Distributed computing and reliable communication in sensor networks using multi-agent systems

Bosse, Stefan; Pantke, Florian

doi:10.1007/s11740-012-0420-8

Cited by 17 publications

(5 citation statements)

References 3 publications

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“…The transaction model bases on the "Web Services Transactions Specification" [6].However, due to our focus on the blocking problem when guaranteeing atomicity, we can use a much simpler transaction model in this paper, i.e., we do not need a certain Web service modelling or composition language like BPEL4WS [7].Web services are invoked by asynchronous messages instead of invoking them by synchronous. Fig.…”

Section: Web Service Transaction Modelmentioning

confidence: 99%

Concurrent Control and Performance Optimization of Distributed Transaction in Heterogeneous Network Environment

Cheng¹,

Liu²,

Qiang³

et al. 2016

Proceedings of the 2016 International Conference on Education, Management and Computer Science

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Abstract. At present, most of the transaction operations are required in the heterogeneous network environment. The transaction operations not only guarantee transaction implemented correctly and effectively, but also keep transactions under the different environment of consistency and integrity. This paper mainly introduces Lock schemes for resolving the concurrent access conflicts in distributed transaction and the control methods of lock granularities applied for different cases are proposed. It provides solutions to concurrent transaction congestion in the network environment, while optimizing the performance of distributed transaction.

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Section: Web Service Transaction Modelmentioning

confidence: 99%

Concurrent Control and Performance Optimization of Distributed Transaction in Heterogeneous Network Environment

Cheng¹,

Liu²,

Qiang³

et al. 2016

Proceedings of the 2016 International Conference on Education, Management and Computer Science

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“…Interaction between nodes can be implemented using agents to manage and distribute information and energy. Agents are suitable for implementation on microchip level (Bosse and Pantke, 2013), as shown in Figure 15.…”

Section: Management On Network Levelmentioning

confidence: 99%

Tool chain for harvesting, simulation and management of energy in Sensorial Materials

Behrmann

Budelmann

Bosse

et al. 2013

Journal of Intelligent Material Systems and Structures

Self Cite

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The continuing decrease in size and energy demand of electronic sensor circuits allows endowing engineering structures and, to an increasing degree, materials with integrated sensing and data processing capabilities. Materials that adhere to this description are designated as Sensorial Materials. Their development is multidisciplinary and requires knowledge beyond materials science in fields like sensor science, computer science, energy harvesting, microsystems technology, low-power electronics, energy management, and communication. Development of such materials will benefit from systematic support for bridging research area boundaries. The present article introduces the backbone of an easy-to-use toolbox for layout of the energy supply of smart sensor nodes within a sensorial material. The fundamental approach is transferred from rapid control development, where a comparable MATLAB/Simulink tool chain is already in use. The main goal is to manage limited power resources without unacceptably compromising functionality in a given application scenario. The toolbox allows analysis of the modeled system in terms of energy and power and allows analyzing factors such as energy harvesting, use of predictive power estimation, power saving (e.g. sleep modes), model-based cognitive data reduction methods, and energy aware algorithm switching. It is linked to a simulation environment allowing analysis of energy demand and production in a specific application scenario. Its initial version presented here supports single selfpowered sensor nodes. A broad set of application cases is used to develop scenario-dependent solutions with minimum energy needs and thus demonstrate the use of the toolbox and the associated development process. The initial test case is a large-scale sensor network with optical fiber-based data and energy transmission, for which optimization of energy consumption is attempted. The toolbox can be used to improve the power-aware design of sensor nodes on digital hardware level using advanced high-level synthesis approaches and provides input for sensor node and sensor network level.

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“…In distributed coordination control, agents interact cooperatively through decentralized controllers using limited inter-agent communication and local information. Due to its wide range of applications, such as sensor networks, multi-robots, multiple economic systems and so forth, consensus plays a significant role in the research of distributed coordination control [ 1 , 2 , 3 ]. Although many studies on the consensus tracking problem have been published to date, most of them suffer from significant drawbacks, including vulnerability against disturbances and faults in control agents [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Distributed Consensus Tracking Control of Chaotic Multi-Agent Supply Chain Network: A New Fault-Tolerant, Finite-Time, and Chatter-Free Approach

Liu

Jahanshahi

Volos

et al. 2021

Entropy

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Over the last years, distributed consensus tracking control has received a lot of attention due to its benefits, such as low operational costs, high resilience, flexible scalability, and so on. However, control methods that do not consider faults in actuators and control agents are impractical in most systems. There is no research in the literature investigating the consensus tracking of supply chain networks subject to disturbances and faults in control input. Motivated by this, the current research studies the fault-tolerant, finite-time, and smooth consensus tracking problems for chaotic multi-agent supply chain networks subject to disturbances, uncertainties, and faults in actuators. The chaotic attractors of a supply chain network are shown, and its corresponding multi-agent system is presented. A new control technique is then proposed, which is suitable for distributed consensus tracking of nonlinear uncertain systems. In the proposed scheme, the effects of faults in control actuators and robustness against unknown time-varying disturbances are taken into account. The proposed technique also uses a finite-time super-twisting algorithm that avoids chattering in the system’s response and control input. Lastly, the multi-agent system is considered in the presence of disturbances and actuator faults, and the proposed scheme’s excellent performance is displayed through numerical simulations.

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Distributed computing and reliable communication in sensor networks using multi-agent systems

Cited by 17 publications

References 3 publications

Concurrent Control and Performance Optimization of Distributed Transaction in Heterogeneous Network Environment

Concurrent Control and Performance Optimization of Distributed Transaction in Heterogeneous Network Environment

Tool chain for harvesting, simulation and management of energy in Sensorial Materials

Distributed Consensus Tracking Control of Chaotic Multi-Agent Supply Chain Network: A New Fault-Tolerant, Finite-Time, and Chatter-Free Approach

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