Security and Privacy in Wireless Sensor Networks Using Intrusion Detection Models to Detect DDOS and Drdos Attacks: A Survey

Wireless sensor networks (WSN) playa significant role in the collection and transmission of data. The principal data collectors and broadcasters are small wireless sensor nodes. As a result of their disorganized layout, the nodes in this network are vulnerable to intrusion. Every aspect of human life includes some form of technological interaction. While the Covid-19 pandemic has been ongoing, the whole corporate and academic world has gone digital. As a direct result of digitization, there has been a rise in the frequency with which Internet-based systems are attacked and breached. The Distributed Denial of Service (DDoS) and Distributed Reflective Denial of Service (DRDoS) assaults are new and dangerous type of cyberattacks that can quickly bring down any service or application that relies on the Internet's infrastructure. Cybercriminals are always refining their methods of attack and evading detection by using techniques that are out of date. Traditional detection systems are not suited to identify novel DDoS attacks since the volume of data created and stored has expanded exponentially in recent years. This research provides a comprehensive overview of the relevant literature, focusing on deep learning for DDoS and DRDoS detection. Due to the expanding number of loT gadgets, distributed DDoS and DRDoS attacks are becoming more likely and more damaging. Due to their lack of generalizability, current attack detection methods cannot be used for early detection of DDoS and DRDoS, resulting in significant load or service degradation when implemented at the endpoint. In this research, a brief review is performed on the models that are used for identification of DDoS and DRDoS attacks. The working of the existing models and the limitations of the models are briefly analyzed in this research. © 2023 IEEE.

Non-Cooperative Game for Edge Server Placement in Wireless Body Area Network

The outbreak of the coronavirus disease 2019 (COVID-19) has become the worst public health event in the whole world, threatening the physical and mental health of hundreds of millions of people. However, because of the high survivability of the virus, it is impossible for humans to eliminate viruses completely. For this reason, it is particularly important to strengthen the prevention of the transmission of viruses and monitor the physical status of the crowd. Wireless sensors are a key player in the fight against the current global outbreak of the Covid-19 pandemic, where they are playing an important role in monitoring human health. The Wireless Body Area Network (WBAN) composed of these wireless sensor devices can monitor human health data without interference for a long time, and update the data in almost real time through the Internet of Things (IoT). However, because the data monitored by the devices is relatively large and the transmission distance is long, only transmitting the data to medical centers through the personal devices (PB) cannot get feedback in time. We propose a non-cooperative game-based server placement method, which is named ESP-19 to improve the efficiency of transmission data of wireless sensors. In this paper, experimental tests are conducted based on the distribution of Shanghai Telecom's base stations, and then the performance of ESP-19 is evaluated. The results show that the proposed method in this paper outperforms the comparison method in terms of service delay. © 2022 IEEE.

Multihop Clustering Routing Protocol Based on Improved Coronavirus Herd Immunity Optimizer and Q-Learning in WSNs

Wireless sensor networks (WSNs) are composed of a large number of spatially distributed sensor nodes to monitor and transmit information from the environment. However, the batteries used by these sensor nodes have limited energy and cannot be charged or replaced due to the harsh deployment environment. This energy limitation will seriously affect the lifetime of the network. Therefore, the purpose of this research is to reduce energy consumption and balance the load of sensor nodes by clustering routing protocols, so as to prolong the lifetime of the network. First, the coronavirus herd immune optimizer is improved and used to optimize the network clustering. Second, the cluster heads (CHs) are selected according to the energy and location factors in the clusters, and a reasonable CH replacement mechanism is designed to avoid the extra communication energy consumption caused by the frequent replacement of CHs. Finally, a multihop routing mechanism between the CHs and the base station is constructed by Q-learning. Simulation results show that the proposed work can improve the structure of clusters, enhance the load balance of nodes, reduce network energy consumption, and prolong the network lifetime. The appearance time of the first energy-depleted node is delayed by 25.8%, 85.9%, and 162.2% compared with IGWO, ACA-LEACH, and DEAL in the monitoring area of $300×300 m, respectively. In addition, the proposed protocol shows better adaptability in varying dynamic conditions. © 2001-2012 IEEE.

CSI-Based Joint Location and Activity Monitoring for COVID-19 Quarantine Environments

The recent SARS-COV-2 virus, also known as COVID-19, badly affected the world's healthcare system due to limited medical resources for a large number of infected human beings. Quarantine helps in breaking the spread of the virus for such communicable diseases. This work proposes a nonwearable/contactless system for human location and activity recognition using ubiquitous wireless signals. The proposed method utilizes the channel state information (CSI) of the wireless signals recorded through a low-cost device for estimating the location and activity of the person under quarantine. We propose to utilize a Siamese architecture with combined one-dimensional convolutional neural networks (1-D-CNNs) and bi-directional long short-term memory (Bi-LSTM) networks. The proposed method provides high accuracy for the joint task and is validated on two real-world testbeds, first, using the designed low-cost CSI recording hardware, and second, on a public dataset for joint activity and location estimation. The human activity recognition (HAR) results outperform state-of-the-art machine and deep learning methods, and localization results are comparable with the existing methods. © 2001-2012 IEEE.

Networking Data and Intelligent Management in the Post-COVID19 Era: A Report on APNOMS 2021

This article presents a report on APNOMS 2021, which was held on September 8–10, 2021 in Tainan, Taiwan. The theme of APNOMS 2021 was "Networking Data and Intelligent Management in the Post-COVID19 Era.”. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Online physics practicum supported by wireless sensor network

This study proposes an online practicum model supported by Wireless Sensor Network (WSN) to implement a physics practicum after the Covid 19 Pandemic. This system is guided by exploratory inquiry questions to help structure students' mindsets in answering investigative questions. Online practicum is also integrated with video conferencing, chat, evaluation system, and lab inquiry stages. The sensor measurement process is carried out directly via live streaming video, where the sensor measurement results are sent in real-time to the website via an internet connection. This study was conducted on 25 students (10 male and 15 female) who were prospective physics teachers. This study used a pre-experimental method with a one-group pretest and post-test design. The study results show that the online practicum model supported by WSN can effectively increase the inquiry skill of prospective physics teacher students. Usability test results obtained an average score of SUS 91.63, which means the practicum system can be categorized as having a user acceptance level of Excellent.

An Autonomous IoT-based Contact Tracing Platform in a COVID-19 Patient Ward

Since 2020, the coronavirus disease (COVID-19) pandemic has had a substantial impact on all community sectors worldwide, particularly the health care sector. Healthcare workers (HCWs) are at risk of COVID-19 infection due to occupational exposure to infectious patients, visitors, and staff. Contact tracing of close physical interaction is an essential control measure, especially in hospitals, to prevent onward transmission during an outbreak event. In this article, we propose an IoT-based contact tracing system for subject identification, interaction tracking and data transmission in hospital wards. The system, based on Bluetooth Low Energy (BLE) devices, tracks the duration of interactions between different HCWs, and the time each HCW spends within the patient rooms using additional information from proximity sensors in the hallway or on the door frame of the patient room. The collected data are transferred via Long Range (LoRa) wireless technology and further analyzed to inform infection prevention activities. The suggested system’s performance is evaluated in a COVID-19 patient ward with both standard and negative pressure isolation rooms, and the current system’s capabilities and future research prospects are briefly discussed. IEEE

Azolla Quantity Prediction using WSN and Machine Learning

Due to COVID-19 pandemic, the expenditures on pellets and feeds in broiler and fish industries increase every year, leading to price overshoots in various agricultural products. Azolla is an emerging protein source alternative for tilapia and other livestock breeders that is known for its fast reproduction. This study aims to enhance the yield production of Azolla ponds in Nevalga Farm, Brgy. Sala, City of Cabuyao, Laguna by employing wireless sensor network (WSN) technology and predictive machine-learning (ML) methods. LoRa-based WSN was designed to measure the parameters that affect the growth and reproduction of Azolla. Throughout the 24-day monitoring period, the average received signal strength indication (RSSI) and signal-to-noise ratio (SNR) of the packets from the three sensing nodes ranged from -50.86 dBm to -71.39 dBm and 8.92 dB to 9.81 dB, respectively. A total of 3582 data sets were obtained during the observation. Among the three regression ML models used, K-Nearest Neighbor algorithm outperformed Linear Regression and Support Vector Machine in predicting Azolla quantity parameters on both training and validation datasets by yielding the smallest values of root mean square error (RMSE) and absolute error on the seven quantity indicators and achieving squared correlation that varied from 0.935 to 0.997. © 2022 IEEE.

Multi-hop Clustering Routing Protocol Based on Improved Coronavirus Herd Immunity Optimizer and Q-Learning in WSNs

Wireless sensor networks (WSNs) are composed of a large number of spatially distributed sensor nodes to monitor and transmit information from the environment. However, the batteries used by these sensor nodes have limited energy and can not be charged or replaced due to the harsh deployment environment. This energy limitation will seriously affect the lifetime of the network. Therefore, the purpose of this research is to reduce energy consumption and balance the load of sensor nodes by clustering routing protocols, so as to prolong the lifetime of the network. Firstly, the coronavirus herd immune optimizer is improved and used to optimize the network clustering. Secondly, the cluster heads are selected according to the energy and location factors in the clusters, and a reasonable cluster head replacement mechanism is designed to avoid the extra communication energy consumption caused by the frequent replacement of cluster heads. Finally, a multi-hop routing mechanism between the cluster heads and the base station is constructed by Q-learning. Simulation results show that the proposed work can improve the structure of clusters, enhance the load balance of nodes, reduce network energy consumption and prolong the network lifetime. The appearance time of the first energy-depleted node is delayed by 25.8%, 85.9% and 162.2% compared with IGWO, ACA-LEACH and DEAL in the monitoring area of 300m ×300m, respectively. In addition, the proposed protocol shows better adaptability in varying dynamic conditions. IEEE

A secure communication using multifactor authentication and key agreement techniques in internet of medical things for COVID-19 patients

Currently, Internet of Medical Things (IoMT) gained popularity because of an ongoing pandemic. A few developed countries plan to deploy the IoMT for improving the security and safety of frontline workers to decrease the mortality rates of COVID-19 patients. However, IoMT devices share the information through an open network which leads to increased vulnerability to various attacks. Hence, electronic health management systems remain many security challenges, like recording sensitive patient data, secure communication, transferring patient information to other doctors, providing the data for future medical diagnosis, collecting data from WBAN, etc. In addition, the sensor devices attached to the human body are resource-limited and have minimal power capacity. Hence, to protect the medical privacy of patients, confidentiality and reliability of the system, the register sensor, doctor and server need to authenticate each other. Therefore, rather than two factors, in this work, a multifactor authentication protocol has been proposed to provide more secure communication. The presented scheme uses biometric and fuzzy extractors for more security purposes. Furthermore, the scheme is proved using informal and formal security verification BAN logic, ProVerif and AVISPA tools. The ProVerif simulation result of the suggested scheme shows that the proposed protocol achieves session key secrecy and mutual authentication

Passive and Privacy-Preserving Human Localization: A Social Distancing Approach Using Commercial Millimeter-Wave Access Points

The pandemic outbreak has profoundly changed our life, especially our social habits and communication behaviors. While this dramatic shock has heavily impacted human interaction rules, novel localization techniques are emerging to help society in complying with new policies, such as social distancing. Wireless sensing and machine learning are well suited to alleviate virus propagation in a privacy-preserving manner. However, their wide deployment requires cost-effective installation and operational solutions.

Covid-19 Symptoms Detection System for Campus Access using WSN

The Covid-19 pandemic caused a lot of dramatic changes to the international education system. Regardless of the advantages of the online-based education system, the conventional on-campus education system has a lot of benefits that cannot be ignored, especially for lab-based courses. The main goal of this proposed paper is to either mitigate or eliminate the hazards of the Covid-19 virus's spreading between students and staff, so they can attend on-campus events and activities safely. In this paper, we proposed an Arduino-based solution to automate the detection of the Covid-19 symptoms of students and staff within a campus. The proposed solution consists of two separate sub-systems. The first sub-system is the Covid-19 Symptoms Detection System (Covid-19 SDS), which is responsible for detecting Covid-19 symptoms by measuring the temperature and heart rate. The second sub-system is the Campus Authenticator Sign-on System (CASS), which is responsible for checking whether the person is authorized to have access or not to the campus. We used Wireless Sensor Network (WSN) and Near Field Communication (NFC) for the data exchange between the nodes. The experiments showed an acceptable reading precision with an error margin of around ± 1.35. © 2022 IEEE.

Sensor Networks with Edge Intelligence for Reliable Air Quality Monitoring in the Covid-19 Era

Rapid advancements in affordable, miniaturised air pollution sensor technologies offer the potential to capture the high variability of personal exposure to air pollution during daily life with unprecedented spatial and temporal resolution. However, concerns remain regarding the suitability of these novel technologies for scientific and policy purposes due to their lack of reliability. The aim of this work is the development of three types of portable air quality devices that monitor particulate matter, differential pressure and outdoor emissions (CO, CO2, O3 and VOCs) with high reliability using low-cost sensors and communicating measurements to the cloud in real time. Reliability is strengthened in all three places: at the sensor level, the device/edge level and at the cloud, cashing data until network connectivity is restored. In order to evaluate their efficiency, two case studies were deployed: (a) in a modern industrial setting and (b) in an IT office space in Greece and the findings are reported.

IoT-Based Epidemic Monitoring via Improved Gated Recurrent Unit Model

During the Coronavirus Disease 2019 (COVID-19) pandemic, non-contact health monitoring and human activity detection by various sensors have attracted tremendous attention. Robot monitoring will result in minimizing the life threat to health providers during the COVID-19 pandemic period. How to improve the performance and generalization of the monitoring model is a critical but challenging task. This paper constructs an epidemic monitoring architecture based on multi-sensor information fusion and applies it in medical robots’ services, such as patient-care, disinfection, garbage disposal, etc. We propose a gated recurrent unit model based on a genetic algorithm (GA-GRU)to realize the effective feature selection and improve the effectiveness and accuracy of the localization, navigation, and activity monitoring for indoor wireless sensor networks (WSNs). By using two GRU layers in the GA-GRU, we improve the generalization capability in multiple WSNs. All these advantages of GA-GRU make it outperform other representative algorithms in a variety of evaluation metrics. The experiments on the WSNs verify that the proposed GA-GRU leads to successful runs and provides optimal performances. These results suggest the GA-GRU method may be preferable for epidemic monitoring in medicine and allied areas with particular relation to the control of the epidemic or pandemic such as COVID-19 pandemic.

Statistical Study of User Perception of Smart Homes during Vital Signal Monitoring with an Energy-Saving Algorithm.

Sensor networks are deployed in people's homes to make life easier and more comfortable and secure. They might represent an interesting approach for elderly care as well. This work highlights the benefits of a sensor network implemented in the homes of a group of users between 55 and 75 years old, which encompasses a simple home energy optimization algorithm based on user behavior. We analyze variables related to vital signs to establish users' comfort and tranquility thresholds. We statistically study the perception of security that users exhibit, differentiating between men and women, examining how it affects the person's development at home, as well as the reactivity of the sensor algorithm, to optimize its performance. The proposed algorithm is analyzed under certain performance metrics, showing an improvement of 15% over a sensor network under the same conditions. We look at and quantify the usefulness of accurate alerts on each sensor and how it reflects in the users' perceptions (for men and women separately). This study analyzes a simple, low-cost, and easy-to-implement home-based sensor network optimized with an adaptive energy optimization algorithm to improve the lives of older adults, which is capable of sending alerts of possible accidents or intruders with the highest efficiency.

Continuous EMF Monitoring as an Emergency and Disaster Detection Tool

A number of applications founded on electromagnetic field (EMF) has increased, since wireless personal communication devices are used by a large number of people. Simultaneously, the controversy about adverse health effects of EMF exposure is in a focus of the public debate. Thus, there are constant demands for comprehensive investigation and monitoring of existing exposure to EMF. In last decade, the wireless sensors networks emerged as an innovative solution for EMF monitoring in the environment. The newest established is Serbian EMF RATEL network that performs continuous wideband monitoring, counting contribution of all active EMF sources, in particular frequency range and in the vicinity of observed location. Besides the monitoring for the health protection purposes, this network can be used as an emergency and disaster detection tool, as demonstrated in a case study of COVID-19 presence in campus of the University of Novi Sad. In this paper, technical details of the Serbian EMF RATEL monitoring network are presented, along with monitoring results from two campus locations, which clearly indicate some significant changes in the EMF level. © 2022 IEEE.

CSI-based Joint Location and Activity Monitoring for COVID-19 Quarantine Environments

The recent SARS-COV-2 virus, also known as COVID-19, badly affected the world’s healthcare system due to limited medical resources for a large number of infected human beings. Quarantine helps in breaking the spread of the virus for such communicable diseases. This work proposes a non-wearable/contactless system for human location and activity recognition using ubiquitous wireless signals. The proposed method utilizes the Channel State Information (CSI) of the wireless signals recorded through a low-cost device for estimating the location and activity of the person under quarantine. We propose to utilize a Siamese architecture with combined one-dimensional Convolutional Neural Networks (1D-CNN) and Bi-directional long-short term memory (Bi-LSTM) networks. The proposed method provides high accuracy for the joint task and is validated on two real-world testbeds. First, using the designed low-cost CSI recording hardware, and second, on a public dataset for joint activity and location estimation. The HAR results outperform state-of-the-art machine and deep learning methods, and localization results are comparable with the existing methods. IEEE

Wireless sensor network enabled real-time remote intelligent health monitoring and management system using Internet of Things (IoT) sensing with cloud-based processing during covid-19 situation

Mankind was living quite an ordinary life when Covid 19 pandemic struck. The whole world was in turmoil and was busy trying to make the situation normal again. But it was impossible to regain the old scenario and people had to accept the new normal. The new normal demands people to follow different guidelines out of which maintaining a social distance of 6 feet was a prominent one. Even in this situation occurrence of any disease does not stop and there are always some patients visiting a doctor. Also, a doctor doesn't always have the luxury to visit every place to see patients, especially in rural areas where there is a transportation problem. So we have come up with a cloud-based system that will use the internet of things to diagnose a patient. This device will contain different sensors like temperature sensor, body oxygen level sensor, blood pressure sensor, heart rate sensor, and height and weight measuring gadgets to measure the body parameters of the patient and then store this information in the secured cloud which can then be accessed by the doctor to diagnose the patient. The sensors will be embedded in an Arduino and it will be connected to the cloud wirelessly with the help of a GSM module and node MCU. Also, a laptop will be present to connect the patient and the doctor in video mode for conversations. This system will also generate a prescription provided by the doctor which can be used anywhere. Thus, this device will not only promote social distancing but also it will prohibit the spread of diseases that are communicable. The doctors can work from the comfort of their home without touching a patient and also without traveling long distances to remote locations.

Temperature Monitoring Mechanism for Covid-19 Using WSN

Internet of Things (IoT) and wireless sensor networks (WSN) are utilized in a variety of control systems, such as environmental monitoring, home automation, and chemical/biological assault detection. As 2019 finished, coronavirus sickness known as COVID-19 began multiplying all around the world has caused a disturbing circumstance worldwide. This infection causes pneumonia with different manifestations, for example fatigue, dry hack and fever. Early discovery of coronavirus might secure many contaminated people. This paper presents a plan of coronavirus observing component [COC] dependent on Wireless sensor organization and Internet of Things to screen individuals during their quarantine. Mechanism relies upon checking the patient wellbeing information when there is a high fever or troublesome in relaxing. © 2022 IEEE.

Protocol Wireless Medical Sensor Networks in IoT for the Efficiency of Healthcare

In this article, a new medical communication scheme, protocol wireless medical sensor networks for the efficiency of healthcare (PWMSN4EoCH), shorten by (PEH), which uses hasty strategy and random network coding (RNC), is proposed. The new concept improves the performance of the healthcare network. It quickly analyzes the medical network description, focusing on some basic parameters for narrowband Internet of Things (NB-IoT) systems in wireless mesh networks (WMNs). This PEH effectively meets the requirements prescribed for wireless telemedicine applications in which medical sensors (MSs) share the downlink and uplink resources to its neighborhood, including wireless health hubs (WHHs) and wireless base stations (WBSs) for controlling the health of the human body. The PEH scheme substantially accelerates the implementation devices of telemedicine for patient satisfaction. In contrast, the state-of-the-art technique (SoAT) scheme, which is currently used, misses the entirety of the proposed principle. The proposed scheme is compared with the SoAT in terms of message size (bytes), round-trip time (RTT) (ms), overall network capacity (ONC) (bytes/s, and delivery delay (DD) in ms. Our investigation has proved that the RTT, ONC, and DD of the proposed PEH are much better than the SoAT schemes, achieving 64%, 66%, and 71%, respectively. The simulation studies clearly indicate that the PEH introduces more than 64% performance enhancement over the SoAT scheme.