Pub Date : 2022-12-19DOI: 10.2174/2210327913666221219151456
Bharti Rana, Simran, Yashwant Singh
Internet-of-things (IoT) has been developed for use in a variety of fields in recent years. The IoT network is embedded with numerous sensors that can sense data directly from the environment. The network's sensing components function as sources, observing environmental occurrences and sending important data to the appropriate data centers. When the sensors detect the stated development, they send the data to a central station. On the other hand, sensors have limited processing, energy, transmission, and memory capacities, which might have a detrimental influence on the system. We have suggested an energy-efficient framework based on Swarm Intelligence in IoT. The idea behind using Swarm Intelligence is the probabilistic-based global search phenomena that suit well for IoT networks because of the randomization of nodes. Our framework considers the prominent metaheuristic concepts responsible for the overall performance of the IoT network. Our current research is based on lowering sensor energy consumption in IoT networks, resulting in a longer network lifetime. This study selects the most appropriate potential node in the IoT network to make it energy-efficient. It suggests a technique combining PSO's exploitation capabilities with the GWO's exploration capabilities to avoid local minima problems and convergence issues. The proposed method PSGWO is compared with the traditional PSO, GWO, Hybrid WSO-SA, and HABC-MBOA algorithms based on several performance metrics in our research study. The results of our tests reveal that this hybrid strategy beats all other ways tested, and the energy consumption rate of the proposed framework is decreased by 23.8% in the case of PSO, 20.2% in the case of GWO, 31.5% in the case of hybrid WSO-SA, and 29.6% in the case of HABC-MBOA, respectively. In this study, several performance parameters, including energy consumption, network lifetime, live nodes, temperature, and throughput, are taken into account to choose the best potential node for the IoT network. Using various simulations, the performance of the proposed algorithm was evaluated and compared to the metaheuristic techniques. Moreover, PSGWO is found to be improved, and the energy consumption rate is decreased.
{"title":"PSGWO: An Energy-efficient Framework in IoT based on Swarm Intelligence","authors":"Bharti Rana, Simran, Yashwant Singh","doi":"10.2174/2210327913666221219151456","DOIUrl":"https://doi.org/10.2174/2210327913666221219151456","url":null,"abstract":"\u0000\u0000Internet-of-things (IoT) has been developed for use in a variety of fields in recent years. The IoT network is embedded with numerous sensors that can sense data directly from the environment. The network's sensing components function as sources, observing environmental occurrences and sending important data to the appropriate data centers. When the sensors detect the stated development, they send the data to a central station. On the other hand, sensors have limited processing, energy, transmission, and memory capacities, which might have a detrimental influence on the system.\u0000\u0000\u0000\u0000We have suggested an energy-efficient framework based on Swarm Intelligence in IoT. The idea behind using Swarm Intelligence is the probabilistic-based global search phenomena that suit well for IoT networks because of the randomization of nodes. Our framework considers the prominent metaheuristic concepts responsible for the overall performance of the IoT network. Our current research is based on lowering sensor energy consumption in IoT networks, resulting in a longer network lifetime.\u0000\u0000\u0000\u0000This study selects the most appropriate potential node in the IoT network to make it energy-efficient. It suggests a technique combining PSO's exploitation capabilities with the GWO's exploration capabilities to avoid local minima problems and convergence issues. The proposed method PSGWO is compared with the traditional PSO, GWO, Hybrid WSO-SA, and HABC-MBOA algorithms based on several performance metrics in our research study.\u0000\u0000\u0000\u0000The results of our tests reveal that this hybrid strategy beats all other ways tested, and the energy consumption rate of the proposed framework is decreased by 23.8% in the case of PSO, 20.2% in the case of GWO, 31.5% in the case of hybrid WSO-SA, and 29.6% in the case of HABC-MBOA, respectively.\u0000\u0000\u0000\u0000In this study, several performance parameters, including energy consumption, network lifetime, live nodes, temperature, and throughput, are taken into account to choose the best potential node for the IoT network. Using various simulations, the performance of the proposed algorithm was evaluated and compared to the metaheuristic techniques. Moreover, PSGWO is found to be improved, and the energy consumption rate is decreased.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"96 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72537768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.2174/2210327913666221216160435
Manasha Saqib, Ayaz Hassan Moon
With the advancements of ubiquitous computing, wireless sensor networks, and machine-to-machine communication, the Internet of Things (IoT) has become a constantly growing concept. The IoT is a new paradigm that interconnects all smart physical devices to provide smart services to users. It effectively delivers user-required services by utilising internet connectivity, sensors, and various technologies and protocols for the analysis and collection of data. IoT is predicted to permeate practically every facet of daily life, from smart cities to health care, smart agriculture, logistics and retail, and even smart living and smart ecosystems. Since IoT systems are comprised of heterogeneous hardware and networking technologies, integrating them to the software/application level to extract information from massive amounts of data is a difficult task. In this survey, the definitions, elements, working, architecture, fundamental technologies, key challenges, and potential applications of IoT are systematically reviewed. Initially, the various definitions and elements of IoT are introduced, followed by an explanation of how an IoT works. Additionally, an outline of IoT in the context of the architecture is presented. The primary enabling technologies that will drive IoT research in the near future are examined in this paper. Furthermore, the major key challenges that the research community must address, as well as potential solutions, are investigated. Finally, the paper concludes with some potential IoT applications to demonstrate the concept's feasibility in real-world scenarios. The goal of this survey is to assist future researchers in identifying IoT-specific challenges and selecting appropriate technology based on application requirements.
{"title":"A Concise Review on Internet of Things: Architecture, Enabling Technologies, Challenges, and Applications","authors":"Manasha Saqib, Ayaz Hassan Moon","doi":"10.2174/2210327913666221216160435","DOIUrl":"https://doi.org/10.2174/2210327913666221216160435","url":null,"abstract":"\u0000\u0000With the advancements of ubiquitous computing, wireless sensor networks, and machine-to-machine communication, the Internet of Things (IoT) has become a constantly growing concept. The IoT is a new paradigm that interconnects all smart physical devices to provide smart services to users. It effectively delivers user-required services by utilising internet connectivity, sensors, and various technologies and protocols for the analysis and collection of data. IoT is predicted to permeate practically every facet of daily life, from smart cities to health care, smart agriculture, logistics and retail, and even smart living and smart ecosystems. Since IoT systems are comprised of heterogeneous hardware and networking technologies, integrating them to the software/application level to extract information from massive amounts of data is a difficult task.\u0000\u0000\u0000\u0000In this survey, the definitions, elements, working, architecture, fundamental technologies, key challenges, and potential applications of IoT are systematically reviewed. Initially, the various definitions and elements of IoT are introduced, followed by an explanation of how an IoT works. Additionally, an outline of IoT in the context of the architecture is presented. The primary enabling technologies that will drive IoT research in the near future are examined in this paper. Furthermore, the major key challenges that the research community must address, as well as potential solutions, are investigated. Finally, the paper concludes with some potential IoT applications to demonstrate the concept's feasibility in real-world scenarios.\u0000\u0000\u0000\u0000The goal of this survey is to assist future researchers in identifying IoT-specific challenges and selecting appropriate technology based on application requirements.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87063879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-16DOI: 10.2174/2210327913666221216152446
Akshatha P S, S. D. Dilip Kumar, V. K. R.
MQTT is an open standard protocol promoted by OASIS and ISO, which allows devices to transport messages using the publish/subscribe model. MQTT is more prevalent than other application layer protocols of the Internet of Things (IoT) due to its lightweight nature, low bandwidth usage, application demand, etc. It is easy and straightforward to use the protocol, making it optimal for communication in resource-constrained situations such as machine-to-machine (M2M), Wireless Sensor Networks (WSNs), and in IoT circumstances in which the actuator and sensor nodes connect with applications through the MQTT message broker. A few review papers on MQTT protocol are available in the literature that focuses on broker details, comparison of IoT protocols, and limitations. In this paper, an overview of MQTT, existing survey work on MQTT, publication statistics, MQTT protocol performance evaluation, applications of MQTT, security issues of MQTT, comparison between MQTT and MQTT-SN, tools available or MQTT and available MQTT brokers to provide service are discussed. Graphs and comparison tables are presented to show the outcomes of the application and performance evaluation. The scope of this review paper is also to contribute a novel taxonomy of application layer protocols, their merits and demerits, correlation of MQTT with other application layer protocols, existing works of MQTT protocol to improve reliability, efficiency, security, issues, and challenges in MQTT, as well as future directions of MQTT.
{"title":"MQTT Implementations, Open Issues, and Challenges: A Detailed Comparison and Survey","authors":"Akshatha P S, S. D. Dilip Kumar, V. K. R.","doi":"10.2174/2210327913666221216152446","DOIUrl":"https://doi.org/10.2174/2210327913666221216152446","url":null,"abstract":"\u0000\u0000MQTT is an open standard protocol promoted by OASIS and ISO, which allows devices to transport messages using the publish/subscribe model. MQTT is more prevalent than other application layer protocols of the Internet of Things (IoT) due to its lightweight nature, low bandwidth usage, application demand, etc. It is easy and straightforward to use the protocol, making it optimal for communication in resource-constrained situations such as machine-to-machine (M2M), Wireless Sensor Networks (WSNs), and in IoT circumstances in which the actuator and sensor nodes connect with applications through the MQTT message broker. A few review papers on MQTT protocol are available in the literature that focuses on broker details, comparison of IoT protocols, and limitations.\u0000In this paper, an overview of MQTT, existing survey work on MQTT, publication statistics, MQTT protocol performance evaluation, applications of MQTT, security issues of MQTT, comparison between MQTT and MQTT-SN, tools available or MQTT and available MQTT brokers to provide service are discussed. Graphs and comparison tables are presented to show the outcomes of the application and performance evaluation. The scope of this review paper is also to contribute a novel taxonomy of application layer protocols, their merits and demerits, correlation of MQTT with other application layer protocols, existing works of MQTT protocol to improve reliability, efficiency, security, issues, and challenges in MQTT, as well as future directions of MQTT.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88943246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-13DOI: 10.2174/2210327913666221213092904
Sunayana Jadhav, R. Daruwala
Event detection and monitoring applications involve highly populated sensor nodes in Wireless Sensor Networks (WSNs). Dense deployment of nodes leads to correlated sensor observations in the spatial and temporal domain. Most of the previous works focused on constant sensing radii for spatially correlated sensor observations. However, in real time scenario, the sensor nodes may have variable sensing coverage areas, which comprise a Heterogeneous WSN. Spatial correlation model discussed in prior literature focused on Homogeneous sensing of sensor nodes. But, real time scenario the condition changes due to interferences obstructing sensing areas. Also, different manufacturers may provide different specifications for sensing areas, thus resulting into Heterogeneous sensing. To address this issue, we present an Enhanced Weighted Spatial Correlation Model for Heterogeneous sensor nodes in WSNs. The mathematical framework considers the spatial coordinates of sensor nodes, the distances between the sensor nodes, and their sensing coverage. Furthermore, the correlation coefficient is calculated in terms of overlapping areas for randomly deployed nodes. Performance of the correlation model is evaluated and analyzed in terms of event distortion function. In addition to this, a macro and micro-zone concept is introduced, wherein sensor information is weighted for better event estimation at the sink node. Moreover, dynamic weighing of nodes like Inverse, Shepard’s and Gaussian distance weighing algorithms are simulated and analyzed for minimal event distortion. Over and above, the system performance is evaluated for different approaches considering reporting nodes with and without clustering of sensor nodes for macro and micro-zone concept. Simulation results for the Enhanced Weighted Spatial Correlation Model developed are obtained using MATLAB software. In order to evaluate the performance of the enhanced correlation model considering Macro and Micro-zone concept, simulations are carried out inMATLAB. Simulations are performed for trials and averaging of the values are finally used for analysis of results. The comparative study shows an improved system performance in terms of minimal distortion obtained for non-clustered nodes; thereby reducing the computational complexity of cluster formation. Furthermore, the dynamic weighing algorithms outperform the existing fixed weighing algorithms for the correlation model with the lowest distortion function. Moreover, in the above algorithms, the event distortion gradually decreases and later becomes constant with the increase in the number of representative nodes. Hence, it illustrates that minimal distortion can be achieved by activating lesser number of representative nodes, thereby preserving the energy of other sensor nodes and increasing the lifetime of WSNs.
{"title":"An Enhanced Spatial Correlation Framework for Heterogenous Wireless Sensor Networks","authors":"Sunayana Jadhav, R. Daruwala","doi":"10.2174/2210327913666221213092904","DOIUrl":"https://doi.org/10.2174/2210327913666221213092904","url":null,"abstract":"\u0000\u0000Event detection and monitoring applications involve highly populated sensor nodes in Wireless Sensor Networks (WSNs). Dense deployment of nodes leads to correlated sensor observations in the spatial and temporal domain. Most of the previous works focused on constant sensing radii for spatially correlated sensor observations. However, in real time scenario, the sensor nodes may have variable sensing coverage areas, which comprise a Heterogeneous WSN.\u0000\u0000\u0000\u0000Spatial correlation model discussed in prior literature focused on Homogeneous sensing of sensor nodes. But, real time scenario the condition changes due to interferences obstructing sensing areas. Also, different manufacturers may provide different specifications for sensing areas, thus resulting into Heterogeneous sensing.\u0000\u0000\u0000\u0000To address this issue, we present an Enhanced Weighted Spatial Correlation Model for Heterogeneous sensor nodes in WSNs.\u0000\u0000\u0000\u0000The mathematical framework considers the spatial coordinates of sensor nodes, the distances between the sensor nodes, and their sensing coverage. Furthermore, the correlation coefficient is calculated in terms of overlapping areas for randomly deployed nodes. Performance of the correlation model is evaluated and analyzed in terms of event distortion function. In addition to this, a macro and micro-zone concept is introduced, wherein sensor information is weighted for better event estimation at the sink node. Moreover, dynamic weighing of nodes like Inverse, Shepard’s and Gaussian distance weighing algorithms are simulated and analyzed for minimal event distortion. Over and above, the system performance is evaluated for different approaches considering reporting nodes with and without clustering of sensor nodes for macro and micro-zone concept. \u0000Simulation results for the Enhanced Weighted Spatial Correlation Model developed are obtained using MATLAB software.\u0000\u0000\u0000\u0000In order to evaluate the performance of the enhanced correlation model considering Macro and Micro-zone concept,\u0000simulations are carried out inMATLAB. Simulations are performed for trials and averaging of the values are finally\u0000used for analysis of results.\u0000\u0000\u0000\u0000The comparative study shows an improved system performance in terms of minimal distortion obtained for non-clustered nodes; thereby reducing the computational complexity of cluster formation. Furthermore, the dynamic weighing algorithms outperform the existing fixed weighing algorithms for the correlation model with the lowest distortion function.\u0000\u0000\u0000\u0000Moreover, in the above algorithms, the event distortion gradually decreases and later becomes constant with the increase in the number of representative nodes. Hence, it illustrates that minimal distortion can be achieved by activating lesser number of representative nodes, thereby preserving the energy of other sensor nodes and increasing the lifetime of WSNs.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90445030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-09DOI: 10.2174/2210327913666221209143445
Mersha Nigus, H.L Shashirekh
This research uses the Ethiopian HICE survey dataset. Predicting food insecurity is critical in presenting the household's situation to the appropriate agencies that take preventative and intervention measures. This research paper's primary goal is to predict households' food security status using ensemble learning models. We use five base classifiers and a voting strategy for ensemble classification to enhance the performance of different base classifiers. Backward feature elimination and hard and soft voting-based ensemble learning are used to evaluate household food security. The training set for the basic classifiers is composed of the features that have been selected. Each ML classifier makes its prediction about the class label with the help of an ensemble learning method. For making decisions, hard voting uses a simple majority, whereas soft vote employs a weighted probability. To determine the final prediction. Ethiopian household income, consumption, and expenditure dataset are used to test the proposed ensemble learning approach. The backward feature elimination approach improved the model's performance by removing irrelevant and redundant features. Random forest, gradient boosting, multi-layer perceptron, K-nearest Neighbor, and Extra Tree classifiers were used to predict the family's level of food security. Finally, the authors compare the accuracy of ensemble and base classifiers. The experiment result shows that the RF classifier surpasses the other base and ensemble classifiers and scored 99.98% accuracy. Because a Random forest classifier is an ensemble learning classifier that uses several decision trees, the final prediction is computed based on the majority vote of the several trees. The comparison result of hard and soft voting reveals that soft voting outperforms hard voting before and after feature selection with accuracies of 99.79% and 99.77%, respectively. Based on the result obtained, ensemble learning plays a significant role in predicting household food security status and implementing hard and soft voting. The RF classifier surpasses the other base and ensemble classifiers with an accuracy of 99.98%. From ensemble methods, soft voting surpasses hard voting with an accuracy score of 99.79%.
{"title":"Prediction of Household Food Security Status Using Ensemble Learning Models","authors":"Mersha Nigus, H.L Shashirekh","doi":"10.2174/2210327913666221209143445","DOIUrl":"https://doi.org/10.2174/2210327913666221209143445","url":null,"abstract":"\u0000\u0000This research uses the Ethiopian HICE survey dataset. Predicting food insecurity is critical in presenting the household's situation to the appropriate agencies that take preventative and intervention measures.\u0000\u0000\u0000\u0000This research paper's primary goal is to predict households' food security status using ensemble learning models.\u0000\u0000\u0000\u0000We use five base classifiers and a voting strategy for ensemble classification to enhance the performance of different base classifiers. Backward feature elimination and hard and soft voting-based ensemble learning are used to evaluate household food security. The training set for the basic classifiers is composed of the features that have been selected. Each ML classifier makes its prediction about the class label with the help of an ensemble learning method. For making decisions, hard voting uses a simple majority, whereas soft vote employs a weighted probability. To determine the final prediction. Ethiopian household income, consumption, and expenditure dataset are used to test the proposed ensemble learning approach. \u0000The backward feature elimination approach improved the model's performance by removing irrelevant and redundant features. Random forest, gradient boosting, multi-layer perceptron, K-nearest Neighbor, and Extra Tree classifiers were used to predict the family's level of food security. Finally, the authors compare the accuracy of ensemble and base classifiers.\u0000\u0000\u0000\u0000The experiment result shows that the RF classifier surpasses the other base and ensemble classifiers and scored 99.98% accuracy. Because a Random forest classifier is an ensemble learning classifier that uses several decision trees, the final prediction is computed based on the majority vote of the several trees. The comparison result of hard and soft voting reveals that soft voting outperforms hard voting before and after feature selection with accuracies of 99.79% and 99.77%, respectively.\u0000\u0000\u0000\u0000Based on the result obtained, ensemble learning plays a significant role in predicting household food security status and implementing hard and soft voting. The RF classifier surpasses the other base and ensemble classifiers with an accuracy of 99.98%. From ensemble methods, soft voting surpasses hard voting with an accuracy score of 99.79%.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74593494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-24DOI: 10.2174/2210327913666221024160809
Qutaiba Ibrahim
This paper proposes an efficient employment of a self-powered VANET infrastructure. Miscellaneous techniques and algorithms are suggested to help the realization of such a framework. The current work attempts to enhance the network architecture of the Green VANET by adopting the self-powered fog computing concept for better networking, computing, and storage performance. The green fog layer consists of three components: a self-powered edge server, Wireless Solar Routers (WSRs), and a new device resulted from the integration between a solar-powered Smart Camera (SC) and a solar-powered Road Side Unit (RSU) in order to create a better sensing mechanism of the road traffic. A proper power management strategy is suggested and installed locally in the self-powered devices to decrease their power utilization by 80% and to lengthen the lifetime of their batteries from 17 to 64 hours. The different methods and algorithms suggested in this paper are realized and tested using an experimental framework based on mix of evaluation kits. It is noticed that the suggested power management algorithm can adjust the duty cycling according to the accessible energy levels and thus the SC-RSU nodes and the WSRs keep on working in a pre-managed and arranged manner.
{"title":"An Efficient Power Management Strategy of a Solar Powered Smart Camera-Road Side Unit Integrated Platform","authors":"Qutaiba Ibrahim","doi":"10.2174/2210327913666221024160809","DOIUrl":"https://doi.org/10.2174/2210327913666221024160809","url":null,"abstract":"\u0000\u0000This paper proposes an efficient employment of a self-powered VANET infrastructure. Miscellaneous techniques and algorithms are suggested to help the realization of such a framework.\u0000\u0000\u0000\u0000The current work attempts to enhance the network architecture of the Green VANET by adopting the self-powered fog computing concept for better networking, computing, and storage performance.\u0000\u0000\u0000\u0000The green fog layer consists of three components: a self-powered edge server, Wireless Solar Routers (WSRs), and a new device resulted from the integration between a solar-powered Smart Camera (SC) and a solar-powered Road Side Unit (RSU) in order to create a better sensing mechanism of the road traffic.\u0000\u0000\u0000\u0000A proper power management strategy is suggested and installed locally in the self-powered devices to decrease their power utilization by 80% and to lengthen the lifetime of their batteries from 17 to 64 hours.\u0000\u0000\u0000\u0000The different methods and algorithms suggested in this paper are realized and tested using an experimental framework based on mix of evaluation kits. It is noticed that the suggested power management algorithm can adjust the duty cycling according to the accessible energy levels and thus the SC-RSU nodes and the WSRs keep on working in a pre-managed and arranged manner.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"83 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89122642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-24DOI: 10.2174/2210327913666221024152931
Sanmukh Kaur, Pranjul Kumar, D. Mehrotra
Vehicular Ad-Hoc Network (VANET) is integral to lessen road mis-happenings and to effortlessly control huge traffic on highways. Numerous protocols are researched and implemented for creating secure medium between vehicular nodes. One of the visible problems is loss of data between the nodes which leads to delay, collision, and accidents in the VANET. This paper investigates traffic security and safety problems faced in VANET and provides a solution for it. For analyses of traffic safety issues, 3 types of Medium Access Control (MAC) protocols were compared namely Conventional, Contention-Based and Contention-Free MAC Protocols. Plentiful of performance metrics have been studied under the transportation security issues including signal received with error, throughput, and MAC Overhead and Packet loss. By comparing the MAC Protocols, it can be concluded that Contention-Free multi-channel SD-TDMA is better with security mechanism for continuous and safe communication between the vehicular nodes. It can be utilized in moderate to heavy traffic scenario to have faster and safe communication.
{"title":"Improvised Protocol for Enhancement of Security in Internet of Vehicles","authors":"Sanmukh Kaur, Pranjul Kumar, D. Mehrotra","doi":"10.2174/2210327913666221024152931","DOIUrl":"https://doi.org/10.2174/2210327913666221024152931","url":null,"abstract":"\u0000\u0000Vehicular Ad-Hoc Network (VANET) is integral to lessen road mis-happenings and to effortlessly control huge traffic on highways. Numerous protocols are researched and implemented for creating secure medium between vehicular nodes.\u0000\u0000\u0000\u0000One of the visible problems is loss of data between the nodes which leads to delay, collision, and accidents in the VANET. This paper investigates traffic security and safety problems faced in VANET and provides a solution for it.\u0000\u0000\u0000\u0000For analyses of traffic safety issues, 3 types of Medium Access Control (MAC) protocols were compared namely Conventional, Contention-Based and Contention-Free MAC Protocols. Plentiful of performance metrics have been studied under the transportation security issues including signal received with error, throughput, and MAC Overhead and Packet loss.\u0000\u0000\u0000\u0000By comparing the MAC Protocols, it can be concluded that Contention-Free multi-channel SD-TDMA is better with security mechanism for continuous and safe communication between the vehicular nodes. It can be utilized in moderate to heavy traffic scenario to have faster and safe communication.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79116097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.2174/2210327913666221021110816
M. Ravi, T. Sheikh, Y. Bulo
Non-orthogonal multiple access (NOMA) is viewed as the key multiple access technology for 5G and beyond networks, attracting the attention of academics and Industries. NOMA and the multiple input multiple output (MIMO-NOMA) technology can improve a system’s throughput, latency, and energy efficiency (EE) in future-generation communication networks. The objective of this paper is to achieve maximum EE by applying the Max-min Power Control Algorithm (MMPCA) through sub-channel optimization, resource allocation (RA) optimization, access point selection (APS), and user association. The EE results obtained with and without using MMPCA are compared to the RA optimization from a conventional water-filling algorithm (WFA). This paper formulates a framework for user-centric (UC) joint resource allocation, such as backhaul connection via beam-forming and Access point AP to user connection via MIMO-NOMA. The user without interference is decoded using the NOMA principle. The MMPCA was also used to optimize cooperative power allocation, sub-channel allocation, and efficient user association. The RA for EE is framed as a mixed non-convex and non-linear function using successive convex approximation and sum ratio decoupling convert in convex and linear. A bisection method was used to achieve optimal RA, user association, and sub-channel assignment. The simulation shows energy efficiency (EE) improvement. Similarly, it is observed that MMPCA outperforms the WFA.
{"title":"Energy Efficiency and Resource Allocation Optimization with MIMO-NOMA and Backhaul Beam-Forming in User Centric Ultra Dense Networks","authors":"M. Ravi, T. Sheikh, Y. Bulo","doi":"10.2174/2210327913666221021110816","DOIUrl":"https://doi.org/10.2174/2210327913666221021110816","url":null,"abstract":"\u0000\u0000Non-orthogonal multiple access (NOMA) is viewed as the key multiple access technology for 5G and beyond networks, attracting the attention of academics and Industries. NOMA and the multiple input multiple output (MIMO-NOMA) technology can improve a system’s throughput, latency, and energy efficiency (EE) in future-generation communication networks.\u0000\u0000\u0000\u0000The objective of this paper is to achieve maximum EE by applying the Max-min Power Control Algorithm (MMPCA) through sub-channel optimization, resource allocation (RA) optimization, access point selection (APS), and user association. The EE results obtained with and without using MMPCA are compared to the RA optimization from a conventional water-filling algorithm (WFA).\u0000\u0000\u0000\u0000This paper formulates a framework for user-centric (UC) joint resource allocation, such as backhaul connection via beam-forming and Access point AP to user connection via MIMO-NOMA. The user without interference is decoded using the NOMA principle. The MMPCA was also used to optimize cooperative power allocation, sub-channel allocation, and efficient user association. The RA for EE is framed as a mixed non-convex and non-linear function using successive convex approximation and sum ratio decoupling convert in convex and linear. A bisection method was used to achieve optimal RA, user association, and sub-channel assignment.\u0000\u0000\u0000\u0000The simulation shows energy efficiency (EE) improvement. Similarly, it is observed that MMPCA outperforms the WFA.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"98 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85901292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-12DOI: 10.2174/2210327912666221012154428
Y. Sucharitha, Pundru Chandra Shaker Reddy
Mobile workstations are frequently used in heterogeneous network's challenging environments. Users must move between various networks for a myriad of purposes, including vertical handover. At this time, it is critical for the mobile station to quickly pick the most appropriate networks from all identified alternative connections with the decision outcome, avoiding the ping-pong effect to the greatest extent feasible. Based on a combination of network characteristics as well as user choice, this study offers a heterogeneous network selection method. This technique integrates three common Multi-Attribute Decision-Making (MADM) techniques, notably the Fuzzy Analytic Hierarchy Process (FAHP), Entropy, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), to take into consideration user preferences for every prospective network as well as the real scenario of heterogeneous networks. For different traffic classes, FAHP is first utilized to determine the weights of network parameters and the utility numbers of total options available. Next, entropies and TOPSIS are utilized to obtain only the unbiased weightages of network factors and utility principles of totally different options. The most suitable networks, whose utility number is the greatest and larger than that of the equivalent number of present networks of the phone station, are chosen to provide accessibility based on the utility numbers of each prospective system as a limit. The suggested method not only eliminates a particular algorithm's one-sided character but also dynamically changes the percentage of each method in the desired outcome based on real needs. The proposed model was compared to the three existing hybrid methods. The results showed that it could precisely choose the optimized network connectivity and significantly reduce the value of vertical handoffs. It also provides the requisite Quality of Service (QoS) and Quality of Everything (QoE) in terms of the quantitative benefits of vertical handovers.
{"title":"An Autonomous Adaptive Enhancement Method Based on Learning to Optimize Heterogeneous Network Selection","authors":"Y. Sucharitha, Pundru Chandra Shaker Reddy","doi":"10.2174/2210327912666221012154428","DOIUrl":"https://doi.org/10.2174/2210327912666221012154428","url":null,"abstract":"\u0000\u0000Mobile workstations are frequently used in heterogeneous network's challenging environments. Users must move between various networks for a myriad of purposes, including vertical handover. At this time, it is critical for the mobile station to quickly pick the most appropriate networks from all identified alternative connections with the decision outcome, avoiding the ping-pong effect to the greatest extent feasible.\u0000\u0000\u0000\u0000Based on a combination of network characteristics as well as user choice, this study offers a heterogeneous network selection method. This technique integrates three common Multi-Attribute Decision-Making (MADM) techniques, notably the Fuzzy Analytic Hierarchy Process (FAHP), Entropy, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), to take into consideration user preferences for every prospective network as well as the real scenario of heterogeneous networks. For different traffic classes, FAHP is first utilized to determine the weights of network parameters and the utility numbers of total options available. Next, entropies and TOPSIS are utilized to obtain only the unbiased weightages of network factors and utility principles of totally different options.\u0000\u0000\u0000\u0000The most suitable networks, whose utility number is the greatest and larger than that of the equivalent number of present networks of the phone station, are chosen to provide accessibility based on the utility numbers of each prospective system as a limit. The suggested method not only eliminates a particular algorithm's one-sided character but also dynamically changes the percentage of each method in the desired outcome based on real needs.\u0000\u0000\u0000\u0000The proposed model was compared to the three existing hybrid methods. The results showed that it could precisely choose the optimized network connectivity and significantly reduce the value of vertical handoffs. It also provides the requisite Quality of Service (QoS) and Quality of Everything (QoE) in terms of the quantitative benefits of vertical handovers.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"2013 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73293533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-05DOI: 10.2174/2210327912666220805124234
R. Gill, Tarun Dubey
A Wireless Sensor Network (WSN) consists of a large number of sensor nodes deployed randomly over an area that can sense the surrounding environment to gather some data and interconnect over a wireless channel to share the information with neighboring nodes or with some user over the internet. WSN has a wide range of applications in the field of military, healthcare, industry, agriculture, livestock farming, and smart cities. The pertinence of WSN in healthcare, defense, agriculture, and industry is discussed in detail in the background section of this paper. The objective of this paper is to examine and simulate Dijkstra’s Algorithm, Bellman Ford’s Algorithm, and Floyd-Warshall’s Algorithm applied for routing in WSN Simulation is completed on CupCarbon U-one 4.2 simulator for these algorithms to compute the shortest distance between a randomly deployed source node and a destination node in different sized networks. Simulation of the three algorithms is carried out considering the vital simulation parameters including sensor radius, radio range, and traffic. Also, Simulation is carried out to show the implementation of Floyd Warshall’s algorithm in the field of smart mobility. The results obtained show that the simulation time for the calculation of the shortest route from source to destinations for the three algorithms is the same which is also suitable for various applications of smart mobility, smart accident management, and smart traffic management. The simulation results are also examined to measure the performance of each algorithm and its suitability in the context of WSN. The epilogue of this paper is provided in the conclusion section.
{"title":"Study of Different Techniques used in WSN for Smart Mobility","authors":"R. Gill, Tarun Dubey","doi":"10.2174/2210327912666220805124234","DOIUrl":"https://doi.org/10.2174/2210327912666220805124234","url":null,"abstract":"\u0000\u0000A Wireless Sensor Network (WSN) consists of a large number of sensor nodes deployed randomly over an area that can sense the surrounding environment to gather some data and interconnect over a wireless channel to share the information with neighboring nodes or with some user over the internet. WSN has a wide range of applications in the field of military, healthcare, industry, agriculture, livestock farming, and smart cities. The pertinence of WSN in healthcare, defense, agriculture, and industry is discussed in detail in the background section of this paper.\u0000\u0000\u0000\u0000The objective of this paper is to examine and simulate Dijkstra’s Algorithm, Bellman Ford’s Algorithm, and Floyd-Warshall’s Algorithm applied for routing in WSN\u0000\u0000\u0000\u0000Simulation is completed on CupCarbon U-one 4.2 simulator for these algorithms to compute the shortest distance between a randomly deployed source node and a destination node in different sized networks. Simulation of the three algorithms is carried out considering the vital simulation parameters including sensor radius, radio range, and traffic. Also, Simulation is carried out to show the implementation of Floyd Warshall’s algorithm in the field of smart mobility.\u0000\u0000\u0000\u0000The results obtained show that the simulation time for the calculation of the shortest route from source to destinations for the three algorithms is the same which is also suitable for various applications of smart mobility, smart accident management, and smart traffic management.\u0000\u0000\u0000\u0000The simulation results are also examined to measure the performance of each algorithm and its suitability in the context of WSN. The epilogue of this paper is provided in the conclusion section.\u0000","PeriodicalId":37686,"journal":{"name":"International Journal of Sensors, Wireless Communications and Control","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72551605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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