1,213 research outputs found

    On Mobility Management in Multi-Sink Sensor Networks for Geocasting of Queries

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    In order to efficiently deal with location dependent messages in multi-sink wireless sensor networks (WSNs), it is key that the network informs sinks what geographical area is covered by which sink. The sinks are then able to efficiently route messages which are only valid in particular regions of the deployment. In our previous work (see the 5th and 6th cited documents), we proposed a combined coverage area reporting and geographical routing protocol for location dependent messages, for example, queries that are injected by sinks. In this paper, we study the case where we have static sinks and mobile sensor nodes in the network. To provide up-to-date coverage areas to sinks, we focus on handling node mobility in the network. We discuss what is a better method for updating the routing structure (i.e., routing trees and coverage areas) to handle mobility efficiently: periodic global updates initiated from sinks or local updates triggered by mobile sensors. Simulation results show that local updating perform very well in terms of query delivery ratio. Local updating has a better scalability to increasing network size. It is also more energy efficient than ourpreviously proposed approach, where global updating in networks have medium mobility rate and speed

    A Level-Wise Periodic Tree Construction Mechanism for Sleep Scheduling in WSN

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    The wireless sensor network(WSN) has been extensively used to monitor and control the natural ecosystem on a large scale like air quality, natural life, etc. Low battery power,low storage, and limited processing ability are the most critical areas of concern in WSN. To reduce energy utilization, the sensor nodes in WSN work in a cyclic process between active and sleep mode. A certain number of nodes are chosen active and they areresponsible for sensing as well as data transmission and rest of the nodes are gone to sleep. In order to lengthen the lifetime of network, in this paper we proposed a level wise periodic tree construction algorithm that uses a specific set of nodes to participate in tree construction, instead of all the nodes, to minimize the energy consumption. In this proposed approach, the main idea is to put the nodes, which are currently active and have already spent a significant amount of energy, to sleep mode, while giving chances to the leaf nodes, which has comparatively spent less energy, to become an active node and maintain connectivity. The performance of the proposed protocol is evaluated usingthe Castalia simulator. The simulation results show that the proposed level-wise periodic tree construction approach increases the durability of the network in conjunction with the non-level approach

    On Mobility Management in Multi-Sink Sensor Networks for Geocasting of Queries

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    In order to efficiently deal with location dependent messages in multi-sink wireless sensor networks (WSNs), it is key that the network informs sinks what geographical area is covered by which sink. The sinks are then able to efficiently route messages which are only valid in particular regions of the deployment. In our previous work (see the 5th and 6th cited documents), we proposed a combined coverage area reporting and geographical routing protocol for location dependent messages, for example, queries that are injected by sinks. In this paper, we study the case where we have static sinks and mobile sensor nodes in the network. To provide up-to-date coverage areas to sinks, we focus on handling node mobility in the network. We discuss what is a better method for updating the routing structure (i.e., routing trees and coverage areas) to handle mobility efficiently: periodic global updates initiated from sinks or local updates triggered by mobile sensors. Simulation results show that local updating perform very well in terms of query delivery ratio. Local updating has a better scalability to increasing network size. It is also more energy efficient than ourpreviously proposed approach, where global updating in networks have medium mobility rate and speed

    Distributed Detection and Estimation in Wireless Sensor Networks

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    In this article we consider the problems of distributed detection and estimation in wireless sensor networks. In the first part, we provide a general framework aimed to show how an efficient design of a sensor network requires a joint organization of in-network processing and communication. Then, we recall the basic features of consensus algorithm, which is a basic tool to reach globally optimal decisions through a distributed approach. The main part of the paper starts addressing the distributed estimation problem. We show first an entirely decentralized approach, where observations and estimations are performed without the intervention of a fusion center. Then, we consider the case where the estimation is performed at a fusion center, showing how to allocate quantization bits and transmit powers in the links between the nodes and the fusion center, in order to accommodate the requirement on the maximum estimation variance, under a constraint on the global transmit power. We extend the approach to the detection problem. Also in this case, we consider the distributed approach, where every node can achieve a globally optimal decision, and the case where the decision is taken at a central node. In the latter case, we show how to allocate coding bits and transmit power in order to maximize the detection probability, under constraints on the false alarm rate and the global transmit power. Then, we generalize consensus algorithms illustrating a distributed procedure that converges to the projection of the observation vector onto a signal subspace. We then address the issue of energy consumption in sensor networks, thus showing how to optimize the network topology in order to minimize the energy necessary to achieve a global consensus. Finally, we address the problem of matching the topology of the network to the graph describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R. Chellapa and S. Theodoridis, Eds., Elsevier, 201

    IoT Transmission Technologies for Distributed Measurement Systems in Critical Environments

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    Distributed measurement systems are spread in the most diverse application scenarios, and Internet of Things (IoT) transmission equipment is usually the enabling technologies for such measurement systems that need to feature wireless connectivity to ensure pervasiveness. Because wireless measurement systems have been deployed for the last years even in critical environments, assessing transmission technologies performances in such contexts is fundamental. Indeed, they are the most challenging ones for wireless data transmission due to their intrinsic attenuation capabilities. Several scenarios in which measurement systems can be deployed are analysed. Firstly, marine contexts are treated by considering above-the-sea wireless links. Such setting can be experienced in whichever application requiring remote monitoring of facilities and assets that are offshore installed. Some instances are offshore sea farming plants, or remote video monitoring systems installed on seamark buoys. Secondly, wireless communications taking place from the underground to the aboveground are covered. This scenario is typical of precision agriculture applications, where the accurate measurement of underground physical parameters is needed to be remotely sent to optimise crops reducing the wastefulness of fundamental resources (e.g., irrigation water). Thirdly, wireless communications occurring from the underwater to the abovewater are addressed. Such situation is inevitable for all those infrastructures monitoring conservation status of underwater species like algae, seaweeds and reef. Then, wireless links happening traversing metal surfaces and structures are tackled. Such context is commonly encountered in asset tracking and monitoring (e.g., containers), or in smart metering applications (e.g., utility meters). Lastly, sundry harsh environments that are typical of industrial monitoring (e.g., vibrating machineries, harsh temperature and humidity rooms, corrosive atmospheres) are tested to validate pervasive measurement infrastructures even in such contexts that are usually experienced in Industrial Internet of Things (IIoT) applications. The performances of wireless measurement systems in such scenarios are tested by sorting out ad-hoc measurement campaigns. Finally, IoT measurement infrastructures respectively deployed in above-the-sea and underground-to-aboveground settings are described to provide real applications in which such facilities can be effectively installed. Nonetheless, the aforementioned application scenarios are only some amid their sundry variety. Indeed, nowadays distributed pervasive measurement systems have to be thought in a broad way, resulting in countless instances: predictive maintenance, smart healthcare, smart cities, industrial monitoring, or smart agriculture, etc. This Thesis aims at showing distributed measurement systems in critical environments to set up pervasive monitoring infrastructures that are enabled by IoT transmission technologies. At first, they are presented, and then the harsh environments are introduced, along with the relative theoretical analysis modelling path loss in such conditions. It must be underlined that this Thesis aims neither at finding better path loss models with respect to the existing ones, nor at improving them. Indeed, path loss models are exploited as they are, in order to derive estimates of losses to understand the effectiveness of the deployed infrastructure. In fact, some transmission tests in those contexts are described, along with providing examples of these types of applications in the field, showing the measurement infrastructures and the relative critical environments serving as deployment sites. The scientific relevance of this Thesis is evident since, at the moment, the literature lacks a comparative study like this, showing both transmission performances in critical environments, and the deployment of real IoT distributed wireless measurement systems in such contexts
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