Unattended Areas Research Articles

Automated Fire Sensing and Fire Extinguisher Module

The Hazard of fire and fire explosions poses significant risks to both property and human safety, necessitating immediate action to minimize damage. Conventional fire detection methods that rely on human observation may result in delays, particularly in unattended areas. To address these challenges, a specialized fire detection and extinguishing system is being developed with the capability to autonomously detect and extinguish fires. This system will also promptly notify individuals through messages and phone calls upon fire detection, improving response times and ensuring immediate awareness of fire incidents. Equipped with Arduino microcontrollers, the system integrates advanced sensors for precise detection of heat and smoke, allowing accurate pinpointing of fire locations. The use of Arduino technology provides adaptable functionality in various environments, ranging from residential to commercial settings. Effective communication of fire alerts is essential for promptly notifying firefighters and occupants, facilitating timely intervention and safe evacuations. The objective is to create an efficient system that harnesses modern technology to enhance fire safety measures, ultimately reducing fire related risks and effectively protecting lives.

Cybersecurity and Compliance in Healthcare: A Study on Key Management and Other Regulatory Requirements

Typically, WSNs are implemented in several applications with various topologies. Nodes use wireless mode of communication, and unattended areas are typically selected for WSN deployment. Attack risk is higher in WSNs. Furthermore, the WSN nodes have severe resource constraints. It is quite difficult to provide security in such a setting. Numerous protocols for key management chores and encryption strategies for maintaining security in the WSN environment have been documented in literature. The criticality of the data being transferred over the network determines how complex the assault will be. Applications that monitor agriculture, for example, are safe from attacks. However, applications related to the military and healthcare could attract attackers who are far more skilled. Applications that involve process control and habitat monitoring attract attackers with medium to low skill levels. There isn't a single protocol available right now that could be flexible enough to meet the different requirements of the apps, which typically have variable security requirements. For these different applications, there are differences in the key management task, which is strongly related to the security requirements. As a result, the precise kind and complexity of the key management technique must be chosen and built in accordance with the necessary level of security, the capabilities of the hardware devices available, and the network topology that is used.

The roles of LinkedIn-based skill endorsements and LinkedIn-based hiring recommendations on hiring preferences: evidence from Bangladeshi employers

Purpose LinkedIn is a popular and maybe the best job-oriented social networking site (SNS) around the world. Numerous individuals (mostly, jobseekers), as well as the companies (mostly, hiring organizations) have LinkedIn accounts. This study intends to reveal the roles of LinkedIn-based skill endorsements (LSEs) as well as LinkedIn-based hiring recommendations (LHRs) on the hiring preferences (HPs) of Bangladeshi employers. Design/methodology/approach The authors purposefully selected 388 valid respondents (employers) from different Bangladeshi job sectors. Based on their responses, the authors applied SPSS 25 for explanatory statistics and a “Covariance-based Structural Equation Modeling (CB-SEM)” (with AMOS 25) to measure the hypothesized associations. Findings After appropriate analysis, the authors revealed a momentous positive role of LHR on HP, whereas LSE was identified to have an inconsiderable role on HP. Originality/value In this empirical study, the authors tried to highlight the relatively unattended area of Bangladeshi employers' HPs that can be affected by LSEs and LHRs. The authors believe that this study will encourage the researchers to unveil the impacts of LinkedIn-based profile characteristics on job candidates' employability. This empirical study can also aid the employers and policymakers to look for a new avenue for hiring competent talents.

Self‐Feedback Sensing Properties of Self‐Powered Multi‐Parameter Meteorological Sensor with Omnidirectional Array Structure

AbstractFrequent meteorological disasters pose more and more extensive threats to human life and property. However, the traditional meteorological sensors based on external power supply, for natural disaster monitoring and early warning, are severely limited in remote and unattended areas. Herein, a self‐powered multi‐parameter meteorological sensor (SMPM‐sensor) for various weather monitoring is proposed, which can monitor the weather through the feedback of electrical energy output while collecting natural energy. The designed 360° omnidirectional array structure facilitates efficient collection of all‐weather solar energy and rain energy in different directions and monitoring the light intensity without the influence of solar azimuth. Based on the four‐segmented disc‐shaped structure and the introduced Ecoflex transition layer, the frequency and amplitude of the wind electrical output can be effectively improved. Owing to structure and material optimizations, a good linearity of 0.9746 between the current and the solar illuminance is exhibited, a wide monitoring range of rainfall intensity changes from 0.1 to 11.9 mm min−1, and the linearity between wind speed and voltage frequency can be as high as 0.9986. Therefore, based on excellent sensing performances, good stability, and real‐time characteristic, the fabricated SMPM sensor provides a potential technological strategy for the field of meteorological disaster monitoring.

Clone Node Detection in Heterogeneous WSN with Low Memory Overhead

In unattended areas, the wireless sensor network is deployed and the nodes are always open to attacks. An adversary can capture a node and can deploy many nodes, which are clone of the captured, in the network called clone node or replicated node by using the credential information retrieved from captured node. These clone nodes can damage the network directly or indirectly. This attack is called node replication o attack or clone node attack. In this area, so many works are introduced and all of these methods use a random key or code, or location information, to detect clone nodes. This paper presents a method that does not use any of this information. The simulation results show that it performs better than previous methods.

The Detection Scheme Against Selective Forwarding of Smart Malicious Nodes With Reinforcement Learning in Wireless Sensor Networks

Wireless sensor networks (WSNs) are extremely vulnerable to different attacks because of open communication, and distribution in unattended areas. The selective forwarding attack is one of the most difficult inside attacks to be detected for two reasons. The node in a harsh environment has to drop some data packets, and the smart malicious node frequently eludes detection. In this paper, we model a selective forwarding attack of smart malicious nodes with a reinforcement learning (RL) algorithm. To effectively detect the selective forwarding attack under a harsh environment, we design the double-threshold density peaks clustering (DT-DPC) algorithm. Abnormal nodes are identified as malicious and isolated owing to continuous abnormalities. Suspicious nodes are determined by the neighbor voting method because malicious behaviors show up separately and a harsh environment universally disturbs agglomerate nodes. Even if smart malicious nodes elude the detection by an RL algorithm, DT-DPC improves the network throughput. The simulation results show that DT-DPC has a low false detection rate (FDR) of around 1% and a missed detection rate (MDR) of around 10%. The network throughput increases by about 4% under a harsh environment.

Duckweed as a future food: Evidence from metabolite profile, nutritional and microbial analyses

Duckweed species are nutritionally meaningless plant which grow wildly in unattended areas. Therefore, understanding the metabolites content in duckweed species is essential for designing a future food products. Here, we report an untargeted Gas Chromatography-Mass Spectrometry (GC-MS)-based metabolomics approach for comprehensively discriminating between Lemna minor and Wolffia globosa of duckweeds species using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). Ten differential metabolites levels were tentatively identified between L. minor and W. globosa. Relative to W. globosa, L. minor appeared to enrich with 5-Hydroxyl-L-tryptophan, Tocopheryl acetate, Naringenin, α-linolenic acid and glutamic acid. Furthermore, the nutritional and microbial analyses of ice cream formulated with dried L. minor were investigated. The nutritional analysis results show that relative to control, the ice cream with 2% dried L. minor had significantly increased protein, fiber and ash content. In addition, total plate count (TPC) for microbial analysis of duckweed ice cream was performed. The result suggested that the small amount of bacteria (3.82 cfu/g) was traced in formulated ice cream with 2% dried L. minor. Overall, the metabolites profile, nutritional and microbial analyses of food used L. minor plant indicate that duckweed is a good candidate for future food.

Identifying malicious nodes in wireless sensor networks based on correlation detection

The wireless sensor network (WSN) is a multi-hop wireless network that comprises multiple sensor nodes arranged in a self-organized manner. It is usually deployed in unattended areas where sensor nodes can easily be infiltrated by attackers who can affect the detection results by injecting false data. This paper proposes a malicious-node identification method based on correlation theory that prevents fault data injection attacks. First, anomalies among similar types of sensor data are detected based on time correlation. Second, malicious nodes are identified based on spatial correlation. Third, the identified malicious nodes are verified based on event correlation. The experimental results and their comparison with those of existing methods show that the proposed scheme has better recall with lower false-positive and false-negative rates than those of the traditional fuzzy reputation model and weighted-trust-based methods.

Automatic detection of urban vacant land: An open-source approach for sustainable cities

With urban vacant land including brownfield land attracting increasing attention; spatial information on vacant land is crucial for city planners and decision-makers. Yet vacant land mapping faces the challenges of confusing terminology, reliance on inefficient registration systems, and the cost of data collection. Automatic site detection has been attempted using classification of remote sensing images. However, because the morphology of vacant land can cover such diverse aspects as derelict structures, bare soil, and vegetation or a mix thereof, even when commercial high-resolution images are used, it remains difficult to achieve this goal.This study starts by defining an urban vacant land typology suitable for automatic site detection. It covers four categories: transportation-associated land; natural sites with unfavorable conditions such as wetlands, steep slopes, or riverbanks; unattended areas or reserve parcels as “leftover spaces” within the urban fabric; and brownfield sites previously used but now abandoned.The study goes on to describe the rule-based data fusion method applied for site detection. The fusion procedure combines remote sensing images, GIS layers and citizen science data. For each type of vacant land, a separate data processing flow was developed. The method was tested in 63 urban and rural districts in Germany, identifying a large number of vacant sites.The study used an open-source approach. Open-source spatial data was collected for analysis, aerial photos provided by open-data projects were used for quality checks, and free software was used for data processing. This means that, information on vacant land is retrievable by local administrations irrespective of their financial circumstances. Depending on local urban development strategies, the detected sites can be used to support different sustainability goals.

Evaluating DoS jamming attack on reactive routing protocol in wireless sensor networks

Wireless sensor networks (WSNs) over the years have emerged as the enabling underlining infrastructure for new wireless technology trends such as Internet-of Things (IoT) and Fog Computing. Its application has spread across diverse fields such as agriculture, military, healthcare and home automation. Despite its promising attributes, it is characterized by its extremely limited resources such as battery energy and memory. Additionally, its deployment in hostile and unattended areas make it vulnerable to security attacks. One of such attacks is the denial of service (DoS) jamming attack that is perpetrated by malicious nodes emitting radio frequency signals to disrupt and interfere with the normal functions of the sensor nodes in the network. This eventually causes a denial of service in the network. Different routing protocols have been proposed over the years to guarantee reliable communication and maintain the network lifetime and functionality for a reasonable duration, notwithstanding DoS jamming attack. Therefore, in this work, we evaluate the effect of a constant jamming DoS attack on two key reactive routing protocols in WSN, ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR). Metrics such as packet sending ratio (PSR), packet loss (PL) and transmitted packets are used to measure the impact of constant jamming DoS attack in the network. Simulation results using network simulation 2 (NS2) and trace graph show that, irrespective of the adopted reactive routing protocol, the impact of the jamming attack is the same.

An efficient mutual authentication and key agreement scheme without password for wireless sensor networks

Wireless sensor networks (WSNs) are usually deployed in hostile or unattended areas, and users need to obtain real-time data from WSNs. The data collected by sensor nodes are usually relatively private and sensitive, so users must pass the identity authentication before obtaining the information perceived by sensor nodes. In order to enhance the security of wireless sensor networks and prevent illegal users from accessing sensor nodes, this paper designs an efficient and secure identity authentication protocol for wireless sensor networks. The proposed protocol makes full use of the advantages of lightweight cryptographic primitives such as physical unclonable function, one-way hash function and bitwise exclusive operation. Moreover, users only need to use biometrics (such as fingerprints, iris) to access the remote systems and do not need to rely on any password, which avoids the trouble of memorizing and losing password. To prove the scheme’s security, the well-known formal security proof with random oracle model and traditional heuristic discussion are given. Additionally, the performance comparison results show that our scheme has less communication overhead and computation complexity, and is effective for the resource constrained sensor devices in WSNs.

Detection of Helicobacter pylori in Patients with Oral Squamous Cell Carcinoma and Oral Potentially Malignant Disorders at a Tertiary Care Centre in Gujarat: A Case-Control Study

Introduction: Oral squamous cell carcinoma (OSCC) contribute to 90% of cancer cases in head and neck region and entails remarkable morbidity and mortality inspite of immense research and advances. Amongst other causes, infection with Helicobacter pylori is an emerging cause of OSCC. There is still perplexity in the exact etiopathogenesis of H. pylori related oral cancer. Aim: In order to explore this much unattended area, present study was aimed to find out the association between H. pylori in premalignant disorders and OSCC. Materials and methods: A Prospective case-control Pilot study of 35 patients (11 confirmed cases of Oral squamous cell carcinoma and 24 with oral potentially malignant disorders along with 15 age and sex matched healthy control) from June 2018 - September 2018 was conducted in the Department of Microbiology, Smt. B. K. Shah Medical Institute, Piparia, Gujarat. H. pylori was detected by methods like Rapid urease test, Gram’s staining and Serology. Results: H. pylori was detected in five cases with OSCC with male predominance and mean age 45.6 yrs. All the five positive patients were severely addicted to tobacco and betel quid since decades. Tobacco was found to be the major risk of OSCC with the OD of 16.19, followed by betel quid (OD-4.56) and H. pylori infection (OD-0.83). Conclusion: The results of this pilot study do not establish a definite causal relationship between H. pylori and OSCC due to the low sample size. Study definitely offers an avenue for further work on larger populations to confirm this possible association.

GAPSO-H: A hybrid approach towards optimizing the cluster based routing in wireless sensor network

Abstract Wireless Sensor Networks (WSNs) have left an indelible mark on the lives of all by aiding in various sectors such as agriculture, education, manufacturing, monitoring of the environment, etc. Nevertheless, because of the wireless existence, the sensor node batteries cannot be replaced when deployed in a remote or unattended area. Several researches are therefore documented to extend the node's survival time. While cluster-based routing has contributed significantly to address this issue, there is still room for improvement in the choice of the cluster head (CH) by integrating critical parameters. Furthermore, primarily the focus had been on either the selection of CH or the data transmission among the nodes. The meta-heuristic methods are the promising approach to acquire the optimal network performance. In this paper, the ‘CH selection’ and ‘sink mobility-based data transmission’, both are optimized through a hybrid approach that consider the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm respectively for each task. The robust behavior of GA helps in the optimized the CH selection, whereas, PSO helps in finding the optimized route for sink mobility. It is observed through the simulation analysis and results statistics that the proposed GAPSO-H (GA and PSO based hybrid) method outperform the state-of-art algorithms at various levels of performance metrics.

AirScope: Mobile Robots-Assisted Cooperative Indoor Air Quality Sensing by Distributed Deep Reinforcement Learning

Indoor air pollution has become a growing health risk, but it is challenging to provide low-cost air quality monitoring for the indoor environment. In this article, we present “AirScope,” a mobile sensing system that employs cooperative robots to monitor the indoor air quality. Since the wireless coverage can be incomplete in some indoor areas, AirScope allows the robots to defer uploading the data to the central server by utilizing their own data buffers. In order to guarantee the timeliness of the data in the server, AirScope aims to minimize the average data latency by properly planning the routes of the robots. Such a route planning strategy has to be implemented in a distributed way since the robots that are out of wireless coverage can only make plans on their own. In addition, the cooperation of the robots is also necessary because the aggregation of the robots in a small area increases the average data latency of the other unattended areas. To solve this distributed and cooperative routing planning problem, we propose a solution based on distributed deep $Q$ -learning (DDQL). We evaluate the system performance by simulations and real-world experiments. The results show that AirScope is effective to reduce data latency, where the proposed DDQL is 8% better than the greedy algorithm and 24% better than the random strategy.

Particle swarm optimization based energy efficient clustering and sink mobility in heterogeneous wireless sensor network

In a WSN, sensor node plays a significant role. Working of sensor node depends upon its battery's life. Replacements of batteries are found infeasible once they are deployed in a remote or unattended area. Plethora of research had been conducted to address this challenge, but they suffer one or the other way. In this paper, a particle swarm optimization (PSO) algorithm integrated with an energy efficient clustering and sink mobility ((PSO-ECSM) is proposed to deal with both cluster head selection problem and sink mobility problem. Extensive computer simulations are conducted to determine the performance of the PSO-ECSM. Five factors such as residual energy, distance, node degree, average energy and energy consumption rate (ECR) are considered for CH selection. An optimum value of these factors is determined through PSO-ECSM algorithm. Further, PSO-ECSM addresses the concern of relaying the data traffic in a multi-hop network by introducing sink mobility. PSO-ECSM's performances are tested against the state-of-the-art algorithms considering five performance metrics (stability period, network, longevity, number of dead nodes against rounds, throughput and network's remaining energy). Statistical tests are conducted to determine the significance of the performance. Simulation results show that the PSO-ECSM improves stability period, half node dead, network lifetime and throughput vis-à-vis ICRPSO by 24.8%, 31.7%, 9.8 %, and 12.2%, respectively.

Learning L2 idioms in a CALL environment: the role of practice intensity, modality, and idiom properties

Idiomatic expressions like hit the road or turn the tables are known to be problematic for L2 learners, but research indicates that learning L2 idiomatic language is important. Relatively few studies, most of them focusing on English idioms, have investigated how L2 idioms are actually acquired and how this process is affected by important idiom properties like transparency (the degree to which the figurative meaning of an idiom can be inferred from its literal analysis) and cross-language overlap (the degree to which L2 idioms correspond to L1 idioms). The present study employed a specially designed CALL system to investigate the effects of intensity of practice and the reading modality on learning Dutch L2 idioms, as well as the impact of idiom transparency and cross-language overlap. The results show that CALL practice with a focus on meaning and form is effective for learning L2 idioms and that the degree of practice needed depends on the properties of the idioms. L2 learners can achieve or even exceed native-like performance. Practicing reading idioms aloud does not lead to significantly higher performance than reading idioms silently.These findings have theoretical implications as they show that differences between native speakers and L2 learners are due to differences in exposure, rather than to different underlying acquisition mechanisms. For teaching practice, this study indicates that a properly designed CALL system is an effective and an ecologically sound environment for learning L2 idioms, a generally unattended area in L2 classes, and that teaching priorities should be based on degree of transparency and cross-language overlap of L2 idioms.

Appropriate Cluster Head Determination Strategy for extending the Wireless Sensor Network’s (WSN’s) Lifetime

WSN is made with immense number of sensor’s which are deployed densely over an unattended area is responsible for taking environmental measurements, process the data and finally transmit the sensed data over a wireless channel to the sink that makes decisions based on these sensor’s readings. It is well known that the energy consumed for transferring one bit of data from individual sensor to sink is equivalent to a large number of arithmetic operations to be performed in a sensor processor. Thus, node clustering have been applied to hierarchical sensor networks with heterogeneity to augment the network existence whereas diminishing the necessary energy consumption. For that reason, Seven Level Balanced Energy Efficient Network Integrated Super Heterogeneous (SL-BEENISH) algorithm was designed and implemented. Simulation outcomes clearly demonstrated that SL-BEENISH attain better in comparison with TDEEC, BEENISH and IBEENISH with more stability period and network duration.

A Trust-Based Active Detection for Cyber-Physical Security in Industrial Environments

For the cyber-physical systems (CPS) in the smart industrial environments, a larger amount of smart sensor nodes, processors and actuators are deployed to sense information from physical world, which is vulnerable to be attacked because the nodes are deployed in unattended areas. Security is a pivotal issue for CPS, therefore, a trust-based active detection (TBAD) scheme is proposed for improving the reliability of collecting data packets and reducing the data redundancy. In TBAD scheme, the trust of the nodes is evaluated by the neighboring nodes and the evaluation results are added into the header of data packets. Thus, according to the reliability of data packets collected by the unmanned aerial vehicle (UAV), the trust of sensor nodes is evaluated by the UAV. In addition, the evaluation trust of nodes stored in the header of data packets will be detected when the UAV suspects the stored trust of sensor nodes. Then, the trust of corresponding sensor nodes is adjusted according to the detection results. The sensor nodes with higher trust are selected to form movement trajectory. A series of simulation experiments are conducted to evaluate the performance of the scheme. The results illustrate that the proposed scheme can greatly improve the efficiency and security of data routing in CPS.

Ant Colony Based Data Aggregation in Real Time Wireless Sensor Networks

Sensing the natural phenomenon, sensing the eventual activities, sensing the abrupt change in human developed systems has a wide number of applications. To sense, one needs the tiny sensor devices called sensor nodes. To collect the data from these devices, one has to arrange these tiny devices into a intelligently managed network, called as sensor networks. Due to mostly unattended application areas and zones, these devices are supposed to be wireless which adds one more dimension to our discussion, known as wireless sensor networks. So basically these are an intelligent arrangement of small, tiny sensing devices with wide array of attributes, namely, sensing ability, low communication range mechanism, limited power sources, small processing ability, small memory. With these limitations, even handling a small data generated/sensed in the network becomes crucial to handle. Therefore there has to be an effective data aggregation technique. The prime goal of any data aggregation algorithms is to collect/retrieve/gather the data in an efficient possible way so that the effort to communicate the data over the network can be reduced. Less work from the network means less energy dissipated, thus lifetime can be enhanced. An effective data aggregation technique reduces the latency also. Small network latency is crucial for time critical applications in real time wireless sensor networks. Ant colony based optimization provides us such a possibility to find an optimal route for efficient data aggregation with significantly reduced network latency. In our work, Ant colony based Data Aggregation in Real Time wireless sensor networks (ACDAR), Ants use their pheromones to establish a new path or follow already established path by other ants. Similarly data packets in wireless sensor networks can take advantage of freshly established paths by Route Ants. As the results received, it is verified that number of packets successfully delivered to the sink have increased and the miss ratio, cumulative packets consumption have improved, network latency has decreased.

SDSRS: A Novel White-Box Cryptography Scheme for Securing Embedded Devices in IIoT

In this article, with the rapid development of industrial Internet of Things, a large number of embedded devices, such as sensors and tag readers, have been widely deployed for gathering and sending data. These devices are commonly unreliable and vulnerable to many threats, because they are located in unattended areas which are vulnerable to device capture attacks. Such environments can be regarded as white-box attack contexts, in which the adversary has total visibility and full control of the implementations. White-box cryptography (WBC) aims to protect implementations of symmetric encryption algorithms in white-box attack contexts. Unfortunately, existing WBC schemes are vulnerable to various attacks, and most of them are insufficiently secure in strict white-box attack contexts. Based on the investigation of existing designs and the corresponding cryptanalysis, we propose a novel design approach for securing WBC schemes, which is named state-dependent selectable random substitutions (SDSRS). It uses SDSRSs to defeat various related white-box cryptanalytic approaches. With special considerations for IIoT systems, such as high performance for supporting real-time applications and small block size for fitting industrial protocols, a concrete WBC scheme designed with the proposed approach has been provided. Our theoretical analysis shows that the proposed scheme is secure. Additionally, experimental results indicate that the scheme performs well in practice, and it is significantly efficient in time and energy consumptions compared with existing secure white-box cryptographic schemes.