Simulation and Synchronous Generation of Radar Signals at Geographically Distributed Sensors for Testing Emitter Location Systems

2021 ◽  
pp. 1-15
Author(s):  
Sudha Rani Suram ◽  
Niranjan Prasad ◽  
Sasibhushana Rao Gottapu
Keyword(s):  
Distributed Sensors ◽  
Emitter Location ◽  
Geographically Distributed ◽  
Radar Signals ◽  
Synchronous Generation ◽  
Location Systems

Predicting Spatio-Temporal Phenomena of Mobile Resources in Sensor Cloud Infrastructure

2021 ◽  
Vol 7 (3) ◽  
pp. 1-38
Author(s):  
Sunanda Bose ◽  
Sumit Kumar Paul ◽  
Nandini Mukherjee
Keyword(s):  
Probabilistic Models ◽  
Cloud Infrastructure ◽  
Distributed Sensors ◽  
Efficient Resource ◽  
Geographically Distributed ◽  
Spatio Temporal ◽  
Sensor Cloud ◽  
Cloud Infrastructures ◽  
Cloud Technologies ◽  
Fixed Region

Integration of sensor and cloud technologies enable distributed sensing and data collection. We consider a scenario when sensing requests are originated from sensor aware applications that are hosted inside sensor-cloud infrastructures. These requests need to be satisfied using geographically distributed sensors. However, if the sensing resources are mobile, then sensing territory is not limited to a fixed region, rather spatially diverse. In this work, we present a generic scheme for integrating spatio-temporal information of mobile sensors for Internet of Things– (IoT) based environment monitoring system. A set of algorithms are proposed in this work to model spatial and temporal features of mobile resources and exploit resource mobility. We also propose probabilistic models to measure feasibility of a resource to sense a specific spatio-temporal phenomenon. We rank the resources based on their feasibility of satisfying the sensing requests and later use the information for efficient resource allocation and scheduling.

scholarly journals Smart outlier detection of wireless sensor network

2016 ◽  
Vol 29 (3) ◽  
pp. 383-393 ◽  
Author(s):  
Sahar Kamal ◽  
Rabie Ramadan ◽  
Fawzy El-Refai
Keyword(s):  
Outlier Detection ◽  
Wireless Sensor ◽  
Data Sets ◽  
High Detection Rate ◽  
Data Set ◽  
Distributed Sensors ◽  
Detection Techniques ◽  
High Detection ◽  
Geographically Distributed ◽  
Outlier Data

Data sets collected from wireless sensor networks (WSN) are usually considered unreliable and subject to errors due to limited sensor capabilities and hard environment resulting in a subset of the sensors data called outlier data. This paper proposes a technique to detect outlier data base on spatial-temporal similarity among data collected by geographically distributed sensors. The proposed technique is able to identify an abnormal subset of data collected by sensor node as outlier data. Moreover, the proposed technique is able to classify this abnormal observation, an error data set or event affected set. Simulation result shows that high detection rate is achieved compared to conventional outlier detection techniques while preserving low positive false alarm rate.

scholarly journals GASP: Genetic Algorithms for Service Placement in Fog Computing Systems

Algorithms ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 201 ◽  
Author(s):  
Claudia Canali ◽  
Riccardo Lancellotti
Keyword(s):  
Genetic Algorithms ◽  
Data Streams ◽  
Smart City ◽  
Fog Computing ◽  
Distributed Sensors ◽  
Computing Paradigm ◽  
Efficient Management ◽  
Geographically Distributed ◽  
The Stability ◽  
The One

Fog computing is becoming popular as a solution to support applications based on geographically distributed sensors that produce huge volumes of data to be processed and filtered with response time constraints. In this scenario, typical of a smart city environment, the traditional cloud paradigm with few powerful data centers located far away from the sources of data becomes inadequate. The fog computing paradigm, which provides a distributed infrastructure of nodes placed close to the data sources, represents a better solution to perform filtering, aggregation, and preprocessing of incoming data streams reducing the experienced latency and increasing the overall scalability. However, many issues still exist regarding the efficient management of a fog computing architecture, such as the distribution of data streams coming from sensors over the fog nodes to minimize the experienced latency. The contribution of this paper is two-fold. First, we present an optimization model for the problem of mapping data streams over fog nodes, considering not only the current load of the fog nodes, but also the communication latency between sensors and fog nodes. Second, to address the complexity of the problem, we present a scalable heuristic based on genetic algorithms. We carried out a set of experiments based on a realistic smart city scenario: the results show how the performance of the proposed heuristic is comparable with the one achieved through the solution of the optimization problem. Then, we carried out a comparison among different genetic evolution strategies and operators that identify the uniform crossover as the best option. Finally, we perform a wide sensitivity analysis to show the stability of the heuristic performance with respect to its main parameters.

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