Fine-grained analysis of packet losses in wireless sensor networks

Shebaro, Bilal; Midi, Daniele; Bertino, Elisa

doi:10.1109/sahcn.2014.6990368

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…The first models for packet loss only considered errors in the physical layer. At the physical layer, the main causes of packet loss in Wi-Fi networks are low signal power, noise, interference, and multipath fading [10], [11]. However, packet loss in Wi-Fi networks can have many causes, including physical and link layer problems.…”

Section: Introductionmentioning

confidence: 99%

MAC-Layer Packet Loss Models for Wi-Fi Networks: A Survey

Silva

Pedroso

2019

IEEE Access

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Technical reports indicate that wireless and mobile devices will account for 71% of all IP traffic by 2022, an increase of 19% over four years. This increase is related to advances in wireless data communication technologies. Wireless networks have become one of the most important ways to connect devices to the Internet, therein improving productivity and encouraging information sharing. IEEE 802.11, known as Wi-Fi, has become the main standard for wireless local area networks. The most important metrics for measuring the quality of Wi-Fi are delay, jitter, and packet loss. Packet loss occurs when one or more packets fail to reach their destination and can occur for a variety of reasons. Packet loss influences the user's perceived quality of applications over Wi-Fi networks, mainly multimedia and real-time applications. The availability of accurate models for packet loss in Wi-Fi networks enables the development of more efficient methods for performance analysis and network design, as well as better computational simulations. Modeling packet loss in such networks presents a major challenge because packets may be lost for many different reasons, including signal attenuation, noise, multipathing, signal refraction, thermal noise, competition for media access and buffer issues. In this paper, we provide an overview of the causes of packet loss and a comprehensive survey of the available models for packet loss in Wi-Fi networks. The potential benefits of the survey are: (i) the systematic presentation of available packet loss models for Wi-Fi networks, their parameters, and respective packet loss rate evaluation, (ii) comparison of models considering validation scenarios and input parameters, and (iii) description of open issues and future research directions. We hope that our analysis will help researchers understand the most important characteristics of the packet loss process in Wi-Fi networks and the strengths and weaknesses of the main packet loss models.INDEX TERMS Gilbert-Elliot model, packet loss model, packet loss rate, Wi-Fi communication.

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Section: Introductionmentioning

confidence: 99%

MAC-Layer Packet Loss Models for Wi-Fi Networks: A Survey

Silva

Pedroso

2019

IEEE Access

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“…Though, this approach was efficient for estimating packet delivery condition and allied loss rate, it could not rectify the loss problem in the network. In [31] authors applied received signal strength indicator (RSSI), the link quality indicator (LQI), and the packet reception rate (PRR) to perform routing decision in WSNs. However, this approach could not deal with dynamic topology and varying network condition.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the simultaneous node activities such as functional-as-routers for multi-hop forwarding too impose queuing overflow. When the route-traffic exceeds a suitable limit or threshold its results into limited data traffic, low data rate transmission that eventually causes QoS violation [31] [32]. Practically, the node-queuing overflow can be visualized in terms of data drop by neighbouring or forwarding node, whose selection as BFN can lead unreliable path (say, BFL) selection.…”

Section: Queuing Overflow Conditionsmentioning

confidence: 99%

Multi-Objective Optimization Assisted Network Condition Aware QOS-Routing Protocol for MANETS: MNCQM

K¹,

Siddesh²

2019

IJCNC

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The exponential rise in wireless communication systems and allied applications has revitalized academiaindustries to achieve more efficient data transmission system to meet Quality-of-Service (QoS) demands. Amongst major wireless communication techniques, Mobile Ad-hoc Network (MANET) is found potential to provide decentralized and infrastructure less communication among multiple distributed nodes across network region. However, dynamic network conditions such as changing topology, congestion, packet drop, intrusion possibilities etc often make MANET's routing a tedious task. On the other hand, mobile network feature broadens the horizon for intruders to penetrate the network and causes performance degradation. Unlike classical MANET protocols where major efforts have been made on single network parameter based routing decision, this research paper proposes a novel Elitist Genetic Algorithm (EGA) Multi-Objective Optimization assisted Network Condition Aware QoS-Routing Protocol for Mobile Ad-hoc Networks (MNCQM). Our proposed MNCQM protocol exhibits two phase implementation where at first it performs node-profiling under dynamic network topology for which three factors; irregular MAC information exchange, queuing overflow and topological variations have been considered. Towards this objective node features like Packet Forwarding Probability (PFP) at the MAC layer, Success Probability of Data Transmission (SPDT) of a neighboring node, and Probability of Successful Data Delivery (PSDD) have been obtained to estimate Node-Trustworthiness Index (NTI), which is further used to eliminate untrustworthy nodes. In the second phase of implementation, a novel Evolutionary Computing assisted nondisjoint best forwarding path selection model is developed that exploits node's and allied link's connectivity and availability features to identify the quasi-sub-optimal forwarding paths. EGA algorithm intends to reduce hop-counts, connectivity-loss and node or link unavailability to estimate best forwarding node. One key feature of the proposed model is dual-supplementary forwarding path selection that enables alternate path formation in case of link outage and thus avoids any iterative network discovery phase.

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“…Monitoring sensors is a complex task as there could be multiple factors that could lead to sensor malfunctions such as calibration errors,environmental conditions, attacks, decay of sensor energy, etc. Different monitoring techniques may thus have to be combined and deployed, ranging from simple techniques, such as profiling sensor baseline behaviour, to complex techniques, such as fine-grained diagnosis techniques for sensors [6].…”

Section: Sensor Deployment and Managementmentioning

confidence: 99%