## 4 Software and Architecture Orchestration in CPS Environments
### Network Constraints and Features  
The rise of software-defined networking (SDN) heralds a prospective network approach, endowing its underlying networks and devices with programmability. Presently, SDN is deployed as the novel networking architecture within conventional Wireless Sensor Networks (WSNs). Such investigations contribute to progressing the networking architecture of Cyber-Physical Systems (CPS) and reshaping the landscape of future networks. Common configurations of industrial networks primarily rely on managed devices equipped with features like priority queuing and access control protocols. Additionally, it is imperative to incorporate physical redundancy and encompass traditional protocols designed to fulfill general network requirements [41]. The delay prerequisites hinge on the industrial application. Situations where the worst-case delay serves as a targeted metric may find best-effort services inadequate. For hard real-time services, there is a demand for time-sensitive networks, wherein the control and management planes assure a low deterministic latency and minimal jitter. An intelligently centralized controller, along with network awareness, holds the potential to provide Quality of Service (QoS) support for vital applications. As stated by [41], it can diminish complexity and expenses while enhancing flexibility. In the pursuit of realizing self-reconfigurable Cyber-Physical Systems (CPSs), most studies have underscored the adoption of closed-loop feedback SDN systems. Hence, in contrast to Wireless Sensor Networks (WSNs), Cyber-Physical Systems (CPS) represent a sophisticated systemic architecture tasked with executing control functions bridging the cyber and physical domains. This involves delivering more intelligent management and feedback capabilities. However, in industrial contexts that integrate mobility and autonomy features, other attributes are gaining prominence. As a result, the application of the low-energy adaptive clustering hierarchy (LEACH) algorithm was executed to amalgamate sensor data within a Wireless Sensor Network (WSN). The introduction of sophisticated communication technologies, such as ultra-wideband communication, aimed to optimize energy efficiency. Additionally, the integration of inventive approaches like spatial-temporal coverage optimization, cooperative Multiple-Input Multiple-Output (MIMO), Non-orthogonal Multiple Access (NOMA), clustering hierarchy, and cognitive radio networking technologies have been implemented. These forward-thinking measures collectively contribute to the progression of WSNs, elongating their lifespan and enhancing overall energy efficiency [44; 45; 46; 47]. Recent computing paradigms within networks, such as Transparent Computing (TC), Fog Computing (Fog), Mobile Edge Computing (MEC), and Cloudlet, have garnered substantial attention in both industry and academia. These models leverage small-scale edge servers with restricted computation resources to promptly serve end-users at the network edge. Fog, MEC, and Cloudlet can be considered as expansions of cloud services to the network edge, given their utilization of comparable computation offloading and storage management approaches. Nevertheless, in the TC perspective, there is a clear separation of computing and storage between end-devices and remote servers, and it promotes the distribution of computing tasks among end-devices and their nearby counterparts, while fetching software and data from remote servers [48]. All these paradigms will exert influence and shape the digitization and softwarization of systems or processes. In other words, their specific characteristics and their associated models and development frameworks must be considered during the implementation of CPS. For a better understanding of this dependency, from the different communications models of IoT, let us briefly enumerate the main characteristics of these paradigms of computing and communication.