Topology control for Wireless Sensor Networks (WSN) is a frequently tackled challenge, for which no satisfying general solution for realistic deployments has been found to the current day. Aiding to minimize unnecessary transmissions, it nevertheless represents a crucial function of WSN, in the light of their pursuit of efficiency.kTC is a new WSN topology control that unlike prior art neither relies on location information, nor on complex geometric structures, which could leave doubts about a practical feasibility. Even though location-free approaches have been proposed to circumvent systematic problems, they do not address issues like robustness and adaptability satisfyingly, which may lead to disconnection in real world deployments. kTC is a locationfree approach that adapts topologies dynamically in face of changing environmental influences. It is based on a local, patternbased heuristic, and transmitting only two messages per node to construct the topology it is highly scalable. The graphs kTC creates are symmetric, connected, and planar; they have bounded degree and nodes are θ-separated. Simulative evaluations indicate that kTC outperforms known topology control schemes. A preliminary deployment on a sensor testbed corroborates the obtained results and acts as proof of concept for kTC.

Abstract-Gossip-based epidemic protocols are used to aggregate data in distributed systems. This fault-tolerant approach does neither require maintenance of any global network state nor knowledge of network structure. However, although gossip-based aggregation algorithms scale well for graphs with good expansion, their efficiency for sparse graphs is unexamined. In this paper we analyze the feasibility and efficiency of a gossip aggregation protocol in wireless networks with low expansion. We propose a modification of the existing aggregation algorithm for use in locality-aware, sparse, static wireless networks. Our protocol terminates autonomously, uses less bandwidth than the original version, and removes the need for the leader election process while counting network nodes. Aggregates are calculated only over nodes placed in the vicinity, and nodes communicate only with their immediate neighbors by using a wireless broadcast.We evaluate our approach by simulation on sparse, irregular graphs with low expansion for the simplified system model. Furthermore, we analytically assess the worst-case convergence time of this protocol for sparse wireless networks and also for the simplified system model.

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First response communication is tackled by several independent research groups. While there are existing prototypes and simulated results, comparison of first response solutions is hardly possible so far. We have built an universal XML based description format to handle all relevant settings and actions typical for first response scenarios. In addition we implemented a user-friendly movement and environment simulator which interacts with the network simulation on top of the simulated movement. The chosen data structure have proven to be well suited for describing settings and actions found in a first response scenario. The simulator combines movement and network simulation and therefore enables both, fine grained movement models and location aware network models with reciprocal interdependencies. The simulation results of the chosen communication approach are therefore finer grained than using a network or movement simulator separately. MOTIVATIONEfficient communication infrastructures are vital especially for handling larger scale disasters on site. Existing communication mechanisms have difficulties in case of damaged or destroyed infrastructure. The self organizing capabilities of P2P enable fast deployment without the need of manual configuration of individual peers. Some of the existing P2P solutions e.g. Groove Office [1] are already successfully used within first response situations. Especially in unreliable and fast changing environments with limited infrastructure, P2P technology can be used as a basis for communication infrastructures. In this paper, we present a movement simulator which tightly, but not exclusively, interacts with the P2P simulator PlanetSim [2]. Although there are other movement simulators (such as [3] or [4]), they are often hard to tailor into the specifics of first response mobile P2P networks. We have developed a behavior based peer movement model, which is especially used to evaluate communication paradigms in a first response scenario. Our contribution is an extensible discrete simulation framework which supports feedback loops from the movement simulation to the network simulation and vice versa. The simulation framework especially enables a detailed analysis of the reciprocal dependencies of wireless networks and moving peers. Figure 1 shows the work flow, which is used to describe a scenario setup including dynamic network traffic and peer movement data. Please note that further steps can be implemented at any time. Figure 1. XML WorkflowThe simulation starts from the static world model, which describes the disaster settings. The world model is enriched by acquired movement data as well as simulated network data, which can be added iteratively. This paper is organized following the structure of the proposed workflow. In section 2. we present the basic description of the data structure for disaster simulation. This is divided in three parts: World Model, Movement Data and Network Data. Section 3. presents the approach of the movement simulation layer, which is divided in the e...

Peer-to-Peer networks are divided into two main classes: unstructured and structured. Overlays from the first class are better suited for exhaustive search, whereas those from the second class offer very efficient key-value lookups. In this paper we present a novel overlay, Path-Finder, which combines the advantages of both classes within one single overlay for the first time. Our evaluation shows that PathFinder is comparable or even better in terms of lookup and complex query performance than existing peer-to-peer overlays and scales to millions of nodes.