OPTIMAL ROUTING ON NARROW CHANNELS

Channel routing is one of the basic problems in VLSI routing. While the minimum width can be found in linear time in the single row routing problem, the complexity of the channel routing problem is not fully understood yet. A solution can be found, even in linear time, in the unconstrained model, but the complexity of determining the minimum width is not known. The present article concentrates on the Manhattan model where horizontal and vertical wire segments are positioned on different sides of the board. In this case, the routing problem is known to be NP-complete. Hence there is no hope to find an algorithm whose running time is polynomial both in the length and the width of the channel. The width of the channel is usually much smaller than the length, thus, an algorithm, whose running time is exponential in the width and polynomial' in the length can be efficient in the case of a narrow channel. We show that the channel routing problem in the Manhattan model is solvable in linear time if the length of the input is proportional to the length of the channel, and the width does not belong to the input

The Single Row Routing Problem Revisited: A Solution Based on Genetic Algorithms

With the advent of VLSI technology, circuits with more than one million transistors have been integrated onto a single chip. As the complexity of ICs grows, the time and money spent on designing the circuits become more important. A large, often dominant, part of the cost and time required to design an IC is consumed in the routing operation. The routing of carriers, such as in IC chips and printed circuit boards, is a classical problem in Computer Aided Design. With the complexity inherent in VLSI circuits, high performance routers are necessary. In this paper, a crucial step in the channel routing technique, the single row routing (SRR) problem, is considered. First, we discuss the relevance of SRR in the context of the general routing problem. Secondly, we show that heuristic algorithms are far from solving the general problem. Next, we introduce evolutionary computation, and, in particular, genetic algorithms (GAs) as a justifiable method in solving the SRR problem. Finally, an efficient O(nk) complexity technique based on GAs heuristic is obtained to solve the general SRR problem containing n nodes. Experimental results show that the algorithm is faster and can often generate better results than many of the leading heuristics proposed in the literature

Power Aware Routing for Sensor Databases

Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensor network databases like TinyDB are the dominant architectures to extract and manage data in such networks. Since sensors have significant power constraints (battery life), and high communication costs, design of energy efficient communication algorithms is of great importance. The data flow in a sensor database is very different from data flow in an ordinary network and poses novel challenges in designing efficient routing algorithms. In this work we explore the problem of energy efficient routing for various different types of database queries and show that in general, this problem is NP-complete. We give a constant factor approximation algorithm for one class of query, and for other queries give heuristic algorithms. We evaluate the efficiency of the proposed algorithms by simulation and demonstrate their near optimal performance for various network sizes

Adaptive Processing of Spatial-Keyword Data Over a Distributed Streaming Cluster

The widespread use of GPS-enabled smartphones along with the popularity of micro-blogging and social networking applications, e.g., Twitter and Facebook, has resulted in the generation of huge streams of geo-tagged textual data. Many applications require real-time processing of these streams. For example, location-based e-coupon and ad-targeting systems enable advertisers to register millions of ads to millions of users. The number of users is typically very high and they are continuously moving, and the ads change frequently as well. Hence sending the right ad to the matching users is very challenging. Existing streaming systems are either centralized or are not spatial-keyword aware, and cannot efficiently support the processing of rapidly arriving spatial-keyword data streams. This paper presents Tornado, a distributed spatial-keyword stream processing system. Tornado features routing units to fairly distribute the workload, and furthermore, co-locate the data objects and the corresponding queries at the same processing units. The routing units use the Augmented-Grid, a novel structure that is equipped with an efficient search algorithm for distributing the data objects and queries. Tornado uses evaluators to process the data objects against the queries. The routing units minimize the redundant communication by not sending data updates for processing when these updates do not match any query. By applying dynamically evaluated cost formulae that continuously represent the processing overhead at each evaluator, Tornado is adaptive to changes in the workload. Extensive experimental evaluation using spatio-textual range queries over real Twitter data indicates that Tornado outperforms the non-spatio-textually aware approaches by up to two orders of magnitude in terms of the overall system throughput

Efficient Compressive Sampling of Spatially Sparse Fields in Wireless Sensor Networks

Wireless sensor networks (WSN), i.e. networks of autonomous, wireless sensing nodes spatially deployed over a geographical area, are often faced with acquisition of spatially sparse fields. In this paper, we present a novel bandwidth/energy efficient CS scheme for acquisition of spatially sparse fields in a WSN. The paper contribution is twofold. Firstly, we introduce a sparse, structured CS matrix and we analytically show that it allows accurate reconstruction of bidimensional spatially sparse signals, such as those occurring in several surveillance application. Secondly, we analytically evaluate the energy and bandwidth consumption of our CS scheme when it is applied to data acquisition in a WSN. Numerical results demonstrate that our CS scheme achieves significant energy and bandwidth savings wrt state-of-the-art approaches when employed for sensing a spatially sparse field by means of a WSN.Comment: Submitted to EURASIP Journal on Advances in Signal Processin