Energy harvesting Internet of Things (IoT) devices are capable of sensing only intermittent and coarse-grained data due to sleep scheduling; therefore, we develop a restoration mechanism (e.g., probabilistic matrix factorization (PMF)) that exploits spatial and temporal correlations of data to build up an environmental monitoring system. However, even with a well-designed restoration mechanism, a high accuracy of the environmental map cannot be achieved if an appropriate sleep scheduling of IoT devices is not incorporated (e.g., if IoT devices at necessary locations are in sleep mode or are not involved in restoration due to their insufficient energy). In this paper, we propose a restoration-aware sleep scheduling (RASS) framework for energy harvesting IoT-based environmental monitoring systems. Here, RASS involves customized deep reinforcement learning (DRL) considering the restoration mechanism, using which the controller performs sleep scheduling to achieve high accuracy of the restored environmental map while avoiding energy outage of IoT devices. The evaluation results demonstrate that RASS can achieve an environmental map with 5% or a lower difference from the actual values and fair energy consumption among IoT devices.

Current 5G networks suffer from high signaling overhead due to highly mobile vehicles. In this paper, we first introduce a personalized handover function (denoted μ HF) that consolidates all handover-related functionalities for individual mobile devices (MDs). Recognizing that the location of μ HF affects overall handover performance , we propose a mobility-aware μ HF provisioning system (MA- μ HFPS), which utilizes a central controller to collect mobility information for each MD and provisions μ HFs in the edge cloud for MDs that are expected to have high mobility for a long time. To minimize signaling overhead and migration cost for handover-related information from the central cloud to the edge cloud while ensuring that the average required resource of the edge cloud remains below a specific threshold, we formulate a constrained Markov decision process (CMDP) problem. By converting the CMDP problem into a linear programming (LP) model, we can achieve an optimal stochastic policy using a traditional algorithm with low complexity. Evaluation results demonstrate that MA- μ HFPS significantly reduces signaling overhead with a small state migration cost compared to the traditional handover management system .

Distributed serverless edge clouds and split computing are promising technologies to reduce the inference latency of large-scale deep neural networks (DNNs). In this article, we propose a dynamic split computing framework (DSCF) in distributed serverless edge clouds. In DSCF, the edge cloud orchestrator dynamically determines 1) splitting point and 2) warm status maintenance of container instances (i.e., whether or not to maintain each container instance in a warm status). For optimal decisions, we formulate a constrained Markov decision process (CMDP) problem to minimize the inference latency while maintaining the average resource consumption of distributed edge clouds below a certain level. The optimal stochastic policy can be obtained by converting the CMDP model into a linear programming (LP) model. The evaluation results demonstrate that DSCF can achieve less than half the inference latency compared to the local computing scheme while maintaining sufficient low resource consumption of distributed edge clouds.

Split computing is a promising approach to reduce the inference latency of deep neural network (DNN) models. In this paper, we propose a two-phase split computing framework (TSCF). In TSCF, for vertical inter-layer splitting between the computing nodes at different levels (e.g., central and edge clouds), a shortest path problem in a directed graph is formulated and a pruning-based low-complexity solution is devised. In addition, for horizontal intra-layer splitting between the computing nodes at the same level (e.g., edge clouds), the execution units of a specific layer are further divided and distributed to the computing nodes at the same level proportionally to their available resources. The evaluation results demonstrate that TSCF can reduce inference latency more than 38.8% compared to the traditional inter-layer splitting scheme by efficiently using the resources of distributed computing nodes. In addition, it is demonstrated that near-optimal performance in terms of inference latency can be achieved even with a pruning-based low-complexity solution.

In the conventional split computing approach based on the external computing node (e.g., cloud), Internet of Things (IoT) devices suffer from high network latency. In this article, we introduce an unmanned aerial vehicle (UAV)-assisted split computing system (USCS) where UAV patrols around the IoT device and IoT device offloads performing the tail model inference to UAV. To minimize the energy consumption while maintaining a sufficiently low inference completion time, IoT device makes two types of decisions: 1) the timing of starting the split computing (i.e., whether to conduct the split computing or delay) and 2) the splitting point. By formulating a constrained Markov decision process (CMDP) problem and converting the CMDP model into a linear programming (LP) model, the decisions of the IoT device can be optimized. The evaluation results show that the USCS can significantly reduce energy consumption while satisfying the inference completion time requirement.

Vehicular sensing has become attracting an increasing research interest for cost-effective monitoring in urban areas. Even though multiple types of sensing data are required to form a multidimensional sensing map in urban sensing applications, most of the previous works have only considered the sensing quality of single sensor type. In this article, we formulate an optimization problem of task allocation to improve the overall sensing quality in multidimensional vehicular urban sensing. To mitigate the high complexity of the formulated problem, we prove the submodularity of the objective function and present a low-complexity heuristic algorithm called sensing quality-aware task allocation (SQTA) leveraging the property of submodular optimization. Extensive experiments have been conducted by using two real-world data sets, which demonstrate that SQTA can improve the average sensing quality of multiple sensor types and also guarantee sufficient levels of the sensing quality of all sensor types.

Serverless edge computing is an emerging concept where only required functions are defined and executed as container instances at the edge cloud. The edge cloud has finite resources; therefore, sophisticated resource management is indispensable to accommodate more requests. In this article, we propose a function-aware resource management (FARM) framework for serverless edge computing that defines per-function queues to maximally utilize edge cloud resources. The FARM framework optimally determines: 1) which container instances should be maintained as warm status and 2) the amount of computing resources assigned to them. The FARM framework specifically formulates a constrained Markov decision process problem to minimize the memory resource consumption for the warm status maintenance while guaranteeing on-time task completion and converts it to a linear programming model to derive the optimal solution. The evaluation results show that the FARM framework can reduce the memory resource consumption of the edge cloud while meeting the on-time task completion.

Serverless architecture enables various intelligent applications to be run without managing infrastructure. In this architecture, the computing cost is generally proportional to the number of requested stateless functions and this number can affect the task completion time and, thus, it is prominent to decide an appropriate number of requested stateless functions. In this article, we propose a latency-guaranteed and energy-efficient task offloading (LETO) system where an Internet of Things (IoT) device decides the number of stateless functions requested to the cloud by considering the deadline on the task completion time and its energy level. To minimize the computing cost while guaranteeing sufficiently short task completion time and low energy outage probability, we formulate a constrained Markov decision process (CMDP) problem and convert the CMDP problem into an equivalent linear programming (LP) model. By solving the LP model, the optimal policy on the number of requested stateless functions can be achieved. Evaluation results illustrate that LETO can cut down the operating expenditure (OPEX) by up to 59% compared to a latency-guaranteed offloading scheme while keeping the task completion time and the energy outage probability below desirable levels.

In the distributed task offloading system, the desired task completion time cannot be achieved when lots of mobile devices offload simultaneously the tasks with high complexity to a specific edge cloud. In this research, we present a neighbor-aware distributed task offloading algorithm (NA-DTOA) where the IoT device takes its energy status and the decisions of neighbor devices into account for the decisions on whether to process the task by itself and on which edge cloud is selected to offload the task. To decrease the task completion time while maintaining the average energy outage probability below the specified threshold, we design a constrained stochastic game model. To achieve the solution of the model, a best response dynamics-based algorithm is devised. The evaluation results reveal that, compared to a probability-based scheme, NA-DTOA reduces the average task completion time by almost 47% while ensuring a substantially low-average energy outage probability of 0.03.

In federated learning (FL), which clients and quantization levels are selected for the deep model parameters has a significant impact on learning time as well as learning accuracy. This is not a trivial issue because it is also significantly affected by factors, such as computational power, communication capacity, and data distribution. Considering these factors, we formulate a joint optimization problem for clustering and selecting clusters with quantization levels. Due to the high complexity of the formulated problem, we propose a situation-aware cluster and quantization level selection (SITUA-CQ) algorithm. In this algorithm, the FL server first assembles clients into clusters to mitigate the impact of biased data distributions and determines the most suitable clusters and quantization levels based on their computing power and channel quality. Extensive simulation results show that SITUA-CQ can reduce the round time by up to 80.3% compared to conventional algorithms.

In vehicular edge computing (VEC), where vehicles offload their tasks to nearby edge clouds, it is not a trivial issue to design an optimal task offloading policy due to the dynamic nature of VEC environment and limited information on computing and communication resources. In this paper, we propose a belief-based task offloading algorithm (BTOA) where a vehicle selects target edge clouds (for computing) and subchannels (for communications) based on its belief, and observe their current resource and channel conditions. Based on the observed information, the vehicle finally determines the most appropriate edge cloud and subchannel. Evaluation results under a realistic traffic scenario demonstrate that BTOA can reduce the total latency of the task offloading over 42% compared to a conventional offloading algorithm where the target edge clouds and subchannels are determined without any real observations.

In federated learning (FL), if the participating mobile devices have low computing power and poor wireless channel conditions and/or they do not have sufficient data for various classes, a long convergence time is required to achieve the desired model accuracy. To address this problem, we first formulate a constrained Markov decision process (CMDP) problem that aims to minimize the average time of rounds while maintaining the numbers of trained data and trained data classes above certain numbers. To obtain the optimal scheduling policy, the formulated CMDP problem is converted into an equivalent linear programming (LP). Additionally, to overcome the problem of the curse of dimensionality in CMDP, we develop a joint client selection and bandwidth allocation algorithm (J-CSBA) that jointly selects appropriate mobile devices and allocates suitable amount of bandwidth to them at each round by considering their data information, computing power, and channel gain. Evaluation results validate that J-CSBA can reduce the convergence time by up to$49\%$compared to a conventional random scheme.

In federated learning (FL), which clients are selected and which quantization levels are chosen for the deep model parameters have significant impacts on the learning time as well as the learning accuracy. In this paper, we formulate a joint optimization problem on the client and quantization level selections. As a low complexity solution to the formulated problem, we develop a performance-aware client and quantization level selection (PA-CQLS) algorithm where the FL server estimates the individual round times of clients based on their computing power and channel quality, and determines the most appropriate clients and quantization levels accordingly. Simulation results show that PA-CQLS can reduce the round time by up to 70% compared to conventional algorithms.

Inappropriate service migrations can lead to undesirable situations, such as high traffic overhead, long service latency, and service disruption. In this article, we propose an application-aware migration algorithm (AMA) with prefetching. In AMA, a mobile device sends a service offloading request to the controller. After receiving this request, the controller determines the initial service cloud where virtual machine (VM) of the service initially operates by considering the application type. In addition, it periodically decides where to migrate VM and prefetch its core part considering the mobility of the mobile device and application type. To minimize the generated traffic volume while satisfying the requirements of the application, a constrained Markov decision process (CMDP) is formulated and its optimal policy is obtained via linear programming. Evaluation results demonstrate that AMA with the optimal policy can reduce the generated traffic volume while satisfying the requirements of the application (i.e., service latency and probability of service disruption).

The computation offloading, where Internet of Things (IoT) devices transfers their task to an external cloud, has several advantages such as low energy consumption of IoT devices and fast response time. To maximize these advantages, IoT devices can exploit the nearest edge cloud. However, frequent offloadings to the nearest edge cloud can cause a location privacy vulnerability due to the proximity of the edge cloud from IoT devices, which is a critical issue in smart city IoT applications. To address this problem, we propose a location privacy-guaranteed offloading algorithm (LPGA) in cache-enabled edge cloud environments. In LPGA, an IoT device decides where to offload the task (i.e., edge cloud or central cloud) with the consideration of the privacy level on its location and the cache hit probability. To minimize the generated traffic volume while maintaining low energy outage probability and providing a sufficient level of location privacy, a constrained Markov decision process (CMDP) problem is developed and it is converted into an equivalent linear programming (LP) model to achieve the optimal policy for offloading. Evaluation results demonstrate LPGA can reduce the traffic volume up to 39 percent compared to a central cloud-based offloading scheme while maintaining the energy outage probability below a certain level and providing required location privacy level.

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.

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.

To support network slicing and service function chaining (SFC) at a time, we propose a novel hierarchical identifier (HID)-based 5G architecture. For this, we first introduce HID which consists of network slice selection assistance information (NSSAI) and service path ID (SPI). Based on HID, a user can attach a specific network slice and flows generated by the user can be processed by a set of service functions (SFs) in a sequence. Meanwhile, when the incoming flow to a specific slice unexpectedly increases, the slice cannot handle incoming flow due to its limited capacity, which degrades users' quality of service (QoS). To alleviate this issue and efficiently utilize network resources, we introduce a concept of the backup slice shared by different services. Evaluation results demonstrate that the proposed architecture can achieve better performance in terms of the average system blocking probability and utilization in dynamic environments.

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.

In vehicular edge clouds, tasks from vehicles are processed nearby edge nodes (ENs) and thus low latency services can be provided. However, under high vehicular mobility, frequent service migration between two ENs and increased handover latency can be observed. In this paper, we introduce a trajectory-aware edge node clustering (TENC) scheme in which multiple ENs form a cluster depending on the trajectory of a target vehicle. To attain the optimal performance, we formulate an optimization problem by means of a constrained Markov decision process (CMDP). Evaluation results demonstrate that the obtained optimal policy can minimize service delay significantly.

To overcome the limitation of standalone edge computing in terms of computing power and resource, a concept of distributed edge computing has been introduced, where application tasks are distributed to multiple edge clouds for collaborative processing. To maximize the effectiveness of the distributed edge computing, we formulate an optimization problem of task allocation minimizing 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 algorithm (DATA). Evaluation results demonstrate that DATA can reduce the completion time up to by 18% compared to conventional dependency-unaware task allocation schemes.

In environmental monitoring systems, unnecessary transmissions can occur when an Internet of Things (IoT) device transmits its data without any consideration on neighbors' transmissions. In this paper, we propose a neighbor-aware energy-efficient monitoring system (NA-EEMS) for energy harvesting IoT devices. In NA-EEMS, to exploit spatial correlation among IoT devices, geographically proximate IoT devices transmit their sensed data in a distributed manner by means of a constraint stochastic game. We devise a best response dynamics-based algorithm to obtain a multipolicy constrained Nash equilibrium. Evaluation results demonstrate that NA-EEMS can improve the network lifetime while preserving the monitoring probability above a desired level.

In mobile crowdsensing (MCS), a participant selection strategy should be carefully designed to guarantee sufficient coverage and avoid unnecessary energy consumption. In this paper, we propose a coverage-guaranteed and energy-efficient participant selection (CG-EEPS) strategy, in which the MCS server determines participants based on the data usage profile and mobility level of mobile devices. In addition, CG-EEPS adopts a piggyback approach of sensory data for energy-efficient transmissions. To attain the optimal performance in CG-EEPS, a constraint Markov decision process (CMDP) problem is formulated and its optimal policy is obtained by a linear programming. To address the curse of dimensionality in CMDP, a greedy heuristic is proposed and evaluated. Trace-driven evaluation results demonstrate that CG-EEPS can achieve sufficient coverage rate only with 20% of participants compared to random selection schemes.

Radio frequency (RF) energy transfer has received high attention as a promising technology for wireless sensor networks (WSNs) due to its flexibility of energy supply. However, unplanned RF energy transmissions may lead to increased energy consumption in the main grid. To address this problem, we first develop the energy queuing models for base station (BS) and sensor node (SN). Based on them, we propose an energy utilization-aware operation control algorithm (EU-OCA) to minimize the energy outage probabilities of SNs while maintaining the energy consumption of the main grid below a certain level. In EU-OCA, a controller determines jointly the active/sleep modes and the transmission powers of renewable energy-based BSs with the consideration of the statistical information on the energy arrival of BSs and the energy consumption of SNs. Evaluation results demonstrate that EU-OCA can achieve longer lifetime compared to other BS operation control algorithms while maintaining the energy consumption of the main grid below a target level.

To provide an accurate environmental map (EM) while avoiding unnecessary transmissions of Internet of Things (IoT) devices, we propose a spatiotemporal correlation-based environmental monitoring system (ST-EMS). In ST-EMS, IoT devices decide whether to transmit the sensed data to an IoT gateway (GW) or not by considering the temporal correlation in the sensed data and energy level. Through a Markov decision process (MDP) formulation, the optimal policy is obtained and it is proved that the optimal policy of MDP has an implementation-friendly threshold structure by using the submodularity concept. Also, the IoT GW in ST-EMS restores EM and improves its accuracy by exploiting the spatial correlation among sensed data using probabilistic matrix factorization. Evaluation results demonstrate that ST-EMS can improve the expected total reward significantly compared with other schemes and achieve low mean square error of 1% in EM restoration.

Even though energy harvesting is a promising technology for energy-efficient surveillance systems, energy harvesting levels are highly dynamic depending on the time and location. Thus, the deployment of nonenergy-harvesting sensor nodes (NHSs) and sophisticated sleep scheduling of sensor nodes are necessary for performance guaranteed surveillance systems. In this paper, we present a software-defined surveillance system (SDSS) in which a centralized controller determines the sleep schedules of energy harvesting and NHSs on the basis of the collected information such as the spatial distribution of targets and the energy levels of sensor nodes. To derive the optimal sleep schedules minimizing the number of active sensor nodes while providing sufficient surveillance performance, a constraint Markov decision process problem is formulated and the optimal policy on sleep scheduling is obtained by linear programming. The evaluation results demonstrate that the SDSS with the optimal policy can reduce energy consumption by employing fewer active sensor nodes while providing the required level of target monitoring probability.

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.

Service function chaining (SFC) is an emerging technique that provides steering of traffic flows through an ordered set of service functions (SFs). In SFC, high availability is one of the most important issues to be addressed. SF instances within the chain can become unavailable when SF instances are overloaded or failed (e.g., power outage). Therefore, in order to realize highly available SFC, load balancing and fault management for SF instances must be provided. In this paper, we propose an overload and failure management (OFM) module in SFC that consists of the overload management (OM) module and the failure management (FM) module. In the OM module, when the current load at an SF instance exceeds a low-level threshold, a backup SF instance is prepared in advance. Meanwhile, if the current load further exceeds a high-level threshold, flow migration from the current SF instance to the backup SF instance is triggered. The FM module detects the failure of the SF instance by using a failure alarm. Upon detecting the failure, flow migration to the backup SF instance is triggered. We implement the OFM module in OpenDaylight (ODL) and present the experimental validation results.

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 longtime. 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.

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.

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.

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.

Current evolved packet core (EPC) systems in LTE/LTE-A networks suffer from the exponentially increased mobile traffic and thus research on new EPC architectures is ongoing. In this paper, we introduce a virtualized EPC (vEPC) system where middlebox functions of the existing EPC systems are implemented in virtualized software modules and the virtualized software modules operate over selected physical service nodes. To minimize the impact of the increased transmission cost in vEPC, the optimal placement of middlebox functions is also investigated. Numerical results demonstrate that the proposed scheme can achieve lower packet transmission cost than the conventional one.

Recently, a data packet forwarding scheme between evolved node Bs (eNBs) in long-term evolution (LTE)/LTE-advanced (LTE-A) networks has been proposed to reduce the signaling overhead and delay incurred in the data path switching scheme. However, the conventional data packet forwarding scheme suffers from the increased delay when the length of the data packet forwarding chain is inappropriately long. To attain the optimal handover performance in LTE/LTE-A networks, we propose an optimized and distributed data packet forwarding scheme where an optimal length of the forwarding chain is obtained by a Markov decision process (MDP). Numerical results demonstrate that the proposed scheme achieves the optimal and adaptive performance in diverse network environments.

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.