As well known, Internet of medical things (IoMT) produces large amounts of medical data and promotes the medical data sharing which serves the data user (i.e., physicians) to boost the clinical treatment and medical research. To protect data user’s privacy and data security during the sharing of medical data, data user must have a self-sovereign decentralized identity (DID) and data access authority. In existing solutions, data user’s privacy protection and authenticated-key-agreement (AKA) for protecting data security are worked independently, which easily results in typical security attacks (e.g., phishing inquiry attacks, ephemeral secret leakage attacks) during data access and system computing overload. To solve the challenge, a new credential-embedded authentication and key agreement scheme (CAKA) is proposed, which can seamlessly combine DID-credentials into AKA. First, CAKA supports bilateral authentication by allowing a digital user to authenticate its service provider, which can enhance the security of unilateral scheme (such as CanDID, IEEE S&P, 2021) and prevent phishing query attacks. Second, for secure data session communication, the user’s DID-credentials are used as the kernel of the session key (SK) generation. In security analysis and performance metrics comparisons, the results indicate that CAKA holds a significant advantage, especially, the storage costs, communication costs and computation costs consumed in CAKA are at least 43% reduction, compared to alternatives. In simulation experiments of CAKA, the results show that decentralized identity authentication and session key agreement are both less than 15 ms, that means CAKA is a practical and promising solution to medical data sharing.

A Non-invasive blood Glucose Monitoring (NGM) method is desired for diabetes patients. Unfortunately, there is no practical NGM technique available for considering clinical accuracy requirements. One of the major causes is raised from the interferences of Environmental Background (EB) and Physiological Background (PB) noises. In this study, we propose a novel NGM method, called NIV-NGM. Based on the near-infrared video of a user's finger, NIV-NGM automatically separates the blood vessel and the background (other tissues) by an image segmentation algorithm and estimates the glucose level using features extracted from them. Compared with other NGM methods, NIV-NGM can differentiate the glucose-related information from the background, and also compensates for the measurement results due to the background noises. Clinical experimental results demonstrate that NIV-NGM is robust and immune from EB and PB noise influence, while can achieve the expected monitoring accuracy$(Clarke(A)=0.96)$.

Non-invasive Glucose Measurement (NGM) technology is promising and desired for patients with hyperglycemia or hypoglycemia. In various kinds of NGM technologies, a prediction algorithm model plays a special role that is to map a group of physical signals to a glucose level of a person at a given time. Unfortunately, there is no practical solution available to the public, under the circumstances of different skin color, skin thickness, physiological differences, etc. In this paper, a Transfer and Collaborative Learning (TCL) method for personalized NGM modeling is proposed, and an Artificial Neural Network Model with TCL (ANN-TCL) is established for predicting glucose concentration, in which model parameters can be tuned according to individual physiological conditions. To verify performance of the proposal, it is embedded into our developed NGM system, and compared with alternate solutions. Clinical trials in the PLA NAVY General Hospital demonstrate that this proposal can reduce the impact of individual discrepancies (IDs), and achieve expected results (R2=0.82) for all test subjects. Obviously, it is helpful to each patient with our NGM system for the purpose of glucose self-monitoring. The unique gain benefits from a special design of the self-learning strategy which can fully take advantage of both universal and personal information.

Nowadays, stroke is regarded as the first killer of public health, causing around 5 million deaths every year worldwide. However, study shows that stroke can be predicted and prevented if people's Blood Pressure (BP) statuses are appropriately monitored daily, since these malignant events have some foreboding physiological signals. Unfortunately, there is no any BP devices to find these signals in real-time, even for Ambulatory Blood Pressure Monitor (ABPM) devices. Furthermore, all data of BP and ABPM devices are collected via a deflation-type mode, which couldn't capture most of the vasculopaty due to elastic deformation problem of blood vessels. To solve these challenges above, a new ABPM system is designed as a Mobile Health (mHealth) system for stroke risk early-warning, in which a customized inflation-type BP measurement, i.e., SILU algorithm, is proposed. Our clinical results demonstrate that the system is a useful tool for stroke prevention, and other BP-related diseases early-risk monitoring.

Non-invasive blood glucose measurement is a crucial challenge in both academic and industry communities. Currently, most of non-invasive solutions are developed based on optical signals. However, their accuracy is still far from clinical requirements if these measured optical signals directly used to estimate corresponding glucose levels. To solve this challenge, a novel Back-propagation Monte Carlo (BpMC) algorithm is proposed to retrieve bio-optical properties in human multilayered tissues. Build on BpMC algorithm, two non-invasive blood glucose estimation models, namely BpMC-DEE and BpMC-CNN, are conceived. In contrast to existing black-box solutions, BpMC-DEE is a white-box model that is more reliable in clinical. BpMC-CNN is a gray-box model whose results are more accurate in cost of a larger dataset and higher computing complexity. BpMC-DEE and BpMC-CNN are embedded and implemented into our designed noninvasive device - Earlight, for clinical trials. The clinical trial results demonstrate that correlation coefficients of these two models reach 0.852 and 0.895, respectively, referring to invasive glucometers. In terms of Clarke Error Grids, our proposals account for 90.6% and 93.5% statistic points in regions A and B, respectively. Moreover, the BpMC algorithm can be applied to other components measurement of biological tissues.

Ventricular tachyarrhythmia, in particular Ventricular Fibrillation (VF), is the primary arrhythmic event to cause Sudden Cardiac Death (SCD). Thus, the quick and accurate detection of a VF event is crucial for capturing the life-threat cardiac arrhythmias in real time. However, almost all existing VF detection algorithms are challenged by low accuracy or/and high complexity. To address this challenge, a Successive Projections Algorithm (SPA)-weighted Arrhythmias-risk ValuatING (SAVING) algorithm is proposed, in which SPA method is first applied to valuate arrhythmias-risk clinical features with using fisher criterion. Clinical trials show that it can obtain accuracy (Ac) of 96.81%, sensitivity (Se) of 95.41%, and specificity (Sp) of 97.15% in our WE-CARE 2.0 system. Compared to conventional algorithms, clinical tests demonstrate that the SAVING algorithm achieves comparative gains, which is a useful tool that can be embedded into WE-CARE systems and Automatic External Defibrillators.

As defined by WHO and ITU, mobile health (mHealth) is expected to change healthcare service modes and will be at the core of the future healthcare system. As for considering human activities, hotspot and indoor environments are the major scenarios to carry mHealth services (e.g., homecare). To accommodate mHealth services, dense/superdense small-cell layout in Heterogeneous Networks (HetNets) is a typical deployment scenario. On the other hand, mHealth services should be guaranteed with reliability and security in any wireless networks, since health-risk or medical-related information involved. In this study, we focus on how to guarantee control-information delivery in HetNets, so that mHealth users can be allowed to access wireless facilities reliably. To handle this challenge, we extend Interference Self-Coordination theory into HetNets, in order to enable service-sensitive healthcare services in mobile networks if needed. The system-level simulation experiments demonstrate that our proposal can achieve significant performance gain: the SINR of control channel is increased by around +104%; the packet error-loss rate can be reduced by -73%; the packet delay is reduced by -46%. These gains are valuable for quality-sensitive applications in HetNets, such as mHealth.

Heterogeneous Networks (HetNets), in which small cells are ultra-densely deployed within a macro cell, is widely regarded as a key solution to mobile traffic offloading. However, this kind of traffic offloading is confronted by two challenges. Firstly, the number of users in any small cell is really limited due to the larger power gap between macros and small cells in HetNets. In return, this phenomenon may degrade traffic offloading performance. Secondly, spectrum efficiency may be deteriorated since uplink channel states are not involved in the conventional cell attachment strategies. To address these challenges, we propose a best-fit cell attachment where downlink and uplink are decoupled and attached to cells independently. Specifically, in this paper, we conceive best-fit strategies for downlink and uplink cell attachment, for taking into account of both channel quality and cell load. And then, their implementation issues in different scenarios are discussed in detail. System-level simulation results demonstrated that our proposal can effectively offload traffic from macros to small cells, and improve data rates for cell-edge users significantly.

In Energy-Harvesting Wireless Body Area Networks (EH-WBANs), how to realize a power management strategy to enable lifetime operation is one of the most concern problems. To solve this problem, a feasible solution should be used to manage the harvested energy in sensor nodes in an EH-WBAN. So far, there are extensive studies about this issue for EH-WBANs. Unfortunately, the significant character of energy harvesting rates is ignored in existing solutions, which has immediate impact on the effectiveness and efficiency of energy harvesting in EH-WBANs. To handle this critical challenge, this paper propose a harvesting-rate oriented self-adaptive algorithm for enabling lifetime operation in EH-WBANs, in which a duty cycle can be dynamically adjusted to maintain the harvested energy amount always greater than the power consumption. In this study, the widely cited TDMA-based frame structure is chosen as the EH-WBAN architecture as well. To exploit the full potentials of the TDMA-based frame structure for energy harvesting, a self-adaptive mechanism is devised to adjust the on-to-off ratio within a duty cycle automatically in the proposal, in which the information of energy harvesting rates can be taken into account, regardless of the rates how varying in a nondeterministic manner and among various sensor nodes in EH-WBANs. To evaluate this algorithm, a demo is configured, which test results demonstrate the proposal is a promising candidate for realizing the lifetime operation in EH-WBANs.

In Orthogonal Frequency Division Multiplexing (OFDM)-based mobile networks (e.g., Long Term Evolution Advanced), Downlink Control Information (DCI) in a Physical Downlink Control Channel (PDCCH) plays a unique role in carrying control signaling and scheduling information, which enables the flexibility and diversity of radio resource utilization. Due to system configuration limitations, power dimension is the only potential to enhance DCI reliability. Based on this motivation, we apply the synergy concept to power dimension: called Power Synergy, which can deal with negative effects caused by Cell-specific Reference Signal (CRS) power boosting, and also allow flexibly power lending between OFDM symbols in the physical control region. Under the constraints of the Block Error Rate (BLER) threshold 102given for PDCCHs and the CRS full-cell coverage requirement, performance tests reveal there are significant profits (for example, 10-dB gain available for enhancing DCI reliability in the worst case) from the power headroom in the physical control region. This valuable gain is helpful to accommodate emerging mobile broadband services (e.g., mobile health) in LTE/LTE-A networks.

Adverse Drug Reactions (ADRs) has become a worldwide problem that draws the attention of people from all racial and ethnic groups. The number of deaths caused by ADRs has greatly increased and led to many drug withdrawals in the last decades. Recent research findings indicate that most ADRs can be effectively prevented to some extent by using computer-aided information technologies. Though many spontaneous reporting systems (SRSs) have been built to enhance the pharma-covigilance, the ADRs data is still very sparse because the large amount of reports obtained from consumers contains insufficient hints to identify a possible causal relationship between an adverse event and drug. Based on this motivation, we developed Adverse-Tracking, a spontaneous reporting system of ADRs via crowd-sourcing. Our proposed system interacts with consumers through a Q&A interface and collects the ADR reports. The decision tree support vector machine (DTSVM) based on the genetic algorithm is used in our system to automate the Q&A procedure. We carried out experiments to evaluate the performance at Peking University First Hospital. As demonstrated by the results, our system is an efficient tool to track and discover adverse events in the consumers' reports of ADRs, which facilitates the detection of “signal”.

Mobile Health (mHealth) is expected to play a special role in today and the future healthcare delivery. Based on this trend, we design a Smart Wristlet mHealth system with mobile interface. The designed Smart Wristlet is dedicated to offer real-time alert for elderly fall, which is the most important when population ageing is becoming. In the Smart Wristlet mHealth system, fall detection is the “bottleneck” of the system operation. To remove this bottleneck away, we propose a fall perception solution for elderly care. In this proposal, we abstract and construct primitive-based features from raw data collected by the Smart Wristlet mHealth system, in which the most valuable features can be selected by using a TF-IDF (Term Frequency-Inverse Document Frequency) metric. In reality, these selected features are the most effective to perform fall detection. Our system tests and clinical trials demonstrate that this proposal is eligible to turn the Smart Wristlet mHealth system into a real solution for elderly care. Results show that the recognition precision and recall can reach 93% and 88%, respectively. Compared with existing solutions, the gain from our proposal is an efficient prevention method for elderly fall, and can save more than 800 million dollars per year at today's socio-economic level.

Recently, cardiovascular disease (CVD) has become one of the leading death causes worldwide, and it contributes to 41% of all deaths each year in China. This disease incurs a cost of more than 400 billion US dollars in China on the healthcare expenditures and lost productivity during the past ten years. It has been shown that the CVD can be effectively prevented by an interdisciplinary approach that leverages the technology development in both IT and electrocardiogram (ECG) fields. In this paper, we present WE-CARE , an intelligent telecardiology system using mobile 7-lead ECG devices. Because of its improved mobility result from wearable and mobile ECG devices, the WE-CARE system has a wider variety of applications than existing resting ECG systems that reside in hospitals. Meanwhile, it meets the requirement of dynamic ECG systems for mobile users in terms of the detection accuracy and latency. We carried out clinical trials by deploying the WE-CARE systems at Peking University Hospital. The clinical results clearly showed that our solution achieves a high detection rate of over 95% against common types of anomalies in ECG, while it only incurs a small detection latency around one second, both of which meet the criteria of real-time medical diagnosis. As demonstrated by the clinical results, the WE-CARE system is a useful and efficient mHealth (mobile health) tool for the cardiovascular disease diagnosis and treatment in medical platforms.

Cardiovascular disease (CVD) is a major issue to public health. It contributes 41% to the Chinese death rate each year. This huge loss encouraged us to develop a Wearable Efficient teleCARdiology systEm (WE-CARE) for early warning and prevention of CVD risks in real time. WE-CARE is expected to work 24/7 online for mobile health (mHealth) applications. Unfortunately, this purpose is often disrupted in system experiments and clinical trials, even if related enabling technologies work properly. This phenomenon is rooted in the overload issue of complex Electrocardiogram (ECG) data in terms of system integration. In this study, our main objective is to get a system light-loading technology to enable mHealth with a benchmarked ECG anomaly recognition rate. To achieve this objective, we propose an approach to purify clinical features from ECG raw data based on manifold learning, called the Manifold-based ECG-feature Purification algorithm. Our clinical trials verify that our proposal can detect anomalies with a recognition rate of up to 94% which is highly valuable in daily public health-risk alert applications based on clinical criteria. Most importantly, the experiment results demonstrate that the WE-CARE system enabled by our proposal can enhance system reliability by at least two times and reduce false negative rates to 0.76%, and extend the battery life by 40.54%, in the system integration level.

For system-level information in OFDM-based mobile networks, the full-cell coverage requirement raises the issue of Inter-Cell Interference (ICI) in physical control channels. Unfortunately, conventional approaches (e.g., scheduling, beamforming) cannot effectively deal with this kind of ICI because of configuration limitations in physical control channels. To solve this challenge, this study defines a new concept of `Interference Self-Coordination (ISC)' for enhancing this system-level information delivery. Specifically, we apply ISC to relieve the ICI effect over the Physical Downlink Control Channel (PDCCH), the most important physical control channel in Long-Term Evolution (LTE/LTE-Advanced) networks, in which Physical Cell Identifier (PCI) planning naturally serves as a self-coordination mechanism. We prove that PCI planning is the most effective solution to relieve ICI over PDCCHs, under the constraint of non-orthogonal control regions between neighboring cells. Since PCI planning is an NP-Complete problem, heuristic-strategy algorithms are devised for implementation, which are self-adaptive to dynamic configurations of the network and system, and compatible with any applicable ICI solutions. The numerical analysis and system-level simulation experiment results demonstrate that this proposal can increase overall system throughput by up to 17%, and also improve cell-edge throughput by up to 42%. In the worst 5% of sectors, the proposal can still obtain 45% system throughput and 200% cell-edge throughput gain.

In Long Term Evolution-Advanced (LTE-A) networks, the quasi-static configuration of Control Channel Element (CCE) creates multiple challenges to real-time mobile health (mHealth) applications. To handle these challenges, we propose a novel solution, Service-oriented Self-adaptive CCE (S2-CCE) configuration mechanism. This proposal can dynamically configure the just-enough number of CCEs for mHealth users according to their scenarios, which can avoid the overutilization of the limited CCE resources. Furthermore, this self-adaptive CCE configuration is also self-aware to service quality from an mHealth user, which can solve the time-sensitive concern in mHealth applications, for example, emergency call and Consultation Video for the point of care. Consequently, we conceive an algorithm to implement the S2-CCE mechanism, in which the initial CCE configuration is based on the Channel Quality Indicator (CQI) reported by an mHealth user from a real deployment, and then this initialized CCE configuration can be intelligently adjusted according to QoS (Quality of Service) requirements from the mHealth user by introducing a concept of Service-Aware Degree (SAD). Our LTE-A system-level simulation experiments demonstrate that the S2-CCE can reduce average delay remarkably for mHealth applications (including HL7, DICOM mHealth users achieving 50% lower delay in contrast with non-service-aware method when there are 50 mHealth users in the cell). And it achieves high user satisfaction (almost 100%) in multi-user scenarios given a milder relaxation to user fairness.

To save signaling overhead, Semi-Persistent Scheduling (SPS) is the standard for delivering Voice-over-IP (VoIP) in OFDM-based mobile networks. But uncompressed or variable-size compressed VoIP packets are problematic for SPS because they spill over the assigned semi-persistent resource whenever the ROHC (Robust Header Compression) mechanism is in an initialization or reset period, propagating system latency and accumulating unnecessary signaling overhead. In response, we propose a Self-Adaptive Scheduling (SAS) algorithm, where dynamic scheduling is used as an adaptive mechanism to assist in scheduling oversized packets, triggered by buffer information already known to the system, without requiring dedicated signaling. To analyze the performance of our proposal in theory, we created a novel theoretical model to describe VoIP traffic considering the uncompressed voice state. This is, to our best knowledge, the first effort to model the effect of uncompressed packets on VoIP performance in mobile networks from the system-level point of view. Theoretical analysis and simulation experiments validate our proposal, demonstrating its ability to enhance the VoIP user experience for a minor signaling overhead cost.

Cardiovascular disease (CVD), principally heart disease and stroke, has become the worldwide leading killer for people of all racial and ethnic groups. In China, this disease contributes 41% to all deaths each year, and it costs the country more than 400 billion US dollars in last decade, including health expenditures and lost productivity, which continues to grow as the population size increases. Recent research findings indicate that CVD can be effectively prevented to some extent by an interdisciplinary approach that combines ICT (Information and Communication Technology) and healthcare applications. Based on this motivation, we developed WE-CARE, a Wearable Efficient teleCARdiology systEm using mobile 7-lead ECG devices. Our WE-CARE system surpasses existing resting ECG systems that reside in hospitals owing to its improved mobility brought by wearable and mobile ECG devices. Meanwhile, it outperforms those conventional dynamic 1-lead or 3-lead ECG systems for mobile users in terms of the appropriate ECG information required for clinical proposes. We carried out clinical trials by deploying the WE-CARE systems at People Hospital of Peking University. The results showed that our solution achieves a high detection rate over 95% against common types of anomalies in ECG signals, while its risk alert delay is limited around one second, both of which match the criteria of real-time healthcare monitoring very well. As demonstrated by the results, the WE-CARE system is a useful and efficient tool for the cardiovascular disease prevention and daily monitoring.

In OFDM-based mobile networks, the full-cell coverage of SCI (System-level Control Information) must be guaranteed for a user that can communicate with its eNB (evolved Node Base station) system. The requirement of SCI coverage causes severe ICI (Inter-Cell Interference) effect. Furthermore, the negative effect is intensified owing to single-frequency networking and physical control region configuration in OFDM-based networks. To enhance the reliability of SCI under the coverage constraint, we investigated how SCI is carried in the Physical Downlink Control Channel (PDCCH) in the common search space, and proposed a solution of Conflict Coordination on PDCCH via PCI (Physical Cell Identifier) Planning (CCP3). In our proposal, PCI planning is used to relieve the ICI effect of the SCI. Thus, a heuristic algorithm is developed for the PCI planning since it is an NP-Complete optimization problem. The link-level simulation experiments and numerical analysis results demonstrate that the proposed CCP3can improve effective SINR (Signal to Interference plus Noise Ratio) of PDCCH significantly, and reduce RE (Resource Element)-occupation conflict probability of PDCCH around 50%. To the best of our knowledge, this study is the first effort to strengthen the SCI reliability from the view of networking optimization.

To provide healthcare services in mobile networks, GBR (Guaranteed Bit Rate)-oriented mechanism is expected to offer stable transmission for vital life and health information. Unfortunately, a kind of GBR-based applications is aggressive to exhaust limited bandwidths in a mobile network. To facilitate such a GBR-driven application, radio bandwidth scheduling should be agile and flexible. Motivated by this trend, we propose a Best-fit Carrier Dial-up (BCD) algorithm, which can dynamically schedule demanded bandwidth for GBR-oriented healthcare applications. In fact, this proposal is developed on the basis of Carrier Aggregation (CA), which is the typical new feature in LTE-A (Long Term Evolution-Advanced) networks. Compared with the CA technology, our proposal has two favorable properties: (1) the BCD algorithm uses the LTE-A own system information to select the best-fit carrier in order to avoid extra signaling overhead. Thus it is suitable for healthcare services which are time-sensitive; (2) the BCD algorithm can dial-up bandwidth on demand at a small-scale granularity. Most valuably, this proposal is backward compatible with CA standards seamlessly. In this study, medical image and consulting video are chosen for simulation experiments. Results verify that our proposal can finely tailor radio bandwidth allocation for the time-sensitive and stable healthcare services at a required GBR level.

In OFDM-based mobile networks, uplink synchronization is essential in order for an eNB (evolved Node Base station) to handle a variety of interference issues. To guarantee uplink synchronization control, the HARQ (Hybrid Automatic Repeat-reQuest) mechanism is applied into the process of its TAC (Timing Advance Command) transmission in current standards. But HARQ conflicts with the TAC scheduling mechanism in nature, because TAC control information is typically scheduled for transmission at the end of a synchronization period in order to save signaling overhead by reducing TAT (Time Alignment Timer) restart frequency. To handle the conflict between the extra HARQ retransmission delay and TAC scheduling mechanism, we propose a novel TO (Time Offset)-TAC solution that can remove the side effects from TAC information delivery caused by the extra delay of HARQ retransmission. We conduct system-level simulation experiments to evaluate our proposal's performance gain. Results demonstrate that our solution can avoid losing synchronization due to its TAT expiration, at a slight penalty of signaling overhead. This study is the first effort at addressing how to guarantee uplink time alignment in the control layer, which is the basis for suppressing interference from “time-staggered symbols” among multiple paths.

ECG analysis is universal and important in miscellaneous medical applications. However, high computation complexity is a problem which has been shown in several levels of conventional data mining algorithms for ECG analysis. In this paper, we presented a novel manifold approach to visualize and analyze the ECG signal. According to regularity of the data, our algorithm can discover the intrinsic structure and represent the streaming data with a 1-D manifold on a 2-D space. Furthermore, the proposed algorithm can reliably detect the anomaly in ECG streaming data. We evaluated the performance of the algorithm with two different anomalies in wearable applications: for the anomaly from heart disorders such as apnea, arrythmia, our algorithm could achieve up to 90% recognition rate, for the anomaly from the ECG device, our algorithm could detect the outlier with 100%.

Today, IT technology plays a key role in promoting health services which provide medical information over a digital information system. Unfortunately, the digitized data may be not understood by medical professionals due to the limitation of conventional medical signal recognition. To deal with this challenge, we design a 3R (Retiming, Regeneration, Reshaping) dataflow engine that can restore electrophysiological data into its original medical patterns. We carried out clinical trials on our cloud platform in PKU's People Hospital. Clinical results proved that our solution produces high playback accuracy and matches with medical diagnosis criteria very well. With the proof of our clinical tests, this solution can be a very useful tool for clinical treatment and diagnosis in medical platforms.

An optical access network should offer low-cost reliable services to its end users. To address this problem, an optimal solution is needed which can turn an optical access architecture into a self-healing system. Hence, we propose the Self-Healing Optical Access Network (SHOAN), in which two or more optical access architectures are partners of each other, and they are interconnected by elementary optical crossbar switches into a simple mesh network. In SHOAN, the crossbar switches can keep each access architecture as an independent and closed system for only serving its own end users in normal state. But the crossbars become open in fault scenarios. Whenever a failure occurs in the network, the fault can be monitored and affected services can be recovered by the partner of the access architecture that is affected. Such an interconnected optical access network can withstand failures in its transmission paths, and recover network services in a self-healing way. Compared to existing solutions (e.g., dual-home architecture), illustrative examples demonstrate that SHOAN has many desirable properties: (1) it is robust because risks are disjointed, (2) it is reliable because service recovery is given top priority, and (3) it has low cost because redundant backup components are not necessary since the partner's resources act as backup resources. Analysis results show that SHOAN can minimize disruption duration and network cost for broadband access services.

With emerging applications in packet-transport networks such as IP-over-WDM networks (e.g., eBanking), packets need to be delivered within a bounded delay (i.e., given deadlines) with high probability. To satisfy such requirement, we propose an Inbuilt-Burstification Urgency-driven Scheduling (iBUS) Algorithm in IP-over-WDM networks. In this approach, packets are assembled into bursts according to their destinations by a preset timer. The timing burstification can reduce the amount of contentions, and support deadline constraints. Then all assembled bursts need to be scheduled efficiently with the deadline constraint. We introduce the concept of urgency degree as a scheduling metric. Bursts are scheduled according to their urgency degrees so that utilization of available network resources can be maximized under the constraint of deadlines. We also study a high-level variant of our algorithm,beta-iBUS, where the parameterbetais used to truncate the acceptable range of urgency degrees [0, 1] into [0,beta] (wherebetales 1) so that invalid scheduling caused by high urgency degrees can be minimized. Simulation experiments demonstrate that our proposal can achieve limited loss probabilities with the constraint of bounded delay.

In the challenging social/geographical environment in Africa, how to design a robust network while tolerating the traffic imbalance is studied in this paper. We propose the Africa TWO network which connects all countries in Africa by employing two concentric rings. We investigate an Integer Linear Programming model and develop heuristic algorithms to deal with some special issues in Africa, such as a large desert in North Africa, high-speed network connectivity to the rest of the world through a few existing undersea fiber cables, and traffic imbalance caused by the differential populations of the various countries. To further exploit the properties of the designed network, we propose Little-Arc-First (LAF) routing algorithm, First-Matching (FM) wavelength assignment, and Sharing Peer Protection (SPP) scheme. The proposed algorithms LAF and FM are more efficient than existing algorithms. Sharing Peer Protection (SPP) scheme achieves high reliability in a cost- effective manner. We also address how to upgrade the network to achieve longer network lifetime for overall economic benefit. Our simulation experiments demonstrate that the concentric two-ring network is a natural candidate not only for the African continent, but can also be extended to other countries and regions.

We propose a scheduling-aware burst assembly algorithm to provide bandwidth-guaranteed and delay-bounded services for IP packets in IP-over-WDM networks. To transport IP packets efficiently over a network, they are assembled into bursts, and delivered within a given deadline. All IP packets in a burst should satisfy the deadline requirement, and, in turn, all assembled bursts are guaranteed to be served. To achieve this goal, the assembly and the scheduling processes of each burst need to be performed concurrently within the same active time-window (called joint window). The joint window size is set considering the deadlines of packets in a burst so that each of them (and, in turn, the burst itself) can satisfy its deadline. To guarantee service-availability for each assembled burst, the scheduling information (e.g., when and how long a slot is free for a burst) is fed back to the burst assembly process in each joint window. This scheduling information is used to determine when and how long an assembly process will be conducted. Those packets, which can not make the deadline, are not considered for the burst assembly process. Although, in our algorithm, the packets are transmitted in a burst (as described above), we investigate the packet-level performance for a IP-over-WDM network. The simulation experiments demonstrate that our algorithm can achieve low packet loss probabilities, and guarantee the delay-bound for delivered packets (loss probability and delay-bound are key factors for emerging IP services).

In the challenging social/geographical environment in Africa, how to design a robust network while tolerating the traffic imbalance is the major goal in the paper. To achieve the goal, we propose and analyze the design of a symmetrical concentric two-ring network to connect all countries in Africa. We develop optimization schemes while dealing with some special issues in Africa, such as a large desert in North Africa, high-speed network connectivity to the rest of the world through a few undersea fiber cables, and traffic imbalance caused by the differential populations of the various countries. Our simulation experiments demonstrate that the concentric two-ring network design is a natural candidate for the African continent.

This paper proposes a novel framework of bandwidth provisioning based on optical burst switching (OBS), called optical flooding cluster switching (OFCS), which is developed to achieve a better resource allocation and efficiency by fully exploiting the potential characteristics of data-dominated traffic with a high self-similarity and burstiness in the modern communication networks. The concept of flooding clusters is first defined, which serves as basis of the study. The design objectives and the working principles of the OFCS scheme are introduced, where the corresponding advantages against the pure OBS, pure OCS (optical circuit switching), and the hybrid OBS/OCS architectures, are discussed. A comprehensive analytical model on the burst drop rate is formulated for the proposed scheme. To verify the analytical model and compare with the other counterparts, extensive simulation efforts have been addressed. The results reveal that OFCS can overcome the non-predictiveness of traffic self-similarity, where the flooding clusters are set up for a partial prediction of the burst arrival. We conclude that the flooding effect of data-dominated traffic can be successfully smoothed out by the proposed OFCS scheme