Development of the Internet of Things (IoT) opens many new challenges. As IoT devices are getting smaller and smaller, the problems of so-called “constrained devices” arise. The traditional Internet protocols are not very well suited for constrained devices comprising localized network nodes with tens of devices primarily communicating with each other (e.g., various sensors in Body Area Network communicating with each other). These devices have very limited memory, processing, and power resources, so traditional security protocols and architectures also do not fit well. To address these challenges the Fog computing paradigm is used in which all constrained devices, or Edge nodes, primarily communicate only with less-constrained Fog node device, which collects all data, processes it and communicates with the outside world. We present a new lightweight secure self-authenticable transfer protocol (SSATP) for communications between Edge nodes and Fog nodes. The primary target of the proposed protocol is to use it as a secure transport for CoAP (Constrained Application Protocol) in place of UDP (User Datagram Protocol) and DTLS (Datagram Transport Layer Security), which are traditional choices in this scenario. SSATP uses modified header fields of standard UDP packets to transfer additional protocol handling and data flow management information as well as user data authentication information. The optional redundant data may be used to provide increased resistance to data losses when protocol is used in unreliable networks. The results of experiments presented in this paper show that SSATP is a better choice than UDP with DTLS in the cases, where the CoAP block transfer mode is used and/or in lossy networks.
This paper intends to precisiate the well-known and widespread definitions of both smart and intelligent agent (SA; IA), as well as the smart and intelligent multi-agent system (SS/II_MAS). The use of a unified and standardized agent and multi-agent system description based on definitions of the general systems theory is delivered and proposed as well. The intellectics of multi-agent systems is considered as a kind of an extension of the agent intelligence. Three typical features of human intellectual activities are proposed to be implemented and simulated in an agent/multi-agent system as the basic paradigms for agent and multi-agent system intellectics. As underlined in the paper, operation according to those paradigms (recognition and classification, behavior according to a set of fuzzy rules, and operation according to some prescribed tendency) is solidly mathematically based (correspondingly: mathematical programing, fuzzy logic and stochastic approximation). Finally, results of computerized modeling and simulation are delivered demonstrating the practical vitality and efficiency of the theoretical approach to the realization of the intelligent environment of the Internet of Things and Services (IoT&S) for user's comfort in two projects: "Research and Development of Internet Infrastructure for IoT& S in the Smart Environment (IDAPI)" and "Research on Smart Home Environment and Development of Intelligent Technologies (BIATech)".
This paper addresses the issues of decision-making methods and their usage capabilities for intelligent control based on the habits of home residents. Learning from the behaviour of the resident is essential for the system to adapt and provide intelligent control based on behavioural habits. However, even deeply ingrained habits are subject to change over time. Therefore, an intelligent system has to respond to a changing and diverse environment. Various decision-making methods have the potential of a number of benefits in providing intelligent control for smart home systems. In this paper, concurrent decision-making methods, including Artificial Neural Networks, Fuzzy Logic, Linear Programing and Bayesian technique, are employed with proposed algorithms in order to provide control based on the habits of residents. These approaches are tested and compared in experimental scenarios for intelligent lightning control with the constant and changing habits of the residents.
This work presents the solution based on the augmenting sequence of linear programming problems (LPP) as a tool for intellectualizing home environment. The proposed solution empowers the intelligent decision making procedure which can be applied to various intelligent control applications. The augmenting self-training procedure based on LPP approach is presented as well, which allows making reasonable decisions having only limited data about the controlled environment. The method permits retraining the decision making system when new data is available. As a proof of concept, this solution is applied to intelligent light control application. The obtained simulation results show the method's capability in making reasonable decisions according to users preferences.
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