Maintaining network connectivity and performance in robot teams

In this paper, we present an experimental study of strategies for maintaining end-to-end communication links for tasks such as surveillance, reconnaissance, and target search and identification, where team connectivity is required for situational awareness. Our main contributions are three fold: (a) We present the construction of a radio signal strength map that can be used to plan multi-robot tasks and also serve as useful perceptual information. We show how a nominal model of an urban environment obtained by aerial surveillance is used to generate strategies for exploration. (b) We present reactive controllers for communication link maintenance; and (c) we consider the differences between monitoring signal strength versus data throughput. Experimental results, obtained using our multi-robot testbed in three representative urban environments, are presented with each of our main contributions

A Survey of Applying Ad Hoc Wireless Sensor Actuator Networks to Enhance Context-Awareness in Environmental Management Systems

Sensor mesh networking is set to be one of the key tools for the future of Ambient Intelligence (AmI) due to new emerging technologies in Ad hoc Wireless Sensor Networks (AWSNs). AWSNs symbolize the new generation of sensor networks with many promising advantages applicable to most networked environments. Unfortunately, however, these practical technologies have some technical problems and, as a consequence, this fascinating field has created novel and interesting challenges, which in turn, have inspired many ongoing research projects and more are likely to follow. Almost certainly, there will be notable improvements in the management of control/actuator networks as a consequence of enhancing the sensitivity capabilities of systems. With an emphasis on Ad hoc Wireless Sensor Actuator Networks (AWSANs) this study presents a systematic analysis of the different existing techniques to improve such systems. It also discusses, analyzes and summarizes the advantages these technologies offer in certain applications and presents a generic solution, in the form of a case study, for an AmI system to enhance the overall environmental management of a campus based on a hierarchical network using an AWSAN

Swarming Reconnaissance Using Unmanned Aerial Vehicles in a Parallel Discrete Event Simulation

Current military affairs indicate that future military warfare requires safer, more accurate, and more fault-tolerant weapons systems. Unmanned Aerial Vehicles (UAV) are one answer to this military requirement. Technology in the UAV arena is moving toward smaller and more capable systems and is becoming available at a fraction of the cost. Exploiting the advances in these miniaturized flying vehicles is the aim of this research. How are the UAVs employed for the future military? The concept of operations for a micro-UAV system is adopted from nature from the appearance of flocking birds, movement of a school of fish, and swarming bees among others. All of these natural phenomena have a common thread: a global action resulting from many small individual actions. This emergent behavior is the aggregate result of many simple interactions occurring within the flock, school, or swarm. In a similar manner, a more robust weapon system uses emergent behavior resulting in no weakest link because the system itself is made up of simple interactions by hundreds or thousands of homogeneous UAVs. The global system in this research is referred to as a swarm. Losing one or a few individual unmanned vehicles would not dramatically impact the swarms ability to complete the mission or cause harm to any human operator. Swarming reconnaissance is the emergent behavior of swarms to perform a reconnaissance operation. An in-depth look at the design of a reconnaissance swarming mission is studied. A taxonomy of passive reconnaissance applications is developed to address feasibility. Evaluation of algorithms for swarm movement, communication, sensor input/analysis, targeting, and network topology result in priorities of each model\u27s desired features. After a thorough selection process of available implementations, a subset of those models are integrated and built upon resulting in a simulation that explores the innovations of swarming UAVs

An energy efficient coverage guaranteed greedy algorithm for wireless sensor networks lifetime enhancement

One of the most significant difficulties in Wireless Sensor Networks (WSNs) is energy efficiency, as they rely on minuscule batteries that cannot be replaced or recharged. In battery-operated networks, energy must be used efficiently. Network lifetime is an important metric for battery-powered networks. There are several approaches to improve network lifetime, such as data aggregation, clustering, topology, scheduling, rate allocation, routing, and mobile relay. Therefore, in this paper, the authors present a method that aims to improve the lifetime of WSN nodes using a greedy algorithm. The proposed Greedy Algorithm method is used to extend the network lifetime by dividing the sensors into a number of disjoint groups while satisfying the coverage requirements. The proposed Greedy algorithm has improved the network lifetime compared to heuristic algorithms. The method was able to generate a larger number of disjoint groups