We focus on range query processing on large-scale, typically distributed infrastructures. In this work we present the ART (Autonomous Range Tree) structure, which outperforms the most popular decentralized structures, including Chord (and some of its successors), BATON (and its successor) and Skip-Graphs. ART supports the join/leave and range query operations in O(log log N) and O(log2b logN +|A|) expected w.h.p number of hops respectively, where the base b is a double-exponentially power of two, N is the total number of peers and |A| the answer size.

In this paper we introduce a novel distributed simulation environment with GUI for P2P simulations (D-P2P-Sim). The key aim is to provide the appropriate integrated set of tools in a single software solution to evaluate the performance of various protocols. The basic architecture of the distributed P2P simulator is based on a multi-threading, asynchronous, message passing and distributed environment with graphical user interface to facilitate ease of use by both researchers and programmers.

After the debunking of some myths about why P2P overlays are not feasible in sensornets, many such solutions have been proposed. None of the existing P2P overlays for sensornets provide "Energy-Level Application and Services". On this purpose and based on the efficient P2P method presented in [16], we design a novel P2P overlay for Energy Level discovery in a sensornet, the so-called ELDT (Energy Level Distributed Tree). Sensor nodes are mapped to peers based on their energy level. As the energy levels change, the sensor nodes would have to move from one peer to another and this oparation is the most crucial for the efficient scalability of the proposed system. Similarly, as the energy level of a sensor node becomes extremelly low, that node may want to forward it's task to another node with the desired energy level. The adaptation of the P2P index presented in [16] quarantees the best-known query performance of the above operation. We experimentally verify this performance via an appropriate simulator we have designed for this purpose.

We present a time-efficient approach to index objects moving on the plane to efficiently answer range queries about their future positions. Each object is moving with non small velocity u, meaning that the velocity value distribution is skewed (Zipf) towards umin in some range [umin, umax], where umin is a positive lower threshold. Our algorithm enhances a previously described solution [18] by accommodating the ISB-tree access method as presented in [6]. Experimental evaluation shows the improved performance, scalability and efficiency of the new algorithm.