A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> a: NETWORK DEPLOYMENT <s> The reliability and real time of industrial wireless sensor networks (IWSNs) are the absolute requirements for industrial systems, which are two foremost obstacles for the large-scale applications of IWSNs. This paper studies the multi-objective node placement problem to guarantee the reliability and real time of IWSNs from the perspective of systems. A novel multi-objective node deployment model is proposed in which the reliability, real time, costs and scalability of IWSNs are addressed. Considering that the optimal node placement is an NP-hard problem, a new multi-objective binary differential evolution harmony search (MOBDEHS) is developed to tackle it, which is inspired by the mechanism of harmony search and differential evolution. Three large-scale node deployment problems are generated as the benCHmarks to verify the proposed model and algorithm. The experimental results demonstrate that the developed model is valid and can be used to design large-scale IWSNs with guaranteed reliability and real-time performance efficiently. Moreover, the comparison results indicate that the proposed MOBDEHS is an effective tool for multi-objective node placement problems and superior to Pareto-based binary differential evolution algorithms, nondominated sorting genetic algorithm II (NSGA-II) and modified NSGA-II. <s> BIB001 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> a: NETWORK DEPLOYMENT <s> In recent years, industrial wireless networks (IWNs) have been transformed by the introduction of mobile nodes, and they now offer increased extensibility, mobility, and flexibility. Nevertheless, mobile nodes pose efficiency and reliability challenges. Efficient node deployment and management of channel interference directly affect network system performance, particularly for key node placement in clustered wireless networks. This study analyzes this system model, considering both industrial properties of wireless networks and their mobility. Then, static and mobile node coverage problems are unified and simplified to target coverage problems. We propose a novel strategy for the deployment of clustered heads in grouped industrial mobile wireless networks (IMWNs) based on the improved maximal clique model and the iterative computation of new candidate cluster head positions. The maximal cliques are obtained via a double-layer Tabu search. Each cluster head updates its new position via an improved virtual force while moving with full coverage to find the minimal inter-cluster interference. Finally, we develop a simulation environment. The simulation results, based on a performance comparison, show the efficacy of the proposed strategies and their superiority over current approaches. <s> BIB002
The problem of network deployment is to identify the location of network nodes and access points (AP) with consideration for the densification, distribution, and mobility, of users (i.e., mostly machines) among others. To improve the delay performance and reliability of the wireless network, node placement shall consider different characteristics of industrial systems such as the wireless channel, including fading, multipath, and performance requirements. In particular, the mobility patterns of users in industrial networks are mostly static or near-static. In addition, the mobility of users is typically limited and regular (e.g., robotic arms in an assembly line). In addition, as mentioned above, traffic is characterized by small packets, small communication distance, etc. To understand the network deployment problem, we show three typical deployment scenarios in industrial plants, which consist of wireless access points, sensors, and actuators in Figure 12 . The first deployment scenario in Figure 12a indicates industrial processes that require both sensors and a single actuator. The second deployment scenario, shown in Figure 12b , indicates large numbers of both sensors and actuators involved in processing complex and comprehensive tasks. Furthermore, the third deployment scenario, shown in Figure 12c , illustrates information collection only (i.e., deploying a large number of sensors to collect data from different aspects of the industrial process, without actuation). To design effective and efficient deployment schemes, the mobility and network traffic patterns of machine devices in I-IoT shall be understood, and all possible mobility scenarios should be covered. Then, the node placement problems can be reduced to optimization problems with the consideration of the mobility patterns and performance requirements BIB002 , BIB001 . For example, Li et al. BIB002 studied the localization of both static and mobile nodes with the consideration of interference and coverage in the industrial wireless network. The node placement was formalized as a coverage problem and solved through the proposed double-layer Tabu search.