I2oT Advanced Direction of the Internet of Things文档格式.docx

1、 knowledge producing； strategy formulation； intelligent internet of things T1 Introduction here are two main motivations for expanding the Internet to the Internet of Things （IoT）. The first motivation is to expand the amount of information shared by databases and objects in the real world. The seco

2、nd motivation is to enable users not only to share information but also control objects in the real world. These make IoT much more attractive in society. In other words， IoT is a good advancement of the conventional Internet. In terms of technological development， however， IoT is still in its infan

3、cy and can be greatly improved by endowing IoT functions with much more intelligence 1. Significant progress has been made in artificial intelligence （AI） over the past decade. All AI technologies needed to make IoT more intelligent and evolve into I2oT are now feasible. The main concern at the mome

4、nt is how to understand and effectively apply AI technologies to current IoT systems. 2 A Brief Description of IoT The purpose of IoT is to expand the functions of existing Internet and make it more useful. With IoT， users can share not only information provided by humans and contained in databases

5、but also information provided by things in physical world. The simplified functional model of IoT is shown in Fig. 1. As in Fig. 1， IoT has sensors， for acquiring information about the state of things； an embedded processor， for producing orders that regulate the state of things； wireless technology

6、， for transferring information from sensors to Internet and Internet to controller； and control unit， for executing human orders regulating the state of things. Take IoT for maintaining room temperature for example. A standard room temperature is designated in advance， and the actual room temperatur

7、e is acquired by the sensor（s） and transferred via wireless to Internet. After receiving the actual room temperature， the embedded processor compares it with the designated value and generates an order to regulate the room temperature and keep it within a certain range. This order is immediately sen

8、t to the control unit via the Internet and wireless unit and is executed by the actuator of the control unit. If information about the state of the thing concerned can be acquired by sensors and controlled by actuators， and if the function performed by the embedded processor is not too complicated，

9、the IoT technology is feasible. If physical things and their environment in IoT become complex， the functions of the required embedded processors also become complex， and conventional technologies of the current IoT will no longer be satisfactory. Unfortunately， problems with complex factors are ver

10、y often important to economic and social development. A typical example is air pollution over a large area. Another typical example is global warming. People want to know information about the air quality and weather conditions and control them in certain ways. Therefore， efficiently dealing with co

11、mplex problems is an unavoidable responsibility of scientists. The most promising approach to handling such complex problems is artificial intelligent. The reason for this proposal is the fact that central need for solving complex problems is the learning ability. 3 Fundamental Concepts and Principl

12、es of Artificial Intelligence In a narrow sense， AI has traditionally implied the simulation of logical human thinking using computer technology. Within this framework， the fields of artificial neural networks （ANNs） 2-4 and sensor?motor systems （SMSs） 5-7 were considered extraneous， even though bot

13、h fields have been concerned with simulating the functions of the human brain. ANN and SMS had to form a new discipline called computational intelligence （CI）. Computational intelligence has become the other approach to AI. It is more reasonable for the term AI to encompass both AI in narrow sense a

14、nd CI. In the contemporary sense， AI is now re?termed unified AI 8-9. In this paper， AI means unified AI， a general term representing the theory and technology related to simulating intellectual abilities of human being， including the ability to understand and solve problems. What follows is a brief

15、 explanation of how AI can handle complex problems 10-12. What AI simulates and offers is not anything else but the learning ability of human beings， i.e.， learning to understand and solve the problem. Therefore， learning is the central feature in AI and learning?technology is the key to handling pr

16、oblems. The simplest model for AI is roughly abstracted in Fig. 2. Ontological information （OI） in Fig. 2 is information about the state and pattern of the state variance that are presented by the object in the environment of the outside world and that are the resources and clues for learning to und

17、erstand the problem. On the other hand， the subjects action or reaction applied to the object can be learnt based on an understanding of the problem. A more specific functional model of the technologies in AI is shown in Fig. 3. In Fig. 3， AI technologies are interconnected and interact with each ot

18、her. 3.1 Categories of AI Technology 3.1.1 Perception This technology is used to acquire the OI about the object or problem in its environment. It is also the technology for converting OI to epistemological information （EI）. Epistemological information is information perceived by the subject about t

19、he trinity of the form （syntactic information）， content/meaning （semantic information）， and utility/value （pragmatic information） concerning OI. Unlike the traditional concept of information proposed by Claude Shannon， EI comprises the trinity of the form， content/meaning， and utility/value and is t

20、he basis of learning. This is why EI is also often called comprehensive information. The essential function of perception is to convert OI to EI. This is the first class of information conversion in AI. 3.1.2 Cognition The main function of cognition technology is to convert EI， which is perceived by

21、 the subject from OI， into the corresponding knowledge about the object. This is the second class of information conversion needed in AI. The only possible approach to converting EI to knowledge must be learning there is no other way. 3.1.3 Decision?Making The technology used in decision?making conv

22、erts EI to intelligent strategy （IS） based on knowledge support and is directed by the goal of problem solving. The strategy is just the procedural guidance for problem?solving. This is the third class of information conversion in AI. The radical function of decision?making technology is learning to

23、 find the optimal solution for a given problem. There are usually a number of ways of achieving the designated goal from a starting point expressed by EI. A decision should be made through intelligent use， via learning， of the relevant knowledge provided. 3.1.4 Strategy?Execution This technology is

24、used to convert the IS into intelligent action （IA） that will solve the problem. 3.1.5 Strategy?Optimization Because of various non?ideal factors in all sub?processes in Fig. 3， there are often errors when intelligent action is applied. These errors are regarded as new information and are fed back t

25、o the input of the perception of the model. With this new information， the knowledge can be improved via learning， and the strategy can be optimized. Such an optimization process might continue many times until the error is sufficiently small. In sum， all the AI technologies hereto mentioned are lea

26、rning?based， and this is why AI is powerful. 3.2 Implementation Issues for the Three Classes of Information Conversion Perception technology can be implemented using the model in Fig. 4， which converts OI to EI， the trinity of X， Y and Z， and is the first class of information conversion. Fig. 4 show

27、s that the ontological information （denoted S） is applied to the input of the perception model and mapped to the corresponding syntactic information （denoted X）. Next， the pragmatic information （denoted Z） can be retrieved from the knowledge base， in which many X?Z pairs， X（i）， Z（i）， are stored. Whe

28、n X is matched with X（i0）， then Z（i0） is regarded as the pragmatic information corresponding to X. In case no math can be found， the equation can be used to find Z；Z = Cor （X， G）（1）where X and G are expressed as vectors； and Cor is the correlation operation. Because X and Z are now available， the se

29、mantic information Y can be inferred from：Y = （X， Z）S （2）where S is the space of semantic information， and is the logic operation mapping the pair of （X， Z） to Y in S. This means that Y is a subset of S when both X and Z are simultaneously valid. In other words， Y is determined by the joint conditions of X and Z （Fig. 5）. As a result， OI is converted into EI， which is the trinity of X， Y and Z， via the model in Fig. 4. This technology is completely feasible in practice. Cognition technology can be implemented using the model in Fig. 6， with