Browsing by Author "Deng, Der-Jiunn"

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3GPP NR Sidelink Transmissions Toward 5G V2X
(2020-01-01)
Lien, Shao-Yu
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Deng, Der-Jiunn
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Lin, Chun-Cheng
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Tsai, Hua-Lung
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Chen, Tao
Featuring direct communications between two user equipments (UEs) without signal relay through a base station, 3GPP sidelink transmissions have manifested their crucial roles in the Long-Term Evolution (LTE) Advanced (LTE-A) for public safety and vehicle-to-everything (V2X) services. With this successful development in LTE-A, the evolution of sidelink transmissions continues in 3GPP New Radio (NR), which renders sidelink an inevitable component as well as downlink and uplink. Targeting at offering low latency, high reliability and high throughout V2X services for advanced driving use cases, a number of new sidelink functions not provided in the LTE-A are supported in NR, including the feedback channel, grant-free access, enhanced channel sensing procedure, and new control channel design. To fully comprehend these new functions, this paper therefore provides essential knowledge of 3GPP NR sidelink transmissions, including the physical layer structure, resource allocation mechanisms, resource sensing and selection procedures, synchronization, and quality-of-service (QoS) management. Furthermore, this paper also provides performance evaluation to assess the gains brought from the new control channel design. As NR sidelink transmissions have been regarded as a foundation to provide advanced services other than V2X in future releases (e.g., advanced relay), potential enhancements are also discussed to serve the urgent demand in corresponding normative works.
A Bat-inspired Algorithm for Router Node Placement with Weighted Clients in Wireless Mesh Networks
(2014)
Lin, Chun-Cheng
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Li, Yan-Sing
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Deng, Der-Jiunn
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工業工程與管理學系
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Department of Industrial Engineering and Management
This paper considers the problem of the dynamic router node placement (dynRNP) with weighted clients in wireless mesh networks (WMNs), in which both mesh clients and mesh router have mobility, and mesh clients can switch on or off their network access at different times. The main objective of the conventional dynRNP problem is to maximize both network connectivity and client coverage, i.e., the size of the greatest subgraph component of the WMN topology and the number of the clients within radio coverage of mesh routers, respectively. Furthermore, this paper considers a more complicated dynRNP problem in WMNs, in which each client is associated with a weighted value (i.e., the mesh clients with higher weights values are served with larger priority), so that the two objective measures are redefined with consideration of clients\' weights. We resolve the weighted problem by a bat-inspired algorithm (BA) with dynamic local search selection scheme, which simulates the echolocation of bats to find the optimal solution. Finally, the performance of the proposed BA is compared with the original BA and PSO, and the effects of weights are analyzed.
A Genetic Algorithm Approach for Detecting Hierarchical and Overlapping Community Structure in Dynamic Social Networks
(2013)
Lin, Chun-Cheng
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Liu, Wan-Yu
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Deng, Der-Jiunn
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工業工程與管理學系
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Department of Industrial Engineering and Management
Social networks are merely a reflection of certain realities among people that have been identified. But in order for people or even computer systems (such as expert systems) to make sense of the social network, it needs to be analyzed with various methods so that the characteristics of the social network can be understood in a meaningful context. This is challenging not only due to the number of people that can be on social networks, but the changes in relationships between people on the social network over time. In this paper, we develop a method to help make sense of dynamic social networks. This is achieved by establishing a hierarchical community structure where each level represents a community partition at a specific granularity level. By organizing each level of the hierarchical community structure by granularity level, a person can essentially "zoom in" to view more detailed (smaller) communities and "zoom out" to view less detailed (larger) communities. Communities consisting of one or more subsets of people having relatively extensive links with other communities are identified and represented as overlapping community structures. Mechanisms are also in place to enable modifications to the social network to be dynamically updated on the hierarchical and overlapping community structure without recreating it in real time for every modification. The experimental results show that the genetic algorithm approach can effectively detect hierarchical and overlapping community structures.
A MapReduce-Based Ensemble Learning Method with Multiple Classifier Types and Diversity for Condition-Based Maintenance with Concept Drifts
(2017-11-01)
Lin, Chun-Cheng
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Shu, Lei
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Deng, Der-Jiunn
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Yeh, Tzu-Lei
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Chen, Yu-Hsiang
Condition-based maintenance in Industry 4.0 collects a huge amount of production datastreams continuously from the Internet of Things attached to machines to forecast the time when to maintain machines or replace components. However, as conditions of machines change dynamically with time owing to machine aging, malfunction, or replacement, the concept of capturing the forecasting pattern from the datastream could drift unpredictably, so it is hard to find a robust forecasting method with high precision. Therefore, this work proposes an ensemble learning method with multiple classifier types and diversity for condition-based maintenance in manufacturing industries, to address the bias problem when using only one base classifier type. Aside from manipulating data diversity, this method includes multiple classifier types, dynamic weight adjusting, and data-based adaption to concept drifts for offline learning models, to promote precision of the forecasting model and precisely detect and adapt to concept drifts. With these features, the proposed method requires powerful computing resources to efficiently respond to practical condition-based maintenance applications. Therefore, the implementation of this method based on the MapReduce framework is proposed to increase computational efficiency. Simulation results show that this method can detect and adapt to all concept drifts with a high precision rate.
A Novel Joint Problem of Routing, Scheduling, and Variable-Width Channel Allocation in WMNs
(2014)
Lin, Chun-Cheng
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Liu, Wan-Yu
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Chou, Chun-Hung
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Deng, Der-Jiunn
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工業工程與管理學系
This paper investigates a novel joint problem of routing, scheduling, and channel allocation for single-radio multichannel wireless mesh networks in which multiple channel widths can be adjusted dynamically through a new software technology so that more concurrent transmissions and suppressed overlapping channel interference can be achieved. Although the previous works have studied this joint problem, their linear programming models for the problem were not incorporated with some delicate constraints. As a result, this paper first constructs a linear programming model with more practical concerns and then proposes a simulated annealing approach with a novel encoding mechanism, in which the configurations of multiple time slots are devised to characterize the dynamic transmission process. Experimental results show that our approach can find the same or similar solutions as the optimal solutions for smaller-scale problems and can efficiently find good-quality solutions for a variety of larger-scale problems.
A Triangular NodeTrix Visualization Interface for Overlapping Social Community Structures of Cyber-Physical-Social Systems in Smart Factories
(2020-01-01)
Lin, Chun-Cheng
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Deng, Der-Jiunn
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Jhong, Shun-Yu
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工業工程與管理學系
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Department of Industrial Engineering and Management
This work proposes a cyber-physical-social system (CPSS) framework for smart factories which associates the social-network-based physical factory circumference with the cyber control through a visualization interface based on NodeTrix. The NodeTrix representation consisting of adjacency matrices and node-link diagrams has been popularly used in social network analysis. Different from previous works on NodeTrix based on square-shaped matrices, the proposed NodeTrix applies triangle-shaped matrices for visualizing overlapping communities to save the displaying space, and a simulated annealing algorithm is used to reduce the crossings among links and communities. In the CPSS for smart factories, working-in-process (WIP) products are categorized according to their attributes, and are placed at different storage locations for later different production processes. By the NodeTrix-based interface for CPSS, when users with different domains and experiences work together to make instant decisions on production and logistics, the physical production environment will be adjusted accordingly and immediately through technological support of Industry 4.0. Through the interface, the information on locations, parameters, and interoperability of WIP products can be visualized and adjusted in time. By comparison with other works, the proposed representation looks promising in displaying social networks for the CPSS with fewer crossings within a smaller screen space.
Applying Simulated Annealing to Joint Routing, Scheduling and Variable-Width Channel Allocation for Multi-hop WMNs
(2013)
Lin, Chun-Cheng
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Liu, Wan-Yu
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Chou, Chun-Hung
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Deng, Der-Jiunn
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工業工程與管理學系
This pager studies the joint routing, scheduling, and channel assignment problem in single-radio multi-channel WMNs. All the three subproblems are related to the packet transmission rate: the routing problem is to find the transmission paths for packets; the scheduling problem is to plan the transmission order of packets; the channel assignment problem is related to signal interference and synchronization. The joint problem has been studied in the past, but they can only solve small-scale problems and did not consider the effect of signal interference, so that their methods are not efficient and do not meet the practical scenario. With regards to this, we propose a simulated annealing approach with a new coding scheme that divides time into a number of time slots to model the dynamic process of packets. In addition, this paper demonstrates a variable-bandwidth channel allocation method that achieves a good balance between higher synchronization and better control of interference. Experimental results show that our proposed simulated annealing approach can find the same or similar solutions with the exact methods for the cases with a small number of nodes and low traffic load.
Concept Drift Detection and Adaption in Big Imbalance Industrial IoT Data Using an Ensemble Learning Method of Offline Classifiers
(2019-01-01)
Lin, Chun-Cheng
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Deng, Der-Jiunn
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Kuo, Chin-Hung
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Chen, Linnan
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工業工程與管理學系
In a smart factory, thousands of industrial Internet of Things (IIoT) devices or sensors are installed in production machines to collect big data on machine conditions and transmit it to a cyber-physical system in the cloud center of the factory. Then, the system employs a variety of condition-based maintenance (CBM) methods to predict the time point when machines start to be operated abnormally and to maintain them or replace their components in advance so as to avoid manufacturing enormous detective products. CBM suffers from problems of concept drifts (i.e., the distribution of fault patterns may change over time) and imbalance data (i.e., the data with faults accounts for a minority of all data). Ensemble learning that integrates the diversity of multiple classifiers provides a high-performance solution to address these problems. In practice, most companies may not have a sufficient budget to establish a sound infrastructure to support real-time online classifiers, but may have off-the-shelf offline classifiers in their existing systems. However, most previous works on ensemble learning only focused on supporting online classifiers. Consequently, this work proposes an ensemble learning algorithm that supports offline classifiers to cope with three-stage CBM with concept drifts and imbalance data, in which Stages 1 (training an ensemble classifier) and 3 (creating a new ensemble) employ an improved Dynamic AdaBoost.NC classifier and the SMO method to address imbalance data; and Stage 2 (detecting concept drifts in imbalance data) employs an improved LFR (Linear Four Rates) method. The experimental results on datasets with different degrees of imbalance show that the proposed method can successfully detect all concept drifts, and has a high accuracy rate in detecting minority-class data, which is over 94%.
Dynamic Multiservice Load Balancing in Cloud-Based Multimedia System
(2014-03-01)
Lin, Chun-Cheng
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Chin, Hui-Hsin
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Deng, Der-Jiunn
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工業工程與管理學系
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Department of Industrial Engineering and Management
Consider a centralized hierarchical cloud-based multimedia system (CMS) consisting of a resource manager, cluster heads, and server clusters, in which the resource manager assigns clients' requests for multimedia service tasks to server clusters according to the task characteristics, and then each cluster head distributes the assigned task to the servers within its server cluster. For such a complicated CMS, however, it is a research challenge to design an effective load balancing algorithm that spreads the multimedia service task load on servers with the minimal cost for transmitting multimedia data between server clusters and clients, while the maximal load limit of each server cluster is not violated. Unlike previous work, this paper takes into account a more practical dynamic multiservice scenario in which each server cluster only handles a specific type of multimedia task, and each client requests a different type of multimedia service at a different time. Such a scenario can be modelled as an integer linear programming problem, which is computationally intractable in general. As a consequence, this paper further solves the problem by an efficient genetic algorithm with an immigrant scheme, which has been shown to be suitable for dynamic problems. Simulation results demonstrate that the proposed genetic algorithm can efficiently cope with dynamic multiservice load balancing in CMS.
Dynamic Weighted Fog Computing Device Placement Using a Bat-Inspired Algorithm with Dynamic Local Search Selection
(1970-01-01)
Lin, Chun-Cheng
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Deng, Der-Jiunn
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Suwatcharachaitiwong, Sirirat
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Li, Yan-Sing
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工業工程與管理學系
This work investigates the dynamical weighted deployment of mobile fog computing devices to support a mobile edge computing environment, in which each edge device is associated with a weight to reflect its importance based on the application. Since edge devices are mobile and could be switched off, it is challenging to dynamically optimize the deployment to adapt to dynamic change. This work further models the problem mathematically and solves it by a bat-inspired algorithm (BA), which searches the optimal solutions by simulating the food-searching behavior of bats via echolocation. Furthermore, three local search methods designed specifically for this problem are integrated into the BA, and a dynamic local search selection mechanism is proposed to adjust the probabilities of choosing the three local search methods iteratively in the BA main loop. Simulation results show outperformance of the proposed BA over the BA without local search and the previous approach.
E-BEB: Enhanced Binary Exponential Backoff Algorithm for Multi-hop Wireless Ad-hoc Networks
(2014-05-01)
Chin, Hui-Hsin
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Lin, Chun-Cheng
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Deng, Der-Jiunn
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工業工程與管理學系
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Department of Industrial Engineering and Management
Binary exponential backoff algorithm is the de-facto medium access control protocol for wireless local area networks, and it has been employed as the standard contention resolution algorithm in multi-hop wireless ad-hoc networks. However, this algorithm does not function well in multi-hop wireless environments due to its several performance issues and technical limitations. In this paper, we propose a simple, efficient, priority provision, and well performed contention resolution algorithm called enhanced binary exponential backoff (E-BEB) algorithm for impartial channel access in multi-hop wireless ad-hoc networks. We also provide a simple and accurate analytical model to study the system saturation throughput of the proposed scheme. Simulations are conducted to evaluate the performance of E-BEB algorithm. The results show that the E-BEB algorithm can alleviate the fairness problem and support multimedia transmission in multi-hop wireless environments.
Energy-efficient placement and sleep control of relay nodes in heterogeneous small cell networks
(2017-02)
Lin, Chun-Cheng
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Deng, Der-Jiunn
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Jhong, Shun-Yu
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工業工程與管理學系
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Department of Industrial Engineering and Management
Small cell (SC) provides low-power radio access with a relatively smaller coverage range than a macrocell. It has been regarded as a key solution for offloading traffic in the future 5G system. Previous works on energy-efficient placement of base stations (BSs) in cellular networks introduced placement and sleep control of low-power relay stations (RSs) to reduce the total power consumption for downlink transmission from BSs to users. As compared with legacy RSs, SC can serve as an RS with a larger coverage range though consuming a bit more circuit power. Hence, this work additionally considers to deploy small cells (SCs) in a one-dimensional highway cellular network with BSs and legacy RSs, and further models the power consumption minimization problem for placement and sleep control of legacy RSs and SCs in this network. Since the problem is hard to be solved analytically, a genetic algorithm with dynamic operator-selection mechanism is further proposed to resolve this problem. Performance of the proposed algorithm is evaluated via simulation with a practical experimental parameter setting.
Extending the Lifetime of Dynamic Underwater Acoustic Sensor Networks Using Multi-Population Harmony Search Algorithm
(2016-06-01)
Lin, Chun-Cheng
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Deng, Der-Jiunn
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Wang, Shang-Bin
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工業工程與管理學系
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Department of Industrial Engineering and Management
Like wireless sensor networks, lifetime of sensors is the main constraint for performance of underwater acoustic sensor networks (UASNs). Most previous works on UASNs did not consider dynamics of networks, i.e., as time goes by, in practice, part of sensors may be malfunctioned, deplete their battery power, or get lost due to violent underwater environment changes. Therefore, this paper considers a UASN in ocean and proposes a sleep scheduling scheme in which sensor nodes and autonomous underwater vehicles in this network can dynamically choose to sleep or work to adapt to the environmental change. The concerned problem is to dynamically determine a sufficient number of active nodes in the UASN at different times, such that the targets required to be detected are covered. A special static scenario of the problem has been shown to be NP-complete. Hence, this paper proposes an improved multi-population harmony search algorithm to solve this dynamic problem. By simulation, the proposed algorithm shows high performance in terms of extending network lifetime, robustness, and computing time.
Forecasting Rare Faults of Critical Components in LED Epitaxy Plants Using a Hybrid Grey Forecasting and Harmony Search Approach
(2016-12)
Lin, Chun-Cheng
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Deng, Der-Jiunn
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Kang, Jia-Rong
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Chang, Sen-Chia
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Chueh, Chuang-Hua
In the light emitting diode (LED) manufacturing industry, the most expensive and crucial facilities are manufacturing machines. Condition-based maintenance (CBM) for crucial components of a manufacturing machine aims to forecast in advance the precise time when some aging component will be broken and replace it in time, to avoid performing abnormally to manufacture defect products. This study focuses on the CBM for a crucial component called particle filter of a pneumatic conveyor machine in the LED epitaxy plant. Conventional forecasting methods were based on the theory of statistics, which requests a large number of data samples and assumes some probability distribution. With advance of machine technology, however, the data samples of broken particle filters to be collected are very few, such that those conventional methods cannot be applied. As a result, this study proposes a novel hybrid grey forecasting and harmony search approach, in which grey forecasting was shown to perform well for small data samples. In the proposed method, operating conditions of particle filters are monitored and collected by industrial sensors. Then, those data are preprocessed by data filtering and clustering. Finally, a hybrid grey forecasting and harmony search approach is used to fit the curve of the aging condition of a particle filter. Numerical analysis of a real example in an LED epitaxy plant shows that the proposed method performs better than conventional methods.
Forecasting Rare Faults of Critical Components in LED Epitaxy Plants Using a Hybrid Grey Forecasting and Harmony Search Approach
(2016-12)
Lin, Chun-Cheng
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Deng, Der-Jiunn
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Kang, Jia-Rong
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Chang, Sen-Chia
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Chueh, Chuang-Hua
In the light emitting diode (LED) manufacturing industry, the most expensive and crucial facilities are manufacturing machines. Condition-based maintenance (CBM) for crucial components of a manufacturing machine aims to forecast in advance the precise time when some aging component will be broken and replace it in time, to avoid performing abnormally to manufacture defect products. This study focuses on the CBM for a crucial component called particle filter of a pneumatic conveyor machine in the LED epitaxy plant. Conventional forecasting methods were based on the theory of statistics, which requests a large number of data samples and assumes some probability distribution. With advance of machine technology, however, the data samples of broken particle filters to be collected are very few, such that those conventional methods cannot be applied. As a result, this study proposes a novel hybrid grey forecasting and harmony search approach, in which grey forecasting was shown to perform well for small data samples. In the proposed method, operating conditions of particle filters are monitored and collected by industrial sensors. Then, those data are preprocessed by data filtering and clustering. Finally, a hybrid grey forecasting and harmony search approach is used to fit the curve of the aging condition of a particle filter. Numerical analysis of a real example in an LED epitaxy plant shows that the proposed method performs better than conventional methods.
IEEE 802.11ba Wake-Up Radio: Performance Evaluation and Practical Designs
(2020-01-01)
Deng, Der-Jiunn
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Lien, Shao-Yu
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Lin, Chun-Cheng
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Gan, Ming
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Chen, Hsing-Chung
Featuring a long operation time to avoid frequent battery replacements, highly energy-efficient radios have shown their pivotal functions for low-power and battery-driven devices in supporting manifold Internet-of-Things (IoT) services. To this end, prolific systems for highly energy-efficient radios have been widely deployed, but they impose a critical challenge of sacrificed data rates to preclude the applications that both require high energy efficiency and high data rates. Recently, this challenge has driven the concept of equipping two radio-chains on a low-power device, known as the primary connectivity radio (PCR) and companion connectivity radio (CCR). The PCR supports high data rate transmissions, and switches to the sleeping mode when no data should be received. In this case, the CCR stays awake to monitor traffic, and wakes up the PCR when data requiring to be received arrives. However, to practice such a concept, sophisticated operations in the physical (PHY) layer and medium access control (MAC) layers are required. Consequently, the IEEE 802.11 Task Group "ba" (TGba) has been formed to launch the normative works since 2017. In the normative development, the most challenging issue lies in the design of a new frame structure especially the preamble sequence, by which the CCR in a station (STA) can efficiently synchronize with an access point (AP) and the PCR can be promptly waken up when an AP wishes to transmit data to an STA. To comprehend such a crucial foundation for the next generation low-power IoT devices, this paper provides comprehensive knowledge of state-of-the-art PHY/MAC operations of IEEE 802.11ba. Most importantly, a preamble sequence design for synchronization is further proposed for the new frames supported in IEEE 802.11ba. Through comprehensively evaluating the performance of different designs (in terms of data rate configuration and synchronization sequences), we justify the outstanding performance of the proposed design in terms of the synchronization error rate and packet error rate, to satisfy the urgent demands in the normative works of IEEE 802.11ba.
Key Design of Driving Industry 4.0: Joint Energy-Efficient Deployment and Scheduling in Group-Based Industrial Wireless Sensor Networks
(2016-10)
Lin, Chun-Cheng
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Deng, Der-Jiunn
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Chen, Zheng-Yu
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Chen, Kwang-Cheng
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交大名義發表
In the Industry 4.0 framework based on IoT and smart manufacturing, it is essential to support factory automation and flexibility in harsh or dynamic industrial environments. State-of-the-art technology suggests building a controlled work-space using large-scale deployment of wireless sensors. To overcome the technological challenges in scalability and heterogeneity for large-scale industrial deployment, group-based industrial wireless sensor networks (GIWSNs) are suggested, in which wireless sensors are divided into multiple groups for multiple monitoring tasks, and each group of sensors is deployed densely within a subarea in a large plant or along a long production/assembly line, while connectivity between groups is required. As wireless sensors are equipped with batteries with limited power, it has been challenging to plan sleep schedules of sensors, which are influenced significantly by deployment of such a large-scale GIWSN. However, most previous works on wireless sensor networks independently investigated deployment and sleep scheduling problems, both of which have been shown to be NP-hard. Therefore, this work jointly considers deployment and sleep scheduling of sensors in a GIWSN along a production line. Via the theory of symmetries, we alleviate the computational concerns from multiple groups to one group and another medium-size group. Then we propose a hybrid harmony search and genetic algorithm, which incorporates deployment and sleep schedules to reduce energy consumption. Simulations verify this joint methodology to effectively achieve energy efficiency.
Latency Control in Software-Defined Mobile-Edge Vehicular Networking
(2017-08-01)
Deng, Der-Jiunn
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Lien, Shao-Yu
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Lin, Chun-Cheng
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Hung, Shao-Chou
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Chen, Wei-Bo
A cloud radio access network deployed on top of edge networks as a software-defined radio access network architecture has been regarded as a promising paradigm for the next generation mobile networks, but it has not received considerable attention in the vehicular industry due to distinct design needs. For mobile networks, infrastructures provide powerful computation capability for universal radio resource optimization, and therefore an inherent "cloud-down" design is favorable. On the contrary, to deploy vehicular technologies, sophisticated processing capability has been a baseline feature for the next generation driving machines, which may not solely rely on wireless infrastructures to guarantee driving safety, so an "edge-up" design is preferred. Consequently, this article surveys the recent works on software-defined vehicular networks and proposes a series of edge-up software-defined networking designs, particularly emphasizing the most crucial function of latency control to support possible wireless applications for the next generation driving machines. The proposed designs thus create a paradigm shift to enable software-defined mobile-edge vehicular networking.
Lifetime Enhancement of Dynamic Heterogeneous Wireless Sensor Networks with Energy-Harvesting Sensors
(2017-10-01)
Lin, Chun-Cheng
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Chen, Yung-Chiao
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Chen, Jiann-Liang
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Deng, Der-Jiunn
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Wang, Shang-Bin
Lifetime enhancement has been the major constraint of developing wireless sensor networks (WSNs). Most of previous related works separately considered dynamics and heterogeneity of WSNs, and did not consider energy-harvesting (EH) sensors, which can absorb natural power (e.g., solar and wind power) to extend lifetime of sensor devices. Therefore, this work investigates the problem of extending the lifetime of dynamic heterogeneous WSNs with EH sensors to enhancing the total WSN lifetime. This problem can be characterized as finding the maximal number of covers each of which is a part of all sensors so that all targets can be monitored by these sensors. Since the case for static WSNs has been shown to be NP-complete, the concerned problem is also NP-complete. Hence, this work first models this problem mathematically, and then proposes a novel harmony search algorithm with multiple populations and local search (HSAML) for this problem with dynamics, heterogeneity, and EH sensors. By simulation, the network lifetime, stability, and executing time of the proposed algorithm are analyzed. From experimental results, the proposed HSAML performs better than the conventional algorithm in terms of average network lifetime for larger-scale problems (i.e., when the number of common and EH sensors is small). In addition, the results confirm that adding EH sensors really helps extend the total WSN lifetime.
MANY-OBJECTIVE SENSOR SELECTION IN IOT SYSTEMS
(2017-06-01)
Lin, Chun-Cheng
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Deng, Der-Jiunn
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Lu, Liang-Yi
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交大名義發表
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工業工程與管理學系
The Internet of Things connects physical objects through sensor devices with multiple functionalities. At the planning stage of deploying an IoT system, we are concerned about sensor selection in the IoT system, which allocates predefined IoT services to multiple sensor devices so as to optimize one or more objectives associated with these allocations, under energy and distance constraints. The sensor selection problem that optimizes a utility function in other applications has been shown to be NP-hard, and the number of IoT services concerned is enormous in practice. Hence, it is suitable to apply evolutionary algorithms (EAs) for solving the large-scale problem with multiple objectives. Recently, the paradigm of multiple-objective EAs (which often address only two or three objectives) has advanced to many-objective EAs (which are intended to address four or more objectives that may be in conflict with each other in many cases). Therefore, this article considers many objectives of the sensor selection problem in the IoT system, including optimization of communication energy consumption, energy balancing on all devices, energy harvesting, green concerns, and QoS. The problem is resolved by a tailored many-objective EA based on decomposition to increase computational efficiency and solution quality. By simulation, the proposed EA is shown to be promising through scatter charts and parallel coordinates.