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IoT for Health Monitoring
Published in Rashmi Gupta, Arun Kumar Rana, Sachin Dhawan, Korhan Cengiz, Advanced Sensing in Image Processing and IoT, 2022
Anshu Saxena, Asmita A. Moghe, Ratish Agarwal
Jaiswal et al. [2] developed an .0automated system using Raspberry Pi by collecting patients' statistics from medical devices fitted with sensors and further processing this data by uploading it to the medical centre's cloud. They also used docker containers for storage distribution, etc. Data through sensor nodes is collected, encoded, and transferred over a wireless channel to the server through the necessary software. Data (patient's temperature, blood pressure, etc.) is input into Raspberry Pi before being pushed to the server. The docker container and the local database in the server are used for further processing of collected data and providing it to the doctor, nursing staff, and hospital to monitor and diagnose health problems. Proper authentication and data encryption are employed among the docker container users and the rest of the components. As well as data storage, the system also provides services like gathering information from doctors, users, hospitals, etc. Instead of using virtual machines, in this system, the docker container and server are loaded in Raspberry Pi. The container receives data from the sensors. Containers in the network receive data and also send it to the local docker server for processing. The local server helps in emergency situations by sending an alert message to the doctor/caregiver. At the same time, it also communicates with the remote server by forwarding to it the sensed data for processing and distribution at the global level to health experts for advice and diagnosis.
Enhanced Intrusion Detection Response and Mitigating Attacks Using Umpiring Security Models for Wireless Sensor Networks
Published in Amit Kumar Tyagi, Ajith Abraham, A. Kaklauskas, N. Sreenath, Gillala Rekha, Shaveta Malik, Security and Privacy-Preserving Techniques in Wireless Robotics, 2022
A. Kathirvel, D. Sudha, S. Navaneethan, M. Subramaniam
Intrusion detection is one of the WSN applications that has grown in popularity due to its many different implementations, such as wireless sensors deployed in an ad hoc manner to track any military environment (Raza et al., 2015). WSNs are densely distributed, small, reduced, limited, transportable network devices that track an entity, evaluate a phenomenon, or regulate a method. Sensor nodes are employed in a variety of technology domains, such as personal applications Smart home, chosen profession such as sales tracking, commercial uses such as architectural and control, and medical operations such as enemy target detection and tracking are all examples of applications (Mrugala et al., 2017; Matyas and Kur (2013), Matyas and Kur (2013), Miao et al., 2014, Josh Kumar et al., 2015 & 2018). The Internet of Things (IoT) as a WSNs’ future appears to be based on a novel concept. It is assumed that every aspect of human life will be equipped with sensors that will communicate with one another to form a network that will make things much easier. Sensor nodes enter the internet dynamically in the Internet of Things, and they use internet resources to communicate and execute their tasks (Murad et al., 2013).
Dynamic Modeling on Malware and Its Defense in Wireless Computer Network Using Pre-Quarantine
Published in Gautam Kumar, Dinesh Kumar Saini, Nguyen Ha Huy Cuong, Cyber Defense Mechanisms, 2020
Yerra Shankar Rao, Hemraj Saini, Ranjita Rath, Tarini Charan Panda
Coupled with the progress of the digital era, increasing development of network applications and cloud computing, networks have become an inevitable part of our daily life. Today’s enterprise systems and networks are frequent targets of malicious attacks such as worms, viruses, spyware, and intrusions that can disrupt or even disable critical services. Among the popular networks, the wireless sensor network is the most vulnerable to attacks of malicious codes due to the structural constraint of its sensor nodes and absence of physical security. A wireless network is a group of sensor nodes which sense, compute, and gather information from the physical environment, and transmit the collected data to a central station. A sensor node is a low-power device which comprises an array of sensors, radio unit, processor unit, memory unit, and power unit. Wireless sensor networks are used in military area monitoring, weather monitoring, healthcare monitoring, vehicle tracking, earth sensing, disaster management, and daily life applications.
An IoT-based Framework of Vehicle Accident Detection for Smart City
Published in IETE Journal of Research, 2023
Pankaj P. Tasgaonkar, Rahul Dev Garg, Pradeep Kumar Garg
An Intelligent Transportation System consists of advanced sensors, high-speed communication, and information technology for the pedestrians, drivers of the road vehicles, and the monitoring stations [37]. The sensors like cameras, RFID readers have been installed either on the roadside, inductive loops and magnetic sensors, etc., under the road or they are present on vehicles like accelerometers, GPS, ultrasonic sensors, etc. Environmental sensors also give information related to rain, cyclone, landslide with large LCDs showing the warnings. The sensor nodes obtain the power from an external battery or a solar cell. Routing protocols in WSN can handle more complex processing and computations requirements. There is a chance that a third party can influence the sensor data and wrong information is passed to the end-users, to avoid such circumstances, security is assigned with encrypted algorithms. Neural network was used to detect crash events from computing the coefficients as a feature vector. Acoustic signals were considered and database of road vehicle crashes, braking, and traffic were stored. [38]. In the crash-sensing algorithm [39], this change of velocity, v(t), is compared to the threshold value, Vth, to provide the effective crash detection decision. The change of velocity, v(t), is the integration of the acceleration signal.
An Empirical Evaluation of Various Digital Signature Scheme in Wireless Sensor Network
Published in IETE Technical Review, 2022
Pankaj Kumar, Saurabh Kumar Sharma
Battery is the bottleneck in WSNs. Since the sensor node is powered by a battery that provides limited energy resource, the life of a sensor node is limited. The major functionality of a sensor node is to recognize events, conduct data processing and convey the processed data to base station. Thereby the energy consumption can be split up into three areas: event sensing, data processing and data communication. Table 7 shows the energy consumption of different digital signature algorithms. It can be observed that RSA is consuming 304 mJ of energy, whereas 684 mJ [29,30] of energy is required by an ELGamal algorithm for signature size of 1024 bits. It means RSA is more efficient, as it requires less energy consumption and less computing power as compared to ELGamal. Hence, the RSA algorithm increases the lifetime of the network compared to ELGamal [30].
DDC protocol to protract network lifetime of wireless sensor networks
Published in International Journal of Computers and Applications, 2022
Yogita Yogita, Vipin Pal, Anju Yadav
A wireless sensor network [1,2] is a collection of various densely deployed sensor nodes and has been turned up as a topic of prominent interest due to its wide range of applications [2,3]. The sensor node is a primary element of a wireless sensor network that senses the application area and sends the sensed data to the base station. Sensor nodes have limited battery power and in most of the cases because of harsh and remote application area there are very less chances of human intervention for replacing or recharging the sensor node's battery once the network is deployed. So, it can be concluded that energy of sensor nodes is a very precious resource and is a vital factor for wireless sensor network lifetime. Clustering methodology has been examined as an energy-efficient approach for wireless sensor networks [4]. It organizes nodes in independent clusters and selects one head for each cluster which has the complete responsibility of the cluster. Performance of a clustering algorithm gets affected by various clustering issues, for example, cluster head selection, round-time duration, number of member nodes in the cluster and many more [5].