Device based Multi-User Tracking System using Internet of Things

In Light Dependent Resistor (LDR) sensor-based user is localized based on the event and the intensity of the room light when a user enters inside a room and switch ON the lights, the intensity goes high, an entry is noti?ed. An exit is noti?ed when a user switches OFF the light and exit the room. Moreover, the model remains prone to more error in multi user localization because multiple users may enter inside same room at same time and the lights of many rooms remain ON which makes more difficult to localize a user. In order to overcome this ambiguity of light sensors, two passive infrared (PIR) sensor with radio frequency identi?cation (RFID) tag-based model has been proposed, where every user has a tag. In this system, 10 PIR sensors and 5 RFID readers were attached to house room (10.0 m * 6.0m). An entry is noti?ed if the following pattern form, the outer PIR detects a motion and waits for few seconds, next the RFID reader reads the tag given to the user and ?nally the inner PIR detects a motion within the given time delay. An exit of a user is noti?ed only if the pattern from inner PIR to outer PIR is followed with the given time delay. The RFID tag is used to identify which user has entered a room at a particular time and also ensures unauthorized entry. The LDR based system gives accuracy nearby 20% but the multi-person tracking in a binary infrared sensor network-based system gives accuracy near about 90%. In this paper, the proposed PIR sensor along with RFID based indoor navigation system gives accuracy near about 94%.                              &nbsp

Minimizing the Localization Error in Wireless Sensor Networks Using Multi-Objective Optimization Techniques

When it comes to remote sensing applications, wireless sensor networks (WSN) are crucial. Because of their small size, low cost, and ability to communicate with one another, sensors are finding more and more applications in a wide range of wireless technologies. The sensor network is the result of the fusion of microelectronic and electromechanical technologies. Through the localization procedure, the precise location of every network node can be determined. When trying to pinpoint the precise location of a node, a mobility anchor can be used in a helpful method known as mobility-assisted localization. In addition to improving route optimization for location-aware mobile nodes, the mobile anchor can do the same for stationary ones. This system proposes a multi-objective approach to minimizing the distance between the source and target nodes by employing the Dijkstra algorithm while avoiding obstacles. Both the Improved Grasshopper Optimization Algorithm (IGOA) and the Butterfly Optimization Algorithm (BOA) have been incorporated into multi-objective models for obstacle avoidance and route planning. Accuracy in localization is enhanced by the proposed system. Further, it decreases both localization errors and computation time when compared to the existing systems

Research Trend Topic Area on Mobile Anchor Localization: A Systematic Mapping Study

Localization in a dynamic environment is one of the challenges in WSN localization involving dynamic sensor nodes or anchor nodes. Mobile anchors can be an efficient solution for the number of anchors in a 3-dimensional environment requiring more local anchors. The reliability of a localization system using mobile anchors is determined by various parameters such as energy efficiency, coverage, computational complexity, and cost. Various methods have been proposed by researchers to build a reliable mobile anchor localization system. This certainly shows the many research opportunities that can be carried out in mobile anchor localization. The many opportunities in this topic will be very confusing for researchers who want to research in this field in choosing a topic area early. However, until now there is still no paper that discusses systematic mapping studies that can provide information on topic areas and trends in the field of mobile anchor localization. A systematic Mapping Study (SMS) was conducted to determine the topic area and its trends, influential authors, and produce modeling topics and trends from the resulting modeling topics. This SMS can be a solution for researchers who are interested in research in the field of mobile anchor localization in determining the research topics they are interested in for further research. This paper gives information on the mobile anchor research area, the author who has influenced mobile anchor localization research, and the topic modeling and trend that potentially promissing research in the future. The SMS includes a chronology of publications from 2017-2022, bibliometric co-occurrence, co-author analysis, topic modeling, and trends. The results show that the development of mobile anchor localization publications is still developing until 2022. There are 10 topic models with 6 of them included in the promising topic. The results of this SMS can be used as preliminary research from the literacy stage, namely Systematic Literature Review (SLR)

Localization Context-Aware Models for Wireless Sensor Network

Wireless sensor networks (WSNs) are emerging as the key technology to support the Internet of Things (IoT) and smart objects. Small devices with low energy consumption and limited computing resources have wide use in many applications and different fields. Nodes are deployed randomly without a priori knowledge of their location. However, location context is a fundamental feature necessary to provide a context-aware framework to information gathered from sensors in many services such as intrusion detection, surveillance, geographic routing/forwarding, and coverage area management. Nevertheless, only a little number of nodes called anchors are equipped with localization components, such as Global Positioning System (GPS) chips. Worse still, when sensors are deployed in an indoor environment, GPS serves no purpose. This chapter surveys a variety of state-of-the-art existing localization techniques and compares their characteristics by detailing their applications, strengths, and challenges. The specificities and enhancements of the most popular and effective techniques are as well reported. Besides, current research directions in localization are discussed

Error Minimization in Indoor Wireless Sensor Network Localization Using Genetic Technique

Using the genetic technique, error minimisation in indoor wireless sensor network localisation improves indoor wireless sensor network localisation during this field research. Sensor localisation-based techniques; several wireless device network applications require awareness of each node's physical location. The discovery of the position complete utilising range measurements also as sensor localisation received signal strength in time of arrival and sensor localisation received signal strength in a time difference of arrival and angle of arrival. WSN in positioning algorithms like the angle of arrival between two neighbour nodes. A wireless sensor network using positioning techniques in the area is assumed as localisation. WSNs always operate in an unattended manner, various situations like dynamic situations in the wireless network. It's impossible to exchange sensor manner after deployment. Therefore, a fundamental objective is to optimise the sensor manner lifetime. There has been much specialising in mobile sensor networks, and we have even seen the event of small-profile sensing devices that are ready to control their movement. Although it's been shown that mobility alleviates several issues regarding sensor network coverage and connectivity, many challenges remain node localisation in wireless device network is extremely important for several applications and received signal strength indicator has the capability of sensing, actuating the environmental data the actual-time and favourable information are often collected using the sensor in WSN systems. WSN is often combined with the internet of things to permit the association and extensive access to sensor data, and genetic techniques search the position of the nodes in WSN using all anchor nodes. A proposed algorithm as a genetic technique supported received signal strength, angle of arrival, receptive wireless device and also localisation wireless network. In the study, this paper problem that accuracy is low and error more, but the proposed algorithm overcomes this problem and minimises the error rate. Finally, the simplest possible location satisfies each factor with a minimal error rate and absolute best solution using GA

Improved Recursive DV-Hop Localization Algorithm with RSSI Measurement for Wireless Sensor Networks

Abstract: Please refer tp full text to view abstrac

Data and resource management in wireless networks via data compression, GPS-free dissemination, and learning

“This research proposes several innovative approaches to collect data efficiently from large scale WSNs. First, a Z-compression algorithm has been proposed which exploits the temporal locality of the multi-dimensional sensing data and adapts the Z-order encoding algorithm to map multi-dimensional data to a one-dimensional data stream. The extended version of Z-compression adapts itself to working in low power WSNs running under low power listening (LPL) mode, and comprehensively analyzes its performance compressing both real-world and synthetic datasets. Second, it proposed an efficient geospatial based data collection scheme for IoTs that reduces redundant rebroadcast of up to 95% by only collecting the data of interest. As most of the low-cost wireless sensors won’t be equipped with a GPS module, the virtual coordinates are used to estimate the locations. The proposed work utilizes the anchor-based virtual coordinate system and DV-Hop (Distance vector of hops to anchors) to estimate the relative location of nodes to anchors. Also, it uses circle and hyperbola constraints to encode the position of interest (POI) and any user-defined trajectory into a data request message which allows only the sensors in the POI and routing trajectory to collect and route. It also provides location anonymity by avoiding using and transmitting GPS location information. This has been extended also for heterogeneous WSNs and refined the encoding algorithm by replacing the circle constraints with the ellipse constraints. Last, it proposes a framework that predicts the trajectory of the moving object using a Sequence-to-Sequence learning (Seq2Seq) model and only wakes-up the sensors that fall within the predicted trajectory of the moving object with a specially designed control packet. It reduces the computation time of encoding geospatial trajectory by more than 90% and preserves the location anonymity for the local edge servers”--Abstract, page iv