Kurt W. Derr

Multi-robot, multi-target Particle Swarm Optimization search in noisy wireless environments

Multiple small robots (swarms) can work together using Particle Swarm Optimization (PSO) to perform tasks that are difficult or impossible for a single robot to accomplish. The problem considered in this paper is exploration of an unknown environment with the goal of finding a target(s) at an unknown location(s) using multiple small mobile robots. This work demonstrates the use of a distributed PSO algorithm with a novel adaptive RSS weighting factor to guide robots for locating target(s) in high risk environments. The approach was developed and analyzed on multiple robot single and multiple target search. The approach was further enhanced by the multi-robot-multi-target search in noisy environments. The experimental results demonstrated how the availability of radio frequency signal can significantly affect robot search time to reach a target.

Wireless based object tracking based on neural networks

Location based services (LBS), context aware applications, and people and object tracking depend on the ability to locate mobile devices, also known as localization, in the wireless landscape. Localization enables a diverse set of applications that include, but are not limited to, vehicle guidance in an industrial environment, security monitoring, self-guided tours, personalized communications services, resource tracking, mobile commerce services, guiding emergency workers during fire emergencies, habitat monitoring, environmental surveillance, and receiving alerts. This paper presents a new neural network approach (LENSR) based on a competitive topological counter propagation network (CPN) with k-nearest neighborhood vector mapping, for indoor location estimation based on received signal strength. The advantage of this approach is both speed and accuracy. The tested accuracy of the algorithm was 90.6% within 1 meter and 96.4% within 1.5 meters. Several approaches for location estimation using WLAN technology were reviewed for comparison of results.

Extended Virtual Spring Mesh (EVSM): The Distributed Self-Organizing Mobile Ad Hoc Network for Area Exploration

Mobile Ad hoc NETworks (MANETs) are distributed self-organizing networks that can change locations and configure themselves on the fly. This paper focuses on an algorithmic approach for the deployment of a MANET within an enclosed area, such as a building in a disaster scenario, which can provide a robust communication infrastructure for search and rescue operations. While a virtual spring mesh (VSM) algorithm provides scalable, self-organizing, and fault-tolerant capabilities required by a MANET, the VSM lacks the MANETs capabilities of deployment mechanisms for blanket coverage of an area and does not provide an obstacle avoidance mechanism. This paper presents a new technique, an extended VSM (EVSM) algorithm that provides the following novelties: 1) new control laws for exploration and expansion to provide blanket coverage, 2) virtual adaptive springs enabling the mesh to expand as necessary, 3) adapts to communications disturbances by varying the density and movement of mobile nodes, and 4) new metrics to assess the performance of the EVSM algorithm. Simulation results show that EVSM provides up to 16% more coverage and is 3.5 times faster than VSM in environments with eight obstacles.

Wireless Sensor Networks - Node Localization for Various Industry Problems

Fast and effective monitoring following airborne releases of toxic substances is critical to mitigate risks to threatened population areas. Electrically powered systems in industrial settings require monitoring of emitted electromagnetic fields to determine the status of the equipment and ensure their safe operation. In situations such as these, wireless sensor nodes (WSNs) at fixed predetermined locations provide monitoring to ensure safety. A challenging algorithmic problem is determining the locations to place these WSNs while meeting several criteria: (1) to provide complete coverage of the domain; (2) to create a topology with problem-dependent node densities; and (3) to minimize the number of WSNs. This paper presents a novel approach, advancing front mesh generation with constrained Delaunay triangulation and smoothing (AFECETS) that addresses these criteria. A unique aspect of AFECETS is the ability to determine WSN locations for areas of high interest (hospitals, schools, and high population density areas) that require higher density of nodes for monitoring environmental conditions, a feature that is difficult to find in other research work. The AFECETS algorithm was tested on several arbitrary shaped domains. AFECETS simulation results show that the algorithm provides significant reduction in the number of nodes, in some cases over 40%, compared with an advancing front mesh generation algorithm; maintains and improves optimal spacing between nodes; and produces simulation run times suitable for real-time applications.

Adaptive Control Parameters for Dispersal of Multi-Agent Mobile Ad Hoc Network (MANET) Swarms

A mobile ad hoc network is a collection of independent nodes that communicate wirelessly with one another. This paper investigates nodes that are swarm robots with communications and sensing capabilities. Each robot in the swarm may operate in a distributed and decentralized manner to achieve some goal. This paper presents a novel approach to dynamically adapting control parameters to achieve mesh configuration stability. The presented approach to robot interaction is based on spring force laws (attraction and repulsion laws) to create near-optimal mesh like configurations. In prior work, we presented the extended virtual spring mesh (EVSM) algorithm for the dispersion of robot swarms. This paper extends the EVSM framework by providing the first known study on the effects of adaptive versus static control parameters on robot swarm stability. The EVSM algorithm provides the following novelties: 1) improved performance with adaptive control parameters and 2) accelerated convergence with high formation effectiveness. Simulation results show that 120 robots reach convergence using adaptive control parameters more than twice as fast as with static control parameters in a multiple obstacle environment.

Wireless Sensor Network Configuration—Part I: Mesh Simplification for Centralized Algorithms

This is the first of a two-part investigation of centralized and decentralized approaches for determining the optimal configuration of a sensor network. In this first part, we present a centralized approach for the generation of mesh (wireless sensor) network configurations that provide complete sensing coverage and communication connectivity of a domain. A challenging problem in deploying wireless sensor networks is maximizing coverage in irregular shaped polygonal areas while maintaining a high degree of node connectivity. The novelties presented in this paper are: 1) a centralized mesh simplification technique, the Iterative Node Removal with Constrained Delaunay Triangulation and Smoothing (INRCDTS) algorithm, and 2) a centralized mesh generation approach with INRCDTS that may be used for any nonintersecting closed polygonal area. Additionally, we provide a comparison of two centralized mesh generation techniques. The INRCDTS was built and tested as an enhancement of two traditional mesh generation techniques: advancing front technique and Matlab partial differential equation toolbox. The INCRCDTS introduces the ability to tune the generated mesh configuration to the number of nodes and nodal spacing. The INRCDTS enhancement has proven to increase the uniformity of the mesh in an irregular shaped polygonal area relative to advancing front and MATLAB partial differential equation algorithms by 23% and 41%, respectively.

Wireless Sensor Network Configuration—Part II: Adaptive Coverage for Decentralized Algorithms

This is the second of a two-part investigation of the generation of wireless sensor network (WSN) configurations that: 1) maximize coverage of irregular shaped polygonal areas and 2) maintain a high degree of node connectivity. The first-part of the investigation presented centralized algorithms for the generation of mesh (wireless sensor) network configurations that maximize coverage and connectivity. In this second part, we present a decentralized and distributed approach using an Extended Virtual Spring Mesh (EVSM)-Adaptive Coverage Algorithm and Protocol (ACAP) algorithm. The EVSM-ACAP algorithm represents an extension of EVSM algorithm with the newly developed ACAP. ACAP provides adaptive coverage and configuration of the mesh network by dynamically adjusting the sensing range of sensor nodes. EVSM-ACAP is compared to centralized mesh generation algorithms (described in the part one of the investigation), as well as other decentralized algorithms from artificial physics, for the control of large numbers of physical agents in sensor networks. EVSM-ACAP is shown to produce a sensor network deployment with an average sensor spacing within 1.6% of the desired spacing, versus 5.75% for the best centralized algorithmic approach. To the best of our knowledge, this is the first time that these centralized mesh network configuration algorithms have been contrasted with the scalable, robust, decentralized algorithms of artificial physics and EVSM.

Intelligent control in automation based on wireless traffic analysis

Wireless technology is a central component of many factory automation infrastructures in both the commercial and government sectors, providing connectivity among various components in industrial realms (distributed sensors, machines, mobile process controllers). However wireless technologies provide more threats to computer security than wired environments. The advantageous features of Bluetooth technology resulted in Bluetooth units shipments climbing to five million per week at the end of 2005 [1, 2]. This is why the real-time interpretation and understanding of Bluetooth traffic behavior is critical in both maintaining the integrity of computer systems and increasing the efficient use of this technology in control type applications. Although neuro-fuzzy approaches have been applied to wireless 802.11 behavior analysis in the past, a significantly different Bluetooth protocol framework has not been extensively explored using this technology. This paper presents a new neuro-fuzzy traffic analysis algorithm of this still new territory of Bluetooth traffic. Further enhancements of this algorithm are presented along with the comparison against the traditional, numerical approach. Through test examples, interesting Bluetooth traffic behavior characteristics were captured, and the comparative elegance of this computationally inexpensive approach was demonstrated. This analysis can be used to provide directions for future development and use of this prevailing technology in various control type applications, as well as making the use of it more secure.

ns-3 Web-Based User Interface: Power Grid Communications Planning and Modeling Tool

All utilities have invested in some level of engineering tools and qualified staff required to modernize the power grid. These engineering tools vary in their level of complexity and fidelity. Most of the existing tools are either application specific or have not been designed to work together to assist in assembling comprehensive first order smart grid build-out cost estimations and planning, especially with regard to communications. There is a need for software to assist utility owners, operators, engineers, and consultants with smart grid communications planning and engineering. The development of a web-based Power Grid Communications Planning and Modeling Tool using ns-3 is in process that will address these needs. This tool models use cases for power grid communications, abstracting away the details of programming in ns-3. The tool will be made open source at the completion of the initial version.

DSTiPE algorithm for fuzzy spatio-temporal risk calculation in wireless environments

Time and location data play a very significant role in a variety of factory automation scenarios, such as automated vehicles and robots, their navigation, tracking, and monitoring, to services of optimization and security. Pervasive wireless capabilities combined with time and location information are enabling new applications in areas such as transportation systems, health care, elder care, military, emergency response, critical infrastructure, and law enforcement. A wireless object in proximity to some area for a duration of time may pose a risk hazard to the environment. This paper presents a novel fuzzy based spatio-temporal risk calculation DSTiPE method that a wireless object may present to the environment. The presented Matlab based application for cluster extraction is verified on a diagonal vehicle movement example.