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The interaction between agents in multiagent-based control systems requires peer to peer communication between agents avoiding central control. The sensor nodes represent agents and produce measurement data every time step. The nodes exchange time series data by using the peer to peer network in order to calculate an aggregation function for solving a problem cooperatively. We investigate the aggregation process of averaging data for time series data of nodes in a peer to peer network by using the grouping algorithm of Cichon et al. 2018. Nodes communicate whether data is new and map data values according to their sizes into a histogram. This map message consists of the subintervals and vectors for estimating the node joining and leaving the subinterval. At each time step, the nodes communicate with each other in synchronous rounds to exchange map messages until the network converges to a common map message. The node calculates the average value of time series data produced by all nodes in the network by using the histogram algorithm. The relative error for comparing the output of averaging time series data, and the ground truth of the average value in the network will decrease as the size of the network increases. We perform simulations which show that the approximate histograms method provides a reasonable approximation of time series data.
We propose secure multi-party computation techniques for the distributed computation of the average using a privacy-preserving extension of gossip algorithms. While recently there has been mainly research on the side of gossip algorithms (GA) for data aggregation itself, to the best of our knowledge, the aforementioned research line does not take into consideration the privacy of the entities involved. More concretely, it is our objective to not reveal a node's private input value to any other node in the network, while still computing the average in a fully-decentralized fashion. Not revealing in our setting means that an attacker gains only minor advantage when guessing a node's private input value. We precisely quantify an attacker's advantage when guessing - as a mean for the level of data privacy leakage of a node's contribution. Our results show that by perturbing the input values of each participating node with pseudo-random noise with appropriate statistical properties (i) only a minor and configurable leakage of private information is revealed, by at the same time (ii) providing a good average approximation at each node. Our approach can be applied to a decentralized prosumer market, in which participants act as energy consumers or producers or both, referred to as prosumers.
Nowadays the processing power of mobile phones, smartphones and PDAs is increasing as well as the transmission bandwidth. Nevertheless there is still the need to reduce the content and the need of processing the data. We discuss the proposals and solutions for dynamic reduction of the transmitted content. For that, device specific properties are taken into account, as much as for the aim to reduce the need of processing power at the client side to be able to display the 3D (virtual reality) data. Therefore, well known technologies, e.g. data compression are combined with new developed ideas to reach the goal of adaptive content transmission. To achieve a device dependant reduction of processing power the data have to be preprocessed at the server side or the server even has to take over functionality of weak mobile devices.
Signal detection and bandwidth estimation, also known as channel segmentation or information channel estimation, is a perpetual topic in communication systems. In the field of radio monitoring this issue is extremely challenging, since unforeseeable effects like fading occur accidentally. In addition, most radio monitoring devices normally scan a wide frequency range of several hundred MHz and have to detect a multitude of different signals, varying in signal power, bandwidth and spectral shape. Since narrowband sensing techniques cannot be directly applied, most radio monitoring devices use Nyquist wideband sensing to discover the huge frequency range. In practice, sensing is normally conducted by an FFT sweep spectrum analyzer that delivers the power spectral density (PSD) values to the radio monitoring system. The channel segmentation is the initial step of a comprehensive signal analysis in a radio monitoring system based on the PSD values. In this paper, a novel approach for channel segmentation is presented that is based on a quantization and a histogram evaluation of the measured PSD. It will be shown that only the combination of both evaluations will lead to an successful automatic channel segmentation. The performance of the proposed algorithm is shown in a real radio monitoring szenario.
Energy management in distribution grids is one of the key challenges that needs to be overcome to increase the share of fluctuating renewable energies. Current control systems for energy management mainly demonstrate centralized- or decentralized-hierarchical control structures. Very few systems manifest a fully decentralized multiagent-based control structure. Multiagent-based control systems promise to be an advantageous approach for the future distributed energy supply system because no central control entity is necessary, which eases parameterization in case of grid topology changes, and the agents are more stable against failures and changes of control topologies. Research is necessary to prove these benefits. In this study, we introduce a design of a multiagent-based voltage control system for low-voltage grids. In detail we introduce cooperative decision-making processes and software solutions that allow the agents to perceive and control their environment, the agent-discovery and localization in different types of communication networks, agent-to-agent communication, and the integration of the multiagent system in existing grid-control infrastructures. Furthermore, the study proposes how different existing technologies can be combined into an applicable multiagent-based voltage control system: the Java/OSGi-based OpenMUC framework allows a generic field–device interaction; peer-to-peer discovery and session establishment functionalities are combined with the agent communication defined by the Foundation for Intelligent Physical Agents (FIPA). The ripple control-signal technology is applied as a fallback communication between the agent and a central grid-control center.
Computing Aggregates on Autonomous, Self-organizing Multi-Agent System: Application "Smart Grid"
(2017)
Decentralized data aggregation plays an important role in estimating the state of the smart grid, allowing the determination of meaningful system-wide measures (such as the current power generation, consumption, etc.) to balance the power in the grid environment. Data aggregation is often practicable if the aggregation is performed effectively. However, many existing approaches are lacking in terms of fault-tolerance. We present an approach to construct a robust self-organizing overlay by exploiting the heterogeneous characteristics of the nodes and interlinking the most reliable nodes to form an stable unstructured overlay. The network structure can recover from random state perturbations in finite time and tolerates substantial message loss. Our approach is inspired from biological and sociological self-organizing mechanisms.