Cognitive radio (CR) technology can leverage intelligence enabled by the integration of machine learning (ML) to successfully deliver pervasive connectivity for next-generation wireless networks. In this, a comprehensive overview of the uses of intelligent cognitive radio in a wide range of existing and emerging wireless technologies, including energy harvesting, physical-layer security, Internet of Things (IoT), mobile communications (vehicular and railway), and unmanned aerial vehicle (UAV) communications, will be presented. The interplay between intelligent CR and current and future technologies will be discussed. Emphasis will be on how the aforementioned techniques can benefit from intelligent CR and vice versa. For each technology, the key motivation for using intelligent networks will be highlighted CR with existing state-of-the-art ML approaches. The problems and prospective research avenues, and a futuristic road map exploring different possibilities for overcoming challenges through trending concepts will also be discussed.
{"title":"The Interplay Between Intelligent Networks and Enabling Technologies for Future Wireless Networks","authors":"W. Hamouda","doi":"10.1145/3551659.3563769","DOIUrl":"https://doi.org/10.1145/3551659.3563769","url":null,"abstract":"Cognitive radio (CR) technology can leverage intelligence enabled by the integration of machine learning (ML) to successfully deliver pervasive connectivity for next-generation wireless networks. In this, a comprehensive overview of the uses of intelligent cognitive radio in a wide range of existing and emerging wireless technologies, including energy harvesting, physical-layer security, Internet of Things (IoT), mobile communications (vehicular and railway), and unmanned aerial vehicle (UAV) communications, will be presented. The interplay between intelligent CR and current and future technologies will be discussed. Emphasis will be on how the aforementioned techniques can benefit from intelligent CR and vice versa. For each technology, the key motivation for using intelligent networks will be highlighted CR with existing state-of-the-art ML approaches. The problems and prospective research avenues, and a futuristic road map exploring different possibilities for overcoming challenges through trending concepts will also be discussed.","PeriodicalId":423926,"journal":{"name":"Proceedings of the 25th International ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121855153","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}
Yuchen Li, W. Liang, Jing Li, Xiuzhen Cheng, Dongxiao Yu, A. Zomaya, Song Guo
The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and edge intelligence arises to provision real-time deep neural network (DNN) inference services for mobile users. To perform efficient and effective DNN model training in edge environments while preserving training data security and privacy of IoT devices, federated learning has been envisioned as an ideal learning paradigm for this purpose. In this paper we study energy-aware DNN model training in an edge environment. We first formulate a novel energy-aware, device-to-device (D2D) assisted federated learning problem with the aim to minimize the global loss of a training DNN model, subject to bandwidth capacity on an edge server and the energy capacity on each IoT device. We then devise an efficient heuristic algorithm for the problem. The crux of the proposed algorithm is to explore the energy usage of neighboring devices of each device for its local model uploading, by reducing the problem to a series of maximum weight matching problems in corresponding auxiliary graphs. We finally evaluate the performance of the proposed algorithm through experimental simulations. Experimental results show that the proposed algorithm is promising.
{"title":"Energy-Constrained D2D Assisted Federated Learning in Edge Computing","authors":"Yuchen Li, W. Liang, Jing Li, Xiuzhen Cheng, Dongxiao Yu, A. Zomaya, Song Guo","doi":"10.1145/3551659.3559062","DOIUrl":"https://doi.org/10.1145/3551659.3559062","url":null,"abstract":"The surging of deep learning brings new vigor and vitality to shape the prospect of intelligent Internet of Things (IoT), and edge intelligence arises to provision real-time deep neural network (DNN) inference services for mobile users. To perform efficient and effective DNN model training in edge environments while preserving training data security and privacy of IoT devices, federated learning has been envisioned as an ideal learning paradigm for this purpose. In this paper we study energy-aware DNN model training in an edge environment. We first formulate a novel energy-aware, device-to-device (D2D) assisted federated learning problem with the aim to minimize the global loss of a training DNN model, subject to bandwidth capacity on an edge server and the energy capacity on each IoT device. We then devise an efficient heuristic algorithm for the problem. The crux of the proposed algorithm is to explore the energy usage of neighboring devices of each device for its local model uploading, by reducing the problem to a series of maximum weight matching problems in corresponding auxiliary graphs. We finally evaluate the performance of the proposed algorithm through experimental simulations. Experimental results show that the proposed algorithm is promising.","PeriodicalId":423926,"journal":{"name":"Proceedings of the 25th International ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126150640","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}
Gabriele Proietti Mattia, Marco Magnani, R. Beraldi
When deploying a distributed application in the Fog or Edge computing environments, the average service latency among all the involved nodes can be an indicator of how much a node is loaded with respect to the other. Indeed, only considering the average CPU time, or the RAM utilisation, for example, does not give a clear depiction of the load situation because these parameters are application- and hardware-agnostic. They do not give any information about how the application is performing from the user perspective and they cannot be used for a QoS-oriented load balancing of the system. Moreover, due to the displacement of the nodes and the heterogeneity of the computing devices the necessity of a load balancing algorithm is clear. In this paper, we propose a load balancing approach that is focused on the service latency with the objective to level it across all the nodes in a fully decentralized manner, in this way no user will experience a worse QoS than the other. By providing a differential model of the system and an adaptive heuristic to find the solution to the problem, we show both in simulation and in a real-world deployment that our approach is able to level the service latency among a set of heterogeneous nodes organized in different topologies.
{"title":"A Latency-levelling Load Balancing Algorithm for Fog and Edge Computing","authors":"Gabriele Proietti Mattia, Marco Magnani, R. Beraldi","doi":"10.1145/3551659.3559048","DOIUrl":"https://doi.org/10.1145/3551659.3559048","url":null,"abstract":"When deploying a distributed application in the Fog or Edge computing environments, the average service latency among all the involved nodes can be an indicator of how much a node is loaded with respect to the other. Indeed, only considering the average CPU time, or the RAM utilisation, for example, does not give a clear depiction of the load situation because these parameters are application- and hardware-agnostic. They do not give any information about how the application is performing from the user perspective and they cannot be used for a QoS-oriented load balancing of the system. Moreover, due to the displacement of the nodes and the heterogeneity of the computing devices the necessity of a load balancing algorithm is clear. In this paper, we propose a load balancing approach that is focused on the service latency with the objective to level it across all the nodes in a fully decentralized manner, in this way no user will experience a worse QoS than the other. By providing a differential model of the system and an adaptive heuristic to find the solution to the problem, we show both in simulation and in a real-world deployment that our approach is able to level the service latency among a set of heterogeneous nodes organized in different topologies.","PeriodicalId":423926,"journal":{"name":"Proceedings of the 25th International ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128668886","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}
Clément Courageux-Sudan, Loic Guegan, Anne-Cécile Orgerie, M. Quinson
Wi-Fi networks are extensively used to provide Internet access to end-users and to deploy applications at the edge. By playing a major role in modern networking, Wi-Fi networks are getting bigger and denser. However, studying their performance at large-scale and in a reproducible manner remains a challenging task. Current solutions include real experiments and simulations. While the size of experiments is limited by their financial cost and potential disturbance of commercial networks, the simulations also lack scalability due to their models' granularity and computational runtime. In this paper, we introduce a new Wi-Fi model for large-scale simulations. This model, based on flow-level simulation, requires fewer computations than state-of-the-art models to estimate bandwidth sharing over a wireless medium, leading to better scalability. Comparing our model to the already existing Wi-Fi implementation of ns-3, we show that our approach yields to close performance evaluations while improving the runtime of simulations by several orders of magnitude. Using this kind of model could allow researchers to obtain reproducible results for networks composed of thousands of nodes much faster than previously.
{"title":"A Flow-Level Wi-Fi Model for Large Scale Network Simulation","authors":"Clément Courageux-Sudan, Loic Guegan, Anne-Cécile Orgerie, M. Quinson","doi":"10.1145/3551659.3559022","DOIUrl":"https://doi.org/10.1145/3551659.3559022","url":null,"abstract":"Wi-Fi networks are extensively used to provide Internet access to end-users and to deploy applications at the edge. By playing a major role in modern networking, Wi-Fi networks are getting bigger and denser. However, studying their performance at large-scale and in a reproducible manner remains a challenging task. Current solutions include real experiments and simulations. While the size of experiments is limited by their financial cost and potential disturbance of commercial networks, the simulations also lack scalability due to their models' granularity and computational runtime. In this paper, we introduce a new Wi-Fi model for large-scale simulations. This model, based on flow-level simulation, requires fewer computations than state-of-the-art models to estimate bandwidth sharing over a wireless medium, leading to better scalability. Comparing our model to the already existing Wi-Fi implementation of ns-3, we show that our approach yields to close performance evaluations while improving the runtime of simulations by several orders of magnitude. Using this kind of model could allow researchers to obtain reproducible results for networks composed of thousands of nodes much faster than previously.","PeriodicalId":423926,"journal":{"name":"Proceedings of the 25th International ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133706595","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}
To achieve an infinite lifetime of sensing infrastructure in Internet-of-Things, battery-less wireless powered sensor networks (WPSNs) are an important step. The nodes in battery-less WPSNs harvest and store energy in super-capacitors from RF signal which are periodically transmitted by power beacons (PBs) or chargers. However, using multiple power chargers requires a focus on a crucial problem of interference. The sensor nodes which are covered by more than one power beacons become unreliable because of the overlapping signals from chargers since the overlap can be constructive or destructive. In this paper, we propose an algorithm to optimize the number and placement of power beacons such that interference can be reduced. The result shows that our proposed optimal power beacon location (OPBL) algorithm reduces interference in 60% of cases and also reduces data transmission time (DTT) by 30% in 24% of cases in comparison to the state-of-the-art.
{"title":"Interference Aware Heuristics to Optimize Power Beacons for Battery-less WSNs","authors":"Akash Kumar, Jagpreet Singh","doi":"10.1145/3551659.3559060","DOIUrl":"https://doi.org/10.1145/3551659.3559060","url":null,"abstract":"To achieve an infinite lifetime of sensing infrastructure in Internet-of-Things, battery-less wireless powered sensor networks (WPSNs) are an important step. The nodes in battery-less WPSNs harvest and store energy in super-capacitors from RF signal which are periodically transmitted by power beacons (PBs) or chargers. However, using multiple power chargers requires a focus on a crucial problem of interference. The sensor nodes which are covered by more than one power beacons become unreliable because of the overlapping signals from chargers since the overlap can be constructive or destructive. In this paper, we propose an algorithm to optimize the number and placement of power beacons such that interference can be reduced. The result shows that our proposed optimal power beacon location (OPBL) algorithm reduces interference in 60% of cases and also reduces data transmission time (DTT) by 30% in 24% of cases in comparison to the state-of-the-art.","PeriodicalId":423926,"journal":{"name":"Proceedings of the 25th International ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116068825","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}
V. Stanciu, Michael Steen, C. Dobre, Andreas Peter
Pedestrian dynamics are nowadays commonly analyzed by leveraging Wi-Fi signals sent by devices that people carry with them and captured by an infrastructure of Wi-Fi scanners. Emitting such signals is not a feature for devices of only passersby, but also for printers, smart TVs, and other devices that exhibit a stationary behavior over time, which eventually end up affecting pedestrian crowd measurements. In this paper we propose a system that accurately counts nonstationary devices sensed by scanners, separately from stationary devices, using no information other than the Wi-Fi signals captured by each scanner in isolation. As counting involves dealing with privacy-sensitive detections of people's devices, the system discards any data in the clear immediately after sensing, later working on encrypted data that it cannot decrypt in the process. The only information made available in the clear is the intended output, i.e. statistical counts of Wi-Fi devices. Our approach relies on an object, which we call comb, that maintains, under encryption, a representation of the frequency of occurrence of devices over time. Applying this comb on the detections made by a scanner enables the calculation of the separate counts. We implement the system and feed it with data from a large open-air festival, showing that accurate anonymized counting of nonstationary Wi-Fi devices is possible when dealing with real-world detections.
{"title":"Anonymized Counting of Nonstationary Wi-Fi Devices When Monitoring Crowds","authors":"V. Stanciu, Michael Steen, C. Dobre, Andreas Peter","doi":"10.1145/3551659.3559042","DOIUrl":"https://doi.org/10.1145/3551659.3559042","url":null,"abstract":"Pedestrian dynamics are nowadays commonly analyzed by leveraging Wi-Fi signals sent by devices that people carry with them and captured by an infrastructure of Wi-Fi scanners. Emitting such signals is not a feature for devices of only passersby, but also for printers, smart TVs, and other devices that exhibit a stationary behavior over time, which eventually end up affecting pedestrian crowd measurements. In this paper we propose a system that accurately counts nonstationary devices sensed by scanners, separately from stationary devices, using no information other than the Wi-Fi signals captured by each scanner in isolation. As counting involves dealing with privacy-sensitive detections of people's devices, the system discards any data in the clear immediately after sensing, later working on encrypted data that it cannot decrypt in the process. The only information made available in the clear is the intended output, i.e. statistical counts of Wi-Fi devices. Our approach relies on an object, which we call comb, that maintains, under encryption, a representation of the frequency of occurrence of devices over time. Applying this comb on the detections made by a scanner enables the calculation of the separate counts. We implement the system and feed it with data from a large open-air festival, showing that accurate anonymized counting of nonstationary Wi-Fi devices is possible when dealing with real-world detections.","PeriodicalId":423926,"journal":{"name":"Proceedings of the 25th International ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123906068","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}
Zhiwei Bao, Yulin Hu, Peng Sun, A. Boukerche, A. Schmeink
We consider an update-and-decide IoT-based wireless network, where information packets generated from dual sources are co-stored in the transmitter's buffer, while decisions are made at the destination. Two practical assumptions about the communications between the transmitter and destination are taken into account: the communications are operating with finite blocklength (FBL) codes, and truncated hybrid automatic repeat request (HARQ) schemes are exploited to improve the FBL reliability, i.e., the number of allowed rounds of (re)transmissions is finite. For the first time, this paper characterizes the timeliness of status updates, namely age upon decisions (AuD) (which highlights the timeliness of the information at decisions in comparison to the concept of age of information), for such truncated HARQ-assisted wireless network. First, we characterize the inter-arrival time between two adjacent successfully transmitted packets, while taking into consideration the preemption policy and the randomness of the number of preempted packets from the same source. In particular, the probability density function, statistical performance of such inter-arrival time are derived. Following these characterizations, we propose a new approach to determine the average AuD and obtain a closed-form expression accordingly. Via simulations, we evaluate the performance and conclude a set of guidelines for designs on the considered network.
{"title":"Average Age Upon Decisions of Wireless Networks with Truncated HARQ in the Finite Blocklength Regime","authors":"Zhiwei Bao, Yulin Hu, Peng Sun, A. Boukerche, A. Schmeink","doi":"10.1145/3551659.3559046","DOIUrl":"https://doi.org/10.1145/3551659.3559046","url":null,"abstract":"We consider an update-and-decide IoT-based wireless network, where information packets generated from dual sources are co-stored in the transmitter's buffer, while decisions are made at the destination. Two practical assumptions about the communications between the transmitter and destination are taken into account: the communications are operating with finite blocklength (FBL) codes, and truncated hybrid automatic repeat request (HARQ) schemes are exploited to improve the FBL reliability, i.e., the number of allowed rounds of (re)transmissions is finite. For the first time, this paper characterizes the timeliness of status updates, namely age upon decisions (AuD) (which highlights the timeliness of the information at decisions in comparison to the concept of age of information), for such truncated HARQ-assisted wireless network. First, we characterize the inter-arrival time between two adjacent successfully transmitted packets, while taking into consideration the preemption policy and the randomness of the number of preempted packets from the same source. In particular, the probability density function, statistical performance of such inter-arrival time are derived. Following these characterizations, we propose a new approach to determine the average AuD and obtain a closed-form expression accordingly. Via simulations, we evaluate the performance and conclude a set of guidelines for designs on the considered network.","PeriodicalId":423926,"journal":{"name":"Proceedings of the 25th International ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132090869","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}
WLANs, which have overtaken wired networks to become the primary means of connecting devices to the Internet, are prone to performance issues due to the scarcity of space in the radio spectrum. As a response, IEEE 802.11ax and subsequent amendments aim at increasing the spatial reuse of a radio channel by allowing the dynamic update of two key parameters in wireless transmission: the transmission power (TX_POWER) and the sensitivity threshold (OBSS_PD). In this paper, we present INSPIRE, a distributed online learning solution performing local Bayesian optimizations based on Gaussian processes to improve the spatial reuse in WLANs. INSPIRE makes no explicit assumptions about the topology of WLANs and favors altruistic behaviors of the access points, leading them to find adequate configurations of their TX_POWER and OBSS_PD parameters for the ''greater good" of the WLANs. We demonstrate the superiority of INSPIRE over other state-of-the-art strategies using the ns-3 simulator and two examples inspired by real-life deployments of dense WLANs. Our results show that, in only a few seconds, INSPIRE is able to drastically increase the quality of service of operational WLANs by improving their fairness and throughput.
{"title":"INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse in Dense WLANs","authors":"Anthony Bardou, Thomas Begin","doi":"10.1145/3551659.3559050","DOIUrl":"https://doi.org/10.1145/3551659.3559050","url":null,"abstract":"WLANs, which have overtaken wired networks to become the primary means of connecting devices to the Internet, are prone to performance issues due to the scarcity of space in the radio spectrum. As a response, IEEE 802.11ax and subsequent amendments aim at increasing the spatial reuse of a radio channel by allowing the dynamic update of two key parameters in wireless transmission: the transmission power (TX_POWER) and the sensitivity threshold (OBSS_PD). In this paper, we present INSPIRE, a distributed online learning solution performing local Bayesian optimizations based on Gaussian processes to improve the spatial reuse in WLANs. INSPIRE makes no explicit assumptions about the topology of WLANs and favors altruistic behaviors of the access points, leading them to find adequate configurations of their TX_POWER and OBSS_PD parameters for the ''greater good\" of the WLANs. We demonstrate the superiority of INSPIRE over other state-of-the-art strategies using the ns-3 simulator and two examples inspired by real-life deployments of dense WLANs. Our results show that, in only a few seconds, INSPIRE is able to drastically increase the quality of service of operational WLANs by improving their fairness and throughput.","PeriodicalId":423926,"journal":{"name":"Proceedings of the 25th International ACM Conference on Modeling Analysis and Simulation of Wireless and Mobile Systems","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130715392","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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