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2010 IEEE International Conference on Communications

作者： Jouini, Wassim Ernst, Damien Moy, Christophe Palicot, Jacques SUPELEC/IETR France University of Liège France

ISBN: (纸本)9781424464043

In this paper, we consider the problem of exploiting spectrum resources for a secondary user (SU) of a wireless communication network. We suggest that upper confidence bound (UCB) algorithms could be useful to design decision making strategies for SUs to exploit intelligently the spectrum resources based on their past observations. The algorithms use an index that provides an optimistic estimation of the availability of the resources to the SU. The suggestion is supported by some experimental results carried out on a specific dynamic spectrum access (DSA) framework.

关键词： Cognitive Radio Dynamic Spectrum Access upper confidence bound algorithm

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ATWR-SMR: An Area-Constrained Truthful-Worker Recruitment-Based Sensing Map Recovery Scheme for Sparse MCS in Extreme-Environment Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2024年第3期11卷 3711-3724页

作者： Fu, Xiangwan Liu, Anfeng Xiong, Neal N. Wang, Tian Zhang, Shaobo Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Sul Ross State Univ Dept Comp Sci & Math Alpine TX 79830 USA Beijing Normal Univ Artificial Intelligence & Future Networks Zhuhai 519000 Guangdong Peoples R China UIC Zhuhai 519087 Guangdong Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Hunan Peoples R China

Sparse mobile crowdsensing (SMCS) is a prospective solution for large-scale data sensing through mobile devices of Internet of Things (IoT) systems where IoT systems cannot obtain the sensing data of the area under extreme environments. The unsensed area data can be obtained by the data inference algorithm trained by the sensed data of recruited workers. However, recruited workers may upload false data in exchange for payment, and the platform is unable to distinguish between true and false data. In this article, our goal is to maximize the SMCS platform's total profit, where the platform cannot verify the authenticity of the sensed data, and the requester's payment is based on the sensing task's data quality. To meet the objective, we propose the area-constrained truthful worker recruitment-based sensing map recovery (ATWR-SMR) scheme, which includes the area constraint, the area-constrained truthful worker recruitment, and the sensing map recovery. 1) The area constraint establishes the importance of areas by history data differences in the sliding window. 2) The truthful worker recruitment identifies trustworthy workers by the truthful upper confidence bound algorithm and recruits low-cost trustworthy workers to sense high-importance areas. 3) The sensing map recovery infers the unsensed data by the deep matrix factorization algorithm trained by the history truthful data set. Finally, we verify the effectiveness of the ATWR-SMR scheme in improving the total profit of the platform through extensive comparison experiments based on the China air quality data set.

关键词： Sensors History Internet of Things Recruitment Inference algorithms Crowdsensing Costs Internet of Things (IoT) sensing map recovery sparse mobile crowdsensing (SMCS) truthful worker recruitment upper confidence bound algorithm

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Learning-Based Adaptive Sensor Selection Framework for Multi-Sensing WSN

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IEEE SENSORS JOURNAL 2021年第12期21卷 13551-13563页

作者： Ghosh, Sushmita De, Swades Chatterjee, Shouri Portmann, Marius Indian Inst Technol Delhi UQ IITD Acad Dept Elect Engn New Delhi 110016 India Univ Queensland Sch IT & Elect Engn Brisbane Qld 4072 Australia

Wireless sensor nodes equipped with multiple sensors often have limited energy availability. To optimize the energy sustainability of such sensor hubs, in this paper a novel adaptive sensor selection framework is proposed. Multiple sensors monitoring different parameters in the same environment often possess cross-correlation, which makes the system predictive. To this end, a learning-based optimization strategy is developed using upper confidence bound algorithm to select an optimum active sensor set in a measurement cycle based on the cross-correlations among the parameters, energy consumed by the sensors, and the energy available at the node. Further, a Gaussian process regressor-based prediction model is used to predict the parameter values of inactive sensors from the cross-correlated parameters of active sensors. To evaluate the performance of the proposed framework in real-life applications, an air pollution monitoring sensor node consisting of seven sensors is deployed in the campus that collects data at a default high sampling rate. Simulation results validate the efficiency and efficacy of the proposed framework. Compared to the current state-of-the-art the proposed algorithm is 54% more energy efficient, with complexity O(2(P)) for P sensors in the node, while maintaining an acceptable range of sensing error.

关键词： Adaptive sensor selection framework energy saving Gaussian process regression model Multi-parameter sensor hub parameter cross-correlation reinforcement learning upper confidence bound algorithm

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Multi-strategy-based artificial bee colony algorithm for AUV path planning with angle constraints

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OCEAN ENGINEERING 2024年第Part3期312卷

作者： Xu, Jian Han, Zhiyong Yin, Liangang Yan, Zheping Yu, Yuyang Ma, Guangzhi Harbin Engn Univ Harbin 150001 Peoples R China Harbin Engn Univ Qingdao Innovat & Dev Ctr Qingdao 266000 Peoples R China

This paper focuses on a path planning problem of an autonomous underwater vehicle (AUV) traversing target points at desired angles in an obstacle environment with eddy currents. A tangent-spatial partition method for path planning model construction is developed, which guarantees the direction of arrival in addition to extending the scope of waypoint search. An improved artificial bee colony algorithm integrating with multiple evolutionary strategies (ABC-MES) is proposed. More specifically, a population initialization method that follows the best point set principle is presented to increase population diversity and accelerate convergence. A multi-strategy evolutionary approach is developed in the employed bees phase to enhance population quality and establish a decision support database for subsequent upper confidence bound (UCB) learning. The UCB algorithm is then applied in the onlooker bee stage for assessment and screening of the optimal evolutionary strategy according to the accumulation of prior knowledge and the exploration of new knowledge. Finally, the T-distribution and reversal learning are explored in the scout bee stage to update old nectar sources to prevent the algorithm from falling into the local optimal prematurely. The strong ability of the proposed ABC-MES algorithm to jump out of the local optima is ensured by comparing it with 9 existing algorithms in terms of accuracy and stability with the help of 28 test functions. Simulation results reveal that the path generated by the proposed ABC-MES algorithm has lower overall cost accounting for time efficiency, navigation distance and energy consumption.

关键词： Artificial bee colony algorithm Path planning Multi-strategy co-evolution upper confidence bound algorithm

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Multi-armed bandit based policies for cognitive radio's decision making issues

Multi-armed bandit based policies for cognitive radio's deci...

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3rd International Conference on Signals, Circuits and Systems

作者： Jouini, Wassim Ernst, Damien Moy, Christophe Palicot, Jacques SUPELEC/IETR France University of Liège France

ISBN: (纸本)9781424443970

We suggest in this paper that many problems related to Cognitive Radio's (CR) decision making inside CR equipments can be formalized as Multi-Armed Bandit problems and that solving such problems by using upper confidence bound (UCB) algorithms can lead to high-performance CR devices. An application of these algorithms to an academic Cognitive Radio problem is reported.

关键词： Cognitive Radio Decision making Multi-Armed Bandit upper confidence bound algorithm

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Governance Policies in IT Service Support 19

Governance Policies in IT Service Support

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19th International Conference on Enterprise Information Systems (ICEIS)

作者： Puvvala, Abhinay Rai, Veerendra K. Tata Consultancy Serv 54B Hadapsar Ind Estate Pune 411013 Maharashtra India

ISBN: (纸本)9789897582479

IT Service support provider, whether outsourced or kept in-house, has to abide by the Service Level Agreements (SLA) that are derived from the business needs. Critical for IT Service support provider are the human resources that are expected to resolve tickets. It is essential that the policies, which govern the tickets' movement amongst these resources, follow the business objectives such as service availability and cost reduction. In this study, we propose an agent based model that represents an IT Service Support system. A vital component in the model is the agent 'Governor', which makes policy decisions by reacting to changes in the environment. The paper also studies the impact of various behavioural attributes of the Governor on the service objectives.

关键词： IT Service Support Agent based Model Governance Policies upper confidence bound algorithm

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Delay-reliability-aware protocol adaption and quality of service guarantee for message queuing telemetry transport-empowered electric Internet of things

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2022年第5期18卷

作者： Zhang, Hao Zhang, Hongshuo Wang, Zhao Zhou, Zhenyu Wang, Qiang Xu, Guangyuan Yang, Junzhong Gan, Zhong North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China XJ Grp Corp Xuchang Henan Peoples R China Smart Grid Anhui Elect Power Co Ltd Chuzhou Power Supply Co Chuzhou Peoples R China Smart Grid Jiangsu Elect Power Co Ltd Taizhou Power Supply Co Taizhou Peoples R China State Grid Shanghai Municipal Elect Power Co Power Dispatching & Control Ctr Shanghai Peoples R China

Message queuing telemetry transport has emerged as a promising communication protocol for resource-constrained electric Internet of things due to high bandwidth utilization, simple implementation, and various quality of service levels. Enabled by message queuing telemetry transport, electric Internet of things gateways adopt dynamic protocol adaptation, conversion, and quality of service level selection to realize bidirectional communication with massive devices and platforms based on heterogeneous communication protocols. However, protocol adaptation and quality of service guarantee in message queuing telemetry transport-empowered electric Internet of things still faces several challenges, such as unified communication architecture, differentiated quality of service requirements, lack of quality of service metric models, and incomplete information. In this paper, we first establish a unified communication architecture for message queuing telemetry transport-empowered electric Internet of things for adaptation and conversion of heterogeneous protocols. Second, we formulate the quality of service level selection optimization problem to minimize the weighted sum of packet-loss ratio and delay. Then, a delay-reliability-aware message queuing telemetry transport quality of service level selection algorithm based on upper confidence bound is proposed to learn the optimal quality of service level through dynamically interacting with the environment. Compared with single and fixed quality of service level selection strategies, delay-reliability-aware message queuing telemetry transport quality of service level selection can effectively reduce the weighted sum of delay and packet-loss ratio and satisfy the differentiated quality of service requirements of electric Internet of things.

关键词： Message queuing telemetry transport electric Internet of things protocol adaption quality of service guarantee delay and reliability awareness upper confidence bound algorithm dynamic quality of service level selection

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A Task Oriented Computation Offloading algorithm for Intelligent Vehicle Network With Mobile Edge Computing

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IEEE ACCESS 2019年 7卷 180491-180502页

作者： Liu, Jun Wang, Shoubin Wang, Jintao Liu, Chang Yan, Yan Northeastern Univ Coll Comp Sci & Engn Shenyang 110169 Peoples R China Beijing Inst Remote Sensing Informat Beijing 100085 Peoples R China Shenyang Aerosp Univ Civil Aviat Inst Shenyang 110136 Peoples R China

With the rise of intelligent and connected vehicles (ICVs), new vehicle applications continue to emerge, while the computing capability of vehicles remains limited. Mobile edge computing (MEC) is considered to be the most effective technique for mitigating vehicle computing pressure, with computation offloading being a key technology for MEC. To solve the problem of excessive task processing delay and energy consumption due to the vehicle-limited computing power in the vehicular network, we consider the tasks and the characteristics of MEC, and divide the tasks into indivisible tasks and divisible tasks according to the size of data (that is, whether it affects functionality after segmentation). Then, two computation offloading algorithms are proposed named binary offloading and partial offloading separately. The binary offloading unloads the task to the mobile edge computing server as a whole and selects only an optimal offloading site;thus, an improved upper confidence bound algorithm is adopted. The partial offloading divides the complex tasks with large data volumes through time slots processed by different MEC servers, and uses the Q-learning algorithm to find the most effective offloading strategy. The simulation results show that the total cost of delay and energy consumption of the binary offloading algorithm is lower when processing computationally intensive tasks. When addressing divisible and complex tasks, the partial offloading algorithm improves the real-time performance of the tasks significantly and conserves the energy of the vehicle terminal.

关键词： Mobile edge computing intelligent and connected vehicle upper confidence bound algorithm Markov model Q-learning

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