Priya Bharti

Bio: Priya Bharti is an academic researcher. The author has contributed to research in topics: Sparse approximation & Wireless sensor network. The author has an hindex of 2, co-authored 2 publications receiving 68 citations.

Localization Techniques for Wireless Sensor Networks

Abstract: Wireless sensor networks (WSNs) have recently emerges as promising technology in wireless communication field and gained special attention by research groups. It uses small and cheap gadgets with low energy requirements and limited on board computing resourceswhich communicates with each other’s or base stations without any pre-defined infrastructure. The property of being infrastructure less makes it suitable in distinctive application situations including remotemonitoring, disaster management, military applications and biomedical health observing devices. In many of these applications, node localization is unavoidably one of the important system parameters for example in target tracking if the nodes are not able to obtain the accurate location information, the related task cannot be performed.It is also helpful in routing, network coverage and quarry management of sensors. In general the localization techniques are ordered into two general classifications: range based and range free. In this paper, we discussed the various localization algorithms with their applicable areas, requirements and limitations. Moreover, on conclusion we compare these localization algorithms and analyze the future research directions for the localization algorithms in WSNs.

Fingerprint Compression using Sparse Representation

Abstract: Biometric identification systems are in use for last many years for the purpose of personal identification, uncompressed graphics, audio and video data require considerable storage capacity and transmission bandwidth dealing with such enormous amount of information can often present difficulties. As per my literature survey, there is no such method that uses compressive sensing and adaptive learning dictionary to compress image along with neural network to estimate the results. In the given algorithm, a dictionary of predefined fingerprint patches is constructed which is than quantized and encoded.

Swarm Intelligence Optimization Techniques for Obstacle-Avoidance Mobility-Assisted Localization in Wireless Sensor Networks

Abstract: In many applications of wireless sensor networks (WSNs), node location is required to locate the monitored event once occurs. Mobility-assisted localization has emerged as an efficient technique for node localization. It works on optimizing a path planning of a location-aware mobile node, called mobile anchor (MA). The task of the MA is to traverse the area of interest (network) in a way that minimizes the localization error while maximizing the number of successful localized nodes. For simplicity, many path planning models assume that the MA has a sufficient source of energy and time, and the network area is obstacle-free. However, in many real-life applications such assumptions are rare. When the network area includes many obstacles, which need to be avoided, and the MA itself has a limited movement distance that cannot be exceeded, a dynamic movement approach is needed. In this paper, we propose two novel dynamic movement techniques that offer obstacle-avoidance path planning for mobility-assisted localization in WSNs. The movement planning is designed in a real-time using two swarm intelligence based algorithms, namely grey wolf optimizer and whale optimization algorithm. Both of our proposed models, grey wolf optimizer-based path planning and whale optimization algorithm-based path planning, provide superior outcomes in comparison to other existing works in several metrics including both localization ratio and localization error rate.

A Self-Powered, Real-Time, LoRaWAN IoT-Based Soil Health Monitoring System

Abstract: Typical soil health assessment requires intensive field sampling and laboratory analysis. Although this approach yields accurate results, it can be costly and labor intensive and not suitable for continuous tracking of soil properties. Advances in soil sensor and wireless technologies are poised to replace physical sampling and offline measurement with in-field monitoring. This article reports the development, deployment, and validation of an Internet-of-Things (IoT) system for continuous monitoring of soil health. The end nodes of the proposed system, called soil health monitoring units (SHMUs), are solar powered and can be installed on a field for extended periods of time. Each SHMU transmits soil temperature, moisture, electrical conductivity, carbon dioxide (CO2), and geolocation data wirelessly using long-range wide-area network (LoRaWAN) radio technology. Data are received by a LoRaWAN gateway, which uploads it to a server for long-term storage and analysis. Users can view acquired data through a Web-based dashboard. The following significant experiments were carried out to validate the developed system: 1) a network consisting of eight SHMUs was deployed at an agricultural field site for several weeks and soil health metrics were analyzed using the soil health dashboard; 2) the flexibility of the system was demonstrated by the addition of an extra CO2 sensor allowing an additional variable directly linked to soil health to be recorded; 3) a wireless communication range of 3422 m was estimated at a transmission power of 10 dBm by deploying the developed system on a large field; 4) the average current consumption of a SHMU (including its associated sensors) was estimated to be 13 mA, at this rate, the onboard Li-ion battery is able to sustain a SHMU for several days; and 5) a 7 cm $\times6.5$ cm solar panel was able to fully charge the onboard battery in 14 days while supplying power to the SHMU.

D-LPCN: A distributed least polar-angle connected node algorithm for finding the boundary of a wireless sensor network

Abstract: A boundary of wireless sensor networks (WSNs) can be used in many fields, for example, to monitor a frontier or a secure place of strategic sensitive sites like oil fields or frontiers of a country. This situation is modeled as the problem of finding a polygon hull in a connected Euclidean graph, which represents a minimal set of connected boundary nodes. In this paper we propose a new algorithm called D-LPCN (Distributed Least Polar-angle Connected Node) which represents the distributed version of the LPCN algorithm introduced in [1]. In each iteration, any boundary node, except the first one, chooses its nearest polar angle node among its neighbors with respect to the node found in the previous iteration. The first starting node can be automatically determined using the Minimum Finding algorithm, which has two main advantages. The first one is that the algorithm works with any type of a connected network, given as planar or not. Furthermore, it takes into account any blocking situation and contains the necessary elements to avoid them. The second advantage is that the algorithm can determine all the boundaries of the different connected parts of the network. The proposed algorithm is validated using the CupCarbon, Tossim and Contiki simulators. It has also been implemented using real sensor nodes based on the TelosB and Arduino/XBee platforms. We have estimated the energy consumption of each node and we have found that the consumption of the network depends on the number of the boundary nodes and their neighbors. The simulation results show that the proposed algorithm is less energy consuming than the existing algorithms and its distributed version is less energy consuming than the centralized version.