K-coverage in regular deterministic sensor deployments

An area is k-covered if every point of the area is covered by at least k sensors. K-coverage is necessary for many applications, such as intrusion detection, data gathering, and object tracking. It is also desirable in situations where a stronger environmental monitoring capability is desired, such as military applications. In this paper, we study the problem of k-coverage in deterministic homogeneous deployments of sensors. We examine the three regular sensor deployments - triangular, square and hexagonal deployments - for k-coverage of the deployment area, for k ≥ 1. We compare the three regular deployments in terms of sensor density. For each deployment, we compute an upper bound and a lower bound on the optimal distance of sensors from each other that ensure k-coverage of the area. We present the results for each k from 1 to 20 and show that the required number of sensors to k-cover the area using uniform random deployment is approximately 3-10 times higher than regular deployments

An efficient genetic algorithm for large-scale planning of robust industrial wireless networks

An industrial indoor environment is harsh for wireless communications compared to an office environment, because the prevalent metal easily causes shadowing effects and affects the availability of an industrial wireless local area network (IWLAN). On the one hand, it is costly, time-consuming, and ineffective to perform trial-and-error manual deployment of wireless nodes. On the other hand, the existing wireless planning tools only focus on office environments such that it is hard to plan IWLANs due to the larger problem size and the deployed IWLANs are vulnerable to prevalent shadowing effects in harsh industrial indoor environments. To fill this gap, this paper proposes an overdimensioning model and a genetic algorithm based over-dimensioning (GAOD) algorithm for deploying large-scale robust IWLANs. As a progress beyond the state-of-the-art wireless planning, two full coverage layers are created. The second coverage layer serves as redundancy in case of shadowing. Meanwhile, the deployment cost is reduced by minimizing the number of access points (APs); the hard constraint of minimal inter-AP spatial paration avoids multiple APs covering the same area to be simultaneously shadowed by the same obstacle. The computation time and occupied memory are dedicatedly considered in the design of GAOD for large-scale optimization. A greedy heuristic based over-dimensioning (GHOD) algorithm and a random OD algorithm are taken as benchmarks. In two vehicle manufacturers with a small and large indoor environment, GAOD outperformed GHOD with up to 20% less APs, while GHOD outputted up to 25% less APs than a random OD algorithm. Furthermore, the effectiveness of this model and GAOD was experimentally validated with a real deployment system

Manipulator system man-machine interface evaluation program

Application and requirements for remote manipulator systems for future space missions were investigated. A manipulator evaluation program was established to study the effects of various systems parameters on operator performance of tasks necessary for remotely manned missions. The program and laboratory facilities are described. Evaluation criteria and philosophy are discussed

Distributed drone base station positioning for emergency cellular networks using reinforcement learning

Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network

Observations of the Earth's magnetic field from the shuttle: Using the Spartan carrier as a magnetic survey tool

The shuttle-deployed and recovered Spartan shows promise as an inexpensive and simple support module for potential field measurements. The results of a preliminary engineering study on the applications of the Spartan carrier to magnetic measurements shows: (1) Extension of the mission duration to as long as 7 days is feasible but requires more reconfiguration of the internal systems; (2) On-board recording of Global Positioning System signals will provide position determination with an accuracy consistent with the most severe requirements; and (3) Making Spartan a magnetically clean spacecraft is straight forward but requires labor-intensive modifications to both the data and power systems. As a magnetic survey tool, Spartan would allow surveys at regularly spaced intervals and could make quick-reaction surveys at times of instability in the secular variation

Traffic Alert and Collision Avoidance System (TCAS): Cockpit Display of Traffic Information (CDTI) investigation. Phase 1: Feasibility study

The possibility of the Threat Alert and Collision Avoidance System (TCAS) traffic sensor and display being used for meaningful Cockpit Display of Traffic Information (CDTI) applications has resulted in the Federal Aviation Administration initiating a project to establish the technical and operational requirements to realize this potential. Phase 1 of the project is presented here. Phase 1 was organized to define specific CDTI applications for the terminal area, to determine what has already been learned about CDTI technology relevant to these applications, and to define the engineering required to supply the remaining TCAS-CDTI technology for capacity benefit realization. The CDTI applications examined have been limited to those appropriate to the final approach and departure phases of flight

Reusable Agena study. Volume 2: Technical

The application of the existing Agena vehicle as a reusable upper stage for the space shuttle is discussed. The primary objective of the study is to define those changes to the Agena required for it to function in the reusable mode in the 100 percent capture of the NASA-DOD mission model. This 100 percent capture is achieved without use of kick motors or stages by simply increasing the Agena propellant load by using optional strap-on-tanks. The required shuttle support equipment, launch and flight operations techniques, development program, and cost package are also defined

Design of Ad Hoc Wireless Mesh Networks Formed by Unmanned Aerial Vehicles with Advanced Mechanical Automation

Ad hoc wireless mesh networks formed by unmanned aerial vehicles (UAVs) equipped with wireless transceivers (access points (APs)) are increasingly being touted as being able to provide a flexible "on-the-fly" communications infrastructure that can collect and transmit sensor data from sensors in remote, wilderness, or disaster-hit areas. Recent advances in the mechanical automation of UAVs have resulted in separable APs and replaceable batteries that can be carried by UAVs and placed at arbitrary locations in the field. These advanced mechanized UAV mesh networks pose interesting questions in terms of the design of the network architecture and the optimal UAV scheduling algorithms. This paper studies a range of network architectures that depend on the mechanized automation (AP separation and battery replacement) capabilities of UAVs and proposes heuristic UAV scheduling algorithms for each network architecture, which are benchmarked against optimal designs.Comment: 12 page