The Cyber Physical System (CPS) has become a promising direction that can enrich the interaction between people and people, people and objects, objects and objects in the physical world and the virtual world. Because of the development of CPS, the number and types of smart devices connected to the Internet have rapidly increased, bringing with it the current issues of scalability and efficiency of the Internet of Things(IoT) network. These problems are caused by the key mechanism of large-scale distribution to the Internet of Things network. In this article, we propose CPSS inspired by software-defined networking (SDN) and service-oriented architecture, which is a powerful software-defined cyber physical system to solve the challenges of broader CPS adoption. To test CPSS, we develop a prototype system. Our experimental results show that CPSS can effectively control CPS, and CPSS has greatly improved latency and throughput compared to traditional controller-controlled CPS.

Domain Name System (DNS) is a key infrastructure on the Internet. The Distribution Master (DM) system is used to transmit root zone data from Internet Assigned Numbers Authority (IANA) to the root server. DM is a centralized system that will introduce single points of failure and abuse of authority. To solve the problem of a single point of failure, we adopt the decentralization of blockchain in architecture, and propose a blockchain-based distributed DM architecture, which allows nodes to join and exit at any time. At the technical level, the 3R-PBFT (Replicated, Redundant Practical Byzantine Fault Tolerance) algorithm is proposed to reach a consensus, which increases the security of the system. We use flexible mechanisms to reduce the number of signatures and improve the performance of the system. A threshold signature algorithm is used to ensure the uniqueness of the signature key information, and the increase of DM nodes will not bring about an increase in the number of keys. The advantages of the system structure in stability and scalability were verified by experiments.

In software-defined network, the use of distributed controllers to control forwarding devices has been proposed to solve the issues of scalability and load balance. However, the forwarding devices are statically assigned to the controllers in these distributed systems, which can overload some controllers while others are underutilized. In this paper, we propose an architecture named ASLB (active controller selection load balance), which proactively selects appropriate controllers for load balancing and minimize packet processing delays. We also present a novel active controller selection algorithm (ACS) for ASLB that efficiently schedules traffic from the switch to the controller and designs an intermediate coordinator for actively selecting a controller to serve a request. We built a system and evaluated it on a physical platform. The results show that ASLB is much better than the static allocation scheme in terms of minimizing latency, bandwidth utilization, and throughput.

Deterministic transmission refers to the guaranteed delivery of data to its destination within a specific time frame along an optimal route, making it an indispensable prerequisite for the operation of autonomous vehicles. The in-vehicle networks (IVNs) face significant challenges in the deterministic transmission of large volumes of perception and control data from and to various vehicle components. In this paper, the cooperative scheduling and routing problem among multi-area control units (ACUs) in IVN is discussed from the view of deterministic transmission, and a ResVmix method is proposed to find the optimal two-layer assignment with ACU cooperation. The multi-cycle queuing and forwarding (multi-CQF) mechanism is employed to schedule the perceived data according to different quality of service (QoS) requirements. The cooperation among multiple ACUs enables deterministic routing of data. The two-layer optimization problems on multi-CQF and ACU are formulated as a+ decentralized partially observable Markov decision processes (Dec-POMDP) and solved using the enhanced multi-agent deep reinforcement learning (MADRL) method. Simulation results demonstrate that ResVmix significantly enhances the deterministic transmission performance of IVN. Compared to traditional shortest path algorithms and state-of-the-art MADRL methods, ResVmix achieves a 22.4% increase in arrival rate and a 20.1% increase in confirmed arrivals, respectively.