The cyber-physical system (CPS) is a promising technique that enables a safety-critical industry ecosystem. In general, wireless sensor networks (WSNs) and the Internet of Things are the sensing and communication infrastructures for CPS. Currently, software-defined networking (SDN) have been used as the new networking architecture for typical WSNs and CPS. However, there are two unresolved problems for a software-defined CPS. First, a feasible systemic architecture is a must for software-defined CPS, which should provide a global virtualization management and closed-loop control between the cyber side and the physical side in a CPS. Second, the lifetime of a software-defined CPS scenario needs to be extended for critical applications. To address the above challenges, this paper proposes a systematic virtual networking architecture to perform the global virtualization control and monitoring of a CPS, in which network functions virtualization (NFV) configuration and orchestration can be realized. Moreover, based on the proposed architecture, a novel lifetime extension scheme (NLES), is proposed for a software-defined CPS. To orchestrate the resource dynamically and efficiently, the instant programmability of an SDN and instant deployment capability of NFV are utilized to control the topology of node modes of a CPS. Then, a game theoretic topology decision approach is proposed to control the topology of the clustering and virtual network function deployment of sensors at run-time in a CPS. The experimental results show that NLES has longer lifetime compared to those of the traditional schemes.

NLES: A Novel Lifetime Extension Scheme for Safety-Critical Cyber-Physical Systems Using SDN and NFV

Software-defined networking (SDN) is a representative next generation network architecture, which allows network administrators to programmatically initialize, control, change, and manage network behavior dynamically via open interfaces. SDN is widely adopted in systems like 5G mobile networks and cyber-physical systems (CPS). However, SDN brings new security problems, e.g., controller hijacking, black-hole, and unauthorized data modification. Traditional firewall or IDS based solutions cannot fix these challenges. It is also undesirable to develop security mechanisms in such an ad hoc manner, which may cause security conflict during the deployment procedure. In this paper, we propose OSCO (Open Security-enhanced Compatible OpenFlow) platform, a unified, lightweight platform to enhance the security property and facilitate the security configuration and evaluation. The proposed platform supports highly configurable cryptographic algorithm modules, security protocols, flexible hardware extensions, and virtualized SDN networks. We prototyped our platform based on the Raspberry Pi Single Board Computer (SBC) hardware and presented a case study for switch port security enhancement. We systematically evaluated critical security modules, which include 4 hash functions, 8 stream/block ciphers, 4 public-key cryptosystems, and key exchange protocols. The experiment results show that our platform performs those security modules and SDN network functions with relatively low computational (extra 2.5% system overhead when performing AES-256 and SHA-256 functions) and networking performance overheads (73.7 Mb/s TCP and 81.2Mb/s UDP transmission speeds in 100Mb/s network settings).

/pdf/a-compatible-openflow-platform-for-enabling-security-5c2fm6jk6v.pdf

A Compatible OpenFlow Platform for Enabling Security Enhancement in SDN

Battery energy storage technology is an important part of the industrial parks to ensure the stable power supply, and its rough charging and discharging mode is difficult to meet the application requirements of energy saving, emission reduction, cost reduction, and efficiency increase. As a classic method of deep reinforcement learning, the deep Q-network is widely used to solve the problem of user-side battery energy storage charging and discharging. In some scenarios, its performance has reached the level of human expert. However, the updating of storage priority in experience memory often lags behind updating of Q-network parameters. In response to the need for lean management of battery charging and discharging, this paper proposes an improved deep Q-network to update the priority of sequence samples and the training performance of deep neural network, which reduces the cost of charging and discharging action and energy consumption in the park. The proposed method considers factors such as real-time electricity price, battery status, and time. The energy consumption state, charging and discharging behavior, reward function, and neural network structure are designed to meet the flexible scheduling of charging and discharging strategies, and can finally realize the optimization of battery energy storage benefits. The proposed method can solve the problem of priority update lag, and improve the utilization efficiency and learning performance of the experience pool samples. The paper selects electricity price data from the United States and some regions of China for simulation experiments. Experimental results show that compared with the traditional algorithm, the proposed approach can achieve better performance in both electricity price systems, thereby greatly reducing the cost of battery energy storage and providing a stronger guarantee for the safe and stable operation of battery energy storage systems in industrial parks.

Improved Deep Q-Network for User-Side Battery Energy Storage Charging and Discharging Strategy in Industrial Parks

Critical Infrastructures such as transportation or energy systems are central to the existence of modern societies. Due to the expected level of performance and developments like the shift towards renewable, fluctuating energy generation in Smart Grids, advanced monitoring and control systems are required for stable operation. This in turn increases the dependence on robust communication technology. However, due to the distributed nature of Critical Infrastructures, dedicated communication networks entail high costs. Hence a convergence of public and purpose built networks is pursued, so mission critical applications can be handled by a shared infrastructure. Software-Defined Networking facilitates this by separating network control from physical packet forwarding and centralizing it in a controller, which also creates a single point of failure. Although approaches capable of mitigating this threat to system robustness exist, the specific requirements of e.g. Smart Grids are not sufficiently addressed. Therefore a novel broker-based approach, allowing multiple Software-Defined Networking controllers to work in concert, is presented. The broker determines the correctness of network control decisions via majority votes. This novel concept for control plane fault tolerance, specifically designed to fulfil the challenging demands of Smart Grids, is detailed and contrasted with currently employed solutions. Also, resiliency against failures as well as malicious attacks is achieved, enabling reliable and highly available Critical Infrastructure communications.

Advanced controller resiliency in software-defined networking enabled critical infrastructure communications

Reinforcement learning for electric vehicle charging scheduling: A systematic review

In this paper, we propose a fault-tolerant mechanism that responds quickly to controller failures in cyberphysical systems that have a primary controller and multiple backup controllers for resilient control of physical objects. Using software-defined networking (SDN), the primary and backup controllers are configured to have the same IP address. In the event of controller failure, the other nodes, including physical objects, are not required to use the address resolution protocol (ARP) to find the media access control (MAC) address of the backup controller. The proposed mechanism enables the SDN controller to substitute the MAC address in frames destined for the primary controller with that of the backup controller on an SDN switch.

Fast Controller Switching for Fault-Tolerant Cyber-Physical Systems on Software-Defined Networks

Electric vehicles (EV) are emerging as a promising transportation medium because they provide better energy efficiency and are more environmentally friendly compared to gasoline-fueled vehicles. However, long charge times and low charger coverage hinder EV charging services. To overcome these problems, this paper proposes a reinforcement learning (RL) based optimal management policy that can maximize the utilization of EV chargers while guaranteeing quality of service. The proposed RL based scheme learns the arrival pattern of EVs and adjusts the service area of each charging station in a dynamic environment.

Reinforcement Learning based Charging Station Management for Electric Vehicles

In IEEE 802.11ax networks, multiple uplink wireless-connectivity services are supported by an uplink multiuser multiple-input and multiple-output (MU-MIMO) technique. In this study, we propose a method for uplink multi-user (UL-MU) transmissions in IEEE 802.11ax networks. In the proposed method, stations are clustered on the basis of their transmission times, to efficiently utilize channels for UL-MU transmissions. From the performance evaluation, we demonstrate that the proposed method improves the network throughput performance by improving the channel utilization.

UL-MU Transmissions in IEEE 802.11ax Networks

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Fast Controller Switching for Fault-Tolerant Cyber-Physical Systems on Software-Defined Networks

Reinforcement Learning based Charging Station Management for Electric Vehicles

UL-MU Transmissions in IEEE 802.11ax Networks