Accurately detecting traffic anomalies becomes increasingly crucial in network management. Algorithms that model the traffic data as a matrix suffers from low detection accuracy, while the work using the tensor model often assumes the tensor is regular without considering that network nodes may dynamically join in or leave, which will fail in a practical network with the change of node set as a result of mobility and churn behaviors. We propose a novel Tensor Recovery scheme in a Dynamic Network (TRDN) with traffic data modeled as a practical irregular tensor for accurate anomaly detection. To take advantage of correlations among small tensors, each formed with a short time duration to capture more hidden information in the data for higher detection accuracy, we propose several novel techniques: 1) a new joint tensor factorization model to capture the characteristic shared by the common nodes of small tensors, 2) a tensor partition algorithm to identify the data that can be applied to train the shared parameters efficiently, and 3) a bar-based algorithm that partitions nodes into the minimum number of no-overlapping subsets to form the shared tensor model. Extensive experiments on two Internet traffic data sets, Abilene and GÈANT, demonstrate the effectiveness of the proposed TRDN.
{"title":"Tensor Factorization for Accurate Anomaly Detection in Dynamic Networks","authors":"Xiaocan Li;Jigang Wen;Kun Xie;Gaogang Xie;Wei Liang","doi":"10.1109/TSUSC.2024.3462814","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3462814","url":null,"abstract":"Accurately detecting traffic anomalies becomes increasingly crucial in network management. Algorithms that model the traffic data as a matrix suffers from low detection accuracy, while the work using the tensor model often assumes the tensor is regular without considering that network nodes may dynamically join in or leave, which will fail in a practical network with the change of node set as a result of mobility and churn behaviors. We propose a novel Tensor Recovery scheme in a Dynamic Network (TRDN) with traffic data modeled as a practical irregular tensor for accurate anomaly detection. To take advantage of correlations among small tensors, each formed with a short time duration to capture more hidden information in the data for higher detection accuracy, we propose several novel techniques: 1) a new joint tensor factorization model to capture the characteristic shared by the common nodes of small tensors, 2) a tensor partition algorithm to identify the data that can be applied to train the shared parameters efficiently, and 3) a bar-based algorithm that partitions nodes into the minimum number of no-overlapping subsets to form the shared tensor model. Extensive experiments on two Internet traffic data sets, Abilene and GÈANT, demonstrate the effectiveness of the proposed TRDN.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 3","pages":"439-450"},"PeriodicalIF":3.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modularized plant factories, characterized by machines executing intelligent control requests to automatically take care of crops, have emerged as a sustainable agricultural paradigm, garnering the attention of Internet-of-Things and agricultural researchers for their production stability and energy efficiency. However, the diversity and pluralism of the plant factory components make it difficult to cooperate and produce crops with better qualities. Therefore, appropriate resource allocation and task scheduling strategies become the key points to optimize the quality of production in the factories by immediately telling which component is more suitable to do what in taking care of the crops. To address this challenge, this paper investigates how the machines of the factory can use their unique services and resource to help improve the crops’ quality and model the machine cooperation as an online decision-making problem. An $alpha$-competitive approach called $textsc {OnATS}$ is designed based on the transformation of the original problem, and the experiments show that the proposed algorithm is superior to the baselines. Additionally, this paper explores the impact of different system configurations on the proposed method and shows that the proposed approach has broad applicability.
{"title":"Let Robots Watch Grass Grow: Optimal Task Assignment for Automatic Plant Factory","authors":"Zhengzhe Xiang;Xizi Xue;Yuanyi Chen;Schahram Dustdar;Minyi Guo","doi":"10.1109/TSUSC.2024.3462447","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3462447","url":null,"abstract":"Modularized plant factories, characterized by machines executing intelligent control requests to automatically take care of crops, have emerged as a sustainable agricultural paradigm, garnering the attention of Internet-of-Things and agricultural researchers for their production stability and energy efficiency. However, the diversity and pluralism of the plant factory components make it difficult to cooperate and produce crops with better qualities. Therefore, appropriate resource allocation and task scheduling strategies become the key points to optimize the quality of production in the factories by immediately telling which component is more suitable to do what in taking care of the crops. To address this challenge, this paper investigates how the machines of the factory can use their unique services and resource to help improve the crops’ quality and model the machine cooperation as an online decision-making problem. An <inline-formula><tex-math>$alpha$</tex-math></inline-formula>-competitive approach called <inline-formula><tex-math>$textsc {OnATS}$</tex-math></inline-formula> is designed based on the transformation of the original problem, and the experiments show that the proposed algorithm is superior to the baselines. Additionally, this paper explores the impact of different system configurations on the proposed method and shows that the proposed approach has broad applicability.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 3","pages":"464-474"},"PeriodicalIF":3.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nowadays, the Internet of Things (IoT) plays a significant role in the development of various real-life applications such as smart cities, healthcare, precision agriculture, and industrial automation. Wireless Sensor Networks (WSNs) are a major ingredient of these IoT-based applications. In WSNs, sensor nodes that are close to the Base Station (BS) relay more data packets compared to other nodes, which creates high energy consumption at nodes close to the BS. As a result, an energy imbalance is created among the sensor nodes. Therefore, sensor nodes close to BS die early as compared to the faraway sensor nodes. These early dead nodes drastically increase data collection delay within the network. Furthermore, the early death of the sensor nodes partitions the network into different isolated sub-networks/segments. The formation of isolated segments causes premature death of the network. This paper proposes a Deep Policy Dynamic Programming (DPDP) based intelligent data routing scheme for IoT-enabled WSNs. The proposed scheme identifies an optimal number of Cluster Heads (CHs) and forms clusters to reduce the energy consumption of the deployed sensor nodes and prevent the early death of sensor nodes. Furthermore, the proposed scheme identifies an optimal number of Rendezvous Points (RPs) and designs an optimal path for Mobile Sink (MS) based data collection. Optimal RP selection and path design algorithms prevent the premature death of the network and significantly improve the overall performance of the network. Extensive simulations and test-bed experiments are conducted to test the performance of the proposed scheme. The simulation and test-bed results show that the proposed scheme outperforms as compared to the existing state-of-the-art approaches in terms of network lifetime, network stability, data loss due to buffer overflow, residual energy, and delay.
{"title":"A Deep Policy Dynamic Programming Based Intelligent Data Routing Scheme for IoT-Enabled Wireless Sensor Networks","authors":"Archana Ojha;Sahil Manikchand Chaudhari;Prasenjit Chanak","doi":"10.1109/TSUSC.2024.3462512","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3462512","url":null,"abstract":"Nowadays, the Internet of Things (IoT) plays a significant role in the development of various real-life applications such as smart cities, healthcare, precision agriculture, and industrial automation. Wireless Sensor Networks (WSNs) are a major ingredient of these IoT-based applications. In WSNs, sensor nodes that are close to the Base Station (BS) relay more data packets compared to other nodes, which creates high energy consumption at nodes close to the BS. As a result, an energy imbalance is created among the sensor nodes. Therefore, sensor nodes close to BS die early as compared to the faraway sensor nodes. These early dead nodes drastically increase data collection delay within the network. Furthermore, the early death of the sensor nodes partitions the network into different isolated sub-networks/segments. The formation of isolated segments causes premature death of the network. This paper proposes a Deep Policy Dynamic Programming (DPDP) based intelligent data routing scheme for IoT-enabled WSNs. The proposed scheme identifies an optimal number of Cluster Heads (CHs) and forms clusters to reduce the energy consumption of the deployed sensor nodes and prevent the early death of sensor nodes. Furthermore, the proposed scheme identifies an optimal number of Rendezvous Points (RPs) and designs an optimal path for Mobile Sink (MS) based data collection. Optimal RP selection and path design algorithms prevent the premature death of the network and significantly improve the overall performance of the network. Extensive simulations and test-bed experiments are conducted to test the performance of the proposed scheme. The simulation and test-bed results show that the proposed scheme outperforms as compared to the existing state-of-the-art approaches in terms of network lifetime, network stability, data loss due to buffer overflow, residual energy, and delay.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 3","pages":"451-463"},"PeriodicalIF":3.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-09DOI: 10.1109/TSUSC.2024.3456429
Deepika Saxena;Ashutosh Kumar Singh
Workload pattern learning-based resource management is crucial for cloud computing environments for achieving higher performance, sustainability, fault-tolerance, and quality of service. The existing literature lacks a comprehensive discussion and meta-analysis of workload pattern learning centered cloud resource management. In this context, this paper presents a first comprehensive study about five pattern learning and analysis-driven techniques applied for achieving higher efficiency and performance during multi-constrained cloud resource management. The paper manifests utility and significance of workload pattern learning-based resource management as compared with traditional resource management. The five principle techniques are thoroughly discussed with coherent depiction of intended concept alongwith numerical illustration. The most prominent state-of-the-art models belonging to each technique are further distinguished based on distinct objectives conferring an extensive survey and comparison. Besides, conceptual and theoretical analysis, the leading models underlying the major resource management techniques are implemented on a common platform and thoroughly examined using real-world Google Cluster workload traces. Based on the all-inclusive study and performance evaluation, trade-off discussion among these techniques are capsuled to put forward imperative concluding remarks with concrete open issues and insightful future research directions.
{"title":"Workload Pattern Learning-Based Cloud Resource Management Models: Concepts and Meta-Analysis","authors":"Deepika Saxena;Ashutosh Kumar Singh","doi":"10.1109/TSUSC.2024.3456429","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3456429","url":null,"abstract":"Workload pattern learning-based resource management is crucial for cloud computing environments for achieving higher performance, sustainability, fault-tolerance, and quality of service. The existing literature lacks a comprehensive discussion and meta-analysis of workload pattern learning centered cloud resource management. In this context, this paper presents a first comprehensive study about five pattern learning and analysis-driven techniques applied for achieving higher efficiency and performance during multi-constrained cloud resource management. The paper manifests utility and significance of workload pattern learning-based resource management as compared with traditional resource management. The five principle techniques are thoroughly discussed with coherent depiction of intended concept alongwith numerical illustration. The most prominent state-of-the-art models belonging to each technique are further distinguished based on distinct objectives conferring an extensive survey and comparison. Besides, conceptual and theoretical analysis, the leading models underlying the major resource management techniques are implemented on a common platform and thoroughly examined using real-world Google Cluster workload traces. Based on the all-inclusive study and performance evaluation, trade-off discussion among these techniques are capsuled to put forward imperative concluding remarks with concrete open issues and insightful future research directions.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 3","pages":"418-438"},"PeriodicalIF":3.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Social Internet of Vehicles (SIoVs) is an important information exchange platform to provide comprehensive traffic services by sharing vehicle-aware data. However, traditional data sharing methods can not provide the security of decentralized data sharing, making it possible for some malicious third parties to initiate dishonest behaviors. Additionally, the lack of access control for data sharing in SIoVs easily leads to unauthorized data sharing, thus user privacy is threatened and the source of false data is difficult to be traced. In this paper, we propose a conditional data-sharing privacy-preserving scheme for blockchain-based social internet of vehicles. In our scheme, a lightweight ledger-based blockchain system is designed, which combines with the ciphertext-policy attribute-based encryption method to realize anonymous one-to-many sharing of data with fine-grained access management. Also, a collaborative identity tracing method is constructed to trace malicious users who provide false data. Our scheme can effectively prevent second-hand data sharing and safeguard user privacy. Moreover, related experimental results validate the efficiency of our scheme.
{"title":"Conditional Data-Sharing Privacy-Preserving Scheme in Blockchain-Based Social Internet of Vehicles","authors":"Zhuoqun Xia;Jiahuan Man;Ke Gu;Xiong Li;Longfei Huang","doi":"10.1109/TSUSC.2024.3452228","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3452228","url":null,"abstract":"Social Internet of Vehicles (SIoVs) is an important information exchange platform to provide comprehensive traffic services by sharing vehicle-aware data. However, traditional data sharing methods can not provide the security of decentralized data sharing, making it possible for some malicious third parties to initiate dishonest behaviors. Additionally, the lack of access control for data sharing in SIoVs easily leads to unauthorized data sharing, thus user privacy is threatened and the source of false data is difficult to be traced. In this paper, we propose a conditional data-sharing privacy-preserving scheme for blockchain-based social internet of vehicles. In our scheme, a lightweight ledger-based blockchain system is designed, which combines with the ciphertext-policy attribute-based encryption method to realize anonymous one-to-many sharing of data with fine-grained access management. Also, a collaborative identity tracing method is constructed to trace malicious users who provide false data. Our scheme can effectively prevent second-hand data sharing and safeguard user privacy. Moreover, related experimental results validate the efficiency of our scheme.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 2","pages":"378-395"},"PeriodicalIF":3.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-26DOI: 10.1109/TSUSC.2024.3449977
Pietro Rando Mazzarino;Martina Capone;Elisa Guelpa;Lorenzo Bottaccioli;Vittorio Verda;Edoardo Patti
Integrated modeling and simulation are crucial for optimizing cities’ energy planning. Existing sector-specific analyses have implementation limitations in representing interactions across infrastructures, limiting optimization potentials. An integrated framework simulating multiple interacting components from a systemic perspective could reveal efficiency gains, flexibility, and synergies across urban energy networks to guide sustainable energy transitions. Co-simulation approaches are gaining attention for analyzing complex interconnected systems like District Heating (DH). Traditional single-discipline models present limitations in fully representing the interconnectivity between district heating networks and related subsystems, such as those in buildings and energy generation. Therefore, we propose a co-simulation based framework to simulate DH system behavior while easily integrating models of other subsystems and Functional Mock-up Unit (FMU) simulators. We tested this Plug&Play modular framework for Demand Side Management (DSM) and Storage-based strategies, evaluating their effectiveness in peak reduction while lowering the temperatures of the network.
{"title":"A Modular Co-Simulation Platform for Comparing Flexibility Solutions in District Heating Under Variable Operating Conditions","authors":"Pietro Rando Mazzarino;Martina Capone;Elisa Guelpa;Lorenzo Bottaccioli;Vittorio Verda;Edoardo Patti","doi":"10.1109/TSUSC.2024.3449977","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3449977","url":null,"abstract":"Integrated modeling and simulation are crucial for optimizing cities’ energy planning. Existing sector-specific analyses have implementation limitations in representing interactions across infrastructures, limiting optimization potentials. An integrated framework simulating multiple interacting components from a systemic perspective could reveal efficiency gains, flexibility, and synergies across urban energy networks to guide sustainable energy transitions. Co-simulation approaches are gaining attention for analyzing complex interconnected systems like District Heating (DH). Traditional single-discipline models present limitations in fully representing the interconnectivity between district heating networks and related subsystems, such as those in buildings and energy generation. Therefore, we propose a co-simulation based framework to simulate DH system behavior while easily integrating models of other subsystems and Functional Mock-up Unit (FMU) simulators. We tested this Plug&Play modular framework for Demand Side Management (DSM) and Storage-based strategies, evaluating their effectiveness in peak reduction while lowering the temperatures of the network.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 2","pages":"408-417"},"PeriodicalIF":3.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10648783","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dynamic state estimation (DSE) plays a vitally important role in modern power systems, and the reliance on the communication network often render the systems to cyber-threats. This paper investigates the secure DSE problem for the multi-generator power grids in the presence of randomly occurring cyber-attacks. To facilitate the decentralized DSE, the synchronous generator is decoupled form the large-scale interconnected power grid with the aid of model decoupling method. A hybrid cyber-attack model, which includes three typical and representative attacks (i.e., denial-of-service attacks, bias injection attacks and replay attacks), is designed and launched in a random way. Attention is devoted to the secure algorithm design problem to light the negative impacts on the DSE performance from the nonlinearity/non-Gaussianity and the random occurrences of the cyber-attacks. Specifically, i) a likelihood function modification method is established where the knowledge of the hybrid-attack model is fully considered; and ii) the associated weights of the particles are updated according to the proposed likelihood function to resist the impacts caused by the randomly occurring cyber-attacks. Finally, simulation experiments with four scenarios are implemented on the IEEE 39-bus system and the corresponding analyses show the validity of the decentralized secure DSE scheme.
{"title":"Dynamic State Estimation for Multi-Machine Power Grids Under Randomly Occurring Cyber-Attacks: A Decentralized Framework","authors":"Bogang Qu;Zidong Wang;Bo Shen;Daogang Peng;Dong Yue","doi":"10.1109/TSUSC.2024.3448225","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3448225","url":null,"abstract":"Dynamic state estimation (DSE) plays a vitally important role in modern power systems, and the reliance on the communication network often render the systems to cyber-threats. This paper investigates the secure DSE problem for the multi-generator power grids in the presence of randomly occurring cyber-attacks. To facilitate the decentralized DSE, the synchronous generator is decoupled form the large-scale interconnected power grid with the aid of model decoupling method. A hybrid cyber-attack model, which includes three typical and representative attacks (i.e., denial-of-service attacks, bias injection attacks and replay attacks), is designed and launched in a random way. Attention is devoted to the secure algorithm design problem to light the negative impacts on the DSE performance from the nonlinearity/non-Gaussianity and the random occurrences of the cyber-attacks. Specifically, i) a likelihood function modification method is established where the knowledge of the hybrid-attack model is fully considered; and ii) the associated weights of the particles are updated according to the proposed likelihood function to resist the impacts caused by the randomly occurring cyber-attacks. Finally, simulation experiments with four scenarios are implemented on the IEEE 39-bus system and the corresponding analyses show the validity of the decentralized secure DSE scheme.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 2","pages":"396-407"},"PeriodicalIF":3.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-16DOI: 10.1109/TSUSC.2024.3444949
Kejun Long;Chunlin Li;Kun Jiang;Shaohua Wan
UAV-assisted VEC can provide content caching services for vehicles by flying close to the vehicles for vehicle's QoS. However, in real-world scenarios with traffic congestion, due to the battery capacity and cache space limitations of UAVs, low content response speed and high response latency may occur. Based on this, we proposed a dynamic energy consumption-based content caching strategy in UAV-assisted VEC. We use the PSO algorithm to solve the problem and obtain the optimal UAV deployment location. For content caching, we construct a content caching model by considering UAV deployment, vehicle user preference, UAV cache capacity, and UAV energy consumption with the goal of minimizing content request latency. In addition, we propose an IAFSA-based content caching strategy. We reduce the solution space of the fish swarm algorithm, decrease the number of caching decisions, and improve the convergence performance of AFSA by employing dynamic horizons and step sizes. Experimental results show that the proposed IAFSA effectively reduces the average content request latency of the vehicle, improves the cache hit rate, and reduces the number of content return trips. Particularly, the proposed strategy reduces the average content request latency by more than 9.84% compared to the baseline algorithm.
{"title":"Improved AFSA-Based Energy-Aware Content Caching Strategy for UAV-Assisted VEC","authors":"Kejun Long;Chunlin Li;Kun Jiang;Shaohua Wan","doi":"10.1109/TSUSC.2024.3444949","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3444949","url":null,"abstract":"UAV-assisted VEC can provide content caching services for vehicles by flying close to the vehicles for vehicle's QoS. However, in real-world scenarios with traffic congestion, due to the battery capacity and cache space limitations of UAVs, low content response speed and high response latency may occur. Based on this, we proposed a dynamic energy consumption-based content caching strategy in UAV-assisted VEC. We use the PSO algorithm to solve the problem and obtain the optimal UAV deployment location. For content caching, we construct a content caching model by considering UAV deployment, vehicle user preference, UAV cache capacity, and UAV energy consumption with the goal of minimizing content request latency. In addition, we propose an IAFSA-based content caching strategy. We reduce the solution space of the fish swarm algorithm, decrease the number of caching decisions, and improve the convergence performance of AFSA by employing dynamic horizons and step sizes. Experimental results show that the proposed IAFSA effectively reduces the average content request latency of the vehicle, improves the cache hit rate, and reduces the number of content return trips. Particularly, the proposed strategy reduces the average content request latency by more than 9.84% compared to the baseline algorithm.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 2","pages":"366-377"},"PeriodicalIF":3.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-14DOI: 10.1109/TSUSC.2024.3443450
Atul Banotra;Deepak Mishra;Sudhakar Modem
The Internet of Things (IoT) applications require uninterrupted network operation which is often hindered by battery energy constraints. Literature suggests that solar energy harvesting is a promising approach to powering IoT devices in a sustainable manner. However, the available literature overlooks key factors of determining effective solar panel size and cost while considering the IoT consumption for sustainable operation. This article tackles these pivotal aspects by investigating viability of commercially available solar panels as a sustainable energy source for IoT applications. A novel stochastic computation model is introduced to characterize the unpredictability of solar irradiance across three different time regions of the day. By employing distribution fitting models, the proposed computation model accurately determines the required solar panel size in cm$^{2}$ and panel cost in Indian Rupees for the sustainable operation of the IoT application. Further, the proposed model incorporates the assessment of outage and sustainability probabilities for user-specified solar panel size and cost. These insights are significant in settings where energy efficiency and sustainability are crucial. Numerical results are presented to validate the derived distribution models and performance metrics for sustainable IoT applications. The effectiveness and accuracy of the proposed model are validated by comparing results with baseline model.
{"title":"Stochastic Computation Model for Solar Panel Size and Cost of Sustainable IoT Networks","authors":"Atul Banotra;Deepak Mishra;Sudhakar Modem","doi":"10.1109/TSUSC.2024.3443450","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3443450","url":null,"abstract":"The Internet of Things (IoT) applications require uninterrupted network operation which is often hindered by battery energy constraints. Literature suggests that solar energy harvesting is a promising approach to powering IoT devices in a sustainable manner. However, the available literature overlooks key factors of determining effective solar panel size and cost while considering the IoT consumption for sustainable operation. This article tackles these pivotal aspects by investigating viability of commercially available solar panels as a sustainable energy source for IoT applications. A novel stochastic computation model is introduced to characterize the unpredictability of solar irradiance across three different time regions of the day. By employing distribution fitting models, the proposed computation model accurately determines the required solar panel size in cm<inline-formula><tex-math>$^{2}$</tex-math></inline-formula> and panel cost in Indian Rupees for the sustainable operation of the IoT application. Further, the proposed model incorporates the assessment of outage and sustainability probabilities for user-specified solar panel size and cost. These insights are significant in settings where energy efficiency and sustainability are crucial. Numerical results are presented to validate the derived distribution models and performance metrics for sustainable IoT applications. The effectiveness and accuracy of the proposed model are validated by comparing results with baseline model.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 2","pages":"317-332"},"PeriodicalIF":3.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Internet of Things (IoT) has enabled pervasive networking and multi-modal sensing, offering various services such as remote operations and augmenting existing processes. The military setting has increasingly and notably adopted IoT technologies, such as sensor-rich drones or autonomous vehicles, which provide military personnel with enhanced situational awareness, faster decision-making capabilities, and improved operational precision. However, integrating IoT into military systems introduces new security challenges due to increased connectivity and susceptibility to vulnerabilities. Cyberattacks on military IoT systems can have severe consequences, including operational disruptions and compromises of sensitive information. This article proposes a new perspective on examining threat models in IoT-enhanced combat systems, emphasising approaches for identifying threats, conducting vulnerability assessments, and suggesting countermeasures. It delves into the characteristics and structures of IoT-enhanced combat systems, exploring technical implementations and technologies. Additionally, it outlines five significant areas of focus, including blockchain, machine learning, game theory, protocols, and algorithms, to enhance understanding of IoT-enhanced combat systems. The insights gained from this analysis can inform the development of secure and resilient military IoT systems, ultimately enhancing the safety and effectiveness of military operations.
{"title":"Cybersecurity Solutions and Techniques for Internet of Things Integration in Combat Systems","authors":"Amirmohammad Pasdar;Nickolaos Koroniotis;Marwa Keshk;Nour Moustafa;Zahir Tari","doi":"10.1109/TSUSC.2024.3443256","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3443256","url":null,"abstract":"The Internet of Things (IoT) has enabled pervasive networking and multi-modal sensing, offering various services such as remote operations and augmenting existing processes. The military setting has increasingly and notably adopted IoT technologies, such as sensor-rich drones or autonomous vehicles, which provide military personnel with enhanced situational awareness, faster decision-making capabilities, and improved operational precision. However, integrating IoT into military systems introduces new security challenges due to increased connectivity and susceptibility to vulnerabilities. Cyberattacks on military IoT systems can have severe consequences, including operational disruptions and compromises of sensitive information. This article proposes a new perspective on examining threat models in IoT-enhanced combat systems, emphasising approaches for identifying threats, conducting vulnerability assessments, and suggesting countermeasures. It delves into the characteristics and structures of IoT-enhanced combat systems, exploring technical implementations and technologies. Additionally, it outlines five significant areas of focus, including blockchain, machine learning, game theory, protocols, and algorithms, to enhance understanding of IoT-enhanced combat systems. The insights gained from this analysis can inform the development of secure and resilient military IoT systems, ultimately enhancing the safety and effectiveness of military operations.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 2","pages":"345-365"},"PeriodicalIF":3.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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