Current Issue : July - September Volume : 2019 Issue Number : 3 Articles : 5 Articles
In this paper, we propose an adaptive triangular deployment algorithm that can adjust sensor distribution depending on the\nvariation in communication performance in an underwater environment. To predict the distance between sensor nodes, a\nperformance surface model is implemented by estimating the communication performance based on spatio-temporal\nenvironment factors affecting the communication performance of the underwater sensor node. Subsequently, the performance\nsurface model is applied to the adaptive triangular deployment algorithm and is used to control the distance between nodes.\nTherefore, underwater wireless sensor networks deployed with adaptive triangular deployment algorithms can achieve a\nmaximum connectivity rate with an optimal number of nodes....
In mobile wireless sensor networks, priori-trail planning for the mobile sink is a commonly used solution to data collection from\nthe whole network, for its low protocol overhead. However, these trail-based approaches lack efficient load balance mechanism to\nhandle burstWSN traffic, which needs to be sent to the base station correctly with low delay. This paper proposed a dynamic path\nplanning for mobile sink to balance load and avoid traffic bottleneck. It contains grid partition of the network, priori-trail creation,\nburst-traffic awareness and estimation, resources collaborative strategy, and dynamic routing adjustment. Experiments on NS-2\nplatform show that the proposed algorithm can efficiently balance the regular and burst data traffic with a low-delay and low loss\nrate performance of the network....
TheSoftware DefinedNetworking (SDN) paradigm decouples the logicmodule fromthe forwardingmodule on traditional network\ndevices, bringing a wave of innovation to computer networks. Firewalls, aswell as other security appliances, can largely benefit from\nthis novel paradigm. Firewalls can be easily implemented by using the default OpenFlow rules, but the logic must reside in the\ncontrol plane due to the dynamic nature of their rules that cannot be handled by data plane devices. This leads to a nonnegligible\noverhead in the communication channel between layers, as well as introducing an additional computational load on the control\nplane. To address the above limitations, we propose the architectural design of FORTRESS: a stateful firewall for SDN networks that\nleverages the stateful data plane architecture to move the logic of the firewall from the control plane to the data plane. FORTRESS\ncan be implemented according to two different architectural designs: Stand-Alone and Cooperative, each one with its own peculiar\nadvantages. We compare FORTRESS against FlowTracker, the state-of-the-art solution for SDN firewalling, and show how our\nsolution outperforms the competitor in terms of the number of packets exchanged between the control plane and the data planeâ??we\nrequire 0 packets for the Stand-Alone architecture and just 4 for the Cooperative one. Moreover, we discuss how the adaptability,\nelegant and modular design, and portability of FORTRESS contribute to make it the ideal candidate for SDN firewalling. Finally,\nwe also provide further research directions....
Improving the energy efficiency of underwater acoustic sensor networks (UW-ASNs) is a crucial issue due to the reduced and\nnonrechargeable energy resource of the underwater sensor nodes. In this work, we address the energy sink hole problem in UWASNs\nwhile considering the unique and harsh characteristics of the underwater channel. Our goal is to determine the optimal\ndeployment and routing settings that surmount the energy sink hole problem and hence maximize the network lifetime. We prove\nthat sensors can evenly consume their initial battery power provided that first they adjust their transmission power when they\ntransmit the route through traffic and second they are appropriately placed while deployed. Mainly, we propose a deployment\nscheme and the corresponding balanced routing strategy that lead to uniform energy consumption among all underwater sensors\nsubject to a predefined reliability level at the sink. Specifically, we look for the optimal deployment settings especially in terms of\nnodesâ?? separation distances that help achieving uniform energy consumption in the network while satisfying the application\nrequirement especially in terms of desired information reliability. Jointly, at the routing layer, we assume that each sensor is\nprovided with the possibility of dynamically adjusting its transmission power up to a given number of levels N. To this goal, we\nmainly deal with two main cases: fixed and variable nodes separation distance. For the fixed case, we suppose that any two\nsuccessive nodes in the network are equally spaced, and we strive for deriving the optimal distance as well as the optimal number\nof transmission power levels along with optimal load weight corresponding to every possible transmission power level for every\nsensor node. For the variable case, we deal with two subcases: first, we suppose that the distance separating successive nodes\nfollows an arithmetic progression, and second, we assume that the distance separating successive nodes is following a geometric\nsequence. Note that for both cases, namely, fixed and variable, we succeed to determine the optimal distances separating successive\nnodes and the optimal number N of transmission power levels along with the corresponding optimal load weight that overcome\nthe energy holes problem, and hence the network lifespan is maximized while respecting the desired reliability level....
Low-cost air pollution wireless sensors are emerging in densely distributed networks that\nprovide more spatial resolution than typical traditional systems for monitoring ambient air quality.\nThis paper presents an air quality measurement system that is composed of a distributed sensor\nnetwork connected to a cloud system forming a wireless sensor network (WSN). Sensor nodes are\nbased on low-power ZigBee motes, and transmit field measurement data to the cloud through a\ngateway. An optimized cloud computing system has been implemented to store, monitor, process,\nand visualize the data received from the sensor network. Data processing and analysis is performed\nin the cloud by applying artificial intelligence techniques to optimize the detection of compounds and\ncontaminants. This proposed system is a low-cost, low-size, and low-power consumption method that\ncan greatly enhance the efficiency of air quality measurements, since a great number of nodes could\nbe deployed and provide relevant information for air quality distribution in different areas. Finally,\na laboratory case study demonstrates the applicability of the proposed system for the detection of\nsome common volatile organic compounds, including: benzene, toluene, ethylbenzene, and xylene.\nPrincipal component analysis, a multilayer perceptron with backpropagation learning algorithm, and\nsupport vector machine have been applied for data processing. The results obtained suggest good\nperformance in discriminating and quantifying the concentration of the volatile organic compounds....
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