In patch-and-stitch localization algorithms, a flip error refers to the kind of error in which a patch is stitched to the map as being wrongly reflected. In this paper, we present an anchor-free localization algorithm which tries to detect and prevent flip errors. The flip error prevention is achieved by two filtering mechanisms: the flip ambiguity test and the flip conflict detection. Based on two techniques, we devised an anchor-free localization algorithm and evaluated the performance of the proposed algorithm though simulations. The results show that our algorithm achieves significant performance improvement over the existing algorithms.

A "patch-and-stitchrdquo localization algorithm divides the network into small overlapping subregions. Typically, each subregion consists of a node and all or some of its neighbors. For each subregion, the algorithm builds a local map, called a patch, which is actually an embedding of the nodes it spans in a relative coordinate system. Finally, the algorithm stitches those patches to form a single global map. In a patch-and-stitch algorithm, the stitching order makes an influence on both the performance and the complexity of the algorithm. In this paper, we present a formal framework to deal with stitching orders in patch-and-stitch localization algorithms. In our framework, the stitching order is determined by a stitching scheme and the stitching scheme consists of a stitching policy and a potential function. The potential function is to predict how well a patch will be stitched if patches are stitched according to a given partial order. The stitching policy is a mechanism that determines the stitching order based on the predictions by the potential function. We present various stitching schemes and evaluate them through simulations. In addition, we apply the patch-and-stitch strategy into the anchor-based localization and propose a clustering-based localization algorithm. A potential function is used to partition the network into clusters each of which is centered at an anchor node. For each cluster, a cluster map is constructed via the anchor-free localization algorithm. Then, those cluster maps are combined to form a single global map. We propose a stitching technique for combining those cluster maps and analyze the performance of the algorithm by simulations.

Node localization is an essential problem in ad hoc wireless sensor networks. Map stitching is a type of localization algorithm that has received a great deal of attention recently. In this algorithm, the network is divided into small overlapping sub-regions, each of which creates a local map. Then, the local maps are stitched together to form a single global map. In this paper, we first propose a new technique for map-to-map stitching that exploits every available distances between two maps. Next, we propose a few anchor-free localization algorithms based on our stitching technique. We experimentally studied the performance of our algorithms under various settings. The results show that our method achieves a significant performance improvement upon the existing method.

In this paper, we consider the problem of nonpreemptively scheduling independent jobs so as to minimize overall finish time on an m-dimensional hypercube system. This problem is NP-hard. We propose a polynomial time approximation algorithm and prove that the absolute performance ratio of the algorithm does not exceed 1.875. This is the first algorithm achieving an absolute performance ratio less than two by a constant.

Abstract Broadcasting refers to the process of dissemination of a set of messages originating from one node to all other nodes in a communication network. We assume that, at any given time, a node can transmit a message along at most one incident link and simultaneously receive a message along at most one incident link. We first present an algorithm for determining the amount of time needed to broadcastkmessages in an arbitrary tree. Second, we show that, for everyn, There exists a graph withnnodes whosek‐message broadcast time matches the trivial lower bound ⌈ logn⌉ +k− 1 by designing a broadcast scheme for complete graphs. We call those graphs minimal broadcast graphs. Finally, we construct annnode minimal broadcast graph with fewer than (⌈logn⌉ + 1)2⌈ logn⌉ −1edges.