Identifying the Challenges in Reducing Latency in GSN using Predictors
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Abstract:
Simulations based on real-time data continuously gathered from sensor networks all over the world have received growing attention due to the increasing availability of measured data. Furthermore, predictive techniques have been employed in the realm of such networks to reduce communication for energy-efficiency. However, research has focused on the high amounts of data transferred rather than latency requirements posed by the applications. We propose using predictors to supply data with low latency as required for accurate simulations. This paper investigates requirements for a successful combination of these concepts and discusses challenges that arise.Keywords:
Realm
Wireless Sensor Networks (WSNs) are expected to be a new, revolutionary
technology in the same manner as the Internet. This is due to their special
characteristics such as low power consumption, ad hoc operation, self-maintenance
and many other features. These special characteristics help in reducing the costs of
network manufacture and implementation which extends their applications in a
number of areas such as health and military services. Unfortunately, network
resources such as memory, power and processing capacity constitute a serious
constraint. In addition, they reduce the immunity of the network against external and
internal impacts (such as electromagnetic interference) which make sensor node
operations frequently deviate from the norm, degrading the WSN's functionality. In
some cases the data collected by the network becomes unreliable; the monitoring of
the phenomenon may even fail. To ensure the reliability of the network, several tools
have been proposed to detect and isolate these deviations but most use relatively
high levels of resources. In certain circumstances these state-of-the-art tools are
unable to avoid the instant impact of data deviations on the accuracy of the collected
data and on the network's functionality.
This thesis overcomes these drawbacks by proposing a new, real-time, low
resources usage, distributed performance algorithm that will monitor the accuracy of
collected data and network functionality in large scale dense deployed WSNs. In
order to achieve this, we have used the spatio-temporal correlation between the
measurements of the neighbour nodes in large scale dense deployed WSNs. This
correlation arises due to near proximity (of the nodes) and/or the slow characteristics'
change of monitored phenomenon.
The proposed algorithm has been tested via simulation experiments using different
simulated and real world application data sets. Moreover, it has been tested on a real
network testbed with Mote sensors using continuous reporting and event-driven
applications. The results from these experiments showed a high rate of detection of
changes in the reliability levels of data and in network performance. They also
showed a high level of accuracy in terms of the detection of sensor faults. This,
however, comes alongside certain limitations because of the use of simple passive
analysis with the proposed algorithm.
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Recent advances in wireless sensor networks have led to many new routing protocols specifically designed for sensor networks. Almost all of these routing protocols considered energy efficiency as the ultimate objective in order to maximize the whole network lifetime. However, the introduction of video and imaging sensors has posed additional challenges. Transmission of video and imaging data requires both energy and QoS aware routing in order to ensure efficient usage of the sensors and effective access to the gathered measurements. In this paper, we propose Efficient, Least Cost, Energy-Aware (ELCEA) QoS routing protocol for sensor networks which can also run efficiently with best-effort traffic. The protocol finds a least cost, delay-constrained path for realtime data in terms of link cost that captures nodes' energy reserve, transmission energy, error rate and other communication parameters. Moreover, the throughput for non-real-time data is maximized by adjusting the service rate for both real-time and non-real-time data at the sensor nodes. Simulation results have demonstrated the effectiveness of our approach for different metrics.
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A wireless sensor network consists of a large number of resource-constrained sensor nodes that are self-organized into a multi-hop network and cooperate on a single task. In many situations, sensor networks need to run for a long time once deployed. When the environment changes during their lifetime, updating the code image or application data at the node for a new task becomes necessary, thus making data dissemination a critical issue where a large data object needs to be reliably propagated to all of the nodes in a network. While most of the current sensor nodes are equipped with a multiple-channel radio, the existing data dissemination approaches such as Deluge [1] do not take advantage of multiple channels. Moreover, these approaches mostly focus on the object delivery latency, while energy efficiency is also very important due to the resource constraints of the sensor nodes. This dissertation proposes three novel protocols for reliable bulk data dissemination, named McTorrent, CORD and McCORD, that focus on both object delivery latency and energy efficiency. These protocols use multiple channels, or a core-based two-phase approach, or both techniques to reduce object delivery latency and energy consumption at each node. The results from experiments on both indoor and outdoor testbeds and extensive simulations in various scenarios show that these protocols significantly reduce the latency and/or energy consumption, compared to the existing approaches.
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Despite the advantages of multi-access edge computing in enabling latency-sensitive services and extending the limited computing capabilities of network devices, access communication issues are still often causing the quality of the wireless channels to be severely degraded, preventing the edge resources from being efficiently utilized. Through the deployment of low-cost passive reflecting elements, the recent studies of intelligent reflecting surfaces (IRSs) in wireless networks have shown a great potential for enhancing the quality of the wireless channels and the transmission rates. In this work, motivated by the recent findings, we study the use of an IRS-aided edge computing system for enabling low latency and high reliability computation offloading in the context of a single-user network. Specifically, we optimize the phase shift of the IRS elements along with the device's transmit power and offloading decision, with the objective of minimizing the device's energy consumption. Due to the non-convexity of the problem, we propose a customized sub-optimal solution based on the alternating optimization approach, utilizing novel successive convex approximation techniques. Numerical analysis demonstrates the energy reduction and saving in network resources provided by the optimized use of the IRS, especially for offloading services with higher reliability.
Computation offloading
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Large volumes of real-world observation and measurement data are collected from sensory devices in the Internet of Things (IoT) networks. IoT data is often generated in highly distributed and dynamic environments. Continuous transmission of large volumes of data collected between sensor and head/sink nodes induces a high communication cost for individual nodes. This results in a significant increase in the overall energy cost for IoT applications such as environmental monitoring. Decreasing data transmission between nodes can effectively reduce energy consumption and prolong the network lifetime, especially in battery-powered nodes/networks. In this article, we describe an adaptive method for data reduction (AM-DR), a data reduction approach for reducing the overall data transmission and communication between sensor nodes in IoT networks such that fine-grained sensor readings can be used to reconstruct the original data within a user-defined accuracy boundary. Evaluation with real-world data shows that AM-DR achieves a communication reduction in some scenarios up to 95% while retaining a high prediction accuracy. To fully achieve the energy savings enabled by AM-DR, we provide a communication cost model. The proposed model is also integrated into the LEACH protocol to demonstrate how our proposed approach reduces energy consumption and effectively prolongs the network lifetime.
Data aggregator
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Wireless sensor networks offer a distributed processing environment. Many sensor nodes are deployed in fields that have limited resources such as computing power, network bandwidth, and electric power. The sensor nodes construct their own networks automatically, and the collected data are sent to the sink node. In these traditional wireless sensor networks, network congestion due to packet flooding through the networks shortens the network life time. Clustering or in-network technologies help reduce packet flooding in the networks. Many studies have focused on saving energy in the sensor nodes because the limited available power leads to an important problem of extending the operation of sensor networks as long as possible. However, we focus on the execution time because clustering and local distributed processing already contribute to saving energy by local decision making. In this paper, we present a cooperative processing model based on the processing timeline. Our processing model includes validation of the processing, prediction of the total execution time, and determination of the optimal number of processing nodes for distributed processing in wireless sensor networks. The experiments demonstrate the accuracy of the proposed model, and a case study shows that our model can be used for the distributed application.
Visual sensor network
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There are various unseen and unpredictable networking states in Wireless Sensor Network (WSN) that adversely affect the aggregated data quality. After reviewing the existing approaches of data quality in WSN, it was found that the solutions are quite symptomatic and they are applicable only in a static environment; however their successful applicability on dynamic and upcoming reconfigurable network is still a big question. Moreover, data quality directly affects energy conservation among the nodes. Therefore, the proposed system introduces a simple and novel framework that jointly addresses the data quality and energy efficiency using probability-based design approach. Using a simplified analytical methodology, the proposed system offers solution in the form of selection transmission of an aggergated data on the basis of message priority in order to offer higher data utilization factor. The study outcome shows proposed system offers a good balance between data quality and energy efficiency in contrast to existing system.
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The volume of streaming sensor data from various environmental sensors continues to increase rapidly due to wider deployments of IoT devices at much greater scales than ever before. This, in turn, causes massive increase in the fog, cloud network traffic which leads to heavily delayed network operations. In streaming data analytics, the ability to obtain real time data insight is crucial for computational sustainability for many IoT enabled applications such as environmental monitors, pollution and climate surveillance, traffic control or even E-commerce applications. However, such network delays prevent us from achieving high quality real-time data analytics of environmental information. In order to address this challenge, we propose the Fog Sampling Node Selector (Fossel) technique that can significantly reduce the IoT network and processing delays by algorithmically selecting an optimal subset of fog nodes to perform the sensor data sampling. In addition, our technique performs a simple type of query executions within the fog nodes in order to further reduce the network delays by processing the data near the data producing devices. Our extensive evaluations show that Fossel technique outperforms the state-of-the-art in terms of latency reduction as well as in bandwidth consumption, network usage and energy consumption.
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