A Novel Clock Skew Estimator and Its Performance for the IEEE 1588v2 (PTP) Case in Fractional Gaussian Noise/Generalized Fractional Gaussian Noise Environment

Papers in the literature dealing with the Ethernet network characterize packet delay variation (PDV) as a long-range dependence (LRD) process. Fractional Gaussian noise (fGn) or generalized fraction Gaussian noise (gfGn) belong to the LRD process. This paper proposes a novel clock skew estimator for the IEEE1588v2 applicable for the white-Gaussian, fGn, or gfGn environment. The clock skew estimator does not depend on the unknown asymmetry between the fixed delays in the forward and reverse paths nor on the clock offset between the Master and Slave. In addition, we supply a closed-form-approximated expression for the mean square error (MSE) related to our new proposed clock skew estimator. This expression is a function of the Hurst exponent H, as a function of the parameter a for the gfGn case, as a function of the total sent Sync messages, as a function of the Sync period, and as a function of the PDV variances of the forward and reverse paths. Simulation results confirm that our closed-form-approximated expression for the MSE indeed supplies the performance of our new proposed clock skew estimator efficiently for various values of the Hurst exponent, for the parameter a in gfGn case, for different Sync periods, for various values for the number of Sync periods and for various values for the PDV variances of the forward and reverse paths. Simulation results also show the advantage in the performance of our new proposed clock skew estimator compared to the literature known ML-like estimator (MLLE) that maximizes the likelihood function obtained based on a reduced subset of observations (the first and last timing stamps). This paper also presents designing graphs for the system designer that show the number of the Sync periods needed to get the required clock skew performance (MSE = 10–12). Thus, the system designer can approximately know in advance the total delay or the time the system has to wait until getting the required system’s performance from the MSE point of view.

1 DESIGN OF MODULAR ARCHITECTURE FOR EV AND HEV FOR PASSENGER CARS

Hybridization is important to obtain the advantages of both the engine and motor as the sources of propulsion. This paper discusses the effect of hybridization of powertrain on vehicle performance. The Hybrid architectures are differentiated on the basis percentage of power dependency on the engine and motor. Passenger car with hybridization ratios of 20%, 40%, 60%, 80% and 100% are modelled on MATLAB/Simulink using the backward facing approach with the engine and motor specifications remaining constant. The hybridizations ratios and the energy consumption in terms of fuel and battery energy are obtained from the model and compared. Neural network is implemented to determine the fuel consumption. The outputs can be used by a system designer to determine a desirable hybridization factor based on the requirements dictated by the specific application.

Design Space Exploration for Secure IoT Devices and Cyber-Physical Systems

With the advent of the Internet of Things (IoT) and Cyber-Physical Systems (CPS), embedded devices have been gaining importance in our daily lives, as well as industrial processes. Independent of their usage, be it within an IoT system or a CPS, embedded devices are always an attractive target for security attacks, mainly due to their continuous network availability and the importance of the data they handle. Thus, the design of such systems requires a thorough consideration of the various security constraints they are liable to. Introducing these security constraints, next to other requirements, such as power consumption, and performance increases the number of design choices a system designer must consider. As the various constraints are often conflicting with each other, designers face the complex task of balancing them. System designers facilitate Design Space Exploration (DSE) tools to support a system designer in this job. However, available DSE tools only offer a limited way of considering security constraints during the design process. In this article, we introduce a novel DSE framework, which allows the consideration of security constraints, in the form of attack scenarios, and attack mitigations in the form of security tasks. Based on the descriptions of the system’s functionality and architecture, possible attacks, and known mitigation techniques, the framework finds the optimal design for a secure IoT device or CPS. Our framework’s functionality and its benefits are shown based on the design of a secure sensor system.

Shading on Photovoltaic Collectors on Rooftops

Rooftop photovoltaic (PV) systems in urban environments play an important role in solar electric energy generation. Shading on PV collectors, by self-shading, walls and fences on rooftops, affect negatively the output energy of the PV systems. Increasing the distance between the collector rows, and between the walls and fences near to the collectors, may minimize the shading losses. Practically, this option is usually limited, especially on rooftops. Rooftops may be of different types: horizontal, inclined, and saw-tooth, and may have obscuring structures like walls and fences. The distance between the shading objects and the PV collector rows determine the loss of energy due to shading. The study provides the PV system designer with mathematical expressions for distances from obscuring objects for the deployment of PV systems on rooftops. The optimal inclination and azimuths angles of a PV system on a triangular sloped rooftop are also illustrated.

A Sequential Sampling Algorithm for Multistage Static Coverage Problems

In many system-engineering problems, such as surveillance, environmental monitoring, and cooperative task performance, it is critical to allocate limited resources within a restricted area optimally. Static coverage problem (SCP) is an important class of the resource allocation problem. SCP focuses on covering an area of interest so that the activities in that area can be detected with high probabilities. In many practical settings, primarily due to financial constraints, a system designer has to allocate resources in multiple stages. In each stage, the system designer can assign a fixed number of resources, i.e., agents. In the multistage formulation, agent locations for the next stage are dependent on previous-stage agent locations. Such multistage static coverage problems are nontrivial to solve. In this paper, we propose an efficient sequential sampling algorithm to solve the multistage static coverage problem (MSCP) in the presence of resource intensity allocation maps (RIAMs) distribution functions that abstract the event that we want to detect/monitor in a given area. The agent's location in the successive stage is determined by formulating it as an optimization problem. Three different objective functions have been developed and proposed in this paper: (1) L2 difference, (2) sequential minimum energy design (SMED), and (3) the weighted L2 and SMED. Pattern search (PS), an efficient heuristic algorithm has been used as optimization algorithm to arrive at the solutions for the formulated optimization problems. The developed approach has been tested on two- and higher dimensional functions. The results analyzing real-life applications of windmill placement inside a wind farm in multiple stages are also presented.

Polynomial Load Model Development for Analysing Residential Electric Energy Use Behaviour

Analysing and simulating the dynamic behaviour of home power system as a part of community-based energy system needs load model of either aggregate or dis-aggregate power use. Moreover, in the context of home energy efficiency, development of specific and accurate residential load model can help system designer to develop a tool for reducing energy consumption effectively. In this paper, a new method for developing two types of residential polynomial load model is presented. In the research, computation technique of model parameters is provided based on median filter and least square estimation and implemented by MATLAB. We use AMPDs data set, which have 1-minute data sampling, to show the effectiveness of proposed method. After simulation is carried out, the performance evaluation of model is provided through exploring root mean-squared error between original data and model output. From simulation results, it could be concluded that proposed model is enough for helping system designer to analyse home power energy use.

A survey of incentive engineering for crowdsourcing

With the growth of the Internet, crowdsourcing has become a popular way to perform intelligence tasks that hitherto would be either performed internally within an organization or not undertaken due to prohibitive costs and the lack of an appropriate communications infrastructure. In crowdsourcing systems, whereby multiple agents are not under the direct control of a system designer, it cannot be assumed that agents will act in a manner that is consistent with the objectives of the system designer or principal agent. In situations whereby agents’ goals are to maximize their return in crowdsourcing systems that offer financial or other rewards, strategies will be adopted by agents to game the system if appropriate mitigating measures are not put in place. The motivational and incentivization research space is quite large; it incorporates diverse techniques from a variety of different disciplines including behavioural economics, incentive theory, and game theory. This paper specifically focusses on game theoretic approaches to the problem in the crowdsourcing domain and places it in the context of the wider research landscape. It provides a survey of incentive engineering techniques that enable the creation of apt incentive structures in a range of different scenarios.