1.1 The Fourth Industrial Revolution
The fourth industrial revolution, which is denoted by the term “Industry 4.0,” is in its early stages. The hallmarks of the former three industrial revolutions are as follows:
- Deployment of mechanical production facilities
- Use of electric power for mass production and communications
- The digital revolution
The distinct feature of Industry 4.0, which distinguishes it from its three predecessors, is the fact that it has been predicted a priori instead of being observed by postanalysis. This prediction opens a window of opportunity for futurists as well as visionary individuals and institutes to actively participate and play key roles in engineering the future. Industry 4.0 is built upon the following four pillars [1]:
- Cyber-physical systems (CPS), which include smart products as their subcomponents
- Internet of things (IoT), which relies on machine-to-machine (M2M) communication as an enabling technology
- Internet of services (IoS), which is exemplified by cloud computing as a model for allowing Internet-enabled devices to access a shared pool of configurable computing resources according to their needs
- Smart factories.
Industry 4.0 will produce massive amounts of data. Portions of the produced data that are associated with high volume, variety, and velocity (3 Vs) are referred to as big data. Each one of the above pillars will be briefly described in what follows [2–7].
By definition, the integration of digital computing and a physical environment results in a cyber-physical system. Therefore, many applications can be collected under the umbrella of such systems [8]. A cyber-physical system usually includes a distributed set of different sensors, where the number of sensors depends on the scale of the system. Data gathered by these sensors are used to form a representation of the environment, which is then used for decision-making. Only portions of the gathered data will be useful (i.e., relevant to) the decision-making task. Hence, in accordance with the task at hand, the information extracted from the available data can be divided into two sets: relevant and irrelevant. The former provides the actionable information [9].
In order to perform a task with an acceptable level of risk, a specific amount of information is required, which is called sufficient information. If the actionable information does not meet the information sufficiency criteria from the decision-making perspective, the decision-maker will face an information gap. In Ref. [10], cognitive control was proposed to reduce this gap between the actionable and sufficient information sets by controlling the directed flow of information:
Given a probabilistic dynamic system that includes a perception–action cycle, and ideally mimics the human brain, the function of cognitive control is to adapt the directed flow of information from the perceptual part of the system to its executive part so as to reduce the information gap, which is equivalent to reducing a properly defined risk functional for the task at hand, the reduction being with a probability close to one.
In a cyber-physical system, cognitive and physical controllers play complementary roles. Cognitive complementary actions can influence different parts of the system:
- Cognitive actions may be applied to the environment in order to indirectly affect the perception process.
- Cognitive actions may also be applied to the system itself in order to reconfigure the sensors and/or actuators.
- Inaddition, cognitive actions may also be subsumed in physical-control actions. In such a case, a physical action is applied to the system but with the goal of decreasing the information gap (with or without other goals).
In other words, in large-scale CPS with the requirement of critical decision-making, cognitive control will enhance reliability of the decision-making process. Final decisions (i.e., outputs of the decision-making process) are then sent as a set of commands to different actuators, which are also distributed in the system. As a result, CPS have been significantly transforming the way our modern society perceives the physical world and interacts with it.
The word “thing” in the Internet of Things refers to different entities, such as radio-frequency identification (RFID), sensors, actuators, computers, and mobile wireless devices, which may all be smart products that communicate with each other using a specific addressing scheme and cooperate toward a common goal. “Thing” can also be interpreted as cyber-physical entities and in effect, therefore, IoT can be viewed as a network of CPS [1].
Adopting a systematic viewpoint on organizations has led to the idea of value chain, which refers to activities that are performed by an organization in order to deliver a valuable product or service to market [11]. Regarding the fact that CPS have made the fusion of physical and virtual worlds possible [1], a combination of both physical and virtual value chains must be taken into account. Hence, a service-oriented architecture can be created that promotes distributed production control, which is built on modular assembly stations that are connected by automated guided vehicles. This provides customers with special production technologies and gives them a degree of freedom to somehow custom design the product that they need. Customers can use the assembly stations and the associated automated transportation system through the IoS.
A factory is said to be smart if in a context-aware manner it can help both employees and machines to execute the tasks that are assigned to them. The operation of such factories would be demand-driven. As proposed in [12], what distinguishes a smart factory from an ordinary one is the existence of the so-called calm systems that operate in the background and are able to communicate and interact with their environments. A smart factory can be viewed as a factory, in which CPS communicate over the IoT to facilitate execution of assigned tasks to employees and machines [1]. The idea of calm systems is very similar to the notion of cognitive dynamic systems (CDS) [13], which can play the role of the central nervous system for the smart factory.
It is obvious that all the mentioned pillars of Industry 4.0 rely on communications and networking in one way or another. In this regard, cognitive radio as a smart product will be part and parcel of each one of the pillars. Therefore, it can be expected that cognitive radio networks will play essential roles in the years to come far beyond the initially set mission for improving spectral efficiency. In order to exploit the full potential of cognitive radio networks in light of the fourth industrialrevolution, such networks must be studied as intelligent sociotechnical systems [14]. Therefore, the socioeconomic principles for self-organizing institutions [15] provide guidelines for design and analysis of cognitive radio networks, where decentralized control, competition for limited resources, and vulnerability to both intentional and unintentional errors, are the main characteristics. In such an environment, the emphasis should be on the endurance of the resource-distribution mechanism rather than on its optimality. According to [16], eight design principles must be considered for endurance of self-management of common-pool resources:
- 1. Clearly defined boundaries for determining who has the right to use which portion of the resources.
- 2. Congruence between appropriation and provision rules and the state of the prevailing local environment.
- 3. Collective-choice arrangements.
- 4. Monitoring of both state conditions and users' behavior.
- 5. A flexible scale of punishment for users that violate communal rules.
- 6. Access to fast, cheap conflict-resolution mechanisms.
- 7. Existence of and control over their own institutions without intervention by external authorities.
- 8. Systems of systems as layered or encapsulated common-pool resources, with local resources at the base level.
Regarding the potentials of cognitive radio networks to play key roles in Industry 4.0 especially, in implementing intelligent sociotechnical systems, the primary objective of this book is to introduce a relatively new way of thinking about such networks. With this objective in mind, we find it instructive to start the discussion with the spectrum-supply chain paradigm, which may not be that well known in the signal-processing and communication-systems communities.
The notion of a supply chain may be viewed as follows: A supply chain is made up of different entities, the role of which is to fulfill the request of a specific customer. In addition to manufacturers, suppliers, and customers, a supply chainincludes transporters, warehouses, brokers, and retailers. A supply chain also includes all functions of each party such as new product development, marketing, operations, distribution, finance, and customer service. Supply chains have been gradually evolving to complex structures known as supply chain networks, which are characterized by their dynamic nature due to continuous flow of information, product, and fun...
