rapid. Comprehensive optimality of a migration scheme can be realized through combination ofa current network state.

Rapid migration method of mobile application in cloud network

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Energy-Efficient Provisioning for Service Function Chains to Support Delay-Sensitive Applications in Network Function Virtualization

A dynamic deployment method of SFC with flow awareness and energy perception

Adaptive delay shutdown method of server based on cloud network

Rapid migration method of mobile application in cloud network