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To support dramatically increasing services from internet of thing (IoT) devices with the sporadic and fluctuated generation of short packet traffic, this paper investigates joint dynamic time division duplexing (TDD) and radio resource control (RRC) connection management in a single-cell massive IoT network. Specifically, under the grant-free transmission incurring packet collision, this study models the factors affecting the time resource utilization (TRU) and energy consumption of IoT devices as a comprehensive system utility and further formulates the problem as a decision-making process aiming for balancing the long-term average TRU and energy consumption. To address the formulated problem, based on the deep reinforcement learning framework, this paper designs an experience-driven joint dynamic TDD and RRC connection management scheme that intelligently i) determines the TDD configuration based on the most recent downlink (DL)/uplink (UL) traffic demands and ii) adjusts the RRC state of each IoT device to control the maximum number of transmitting IoT devices. Finally, trace-driven simulation results demonstrate that the proposed scheme outperforms existing benchmarks, such as Static TDD and Dynamic TDD, in terms of transmission success ratio difference (TSRD) (up to 89% reduction), time resource utilization (TRU) (up to $17\times $ increase), and energy consumption (up to 70% reduction) of IoT devices.

This paper proposes a scheme for predicting ground reaction force (GRF) and center of pressure (CoP) using low-cost FSR sensors. GRF and CoP data are commonly collected from smart insoles to analyze the wearer’s gait and diagnose balance issues. This approach can be utilized to improve a user’s rehabilitation process and enable customized treatment plans for patients with specific diseases, making it a useful technology in many fields. However, the conventional measuring equipment for directly monitoring GRF and CoP values, such as F-Scan, is expensive, posing a challenge to commercialization in the industry. To solve this problem, this paper proposes a technology to predict relevant indicators using only low-cost Force Sensing Resistor (FSR) sensors instead of expensive equipment. In this study, data were collected from subjects simultaneously wearing a low-cost FSR Sensor and an F-Scan device, and the relationship between the collected data sets was analyzed using supervised learning techniques. Using the proposed technique, an artificial neural network was constructed that can derive a predicted value close to the actual F-Scan values using only the data from the FSR Sensor. In this process, GRF and CoP were calculated using six virtual forces instead of the pressure value of the entire sole. It was verified through various simulations that it is possible to achieve an improved prediction accuracy of more than 30% when using the proposed technique compared to conventional prediction techniques.

Personal data is becoming increasingly valuable in business, as the insights that can be obtained from data processing continue to improve. However, it also can cause adverse effects on individuals. To improve data quality while satisfying privacy compliance, companies now have focused on collecting informed consent from individuals to directly handle personal data without applying any privacy-preserving techniques. Even though the companies obtain consent to use personal data, to improve transparency and accountability to ensure that companies deal with personal data according to consent, it is necessary for a system to comply with privacy requirements. Therefore, this paper proposes a new consent-based privacy-compliant personal data-sharing system that considers personal data-sharing flows and requirements obtained from enterprises and privacy frameworks, respectively. By analyzing a general process and the roles of actors for data sharing in enterprise environments according to standard privacy frameworks, this paper has proposed system requirements, system architecture, and detailed procedure for a consent-based privacy-compliant processing method that considers compliance checking as well as consent checking. To show the feasibility of the proposed system, this paper demonstrates a prototype and the performance analysis in the lab and real-world environments.

Personal data have become the key to data-driven services and applications whereas privacy requirements are now strongly imposed by regulations. Meanwhile, people find it difficult to understand whether the services and applications handle personal data to comply with their agreements and regulations. Therefore, the need for privacy indicators, which summarize privacy contents as forms of privacy scoring, labels, etc., has increased to empower the users’ rights by providing understandable information about privacy. For firm privacy indicators, proper criteria and methods for evaluating the level of privacy risks and compliance are required. Accordingly, this paper proposes a privacy scoring framework for services in the context of handling personal data, inspired by six standardized indicators. This paper introduces detailed information on standardized indicators and proposes privacy indicators to quantify privacy scores. Also, this paper proposes methods for evaluating privacy policy based on a set of machine learning-based hierarchical binary classifiers and processes for quantifying the level of privacy risks and compliance from privacy-related information. Through analyzing privacy policies and data access lists of more than 10,000 mobile applications on Google Play Store and investigating case studies on privacy scoring of some mobile applications, this paper shows the feasibility of the proposed framework.

This paper proposes a profit maximization model for a data consumer when it buys personal data from data providers (by obtaining consent) through data brokers and provides their new services to data providers (i.e., service consumers). To observe the behavioral models of data providers, the data consumer, and service consumers, this paper proposes the willingness-to-sell model of personal data of data providers (which is affected by data providers’ behavior related to explicit consent), the service quality model obtained by the collected personal data from the data consumer’s perspective, and the willingness-to-pay model of service consumers regarding provided new services from the data consumer. Particularly, this paper jointly considers the behavior of data providers and service users under a limited budget. With parameters inspired by real-world surveys on data providers, this paper shows various numerical results to check the feasibility of the proposed models.

This paper proposes a novel user-preference-aware power scheduling scheme for application at an electric vehicle (EV) charging facility. Here, the preference of the EV user is characterized as a utility function by considering two different factors: 1) a satisfaction factor according to the charged energy, and 2) payment for the received charging service. As a key component of the power scheduling method, this paper proposes a two-stage power charging method and analyzes the advantage of the proposed method from a monetary perspective. In addition, this paper analyzes the economic benefits of a charging facility and EVs using the single-leader, multi-follower Stackelkberg game. By showing the existence of a unique best response for each participant, this paper presents the improvement of financial profit of EV users and charging facility through comparison with the case where the charging facility does not apply a proper power management scheme. Based on the actual world datasets, this paper shows that the proposed power scheduling scheme is feasible for actual environment. With satisfying the constraints, it is possible to reduce overall electricity cost up to 8.59% compared to the case without considering the peak power in EV charging facility.

This paper proposes a novel energy-conscious access point ( $e$ AP) system with cross-layer design to increase the energy efficiency of IoT devices in IEEE 802.11 Wi-Fi networks for reliable IoT services. The proposed $e$ AP system controls the energy resources of IoT devices to extend the lifetime of the IoT device. For this purpose, we develop a new $e$ AP system that considers a cross-layer design with a prompt TCP ACK transmit function, a caching-and-retransmit IoT data function, and a multiple IoT data aggregate function to improve the energy efficiency of the IoT device. In addition, the proposed $e$ AP system has a device energy management module that precisely controls operating parameters, such as the transmission period of IoT packets, the delivery of traffic indication message (DTIM) value of IoT devices, and the transmitting power of IoT devices. These features extend the lifetime of the battery-powered IoT devices while satisfying service requirements for reliable IoT services. The long listening time of TCP ACK messages in receive mode (Rx) results in the high energy consumption of IoT devices due to the large round trip time. The proposed $e$ AP system reduces the reception time of TCP ACK messages in the IoT device, using the prompt TCP ACK transmit function in the $e$ AP. This reduces the long Rx mode time for TCP ACK reception, and increases the short sleep mode time, which results in increase of the energy efficiency of the IoT device. In the energy-saving analyses, we formulate an energy consumption model for the IoT device, and determine the energy-saving gain when the IoT device uses the $e$ AP system model, compared to a legacy AP system model. Our performance evaluation results verify that the proposed $e$ AP system achieves a maximum improvement in energy efficiency of approximately 88%, and 8.4 times improvement in the expected lifetime of the IoT device, compared to the legacy AP system model.

This paper proposes a cooperative multi-agent online reinforcement learning-based (COMORL) bias offset (BO) control scheme for cell range expansion (CRE) in dense heterogeneous networks (HetNets). The proposed COMORL scheme controls BOs for CRE to maximize the number of user equipments (UEs) that satisfy their quality of service (QoS) requirements, especially in terms of delay and data rates. For this purpose, we developed a QoS satisfaction indicator that measures a violation of delay requirements by considering both QoS requirements and signal-to-interference-plus-noise ratio (SINR). In addition, we formulated a Markov decision process (MDP) model that is solved with a cooperative multi-agent online reinforcement learning algorithm. The proposed COMORL scheme maximizes the global utility for load-coupled base stations. Our simulation results verify the proposed COMORL scheme’s effectiveness in terms of throughput, delay satisfaction ratio, and fairness. Specifically, we verify that the proposed COMORL scheme achieves a maximum of approximately 27% and 30% improvement of the delay satisfaction ratio, which is how many UEs satisfy their delay requirement among all of the UEs in a serving BS under medium and full traffic loads, respectively, in a dynamic scenario in comparison to the max-SINR scheme.

Since personal data becomes a valuable asset for IT industries, the necessity of data ecosystems with personal data trading methodologies is continuously increased, and the concept of data brokers is now widely used in the market. Moreover, utilizing the advantages of blockchain, distributed personal data markets have recently been considered because all participants behave selfishly for their profits in the markets. Since the participants may behave maliciously, it is necessary to prevent such behavior, and one of the considered approaches is putting additional deposits as a penalty into blockchain smart contracts. Since data brokers give various advantages to maintain personal data trading markets, many studies have considered both data brokers and blockchain at the same time. However, those studies have mainly focused on putting deposits into data buyers/sellers but not data brokers. However, since data brokers are also major players in the market, it is necessary to consider deposit decision models for data brokers. Hence, this paper proposes a deposit decision model for data brokers in distributed personal data markets using blockchain. Particularly, this paper proposes a profit model with deposits depending on their behavior for handling contracts and a credit level model that puts fewer deposits for a data broker with a higher credit level to motivate the data brokers’ truthful behavior. With the analysis of the proposed models, this paper shows that the models are feasible to motivate data brokers’ truthful behavior by allocating deposits for not only a large enough penalty but also a fair enough incentive.

On a future tactical-battle field network, combat radio nodes will be deployed for various operations, forming a mobile ad-hoc network (MANET). However, because of the nodes' mobility, a single group might be divided into several small groups with fewer nodes. Conversely, several small groups might be merged into one group. In such an environment, an unmanned aerial vehicle (UAV) will provide an effective way to improve network coverage and connectivity among the small groups. However, some issues should be considered for the optimal deployment of the UAV. One issue is to find the proper position of the UAV, which enhances the connectivity among the groups, because a tactical network places a high priority on network survivability rather than throughput maximization. We also need to exploit real topographic information to obtain more accurate connectivity information among nodes. Second, an efficient resource allocation scheme for reliable communications through the UAV should be taken into account. Since most of the links between the UAV and the ground nodes are line-of-sight (LoS), due to the good quality of these links, the traffic via the UAV will be heavy in spite of the limited data slot resources. Moreover, the traffic flows of the network require diverse quality-of-service (QoS), and different priorities should be imposed upon the nodes depending on the involved tactical operations. In this paper, we formulate a UAV positioning problem maximizing group connectivity (GC) and a data slot-allocation problem maximizing GC utility. Subsequently, we propose an optimal positioning and slot allocation algorithm, which satisfies QoS requirement and supports the predetermined priorities of the nodes and the groups. Numerical analysis verifies that the proposed scheme is effective in UAV positioning and slot allocation, outperforming conventional methods.

This paper proposes a joint demand response and energy trading for electric vehicles in an off-grid system. We consider isolated microgrid in a region where, at a given time, some renewable energy generators have superflous energy for sale or to keep in storage facilities, whereas some electric vehicles wish to buy additional energy to meet their deficiency. In our system model, broker lead the market by determining the optimal transaction price by considering a trade-off between commission revenue and power reliability. Buyers and sellers follow the broker's decision by independently submitting a transaction price to the broker. Correspondingly transaction energy is allocated to the buyers in the proportion to their payment, whereas the revenue is allocated to the sellers in proportion to their sales. We investigated the economic benefits of such a joint demand response and energy trading by analyzing its hierarchical decision-making scheme as a single-leader-heterogeneous multi-follower Stackelberg game. With demonstrating an existence of a unique Stackelberg equilibrium, we show that the transaction price in the proposed market model is up to 25.8% cheaper than the existing power market. In addition, we compare the power reliability results with other algorithm to show the suitability of proposed algorithm in the isolated microgrid environment.

With the widespread use of the Internet of Things, data-driven services take the lead of both online and off-line businesses. Especially, personal data draw heavy attention of service providers because of the usefulness in value-added services. With the emerging big-data technology, a data broker appears, which exploits and sells personal data about individuals to other third parties. Due to little transparency between providers and brokers/consumers, people think that the current ecosystem is not trustworthy, and new regulations with strengthening the rights of individuals were introduced. Therefore, people have an interest in their privacy valuation. In this sense, the willingness-to-sell (WTS) of providers becomes one of the important aspects for data brokers; however, conventional studies have mainly focused on the willingness-to-buy (WTB) of consumers. Therefore, this paper proposes an optimized trading model for data brokers who buy personal data with proper incentives based on the WTS, and they sell valuable information from the refined dataset by considering the WTB and the dataset quality. This paper shows that the proposed model has a global optimal point by the convex optimization technique and proposes a gradient ascent-based algorithm. Consequently, it shows that the proposed model is feasible even if the data brokers spend costs to gather personal data.

In this paper, we newly model computation offloading competition when multiple clients compete with each other so as to reduce energy cost and improve computational performance. We consider two types of destination of offloading request, such as a cloudlet and a remote cloud. Here, the cloudlet consists of locally connected mobile terminals with low-latency and high bandwidth but suffering from task overload due to its limited computational capacity. On the other hand, the remote cloud has a high and stable capacity but the high latency. To facilitate the competition model, on the destination sides, we have designed an energy-oriented task scheduling scheme, which aims to maximize the welfare of clients in terms of energy efficiency. Under this proposed job scheduling, as a joint consideration of the destination and client sides, competition behavior among multiple clients for optimal computation offloading is modeled and analyzed as a non-cooperative game by considering a trade-off between different types of destinations. Based on this game-theoretical analysis, we propose a novel energy-oriented weight assignment scheme in the mobile terminal side to maximize mobile terminal energy efficiency. Finally, we show that the proposed scheme converges well to a unique equilibrium and it maximizes the payoff of all participating clients.

With the technical growth and the reduction of deployment cost for distributed energy resources (DERs), such as solar photovoltaic (PV), energy trading has been recently encouraged to energy consumers, which can sell energy from their own energy storage system (ESS). Meanwhile, due to the unprecedented rise of greenhouse gas (GHG) emissions, some countries (e.g., Republic of Korea and India) have mandated using a renewable energy certificate (REC) in energy trading markets. In this paper, we propose an energy broker model to boost energy trading between the existing power grid and energy consumers. In particular, to maximize the profits of energy consumers and the energy provider, the proposed energy broker is in charge of deciding the optimal demand and dynamic price of energy in an REC-based energy trading market. In this solution, the smart agents (e.g., IoT intelligent devices) of consumers exchange energy trading associated information, including the amount of energy generation, price and REC. For deciding the optimal demand and dynamic pricing, we formulate convex optimization problems using dual decomposition. Through a numerical simulation analysis, we compare the performance of the proposed dynamic pricing strategy with the conventional pricing strategies. Results show that the proposed dynamic pricing and demand control strategies can encourage energy trading by allowing RECs trading of the conventional power grid.

This paper presents the design of an event-driven energy trading system among microgrids. Each microgrid can be either a provider or a consumer depending on the status of its energy generation and local demands. Under this approach, an aperiodic market model is newly proposed such that trading occurs when one of the consumers requests energy from the trading market. To promote the trading system, a consumer-side reward concept is introduced. The consumer makes a decision on the size of the posted reward to procure energy depending on its required energy level. Providers then react to this posted reward by submitting their energy bid. Accordingly, the posted reward is allocated to the providers in proportion to their energy bids. Moreover, for practical concerns, a transmission and distribution loss factor is considered as a heterogeneous energy trading system. The problem is then formulated as a non-cooperative Stackelberg game model. The existence and uniqueness of Stackelberg equilibrium (SE) are shown and the closed form of the SE is derived. Using the SE, an optimal trading algorithm for microgrids is provided. The stability of the energy trading system is verified due to the unique SE. In this approach, no expected waiting time for trading is required for sustaining an energy trading market.

5G-based hyper-connected Internet of Things environment is emerging, where various connectivities such as BlueTooth, WiFi, and 3/4/5G are provided. Since they have different communication characteristics, it is difficult to provide the user quality of experience when the handoff takes place according to the movement of the mobile devices between wireless access networks. For solving this problem, we propose a seamless video streaming protocol to satisfy the user quality of experience regardless of handoff. The proposed protocol can dynamically adjust streaming bit rate according to the network condition and also maintain fixed quantity of buffered audio–video data for the stable quality of experience level. The proposed protocol is designed independently of OS versions and CPU performance of terminals, which makes it easy to be implanted in various types of Internet of Things devices. Real-world experimental results show that seamless video streaming service can be guaranteed by carefully predicting the network dynamics over the heterogeneous wireless access networks. The protocol is now commercially deployed for over 5 million Long-Term Evolution subscribers of a South Korea telco, LG U+.

In this paper, a clustering method with proposed distance measurement to extract base load profiles from arbitrary data sets is studied. Recently, smart energy load metering devices are broadly deployed, and an immense volume of data is now collected. However, as this large amount of data has been explosively generated over such a short period of time, the collected data is hardly organized to be employed for study, applications, services, and systems. This paper provides a foundation method to extract base load profiles that can be utilized by power engineers, energy system operators, and researchers for deeper analysis and more advanced technologies. The base load profiles allow them to understand the patterns residing in the load data to discover the greater value. Up to this day, experts with domain knowledge often have done the base load profile realization manually. However, the volume of the data is growing too fast to handle it with the conventional approach. Accordingly, an automated yet precise method to recognize and extract the base power load profiles is studied in this paper. For base load profile extraction, this paper proposes Sample Pearson Correlation Coefficient (SPCC) distance measurement and applies it to Mean-Shift algorithm based nonparametric mode-seeking clustering. The superiority of SPCC distance over traditional Euclidean distance is validated by mathematical and numerical analysis.

This paper proposes a learning-based adaptive imputation method (LAI) for imputing missing power data in an energy system. This method estimates the missing power data by using the pattern that appears in the collected data. Here, in order to capture the patterns from past power data, we newly model a feature vector by using past data and its variations. The proposed LAI then learns the optimal length of the feature vector and the optimal historical length, which are significant hyper parameters of the proposed method, by utilizing intentional missing data. Based on a weighted distance between feature vectors representing a missing situation and past situation, missing power data are estimated by referring to the k most similar past situations in the optimal historical length. We further extend the proposed LAI to alleviate the effect of unexpected variation in power data and refer to this new approach as the extended LAI method (eLAI). The eLAI selects a method between linear interpolation (LI) and the proposed LAI to improve accuracy under unexpected variations. Finally, from a simulation under various energy consumption profiles, we verify that the proposed eLAI achieves about a 74% reduction of the average imputation error in an energy system, compared to the existing imputation methods.