We propose an efficient simulation technique for the CSMA medium access protocol. It is based on the well-established model of using signal to interference and noise ratio (SINR), which is very accurate and allows for highly realistic predictions of collisions. However, traditional implementations require O(n) time to compute interference values. We evaluate how to speed this up by using efficient data structures such as k-d-trees or geometric hash tables in our implementation. There are different levels of accuracy, some of which allow for O(√n + a) lookups, where a is the number of nearby senders. We demonstrate the achievable speedup and discuss accuracy tradeoffs for different settings using the Shawn simulator.

Wireless Sensor Networks (WSNs) are envisioned to become an integral part of the Future Internet. Together with countless other embedded appliances, such resource-constraint devices will form an Internet of Things (IoT) where all kinds of devices extend the Internet to the physical world. In this vision, the seamless and flexible integration of IoT devices ranging from simple sensor nodes to large scale Enterprise IT servers are the basis for novel applications and business processes not possible before. A major challenge is to master the arising challenges of scale, low resources, and heterogeneity. In the Internet and especially in Enterprise IT, heterogeneity is addressed using Service-Oriented Architectures (SOA). However, today's technologies used to realize SOAs are to heavyweight to be used in resource-constraint networks (RCNs). In this paper, we introduce a novel, versatile, and light-weight Web Service transport protocol (called Lean Transport Protocol, LTP) that allows the transparent exchange of Web Service messages between all kinds of resource-constrained devices and server or PC class systems. We describe LTP in detail and show by real-world measurements that LTP has the potential to serve as standard Web Service transport protocol in the Internet of Things.

We will demonstrate a wireless sensor network system for the surveillance of critical areas and properties -- e.g. borders. The system consists of up to 10 sensor nodes that monitor a small border area. The protocols we show focus on detecting trespassers across a predefined area and reporting the detection to a gateway node securely. There, the trespasser's path will be graphically diplayed on a border map. The demonstration features secure protocols for the detection of trespassers, node failure and network partitioning, along with a duty cycle protocol to ensure network longevity. All information pertaining to relevant events in the network or border area will be graphically displayed on a gateway computer.

Information about distances to other nodes in wireless sensor networks has proven advantageous not only for location discovery but is also helpful for context aware applications in general. In this paper we present a novel approach to distance estimation that does neither depend on special hardware nor on unreliable measurements of physical wireless communication properties. Instead, it is inspired by the observation that distant nodes have fewer neighbors in common than close ones and calculates distances from intersection cardinalities of sets of adjacent nodes. We discuss related work and present the new approach in detail including its mathematical foundations. A simulative performance analysis comprising different scenarios shows that our scheme yields competitive results.

This paper presents our novel graphical single-authoring framework, which automatically creates customized user interfaces (UI) for a variety of devices by reusing an existing UI originally designed for large devices. It distinguishes itself from other authoring frameworks by its intuitive graphical support, UI reuse and its extensibility.

In this paper we present a modular and extensible visualization framework for wireless sensor networks. These networks have typically no means of visualizing their state, measurements or computational results. Visualization is therefore a key issue to develop and operate these networks. Data emitted by individual sensor nodes is collected by the visualization software and passed to a flexible multi-layer plug-in mechanism that renders the information on a canvas. Developers can easily adapt existing or develop new custom tailored plug-ins for their specific application.