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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. [ABSTRACT FROM AUTHOR]

In environmental monitoring systems based on the Internet of Things (IoT), sensor nodes (SNs) typically send data to the server via a wireless gateway (GW) at regular intervals. However, when SNs are located far from the GW, substantial energy is expended in transmitting data. This paper introduces a novel unmanned aerial vehicle (UAV)-based environmental monitoring system. In the proposed system, the UAV conducts patrols in the designated area, and SNs periodically transmit the collected data to the GW or the UAV. This transmission decision is made while taking into account the respective distance between both the GW and the UAV. To ensure a high-quality environmental map, characterized by a consistent collection of a satisfactory amount of up-to-date data while preventing energy depletion in the SNs and the UAV, the UAV periodically decides on three types of UAV operations. These decisions involve deciding where to move, deciding whether to relay or aggregate the data from the SNs, and deciding whether to transfer energy to the SNs. For the optimal decisions, we introduce an algorithm, called DeepUAV, using deep reinforcement learning (DRL) to make decisions in UAV operations. In DeepUAV, the controller continually learns online and enhances the UAV's decisions through trial and error. The evaluation results indicate that DeepUAV successfully gathers a substantial amount of the current data consistently while mitigating the risk of energy depletion in SNs and the UAV. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In 5G network slicing environments, if insufficient resources are allocated to the associated resource-intensive service slices, its quality of service (QoS) can be considerably degraded. In this paper, we propose a priority-based dynamic resource allocation scheme (PDRAS), in which a resource management agent maintains slicing information such as priorities, demand profiles, and average resource adjustment time to change allocated resources to slices. To maximize QoS of slices while maintaining the total amount of allocated resources below a certain level, a constrained Markov decision process problem is formulated and the optimal allocation policy is obtained using linear programming. Extensive evaluation results demonstrate that PDRAS with the optimal policy has better performance regarding QoS and the resource usage efficiency compared with other schemes. [ABSTRACT FROM AUTHOR]

Fog node (FN) close to Internet of Things (IoT) devices can be exploited to offload the computing task of IoT devices. However, when lots of tasks are simultaneously offloaded from multiple IoT devices, FN can be overloaded. To mitigate this problem, we introduce a DYSON: DelaYed offloading System with Opportunistic fog Node (OFN) such as vehicles and mobile phones which can opportunistically reduce the load of static FN thanks to their mobility. In DYSON, to maximize the offloading effect of OFN, when IoT device cannot use OFN, it delays the task offloading with the expectation of future contacts of OFN. To assess the performance of DYSON, we develop an analytic model for the opportunistic offloading probability that the task can be offloaded to OFN. Based on the analytic model, we derive the optimal delay timer to maximize the opportunistic offloading probability while maintaining the probability that the task cannot be processed within the deadline below a target probability. Extensive simulation results are provided to show the superiority of DYSON. [ABSTRACT FROM AUTHOR]

To reduce the overloading of the power system due to the increasing use of electric vehicles (EVs), the power system operators employ dynamic cost plans depending on the number of EVs being charged. In this situation, the charging cost of an EV can be reduced by considering other EVs’ charging strategies and avoiding simultaneous charging. In this article, we develop a distributed EV charging mechanism (D-EVCM) where each EV possesses some information, such as the charging probabilities of other EVs, electricity price, current energy level, and expected departure time. Based on the information, an EV periodically decides whether to charge its battery in a distributed manner. To minimize the average charging cost of EV while avoiding the situation where an EV departs from a charging station with insufficient energy level, a constrained stochastic game model is formulated and its solution is obtained using a best response dynamics-based algorithm. The evaluation results show that D-EVCM can reduce the average charging cost over 48% with a sufficient low battery outage probability (e.g., 0.01%) compared to a threshold-based charging scheme. [ABSTRACT FROM AUTHOR]

In battery swapping environments, electric vehicles (EVs) can play roles as battery providers as well as consumers. In this paper, we propose an optimal battery charging algorithm (OBCA) where a battery swapping station (BSS) charges batteries in its storage with the consideration of the profile of the electricity price and the arrival rates of EVs. To maximize the net profit of BSS while maintaining the battery changing probability above a certain level (i.e., maintaining high quality of service (QoS) of BSS), we formulate a constraint Markov decision process (CMDP) problem and the optimal charging schedule for batteries in BSS is obtained by a linear programming (LP). Evaluation results demonstrate that OBCA with the optimal policy can improve the net profit of BSS up to 418% compared to an electric price-aware scheme while maintaining high QoS of BSS. [ABSTRACT FROM AUTHOR]

Satellite communications are perceived as a key enabling technology in 5G and beyond due to their global coverage and network resilience. However, conventional space-terrestrial integrated network approaches cannot avoid long propagation delays, and thus an excessively increased signaling delay for the control plane procedures (e.g., session establishment and handover) is expected. To address this issue, we introduce a novel space mobile network (SMN) architecture in which satellites act on core networks (CNs) and radio access networks (RANs) by provisioning RAN/ CN network functions. Based on the use of SMN, we devised persistent tunnel-based session establishment and cluster-based handover procedures to further reduce the session establishment and handover times, respectively. Simulation results demonstrate that the proposed session establishment and handover procedures can reduce the completion times by 7.8-33.2 percent and 19.7-53.7 percent, respectively, compared to the conventional approaches. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Owing to dynamically changing resources and channel conditions of mobile devices (MDs), when a static deadline-based MD selection scheme is used for federated learning, resource utilization of MDs can be degraded. To mitigate this problem, we propose an adaptive deadline determination (ADD) algorithm for MD selection, where a deadline for each round is adaptively determined with the consideration of the performance disparity of MDs. Evaluation results demonstrate that ADD can achieve the fastest average convergence time among the comparison schemes. [ABSTRACT FROM AUTHOR]

In task offloading systems, it is imperative to guarantee that an offloaded task is completed within a pre-specified deadline. In this paper, we propose a distributed device-to-device (D2D) offloading system (DDOS) in which a task owner opportunistically broadcasts an offloading request that includes its mobility level and task completion deadline. After receiving the request, mobile devices in the vicinity of the task owner employ a constraint stochastic game to decide, in a distributed manner, whether to accept the request or not. We devise a best response dynamics-based algorithm (BRDA) to obtain a multi-policy constrained Nash equilibrium. Evaluation results demonstrate that DDOS can guarantee a high on-time task completion probability, as well as a low energy consumption. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To overcome the limitation of standalone edge cloud in terms of computing power and resource, a concept of distributed edge cloud has been introduced, where application tasks are distributed to multiple edge clouds for collaborative processing. To maximize the effectiveness of the distributed edge cloud, we formulate an optimization problem of task allocation to minimize the application completion time. To mitigate high complexity overhead in the formulated problem, we devise a low-complexity heuristic algorithm called dependency-aware task allocation (DATA) algorithm. Evaluation results demonstrate that DATA can reduce the application completion time up to by 15%–32% compared to conventional dependency-unaware task allocation schemes. [ABSTRACT FROM AUTHOR]

Radio frequency (RF) energy harvesting technique has received high attention as a promising technology for Internet of Things (IoT) based industrial systems due to its flexibility of energy supply. When multiple IoT gateways are used as directional RF energy sources, the direction and phase of RF energy transmissions influence the RF energy transmission efficiency. In this paper, we propose a phase-aware directional energy transmission algorithm (PA-DETA) where directional RF energy sources (e.g., IoT gateways) are connected to a controller and their operation parameters (i.e., RF energy transmission direction and initial phase) are decided by the controller to maximize the total harvested energy while maintaining the energy consumption in RF energy sources and the number of discarded packets due to energy depletion of harvesting nodes below certain levels. To optimize the performance of PA-DETA, we formulate a constraint Markov decision process and the optimal policy on the direction and initial phase of RF energy transmissions is obtained by a linear programming. Evaluation results demonstrate that PA-DETA with the optimal policy outperforms the comparison schemes in terms of the total harvested energy, the energy consumption in RF energy sources, and the packet discard rate. [ABSTRACT FROM AUTHOR]

Recently, the interest in operating long term evolution (LTE) in unlicensed bands (i.e., LTE-U) has increased. However, since LTE does not sense for channel vacancy prior to transmissions, whereas a wireless local area network (WLAN) is based on carrier sensing multiple access, LTE interference severely degrades the performance of WLAN. To maximize the LTE throughput while maintaining fairness between LTE and WLAN, we formulate a joint optimization problem of channel selection and frame scheduling in LTE. As a practical solution of this problem, we propose a low-complexity heuristic algorithm called the sequential operation algorithm (SOA). Evaluation results demonstrate that SOA can achieve an LTE throughput comparable to that of the optimal solution, while maintaining fairness between LTE and WLAN by selecting appropriate channels and conducting effective frame scheduling. [ABSTRACT FROM AUTHOR]

In a vehicle-to-grid (V2G) system, electric vehicles (EVs) can be efficiently used as power consumers and suppliers to achieve microgrid (MG) autonomy. Since EVs can act as energy transporters among different regions (i.e., MGs), it is an important issue to decide where and when EVs are charged or discharged to achieve the optimal performance in a V2G system. In this paper, we propose a mobility-aware V2G control algorithm (MACA) that considers the mobility of EVs, states of charge of EVs, and the estimated/actual demands of MGs and then determines charging and discharging schedules for EVs. To optimize the performance of MACA, the Markov decision process problem is formulated and the optimal policy on charging and discharging is obtained by a value iteration algorithm. Since the mobility of EVs and the estimated/actual demand profiles of MGs may not be easily obtained, a reinforcement learning approach is also introduced. Evaluation results demonstrate that MACA with the optimal and learning-based policies can effectively achieve MG autonomy and provide higher satisfaction on the charging. [ABSTRACT FROM AUTHOR]

Recently, femtocells are widely deployed to offload the traffic from the macrocell. Since conventional location management schemes of femto/macrocell networks do not consider mobility pattern of the mobile node (MN), unnecessary location updates can occur. Specifically, when an MN moves along the contour of the femtocell coverage, the MN frequently executes location update procedures, which causes significant location update cost. To address this problem, we propose a mobility-aware location management (MALM) scheme, where the MN conducts location update only at specific femtocells in which it is expected that the MN stays for a long time. To optimize MALM, a Markov decision process (MDP) problem is formulated and the optimal policy is determined. Evaluation results demonstrate that MALM with the optimal policy can reduce the number of location updates while providing sufficient offloading gain. [ABSTRACT FROM AUTHOR]

In the delayed WiFi offloading, since data are downloaded only through WiFi networks before a predefined delay timer expires, the performance of the delayed WiFi offloading is affected by the delay timer value. To assess the performance of the delayed WiFi offloading, we develop analytical models for the expected saving monetary cost and download completion time. Based on these analytical models, the optimal delay timer is derived to maximally save the monetary cost while maintaining the outage probability on the download time at low level. Evaluation results demonstrate the effects of the average cellular-only residence time and WiFi data rate, and show that better performance can be achieved when the delay timer is set to the optimal value. [ABSTRACT FROM PUBLISHER]

Article synchronization is one of the most fundamental issues in online social networking services (SNSs). In particular, when deploying multiple access networks with different transmission costs, an efficient synchronization scheme should be devised for users to enjoy SNSs in heterogeneous wireless networks. In this paper, we propose an opportunistic push scheme (OPS) that aggregates published articles, opportunistically pushing them through low-cost access networks (e.g., open Wi-Fi networks). To balance the reduced transmission costs and the increased page loading time, we formulate a Markov decision process problem that considers a mobility model based on the users’ social contact pattern. Evaluation results demonstrate that OPS with the optimal policy can reduce the number of transmissions in high-cost access networks, while satisfying users’ quality of experience in terms of the page loading time. [ABSTRACT FROM PUBLISHER]

In cloud storage applications where the data is shared by multiple mobile users, it is essential to provide the consistency among mobile users by means of appropriate synchronization algorithms. In particular, if the data is frequently updated and the number of mobile users sharing the data is large, the synchronization traffic can be significant. Moreover, the excessive synchronization traffic in mobile networks is more important in terms of radio resource utilization and energy consumption. In this paper, we propose an efficient delta synchronization (EDS) algorithm that aggregates the updated data to reduce the synchronization traffic and synchronizes the aggregated one periodically to satisfy the consistency. To find out the optimal policy for the aggregation and the periodical synchronization, an optimization problem is formulated as a Markov decision process (MDP) and a value iteration algorithm is presented for computing the stationary deterministic policy. Numerical results demonstrate that EDS can choose the optimal action that strikes a balance between the reduction of the synchronization traffic and the satisfaction of the consistency. [ABSTRACT FROM PUBLISHER]

Recently, there has been an increasing interest in operating long-term evolution (LTE) in unlicensed bands (i.e., LTE-U). However, since LTE and wireless local area networks (WLANs) are designed to operate in different bands, they have no coexistence mechanism, which leads to significant performance degradation. In particular, since LTE does not sense channel vacancy prior to transmissions, the LTE interference severely affects the WLAN operation. To address this problem, we propose a fair listen-before-talk (F-LBT) algorithm for coexistence of LTE-U and WLAN in unlicensed bands. F-LBT jointly considers the total system throughput and the fairness between LTE-U and WLAN and then allocates an appropriate idle period for WLAN. Evaluation results demonstrate that F-LBT can improve the total system throughput while providing the fairness between LTE-U and WLAN. [ABSTRACT FROM PUBLISHER]

WiFi offloading is a promising technique for addressing the mobile data explosion problem. In this paper, we consider two types of WiFi offloading techniques: 1) opportunistic WiFi offloading, where data offloading is conducted only when a mobile node opportunistically meets WiFi access points (APs); and 2) delayed WiFi offloading, where data transfer is delayed with the expectation of future AP contacts. We developed analytical models on WiFi offloading efficiency, which is defined as the ratio of the amount of offloaded data to the total amount of data. Extensive simulation results are given to validate the analytical models and to demonstrate the effects of the average cellular residence time, the delay bound, the variability of the WiFi residence time, the WiFi data rate, and the average session duration on WiFi offloading efficiency. [ABSTRACT FROM AUTHOR]

Long-term evolution (LTE)/LTE-advanced (LTE-A) networks have recently introduced a data packet forwarding scheme between evolved node Bs (eNBs) to reduce the signaling overhead and the delay incurred in the data path switching scheme, which is a baseline handover scheme in LTE/LTE-A networks. Even with the data packet forwarding scheme, if the length of the forwarding chain is set inappropriately, the data packet forwarding scheme suffers from the degraded throughput. To attain the optimal handover performance in terms of throughput and delay and reduce the signaling overhead, we propose an optimized and distributed data packet forwarding scheme where the optimal length of the forwarding chain is obtained by a Markov decision process (MDP). In addition, a low-complexity value iteration algorithm is devised to solve the optimality equation of MDP in a more practical manner. Real trace-driven evaluation results demonstrate that the proposed scheme determines the optimal length of the forwarding chain adaptively to the applications' quality-of-service (QoS) requirements and reduces the signaling overhead and delay while achieving higher throughput in diverse environments. [ABSTRACT FROM AUTHOR]

Distributed mobility management (DMM) is a promising technology to address the mobile data traffic explosion problem. Since the location information of mobile nodes (MNs) are distributed in several mobility agents (MAs), DMM requires an additional mechanism to share the location information of MNs between MAs. In the literature, multicast or distributed hash table (DHT)-based sharing methods have been suggested; however they incur significant signaling overhead owing to unnecessary location information updates under frequent handovers. To reduce the signaling overhead, we propose a timer-based Bloom filter aggregation (TBFA) scheme for distributing the location information. In the TBFA scheme, the location information of MNs is maintained by Bloom filters at each MA. Also, since the propagation of the whole Bloom filter for every MN movement leads to high signaling overhead, each MA only propagates changed indexes in the Bloom filter when a pre-defined timer expires. To verify the performance of the TBFA scheme, we develop analytical models on the signaling overhead and the latency and devise an algorithm to select an appropriate timer value. Extensive simulation results are given to show the accuracy of analytical models and effectiveness of the TBFA scheme over the existing DMM scheme. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel session-initiation-protocol-based location management framework in opportunistic WiFi networks. In the proposed framework, a mobile terminal needs to update its attachment to the network only when it connects to a new WiFi access point to reduce its location update traffic. In addition, to find out the exact point of attachment, a timer-based session setup procedure is introduced, which can intelligently reduce the session setup latency by looking for a user in either WiFi or cellular systems. We develop analytical models for the location update cost and the session setup latency and investigate the optimization of the timer under the realistic WiFi hotspot residence time model. [ABSTRACT FROM AUTHOR]

In this paper, we propose a vehicular passenger mobility-aware bandwidth allocation (V-MBA) scheme in mobile hotspots. The V-MBA scheme consists of both call admission control and bandwidth adjustment functions to lower handoff vehicle service dropping probability and efficiently utilize resource of base station. Specifically, a handoff priority scheme with guard bandwidth is employed to protect handoff vehicle service. Also, bandwidth is dynamically assigned to each vehicle by exploiting vehicular passenger movement pattern that includes getting on and off events at a station. We evaluate the V-MBA scheme by developing a continuous-time Markov chain model. Simulation results demonstrate that the V-MBA scheme can guarantee low new vehicle service blocking probability and handoff vehicle service dropping probability through flexible bandwidth allocation. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a mobility-aware call admission control (MA-CAC) algorithm with a handoff queue (HQ) in mobile hotspots, where different admission control policies are employed, depending on the vehicle mobility. Specifically, when a vehicle is static, a handoff priority scheme with guard channels is studied to protect vehicular handoff users because handoff users may get on the vehicle. In addition, an HQ is examined during stopping to further accept handoff users. On the other hand, for a moving vehicle, no guard channels for handoff users are allocated to maximize channel utilization. By means of Markov chains, we evaluate MA-CAC with an HQ in terms of new-call blocking probability (NCBP), handoff-call dropping probability (HCDP), handoff-call waiting time in the HQ, and channel utilization. Analytical and simulation results demonstrate that MA-CAC with an HQ can lower both the HCDP and the NCBP while maintaining high channel utilization. [ABSTRACT FROM PUBLISHER]

Nowadays, online social networking services in wireless networks are very popular. In this paper, we propose a timer-based push scheme (TPS) where a number of articles posted during a pre-defined time interval are pushed to users and thus the number of push operations and articles to be pulled can be reduced. We develop an analytical model for TPS and derive the optimal timer minimizing the total cost. Simulation results are given to show the effectiveness of TPS over the existing push N scheme and to validate the analytical results. [ABSTRACT FROM PUBLISHER]

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 ). [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including traffic explosion problem in hierarchical wireless networks; deep learning techniques in signal recognition; and the social network data mining.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]