Jinko Kanno

Decentralized detection and patching of coverage holes in wireless sensor networks

Detection and patching of coverage holes in Wireless Sensor Networks (WSNs) are important measures of Quality of Service (QoS) for security and other applications that emphasize sensor network coverage. In this paper, we model a WSN using simplicial complexes based on its communication graph by which the network can be represented as connections of sensor nodes without knowing exact locations of nodes. Thus, the coverage problem is converted to a connectivity problem under some assumptions presented in the paper. We discuss two major topics in this paper, namely sensor network coverage hole detection and patching. We present a novel, decentralized, coordinate-free, node-based coverage hole detection algorithm. The algorithm can be implemented on a single node with connectivity information gathered from one-hop away neighbors. Thus, the coverage hole detection algorithm can be run on individual nodes and does not require time-consuming, centralized data processing. The hole-patching algorithm is based on the concept of perpendicular bisector line. Every hole-boundary edge has a corresponding perpendicular bisector and new sensor nodes are deployed on hole-boundary bisectors. Deployment of new sensor nodes maintains network connectivity, while reduces coverage holes.

Detecting coverage holes in wireless sensor networks

Wireless sensor network coverage completeness is an important Quality of Service measure. It is frequently assumed that events occurring in the sensor field will always be detected. However, this is not necessary the case, particularly if there are holes in the sensor network coverage. This paper introduces a novel method for detection and relative localization of sensor network coverage holes in coordinate-free networks assuming availability of a network communication graph. We identify sensor nodes that bound coverage holes, called “hole boundary nodes”, by processing information embedded in a communication graph, which is non-planar in general. We create a hole-equivalent planar graph preserving a number and position of holes. Finally, we build a planar simplicial complex, called maximal simplicial complex, which contains the information regarding coverage holes. The proposed method is applicable for both coordinate-available and coordinate-free networks. Two implementation strategies for hole detection are provided, and they are each analyzed to compare runtime and accuracy. Simulation results show effectiveness of the hole detection algorithms.

Measuring Distance between Unordered Sets of Different Sizes

We present a distance metric based upon the notion of minimum-cost injective mappings between sets. Our function satisfies metric properties as long as the cost of the minimum mappings is derived from a semimetric, for which the triangle inequality is not necessarily satisfied. We show that the Jaccard distance (alternatively biotope, Tanimoto, or Marczewski-Steinhaus distance) may be considered the special case for finite sets where costs are derived from the discrete metric. Extensions that allow premetrics (not necessarily symmetric), multisets (generalized to include probability distributions), and asymmetric mappings are given that expand the versatility of the metric without sacrificing metric properties. The function has potential applications in pattern recognition, machine learning, and information retrieval.

Unmanned Ground Vehicle Navigation in Coordinate-Free and Localization-Free Wireless Sensor and Actuator Networks

We present a set of algorithms for the navigation of Unmanned Ground Vehicles (UGVs) towards a set of pre-identified target nodes in coordinate-free and localization-free wireless sensor and actuator networks. The UGVs are equipped with a set of wireless listeners that provide sensing information about the potential field generated by the network of actuators. Two main navigation scenarios are considered: single-UGV, single-destination navigation and multi-UGV, multi-destination navigation. For the single-UGV, single-destination case, we present both centralized and distributed navigation algorithms. Both algorithms share a similar two-phase concept. In the first phase, the system assigns level numbers to individual nodes based on their hop distance from the target nodes. In the second phase, the UGV uses the potential field created by the network of actuators to move towards the target nodes, requiring cooperation between triplets of actuator nodes and the UGV. The hop distance to the target nodes is used to control the main moving direction while the potential field, which can be measured by listeners on the UGV, is used to determine the UGV’s movement. For the multi-UGV, multi-destination case, we present a decentralized allocation algorithm such that multiple UGVs avoid converging to the same destination. After each UGV determines its destination, the proposed navigation scheme is applied. The presented algorithms do not attempt to localize UGVs or sensor nodes and are therefore suitable for operating in GPS-free/denied environments. We also present a study of the communication complexity of the algorithms as well as simulation examples that verify the proposed algorithms and compare their performances.

Multi-UGV multi-destination navigation in coordinate-free and localization-free Wireless Sensor and Actuator Networks

New Unmanned Ground Vehicle (UGV) navigation algorithms in coordinate-free and localization-free Wireless Sensor and Actuator Networks (WSANs) are presented. The algorithms are distributed and designed for multi-UGV multi-destination navigation in a sensor network environment. The proposed method eliminates possible multiplicity of destination nodes. A Leader Election Algorithm is used to uniquely identify destination nodes, followed by a Hop-distance Assignment Algorithm where each node stores hop-distances from all destinations. The multi-UGV multi-destination navigation planning problem is formulated as a task allocation problem, where each UGV is considered as an agent and each destination as a task. Since even in the special case of a single UGV navigation, the problem, being similar to the well-known Traveling Salesman Problem, is NP-complete, an allocation algorithm that is suboptimal with respect to the total traveled distance is presented. A communication complexity analysis is provided. Both experimental and simulation results are presented to illustrate the effectiveness of the proposed navigation method.

3D hand posture recognition from small unlabeled point sets

This paper is concerned with the evaluation and comparison of several methods for the classification and recognition of static hand postures from small unlabeled point sets corresponding to physical landmarks, e.g. reflective marker positions in a motion capture environment. We compare various classification algorithms based upon multiple interpretations and feature transformations of the point sets, including those based upon aggregate features (e.g. mean) and a pseudo-rasterization of the space. We find aggregate feature classifiers to be balanced across multiple users but relatively limited in maximum achievable accuracy. Certain classifiers based upon the pseudo-rasterization performed best among tested classification algorithms. The inherent difficulty in classifying certain users leads us to conclude that online learning may be necessary for the recognition of natural gestures.

Distributed unmanned ground vehicle navigation in coordinate-free and localization-free wireless sensor and actuator networks

We present a distributed algorithm for the navigation of an Unmanned Ground Vehicle (UGV) towards a set of identified target nodes in coordinate-free and localization-free wireless sensor and actuator networks. The navigation algorithm proceeds in two phases: first, a node level is determined based on a hop distance from the target nodes, which is accomplished by the network nodes without the need for any UGV action; and second, the UGV uses potential fields created by the network actuators to move towards the target nodes, requiring cooperation between certain actuator nodes and the UGV. The hop distance to the target nodes is used to control the main moving direction while the potential field, which can be measured by listeners on the UGV, is used to determine the UGVs movement. The major contribution of this paper is that the algorithm is fully distributed compared to the existing results in the field, which is suitable for real-time implementation. Meanwhile, the presenting algorithm uses three actuators to generate a potential field, which makes the algorithm more robust and flexible. A study on the communication complexity of the algorithm is presented as well as simulation examples that verify the presented algorithm.

Unmanned ground vehicle navigation in coordinate-free and localization-free Wireless Sensor and Acutuator Networks

In this paper, we present an algorithm for navigating an unmanned ground vehicle (UGV) through coordinate-free Wireless Sensor and Actuator Networks. The navigation algorithm proceeds in two phases. In the first phase, for each node in the sensor network we compute a hop-level distance from the target node, which indicates the number of communication hops between the node and any destination nodes. In the second phase, sets of nodes are chosen sequentially to induce potential fields that drive the UGV towards the destination. Overall, a hop-distance from the destination nodes is assigned to control main navigation direction while potential fields monitored by listeners on the UGV are employed to determine the UGVs movement. The major contribution of this paper is that our algorithm does not attempt to localize either the UGV or sensor nodes. Therefore, this navigation algorithm overcomes limitations of traditional navigation methods.

Splitter Theorems for 4-Regular Graphs

Let