Intelligent Multi-Agent Based Information Management Methods to Direct Complex Industrial Systems

In recent years, the increasingly complexity of the logistic and technical aspects of the novel manufacturing environments, as well as the need to increase the performance and safety characteristics of the related cooperation, coordi-nation and control mechanisms is encouraging the development of new information management strategies to direct and man- age the automated systems involved in the manufacturing processes. The Computational Intelligent (CI) approaches seem to provide an effective support to the challenges posed by the next generation industrial systems. In particular, the Intelligent Agents (IAs) and the Multi-Agent Systems (MASs) paradigms seem to provide the best suitable solutions. Autonomy, flexibility and adaptability of the agent-based technology are the key points to manage both automated and information processes of any industrial system. The paper describes the main features of the IAs and MASs and how their technology can be adapted to support the current and next generation advanced industrial systems. Moreover, a study of how a MAS is utilized within a productive process is depicted.

Privacy Preserving Demand Side Management Method via Multi-Agent Reinforcement Learning

The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.

Multi-Agent Hierarchical Graph Attention Reinforcement Learning for Grid-Aware Energy Management

The increasing adoption of renewable energy has posed challenges for voltage regulation in power distribution networks.Gridaware energy management,which includes the control of smart inverters and energy management systems,is a trending way to mitigate this problem.However,existing multi-agent reinforcement learning methods for grid-aware energy management have not sufficiently considered the importance of agent cooperation and the unique characteristics of the grid,which leads to limited performance.In this study,we propose a new approach named multi-agent hierarchical graph attention reinforcement learning framework(MAHGA)to stabilize the voltage.Specifically,under the paradigm of centralized training and decentralized execution,we model the power distribution network as a novel hierarchical graph containing the agent-level topology and the bus-level topology.Then a hierarchical graph attention model is devised to capture the complex correlation between agents.Moreover,we incorporate graph contrastive learning as an auxiliary task in the reinforcement learning process to improve representation learning from graphs.Experiments on several real-world scenarios reveal that our approach achieves the best performance and can reduce the number of voltage violations remarkably.

Supply Chain Management System Model of Virtual Enterprises Based on Multi-Agent

Based on the analysis of a virtual enterprise and the development of supply chain management, their integration is proposed. Then, the difference between multi-agent system modeling method and the traditional modeling method is analyzed, and a method based on Java agent framework for multi-agent systems（ JAFMAS） is proposed. By using this method the virtual enterprise＇ s supply chain management system model is established.

Intelligent Parking Management System by Multi-Agent Approach:The Case of Urban Area of Tunis

By coordination and cooperation between multi-agents, this paper proposes the network of intelligent agents which can reduce the search time needed to finding a parking place. Based on multi-agent model, the fined solution is designed to help drivers in finding a parking space at anytime and anywhere. Three services are offered: the search for a vacant place, directions to a parking space and booking a place for parking. The results of this study generated by the platform MATSim transport simulation, show that our approach optimizes the operation of vehicles in a parking need with the aim of reducing congestion, and improve traffic flow in urban area. A comparison between the first method where the vehicles are random and the second method where vehicles are steered to vacant parking spaces shows that the minimization of time looking for a parking space could improve circulation by reducing the number of cars in the morning of 2% and 0.7% of the evening. In addition, the traffic per hour per day was reduced by approximately 4.17%.

Keynote trustmanagement及其在主动网络中的应用

随着Internet应用的迅速发展, 分布式计算机逐渐成为发展主流。传统的基于ACL的资源访问控制机制已经不能很好的适应分布式系统需要。本文在介绍了基于信任管理的访问控制机制后, 深入分析Keynotetrustmanagementsystem, 并在此基础上给出了Keynotetrustmanagementsystem在安全主动组播中的应用构架。该构架能较好解决传统IP组播中未能解决的组播组成员的访问控制与分布式授权问题。

A Method for Trust Management in Cloud Computing: Data Coloring by Cloud Watermarking

With the development of Internet technology and human computing, the computing environment has changed dramatically over the last three decades. Cloud computing emerges as a paradigm of Internet computing in which dynamical, scalable and often virtuMized resources are provided as services. With virtualization technology, cloud computing offers diverse services （such as virtual computing, virtual storage, virtual bandwidth, etc.） for the public by means of multi-tenancy mode. Although users are enjoying the capabilities of super-computing and mass storage supplied by cloud computing, cloud security still remains as a hot spot problem, which is in essence the trust management between data owners and storage service providers. In this paper, we propose a data coloring method based on cloud watermarking to recognize and ensure mutual reputations. The experimental results show that the robustness of reverse cloud generator can guarantee users＇ embedded social reputation identifications. Hence, our work provides a reference solution to the critical problem of cloud security.

Self-Organized Public-Key Management for Mobile Ad Hoc Networks Based on a Bidirectional Trust Model

In traditional networks , the authentication is performed by certificate authoritys（CA）,which can＇t be built in distributed mobile Ad Hoc Networks however. In this pa per, we propose a fully self-organized public key management based on bidirectional trust model without any centralized authority that allows users to generate their public-private key pairs, to issue certificates, and the trust relation spreads rationally according to the truly human relations. In contrast with the traditional self-organized public-key management, the average certificates paths get more short, the authentication passing rate gets more high and the most important is that the bidirectional trust based model satisfys the trust re quirement of hosts better.

Quantum Communication Networks and Trust Management: A Survey

This paper summarizes the state of art in quantum communication networks and trust management in recent years.As in the classical networks,trust management is the premise and foundation of quantum secure communication and cannot simply be attributed to security issues,therefore the basic and importance of trust management in quantum communication networks should be taken more seriously.Compared with other theories and techniques in quantum communication,the trust of quantum communication and trust management model in quantum communication network environment is still in its initial stage.In this paper,the core technologies of establishing secure and reliable quantum communication networks are categorized and summarized,and the trends of each direction in trust management of quantum communication network are discussed in depth.

A Neural Network-Based Trust Management System for Edge Devices in Peer-to-Peer Networks

Edge devices in Internet of Things(IoT)applications can form peers to communicate in peer-to-peer(P2P)networks over P2P protocols.Using P2P networks ensures scalability and removes the need for centralized management.However,due to the open nature of P2P networks,they often suffer from the existence of malicious peers,especially malicious peers that unite in groups to raise each other’s ratings.This compromises users’safety and makes them lose their confidence about the files or services they are receiving.To address these challenges,we propose a neural networkbased algorithm,which uses the advantages of a machine learning algorithm to identify whether or not a peer is malicious.In this paper,a neural network(NN)was chosen as the machine learning algorithm due to its efficiency in classification.The experiments showed that the NNTrust algorithm is more effective and has a higher potential of reducing the number of invalid files and increasing success rates than other well-known trust management systems.

TPM Context Manager and Dynamic Configuration Management for Trusted Virtualization Platform

It is absolutely critical that trusted configuration management which significantly affects trust chain establishment, sealing storage and remote attestation, especially in trusted virtualization platform like Xen whose system configuration changes easily. TPM （trusted platform module） context manager is presented to carry out dynamic configuration management for virtual machine. It manages the TPM command requests and VM （virtual machine） configurations. The dynamic configuration representa- tion method based on Merkle hash tree is explicitly proposed against TCG （trusted computing group） static configuration representation. It reflects the true VM status in real time even if the configuration has changed, and it eliminates the invalidation of configuration representation, sealing storage and remote attestation. TPM context manager supports TCG storage protection, remote attestation etc, which greatly enhances the security on trusted virtualization platform.

Authorization Management Framework Based on Joint Trust-Risk Evaluation

Authorization management is important precondition and foundation for coordinating and resource sharing in open networks. Recently, authorization based on trust is widely used whereby access rights to shared resource are granted on the basis of their trust relation in distributed environment. Nevertheless, dynamic change of the status of credential and chain of trust induces to uncertainty of trust relation. Considering uncertainty of authorization and analyzing deficiency of authorization model only based on trust, we proposes joint trust-risk evaluation and build the model based on fuzzy set theory, and make use of the membership grade of fuzzy set to express joint trust-risk relation. Finally, derivation principle and constraint principle of joint trust-risk relationships are presented. The authorization management model is defined based on joint trust-risk evaluation, proof of compliance and separation of duty are analyzed. The proposed model depicts not only trust relationship between principals, but also security problem of authorization.

Attack Resistant Trust Management Model Based on Beta Function for Distributed Routing in Internet of Things

With the rapid development of Internet of Things (IoT),the issue of trust in distributed routing systems has attracted more research attention.The existing trust management frameworks,however,suffer from some possible attacks in hostile environments,such as false accusation,collusion,on-off,and conflicting behavior.Therefore,more comprehensive models should be proposed to predict the trust level of nodes on potential routes more precisely,and to defeat several kinds of possible attacks.This paper makes an attempt to design an attack-resistant trust management model based on beta function for distributed routing strategy in IoT.Our model can evaluate and propagate reputation in distributed routing systems.We first describe possible attacks on existing systems.Our model is then proposed to establish reliable trust relations between self-organized nodes and defeat possible attacks in distributed routing systems.We also propose a theoretical basis and skeleton of our model.Finally,some performance evaluations and security analyses are provided to show the effectiveness and robustness of our model compared with the existing systems.

Trust management-based and energy efficient hierarchical routing protocol in wireless sensor networks

The single planar routing protocol has a slow convergence rate in the large-scale Wireless Sensor Network(WSN).Although the hierarchical routing protocol can effectively cope with large-scale application scenarios,how to elect a secure cluster head and balance the network load becomes an enormous challenge.In this paper,a Trust Management-based and Low Energy Adaptive Clustering Hierarchy protocol(LEACH-TM)is proposed.In LEACH-TM,by using the number of dynamic decision cluster head nodes,residual energy and density of neighbor nodes,the size of the cluster can be better constrained to improve energy efficiency,and avoid excessive energy consumption of a node.Simultaneously,the trust management scheme is introduced into LEACH-TM to defend against internal attacks.The simulation results show that,compared with LEACH-SWDN protocol and LEACH protocol,LEACH-TM outperforms in prolonging the network lifetime and balancing the energy consumption,and can effectively mitigate the influence of malicious nodes on cluster head selection,which can greatiy guarantee the security of the overall network.

Context and Machine Learning Based Trust Management Framework for Internet of Vehicles

Trust is one of the core components of any ad hoc network security system.Trust management(TM)has always been a challenging issue in a vehicular network.One such developing network is the Internet of vehicles(IoV),which is expected to be an essential part of smart cities.IoV originated from the merger of Vehicular ad hoc networks(VANET)and the Internet of things(IoT).Security is one of the main barriers in the on-road IoV implementation.Existing security standards are insufficient to meet the extremely dynamic and rapidly changing IoV requirements.Trust plays a vital role in ensuring security,especially during vehicle to vehicle communication.Vehicular networks,having a unique nature among other wireless ad hoc networks,require dedicated efforts to develop trust protocols.Current TM schemes are inflexible and static.Predefined scenarios and limited parameters are the basis for existing TM models that are not suitable for vehicle networks.The vehicular network requires agile and adaptive solutions to ensure security,especially when it comes to critical messages.The vehicle network’s wireless nature increases its attack surface and exposes the network to numerous security threats.Moreover,internet involvement makes it more vulnerable to cyberattacks.The proposed TM framework is based on context-based cognition and machine learning to be best suited to IoV dynamics.Machine learning is the best solution to utilize the big data produced by vehicle sensors.To handle the uncertainty Bayesian machine learning statistical model is used.The proposed framework can adapt scenarios dynamically and infer using the maximum possible parameter available.The results indicated better performance than existing TM methods.Furthermore,for future work,a high-level machine learning model is proposed.

Design and Implementation of Trusted-Router Based on Trust Management System

This paper presents a scheme to perform QoS management and assure network security by using the trusted-router based on the Trust Management System.In this trusted-router,every IP packet is forwarded and queued by its trust value,which is the quantification of the network's expectation for this packet's and its owner's behavior in the network.We outline the algorithms to calculate the trust value of the trusted-router and the IP packet.We also introduce the trust-based QoS management algorithm and the deployment of the trusted-routers which carry out this algorithm.The simulation results show that the least trusted IP packets will be dropped to save resources for those highly trusted IP packets.This will ecourage all the elements in the network to keep a good trust record.

Model of Trust Management in Open Network Environment

To keep open network more efficacious and secure, it is necessary that a nice trust model and method of trust management must be developed. The reason why traditional trust models are incomplete in their function to manage trust is explained, and a general model based on hybrid trust model and introducer protocol is provided. The hybrid model is more flexible and efficacious to manage trust compared with hierarchy model and Web model. The introducer protocol is a better solution to build, maintain and refresh the trust relationship in open network environment.

Adaptive Trust Management Framework in P2P Grid Computing

In P2P Grid computing systems, the authorization decision is often tackled by two different trust management methods： policy-based approach, where authorization are built on logical rules and verifiable properties encoded in signed credentials, and reputation-based approach, based on collecting, aggregating and disseminating reputation among the peers. However, the overhead caused by proof of compliance on authorization and the absence of certifying authorities may negate the strong and objective security advantages of policy-based approach, whilst vagueness, complexity and inaccurate characterization caused by reputation evolution may eliminate the quantitative and flexible advantages of reputation-based approach. We propose an adaptive trust management framework, which combines the merit of policy proof and reputation evolution such that authorization is aware of not only the strong and objective security traits, but also the calculability and the availability security traits. Finally, the framework of system is proposed.

A Dynamic Active Multicast Group Access Control Framework Based on Trust Management System

The current multicast model provides no access control mechanism. Any host can send data directly to a multicast address or join a multicast group to become a member, which brings safety problems to multicast. In this paper, we present a new active multicast group access control mechanism that is founded on trust management. This structure can solve the problem that exists in multicast members＇ access control and distributing authorization of traditional IP multicast.

A Cache Considering Role-Based Access Control and Trust in Privilege Management Infrastructure

PMI （privilege management infrastructure） is used to perform access control to resource in an E-commerce or E-government system. With the ever-increasing need for secure transaction, the need for systems that offer a wide variety of QoS （quality-of-service） features is also growing. In order to improve the QoS of PMI system, a cache based on RBAC （Role-based Access control） and trust is proposed. Our system is realized based on Web service. How to design the cache based on RBAC and trust in the access control model is deseribed in detail. The algorithm to query role permission in cache and to add records in cache is dealt with. The policy to update cache is introduced also.