An Overview of Centralised Middleware Components for Sensor Networks

Sensors are increasingly becoming part of our daily lives: motion detection, lighting control, environmental monitoring, and keeping track of energy consumption all rely on sensors. Combining data from this wide variety of sensors will result in new and innovative applications. However, access to these sensors – or the networks formed by them – is often provided via proprietary protocols and data formats, thereby obstructing the development of applications. To overcome such issues, middleware components have been employed to provide a universal interface to the sensor networks, hiding vendor-specific details from application developers. The scientific literature contains many descriptions of middleware components for sensor networks, with ideas from various fields of research. Recently, much attention in literature is aimed at what we, in this paper, define as ‘centralised’ middleware components. These components consider sensor networks that have no capacity – in terms of memory, data storage, and cpu power – to run middleware components (partially) on the sensor nodes. Often, viewed from the position of the middleware component, these sensor networks function as simple data providers for applications In this paper we introduce the term ‘centralised’ for such middleware components, guided by a literature review of existing middleware components for sensor networks. We describe their general architecture, give a description of a representative set of four centralised middleware components, and discuss advantages and disadvantages of these components. Finally, we identify directions of further research that will impact centralised middleware systems in the near future

Networks of Sensors - Operation and Control

Mei, R.D. van der [Promotor]Bhulai, S. [Copromotor

Value Function Discovery in Markov Decision Processes with Evolutionary Algorithms

In this paper we introduce a novel method for discovery of value functions for Markov Decision Processes (MDPs). This method, which we call Value Function Discovery (VFD), is based on ideas from the Evolutionary Algorithm field. VFD’s key feature is that it discovers descriptions of value functions that are algebraic in nature. This feature is unique, because the descriptions include the model parameters of the MDP. The algebraic expression of the value function discovered by VFD can be used in several scenarios, e.g., conversion to a policy (with one-step policy improvement) or control of systems with time-varying parameters. The work in this paper is a first step towards exploring potential usage scenarios of discovered value functions. We give a detailed description of VFD and illustrate its application on an example MDP. For this MDP we let VFD discover an algebraic description of a value function that closely resembles the optimal value function. The discovered value function is then used to obtain a policy, which we compare numerically to the optimal policy of the MDP. The resulting policy shows near-optimal performance on a wide range of model parameters. Finally, we identify and discuss future application scenarios of discovered value functions

Throughput modeling of the IEEE MAC for sensor networks

In this paper we provide a model for analyzing the saturation throughput of the ieee 802.15.4 mac protocol, which is the de-facto standard for wireless sensor networks, ensuring fair access to the channel. To this end, we introduce the concept of a natural layer, which reflects the time that a sensor node typically has to wait prior to sending a packet. The model is simple and provides new insight how the throughput depends on the protocol parameters and the number of nodes in the network. Validation experiments with simulations demonstrate that the model is highly accurate for a wide range of parameter settings of the mac protocol, and applicable to both large and small networks. As a byproduct, we discuss fundamental differences in the protocol stack and corresponding throughput models of the popular 802.11 standard

Retired Life Spending At Least RM1500 Per Month

(布城10日讯）马来西亚 北方大学校长诺阿兹兹指出， 对于居住在城市地区的退休人 士而言，他们每个月至少需要 1500令吉的生活费

Learning Optimal Policies in Markov Decision Processes with Value Function Discovery

In this paper we describe recent progress in our work on Value Function Discovery (VFD), a novel method for discovery of value functions for Markov Decision Processes (MDPs). In a previous paper we described how VFD discovers algebraic descriptions of value functions (and the corresponding policies) using ideas from the Evolutionary Algorithm field. A special feature of VFD is that the descriptions include the model parameters of the MDP. We extend that work and show how additional information about the structure of the MDP can be included in VFD. This alternative use of VFD still yields near-optimal policies, and is much faster. Besides increased performance and improved run times, this approach illustrates that VFD is not restricted to learning value functions and can be applied more generally

Advances in Understanding Xenobiotic Metabolism.

The understanding of how exogenous chemicals (xenobiotics) are metabolized, distributed, and eliminated is critical to determine the impact of the chemical and its metabolites to the (human) organism. This is part of the research and educational discipline ADMET (absorption, distribution, metabolism, elimination, and toxicity). Here, we review the work of Jan Commandeur and colleagues who have not only made a significant impact in understanding of phase I and phase II metabolism of several important compounds but also contributed greatly to the development of experimental techniques for the study of xenobiotic metabolism. Jan Commandeur's work has covered a broad area of research, such as the development of online screening methodologies, the use of a combination of enzyme mutagenesis and molecular modeling for structure-activity relationship (SAR) studies, and the development of novel probe substrates. This work is the bedrock of current activities and brings the field closer to personalized (cohort-based) pharmacology, toxicology, and hazard/risk assessment.publishersversionpublishe