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

An optimal query assignment for wireless sensor networks

With the increased use of large-scale real-time embedded sensor networks, new control mechanisms are needed to avoid congestion and meet required Quality of Service (QoS) levels. In this paper, we propose a Markov Decision Problem (MDP) to prescribe an optimal query assignment strategy that achieves a trade-off between two QoS requirements: query response time and data validity. Query response time is the time that queries spend in the sensor network until they are solved. Data validity (freshness) indicates the time elapsed between data acquisition and query response and whether that time period exceeds a predefined tolerance. We assess the performance of the proposed model by means of a discrete event simulation. Compared with three other heuristics, derived from practical assignment strategies, the proposed policy performs better in terms of average assignment costs. Also in the case of real query traffic simulations, results show that the proposed policy achieves cost gains compared with the other heuristics considered. The results provide useful insight into deriving simple assignment strategies that can be easily used in practice

An Optimal Query Assignment for Wireless Sensor Networks

A trade-off between two QoS requirements of wireless sensor networks: query waiting time and validity (age) of the data feeding the queries, is investigated. We propose a Continuous Time Markov Decision Process with a drift that trades-off between the two QoS requirements by assigning incoming queries to the wireless sensor network or to the database. To compute an optimal assignment policy, we argue, by means of non-standard uniformization, a discrete time Markov decision process, stochastically equivalent to the initial continuous process. We determine an optimal query assignment policy for the discrete time process by means of dynamic programming. Next, we assess numerically the performance of the optimal policy and show that it outperforms in terms of average assignment costs three other heuristics, commonly used in practice. Lastly, the optimality of the our model is confirmed also in the case of real query traffic, where our proposed policy achieves significant cost savings compared to the heuristics.Comment: 27 pages, 20 figure

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

Outlier preservation by dimensionality reduction techniques

Sensors are increasingly part of our daily lives: motion detection, lighting control, and energy consumption all rely on sensors. Combining this information into, for instance, simple and comprehensive graphs can be quite challenging. Dimensionality reduction is often used to address this problem, by decreasing the number of variables in the data and looking for shorter representations. However, dimensionality reduction is often aimed at normal daily data, and applying it to events deviating from this daily data (so-called outliers) can affect such events negatively. In particular, outliers might go unnoticed.In this paper we show that dimensionality reduction can indeed have a large impact on outliers. To that end we apply three dimensionality reduction techniques to three real-world data sets, and inspect how well they preserve outliers. We use several performance measures to show how well these techniques are capable of preserving outliers, and we discuss the results

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