2,556 research outputs found
Collaborative signal and information processing for target detection with heterogeneous sensor networks
In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield
Ready for Tomorrow: Demand-Side Emerging Skills for the 21st Century
As part of the Ready for the Job demand-side skill assessment, the Heldrich Center explored emerging work skills that will affect New Jersey's workforce in the next three to five years. The Heldrich Center identified five specific areas likely to generate new skill demands: biotechnology, security, e-learning, e-commerce, and food/agribusiness. This report explores the study's findings and offers recommendations for improving education and training in New Jersey
A study of remotely booking slot for vehicle using Internet of Things
Internet Of Things (IoT) is a continually growing area which aids us to unite diverse objects. The proposed system exhibits the universal notion of utilizing cloud-based intellectual automotive car parking facilities in smart cities as a notable implementation of the IoT. Such services demonstrate to be a noteworthy part of the IoT and thus serving users in no small amount due to its pure commerce positioned qualities. Electromagnetic fields are being used by RFID to detect and track tags ascribed to objects automatically. The RFID technology is used in this system along with suitable IoT protocols to evade human interference, which reduces the cost. Information is bartered using readers and tags. RFID and IoT technologies are mainly used to automate the guide systems and make them strong and more accurate. Open Service Gateways can be effectively used for this module. This system established on the consequence of IoT and the purposes are solving the chaos, bewilderment, and extensive backlogs in parking spaces like malls and business parks that are customary as a consequence of the increased use of automobiles. The proposed work aims to solve these problems and offer car drivers a hassle-free and instantaneous car parking experience. While a number of nodes are positioned depends on topographical restrictions, positioning of prominent anchor sensor nodes in the smart parking is a primary factor against which the efficiency and cost of the parking system hang. A Raspberry Pi would act as a mini-computer in our system. A suitable smallest path methodology would be cast-off to obtain the shortest distance between the user and every car park in the system. Hence, the pausing time of the user is decreased. This work furthermore includes the practice of remotely booking of a slot with the collaboration of android application exercising smartphones for the communication between the Smart Parking system and the user
A computational fluid dynamics approach for optimization of a sensor network
Presented at the 2006 IEEE International Workshop on Measurement Systems for Homeland Security, Contraband Detection and Personal Safety. Alexandria, VA.We optimize the placement of sensors for detecting a
nuclear, biological, or chemical (NBC) attack in a dense urban
environment. This approach draws from two main areas: (1)
computational fluid dynamic (CFD) simulations and (2) sensor
placement algorithms. The main objective was to minimize detection
time of a NBC sensor network for attacks on a generic
urban environment. To this end we conducted simulations in
the generic urban environment using thirty-three (33) unique
attack locations, thirty-three (33) candidate sensor locations,
prevailing wind conditions, and the properties of the chemical
agent, chlorine gas. A total of ninety-nine (99) simulated attack
scenarios were created (three sets of thirty-three unique attack
simulations) and used for optimization. Simulated surrogate
agent concentration data were collected at each candidate sensor
location as a function of time. The integration of this concentration
data with respect to time was used to calculate the
”consumption” of the network and the sensor placement algorithm
minimized consumption to the network while minimizing
the number of sensors placed. Our results show how a small
number of properly placed sensors (eight(8), in our case) provides
the best achievable coverage (additional sensors do not
help)
A Spark-based genetic algorithm for sensor placement in large scale drinking water distribution systems
Water pollution incidents have occurred frequently in recent years, causing severe damages, economic loss and long-lasting society impact. A viable solution is to install water quality monitoring sensors in water supply networks (WSNs) for real-time pollution detection, thereby mitigating the risk of catastrophic contamination incidents. Given the significant cost of placing sensors at all locations in a network, a critical issue is where to deploy sensors within WSNs, while achieving rapid detection of contaminant events. Existing studies have mainly focused on sensor placement in water distribution systems (WDSs). However, the problem is still not adequately addressed, especially for large scale WSNs. In this paper, we investigate the sensor placement problem in large scale WDSs with the objective of minimizing the impact of contamination events. Specifically, we propose a two-phase Spark-based genetic algorithm (SGA). Experimental results show that SGA outperforms other traditional algorithms in both accuracy and efficiency, which validates the feasibility and effectiveness of our proposed approach
On the design of smart parking networks in the smart cities: an optimal sensor placement model
Smart parking is a typical IoT application that can benefit from advances in
sensor, actuator and RFID technologies to provide many services to its users and parking
owners of a smart city. This paper considers a smart parking infrastructure where sensors
are laid down on the parking spots to detect car presence and RFID readers are embedded
into parking gates to identify cars and help in the billing of the smart parking. Both types
of devices are endowed with wired and wireless communication capabilities for reporting
to a gateway where the situation recognition is performed. The sensor devices are tasked
to play one of the three roles: (1) slave sensor nodes located on the parking spot to detect
car presence/absence; (2) master nodes located at one of the edges of a parking lot to detect
presence and collect the sensor readings from the slave nodes; and (3) repeater sensor nodes,
also called ''anchor'' nodes, located strategically at specific locations in the parking lot to
increase the coverage and connectivity of the wireless sensor network. While slave and
master nodes are placed based on geographic constraints, the optimal placement of the
relay/anchor sensor nodes in smart parking is an important parameter upon which the cost
and e ciency of the parking system depends. We formulate the optimal placement of sensors
in smart parking as an integer linear programming multi-objective problem optimizing the
sensor network engineering e ciency in terms of coverage and lifetime maximization, as
well as its economic gain in terms of the number of sensors deployed for a specific coverage
and lifetime. We propose an exact solution to the node placement problem using single-step
and two-step solutions implemented in the Mosel language based on the Xpress-MPsuite of
libraries. Experimental results reveal the relative e ciency of the single-step compared to
the two-step model on di erent performance parameters. These results are consolidated by
simulation results, which reveal that our solution outperforms a random placement in terms
of both energy consumption, delay and throughput achieved by a smart parking network
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