2.1. Theory of Knowledge Value Transformation
Influenced by Taoism, Chiang developed the HyPloy Model for organic intelligent agents, which acts as a fusion function of intrinsic experiences as well as intention and extrinsic projection, including inter-promoting for cooperation and inter-inhibition for competition, to respond to the extinction of their environment [
6]. With extrinsic projection, an organic agent like homo deus interacts with others to improve its behavior. Based on HyPloy, HyQVS stands for a framework for organic ecosystems that tends to improve quality and value through cooperation and competition among its organic constituents, which leads to the cooperation-competition strategy. An organic ecosystem, such as HyQVS, should have three kinds of capabilities, i.e., communication, cooperation, and coordination that altogether determine the capability for improvement [
1].
The Theory of Knowledge Value Transformation is meant to concretize the improvement of features and values in HyQVS [
6,
7]. To conduct a value-based strategy, the quality features and values must be identified through a planning phase. Assume that the features of individuals in an ecosystem consists of two parts: a common part and a particular part. The common part is the indifference features within the ecosystem that sustains the common goal for the cooperation by orchestrated supported activities. The particular part is the distinct operation structure according to the individual domain to support its unique status as its core competence for competition. The common feature domain joins particular parts of each individual to develop a comparable feature domain for each individual. Now, the comparable feature domain can be seen as a unified feature space, preserving the landscape of the ecosystem and the detailed weights of the constituents. With the features of the ecosystem, we can prepare a transform function for the relations between the common features, individual features, and comparable feature space to generate the comparative value of each individual that builds the value proposition of the ecosystems. The function transforms the commonality and maintains the source generality. The optimization can be expressed as follows:
The optimization should obey a certain law or a in fact consensus. The optimal value that the optimization produces depends on the law for coordination, and the fitness of the law depends on the target for improvement, which typically compensate the internal intention, that is, the so-called optimization strategy. The performance of the optimization algorithm depends on the target for improvement.
2.3. Federation Game and Coalition Game
For the realization of the knowledge value transformation of HyQVS, we define the federation game as is a kind of cooperative evolution game with a gestalt and apply it for coordinating the organic agent of the HyQVS. The federation gestalt can unify sub-cooperative subgroups, viz. coalitions [
9]. The federation should be in charge of global ICT resource planning and sharing, e.g., ITU, ICANN, and the coalitions work for regional ICT resource sharing, e.g., NCC, TWNIC (Taiwan Network Information Center). This suggests that in a union where members of higher positions or a strong subgroup collectively conclude the important decisions for the organization such as the governmental authorities, the core set would focus on stability rather than on the impartiality of all participants [
6,
10]. It is impossible to design a fair coordinated system, such as an auction that satisfies unanimity, non-dictatorship, and independence of irrelevant alternatives simultaneously. Therefore, the authorities should actively become involved in the planning and design of an open ecosystem. Improvements such as within HyQVS can be realized by optimizing the related collective behavior of the organic ecosystem in the cooperative game. Through such kind of cooperative game, we try determining appropriate strategies to acquire an equilibrium for better social welfare.
The organic ecosystem for inclusive sharing economy relates to a coalition between providers and users, risk prevention, and the division of rights and responsibilities among users, corresponding to the symmetry of the participants [
6,
9]. A successful auction must reside in a well-organized collaboration as HyQVS suggested among the authority, providers, and users. To achieve this goal, the cooperation must be fair and stable so that the participants will evolutionally benefit from it via continuous improvement.
Assume that every coalition has a payoff of a single value (which could be physical or potential) that is transferable and allowed to be redistributed to the participants like the theory of knowledge to value transformation indicates. Members of a game all look for the largest economic improvement from the coalition. The gestalt of the coalition should provide the property of synergy, that is, the value derived by the coalition should be at least as large as the sum of their respective payoffs without the coalition. The coalition should have a fair way to payoff, so the members should receive benefits in proportion to their marginal contributions. The gain from a coalition is a function of the number of customers the participants have. Because the coalition should aim at a stable equilibrium, it has to be founded on the egoism to redesign the mechanism by the core set in the game [
10]. The core condition is an un-dominated imputation set that obtains a stable payoff in which no agent would suffer from the new criterion of state modification or resource reallocation, assuming that participants are rational
wrt. behavior economics and the distribution of the payoff satisfies. The above reflects the strategy to keep the coalition sympathetic to help other non-dominant participants benefit from their involvement and contribute to the coalition.
The internal intention of the organic agents within an ecosystem of HyQVS is the most difficult part to deal with, because the intention of some agents may be irrational, and this is the problem that the subjective believes in the Theory of Games and Economic Behavior of von Neumann and Morgenstern could not deal with [
11]. The Shapley value applies primarily in situations when the contributions of each actor are unequal, but each player works in cooperation with each other to obtain the gain or payoff. If participants want to manage stability and fairness in such games, it requires the dominant partners to make a concession [
12]. An important implication of this is that the collective action in the cooperative must generate synergy [
13], so it is no less than their union set. Both parties treat the other as a partner to achieve the same goals in the common domain as indicated in Equation (1). The benefits are on both sides and flow equally to each other.
Instead of mandatory norms with laws, the most valuable support or the concession and payoff comes from the moral and ethics [
14]. For the coalition of providers and users, reciprocation originates from the altruism of the dominant party [
15]. With respect to the inclusive share economy for sustainable ecosystems, altruism is the most incredible supports for cooperation within the coalition game due to reciprocation, and egoism on the contrary damages the cooperation [
16]. This is the reason, we argue for that the sharing economy is inclusive. With an appropriate rule for the optimization of collective behaviors, the following pitfalls of egoism could be avoided:
2.5. 5G Mobile Communication: IMT-2020 and IoT
Following the Radio-communication Assembly of the International Telecommunication Union (ITU) in 2015, 5G communication was entitled IMT-2020 (International Mobile Telecommunication-2020) [
21]. The 5G mobile technique can accomplish enhanced devices and communication capabilities in terms of efficiency, throughput, and latency, as well as a high data rate, and ITU intends to use 5G to connect things, particularly in mid- and long-range [
19]. Thus, 5G can enhance the QoS of the IoT.
Owing to the advent of Internet technology, the world has become more closely connected to information systems, as sensors and actuators are incorporated into a wider variety of objects and then connected to the Internet through the arrangement of wired and wireless networks, such as the IoTs [
22]. Radio frequency identification (RFID) has been used for IoT, and short- and mid-range wireless networks, such as Zigbee, Bluetooth, and Wi-Fi, are suitable for situations where devices are close to the receiving gateway; however, they cannot cover long-range IoT applications, such as smart cities, e-health, and smart agriculture [
2,
4]. In addition to the limitation of distance, the race for bandwidth and latency may occur by these networks because of the narrow bandwidth and high frequency [
2,
4]. With the advent of Cloud and edge computing, mid- and short-range, very low-latency broadband is required for IoT services [
23]. Mobile companies are competing to provide innovative services and better QoS for IoT as mentioned above. However, it depends on an optimal spectrum splitting and efficient auction to provide better QoS, including long range, low latency, and high penetration rate for 5G. Along with the development of 5G technology, NB-IoT has become a popular communication model for D2D-enhanced content uploading with social trustworthiness in 5G IoT [
24], including in Taiwan [
25].
Chiang et al. developed a RFID/NFC and mobile-computing based IoT service framework that classifies the IoT services into three categories,
viz. human-enabled service, device-enabled service, and object-enabled service [
26]. To realize the services, the software on mobile phones and RFID/NFC devices have to translate the unique ID (Identifier) of EPCglobal into the FQDN in the form of DNS (Domain Name System) URL (Unified Resource Locator) and then finds the specific ONS server according to the DNS URL of the web. After that, the ONS server feeds back the URL of the service corresponding to the FQDN to activate the service. Moreover, GPS was used to identify the locations of the users. In the meantime, Chiang et al. also developed a RFID/NFC based Augmented Reality (AR) system which uses RFID/NFC tags, mobile phone camera, and communication interface to realize the tracker and marker indicating the actual location of the object and can be adjusted with the user and the relative distance and angle between the subject matter when the user moves in the environment, and can change the virtual object marking for real-time location positioning [
27,
28]. Chiang and Yeh further developed a two-fold authentication technique with mobile phone number and e-mail address as the ID of a user, not only for the authorization but also for the account rescue [
29]. The IoT service framework for solving problems in the field of O2O (Online to Offline) management of creative cultural derivatives by the National Palace Museum (NPM) in which Exhibition areas and special Exhibition (short-term social network) for antiquities and the stores selling creative cultural derivatives own their IoTs of physical things, and the online shops build the virtual world [
30,
31]. The users can move around the multiple IoTs with different behaviors in response to the purpose of the specific IoT environment. For the time being, the aforementioned development can be considered as the Multiple Social IoTs (MSIoTs).
The advances of mobile technologies lead to a ubiquitous computing environment. Tsai and Chiang put forward the framework in four broad areas: pervasive devices, networking, middleware, and applications in which the organic agents are seamlessly integrated into the environment and provides useful services to humans in their everyday lives on the case of the Ubiquitous Intellectual Properties Management System of NPM including multimedia Digital Archives, Digital Museum, and e-learning project [
32]. Chiang and Huang have put the consensus forming as the starting point of the realization of IoT services so that the organic agents can have a meaningful social relationship with one another [
33]. With regard to Hybrid Cloud with connection IoTs, Chiang has developed an approach for authentication and authorization based on OpenID and Oauth for social networks on the Cloud [
34] and proposed a systematics collaboration framework with respect to interoperation [
35]. On top of the overall framework, collaborative learning gamification with 3D AR courses were developed and used in practice [
34]. What has been done above can be called Multiple Social IoTs and is one of the earliest pilot programs of the so-called Metaverse nowadays [
36].
The study in [
6] analyzed the ecosystems of different topologies with different time complexities. The costs for maintenance and participation in the ecosystems could be the extra social cost and inevitably the friction costs of consequent resource exchanges, including the trust and risk, which is especially important for SIoTs [
37,
38,
39,
40,
41,
42]. It is a matter of connectivity but not the bandwidth. With respect to the research results as above, the SIoTs and MIoTs [
43,
44,
45] fall into the topology of partially centralized agents with some unified arrangements whose complexity is
O(2m + ip + n + jp) or the topology of decentralized autonomous agents whose complexity is
O(2m + n + i + j). The difference depends on whether the IoT ecosystem employs Client/Server architecture or serverless computing architecture. The latter can use Peer-to-Peer (P2P) communication to address the routing issue in delay-tolerance as described by [
40]. Notably, the concept of the agent-oriented IoTs [
46,
47] belongs to decentralized autonomous systems like Blockchain as described in [
6]. The results of the simulation of [
47] is evidence for the complexity of O(2m + n + i + j) and the Open API will be the most important mechanism for the services of this kind of IoTs. Since the spectrum splitting and auction involve only the resource planners and providers but not the developers and users of the IoT services, the consideration for SIoT and MIoT is out of the scope of this study. However, the optimal sharing of 5G mobile spectrum can lead to the enhancement of QoS of almost all kinds of IoTs.
2.7. Cost Function
The spectrum consists of two parts: the cost of spectrum acquisition and equipment construction. The cost for spectrum acquisition is determined by the value of a frequency segment that is calculated with the value of spectrum, 10 MHz per segment according to frequency-devising duplexing (FDD), and 30 million users of mobile communication in Taiwan. The spectrum value is determined by the revenue from the auction for the spectrum provider, such as NCC, however is the variable cost for the users such as the mobile operators, which will be determined during bidding. In contrast, the equipment construction cost is fixed for the spectrum. The cost of the equipment construction can be obtained by multiplying the price of one base station by the number of base stations in each band.
The cost for the base station after 2020 can be predicted based on the highest price of the 4G base stations, that is, NTD 1.8 million. Consequently, the estimated cost for the 5G base station after 2020 can be estimated from the price for 4G multiplied by the average inflation rate, that is, NTD 1,887,317.
Based on the data for 4G in Taiwan, we can speculate on the minimum number of base stations required for 5G. The area covered by one base station for the 600 MHz band is 125% of that for the 700 MHz band. Hence, the number of base stations for the 600 MHz band is 80% of that of the 700 MHz band [
56]. The features of the propagation at 800 MHz and 900 MHz were very similar [
57]. The feature of the propagation of the L-Band lies in an intermediary value between that of the 900 MHz band and the 2.1 GHz band. The area covered by the L-Band is 150% of that of the 2.1 GHz band and 75% of that of the 900 MHz band. The number of base stations for the L-Band is thus 133% of that for the 900 MHz band [
58]. The coverage of the C-Band is approximately 187% smaller than that of the 2.3 GHz band, and the area covered by the 2.3 GHz band is 180% smaller than that of the 900 MHz band, the number of base stations required for the C-Band is thus 336.6% of that for the 900 MHz band [
59].
2.10. Hybrid Metaheuristics for Optimization
There are many well-known optimization algorithms originating from different scientific disciplines, such as random optimization (RO) from mathematics, SA from physics, and GA from biologics, each of which has the underlying spirit of the specific discipline. Researchers have proved the effects of RO, SA, and GA for spectrum splitting. However, in location-based bidding, there are several permutations of segments, and the aggregated result can vary and converge to the local optimum. This is interpreted as a viable characteristic of the nondeterministic result of the authorities’ revenue because it is opportunity revenue [
5,
6]. Chiang has classified the open ecosystems into five types of topologies, among those the type of multi-agents with unified rules which can be optimized with SA has the best efficiency and the type of multi-agents without unified rules which can be optimized with GA is in the second place [
5]. Therefore, we apply SA and GA to the coalition game in the case of spectrum splitting and auction and compare the results.
Particle Swarm Optimization (PSO) is a well-known optimization method that originates from social behavior science. Because of the problem of local optimization and the emphasis of self-organization without centralized control [
61,
62], it is more suitable for partially centralized agents with some unified arrangements [
6]. The Fruit Fly Algorithm (FFA) is a type of cooperative coevolution based on the Parisian approach. A global fitness function evaluates the quality of the population as a whole; only then the fitness of an individual is calculated as the difference between the global fitness values of the population with and without the particular fly, whose individual fitness function has to be evaluated; the fitness of each fly is considered as a level of confidence [
63]. The main difference between the FFA and with PSO is that the FFA is not based on any behavioral model but only builds a geometrical representation. It belongs to the topology of decentralized autonomous agents as formulized in [
6]. Neither Particle Swarm Optimization (PSO) nor Fruit Fly Algorithm (FFA) are suitable for coalition games for spectrum splitting and auction so that we will not consider it in this study.
2.10.1. Random Optimization (RO)
The name RO is attributed to Matyas [
64], who made an early presentation of RO along with basic mathematical analysis, which is also known as direct-search, derivative-free. RO works by iteratively moving to better positions in the search space, which are sampled using, for instance, a normal distribution surrounding the current position.
Rastrigin and Schumer [
65] proposed optimization methods that did not require gradient properties using adaptive step size random search in a single mode. Thus, RO can be used on functions that are not continuous or differentiable, so that it is not convex. Baba [
66] further proved that the global minimum can be reached with a probability of one, even if the performance function is multimodal or the differentiability is not provided.
The RO algorithm showed that for a minimization problem, each round of the variable selection was independent. Starting from a random vector, RO can minimize the risk resulting from randomness. RO is easy to understand and can generally serve as a baseline for optimization. Most importantly, the cooperative game based on Shapley value and Pareto Optimality needs a property of random walk which is the advantage of RO.
Normally, there are two basic strategies for heuristic search: “divide-and-conquer” and “iterative improvement” [
67]. Because RO applies an iterative one that fits the purpose of continuous improvement and does not conquer, it favors symmetry in the auction.
2.10.2. Simulated Annealing (SA)
Kirkpatrick et al. [
68] proposed a simulated annealing algorithm to connect statistical physics to the optimization problem and approximate the global optimum of a given function. The term “annealing” denotes an analogy with thermodynamics involving the method that metals cool and alter their properties.
SA uses temperature to control the magnitude of the changes in the objective function and is effective in handling certain optimization problems that are out of control using combinatorial methods when the number of objects becomes large [
69]. Lin et al. use nearest-neighbor heuristics in accelerated algorithms of SA optimization problems [
70]. Chiang et al. adopted a Boltzmann machine to find the next best temperature to eliminate the local optimum and reach the global optimum in relation to the equilibrium for the global state [
71]. Therefore, it is more suitable for the goal of this study.
SA concurrently extends both divide-and-conquer and iterative improvements. When properly used, SA can maximize revenue and minimize costs simultaneously [
70,
71]. Because it seeks the equilibrium and does not emphasize the conquer only, it can sustain the stability of the 5G ecosystem in the auction.
2.10.3. Genetic Algorithm (GA)
John Holland [
72] developed a GA, which has been largely used in solving optimization problems. Within the GA, individuals adapting to their environment will have a better chance of surviving and reproducing [
73]. It stands for the process of evolution, where the fittest ones are chosen for reproduction to generate progeny of the next generation.
Although GA is deemed robust, there are still some problems, such as the difficulty in choosing the population size, crossover, and mutation rate, because they are principally experimental parameters [
74]. Furthermore, GA cannot always achieve an optimal solution, whereas premature convergence may occur when the fitness values of potential solutions stop improving, and an optimal value has not been reached [
74,
75]. According to our experiences, the biggest problem of GA is that, when the initial condition is wrong, the evolution process will be out of order in terms of Chaos Theory. In a coalition game, the fittest one (i.e., the elite) could be out of control of the authority and damage the equilibrium. GA applies a conquer, and it is possible that the GA does not match the goal of symmetry.
The term metaheuristic was created and is now well accepted for general techniques which are not specific to a particular problem. Many combinatorial optimization problems are very hard to solve optimally, the quality of the results obtained by somewhat unsophisticated metaheuristics is often impressive. A new kind of approximate algorithm has emerged which tries to combine basic heuristic methods in higher level frameworks aimed at efficiently and effectively exploring a search space [
67]. These methods are today commonly called metaheuristics. Such a combination of one metaheuristic with components from other metaheuristics is called a hybrid metaheuristic. For the optimization of spectrum splitting and auction, we develop different kinds of hybrid metaheuristics for cooperative evolution with multiple objectives including revenue, number of segments, quality features, etc. and test them with respect to the goal of coalition game of the 5G ecosystem. For example, we can build the hybrid metaheuristics in using the RO to compensate the effect of divide-and-conquer of GA, for the optimization problems of coalition games.