Pheromone-based In-Network Processing for wireless sensor network monitoring systems

Monitoring spatio-temporal continuous fields using wireless sensor networks (WSNs) has emerged as a novel solution. An efficient data-driven routing mechanism for sensor querying and information gathering in large-scale WSNs is a challenging problem. In particular, we consider the case of how to query the sensor network information with the minimum energy cost in scenarios where a small subset of sensor nodes has relevant readings. In order to deal with this problem, we propose a Pheromone-based In-Network Processing (PhINP) mechanism. The proposal takes advantages of both a pheromone-based iterative strategy to direct queries towards nodes with relevant information and query- and response-based in-network filtering to reduce the number of active nodes. Additionally, we apply reinforcement learning to improve the performance. The main contribution of this work is the proposal of a simple and efficient mechanism for information discovery and gathering. It can reduce the messages exchanged in the network, by allowing some error, in order to maximize the network lifetime. We demonstrate by extensive simulations that using PhINP mechanism the query dissemination cost can be reduced by approximately 60% over flooding, with an error below 1%, applying the same in-network filtering strategy.Fil: Riva, Guillermo Gaston. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Universidad Tecnológica Nacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba; ArgentinaFil: Finochietto, Jorge Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Estudios Avanzados en Ingeniería y Tecnología. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Estudios Avanzados en Ingeniería y Tecnología; Argentin

Finding Shortest Path using Dijkstra in Live Traffic Simuation

These days, a few online administrations give live activity information, for example, Google-Map, Navteq , INRIX Traffic Information Provider , and TomTom NV. Yet at the same time figuring the most limited way on live movement is enormous issue. This is critical for auto route as it helps drivers to decide. In displayed approach server will gather live activity data and afterward declare them over remote system. With this approach any number of customers can be included. This new approach called live movement file time dependant (LTI-TD) empowers drivers to upgrade their briefest way come about by accepting just a little division of the file. The current frameworks were infeasible to tackle the issue because of their restrictive upkeep time and extensive transmission overhead. LTI-TD is a novel answer for Online Shortest Path Computation on Time Dependent Network

Finding Shortest Path using Dijkstra in Live Traffic Simuation

These days, a few online administrations give live activity information, for example, Google-Map, Navteq , INRIX Traffic Information Provider , and TomTom NV. Yet at the same time figuring the most limited way on live movement is enormous issue. This is critical for auto route as it helps drivers to decide. In displayed approach server will gather live activity data and afterward declare them over remote system. With this approach any number of customers can be included. This new approach called live movement file time dependant (LTI-TD) empowers drivers to upgrade their briefest way come about by accepting just a little division of the file. The current frameworks were infeasible to tackle the issue because of their restrictive upkeep time and extensive transmission overhead. LTI-TD is a novel answer for Online Shortest Path Computation on Time Dependent Network

Finding Shortest Path using Dijkstra in Live Traffic Simuation

These days, a few online administrations give live activity information, for example, Google-Map, Navteq , INRIX Traffic Information Provider , and TomTom NV. Yet at the same time figuring the most limited way on live movement is enormous issue. This is critical for auto route as it helps drivers to decide. In displayed approach server will gather live activity data and afterward declare them over remote system. With this approach any number of customers can be included. This new approach called live movement file time dependant (LTI-TD) empowers drivers to upgrade their briefest way come about by accepting just a little division of the file. The current frameworks were infeasible to tackle the issue because of their restrictive upkeep time and extensive transmission overhead. LTI-TD is a novel answer for Online Shortest Path Computation on Time Dependent Network

Finding Shortest Path using Dijkstra in Live Traffic Simuation

These days, a few online administrations give live activity information, for example, Google-Map, Navteq , INRIX Traffic Information Provider , and TomTom NV. Yet at the same time figuring the most limited way on live movement is enormous issue. This is critical for auto route as it helps drivers to decide. In displayed approach server will gather live activity data and afterward declare them over remote system. With this approach any number of customers can be included. This new approach called live movement file time dependant (LTI-TD) empowers drivers to upgrade their briefest way come about by accepting just a little division of the file. The current frameworks were infeasible to tackle the issue because of their restrictive upkeep time and extensive transmission overhead. LTI-TD is a novel answer for Online Shortest Path Computation on Time Dependent Network

Finding Shortest Path using Dijkstra in Live Traffic Simuation

These days, a few online administrations give live activity information, for example, Google-Map, Navteq , INRIX Traffic Information Provider , and TomTom NV. Yet at the same time figuring the most limited way on live movement is enormous issue. This is critical for auto route as it helps drivers to decide. In displayed approach server will gather live activity data and afterward declare them over remote system. With this approach any number of customers can be included. This new approach called live movement file time dependant (LTI-TD) empowers drivers to upgrade their briefest way come about by accepting just a little division of the file. The current frameworks were infeasible to tackle the issue because of their restrictive upkeep time and extensive transmission overhead. LTI-TD is a novel answer for Online Shortest Path Computation on Time Dependent Network

Finding Shortest Path using Dijkstra in Live Traffic Simuation

These days, a few online administrations give live activity information, for example, Google-Map, Navteq , INRIX Traffic Information Provider , and TomTom NV. Yet at the same time figuring the most limited way on live movement is enormous issue. This is critical for auto route as it helps drivers to decide. In displayed approach server will gather live activity data and afterward declare them over remote system. With this approach any number of customers can be included. This new approach called live movement file time dependant (LTI-TD) empowers drivers to upgrade their briefest way come about by accepting just a little division of the file. The current frameworks were infeasible to tackle the issue because of their restrictive upkeep time and extensive transmission overhead. LTI-TD is a novel answer for Online Shortest Path Computation on Time Dependent Network

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Finding Shortest Path using Dijkstra in Live Traffic Simuation

These days, a few online administrations give live activity information, for example, Google-Map, Navteq , INRIX Traffic Information Provider , and TomTom NV. Yet at the same time figuring the most limited way on live movement is enormous issue. This is critical for auto route as it helps drivers to decide. In displayed approach server will gather live activity data and afterward declare them over remote system. With this approach any number of customers can be included. This new approach called live movement file time dependant (LTI-TD) empowers drivers to upgrade their briefest way come about by accepting just a little division of the file. The current frameworks were infeasible to tackle the issue because of their restrictive upkeep time and extensive transmission overhead. LTI-TD is a novel answer for Online Shortest Path Computation on Time Dependent Network