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To guarantee a high data transmission rate in heterogeneous mobile networks, sufficient small base stations (SBSs) and user plane function (UPF) instances should be active. However, the excessive operation of SBSs and UPF instances can increase the operating expenditure (OPEX) for the network operator. To balance the data rate and OPEX, we propose a joint UPF instance–SBS sched-uling algorithm (J-UBSA). In the proposed J-UBSA, a controller periodically determines the appropriate number of active SBSs and UPF instances based on estimated variations in session requests. The decision-making process is for-mulated as a constrained Markov decision process and converted into a linear programming model to obtain the optimal solution using a traditional algo-rithm with low complexity. Evaluation results demonstrate that J-UBSA can substantially reduce the OPEX while guaranteeing a suitable average data rate for mobile devices.

In beyond fifth-generation networks, millimeter wave (mmWave) is considered a promising technology that can offer high data rates. However, due to inter-cell interference at cell boundaries, it is difficult to achieve a high signal-to-interference-plus-noise ratio (SINR) among users in an ultra-dense mmWave network environment (UDmN). In this paper, we solve this problem with the cooperative transmission technique to provide high SINR to users. Using coordinated multi-point transmission (CoMP) with the joint transmission (JT) strategy as a cooperation diversity technique can provide users with higher data rates through multiple desired signals. Nonetheless, cooperative transmissions between multiple base stations (BSs) lead to increased energy consumption. Therefore, we propose a multi-agent Q-learning-based power control scheme in UDmN. To satisfy the quality of service (QoS) requirements of users and decrease the energy consumption of networks, we define a reward function while considering the outage and energy efficiency of each BS. The results show that our scheme can achieve optimal transmission power and significantly improved network energy efficiency compared with conventional algorithms such as no transmit power control and random control. Additionally, we validate that leveraging channel state information to determine the participation of each BS in power control contributes to enhanced overall performance.

To balance the tradeoff between quality of service (QoS) and operating expenditure (OPEX), the service provider should request the appropriate amount of resources to the cloud operator based on the estimated variation of service requests. This paper proposes a popularity-aware service provisioning framework (PASPF), which leverages the network data analytics function (NWDAF) to obtain analytics on service popularity variations. These analytics estimate the congestion level and list of top services contributing most of the traffic change. Based on the analytics, PASPF enables the service provider to request the appropriate amount of resources for each service for the next time period to the cloud operator. To minimize the OPEX of the service provider while keeping the average response time of the services below their requirements, we formulate a constrained Markov decision process (CMDP) problem. The optimal stochastic policy can be obtained by converting the CMDP model into a linear programming (LP) model. Evaluation results demonstrate that the PASPF can achieve less than 50% OPEX of the service provider compared to a popularity-non-aware scheme while keeping the average response time of the services below the requirement.

In the emergency situations, Wi-Fi Direct (WFD) can provide group-based communications between group owner (GO) and group members (GMs). However, when GO disappears voluntarily or involuntarily, the group connectivity is terminated. For the re-connection, a group reformation procedure including device discovery, GO negotiation, Wi-Fi protected setup (WPS), and address configuration is needed, which consumes much time. In addition, current WFD requires user intervention to initiate the group reformation. To mitigate this problem, this paper proposes an AWS: an Autonomous WFD connectivity maintenance Scheme. In AWS, GO determines a candidate GO (CGO) in advance and transmits the GO information including addresses and credentials to CGO. When GO disappears, based on the GO information, CGO activates virtual GO (VGO) that can operate exactly the same as the disappeared GO (e.g., with same addresses and operating channel). Simulation results are given to validate AWS and to show the performance of AWS in terms of the group disruption time.

The split computing approach, where the head and tail models are respectively distributed between the IoT device and cloud, suffers from high network latency especially when the cloud is located far from the IoT device. To mitigate this problem, we introduce a distributed split computing system (DSCS) where an IoT device (called split computing requester) broadcasts a split computing request to its neighboring IoT devices. After receiving the request, the neighboring IoT devices (i.e., requestees) distributively determine whether or not to accept the split computing request by taking into account the unnecessary energy consumption and computation time. To minimize energy consumption while maintaining a specified probability of on-time computing completion, we develop a constrained stochastic game model. Then, a best-response dynamics-based algorithm is used to obtain the Nash equilibrium. The evaluation results demonstrate that the DSCS consumes can reduce more than 20% energy consumption compared to a probabilistic-based acceptance scheme, where the IoT devices accept a split computing request based on a predefined probability, while providing high on-time computing completion probability.

The mobile network allows redundant transmission via disjoint paths to support high-reliability communication (e.g., ultrareliable and low-latency communications [URLLC]). Although redundant transmission can improve communication reliability, it also increases network costs (e.g., traffic and control overhead). In this study, we propose a reliability-guaranteed multipath allocation algorithm (RG-MAA) that allocates appropriate paths by considering the path setup time and dynamicity of the reliability paths. We develop an optimization problem using a constrained Markov decision process (CMDP) to minimize network costs while ensuring the required communication reliability. The evaluation results show that RG-MAA can reduce network costs by up to 30% compared with the scheme that uses all possible paths while ensuring the required communication reliability.

In heterogeneous wireless networked control systems (WNCSs), the age of information (AoI) of the actuation update and actuation update cost are important performance metrics. To reduce the monetary cost, the control system can wait for the availability of a WiFi network for the actuator and then conduct the update using a WiFi network in an opportunistic manner, but this leads to an increased AoI of the actuation update. In addition, since there are different AoI requirements according to the control priorities (i.e., robustness of AoI of the actuation update), these need to be considered when delivering the actuation update. To jointly consider the monetary cost and AoI with priority, this paper proposes a priority-aware actuation update scheme (PAUS) where the control system decides whether to deliver or delay the actuation update to the actuator. For the optimal decision, we formulate a Markov decision process model and derive the optimal policy based on Q-learning, which aims to maximize the average reward that implies the balance between the monetary cost and AoI with priority. Simulation results demonstrate that the PAUS outperforms the comparison schemes in terms of the average reward under various settings.

In the downlink of millimeter wave (mmWave) cellular networks, coordinated multi-point transmission (CoMP) with the joint transmission (JT) strategy can achieve a higher data rate at cell edge by multiple desired signals. However, since cooperative transmissions among multiple cells lead to increased energy consumption for these cells, an energy efficient CoMP-JT strategy is required. In this paper, we propose an energy efficient CoMP-JT (EE-CoMP) scheme with hybrid power, where a small cell base station (SC) can use both renewable power and grid power. In EE-CoMP, when the multiple neighboring SCs conduct a cooperative transmission, these SCs determine whether or not to conduct CoMP-JT in a distributed manner. To minimize the grid energy consumption of SCs while maintaining the outage probability, we consider a stochastic game model with constrains for an EE-CoMP based system. In this way, the policy for conducting CoMP-JT can be obtained by applying a best response algorithm. Evaluation results show that EE-CoMP can reduce the grid energy consumption by 80% compared with a probability-based scheme while providing a sufficiently target outage probability (e.g., 0.1).

The observation satellite can exploit its own storage and computational resources to reduce the transmission delay. However, too excessive usage of these resources can have negative effects on the queuing delay at the relay satellite and/or on conducting other tasks at each observation satellite. In this paper, we proposed a new resource- and neighbor-aware observation transmission scheme (RNA-OTS). In RNA-OTS, each observation satellite decides whether to use its resources and the resources of the relay satellite at each time epoch by considering its resource utilization and transmission policies of neighbor observation satellites. For the optimal decision of each observation satellite in a distributed manner, the operation of observation satellites is modeled by means of a constrained stochastic game, and a best-response-dynamics-based algorithm is devised to find the Nash equilibrium. The evaluation results demonstrate that RNA-OTS can decrease the delay to deliver the observation to the destination by up to 87% compared to a relay-satellite-based scheme while guaranteeing a sufficiently low average utilization of the resources of the observation satellite.

Service function chaining (SFC) is an indispensable technique for Internet service providers to efficiently manage their networks. However, SFC poses requirements of additional processing time of service functions (SFs) and increased routing time owing to detoured paths. In this paper, we introduce the use of a programmable data plane (PDP) to reduce the additional processing and routing times in SFC. We first classify the existing PDP-empowered SFC schemes and analyze their pros and cons. An optimization problem, to find the optimal SF embedding strategy minimizing the SFC completion time while efficiently utilizing the PDP switch resources, is formulated and a flow-aware SF embedding (FASE) algorithm that complementarily combines the redundant SF and re-circulation approaches is devised. FASE is implemented over a commercial PDP switch and experimental results demonstrate that FASE can reduce the SFC completion time by up to 33% compared with conventional approaches while utilizing the switch resources efficiently.

In energy harvesting Internet of Things (IoT) systems, the age of information (AoI) should be maintained at a low level to guarantee the accuracy and reliability of derived decisions. In this paper, we design an information freshness-guaranteed and energy-efficient data generation control system (IFE-DGCS) where an IoT gateway with a directional antenna determines the polling frequency for each sector by selecting a polling sector at periodic decision epochs. When polling data, the IoT gateway transfers the RF energy to IoT devices in the polling sector by means of simultaneous wireless information and power transfer (SWIPT). To minimize the energy outage probability while maintaining the AoI below a certain level, a constrained Markov decision process (CMDP) is formulated and the optimal stochastic policy on the polling sector is obtained by a linear programming (LP). To resolve the curse of the dimensionality problem in CMDP, a greedy IFE-DGCS is developed and its performance is extensively studied. Evaluation results demonstrate that IFE-DGCS with the optimal policy achieves a comparable energy outage probability to the conventional energy-oriented scheme while guaranteeing a sufficiently low AoI.

In device-to-device (D2D) offloading, frequent offloading to a specific mobile device (i.e., an offloadee) can violate the privacy of a task owner and proper offloading results cannot always be guaranteed due to untruthful mobile devices. To address these problems, we propose a privacy-preserving and trustworthy D2D offloading scheme (PPTS) with two steps: 1) a privacy-preserving offloading step and 2) a blockchain-based verification step. In the first step, a task owner selects several offloadees and offloads the tasks redundantly to them to obtain reliable offloading results with a minimum task completion delay while preserving its own privacy at a sufficient level. In addition, the task owner chooses another mobile device as a verifier. In the second step, the task owner and verifier exploit blockchain networks to check whether the offloading results are appropriately processed. We formulate a static game to decide an appropriate redundancy ratio of offloading tasks and incentives for the offloadees.The evaluation results demonstrate that PPTS can provide reliable offloading results with a reduced task completion delay and guarantee a sufficient level of privacy of the task owner.

Cloud storage applications, such as Dropbox and Google Drive, have recently become very popular among mobile users. In these applications, a cloud server is responsible for synchronizing updates to files among mobile users, and thus if files are shared by many mobile users and are frequently updated then the resulting synchronization traffic can be significant. In order to reduce the synchronization traffic with providing acceptable access latency, we propose a fog-assisted aggregated synchronization (FAS) scheme in which the fog computing server and the cloud server conduct localized and aggregated synchronizations, respectively. We develop an analytical model of the FAS scheme based on renewal-reward theory and use it for model-based adjustment of the timer that controls the trade-off between access latency and synchronization traffic. We use analytical and simulation results to give insight into the effects of the timer, the update-to-access ratio, the number of mobile users, and the sensitivity to the arrival process. The analytical and simulation results demonstrate that the FAS scheme can reduce the synchronization traffic significantly with acceptable access latency compared to conventional schemes.

Time synchronization is one of the most fundamental problems on the internet of things (IoT). The IoT requires low power and an efficient synchronization protocol to minimize power consumption and conserve battery power. This paper introduces an efficient method for time synchronization in the IoT called low-power multi-hop synchronization (LPSRS). It employs a reference node scheduling mechanism to avoid packet collisions and minimize the communication overhead, which has a big impact on power consumption. The performance of LPSRS has been evaluated and compared to previous synchronization methods, HRTS and R-Sync, via real hardware networks and simulations. The results show that LPSRS achieves a better performance in terms of power consumption (transmitted messages). In particular, for a large network of 450 nodes, LPSRS reduced the total number of transmitted messages by 53% and 49% compared to HRTS and R-Sync, respectively.

Abstract Proactive content caching in small cells (SCs) (i.e., proactive edge caching) can significantly reduce energy consumption of networks. However, the sleep mode of SCs can make the cached contents in SCs unavailable. Therefore, a joint optimization of edge caching strategy and sleep scheduling should be conducted to maximize the effectiveness of edge caching. In this paper, we propose an energy-efficient proactive edge caching with sleep scheduling (E3CS) where the controller jointly adjusts the caching strategy and the sleep scheduling of SCs by considering the content popularity dynamics. To optimize the performance of E3CS (i.e., minimize the overall energy consumption), we formulate a Markov decision process (MDP) and the joint optimal policy on the caching strategy and sleep scheduling is obtained by a value iteration algorithm. Evaluation results demonstrate that E3CS with the optimal policy outperforms the comparison schemes in terms of the overall energy consumption.

In a non-orthogonal multiple access (NOMA) environment, an Internet of Things (IoT) device achieves a high data rate by increasing its transmission power. However, excessively high transmission power can cause an energy outage of an IoT device and have a detrimental effect on the signal-to-interference-plus-noise ratio of neighbor IoT devices. In this paper, we propose a neighbor-aware NOMA scheme (NA-NOMA) where each IoT device determines whether to transmit data to the base station and the transmission power at each time epoch in a distributed manner with the consideration of its energy level and other devices’ transmission powers. To maximize the aggregated data rate of IoT devices while keeping an acceptable average energy outage probability, a constrained stochastic game model is formulated, and the solution of the model is obtained using a best response dynamics-based algorithm. Evaluation results show that NA-NOMA can increase the average data rate up to 22% compared with a probability-based scheme while providing a sufficiently low energy outage probability (e.g., 0.05).

In energy harvesting cloud radio access networks, the sleep mode switching of small radio remote head (SRRH) and the functional splitting between SRRH and baseband unit are two important challenges for improving energy efficiency and throughput. In this paper, we propose an energy-efficient mode switching mechanism (E2MSM) with flexible functional splitting in which a controller determines the modes of SRRHs and functional splitting level by considering the renewable energy levels and populations of SRRHs. To optimize the performance of E2MSM, a constraint Markov decision process problem is formulated and a joint optimal policy on the sleep scheduling and the functional splitting level is obtained by a linear programming. Evaluation results demonstrate that E2MSM with the optimal policy can achieve comparable throughput to the throughput-oriented scheme while consuming no non-renewable energy.

A novel concept of the edge cloud has recently been introduced to reduce transmission costs in mobile cloud computing services. Heterogeneous networks with diverse radio access networks will be pervasive in the future. In this paper, we propose a spatial and temporal computation offloading decision algorithm (ST-CODA) in edge cloud-enabled heterogeneous networks. In ST-CODA, a mobile device decides where and when to process tasks by means of a Markov decision process with the consideration of the processing time and energy consumption of different computation nodes and the transmission cost in heterogeneous networks. Extensive evaluation results are given to demonstrate the effectiveness of the ST-CODA in terms of the transmission cost, the energy efficiency of the mobile device, and the number of tasks that can be processed before their deadline.

Frequent location updates of individual Internet of Things (IoT) devices can cause several problems (e.g., signaling overhead in networks and energy depletion of IoT devices) in massive machine type communication (mMTC) systems. To alleviate these problems, we design a distributed group location update algorithm (DGLU) in which geographically proximate IoT devices determine whether to conduct the location update in a distributed manner. To maximize the accuracy of the locations of IoT devices while maintaining a sufficiently small energy outage probability, we formulate a constrained stochastic game model. We then introduce a best response dynamics-based algorithm to obtain a multi-policy constrained Nash equilibrium. From the evaluation results, it is demonstrated that DGLU can achieve an accuracy of location information that is comparable with that of the individual location update scheme, with a sufficiently small energy outage probability.

Time synchronization is an essential issue in industrial wireless sensor networks (IWSNs). It assists perfect coordinated communications among the sensor nodes to preserve battery power. Generally, time synchronization in IWSNs has two major aspects of energy consumption and accuracy. In the literature, the energy consumption has not received much attention in contrast to the accuracy. In this paper, focusing on the energy consumption aspect, we introduce an energy-efficient reference node selection (EERS) algorithm for time synchronization in IWSNs. It selects and schedules a minimal sequence of connected reference nodes that are responsible for spreading timing messages. EERS achieves energy consumption synchronization by reducing the number of transmitted messages among the sensor nodes. To evaluate the performance of EERS, we conducted extensive experiments with Arduino Nano RF sensors and revealed that EERS achieves considerably fewer messages than previous techniques, robust time synchronization (R-Sync), fast scheduling and accurate drift compensation for time synchronization (FADS), and low power scheduling for time synchronization protocols (LPSS). In addition, simulation results for a large sensor network of 450 nodes demonstrate that EERS reduces the whole number of transmitted messages by 52%, 30%, and 13% compared to R-Sync, FADS, and LPSS, respectively.

The limited battery capacity of Internet of Things (IoT) devices is a major deployment barrier for IoT-based computing systems. In this paper, we propose an energy efficient cooperative computation algorithm (EE-CCA). In an EE-CCA, a pair of IoT devices decide whether to offload some parts of the task to the opponent by considering their energy levels and the task deadline. To minimize the energy outage probability while completing most of tasks before their deadlines, we formulate a constraint Markov decision process (CMDP) problem and the optimal offloading strategy is obtained by linear programming (LP). Meanwhile, an optimization problem of finding pairs of IoT devices (i.e., IoT device pairing problem) is formulated under the optimal offloading strategy. Evaluation results demonstrate that the EE-CCA can reduce the energy outage probability up to 78 % compared with the random offloading scheme while completing tasks before their deadlines with high probability.