Recently, the Services Oriented Architectures (SOA) have been recognized as the key to the integration and interoperability of different applications and systems that coexist in an organization. However, even though the use of SOA has increased, some applications are unable to use it. That is the case of mission critical information applications, whose requirements such as high reliability, non-stop operation, high flexibility and high performance are not satisfied by conventional SOA infrastructures. In this article we present a novel approach of combining SOA with Autonomous Decentralized Systems (ADS) in order to provide an infrastructure that can satisfy those requirements. We have named this infrastructure Autonomous Decentralized Service Oriented Architecture (ADSOA). We present the concept and architecture of ADSOA, as well as the Loosely Couple Delivery Transaction and Synchronization Technology for assuring the data consistency and high reliability of the application. Moreover, a real implementation and evaluation of the proposal in a mission critical information system, the Uniqueness Verifying Public Key Infrastructure (UV-PKI), is shown in order to prove its effectiveness.

In recent years, society has experienced several changes in its ways and methods of consuming. Nowadays, the diversity and the customization of products and services have provoked that the consumer needs continuously change. Hence, the database systems support e-business processes are required to be timeliness and adaptable to the changing preferences. Autonomous Decentralized Database System (ADDS), has been proposed in order to satisfy the enhanced requirements of current on-line e-business applications. Autonomy and decentralization of subsystems help to achieve short response times in highly competitive situations and an autonomous Coordination Mobile Agent (CMA) has been proposed to achieve flexibility in a highly dynamic environment. However, a problem in ADDS is as the number of sites increases, the distribution and harmonization of product information among the sites are turning difficult. Therefore, many users cannot be satisfied quickly. As a result, system timeliness is inadequate. To solve this problem, a self configuration technology is proposed. This technology can configure the system to the evolving situation dynamically for achieving high response. A simulation shows the effectiveness of the proposed technology in a large-scale system. Finally, an implementation of this technology is presented.

East Japan Railway Company has created new businesses such as life-style business and information technology business on the basis of railway business for sustainable growth. These businesses generate and provide synergy to one another effectively because each business is autonomous decentralized system based on diversified infrastructure. The infrastructure includes not just structure but management, technology, operation and maintenance: we call this “MTOMI Model.” The MTOMI Model is the key concept of JR East's businesses and can generate JR East's ecosystem.

The growing trends in Internet usage for data and knowledge sharing calls for dynamic classification of web contents, particularly at the edges of the Internet. Rather than considering Linked Data as an integral part of Big Data, we propose Autonomous Decentralized Semantic-based Content Classifier (ADSCC) for dynamic classification of unstructured web contents, using Linked Data and web metadata in Content Delivery Network (CDN). The proposed framework ensures efficient categorization of URLs (even overlapping categories) by dynamically mapping the changing user-defined categories to ontologies' category/classes. This dynamic classification is performed by the proposed system that mainly involves three main algorithms/modules: Dynamic Mapping algorithm, Autonomous coordination-based Inference algorithm, and Context-based disambiguation. Evaluation results show that the proposed system achieves (on average), the precision, recall and F-measure within the 93-97% range.

In this paper, we propose a novel Autonomous Decentralized Control (ADC) scheme for indirectly controlling a system performance variable of large-scale and wide-area networks. In a large-scale and wide-area network, since it is impractical for any one node to gather full information of the entire network, network control must be realized by inter-node collaboration using information local to each node. Several critical network problems (e.g., resource allocation) are often formulated by a system performance variable that is an amount to quantify system state. We solve such problems by designing an autonomous node action that indirectly controls, via the Markov Chain Monte Carlo method, the probability distribution of a system performance variable by using only local information. Analyses based on statistical mechanics confirm the effectiveness of the proposed node action. Moreover, the proposal is used to implement traffic-aware virtual machine placement control with load balancing in a data center network. Simulations confirm that it can control the system performance variable and is robust against system fluctuations. A comparison against a centralized control scheme verifies the superiority of the proposal.

In this paper, we introduce a distributed power allocation strategy for random access, that has the capabilities of multipacket reception (MPR) and successive interference cancellation (SIC). The proposed random access scheme is suitable for machine-to-machine (M2M) communication application in fifth-generation (5G) cellular networks. A previous study optimized the probability distribution for discrete transmission power levels, with implicit limitations on the successful decoding of at most two packets from a single collision. We formulate the optimization problem for the general case, where a base station can decode multiple packets from a single collision, and this depends only on the signal-to-interference-plus-noise ratio (SINR). We also propose a feasible suboptimal iterative per-level optimization process; we do this by introducing relationships among the different discrete power levels. Compared with the conventional power allocation scheme with MPR and SIC, our method significantly improves the system throughput; this is confirmed by computer simulations.

It is not easy to provide energy supply based on renewable energy enough to satisfy energy demand anytime and anywhere because the amount of renewable energy depends on geographical conditions and the time of day. In order to maximize the satisfaction of energy demand by renewable energy, surplus energy generated with renewable energy should be stored in batteries, and transmitted to electric loads with high demand somewhere in the electricity system. This paper proposes a novel autonomous decentralized mechanism of energy interchanges between distributed batteries on the basis of the diffusion equation and MCMC (Markov Chain Monte Carlo) for realizing energy supply appropriately for energy demand. Experimental results show that the proposed mechanism effectively works under several situations. Moreover, we discuss a method to easily estimate the behavior of the entire system by each node with the proposed mechanism, and the application potentiality of this estimating method to an efficient method working with non-renewable generators while minimizing the dependence of non-renewable energy, and an incentive mechanism to prevent monopolizing energy in systems.

In this paper, we consider a decentralized failure diagnosis problem for discrete event systems. For a conjunctively codiagnosable system, there exists a conjunctive decentralized diagnoser that can detect the occurrence of any failure within a uniformly bounded number of steps. We present a method for synthesizing such a conjunctive decentralized diagnoser as an online diagnoser.

Mobile ad hoc networks (MANETs) consist of mobile terminals that directly connect with one another to communicate without a network infrastructure, such as base stations and/or access points of wireless local area networks (LANs) connected to wired backbone networks. Large-scale disasters such as tsunamis and earthquakes can cause serious damage to life, property as well as any network infrastructure. However, MANETs can function even after severe disasters have destroyed regular network infrastructure. We have proposed an autonomous decentralized structure formation technology based on local interaction, and have applied it to implement autonomous decentralized clustering on MANETs. This method is known to configure clusters that reflect the network condition, such as residual battery power and the degree of each node. However, the effect of clusters that reflect the network condition has not been evaluated. In this study, we configure clusters using our method, the back-diffusion method, and a bio-inspired method, which is a kind of autonomous decentralized clustering that cannot reflect the network condition. We also clarify the importance of clustering that reflects the network condition, with regard to power consumption and data transfer efficiency.

Differential games are considered an extension of optimal control problems, which are used to formulate centralized control problems in smart grids. Optimal control theory is used to study systems consisting of one agent with one objective, whereas differential games are used to formulate systems consisting of multiple agents with multiple objectives. Therefore, a differential-game-theoretic approach is appropriate for formulating decentralized demand-side energy management systems where there are multiple decision-making entities interacting with each other. Moreover, in many smart grid applications, we need to obtain information for control via communication systems. To formulate the influence of communication availability, differential game theory is also promising because the availability of communication is considered as part of an information structure (i.e., feedback or open-loop) in differential games. The feedback information structure is adopted when information for control can be obtained, whereas the open-loop information structure is applied when the information cannot be obtained because of communication failure. This paper proposes a comprehensive framework for evaluating the performance of demand-side actors in a demand-side management system using each control scheme according to both communication availability and sampling frequency. Numerical analysis shows that the proposed comprehensive framework allows for an analysis of trade-off for decentralized and centralized control schemes.

In this paper, we study conjunctive decentralized diagnosis of discrete event systems (DESs). In most existing works on decentralized diagnosis of DESs, it is implicitly assumed that diagnosis decisions of all local diagnosers are available to detect a failure. However, it may be possible that some local diagnosis decisions are not available, due to some reasons. Letting n be the number of local diagnosers, the notion of (n,k)-conjunctive codiagnosability guarantees that the occurrence of any failure is detected in the conjunctive architecture as long as at least k of the n local diagnosis decisions are available. We propose an algorithm for verifying (n,k)-conjunctive codiagnosability. To construct a reliable conjunctive decentralized diagnoser, we need to compute the delay bound within which the occurrence of any failure can be detected as long as at least k of the n local diagnosis decisions are available. We show how to compute the delay bound.

A decentralized secure protocol for casting trust rating in reputation systems (StR protocol) is lately proposed by Dimitriou and Michalas, and the StR protocol is verified to be faster than the previous work providing anonymous feedback. In this letter, we present new enhanced scheme of StR. Compared with StR protocol, our new approach attains the exactly same security, but requires less processing time and about half communication overheads. Therefore, we improve the performance without sacrificing any security, especially the communication delay is dramatically reduced.

As wireless LAN systems become more widespread, the number of access points (APs) is increasing. A large number of APs cause overlapping cells where nearby cells utilize the same frequency channel. In the overlapping cells, inter-cell interference (ICI) degrades the throughput. This paper proposes an interference-aware multi-cell beamforming (IMB) technique to reduce the throughput degradation in the overlapping cells. The IMB technique improves transmission performance better than conventional multi-cell beamforming based on a decentralized control scheme. The conventional technique mitigates ICI by nullifying all the interference signal space (ISS) by beamforming, but the signal spaces to the user terminal (UT) is also limited because the degree of freedom (DoF) at the AP is limited. On the other hand, the IMB technique increases the signal space to the UT because the DoF at the AP is increased by selecting the ISS by allowing a small amount of ICI. In addition, we introduce a method of selecting the ISS in a decentralized control scheme. In our work, we analyze the interference channel state information (CSI) and evaluate the transmission performance of the IMB technique by using a measured CSI in an actual indoor environment. As a result, we find that the IMB technique becomes more effective as the number of UT antennas in nearby cells increases.

In forthcoming sensor networks, a multitude of sensor nodes deployed over a large geographical area for monitoring traffic, climate, etc. are expected to become an inevitable infrastructure. Clustering algorithms play an important role in aggregating a large volume of data that are produced continuously by the huge number of sensor nodes. In such networks, equal-sized multi-hop clusters which include an equal number of nodes are useful for efficiency and resiliency. In addition, scalability is important in such large-scale networks. In this paper, we mathematically design a decentralized equal-sized clustering algorithm using a partial differential equation based on the Fourier transform technique, and then design its protocol by discretizing the equation. We evaluated through simulations the equality of cluster sizes and the resiliency against packet loss and node failure in two-dimensional perturbed grid topologies.

In this paper, we study decentralized supervisory control of timed discrete event systems, where we adopt the OR rule for fusing local enablement decisions and the AND rule for fusing local enforcement decisions. For any specification language satisfying a certain assumption, we propose a method for constructing a decentralized supervisor that achieves its sublanguage. The proposed method does not require computing the achieved sublanguage.

In this paper, we propose an optimal supervisory control method for discrete event systems (DESs) that have different preferences. In our previous work, we proposed an optimal supervisory control method based on reinforcement learning. In this paper, we extend it and consider a system that consists of several local systems. This system is modeled by a decentralized DES (DDES) that consists of local DESs, and is supervised by a central supervisor. In addition, we consider that the supervisor and each local DES have their own preferences. Each preference is represented by a preference function. We introduce the new value function based on the preference functions. Then, we propose the learning method of the optimal supervisor based on reinforcement learning for the DDESs. The supervisor learns how to assign the control pattern so as to maximize the value function for the DDES. The proposed method shows the general framework of optimal supervisory control for the DDES that consists of several local systems with different preferences. We show the efficiency of the proposed method through a computer simulation.

In this paper, we study decentralized supervisory control of timed discrete event systems, where we adopt the OR rule for fusing local enablement decisions and the AND rule for fusing local enforcement decisions. Under these rules, necessary and sufficient conditions for the existence of a decentralized supervisor that achieves a given specification language are easily obtained from the result of literature. If a given specification language does not satisfy these existence conditions, we must compute its sublanguage satisfying them. The main contribution of this paper is proposing a method for computing such a sublanguage.

In the framework of decentralized supervisory control of timed discrete event systems (TDESs), each local supervisor decides the set of events to be enabled to occur and the set of events to be forced to occur under its own local observation in order for a given specification to be satisfied. In this paper, we focus on fusion rules for the enforcement decisions and adopt the combined fusion rule using the AND rule and the OR rule. We first derive necessary and sufficient conditions for the existence of a decentralized supervisor under the combined fusion rule for a given partition of the set of forcible events. We next study how to find a suitable partition.

In the near future, decentralized network systems consisting of a huge number of sensor nodes are expected to play an important role. In such a network, each node should control itself by means of a local interaction algorithm. Although such local interaction algorithms improve system reliability, how to design a local interaction algorithm has become an issue. In this paper, we describe a local interaction algorithm in a partial differential equation (or PDE) and propose a new design method whereby a PDE is derived from the solution we desire. The solution is considered as a pattern of nodes' control values over the network each of which is used to control the node's behavior. As a result, nodes collectively provide network functions such as clustering, collision and congestion avoidance. In this paper, we focus on a periodic pattern comprising sinusoidal waves and derive the PDE whose solution exhibits such a pattern by exploiting the Fourier method.

Clustering technology is very important in ad hoc networks and sensor networks from the view point of reducing the traffic load and energy consumption. In this paper, we propose a new structure formation mechanism as a tool for clustering. It meets the key clustering requirements including the use of an autonomous decentralized algorithm and a consideration of the situation of individual nodes. The proposed mechanism follows the framework of autonomous decentralized control based on local interaction, in which the behavior of the whole system is indirectly controlled by appropriately designing the autonomous actions of the subsystems. As an application example, we demonstrate autonomous decentralized clustering for a two-dimensional lattice network model, and the characteristics and adaptability of the proposed method are shown. In particular, the clusters produced can reflect the environmental situation of each node given by the initial condition.