In 5G networks, the Legacy Paging mechanism for device connections faces challenges due to limited resources and increasing demands from human-type communication (HTC) and massive machine-type communications (mMTCs). This results in higher connection setup time (CST) under high traffic. Group paging, developed for mMTC, allows multiple devices to respond to a single paging message, but increases energy consumption for HTC due to irregular data transmission patterns. To address this, we propose the deep reinforcement learning-based group configuration control (DRL-GCC) mechanism. DRL-GCC combines legacy and group paging methods to optimize paging for HTC by dynamically adjusting group configurations, using the proximal policy optimization algorithm to reduce energy consumption and satisfy the required CST. Simulation results demonstrate that DRL-GCC significantly reduces energy consumption in the random access channel (RACH) procedure and the overall energy usage of devices while ensuring compliance with the CST constraint compared to its alternative methods. Notably, DRL-GCC achieves up to a 43% reduction in energy used during the RACH procedure and a 9% decrease in total energy consumption compared to the existing benchmarks. These improvements imply DRL-GCC’s ability to balance energy efficiency with operational performance, providing a robust solution for efficient HTC-focused service provisioning in 5G networks.

In a variety of Internet of Things (IoT) applications, there is a growing need to constantly transmit large amounts of real-time data from IoT sensors to enable precise environmental monitoring. However, this constant transmission of data can result in high energy consumption of IoT sensors, which presents a significant challenge for IoT systems. Therefore, this article presents a new approach for controlling the transmission period of IoT sensors called imputation error cluster-based transmission period control (IEC-TPC) framework, with the goal of reducing energy consumption while maintaining accurate data collection. In order to effectively balance energy consumption and data collection accuracy, the proposed approach uses an imputation error centroid (IEC) model that forms clusters based on imputation error vectors and approximates the centroid of each cluster using a logistic function. Additionally, a new imputation error cluster prediction (IECP) model is designed using long short-term memory (LSTM)-based encoding and convolutional neural network (CNN) models to predict the label of each cluster in advance. By combining the IEC model with a numerically modeled energy consumption function, a min–max optimization problem is properly formulated to minimize the worst-case performance of transmission period control. To find the optimal solution, a Uniqueness Condition function is properly defined, and an optimal objective value searching algorithm (O2VSA) leveraging a bisection search is proposed. Performance evaluation shows that the proposed method achieves similar data collection accuracy in CO2 and humidity data sets while reducing energy consumption by 12.92% and 35.83% than other baseline models, respectively. Moreover, the proposed algorithm outperforms the state-of-the-art method in the temperature data set, with 13% lower energy consumption value. The proposed method also achieves a significant reduction in energy consumption of 95.21% compared to the uniform transmission period model in this data set, while maintaining a low data reconstruction error of only 0.42%. Overall, the proposed method offers a promising approach for efficient and accurate transmission period control in IoT applications.

Most deep anomaly detection models are based on learning normality from datasets due to the difficulty of defining abnormality by its diverse and inconsistent nature. Therefore, it has been a common practice to learn normality under the assumption that anomalous data are absent in a training dataset, which we call normality assumption. However, in practice, the normality assumption is often violated due to the nature of real data distributions that includes anomalous tails, i.e., a contaminated dataset. Thereby, the gap between the assumption and actual training data affects detrimentally in learning of an anomaly detection model. In this work, we propose a learning framework to reduce this gap and achieve better normality representation. Our key idea is to identify sample-wise normality and utilize it as an importance weight, which is updated iteratively during the training. Our framework is designed to be model-agnostic and hyperparameter insensitive so that it applies to a wide range of existing methods without careful parameter tuning. We apply our framework to three different representative approaches of deep anomaly detection that are classified into one-class classification-, probabilistic model-, and reconstruction-based approaches. In addition, we address the importance of a termination condition for iterative methods and propose a termination criterion inspired by the anomaly detection objective. We validate that our framework improves the robustness of the anomaly detection models under different levels of contamination ratios on five anomaly detection benchmark datasets and two image datasets. On various contaminated datasets, our framework improves the performance of three representative anomaly detection methods, measured by area under the ROC curve.

With the recent explosive growth of the Internet of Things (IoT), edge computing is emerging as a modern computing paradigm that coexists with the cloud to process massive amounts of data particularly distributed at the edge network. Meanwhile, edge computing directly permits the use of artificial intelligent (AI) models at the edge. Currently in numerous IoT applications, a tremendous amount of data is being measured and transmitted from IoT sensors to monitor surrounding information in real-time. Considering that continuous transmission of sensed data substantially requires high-energy consumption of IoT sensors, this article proposes a proximal policy optimization (PPO)-based autonomous transmission period control (PPO-ATPC) system in IoT edge computing that can automatically and adaptively control the transmission period of each IoT sensor. Here, the design of state, action, and reward is shaped in an unprecedented way and furthermore, a PPO, which is one of the most effective model-free deep reinforcement learning (DRL) algorithms, is fully leveraged to achieve an optimal or nearly optimal policy that can significantly shorten the data volume while maintaining high-data quality. The merits of the proposed PPO-ATPC are extensively validated through quantitative comparison with other alternative approaches using three different sensor data collected from real-time environmental monitoring, and in-depth insights into the effectiveness of PPO-ATPC are further provided from diverse perspectives. The performance results show that total data volume for each data set could be reduced by 73.849%, 89.931%, and 81.310%, with the average root mean square error of 0.322, 13.896, and 0.048.

In distributed computing systems, coding has played an important role to robustify the system against the effect of noise, e.g., stragglers, system failures and communication bottlenecks. Most of the existing work has focused on a simple master-worker model with one master and homogeneous workers. However, real-world systems are typically configured with heterogeneous workers distributed to computing nodes and serve multiple tasks in parallel. In this study, we consider the scenario in which multiple masters perform matrix multiplications using the workers having group heterogeneity. The group heterogeneity models that homogeneous workers are located in the same location and regarded as a group; the workers deployed in the different locations are potentially heterogeneous. We propose an asymptotically optimal worker assignment to multiple masters for coded distributed computing in the presence of heterogeneous groups of workers. Specifically, we present a lower bound for the expected latency in terms of the numbers of workers assigned to the masters and the amount of tasks allocated to workers. Adding the concentration constraints on the number of workers allocated to masters, we can obtain the minimum of the lower bound by taking the optimal worker assignment. We find the optimal worker assignment by converting the problem at hand into a linear programming problem. From both numerical simulations and experiments on Amazon EC2 clusters, we confirm that the effect of the proposed worker assignment is significant in various scenarios.

This article proposes a novel deep learning-based robust Internet of Things (IoT) sensor data transmission period control (DL-RDTPC) framework in an IoT edge computing system. In general, as the data transmission period of IoT sensors increases, the energy consumption of IoT sensors is reduced, and contrarily, the amount of un-transmitted data (i.e., missing values) becomes continuously accumulated. Therefore, the IoT server is in charge of accurately imputing these missing data for reliable data analysis. By addressing this issue, we newly design the imputation accuracy prediction (IAP) module, which captures the complicated relationships between the imputation accuracy and the data transmission period, in order to estimate the imputation accuracy, precisely. For constructing the IAP, three submodules, which include a stacked bidirectional long short-term memory (Bi-LSTM) model, a multihead convolutional neural network (CNN), and a neural network-based period information encoding network (PIEN) are leveraged. To balance the tradeoff between the imputation accuracy and energy consumption regarding the data transmission period, the multiobjective optimization problem is formulated for minimizing the maximum value of both: 1) the energy consumption of IoT sensors obtained from the analytical model and 2) the imputation accuracy predicted from IAP module. The optimal solution is consequently obtained by utilizing the bisection search algorithm. Extensive performance evaluations validate the effectiveness of the proposed RDTPC algorithm in terms of both the average energy consumption (maximum 68% reduction) and missing data imputation accuracy (maximum 64% RMSE reduction) over other benchmarks. Finally, this article provides a practical implementation of the proposed RDTPC framework via the HTTP protocol under the IEEE 802.11-based WLAN network, as well as interworking with the commercial cloud server.

Mobile-edge computing (MEC) is expected to play an important role in the next-generation of Internet-of-Things (IoT) services with artificial intelligence (AI) by providing the sustainable computation capability of resource-constrained IoT devices. Since the finite battery lifetime has been a longstanding challenge of the MEC system for IoT services, the wireless power transfer (WPT) technology has been recently developed for the MEC system in order to support the perpetual operation of IoT devices. In this article, we introduce two resource allocation problems for OFDMA-based WPT-MEC systems: 1) a max–min energy fairness (MMEF) problem and 2) a power sum maximization (PSM) problem. These problems ensure high-performance computations for AI-based applications, where the network reliability and the tremendous power consumption may be required. Moreover, we incorporate a logarithmic nonlinear energy harvesting (EH) model into our formulated problems, which lead to nonconvex mixed-integer nonlinear programming (MINLP) problems. In order to resolve these NP-hard problems, we convert the proposed problems into their equivalent convex forms by applying the continuous relaxation method. The near-optimal solutions are thereby obtained in closed-form expressions by leveraging the Lagrangian duality method for each relaxed problem. Numerical results are presented to validate the merits of the proposed algorithms of MMEF&PSM over the alternative benchmark algorithm and provide significant insights on the effects of the key system parameters, including the number of IoT devices and power transmission subcarriers.

In order to reduce unnecessary data transmissions from Internet of Things (IoT) sensors, this article proposes a multivariate-time-series-prediction-based adaptive data transmission period control (PBATPC) algorithm for IoT networks. Based on the spatio-temporal correlation between multivariate time-series data, we developed a novel multivariate time-series data encoding scheme utilizing the proposed time-series distance measure$\textit {ADMWD}$. Composed of two significant factors for a multivariate time-series prediction, i.e., the absolute deviation from the mean (ADM) and the weighted differential (WD) distance, the$\textit {ADMWD}$considers both the time distance from a prediction point and a negative correlation between the time-series data concurrently. Utilizing the convolutional neural network (CNN) model, a subset of IoT sensor readings can be predicted from encoded multivariate time-series measurements, and we compared the predicted sensor values with actual readings to obtain the adaptive data transmission period. Extensive performance evaluations show a substantial performance gain of the proposed algorithm in terms of the average power reduction ratio (approximately 12%) and average data reconstruction error (approximately 8.32% MAPE). Finally, this article also provides a practical implementation of the proposed PBATPC algorithm via the HTTP protocol under the IEEE 802.11-based WLAN network.

With the evolutionary development of the Internet of Things (IoT), the demand for delay-sensitive applications has increased. In this manner, mobile-edge computing (MEC) has emerged as a promising technology to run these applications on mobile devices. Several studies have been conducted on pricing schemes in the MEC environment, and they have focused on the amount of offloaded data in pricing. However, under the previously proposed pricing policy, the server resource usage of users is not properly reflected in the payment, so users only try to occupy the server resources as much as possible without any consideration. To solve this problem, this article newly proposes a differential pricing scheme in which the unit price per second is determined based on the user’s usage of server computational resources. Also, it suggests the user’s optimal offloading strategy and server’s equilibrium pricing strategy by formulating Stackelberg game based on the proposed pricing scheme and execution delay. Several numerical results show that the proposed pricing scheme and strategies can solve the problems and can increase the efficiency of edge server’s computational resources.

This article studies distributed computing (DC) mechanisms on heterogeneous mobile devices (MDs) for latency reduction (LR) in Internet-of-Things (IoT) services by mitigating the effect of straggling MDs. We propose novel coded DC mechanisms with two different incentive distributions that consider the time-discounting value of processed results and the amount of the workload computed by MDs. Specifically, we consider distributed gradient descent computing with coding when a task publisher (TP) with a limited amount of budget offers incentives to encourage MDs' participation in the computation. To analyze a hierarchical decision-making structure of the TP and MDs, we formulate a strategic competition between them as a Stackelberg game. In the case that the MDs are the leaders, we design a CPU-cycle frequency control scheme to balance each MD's computing speed and energy consumption for obtaining its maximum utility with the incentive mechanisms. As the follower, the TP aims at minimizing latency of the DC, and it follows the MDs' decisions to determine the load allocation for each MD. Then, we design an algorithm achieving the Stackelberg equilibrium, which is shown to be a unique Nash equilibrium of the game. The performance evaluation results show that the proposed mechanisms achieve 39% of LR on average compared to the benchmark mechanism. Furthermore, the results corroborate the efficiency of the proposed mechanisms in terms of the MDs' social welfare.

This article proposes a novel fair and scalable relay control (FSRC) scheme for the Internet-of-Things (IoT) services in long range (LoRa)-based low-power wide-area networks. The proposed FSRC scheme promotes relay operation with low spreading factor (SF) to improve the success probability for distant end-devices (EDs) and the fairness of success probability for each SF region. To achieve this, a theoretical framework for designing the relay operation is analytically developed by considering a practical LoRaWAN MAC protocol as an analytical model. The proposed FSRC scheme encompasses a selective relay operation by considering both signal-to-noise ratio and signal-to-interference ratio and the receive signal strength indicator value for the location-unaware relay selection strategy. Using this model, a genetic algorithm-based relay control strategy is proposed to maximize both coverage probability and minimum success probability for all SF regions by controlling the relay parameters, such as source-relay region and source-relay ratio. Our numerical analysis validates the effectiveness of the proposed FSRC scheme under various parameters in terms of the minimum success probability of each SF region, coverage probability, and fairness. Specifically, we verify that the proposed FSRC scheme achieves a maximum of approximately 37% and 33% improvement of the minimum success probability and coverage probability, respectively, under practical LoRa PHY/MAC parameters, compared to the single-hop environment (without relay operation).

Recently, coding has been a useful technique to mitigate stragglers' effect in distributed computing. However, coding in this context has been mainly explored assuming homogeneous workers, although real-world clusters often consist of heterogeneous workers with different computing capabilities. The uniform load allocation without considering the heterogeneity possibly causes a significant loss in latency. In this article, we suggest the optimal load allocation for coded distributed computing with heterogeneous workers. Specifically, we focus on the scenario that there exist workers having the same computing capability, which can be regarded as a group for analysis. We rely on the lower bound on the expected latency and obtain the optimal load allocation by showing that our load allocation achieves the minimum of the lower bound for a sufficiently large number of workers. Given the proposed optimal load allocation, we derive the optimal code rate to achieve the minimum expected latency. From numerical simulations, when assuming the group heterogeneity, our load allocation reduces the expected latency by orders of magnitude over the existing scheme. Furthermore, from experiments on Amazon EC2 for scenarios with distinct straggler/heterogeneity patterns, we observe that our scheme outperforms the competing schemes reducing the total finishing time by up to 52%.

With the widespread of Internet-of-Things (IoT) environment, a big data concept has emerged to handle a large number of data generated by IoT devices. Moreover, since data-driven approaches now become important for business, IoT data markets have emerged, and IoT big data are exploited by major stakeholders, such as data brokers and data service providers. Since many services and applications utilize data analytic methods with collected data from IoT devices, the conflict issues between privacy and data exploitation are raised, and the markets are mainly categorized as privacy protection markets and privacy valuation markets, respectively. Since these kinds of data value chains (which are mainly considered by business stakeholders) are revealed, data providers are interested in proper incentives in exchange for their privacy (i.e., privacy valuation) under their agreement. Therefore, this article proposes a competitive data trading model that consists of data providers who weigh the value between privacy protection and valuation as well as other business stakeholders. Each data broker considers the willingness-to-sell of data providers, and a single data service provider considers the willingness-to-pay of service consumers. At the same time, multiple data brokers compete to sell their data set to the data service provider as a noncooperative game model. Based on the Nash equilibrium analysis (NE) of the game, the feasibility is shown that the proposed model has the unique NE that maximizes the profits of business stakeholders while satisfying all market participants.

This paper proposes auction-based energy trading among electric vehicles (EVs) with consideration of practical battery status. At an arbitrary time, each EV can be a seller or a buyer according to their residual battery level and required energy for reaching the predefined destination. Under energy trading among EVs, there is an energy pool managed by an auctioneer in the market that gathers surplus energy from sellers and supplies it to buyers using an auction mechanism. Each buyer individually decides the initial bidding price and the amount of energy to buy from sellers according to an expected cost savings compared with buying from macrogrids as well as its battery wear-out cost and charging/discharging efficiency. Similarly, each seller also individually decides its initial selling price and the amount of energy for sale subject to a tradeoff between the received revenue and discharging cost depending on its battery status. Notably, we provide a battery status model of EVs for designing well-defined utilities of both sellers and buyers. We design a naive auction process such that, in the auction process, the auctioneer controls the bidding increment to determine the best prices on a set of energies offered to multiple buyers through an iterative procedure. Finally, we show that distributing the energy based on a well-defined utility function converges to a unique optimal distribution for maximizing the payoff of all participating EVs.

The number of wireless access points (APs) have increased rapidly in recent years to support increasing demands of wireless local area network. However, some of the research findings show that many of these APs are actually idle mainly during off-peak hours, i.e., there is no active user to support within their coverage areas during that time. Nevertheless, to identify availability of users, all APs are remained powered-on always regardless the presence or absence of any active user inside their coverage areas. To save energy during off-peak hours in idle APs, sleep mechanism could be applied. In this paper, we propose a novel sleep mechanism for IEEE 802.11 wireless local area network APs. In our solution, an AP moves into sleep mode to maximize its energy efficiency (EE) while not harming traffic delay requirements. To decide length of sleep interval of an AP, we propose a dynamic sleep boundary decision algorithm which dynamically sets upper and lower bound of sleep duration considering traffic arrival rate and traffic delay requirements at a given time. Numerical results show the proposed scheme can improve EE of an AP significantly compared to the existing schemes without violating traffic delay requirements.

In this paper, we propose a novel energy efficient solution for TDM Passive Optical Networks (TDM-PONs), which is a widely used access network technology, to facilitate local customer internetworking. Here, our prime objectives are to ensure traffic latency among different customers connected through TDM-PON and maximize energy saving performance of Optical Network Units (ONUs), which are TDM-PON equipment situated at customer premises. To do so, we devise a novel sleep management protocols for ONUs. In our proposal, when some ONUs need to exchange traffic, they maintain sleep mode as a group in order to reduce traffic latency among the customers attached with those ONUs. To date, considering traffic forwarding between access and core network entities, researchers have come up with several solutions to manage sleep mode in ONUs in order to improve PON energy saving. However, to the best of our knowledge, how ONUs can manage sleep mode and at the same time satisfy latency requirements while managing data flows among them has been paid little attention. The effectiveness of our solution is validated and compared with other alternative approach by simulation. Simulation results demonstrate that proposed solution reduces inter-ONU traffic delay noticeably compared to other solution while providing satisfactory energy saving performance of ONUs.

For improving Quality of Service in wireless networks, multi-homing techniques have been considered as a promising solution. To date, most of the conventional research efforts have been focusing on user side interests (e.g. improving throughput, minimizing packet loss). However, a service provider could have different interests (e.g. profit, energy efficiency). Nevertheless, to the best of our knowledge, there is no research concern that takes into account the interests of user and service provider side at the same time. In this paper, we first model utility functions of both sides considering the aforementioned issues. Then, based on these utility functions we address the joint pricing and load distribution problem of multi-homing in heterogeneous wireless networks. Here, the problem is formulated into a Stackelberg game. Then, we propose schemes to obtain an optimal solution; so that, both sides (i.e. service providers and users) are satisfied. Finally, we provide rigorous analysis varying different parameters (e.g. cost, rate allocation, energy efficiency) which can potentially affect to the game. In addition to that, we show the proposed schemes are well converged to equilibrium point.

Radio frequency spectrum within 3-300 GHz has been investigated for the next generation of mobile broadband system. The mmWave mobile broadband (MMB) system is expected to achieve high capacity by utilizing immense bandwidth. However, high attenuation property of mmWave generates outage zone where the signal does not reach frequently in MMB system. To cover outage zone, relay operation has been suggested as one of the solutions while improving the energy efficiency. However, there is no analysis on the energy efficiency of user relay considering outage zone in metropolitan MMB system. In this paper, we suggest the analytical model of user relay in MMB system with outage zone. Furthermore, we propose the energy-efficient user relaying scheme to minimize the transmission power of metropolitan MMB system. Simulation results show that user relay improves the energy efficiency and reduces down outage zone in metropolitan MMB system.

The growing mobile data demand has led to the investigation of millimeter-wave (mm-wave) band which makes the communication systems can achieve multi-gigabit data rate. Due to the high attenuation phenomenon of mm-wave, however, the mm-wave system contains the ``dead zones'' where users cannot receive mobile data. To cover dead zones, deployment of relay stations becomes an important issue for constructing mm-wave systems. In addition, according to growing interests on green communication, energy efficiency of the communication system becomes another important issue. In spite of that importance, there is no study about energy efficiency of relaying in the mm-wave system. In this paper, we model the mm-wave systems considering relay operation. And then, we analyze energy efficiency of relay operation compared to direct transmission of the systems. Finally, the numerical results show that the relay operation makes better energy efficiency than direct transmission in certain criteria.

With the spread of Internet of Thing end device, the usage of multiple devices with Internet connectivity is increasing. However the methodology that can effectively interconnect the data or integrate the services being executed on individual things is scarce. One way to solve this problem is converting the things into the flexible virtual objects. In this paper, a method to generate an IoT node into a programmable object form is introduced. In addition, Instance Hosting System and its structure that can service the objects as micro instances are provided. Functionality of the proposed system has been confirmed via realization of Instance Hosting Gateway in an Access Point environment. Through this research, the method on how to effectively and efficiently manage number of IoT nodes is suggested.

Hierarchical cooperation (HC) has been proven in linear capacity scaling for ad hoc networks when density of nodes exists. This technology has recently been studied as a new generation network architecture (i.e., 5G). However, for cellular networks, there have been few studies on MAC protocols for HC in relation to signaling. In particular, energy consumption due to frequently changed clusters for maintaining consecutive ad hoc networks, and with respect to limited battery supply, is an important issue that should be addressed. In this paper, we propose an energy efficient HC based (EEHC-) TDMA MAC protocol aimed at alleviating energy depletion. Through a simulation, we analyze the EEHC-TDMA MAC protocol considering hierarchy, density, and distance. Finally, we show that an EEHC-TDMA MAC with hierarchy reduces energy consumption and also achieves maximum throughput close to the linear capacity scaling.

As the demand for high-quality multimedia service over wireless networks has increased wireless multicast communication has been researched for many years in efforts to achieve high throughput. Recently, cooperative multicast scheduling scheme was proposed with the goal of achieving high throughput with good fairness. However, although much attention has been directed towards energy efficient communication, energy efficient cooperative multicast design has not received much consideration. Part of the reason for this is that significant relay power consumption is required for user cooperation. In this paper, we model Quality of Service (QoS) of multicast in terms of outage probability when the cooperative multicast scheme is adopted. From the model, we obtain analytical results for QoS and power consumption under a varying number of users and cell coverage of the Base Station (BS). Finally, the analytical models can be used to find the optimal B S's transmission power by predicting system performance if system configurations are given. This can help system deployment and system optimization.

During off-peak hours of a day, when incoming and outgoing traffic arrival rate used to be low, a Mobile Terminal (MT) can stay in idle mode for long time. For tracking the idle MTs, a wireless access network invokes them to conduct location update whenever they enter into a new location. This allows the network to route a call successfully. To provide any IP packet based service, Mobile IP (MIP) is very important protocol undeniably. However, Proxy Mobile IP was developed by IETF without considering idle mode condition of MTs. Consequently, an idle MT needs to conduct MIP binding (Layer 3 location update) whenever it moves to a new area of a Mobile Access Gateway (MAG) although it does not have any incoming or outgoing packets during idle periods. This phenomenon unnecessarily increases location update signaling cost. In our previous work, we proposed a mechanism using which only Layer 2 location update is needed when an MT is in idle mode and the Layer 3 location update is conducted after a call arrives for an idle MT in Optical and Wireless Converged Access Networks (OWCAN). This allows saving energy in the mobility management nodes (e.g. MAG, Base Stations) significantly. Based on our previous work, in this paper we propose a novel algorithm for the Optical Network Units (ONUs) of an OWCAN. This algorithm decides when to interrupt modules of a sleeping ONU to wake up and which frames should be forwarded for uplink transmission. We have found that adopting this algorithm an ONU of an OWCAN can save energy significantly. Therefore, our solutions mainly contribute in two-fold to make a green OWCAN. First, it minimizes energy consumption in the mobility management nodes. Second, our solution also reduces energy consumption and improves bandwidth utilization in the optical backhaul.

For multi-homed streaming service, it is important to enhance the throughput by efficient allocation of resources to the most appropriate interfaces of the User Terminal (UT). However, running multiple interfaces simultaneously can significantly contribute to rapid reduction of battery life. In this work, we propose a Power Minimized Rate Allocation Scheme (PMRAS) with optimal rate allocation at each interface with or without packet loss constraint. To develop the PMRAS, we formulate power consumption model based on the network initial interface state (e.g. active or sleep state). To solve convex optimization with multiple constraints, we adopt a Lagrangian algorithm based on dual decomposition. When compared with Rate Proportional additive-increase multiplicative-decrease (AIMD), the proposed algorithm gives significantly reduced total energy consumption with guarantee required Quality of Service (QoS) constraints.

Nowadays Passive Optical Network (PON) requires that Optical Network Units (ONUs) wake up periodically to check if the Optical Line Terminal (OLT) has any message directed to them. This implies that ONUs change from sleeping mode in which they just consume 1 W to active mode in which the consumption goes up to 10 W. In many cases, the OLT does not have any packets for the ONU and it goes to sleep again, what supposes a waste of energy. In this paper, we propose a novel Hybrid ONU that relies on a low-cost and low-energy technology, IEEE 802.15.4, to wake up those ONUs that are going to receive a packet. Our first estimations demonstrates that our solution would save around 25000$ per year and OLT.

To manage multicast group in wired or wireless domain, the hosts need to send internet group management protocol (IGMP) report message when they are queried by the multicast routers. In fact, the bandwidth of wireless domain and mobile nodes' battery power are scarce resources; therefore, it can be big burden for the receiver-driven layered video multicasting (RLVM) receiving mobile hosts to follow the conventional report sending phenomenon, where a host needs to send a report message for subscribing each of the video layers. In this paper, we propose a mechanism for reducing the number of IGMP report messages for RLVM receiving hosts. The protocol overhead and efficiency are evaluated, and compared with conventional IGMPs. Our results show that applying proposed idea in IGMP the number of report messages can be minimized significantly for RLVM services.

We propose a call admission control scheme for soft handoff of CDMA networks based on mobility estimation and mobility history of mobile stations. This work is further improvement of the Eliminating Pseudo Handoff Call (EPHC) proposed by Xiaomin Ma et.al. Therefore we named our proposed scheme as Improved EPHC (IEPHC). The proposed scheme not only improves network throughput but also reduces handoff and new call blocking probability. We derive here the handoff blocking probability. In addition, we present the decision procedures for the call admission control agent, which assigns a channel for handover requester mobile station. Numerical results show that the IEPHC scheme minimizes handover call blocking probability and increases throughput of CDMA cellular networks.

In this paper, we introduce the personalized Internet protocol television (IPTV) services for new business opportunities in next generation networks (NGN) and present the direction and strategies for services deployment in network operators' perspective. Personalized IPTV will be key solutions for value added services over Internet and emergence of Web and NGN will provide open platform and new business model for IPTV services in the future.

The video-on-demand (VoD) service over IP based network is increasingly provided with dedicated unicast stream. In the VoD server's point of view, this unicast transmission is simple and makes no problem when there is enough capacity or the service request arrival rate is moderately low. However, in the real world, the video request popularity is highly skewed, so there are often concentrated requests for one particular content and burst requests in case of special events. Those cases are critical for the unicast transmission servers because the server's capacity is limited from its initial design so it results in high service blocking probability. To cope with this problem, we propose a hybrid transmission mechanism with newly proposing "Multi-session Multicasting" algorithm and adaptive session allocation method. The multi-session multicasting algorithm enables to serve unlimited requests within a fixed boundary of the bandwidth consumption and the adaptive video allocation method enables to decide the efficient transmission method for each videos. Therefore, by transmitting highly requested videos through multi-session multicasting and normal videos with unicast, we have achieved improvement on service blocking probability.

Internet protocol television (IPTV), one of the most emerging services, offers multimedia streaming services with security, reliability, and relevant quality of service (QoS)/quality of experience (QoE). It provides added values to all the involving players including customers and also brings technical and business challenges to those players. For IPTV services, we expect to adopt the next generation network (NGN) environment for high quality and the Web technologies for personalization to meet the customer's necessity. Web can provide an open, flexible, and agile platform. Therefore, in this paper, we propose personalized IPTV services based on Web-based open platform in NGN environment and present functional architecture for Web-based personalized IPTV services. Technical issues for deploying the proposed IPTV services using Web are also provided. The objective of this paper is to analyze the critical architectural issues for developing viable and feasible networking platform models for personalized IPTV service.

Guaranteed end-to-end QoS for real-time services poses a number of new design and deployment challenges in the next generation Broadband convergent Networks (BcN). One of the major issues is preserving end-to-end QoS for real-time provisioning during handoff in the converged network. Mobile IP, proposed for mobility management in current IP network (that itself is a best effort based network) has a significantly high latency period and signaling overhead during handoff. In this paper, we propose a flow-based forwarding scheme in Mobile IPv6 networks to support real-time service provisioning by using the Flow Label field in IPv6 packets. The proposed scheme takes the advantages of rapid establishment of a flow and fast forwarding of packets based on flow state information. Our scheme describes a fast flow handoff algorithm and follows it up with an optimal partitioning of QoS classes based on flow-based maps.

We propose a new optical hybrid switching system that takes advantage of both optical burst switching (OBS) and optical circuit switching (OCS) technologies. This system classifies incoming IP traffic flows into short-lived and long-lived flows. We model the system as a single server queue in a Markovian environment. The burst generation process is assumed to follow a two-state Markov modulated Poisson process (MMPP), and the service rate fluctuates based on the number of concurrent OCS sessions. Results for the mean delay and queue size are derived.

The queuing behavior of voice and data traffic for a proposed voice/data multiplexing system is investigated. For synchronization of packetized voice in the packet-switched network, the master frame format of time-division multiplexing (TDM) is utilized. This scheme has discrete delay characteristics for packetized voice and provides a simple play-out method for voice signals. The performance of voice and data traffic is investigated by obtaining the cumulative distribution of delay of voice packets and the mean delay time of voice and data packets. The performances of the proposed system and the circuit-switched system are compared to time-assignment speech interpolation (TASI) in terms of the loss probability of voice traffic and the maximum allowable number of input voice channels for a given trunk capacity. The proposed system has been found to be more efficient than the circuit-switched system with TASI for integrating multiple voice channels in a single link. For a given trunk capacity, the system can accommodate about twice the number of input voice channels as the circuit-switched system.>

The authors propose a multiplexing frame structure that makes it possible to transmit voice messages synchronously without loss or clipping of contents. This scheme has discrete delay characteristics, and provides a simple play-out method for reproduction of voice signal. The authors investigate its performance by obtaining the cumulative distribution of delay of voice packets and the mean waiting time of data packets. It is concluded that this synchronous frame structure can easily be applied to enhance services with various transmission rates, such as flow control of message streams, node congestion control, and service-class or throughput-class negotiation of channels without significant degradation of trunk utilization.>