Agent-based Management Research Articles

Applications of Fuzzy Logic and Probabilistic Neural Networks in E-Service for Malware Detection

The key point in the process of agent-based management in e-service for malware detection (according to accuracy criteria) is a decision-making process. To determine the optimal e-service for malware detection, two concepts were investigated: Fuzzy Logic (FL) and Probabilistic Neural Networks (PNN). In this study, three evolutionary variants of fuzzy partitioning, including regular, hierarchical fuzzy partitioning, and k-means, were used to automatically process the design of the fuzzy partition. Also, this study demonstrates the application of a feature selection method to reduce the dimensionality of the data by removing irrelevant features to create fuzzy logic in a dataset. The behaviors of malware are analyzed by fuzzifying relevant features for pattern recognition. The Apriori algorithm was applied to the fuzzified features to find the fuzzy-based rules, and these rules were used for predicting the output of malware detection e-services. Probabilistic neural networks were also used to find the ideal agent-based model for numerous classification problems. The numerical results show that the agent-based management performances trained with the clustering method achieve an accuracy of 100% with the PNN-MCD model. This is followed by the FL model, which classifies on the basis of linguistic variables and achieves an average accuracy of 82%.

Agent-Based Management and Coordination of Aircraft at Intersections

This work presents a game-theoretic approach to address the problem of coordination and scheduling of aircraft at intersections, with the goal of avoiding conflicts and potential collisions. The proposed algorithm enables simple agents to work together in a way that leads to cooperative behaviors, resulting in equilibria that improve the overall efficiency of the system. The researchers tested and compared the game-theoretic approach with a centralized approach, specifically FirstCome-First-Serve (FCFS), using data from Mohammed 5 Casablanca airport. The initial results suggest that the game-theoretic model is promising, despite its higher complexity. The approach has the potential to improve the overall coordination and scheduling of aircraft, leading to a more efficient and safe system. The proposed game-theoretic approach is designed to improve the coordination and scheduling of aircraft at intersections, ultimately leading to a safer and more efficient system. The approach is shown to be promising in initial testing, offering a potentially superior alternative to centralized approaches like FCFS. This research highlights the potential benefits of game-theoretic models in addressing complex coordination problems in multi-agent systems.

A Novel Agent-Based Power Management Scheme for Smart Multiple-Microgrid Distribution Systems

In this work, a novel agent-based day-ahead power management scheme is proposed for multiple-microgrid distribution systems with the intent of reducing operational costs and improving system resilience. The proposed power sharing algorithm executes within each microgrid (MG) locally, and the neighboring MGs cooperate via a multi-agent system cooperation scheme, established to model the communication among the agents. The power management for each agent is modeled as a multi-objective optimization problem (MOP) including two objectives: maximizing load coverage and minimizing the operating costs. The proposed MOP is solved using the Nondominated Sorting Genetic Algorithm (NSGA-II), where a set of Pareto optimal solutions is obtained for each agent through the NSGA-II. The final solution is obtained using an Analytical Hierarchical Process. The effectiveness of the proposed scheme is evaluated using a benchmark 4-MG distribution system. It is shown that the proposed power management scheme and the cooperation of agents lead to a higher overall system resilience and lower operation costs during extreme events.

Agent-Based Multipath Management for Supporting Sink Mobility in Wireless Sensor Networks

In wireless sensor networks, sink mobility support is one of the essential functionalities in many applications. With continuous advancement, future applications will require not only sink mobility support but also high-performance data delivery service. Multipath routing is one of the promising technologies for improving data delivery performance by collaboratively using alternative or redundant multiple routing paths. However, existing multipath routing protocols had not dealt with sink mobility. As a result, they lead to bad performance in terms of energy efficiency due to the end-to-end path reconstruction. Consequently, a novel multipath management scheme is required thereby supporting sink mobility without performance degradation. In this paper, we propose a multipath management scheme for supporting sink mobility. The proposed scheme dynamically constructs multipath along the moving path of a sink. In addition, the proposed scheme provides the path shortening schemes according to the sink’s movement for reducing energy consumption. Our simulation results show that the proposed scheme is superior to existing path management schemes in terms of reliability and energy efficiency.

Agent-based management of support systems for distributed brainstorming

In this paper, we describe the design and the management of an agent-based system that supports distributed brainstorming activities. The support system is a highly coordinated IoT application composed of many locally installed interface devices, multimedia communication functions, and cloud functions that process application logic and store meeting data. The system is designed to support a variety of brainstorming sessions, so its functionalities must be modifiable and enable the system to be adapted to different environments and user requirements without any loss of performance. System accessibility should be also ensured from any location for any user. These constraints require a flexible and usable support system.We further discuss the aspects of flexibility and usability that are important in a support system for distributed brainstorming, from which we propose a conceptual schema for flexible and usable support systems. To realize this schema, we present a resource-oriented architecture that can modify the brainstorming support system’s structure and functions. Flexibility is achieved thanks to an agent-based system that manages resources and operates on them according to users’ requests.We also describe the system architecture, which is organized around a set of channels dedicated to different services proposed to the users. We present in detail a video channel that ensures user awareness during synchronized activities. We then conduct several experiments verifying the usability of important channels in the architecture and present the results of these experiments.Finally, we discuss experimental scenarios that show how the system owes its adaptability to management based on an agent organization that supports distributed brainstorming and other activities.

An agent-based cluster head management strategy for hierarchical wireless sensor network

Wireless sensor networks (WSNs) contain a huge number of sensors. These networks are useful for environments that are difficult or dangerous for human deployment. A key challenge and a critical issue in sensor networks is the energy consumption. The control of the WSN is required in order to ensure that the network functions correctly when some components fail. In this paper, we propose a cluster head management strategy (CHMS) for hierarchical protocols to ensure network continuity even in the event of critical energy occurring in the cluster head (CH). This solution maximises the network lifetime and avoids system crash. The principal idea of the CHMS is based on the distribution rules, which are ensured by a specific node called maintenance node. After receiving the alert message from the CH 'energy level dips low', the nearest node from the base station (BS) is selected as an assistant of the CH where it affects the intra data routing function. We implemented the proposed approach by using a multi-agent system (MAS). Simulations results show that the CHMS approach maximise the cluster lifetime and minimise the consumed energy.

An agent-based cluster head management strategy for hierarchical wireless sensor network

An agent-based cluster head management strategy for hierarchical wireless sensor network

Dynamic multi agent-based management and load frequency control of PV/Fuel cell/ wind turbine/ CHP in autonomous microgrid system

This work proposes an adaptive Multi-Input and Single-Output fuzzy controller which is designed in a supervisory manner for a multi-agent system. This paper mainly aims to control the frequency oscillation of each agent and minimize the production cost of the whole interconnected system. In the proposed controller, the load frequency control and management controller loops are integrated into the designing phase. In many parallel works, designing a load frequency controller and managing unit have been carried out separately which leads to disturbance in both outputs. In the present work, the disruption is eliminated by the implementation of the proposed controlling method. To improve the performance of the proposed controller, the key parameters of the controller are tuned using the modified particle swarm optimization algorithm. The designed control system namely “distributed control method” is applied for several independent units (agents). The controller parameters are tuned in a supervisory manner for the considered multi-agent system, where each agent is connected to the adjacent agents. A wide range of operation points is regarded in the designing phase to adjust the parameters in a way that the controller can effectively control the system in the whole of this range.

Dare to Be Different: How to Commit to Investment Beliefs Through Knowledge Management

Knowledge Management (“KM”), in institutional asset management, is defined as the explicit and systematic management of knowledge – and its associated processes of creation, organization, diffusion, use and exploitation – in pursuit of improving portfolio performance and managing risks. Asset owners exist at the nexus of unique knowledge and data flows from partners such as asset managers, banks, and consultants. Innovative asset owners can crowdsource unique data sets from partners and leverage InvestTech to dynamically manage asset allocations (and risk factors). This paper describes the importance of converting KM into a skill that supports alpha generation, and how most asset owners can replicate this process to develop bespoke approaches based on specific relationships, key risks, and objectives. The paper demonstrates how investors can commit to their investment beliefs using KM (or conversely, imply their investment beliefs from investment choices made) around three major areas: (i) investing style (formulaic or discretionary); (ii) optimization method (for setting the Strategic Asset Allocation, (“SAA”) and Tactical Asset Allocation (“TAA”)); and (iii) rebalancing process (mechanical or informed). The paper extends the analysis to show how effective KM can also help with risk-management, with a particular example of a new approach to risk-management, termed “Agent-Based Risk Management,” and also how KM may lead to different implementations for say a public pension plan versus a captive insurance company.

A role-changeable fault-tolerant management strategy towards resilient NoC-based manycore systems

In hierarchically managed network-on-chip (NoC) based manycore systems with over one thousand processor cores, resource management is critical to efficient operation of the whole system. Meanwhile, the fault-tolerant design of the management structure is as important as the fault-tolerant design in the micro-structural level and the system level, which should require more attention towards constructing resilient manycore systems. This paper presents RCFTM, a hierarchical agent-based role-changeable fault-tolerant management strategy based on the modified role-changeable management framework for resilient NoC-based manycore systems. It is a distributed and adaptive strategy which can reconstruct the management hierarchy dynamically and it is capable of providing an intrinsic ability to tolerate various damages to the management hierarchy due to permanent core faults. The fault-tolerant capability of the RCFTM strategy is first evaluated with MATLAB for a large scale manycore system. Results show that the RCFTM strategy has better fault-tolerant capability than the conventional replacement strategy which relies only on backup cores. Then, the RCFTM strategy is implemented in C on a 5×5 NoC-based manycore system, where a full-system simulation platform is utilized. Experiments show that, with 20K bytes ROM and 35.6K bytes RAM footprint for the RCFTM strategy on each agent, the system can successfully tolerate both single and multiple core faults. Results also show that the RCFTM strategy only introduces less than 1.48% computing overhead of a working agent while the system is fault-free.

The development of a RFID and agent-based lot management controller for PROMIS in a client/server structure for IC assembly firm

Many integrated circuit assembly firms today are struggling in a low-profit environment. To survive in the semiconductor industry, it requires assembly firms to do their best efforts in enhancing their competitive advantages. One effective way for an assembly firm to enhance its competitiveness is the introduction of advanced technologies into its shop floor control system (SFCS) to improve assembly yield and lower assembly costs. In this study, two advanced technologies, Radio Frequency Identification and agent-based approach, were used to initiate a lot management controller (LMC), which played as a thin client in a client/server architecture to cooperate with PROMIS, a SFCS being widely used in the semiconductor industry. In addition, an eight-stage software process, which is based on Unified Modeling Language, is proposed to facilitate the development of the LMC.

Domain-Partitioned Element Management Systems Employing Mobile Agents for Distributed Network Management

Network management systems based on mobile agents are efficiently a better alternative than typical client/server based architectures. Centralized management models like SNMP or CMIP based management models suffer from scalability and flexibility issues which are addressed to great extent by flat bed or static mid-level manager models based on mobile agents, yet the use of mobile agents to distribute and delegate management tasks for above stated agent-based management frameworks like initial flat bed models and static mid-level managers cannot efficiently meet the demands of current networks which are growing in size and complexity. In view of the above mentioned limitations, we proposed a domain partitioned network management model based-on mobile agent & Element Management Systems in order to minimize management data flow to a centralized server. Intelligent agent allocated to specific EMS performs local network management and reports the results to the superior manager and finally the global manager performs global network management using those submitted management results. Experimental results of various scenarios of the proposed model have been presented to support the arguments given in favor of the prototype system based on mobile agents.

Mobile Agent Framework for Distributed Network Performance Management

traditional centralized network management approach presents severe efficiency and scalability limitations in large scale networks. The process of data collection and analysis typically involves huge transfers of management data to the manager which consumes considerable network bandwidth and causes bottlenecks at the manager side. Mobile agent technology provides an effective solution to alleviate this burden by distributing the management functionality over the network elements. A Mobile Agent has the ability to autonomously move among network elements to perform the required tasks locally. Thus, the code is transferred to the data location instead of moving the entire data to the manager's site. The present study aims to investigate the effectiveness of using mobile agents to overcome the limitations of the centralized structure. Focusing on the network performance management functional area, a prototype is developed to assess the effectiveness of a distributed mobile-agent-based network management system. The developed prototype installs itself automatically on remote machines and periodically checks their software and hardware status. Experiments are done to measure the network traffic volume when managing a typical network. Practical measurements are compared for the traffic generated by both the developed prototype and the current centralized network management standard (SNMP). This comparison confirms that mobile- agent-based management employs much less traffic than the centralized system. An estimation of the required management delays is provided for both sequential- and parallel- dispatching of the mobile agents.

DIVE-C: Distributed-parallel Virtual Environment on Cloud Computing Platform

In social media services and social network services, it is necessary to collect, analyze and process their big data with low maintenance cost. Therefore, distributed-parallel data processing on cloud platform is getting spotlight as useful solution for them. In this paper, we present a new architecture of DIVE-C: DIstributed-parallel Virtual Environment on Cloud computing platform for distributed parallel data processing applications which offers a transparent virtual computing environment in order to provide a way easy to launch user’s distributed parallel applications. It hides the complexity of the cloud, and helps users to focus on their new applications and core services. DIVE-C uses agent-based resource management scheme to configure VM resources and application deployment for offering various distributed-parallel application models. VM resources are automatically provided by unified cloud management layer. Furthermore, an easy-to-use web interface of DIVE-C offers convenience to users. We implemented a prototype of DIVE-C, and its experiment results show the competitive performance of DIVE-C for dynamic resource and virtual computing environment provisioning for various data processing models.

Formal approach to agent-based dynamic reconfiguration in Networks-On-Chip

A Network-On-Chip (NoC) platform is an emerging topology for large-scale applications. It provides a required number of resources for critical and excessive computations. However, the computations may be interrupted by faults occurring at run-time. Hence, reliability of computations as well as efficient resource management at run-time are crucial for such many-core NoC systems. To achieve this, we utilize an agent-based management system where agents are organized in a three-level hierarchy. We propose to incorporate reallocation and reconfiguration procedures into agents hierarchy such that fault-tolerance mechanisms can be executed at run-time. Task reallocation enables local reconfiguration of a core allowing it to be eventually reused in order to restore the original performance of communication and computations. The contributions of this paper are: (i) an algorithm for initial application mapping with spare cores, (ii) a multi-objective algorithm for efficient utilization of spare cores at run-time in order to enhance fault-tolerance while maintaining efficiency of communication and computations at an adequate level, (iii) an algorithm integrating the local reconfiguration procedure and (iv) formal modeling and verification of the dynamic agent-based NoC management architecture incorporating these algorithms within the Event-B framework.

Multiagent-Based Distributed-Energy-Resource Management for Intelligent Microgrids

Microgrid is a combination of distributed generators, storage systems, and controllable loads connected to low-voltage network that can operate either in grid-connected or in island mode. High penetration of power at distribution level creates such multiple microgrids. This paper proposes a two-level architecture for distributed-energy-resource management for multiple microgrids using multiagent systems. In order to match the buyers and sellers in the energy market, symmetrical assignment problem based on naíve auction algorithm is used. The developed mechanism allows the pool members such as generation agents, load agents, auction agents, grid agents, and storage agents to participate in market. Three different scenarios are identified based on the supply–demand mismatch among the participating microgrids. At the end of this paper, two case studies are presented with two and four interconnected microgrids participating in the market. Simulation results clearly indicate that the agent-based management is effective in resource management among multiple microgrids economically and profitably.

An Intelligent Auction Scheme for Smart Grid Market Using a Hybrid Immune Algorithm

Smart Grid technology is recognized as a key component of the solution to challenges such as the increasing electric demand, an aging utility infrastructure and workforce, and the environmental impact of greenhouse gases produced during electric generation. This paper presents the application of a hybrid optimization algorithm for distributed energy resource (DER) management in Smart Grid operation. The approach emphasizes the advantages of using multiagent systems for profitable operation of a Smart Grid in the energy market. The trading strategy adopted for the auction process is a profit-maximizing adaptive bidding strategy based on risk and competitive equilibrium price prediction. The auctioneer manages the usage of DERs by receiving bids from buyers and asks from sellers. A hybrid-immune-system-based particle swarm optimization is used to minimize the fuel cost for generation assuming realistic market prices for power, distributed generator bids reflecting realistic operational costs, and load bids customized according to the consumers' priorities. The simulation results clearly indicate that the agent-based management is effective in coordinating the various DERs economically and profitably.

Economic agent-based resource management in ad hoc grid environment

The ad hoc grid is a spontaneous organisation of cooperating heterogeneous nodes into a logical community without a fixed infrastructure and with only minimal administrative requirements. Resource management for ad hoc grids is challenging due to the participation of heterogeneous, dynamic, autonomous and ephemeral grid nodes. The paper proposes an ad hoc grid resource management system, the producers and consumers of ad hoc grid resource are modelled as the self-interested decision-makers described in microeconomic theory. All market participants in the ad hoc grid environment including grid resources and services can be represented as agents. We apply economic agents to build ad hoc grid resource management, where ad hoc grid resource consumers and providers can buy and sell ad hoc grid resource based on an underlying economic architecture. The main processes involved in ad hoc grid resource management are resource registration, discovery and resource allocation. The experiments are conducted to compare ad hoc grid resource allocation algorithm with other ad hoc grid resource allocation algorithm. Simulation results show that our proposed algorithm is more efficient than compared allocation scheme.

Security Schemes for a Mobile Agent Based Network and System Management Framework

In previous research work, we proposed an integrated framework to guide the design of a Mobile Agent based Network Management (MANM) system, namely the Mobile Agent based framework for Security enhanced Autonomous network and system Management (MASAM). This framework offered two distinct advantages: (1) the provision of a secure agent-based management infrastructure and (2) the capability of achieving enhanced network management functionalities. In this paper, we propose two novel security schemes, namely the Visibility Domain Protection Scheme (VDPS) and the Visa Based Authentication Scheme (VBAS), for protection of management information and authentication and resource access control of management agents, respectively. These schemes seamlessly fit into the modular MASAM framework and facilitate two major security components defined in its abstract architecture. The efficiency of the two security schemes, focusing on the aspects of security and performance, is demonstrated and evaluation results are compared with the SNMPv3 standard.

COORDINATION OF DISTRIBUTED ENERGY RESOURCE AGENTS

This article describes our research in technologies for the management and control of distributed energy resources. An agent-based management and control system is being developed to enable large-scale deployment of distributed energy resources. Local intelligent agents will allow consumers who are connected at low levels in the distribution network to manage their energy requirements and participate in coordination responses to network stimuli. Such responses can be used to reduce the volatility of wholesale electricity prices and assist constrained networks during summer and winter demand peaks. In our system, the coordination of energy resources is decentralized. Energy resources coordinate each other to realize efficient autonomous matching of supply and demand in large power distribution networks. The information exchange is through indirect (or stigmergic) communications between agents. The coordination mechanism is asynchronous and adapts to change in an unsupervised manner, making it intrinsically scalable and robust.