1,721,105 research outputs found

    An Event-Based Kalman filter for Clock Synchronization

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    The distribution of a time reference has long been a significant research topic in measurement and different solutions have been proposed over the years. In this context, the design of servo clocks plays an important role to get better performances by smoothing the influence of noise sources affecting a synchronization system. A servo clock is asked to provide an adaptive and conservative measure of the time distance between the local clock and the time reference by minimizing, if possible, the energy consumption. In this paper, we propose a servo clock based on an efficient implementation of the Kalman filter (KF), called in the following event-based KF that allows to overcome drawbacks of existing KF-based servo clocks with furthermore a significant reduction of the computational cost. An in-depth analysis of the synchronization uncertainty has been reported to completely characterize the proposed solution; and finally, some guidelines on how to correctly initialize the KF are provided

    ISO/IEC/IEEE 21451 Compliant Sensor Nodes for Energy-Aware Wireless Sensor Networks

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    Much attention is focused nowadays on the developing of energy-aware wireless sensor and actuator network (WSAN) and in particular on implementing network control algorithms by exploiting the ability of a sensor to reliably estimate and report its energy status at network level where energy-saving strategies can be implemented. This poses the attention on how a sensor may transfer information regarding its energy resources at network level in a standardized way. In this paper, we face the problem concerning the realization of energy-aware WSAN by using ISO/IEC/IEEE 21451 compliant sensor nodes. To this end, some guidelines on how exploiting the capability of the current version of the ISO/IEC/IEEE 21451 standard to obtain information about the energy-status of network nodes will be reported and, furthermore, some extensions for overcoming the existing shortcomings will finally be proposed

    Measurable quantities in a synchronization system under attack: A first step to implement a detection system

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    Vulnerability of a synchronization system against different kinds of network attack is gaining a large interest in the last years. In particular, for packet-based systems like PTP, delay attacks cannot be solved by a cryptographic system, since they simply consist in the maliciously delaying timing packets in a network. However, these attacks can be detected by analysing the time series of the different quantities considered by a synchronization system. In particular, possible variations in these time series may point out the occurrence of an attack. In this paper we provide an initial overview of the main issues concerning a delay attack and then we suggest a possible solution based on a slight modification of an existing servo clock

    A Combined Approach for Real-Time Data Compression in Wireless Body Sensor Networks

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    Wireless body sensor networks (WBSNs) represent an enabling technology for unobtrusive patient monitoring. Unlike wireless sensor networks (WSNs), they are characterized by relatively few and heterogeneous sensors placed in, on, or around the human body. An important issue consists in designing efficient solutions for optimizing network resource usage, such as computational capacity, energy, and bandwidth. Compression algorithms for WBSNs need to satisfy more stringent requirements than solutions for typical WSNs. In particular, to guarantee real-time monitoring of vital signals, the algorithms cannot introduce latency. Furthermore, the maximum reconstruction error is usually very small and it needs to be known in advance. In this scenario, we propose a combined compression algorithm, which satisfied the previous requirements. Results obtained by considering different biomedical signals show that a significant compression ratio can be achieved also when very small values of the maximum error are considered

    Reinforcement Learning applied to Network Synchronization Systems

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    The design of suitable clock servo is a well-known problem in the context of network-based synchronization systems. Several approaches can be found in the current literature, typically based on PI-controllers or Kalman filtering. These methods require a thorough knowledge of the environment, i.e. clock model, stability parameters, temperature variations, network traffic load, traffic profile and so on. This a-priori knowledge is required to optimize the servo parameters, such as PI constants or transition matrices in a Kalman filter. In this paper we propose instead a clock servo based on the recent Reinforcement Learning approach. In this case a self-learning algorithm based on a deep-Q network learns how to synchronize a local clock only from experience and by exploiting a limited set of predefined actions. Encouraging preliminary results reported in this paper represent a first step to explore the potentiality of the reinforcement learning in synchronization systems typically characterized by an initial lack of knowledge or by a great environmental variability

    Ottimizzazione delle misure di intensità del traffico in rete

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    In questo lavoro viene presentata un'analisi teorica della misura delle curve di intensità di traffico in una rete a commutazione di pacchetto, con lo scopo di definire criteri che permettano di ottimizzarne la stima. Si vedrà in particolare come l'analisi di incertezza consenta di formulare in modo rigoroso l'argomento, fornendo gli strumenti necessari per valutare l'accuratezza delle misure ottenute in funzione sia dei parametri dello stimatore, sia dei parametri intrinseci del traffic osservato

    Detection of Anomalous Behaviors in Networks From Traffic Measurements

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    Analysis and monitoring of traffic measurements can provide a useful tool for diagnostics, troubleshooting, and performance analysis in networks. This paper presents a method for traffic analysis based on the theory of extreme values. Numerical time series obtained from traffic measurements are processed to compute statistical indicators and analyze them to detect significant local features, which may point to some critical network behavior
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