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Model of wireless sensor networks
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The need for sensor node localization becomes a vital task for wireless sensor networks (WSNs), due to repeatedly growing of WSNs technology. Global position system (GPS) - based sensor node localization provides accurate location, but at the same time undesirably lead to increase the network deployment cost. Also, in indoor environment the GPS-bas...
Citations
... The collected data of the sensor nodes have no meaning until the WSN knows its actual state. Thus, the localization of sensor nodes becomes an important challenge for WSNs [8][9][10]. ...
Text matching is a process of finding the frequency of occurrences of text pattern in a corpus. It's very costly to store, process, and retrieve a vast volume of text data. In this paper, we present a method to keep the massive text corpus in lesser memory space by using text compression and to retrieve the results by matching directly on this compressed corpus without decompression using compressed pattern matching (CPM). The proposed approach also helps to minimize the time taken to perform matching without compromising the false matching results. We used word-based tagged coding to perform text compression and Wavelet Trees for representing the compressed text in memory. The proposed Text Matching in Compressed text using Parallel Wavelet Tree (TMC_PWT) method is quite fast in comparison to other existing text matching algorithms that support CPM. In the context of CPM, the proposed method provides a good compression ratio and does not suffer from the problem of false matching.
Keywords: Modern Information Retrieval, Wavelet Tree, Word-Based Tagged Code, Compressed Pattern Matching.
... Indoor positioning is also another field which is being highly benefited from the use of WSN. The proposed solutions in the literature seek for a balanced system between communication complexity, storage scale, localization error and location accuracy [43]. These distributed solutions use sensor nodes to localize using various localization techniques, the most popular categories are range-based and non range-based algorithms. ...
Radio frequency identification (RFID) and wireless sensors networks (WSNs) are two fundamental pillars that enable the Internet of Things (IoT). RFID systems are able to identify and track devices, whilst WSNs cooperate to gather and provide information from interconnected sensors. This involves challenges, for example, in transforming RFID systems with identification capabilities into sensing and computational platforms, as well as considering them as architectures of wirelessly connected sensing tags. This, together with the latest advances in WSNs and with the integration of both technologies, has resulted in the opportunity to develop novel IoT applications. This paper presents a review of these two technologies and the obstacles and challenges that need to be overcome. Some of these challenges are the efficiency of the energy harvesting, communication interference, fault tolerance, higher capacities to handling data processing, cost feasibility, and an appropriate integration of these factors. Additionally, two emerging trends in IoT are reviewed: the combination of RFID and WSNs in order to exploit their advantages and complement their limitations, and wearable sensors, which enable new promising IoT applications.
A wireless sensor network (WSN) consists of many small, low cost and less computational power sensor nodes. These nodes are uniformly or randomly deployed for gathering vital information from the environment. It is crucial to identify the exact and accurate position of the sensor node as it helps in efficient communication between unknown and known (beacon) nodes. Localization has many applications, such as rescue, traffic controlling and monitoring, underwater cultivation, surveillance, and target tracking. It is also used in day-to-day life in the form of GPS to guide people in their travel. Hence, to avail of the accurate service from localization, the exact position of the sensor is needed. In this work, an ensemble approach is proposed using both DV-Hop and a weighted amorphous algorithm to enhance localization accuracy. Two distance measurements are calculated to obtain the distance from an unknown node to the beacon node by considering hop value and size. Finally, the probabilistic distance estimation is applied to the obtained distances to get the actual distance. Proposed approach is compared with the traditional amorphous and three other improved amorphous algorithms and provides higher accuracy in terms of MAE, MSE, and RMSE.
Wireless sensor networks (WSNs) consist of a number of sensor nodes working together for gathering and retransmitting data or information. WSNs have become increasingly popular due to their wide range of applications. They are typically used for remote environment monitoring in areas where supplying electrical power is difficult. The devices are powered by batteries and by alternative energy sources. Localization is used in WSNs to find the current location of the sensor nodes. Installation of GPS on each sensor node is expensive, further, manually configuring location details on each sensor node is not possible in dense WSNs. To make the deployment of WSNs economical, localization techniques are used. With the help of localization techniques, sensor nodes identify their location based on the information provided by an anchor node or beacon node. As the battery energy is limited for the sensor nodes, the different optimization techniques are required for energy optimization and localization. In this proposed work, the firefly optimization technique and the Hybrid Eagle with firefly Optimization technique for energy optimization with RSSI (Received Signal Strength Indication) positioning method is applied to complete the cluster and cluster head selection to optimize energy and power consumption and to increase network life cycle in WSN power consumption. The performance of both algorithms and parameters is executed in the MAT-LAB simulation platform.KeywordsWSNLocalizationOptimizationFirefly and EagleRSSISensor node
Localization is an essential module for most protocols and applications in Wireless Sensor Networks - Internet of Things(WSN-IoT). Among the well-known approaches available for WSN-IoT localization, the algorithm requires at least one,two, or three beacon nodes-based localization approaches. Many other localization protocols use a small set of beacon nodesfor the localization of sensor nodes. However, still, the authors are not able to provide an accurate and reliable approach inthe field of WSN-IoT. Thus, this work provides an adaptive ensemble localization approach in WSN-IoT. The proposedapproach adaptively uses the concept of available single, two, and three beacons nodes-based localization approachesaccording to the number of available beacon nodes. By comparing available single, two, or three beacons nodes-basedlocalization approaches the simulation results of the proposed work outperformed in terms of fast convergence rate, lesserroneous and higher accuracy with reducing the line of sight problem.
