Self-organizing network (SON) has been introduced as a collection of functions to automatically manage and optimize heterogeneous cellular networks. For network-wide deployment of SON, coordination among SON functions is indispensable as their operational objectives differ. However, since naive coordination among SON functions may lead to high complexity and policy conflicts, a carefully designed coordination framework is required. In this paper, we propose a novel conflict-aware distributed coordination (CADC) framework, employing deep reinforcement learning (DRL)-based coordinators for base station (BS) clusters to manage SON functions at the local level. To minimize inter-cluster dependency, CADC employs a graph-based clustering algorithm that models potential conflict relationships among BSs and groups BSs accordingly, thereby enabling global coordination through localized coordination within clusters. For performance evaluation, CADC is implemented on an ns-3 network simulator integrated with ns3-gym, and extensive simulation results demonstrate that CADC effectively resolves policy conflicts between SON functions compared to baseline schemes while achieving faster convergence.

In-band network telemetry (INT) enables real-time and fine-grained network monitoring but incurs high transmission overhead. To mitigate this, encoding-based INT methods have been introduced to collect telemetry items using fewer bits than their original bits. However, prior works struggle to reduce encoding bit length when the magnitudes of telemetry items vary widely, as they rely on transmitting raw values. To address this challenge, we propose a prediction-based INT framework that effectively minimizes encoding bit length by collecting prediction errors instead of raw values. Our framework leverages both temporal patterns and inter-item correlations to robustly reduce prediction errors, thereby significantly lowering encoding bits while ensuring accurate reconstruction of original values.

With previously unattainable performance metrics like terabit-per-second data rates, extremely low latency, and ubiquitous coverage, the next sixth-generation (6G) wireless communication technology promises to transform connectivity completely. This study highlights the major developments, difficulties, and potential uses of 6G while summarizing the state of research and development at the moment. The core technological foundations of 6G have been investigated, including terahertz (THz) frequency bands, in-band full duplex (IBFD) communication, and artificial intelligence/ML-driven network optimization. Additionally, it has been implied that 6G has the ability to facilitate transformational applications, such as body area networks, extended reality (XR), collaborating robotics (Cobots), smart grid 2.0, autonomous vehicles, intelligent healthcare systems, etc. Major challenges, including ultradense networking (UDN), spectrum allotment, security, and privacy, are evaluated severely. The purpose of this article is to guide future research and build a greater understanding of the revolutionary potential of 6G technology to restructure global communication networks by providing an overview of 6G research and development.

Reconfigurable data center networks (RDCNs), integrating the electrical packet switch (EPS) with the optical circuit switch (OCS), improve network adaptability by enabling high- throughput connections between top-of-rack (ToR) pairs. However, existing RDCN scheduling schemes face challenges in responsiveness, particularly during traffic bursts. In this paper, we propose a novel demand-aware distributed scheduling framework called P4-DADS, utilizing P4-based programmable ToR switches (P4ToR). To prevent conflicts arising from simultaneous OCS port allocations, P4-DADS employs a token-ring-based distributed reservation algorithm, enhanced with an adaptive buffer control (ABC) mechanism. By formulating a Markov decision process (MDP) problem, the optimal ABC policy is obtained through a value iteration algorithm, ensuring that packets are immediately ready for transmission during sudden demand surges. P4-DADS improves network responsiveness and scalability, as evidenced by a 145.95% increase in throughput and a 87.31% reduction in flow completion time. These improvements demonstrate the potential of P4-DADS as a scalable and efficient solution for resource management in RDCN.

Edge federation is a promising approach for reducing the workload of each operator by facilitating resource sharing amongst operators. However, operators often exhibit selfish behavior, prioritizing their own benefits. They may refrain from participating in edge federation or misrepresent the actual value of their resources, which ultimately diminishes the overall benefits of edge federation. To address this issue, we first establish a Vickrey-Clarke-Groves (VCG)-based reverse auction framework for task offloading in edge federation. In the framework, a winner determination problem is formulated as an integer linear programming (ILP) problem. The ILP problem formulated has high computational complexity and cannot be applied to large and dynamic network environments. Thus, we propose an efficient winner and payment (W&P) determination algorithm to obtain sub-optimal solutions in polynomial time. Extensive simulation results demonstrate that the proposed algorithm increases the total profit of edge federation by up to 119.7% and reduces total resource usage by up to 54.8%, respectively, compared to other comparison algorithms.

To tackle the difficulties of running sophisticated deep neural network (DNN) models on mobile devices, split computing presents a viable solution by offloading computations to the edge server. Current split computing schemes typically aim to lower either inference latency or energy use separately; however, optimizing both simultaneously is quite challenging due to numerous shifting factors, such as intensive continuous DNN model inferences, DNN model traits, and device/network conditions. Moreover, in practical applications, edge server overload might lead to substantial queuing delays, adding complexity to the optimization process. This paper outlines a joint optimization problem that simultaneously seeks to minimize both inference latency and energy consumption, with a distinct inclusion of queue clearance latency for an accurate analysis of the continuously generated DNN model inferences. To address this intricate optimization challenge, we introduce a low-complexity heuristic algorithm that sets split point decisions based on the residual energy of mobile devices for each DNN inference cycle. Upon evaluation, our proposed algorithm demonstrates notable improvements by reducing inference latency by between$73.37\%$and$99.39\%$, and cutting down energy usage by between$39.97\%$and$94.67\%$compared to fully local processing on mobile devices.

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.

Given the importance of robustness and resilience in emerging cloud-native networks, effective monitoring for fault detection is paramount and In-band network telemetry (INT) is a key candidate that enables real-time and fine-grained network monitoring with a programmable data plane. However, INT increases bandwidth overhead because network information is inserted directly into the packet header. In this paper, we propose a quantized INT (QINT) to effectively reduce overhead by considering the distribution of raw data. In QINT, the programmable switch encodes a raw telemetry item into a quantized bit stream using the Huffman coding scheme. To do this, QINT monitors the distribution of network telemetry items and encodes high-frequency data that are generated most of the time in a few bits. We implemented QINT on a programmable switch and our experimental results demonstrate that QINT can reduce the relative bandwidth usage by up to 60.6% compared to traditional INT, respectively.

The increasing utilization of newly emerging networks (e.g., private-5G) across industries underscores the need for accurate traffic prediction to manage network resources effectively. However, rapidly emerging networks face challenges in accurate prediction due to limited training data at the early stage and fluctuation in traffic load at the maintenance stage. In response, we propose ISOML (Inter-Service Online Meta-Learning), a novel traffic prediction pipeline designed for newly emerging networks. ISOML utilizes meta-learning to address data scarcity and employs the EWC (Elastic Weight Consolidation) for online learning to learn dynamics of traffic patterns. Experimental validation in real-world datasets demonstrates the efficacy of ISOML in predicting traffic for emerging network environments.

In-network (or on-path) inference over programmable data planes (PDPs) allows fast and low-overhead inference using deep neural networks (DNN). To alleviate the massive processing and deployment cost of in-network inference, distributed deployment on multiple programmable network devices is mainly adopted. However, it is likely to produce a considerable amount of network traffic due to the exclusive forwarding chain and intermediate data between submodels. In this work, we propose a traffic-aware adaptive in-network inference scheme, TINIEE, to maximally reduce the network traffic of in-network inference without causing a significant reduction in classification performance. To this end, we first devise an adaptive inference method on the data plane striking the balance between the classification performance and the network traffic cost. Furthermore, we formulate a traffic minimization problem to decide the proper location of each submodel considering each flow's exit tendency with a predefined confidence threshold. Since the problem is excessively complicated, we devise a low-complexity practical submodel placement algorithm. We implement the proposed scheme on software-programmable switches, and the evaluation results demonstrate that TINIEE reduces network traffic by up to 34.48 % compared to the state-of-the-art, while maintaining sufficiently high classification performance.

In heterogeneous networks (HetNets), the dense deployment of base stations (BSs) often leads to severe signal interference. This interference causes radio link failures (RLFs) and ping-pong handovers (PPs), undermining the connectivity of user equipments (UEs). To address these problems, mobility robustness optimization (MRO) can be an effective solution. However, MRO operation without considering load distribution can overload specific BSs. This can increase signal interference and lower channel quality in high-load areas, degrading MRO performance. To mitigate these issues, we propose a load-aware MRO framework using multi-objective reinforcement learning. In the proposed framework, agents use two separate objective functions to learn the individual impacts of adjusting handover control parameters on both preventing occurrences of RLFs/PPs and distributing load. Through multi-objective learning, our framework minimizes the occurrences of RLFs/PPs while flexibly distributing load across BSs. This prevents additional RLFs/PPs and channel quality degradation caused by load concentration. Simulation results show that the proposed algorithm reduces the occurrence rates of RLFs and PPs by up to 27% and 52%, respectively.

Self-organizing network (SON) has been introduced as a collection of functions to automatically manage and optimize dynamic networks. For network-wide deployment of SON, coordination among SON functions is indispensable. In addition, since naive coordination among SON functions may lead to high complexity and policy conflict, a carefully designed coordination framework needs to be designed. In this paper, we propose a scalable SON coordination framework, based on deep reinforcement learning (DRL), that manages the operation of SON functions to prevent policy conflicts. To reduce coordination complexity, we formulate a Markov decision process (MDP) problem and introduce an efficient DRL algorithm to solve it. Extensive simulation results show that the proposed framework effectively resolves policy conflicts between SON functions compared to baseline schemes and achieves a faster convergence time.

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.

Training on time-series data generated from mobile networks is a resource-intensive and time-consuming task that encounters various training failures. To cope with this issue, we propose CheckBullet, a lightweight checkpoint system to minimize storage requirements and enable fast recovery in mobile networks. First, CheckBullet determines a checkpointing interval based on the characteristics of the model and the timing of failure occurrences. This approach ensures fast recovery while preserving the existing training runtime. Second, CheckBullet quantizes the weight tensor and eliminates duplicate weights, which significantly reduces the overall checkpoint size, leading to a substantial decrease in storage requirements. Third, CheckBullet selects the minimum training loss among the deduplicated checkpoints and merges the selected checkpoints. This approach reduces recovery time while preserving existing training loss. The experimental results show that CheckBullet can reduce the recovery time by$6\times$to$11\times$barely increasing the training runtime. Furthermore, CheckBullet can save storage requirements by up to 70% while maintaining the minimum training loss.

To support a wide range of vertical services in the fifth-generation (5G), a concept of private 5G has been introduced and has gained a great attention. Even though several deployment models for private 5G have been reported in the literature, they have not fully utilized the benefit of modularized control plane (CP) network functions (NFs) for better performance and cost efficiency. However, to leverage this benefit, a sophisticated CP placement design is essential. In this paper, we propose a novel CP framework for private 5G dubbed divide and cache (D&C). In D&C, the dependency and frequency of CP NFs are first analyzed based on the 3rd generation partnership project (3GPP) specification. Based on the analysis results, NFs for private 5G are split into on-premise and edge/public clouds to balance the tradeoff between performance and deployment costs. Also, a new NF called NF profile cache function (NFPCF) is built in on-premise to mitigate the signaling overhead. We implemented D&C over a private 5G testbed using an open-source software (i.e., free5GC) running on multiple virtual machines (VMs). Evaluation results demonstrate that D&C can achieve 19% lower deployment cost at the expense of slightly increased latency compared to the existing model.

With the rapid growth in demand for mobile data fueled by the emergence of new consumer applications that require high quality of service, existing static mobile data pricing plans are no longer suitable. Although several dynamic pricing mechanisms have been proposed in the literature, they have not been widely adopted due to their associated implementation complexity, the lack of trust in the associated platforms, and the absence of automatic enforcement. To address these challenges, we propose a credible trust framework (CREDIT) that leverages well-established Ethereum smart contracts for service-level agreement (SLA) enforcement. CREDIT introduces a novel SLA verification mechanism through fair auditor selection and auditor payoff to ensure truthfulness, and hence reinforces trust between the various parties involved in CREDIT. A detailed game-theoretic analysis is provided to prove the credibility of CREDIT by using the principle of a strong Nash equilibrium. In addition, CREDIT is prototyped by leveraging the smart contracts of Ethereum Blockchain. The results achieved provide a valuable validation of the feasibility of CREDIT.

Most current volumetric DDoS detection and mitigation schemes utilizing data plane programmability are source-based, yet it is challenging to identify an attacker through source analysis because a large number of widespread sources are exploited by the attacker. In this paper, we propose BACKWARD, a victim-centric DDoS attack detection and mitigation scheme that first identifies the victim of the DDoS attack and then only blocks sources that contacted the victim. We implement BACKWARD using the P4 language and present experimental results, which show that BACKWARD is able to achieve higher accuracy in identifying and blocking the attackers compared to the source-based scheme.

To support a wide range of vertical services in fifth-generation (5G), a concept of private 5G has gained a great attention. Even though several deployment models for private 5G have been reported in the literature, they have not fully utilized the benefit of modularized control plane (CP) network functions (NFs) for better performance and cost efficiency. In addition, their qualitative and quantitative analysis has not been conducted yet. In this paper, we propose a novel CP framework for private 5G dubbed divide and cache (D&C). In D&C, the dependency and frequency of CP NFs are first analyzed based on the 3rd generation partnership project (3GPP) specification. Based on the analysis results, NFs are split into on-premise and edge/public clouds to balance the tradeoff between performance and cost. Also, a new NF called NF profile cache function (NFPCF) is built in on-premise to mitigate the signaling overhead. We implemented D&C over a private 5G testbed using an open-source software (i.e., free5GC). Evaluation results demonstrate that D&C can achieve 19% lower deployment cost at the expense of slightly increased latency compared to the existing model.

Distributed machine learning is an effective method to alleviate intensive computation costs of training; however it suffers from network bottlenecks while gathering local results. Recent advent of programmable data planes opened a new avenue, in-network aggregation, which executes gradient aggregations in the middle of the network resolving network bottlenecks and further accelerates distributed machine learning. However, due to resource-constrained features of current programmable data planes, installation of in-network aggregation functionalities throughout the network would impose unacceptable burden, posing a need for sophisticated deployment. In this paper, we consider a problem of deploying in-network aggregation functionalities, so as to minimize the total network traffic in multi-tenant distributed machine learning. Since the formulated problem is an integer linear programming problem, which is known as NP-hard, we propose a traffic aware placement of in-network aggregation (TAPINA) algorithm with lower complexity and near-optimal performance. TAPINA decides aggregation points of multiple tenants sequentially in order of their expected traffics and reuses the already selected aggregation points by other tenants to reduce the overall deployment cost. Simulation results demonstrate that TAPINA shows near-optimal performance, achieving up to 20 % traffic reduction compared to the state-of-the-art algorithm in most cases.

Network Slicing has emerged as a keystone for supporting multiple service verticals over shared 5G network infrastructure. It leverages virtualization techniques to allocate programmable network instances matching service requirements. This requires novel resource allocation mechanisms, as well as new admission control and load balancing policies, to satisfy the diverse quality of service (QoS) requirements of different services, while optimizing the resource utilization across slices. This in turn requires accurate determination of loading on a slice, which not only depends on the type of application, but also on the stringency of QoS requirements, and hence existing metrics such as resource utilization may be inadequate. We introduce a novel load metric that can be used across slices, and illustrate how it can be used for load balancing, slice capacity resizing, and admission control in the proposed cross-slice resource orchestration framework.

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 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.

Owing to the advantages of satisfying service requirements and providing strong security, non-public networks (NPNs) are considered as a promising technology in vertical industries. However, to efficiently manage cloud-native network functions (CNFs) in NPNs, a sophisticated control plane management scheme should be designed. In this paper, we propose a deep Q-network-based CNF placement algorithm (DQN-CNFPA) that jointly minimizes the costs incurred by launching and operating CNFs in edge clouds and the backhaul control traffic overhead. In addition, DQN-CNFPA learns the spatiotemporal patterns in service requests and adaptively places CNFs in edge clouds according to the expected incurred costs. The evaluation results demonstrate that DQN-CNFPA can reduce the total cost by up to 26.2% compared with a conventional scheme that does not learn spatiotemporal service request patterns.

In-network aggregation facilitates accelerated distributed deep learning by utilizing a programmable switch to aggregate gradient packets. However, a straggler problem should be addressed to avoid performance degradation in terms of training time. In this paper, we propose a straggler-aware in-network aggregation (SAINA) scheme to mitigate the straggler problem while preventing accuracy degradation. In SAINA, the programmable switch aggregates local gradients of the fastest$k$workers to exclude stragglers and changes$k$adaptively to balance the tradeoff between training speed and accuracy. To this end, we design a switch-friendly convergence detection (SFCD) algorithm which detects a convergence point and determines$k$at the convergence point. SAINA is implemented over a software programmable switch and experimental results show that the accuracy of SAINA can reach a target accuracy up to 2.84x faster than the existing in-network aggregation scheme.

Vehicle-to-everything (V2X) communications over cellular networks have a great potential for enabling intelligent transportation systems (ITSs), and supporting advanced services such as autonomous driving. However, such services have stringent QoS and security/privacy requirements. Even though the use of blockchain can ensure security and privacy for V2X services, blockchain-based solutions suffer from the issues of high latency, low scalability, and high computation power for mining. To overcome these challenges, we propose a lightweight and secure vehicular edge computing framework. The LS-VEC framework leverages directed acyclic graphs (DAGs) for recording transactions for edge resource allocation and micro-transactions for pricing VEC resources. In addition, an auction theory-based game-theoretic approach is proposed for allocation and pricing of edge resources used for supporting computation offloading.

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.

Unslotted ALOHA has been often employed by several low-power wide-area networks (LPWANs) for Internet of Things (IoT) as a random access (RA) protocol. This work analyzes the performance of unslotted ALOHA systems in terms of throughput and RA delay, and investigates their optimization. Our analysis consists of: 1) two-heterogeneoususer case, whose backoff rate and packet length are different; 2)$N$-homogeneoususer case, whose backoff rate and packet length are identical; and 3) homogeneous users of infinite population model. In the two-user case, we investigate the throughput region of unslotted ALOHA by using a multiobjective optimization problem (MOOP) and derive the Laplace Stieltjes transform (LST) of the probability density function (PDF) of RA delay. For$N$-homogeneous user case, we show how the throughput behaves according to the population size, packet length, and backoff rate. Our work may provide a comprehensive analytical framework for unslotted ALOHA systems.

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.

With the emergence of 5G, wireless sensor networks, and related technologies, Internet of Things (IoT) has gained prominence as an emerging paradigm to meet the demands of flexible, agile, and ubiquitous accessibility of cyberspace from physical systems. However, the current centralized IoT architecture is heavily restricted by the problems of single points of failure, data privacy, security, and robustness. Recently, blockchains have been found attractive as potential solutions to some of these problems, due to their ability to maintain immutable open ledgers that are accessible to everyone but are tamper-proof. In addition, rapid development of edge computing has enabled a large range of new IoT applications. Edge computing pushes cloud services from the network core to the network edges in closer proximity to IoT devices. Thus, blockchain and edge computing are attractive technologies to meet new and existing challenges by enabling new IoT applications and services through secure, reliable, flexible, and powerful devices and systems while motivating new business models in the growing digital economies. They can provide attractive solutions, such as schemes for decentralized services, service virtualization, rapid resource optimization, and flexible and reliable management and maintenance.

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.

General traffic analysis based on deep packet inspection (DPI) techniques at switches cannot grasp the detailed knowledge of network applications going into internal switches, and the statistics-based reports of switches lack flow-level recognition of the traffic. Besides, DPI is generally expensive and has limited performance. Therefore, network-wise accurate flow-awareness by packet sampling is highly desirable for fine-grained quality of service guarantee, internal network management, traffic engineering, security analysis, and so on. In this paper, we propose a Spatial-Temporal Collaborative Sampling (STCS) framework in the flow-aware software-defined networks (SDNs). Particularly, considering the spatial-temporal factors and limits of network resources, the formulated STCS problem aims to maximize the network-wise sampling accuracy of flows including mice flows and elephant flows by characterizing both of the comprehensive influences of switches and the effects on sampling accuracy imposed by the collaborative strategy among switches in the spatial-temporal dimension. We propose a suboptimal approach to address the complex STCS problem in two steps: 1) Top-K switch selection based on the iterative comprehensive influence, and 2) sampling time slot allocation based on the local value maximization. Trace-driven evaluation results demonstrate the effectiveness of the proposed framework on improving the sampling accuracy and reducing redundant packets.

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.

For efficient advertisement in vehicular environments, we propose a software-defined vehicular advertisement platform (SD-VAP) in which a centralized controller determines target vehicles for advertisements. To maximize the advertisement exposure impact in SD-VAP, we formulate a vehicle-to-advertisement matching problem and devise two heuristic algorithms to solve the problem in a practical manner. Extensive simulation results demonstrate that the proposed heuristic algorithms achieve near-optimal performance in terms of the advertisement quality and total advertisement exposure.

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.

General traffic analysis based on Deep Packet Inspection (DPI) techniques at the gateways or access points cannot grasp the detailed knowledge of network applications going among internal nodes, and the statistics-based reports of routers are also lack of flow-level recognition of the traffic in the form of only five tuple. Therefore, network-wise accurate flow-awareness by packet sampling is highly desired for fine-grained quality of service guarantee, internal network management, traffic engineering, and security analysis and so on. In this paper, we propose a Spatio-Temporal Collaborative Sampling (STCS) problem based on the Software-Defined Networking (SDN) technique. The goal of STCS is to maximize the network-wise sampling accuracy of both elephant and mice flows, which considers both of the comprehensive influences of nodes and the effect on sampling accuracy imposed by the collaborative strategy among nodes in the time dimension. We present a approach to calculate the near optimal solution of STCS in two steps: 1) Top-K nodes selection by iterative comprehensive influence, and 2) spatio-temporal cosampling solution based on the local value maximization strategy. We evaluate the proposed approach by a realistic large-scale topology, and the results show that the sampling accuracy can be effectively improved by the method, especially for mice flows, and the redundant ratio of sampled packets is reduced by 34.4%.

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.

In wireless multimedia cognitive radio networks (WMCRNs), to optimize multimedia transmissions and scarce wireless spectrum utilization, a multimedia secondary user (MSU) needs to estimate and/or identify the achievable quality of service (QoS)-levels over the available licensed channels. However, due to the lack of signaling information among MSUs and the primary users (PUs) in uncoordinated environments, identification of the achievable QoS-levels on the available licensed channels is a challenging problem and has not yet been fully explored. To address this challenge, we propose a novel NOn-parametric Bayesian channEls cLustering (NOBEL) scheme. In NOBEL, an infinite Gaussian mixture model-based collapsed Gibbs sampler is adopted to identify the achievable QoS-levels over the feature space, i.e., bitrate, packet delay variation, and packet delivery ratio on the PUs' licensed channels. Real trace-driven evaluation results demonstrate that NOBEL outperforms other baseline clustering techniques and guarantee high accuracy from 98% to 99.5%.

A Tactile Internet-based nanonetwork is an emerging field that promises a new range of e-health applications, in which human operators can efficiently operate and control devices at the nanoscale for remote-patient treatment. A haptic feedback is inevitable for establishing a link between the operator and unknown in-body environment. However, haptic communications over the terahertz band may incur significant path loss due to molecular absorption. In this paper, we propose an optimization framework for haptic communications over nanonetworks, in which in-body nanodevices transmit haptic information to an operator via the terahertz band. By considering the properties of the terahertz band, we employ Brownian motion to describe the mobility of the nanodevices and develop a time-variant terahertz channel model. Furthermore, based on the developed channel model, we construct a stochastic optimization problem for improving haptic communications under the constraints of system stability, energy consumption, and latency. To solve the formulated nonconvex stochastic problem, an improved time-varying particle swarm optimization algorithm is presented, which can deal with the constraints of the problem efficiently by reducing the convergence time significantly. The simulation results validate the theoretical analysis of the proposed system.

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.

The secure distribution of protected content requires consumer authentication and involves the conventional method of end-to-end encryption. However, in information-centric networking (ICN) the end-to-end encryption makes the content caching ineffective since encrypted content stored in a cache is useless for any consumer except those who know the encryption key. For effective caching of encrypted content in ICN, we propose a novel scheme, called the Secure Distribution of Protected Content (SDPC). SDPC ensures that only authenticated consumers can access the content. The SDPC is a lightweight authentication and key distribution protocol; it allows consumer nodes to verify the originality of the published article by using a symmetric key encryption. The security of the SDPC was proved with BAN logic and Scyther tool verification.

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.

With the rapid development of various real-time services, there is urgent need for application-aware capabilities in realtime network to meet the higher demand for network's quality of service (QoS), security policy and so on. Accurate and cost-effective collection of flow information is needed. However, the contradiction between the real-time, accuracy and performance cost in the passive traffic information collection mode of old board makes it difficult to build application-aware realtime network of high-accuracy. In this paper, we propose an agile application-awareness network (A3N) for software-defined networks (SDN). A3N implements a flow-based self-adaptive sampling strategy (FSS) for the incoming traffic and combines a parallel deep packet inspection (DPI) with high-performance to perceive network traffic changes. Experimental results demonstrate the proposed A3N can gain good performance with low costs, and also provide real-time application-aware services with deep operational visibility.

Vehicular cloud (VC) is an emerging technology where multiple vehicles form a cloud to share their abundant resources and carry out a heavy job in a cooperative manner. By using VC, each vehicle can perform various VC applications requiring heavy resources. In this regards, it is not a trivial issue to choose appropriate vehicles to complete the given task according to vehicular mobility and each VC service type. In this paper, we suggest a reliable vehicle selection algorithm (RVSA) to minimize the cost of completing the requested task for solving mixed integer nonlinear programming (MINLP) vehicle selection problem. Evaluation result demonstrates that RVSA can achieve to reduce significantly the task completion cost over a wide range of vehicular mobility compared with other conventional algorithms.

Vehicular ad hoc networks (VANETs) are taking more attention from both the academia and the automotive industry due to a rapid development of wireless communication technologies. And with this development, vehicles called connected cars are increasingly being equipped with more sensors, processors, storages, and communication devices as they start to provide both infotainment and safety services through V2X communication. Such increase of vehicles is also related to the rise of security attacks and potential security threats. In a vehicular environment, security is one of the most important issues and it must be addressed before VANETs can be widely deployed. Conventional VANETs have some unique characteristics such as high mobility, dynamic topology, and a short connection time. Since an attacker can launch any unexpected attacks, it is difficult to predict these attacks in advance. To handle this problem, we propose collaborative security attack detection mechanism in a software-defined vehicular networks that uses multi-class support vector machine (SVM) to detect various types of attacks dynamically. We compare our security mechanism to existing distributed approach and present simulation results. The results demonstrate that the proposed security mechanism can effectively identify the types of attacks and achieve a good performance regarding high precision, recall, and accuracy.

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.

In this paper, we consider the problem of optimal dynamic service function (SF) placement and flow routing in a SF chaining (SFC) enabled network. We formulate a multi-objective optimization problem to maximize the acceptable flow rate and to minimize the energy cost for multiple service chains. We transform the multi-objective optimization problem into a single-objective mixed integer linear programming (MILP) problem, and prove that the problem is NP-hard. We propose a polynomial time algorithm based on linear relaxation and rounding to approximate the optimal solution of the MILP. Extensive simulations are conducted to evaluate the effects of the energy budget, the network topology, and the amount of server resources on the acceptable flow rate. The results demonstrate that the proposed algorithm can achieve near-optimal performance and can significantly increase the acceptable flow rate and the service capacity compared to other algorithms under an energy cost budget.

Mobile collaborative community (MCC) is an emerging technology that allows multiple mobile nodes (MNs) to perform a resource intensive task, such as large content download, in a cooperative manner. In this paper, we introduce a proxy-based collaboration system for the MCC where a content proxy (CProxy) determines the amount of chunks and the sharing order scheduled to each MN, and the received chunks are shared among MNs via Wi-Fi Direct. We formulate a multi-objective optimization problem to minimize both the collaborative content download time and the energy consumption in an MCC, and propose a heuristic algorithm for solving the optimization problem. Extensive simulations are carried out to evaluate the effects of the number of MNs, the wireless bandwidth, the content size, and dynamic channel conditions on the content download time and the energy consumption. Our results demonstrate that the proposed algorithm can achieve near-optimal performance and significantly reduce the content download time and has an energy consumption comparable to that of other algorithms.

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.

Mobile collaborative community (MCC) is an emerging technology where multiple mobile nodes (MNs) conduct a job (e.g., large content download) in a cooperative manner. In this paper, we consider a scenario in which multiple MNs form MCC for content download through wireless wide area network (WWAN) and share of the downloaded content through wireless local area network (WLAN). In the collaborative content download for MCC, the content chunk size assigned to an MN and the sharing order of the received chunk should be carefully determined to reduce the content download time. Therefore, we formulate an optimization problem that jointly considers the chunk size and the sharing order to minimize the content download time. Specifically, the optimization problem is formulated as a mixed integer non-linear programming (MINLP) problem that is known as NP-hard. The original optimization problem is relaxed into a linear programming (LP) problem and a heuristic algorithm minimizing the content download time and operating in a polynomial time is proposed based on the 2-opt algorithm. Simulation results demonstrate that the proposed algorithm can achieve near-optimal performance to the MINLP optimal solution and can reduce the content download time compared with other algorithms by choosing proper chunk size and sharing order.

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.

Quality of service (QoS) aware resource allocation for the uplink of a decode-and-forward relay assisted Orthogonal Frequency Division Multiple Access (OFDMA) based cellular system is investigated. Incorporating relays in the system improves the cell-edge coverage and the system throughput. Resource (relay, subcarrier and power) allocation problem is formulated with the objective of maximizing the total system throughput subject to the satisfaction of user QoS requirements and individual total power constraints of the users and relays. The throughput of each end-to-end link is modeled considering both the direct and relay links. Due to non-convex nature of the original resource allocation problem, the optimal solution is obtained by solving a relaxed problem via two level dual decomposition. The performance of the proposed scheme is evaluated in the scenarios based on LTE-A network model. Numerical results reveal that the proposed scheme guarantees each user's QoS satisfaction at the expense of a slight degradation of the system throughput.

In wireless ad hoc networks, it is difficult to share the information on neighbor devices in a distributed manner. Therefore, efficient neighbor discovery algorithms should be devised for self-organization in wireless ad hoc networks. In this paper, we propose a probabilistic neighbor discovery (PND) algorithm, which aims at reducing the neighbor discovery time by adjusting the transmission probability of advertisement messages through the muiltiplicative-increase/multiplicative-decrease (MIMD) policy. To further improve PND, we consider the collision detection (CD) capability in which a device can distinguish between successful reception and collision of advertisement messages. Simulation results show that the transmission probabilities of PND and PND with CD converge on the optimal value quickly although the number of devices is unknown. As a result, PND and PND with CD can reduce the neighbor discovery time by 15.6% and 57.0%, respectively, compared with the ALOHA-like neighbor discovery algorithm.

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.

In wireless data access applications, reduction of both the access latency and the wireless traffic volume is essential. In this paper, we propose a fast wireless data access scheme (FW-DAS) for wireless data access applications in which data objects are frequently updated and fast access to data objects is indispensable. In FW-DAS, different operation modes are defined depending on the data object popularity, and only popular data objects are proactively pushed to the access point/base station to minimize the access latency while mitigating the traffic load over the wireless link. An analytical model for the access latency is developed and an operation mode selection algorithm is introduced to reduce the access latency. Extensive simulation results show the effects of access-to-update ratio, data popularity, cache size, data object size, and wireless bandwidth. Analytical and simulation results demonstrate that FW-DAS can reduce the access latency with reasonable traffic load compared with poll-each-read (PER)/callback (CB) and their combinations.

Video on demand services have been increasingly proliferated in the Internet. One popular way to disseminate video files among numerous users is to leverage peer-to-peer (P2P) systems (e.g., BitTorrent). However, BitTorrent is not designed with video streaming requirements and hence suffers from long setup delay. In this paper, the drawbacks of existing P2P-based streaming solutions are analyzed in terms of sequential delivery. Then we propose Waterfall that splits the whole swarm into multiple swarms, which are then cascaded by the scene sequence. In this way, peers in a swarm download the chunks of the same video scene from the peers in the same swarm as well as the ones in the preceding swarm that already moved on to the next scene. The average setup delay and maximum playback rate of Waterfall are analyzed. Experiments from a wide area network testbed reveal that Waterfall achieves two to three times higher playback rate and significantly low setup delay than the prior BitTorrent-based streaming solutions.

In this paper, we propose a rate-aware power saving mechanism (RA-PSM) in multi-rate wireless LANs. In RA-PSM, the channel access order is determined depending on the transmission rate (or channel conditions). Since a station with higher transmission rate can request the buffered frames at the access point with higher priority, the overall channel waiting time can be reduced. Preliminary simulation results illustrate that RA-PSM can reduce the average waiting time by 45% compared with the conventional IEEE 802.11 PSM.

In this paper, we analyze the energy efficiency of IEEE 802.11 power save mode in multi-rate environments. We consider two transmission schedules: random and rate-aware schedules. Numerical results demonstrate that the rate-aware schedule can reduce the expected idle time by 36%.

Topology control is to determine the transmission power of each node so as to maintain network connectivity and consume the minimum transmission power. Cooperative Communication (CC) is a new technology that allows multiple nodes to simultaneously transmit the same data. It can save transmission power and extend transmission coverage. However, prior research work on topology control considers CC only in the aspect of energy saving, not that of coverage extension. We observe that CC can bridge (link) disconnected networks and therefore identify the challenges in the development of a centralized topology control scheme, named shape Cooperative Bridges, which reduces transmission power of nodes as well as increases network connectivity. We propose three algorithms that select energy efficient neighbor nodes, which assist a source node to communicate with a destination node: an optimal method and two greedy heuristics. In addition, we consider a distributed version of the proposed topology control scheme. Our findings are substantiated by an extensive simulation study, through which we show that the shape Cooperative Bridges scheme substantially increases the connectivity with tolerable increase of transmission power compared to other existing topology control schemes, which means that it outperforms in terms of a connectivity-to-power ratio.

We propose a contents-aware multicast (CAM) scheme in rate adaptive wireless networks. In CAM, transmission rate is determined depending on the contents type. That is, base layer packets are transmitted by the lowest rate for higher reliability. On the contrary, enhancement layer packets are sent by higher transmission rates. Moreover, enhancement layer packets are combined with base layer packets and redundantly transmitted, which allow higher decoding probability in error-prone wireless networks.

In conventional opportunistic relay selection schemes, one or multiple relay with better channel conditions are selected. However, if frame aggregation for improving MAC throughput is supported, each relay can have different amounts of data and thus conventional selection schemes may choose a worse relay with good channel conditions but little data. Therefore, we propose a novel opportunistic relay selection scheme when frame aggregation is adopted. In the proposed scheme, a relay is selected in a distributed manner by considering the amount of data as well as channel conditions, and therefore the most appropriate relay can be chosen.

In this paper, we propose a consistent random backoff (CRB) scheme to reduce the channel access delay jitter in voice over wireless local area networks (VoWLANs). In the CRB scheme, a contention window (CW) size at each backoff stage is determined by hashing the session identifier and the talk spurt index. Therefore, all packets in the same talk spurt of a session have the same CW sizes if they are transmitted at the same backoff stage. Since a modulo-division operation with the identical maximum CW value is applied, fairness with the legacy backoff scheme (i.e., binary exponential backoff (BEB)) is also provided. Extensive simulation results demonstrate that the CRB scheme can reduce the channel access delay jitter by 54%.

In wireless local area network (WLAN)-based vehicular networks, the performance anomaly problem is serious due to random channel access among different vehicles with diverse channel conditions and association times. To address the performance anomaly problem, we propose a signal to noise ratio (SNR)-based admission control scheme where only vehicles with better channel conditions (or higher transmission rates) are serviced by access points (APs) and thus the impact of vehicles with low transmission rates can be mitigated. The starvation issue of rejected vehicles can be resolved by considering mobility in vehicular environments with multiple intersections. Simulation results demonstrate that the SNR-based admission control scheme can improve the network throughput and the starvation problem diminishes as the number of intersections increases users.

In this paper, we introduce proactive route optimization (PRO) in session initiation protocol (SIP) mobility to reduce the session setup latency. In SIP-PRO, the mobility binding information is prefetched during the location registration step, and it is used for session establishment if it is valid. Proactive route optimization achieves the reduced session setup latency by eliminating traverse over multiple SIP servers.

Network mobility (NEMO) basic support protocol enables seamless mobility in vehicular networks. In this paper, we analyze the session handoff probability in NEMO-based vehicular environments. We develop analytical models for the session handoff probability in two vehicular scenarios: 1) onboard time is deterministic (as in a subway) and 2) on-board time is variable due to traffic condition (as in a car). Numerical results are given to illustrate the effects of on-board time, cell residence time, and session duration.

In the future, most mobile nodes will have multiple radio interfaces, and this feature can be exploited to reduce the transmission cost in wireless data access applications. In this work, we propose cooperative wireless data access algorithms with strong consistency in multi-radio wireless networks. It can be shown that cooperation with neighbor nodes can reduce the expensive transmission cost over wireless links.

In this paper, we investigate the performance of multicast and broadcast services (MBS) in mobile WiMAX systems. We develop an analytical model for the session blocking probability and the session disruption probability. Numerical results are given to demonstrate the effects of session arrival rate and session duration.

Resource management in vehicular WiFi networks is an interesting and challenging issue. In this paper, we propose a mobility-aware call admission control (MA-CAC) algorithm where different admission control policies are employed depending on the mobility. Specifically, when a vehicle is static, a handoff priority scheme with guard channels is examined to protect vehicular handoff users. On the other hand, for a moving vehicle, no guard channels for handoff users are allocated for maximizing channel utilization since there are no vehicular handoff users. By means of Markov chains, we evaluate the MA-CAC algorithm in terms of new call blocking probability, handoff call dropping probability, and channel utilization. Numerical results demonstrate that the MA-CAC algorithm can lower the handoff call dropping probability while maintaining high channel utilization.

Cooperative Communication (CC) is a technology that allows multiple nodes to simultaneously transmit the same data. It can save power and extend transmission coverage. However, prior research work on topology control considers CC only in the aspect of energy saving, not that of coverage extension. We identify the challenges in the development of a centralized topology control scheme, named Cooperative Bridges, which reduces transmission power of nodes as well as increases network connectivity. We observe that CC can bridge (link) disconnected networks. We propose two algorithms that select the most energy efficient neighbor nodes, which assist a source to communicate with a destination node; an optimal method and a greedy heuristic. In addition we consider a distributed version of the proposed topology control scheme. Our findings are substantiated by an extensive simulation study, through which we show that the Cooperative Bridges scheme substantially increases the connectivity while consuming a similar amount of transmission power compared to other existing topology control schemes.

Proxy Mobile IPv6 (PMIPv6) supports multi-homing where a mobile node can connect to a PMIPv6 domain through multiple interfaces for simultaneous access. However, for an interface handoff, PMIPv6 does not allow simultaneous access since all the home network prefixes associated with one interface are associated with another interface of a MN. In this paper, we propose a dynamic home network prefix assignment (DHNPA) scheme where both the fixed prefix model and shared prefix model are used for simultaneous access. Simulation results show that the DHNPA scheme can achieve simultaneous access and provide the information for handoff indication in multi-homing scenario.

Internet services will be pervasive in future intelligent transportation systems. To this end, vehicular Wi-Fi networks are introduced and widely deployed nowadays. However, quantitative performance in vehicular Wi-Fi networks has not been reported in the literature. In this paper, we conduct a measurement study in vehicular Wi-Fi networks to investigate the uplink and downlink throughput under diverse vehicular environments. The measurement results demonstrate that current vehicular Wi-Fi networks have poor quality in terms of downlink throughput and thus efficient resource management schemes need to be devised.

WiMedia MAC protocol supports fully distributed data communications in high data rate wireless personal area networks (WPANs). WiMedia MAC includes a distributed reservation protocol (DRP) for synchronous traffic and a prioritized contention access (PCA) protocol for asynchronous traffic. Since PCA is based on the carrier-sense multiple access with collision avoidance (CSMA/CA) mechanism, it suffers from low throughput due to collision, especially when the number of devices is large. In this paper, we propose a novel channel access scheme called deterministic channel access (DCA), which determines the transmission order among devices by means of beacon frames. Since all devices follow the deterministic transmission order, collision-free channel access can be achieved and thus the throughput can be significantly improved. Extensive simulation results demonstrate that DCA outperforms PCA in terms of throughput under different situations.

It is an important issue to provide resilient services in wireless networks where frequent frame errors are observed. In this paper, we propose a p-persistent frame replication scheme for resilient services in multi-radio wireless networks. In the p-persistent frame replication scheme, packets are transmitted over the primary link and the packets are redundantly sent over the secondary link with probability p. In addition, cooperative retransmission is devised to minimize the overhead in retransmission. Analytical expressions for the reliability, latency, and redundancy in the p-persistent frame replication scheme are derived and validated by extensive simulations. We also develop a binary search algorithm to obtain the optimal p to minimize the redundancy while satisfying the given latency and reliability requirements.

It is usually possible to associate with more than one Access Point (AP) in IEEE 802.11 Wireless LANs. AP selection is a crucial issue because the performance achieved by a user heavily depends on the AP selected. A received signal strength (RSS)-based association mechanism is specified by the IEEE 802.11 standard. However, this does not consider the channel conditions and AP load, which leads to a low throughput and a low user transmission rate. An alternative to using the RSS is to exploit the airtime metric, which provides users with information on how busy the channel is, but the results of our simulations show that the airtime metric cannot indicate the exact status of the channel. In this paper, we present a new association framework using channel occupancy in order to provide users with exact channel status information of APs. Via the proposed scheme, users can compare the achievable throughput provided by each AP and select the best AP. We validate our proposed association method via simulation results, which show that the throughput improvement of the method is worthy of notice.

Network-based mobility management has advantages in easy implementation and deployment, and thus the Internet Engineering Task Force (IETF) is standardizing proxy mobile IPv6 (PMIPv6) for network-based mobility management. In this paper, we propose a practical network mobility (NEMO) solution in PMIPv6 networks, called relay-based NEMO (rNEMO). In rNEMO, a simple amplify-and-forward (AF) or decoding-and-forward (DF) relay station is employed for supporting network mobility; no complicated mobile routers are further needed. We describe the binding update and packet delivery procedures in rNEMO, and we then evaluate it against the NEMO basic support protocol in terms of deployment, processing latency, and security.

Relay transmission is a promising technology for improving the throughput and energy efficiency in multi-rate wireless personal area networks (WPANs). In this paper, we propose a distributed relay MAC (DR-MAC) protocol in Wi-Media WPANs. DR-MAC extends a distributed reservation protocol (DRP) in WiMedia MAC and neighbor information for relay transmission can be collected during the beacon period. Therefore, DR-MAC can minimize control overhead for relay transmission and is compatible to the standard WiMedia MAC protocol. We also introduce a medium access slot (MAS) allocation procedure for maximizing the efficiency in DR-MAC. Compared with direct transmission, extensive simulation results demonstrate that DR-MAC can improve the throughput by 10% and reduce the energy consumption by 26% when the number of devices is 20.

By integrating wireless wide area networks (WWANs) and wireless local area networks (WLANs), mobile hotspot technologies enable seamless Internet multimedia services to users on-board a vehicle. In this paper, we investigate the performance of TCP-Friendly Rate Control (TFRC) protocol supporting multimedia services in mobile hotspots. To quantify the throughput of TFRC flows in mobile hotspots, we first develop discrete-time queuing models for the WWAN link and the WLAN link. We then derive the steady state TFRC throughput using an iterative algorithm. Analytical and extensive simulation results reveal how the end-to-end TFRC throughput is affected by the number of users in a mobile hotspot, the vehicle velocity, the WWAN/WLAN link bandwidth, the retransmission limit, and the buffer size. It is found that the WWAN channel profile and link bandwidth have significant impacts on the TFRC throughput, and therefore suitable resource allocation and admission control are indispensable for the quality and efficiency of multimedia services in mobile hotspots.

In this paper, we investigate efficient data access algorithms in intelligent transportation system (ITS)-based networks with multi-hop wireless links. We introduce a proxy cache (PC) and propose PC-based poll-each-read (P-PER) and PC-based callback (P-CB) data access algorithms to reduce the transmission cost over the bottleneck wireless links. Extensive simulation results are given to demonstrate the performance of P-PER and P-CB. It is shown that P-PER and P-CB can improve the cache hit performance and reduce the transmission cost significantly. A tradeoff between P-PER and P-CB suggests the need to use a hybrid proxy-based approach to attain optimal performance of data access in ITS-based networks with multi-hop wireless links.

We consider downward and upward vertical handovers in integrated wireless LAN and cellular networks, and address wireless profiled TCP premature timeouts due to steep increase of round-trip time and false fast retransmit due to packet reordering. Specifically, we develop a mobile receiver centric loosely coupled cross-layer design, which is easy to implement and deploy, backward compatible with the wireless application protocol version 2 (WAP 2.0) architecture, and robust in the absence of cross-layer information. We propose two proactive schemes which prevent false fast retransmit by equalizing the round-trip delay experienced by all packets and suppress the premature timeouts by carefully inflating retransmission timeout time. We conduct extensive simulations to evaluate the performance in downward and upward vertical handovers. It is demonstrated that the proposed schemes significantly improve the performance in a wide range of network conditions.

Mobility management is a key issue in mobile hotspots which enable ubiquitous Internet services while onboard a vehicle. In this paper, we compare two representative mobility management schemes for mobile hotspots: the network mobility (NEMO) basic support protocol at the network layer and the session initiation protocol (SIP)-based network mobility support protocol at the application layer. We evaluate their salient features and quantify their handoff latency over a wireless fading channel. It is shown that the SIP-based network mobility support protocol can be easily deployed and can reduce the tunneling overhead incurred in the NEMO basic support protocol. However, it can increase the handoff latency due to longer message length.

By introducing a mobility anchor point (MAP), Hierarchical Mobile IPv6 (HMIP6) reduces the signaling overhead and handoff latency associated with Mobile IPv6. However, if a mobile node (MN)'s session activity is high and its mobility is relatively low, HMIPv6 may degrade end-to-end data throughput due to the additional packet tunneling at the MAP. In this paper, we propose an adaptive route optimization (ARO) scheme to improve the throughput performance in HMIPv6 networks. Depending on the measured session-to-mobility ratio (SMR), ARO chooses one of the two different route optimization algorithms adaptively. Specifically, an MN informs a correspondent node (CN) of its on-link care-of address (LCoA) if the CN's SMR is greater than a predefined threshold. If the SMR is equal to or lower than the threshold, the CN is informed with the MN's regional CoA (RCoA). We analyze the performance of ARO in terms of balancing the signaling overhead reduction and the data throughput improvement. We also derive the optimal SMR threshold explicitly to achieve such a balance. Analytical and simulation results demonstrate that ARO is a viable scheme for deployment in HMIPv6 networks.

Mobile hotspot enabling Internet access services in moving vehicles is an important service for ubiquitous computing. In this paper, we propose an analytical framework for studying the packet loss behavior and throughput in a mobile hotspot with heterogeneous wireless links. We first develop a two-state Markov model for the integrated wireless wide area network (WWAN) and wireless local area network (WLAN). We then derive the expressions that describe the experienced packet loss probability, packet loss burst length, and throughput. Finally, we present simulation results to verify the accuracy of our analysis. It is concluded that adaptive and cross-layer approaches should be deployed to improve the performance of mobile hotspots.

In IEEE 802.11 WLANs, packet losses may be due to buffer overflow, transmission errors, or collisions. Therefore, the performance of TCP-Friendly Rate Control (TFRC) in IEEE 802.11 WLANs largely depends on its ability to differentiate packet losses resulting from network congestion (due to buffer overflow and collisions) and those from transmission errors. In this paper, an enhanced TFRC (E-TFRC) protocol is proposed to detect and identify the cause of packet loss events through a novel two-phase loss differentiation algorithm (TP-LDA). The packet losses due to buffer overflow and those due to failed transmissions in WLANs are first differentiated. For failed transmissions, the fraction of those due to collisions is obtained with the assistance of the lower layer. By employing TP-LDA, only the packet losses due to buffer overflow and collisions are notified to the sender for appropriate flow and congestion control. To quantify the performance of TFRC and E-TFRC over WLANs, a continuous-time Markov chain based on a new WLAN link model is developed by considering both collisions and transmission errors. Analytical and simulation results demonstrate that, with appropriate loss differentiation, E-TFRC can achieve higher throughput than TFRC in WLANs with different channel profiles.

Abstract The advent of advanced mobile/wireless systems has been facilitating the battery‐powered mobile computing devices (nodes) to remain always connected to the internet. However, until now, the power‐drain rate of mobile nodes is very high in comparison with the available power of portable batteries. To reduce the energy consumption of mobile nodes, we present an integrated IP paging protocol (IIPP) by integrating the IP‐layer paging protocol based on Mobile IPv4 regional registration (MIPRR) with a power save mechanism. IIPP reduces the frequency of signaling messages between mobile nodes and networks. When not sending or receiving data for a certain time, mobile nodes enter power save mode (PSM), and consume very low power. We formulate analytical models and carry out simulations to evaluate the proposed IIPP. The results show that, compared to MIPRR, IIPP significantly reduces the average power consumption of the mobile node and signaling overheads in the network. Copyright © 2006 John Wiley & Sons, Ltd.

Mobile hotspot enabling Internet access services in moving vehicles is an important service for ubiquitous computing. In this paper, we propose an analytical framework for studying the packet loss rate and throughput in a mobile hotspot with heterogeneous wireless links. We present simulation results to verify the accuracy of our analysis. It is concluded that adaptive and cross-layer approaches should be deployed to improve the performance of mobile hotspots.

In wireless sensor networks (WSNs), reducing energy consumption of resource constrained sensor nodes is one of the most important issues. In this paper, we propose a task aware (TA) MAC protocol, which improves energy efficiency and throughput by introducing a channel access scheme depending on traffic load in WSNs. The amount of traffic load of a sensor node can be estimated by its task activity, where a task is an operation that the sensor node performs based on the schedule set by data dissemination procedures in advance. In addition, the sensor node collects neighbor nodes' task activities and determines its channel access probability using the collected information. Consequently, the sensor node can choose a more suitable channel access probability which is adaptive to its traffic load as well as neighbor's traffic load. We carry out performance analysis using a p-persistent MAC protocol. The results reveal that the TA-MAC protocol exhibits less collisions than the normal p-persistent MAC protocol and thus it achieves energy efficient operations. Also, it can been seen that the TA-MAC protocol improves system throughput compared with other protocols

Mobility support in IEEE 802.11 networks is a challenging issue. Recently, a new scheme, called proactive neighbor caching (PNC), was proposed and adopted as an IEEE standard. The PNC scheme introduces a neighbor graph, which dynamically captures the mobility topology of a wireless network for pre-positioning the context of a mobile host (MH). However, the PNC scheme may result in a significant signaling overhead because the MH's context is propagated to all neighbor access points (APs). We propose a selective neighbor caching (SNC) scheme, which propagates an MH's context to only the selected neighbor APs considering handoff frequencies between APs. When the context transfer is needed, neighbor APs with handoff probabilities equal to or higher than a predefined threshold value are selected. We also derive an optimal threshold value when the target cache hit probability is given. Simulation results reveal that the SNC scheme significantly reduces the signaling overhead while guaranteeing a comparable cache hit probability compared to the PNC scheme.

Although hierarchical mobile IPv6 (HMIPv6) can reduce the signaling overhead and the handoff latency, it results in the non-optimal local routing problem when two mobile nodes communicate in the same mobility anchor point domain. To address this problem, we propose an adaptive local route optimization (ALRO) scheme. The ALRO scheme chooses either the global route optimization scheme or the local route optimization scheme depending on the session-to-mobility ratio (SMR). Based on the proposed analytical model, we find the optimal SMR threshold at which the ALRO scheme shows the best performance. Numerical results demonstrate that the ALRO scheme shows a good performance in terms of the total cost, session delivery time, and session disruption time.

In this paper, we propose a mobility-based load control scheme, which consists of two sub-algorithms: (1) a threshold-based admission control algorithm; and (2) a session-to-mobility ratio (SMR) based replacement algorithm. Here the SMR is defined as a ratio of the session arrival rate to the handoff rate. When the number of mobile nodes (MNs) at a mobile anchor point (MAP) reaches to the full capacity, the MAP replaces an existing MN at the MAP, whose SMR is high, with an MN that just requests a binding update. The replaced MN is redirected to its home agent. We analyze the proposed load control scheme using the Markov chain model in terms of the new MN blocking probability and the ongoing MN dropping probability. By combining the threshold-based admission control with the SMR-based replacement, the above probabilities are lowered significantly compared to the threshold-based admission control alone.

Hierarchical mobile IPv6 (HMIPv6) is an enhanced mobile IPv6 in order to reduce signaling overhead and to support seamless handoff in IP-based wireless/mobile networks. To support more scalable services, HMIPv6 can be organized as a multilevel hierarchy architecture (i.e, tree structure). However, since the multi-level HMIPv6 results in additional packet processing overhead, it is necessary to consider the overall cost and to find the optimal level to minimize the overall cost. In this paper, we investigate this problem, namely the design of a multi-level HMIPv6 with optimal hierarchy. To do this, we formulate the location update cost and the packet delivery cost in the multi-level HMIPv6. Based on the formulated cost functions, we present the optimal hierarchy level in the multi-level HMIPv6 to minimize the total cost. In addition, we investigate the effects of session-to-mobility ratio (SMR) on the total cost and the optimal hierarchy. The numerical results, which show various relationships among network size, optimal hierarchy, and SMR, can be utilized to design an optimal HMIPv6 network.

We evaluate the performance of IP paging with power save mechanism by formulating an analytical model and carrying out simulation study of integrated IP paging protocol (HPP) that integrates both the paging and power save functionality in IP layer. The results show that, compared to the mobile IP regional registration, the UPP significantly reduces the average power consumption of a mobile node and the signaling load in the access networks, while providing link layer independent mobility and power management functions.

Recently, IEEE 802.11 wireless networks have been widely deployed in public areas for mobile Internet services. In the public wireless LAN systems, paging function is necessary to support various advanced services (e.g. voice over IP and message applications) and to provide efficient power management scheme. In next-generation mobile networks, the so-called all-IP networks, the paging function will be supported in the IP layer (i.e. IP paging). When IP paging protocol is deployed in IEEE 802.11 wireless networks, it may utilize the power saving mechanism supported in the IEEE 802.11 standard for more efficient power management. However, since the current power saving mechanism is based on a periodical wake-up mechanism with a fixed interval, it is difficult to optimize the power saving performance. In this paper, we analyze the performance of IP paging protocol over IEEE 802.11 power saving mode (PSM). We define a wake-up cost and a paging delay cost. Then, we study the effect of varying the length of the wake-up interval and the session arrival rate. In addition, we analyze the distribution of the session blocking probability due to the coarse-grained wake-up interval. Also, we investigate the optimal wake-up interval to minimize the total cost through simulations. These results indicate that it is necessary to find the optimal wake-up interval in order to minimize the total cost while satisfying the given paging delay constraints.

Next-generation wireless/mobile networks will be IP-based cellular networks integrating Internet with the existing cellular networks. Recently, hierarchical mobile IPv6 (HMIPv6) was proposed by the Internet engineering task force (IETF) for efficient mobility management. HMIPv6 reduces the amount of signaling and improves the performance of MIPv6 in terms of handover latency. Although HMIPv6 is an efficient scheme, the performance of wireless networks is highly dependent on various system parameters such as user mobility model, packet arrival pattern, etc. Therefore, it is essential to analyze the network performance when HMIPv6 is deployed in IP-based cellular networks. In this paper, we propose an analytic model for the performance analysis of HMIPv6 in IP-based cellular networks, which is based on the random walk mobility model. Based on this analytic model, we formulate location update cost and packet delivery cost. Then, we analyze the impact of cell residence time on the location update cost and the impact of user population on the packet delivery cost. Also, we investigate the variation in the total cost as the relative session size and the MAP domain size are changed. As a result, we present various analytical results in different environments.