The paper addresses the issue of the send-on-delta data collecting strategy tocapture information from the environment. Send-on-delta concept is the signal-dependenttemporal sampling scheme, where the sampling is triggered if the signal deviates by deltadefined as the significant change of its value. It is an attractive scheme for wireless sensornetworking due to effective energy consumption. The quantitative evaluations of send-on-delta scheme for a general type continuous-time bandlimited signal are presented in thepaper. The bounds on the mean traffic of reports for a given signal, and assumed samplingresolution, are evaluated. Furthermore, the send-on-delta effectiveness, defined as thereduction of the mean rate of reports in comparison to the periodic sampling for a givenresolution, is derived. It is shown that the lower bound of the send-on-delta effectiveness(i.e. the guaranteed reduction) is independent of the sampling resolution, and constitutes thebuilt-in feature of the input signal. The calculation of the effectiveness for standard signals,that model the state evolution of dynamic environment in time, is exemplified. Finally, theexample of send-on-delta programming is shown.

In this paper we provide an overview of recent theoretical approaches and technologies that respond to the fundamental challenges of modern factory automation. We classify these major methods and technologies into several groups and, for seven of them - namely: vertical integration of factory automation systems; distributed and decentralised control, smart sensors and actuators in factories; networked control systems and wireless sensors and actuators; autonomy and self-organisation of factories; advanced sensing for factory automation; semantic models of factories; engineering methods of factory automation systems - we report recent research contributions and formulate open technical problems in the domain of modern factory automation.

A rapid progress in intelligent sensing technology creates new interest in a development of analysis and design of non-conventional sampling schemes. The investigation of the event-based sampling according to the integral criterion is presented in this paper. The investigated sampling scheme is an extension of the pure linear send-on- delta/level-crossing algorithm utilized for reporting the state of objects monitored by intelligent sensors. The motivation of using the event-based integral sampling is outlined. The related works in adaptive sampling are summarized. The analytical closed-form formulas for the evaluation of the mean rate of event-based traffic, and the asymptotic integral sampling effectiveness, are derived. The simulation results verifying the analytical formulas are reported. The effectiveness of the integral sampling is compared with the related linear send-on-delta/level-crossing scheme. The calculation of the asymptotic effectiveness for common signals, which model the state evolution of dynamic systems in time, is exemplified.

The paper belongs to the studies that deal with the effectiveness of the particular event-based sampling scheme compared to the conventional periodic sampling as a reference. In the present study, the event-based sampling according to a constant energy of sampling error is analyzed. This criterion is suitable for applications where the energy of sampling error should be bounded (i.e., in building automation, or in greenhouse climate monitoring and control). Compared to the integral sampling criteria, the error energy criterion gives more weight to extreme sampling error values. The proposed sampling principle extends a range of event-based sampling schemes and makes the choice of particular sampling criterion more flexible to application requirements. In the paper, it is proved analytically that the proposed event-based sampling criterion is more effective than the periodic sampling by a factor defined by the ratio of the maximum to the mean of the cubic root of the signal time-derivative square in the analyzed time interval. Furthermore, it is shown that the sampling according to energy criterion is less effective than the send-on-delta scheme but more effective than the sampling according to integral criterion. On the other hand, it is indicated that higher effectiveness in sampling according to the selected event-based criterion is obtained at the cost of increasing the total sampling error defined as the sum of errors for all the samples taken.

This paper focuses on the recovery of bandlimited signals from level-crossing samples by exploiting not only the knowledge of when given levels are crossed by the input, but also the implicit information that the signal stays between neighboring levels in the time intervals between the level crossings. We propose to use the technique of Projection Onto Convex Sets (POCS) for perfect signal reconstruction from either the level crossings, or the associated implicit information. Two POCS algorithms are proposed: iterative POCS and one-step POCS. While the one-step POCS is based on matrix inversion, the iterative projections can be implemented using a chain of standard circuit operations: a low-pass filter and a clipping circuit, respectively. The perfect signal recovery of the infinite projection iteration can be viewed as the completion of the nonperfect input reconstruction achieved in continuous-time DSP (CT-DSP) from level crossings. The comparative analysis of simulation results for both iterative and one-step POCS algorithms shows the importance of a good selection of the initial guess for the POCS reconstruction.