This study introduces ViTrack, an advanced vision-and-wireless-based navigation system that assists visually impaired individuals in indoor environments in a user-controlled, privacy-preserving manner. Traditional visual navigation methods often encounter challenges due to obstructed views, hindering effective tracking and identification. Similarly, wireless positioning systems are prone to signal interference, leading to inaccuracies. ViTrack seamlessly blends vision-based and wireless positioning technologies to provide comprehensive, unobtrusive navigation support. A key element of our approach is using existing surveillance cameras, expanding our observation capabilities while minimizing the need for additional devices for the user. Our system is designed to improve navigation while maintaining simplicity for the user. We exhibit ViTrack within an already-established surveillance camera network. It automatically employs low-cost wireless sniffers to detect users within the observation guiding zone. Simultaneously, vision-based methods ensure collision-free path planning. Our findings highlight the superiority of Long Short-Term Memory (LSTM) neural networks over other neural network architectures regarding localization and tracking accuracy. We also demonstrate the crucial role of data augmentation in mitigating device failure impacts, significantly boosting the system’s reliability.

Traditional vision-based and wireless navigation systems limit visually impaired users due to occlusions, limited camera views, environmental interference, and signal blockage. Continuous tracking in non-overlapping multi-camera networks presents a key challenge in dynamic environments. This paper introduces CamTrack, a novel vision-assisted navigation framework designed to overcome these issues and provide enhanced mobility support for visually impaired individuals. CamTrack employs a novel sensor fusion methodology that unifies existing surveillance camera infrastructure with readily available smartphone sensors and radios, specifically the Inertial Measurement Unit (IMU) and Bluetooth Low Energy (BLE), enabling practical and scalable deployment. The system leverages object detection and deep learning person Re-identification to enable seamless, continuous, and unobtrusive user tracking across multi-camera zones. A key technical contribution is a predictive handover mechanism that utilizes IMU-derived velocity and orientation to anticipate user trajectories, thereby optimizing camera resource allocation, reducing computational overhead, and minimizing latency during transitions. To further enhance robustness, we introduce a camera-handover probability model. Real-world deployment in a smart campus environment validated CamTrack’s effectiveness and practicality in delivering robust, real-time assistive navigation. The analysis of the handover model showed that success rates are lower in sparse camera areas and negatively affected by time spent outside coverage. Quantitative evaluation shows that increasing the number of active cameras from six to nine improves tracking success probability by up to 39% in sparse coverage zones and 13% in dense zones, though at the cost of higher resource utilization. This measurable trade-off highlights how CamTrack enables administrators to balance tracking continuity against deployment cost. It also highlighted the impact of the handover expiry timer on success probability. CamTrack’s use of existing infrastructure offers a scalable and cost-effective solution for smart environments, enhancing accessible navigation.

Detecting conversational groups in proximal spaces is vital for social sensing but remains challenging due to acoustic degradation and privacy concerns. We propose a dual-mode speech-based framework using contextual and temporal cues to identify conversational clusters within Hall’s proxemic zones. The contextual mode fuses word-level embeddings with a novel BERT-based Reply-to Ratio, producing robust semantic affinities. The temporal mode offers a privacy-preserving fallback by estimating turn-taking overlaps from speaking-time distributions and timestamps, avoiding raw audio or text. These affinities form a weighted social graph, with Louvain community detection extracting group structures. Experiments on smartphone-recorded dialogues show a 14% accuracy gain over a hybrid baseline (up to 30% at long distances) for the contextual mode. The temporal mode averages 73% accuracy with an 11% improvement, showing strong performance under distance and privacy constraints.

This paper proposes a novel method for extracting conversation groups by fusing wireless and audio data. We first use wireless signals to identify groups of people that walk together. Then we propose a novel approach for detecting conversation groups using audio data. Our approach examines non-contextual and contextual analyses to align with user permission levels concerning data utilization. We also incorporate turn-taking likelihood, frequent words, topic similarity, and Named Entity Recognition (NER) to identify conversation groups effectively. To further mitigate privacy issues, we propose to use vector feature representations of speech patterns, a subset of relevant keywords, and a vector space representation of the discussed topic. We first convert the audio files into text and analyze the content by identifying frequent words and topics and extracting NER information on the user's phone. Only the featured vectors are received at the third-party server to compute different similarities among conversation participants. By integrating these similarity values, we obtain the non-contextual and contextual-level Similarity between two users, which allows us to determine if they belong to the same conversation group. Experimental results demonstrate that turn-taking likelihood can identify groups by 90%. However, contextual information can achieve high accuracy rates of up to 96.57% and an average F1-score of 94.77%. These findings indicate that our system is highly effective in analyzing data in audio streams for conversation grouping, with potential applications in various social scenarios.

With the rise of smartphones and wearables, research into human movement, particularly group interactions, has gained significant attention. These devices serve as invaluable sources of diverse data streams, encompassing movement patterns, social interactions, and historical wireless signals crucial for group identification. However, the accuracy of wireless signal recognition could be better in close proximity scenarios. Our paper presents a pioneering methodology to overcome this limitation that integrates wireless signal data with environmental light sensor features. This novel fusion enhances mobility group recognition accuracy significantly. We conducted rigorous experiments in a building. Our method consistently demonstrated robust performance in identifying mobility groups accurately. This empirical validation underscores the effectiveness and adaptability of our proposed approach.

This paper introduces NOMA-D with 802.11 DCF, a pioneering protocol that harnesses Non-Orthogonal Multiple Access (NOMA) to boost throughput in industrial wireless networks (IWNs) operating on the 802.11 DCF standard. NOMAD exploits NOMA’s ability to decode superimposed signals from users with different channel conditions, effectively transforming collisions into successful transmissions. This is achieved by strategically allocating power based on channel quality, enabling simultaneous transmissions on the same resource block. By implementing NOMA-D paired transmission within DCF, our analysis demonstrates that the NOMA-D approach can achieve throughput gains surpassing $20 \%$ compared to traditional 802.11 systems across most scenarios. This underscores the potential of NOMA as a promising technique for enhancing the performance of 802.11 networks.

With the explosive growth of mobile data traffic, roadside-unit (RSU) caching is considered an effective way to offload download traffic in vehicular ad hoc networks (VANETs). Many existing works investigate the content placement of RSU caching. However, few of them consider the download deadline constraint when caching the content in the RSUs. In this paper, the main objective is to maximize the hit rate of downloading the requested content from the RSUs before the deadline expires. We propose a Markov-based mobility model and a Markov clustering-based content placement algorithm to group the RSUs into clusters and allocate the content to the cache of the RSUs in the cluster. We also investigate the impact on the cache hit rate under different simulation parameters, such as the total number of RSUs, the cache size, and the number of RSUs visited by vehicles during the download period. According to the simulations conducted, when the region of interest (RoI) is small, the MVP method increases the cache hit rate by at least 21.40% compared to the existing methods. When the RoI is large, our approach outperforms other existing methods by at least 26.16% and at most 337.77%, which significantly increases the efficiency of the download session in VANET.

People usually form a social structure (e.g., a leader-follower, companion, or independent group) for better interactions among them and thus share similar perceptions of visible scenes and invisible wireless signals encountered while moving. Many mobility-driven applications have paid much attention to recognizing trajectory relationships among people. This work models visual and wireless data to quantify the trajectory similarity between a pair of users. We design a visual and wireless sensor fusion system, called ViWise, which incorporates the first-person video frames collected by a wearable visual device and the wireless packets broadcast by a personal mobile device for recognizing finer-grained trajectory relationships within a mobility group. When people take similar trajectories, they usually share similar visual scenes. Their wireless packets observed by ambient wireless base stations (called wireless scanners in this work) usually contain similar patterns. We model the visual characteristics of physical objects seen by a user from two perspectives: micro-scale image structure with pixel-wise features and macro-scale semantic context. However, we model characteristics of wireless packets based on the encountered wireless scanners along the user’s trajectory. Given two users’ trajectories, their trajectory characteristics behind the visible video frames and invisible wireless packets are fused together to compute the visual-wireless data similarity that quantifies the correlation between trajectories taken by them. We exploit modeled visual-wireless data similarity to recognize the social structure within user trajectories. Comprehensive experimental results in indoor and outdoor environments show that the proposed ViWise is robust in trajectory relationship recognition with an accuracy of above 90%.

Many indoor localization techniques have been widely investigated. As the proportion of people with multiple mobile devices is increasing, an interesting research challenge arising from this growth is how to integrate data from multiple devices carried by a mobile user for localizing her/him. This work designs amulti-device joint localizationsystem, calledJoLo, to realize the idea using a group of off-the-shelf mobile devices carried by a user. The proposed system collects wireless fingerprints at the predefined training locations in the environment. Then, the system fuses the real-time wireless measurements observed by the multiple devices of a user into a summary list for localization reference. Finally, three fusion-based positioning algorithms are proposed to determine the user's location based on the fused summary list. The three proposed algorithms reduce mean distance errors in localization to less than one meter in a lab environment. One of the fusion-based algorithms, namely cluster-based DF algorithm, can even improve the performance by 50% on average compared to the existing single-device localization techniques.

In this paper, we propose the ProTrack system to detect proximity levels and extract movement patterns to infer social relationships, which are key factors in many urban computing applications. In ProTrack, the system passively collects wireless broadcasting packets from the WiFi or Bluetooth-enabled mobile devices by multiple wireless scanners deployed in the region of interest. The received signal strength extracted from these packets is used to infer the devices’ spatial and movement features. The proposed ProTrack mobility framework investigates the trajectory level, namely companion, follow, independent, and the proximity level, namely immediate, near, far for a pair of users. Based on the output results, the system can generate a graph-based proximity map and trajectory map to infer social distance and social relationship without depending on complicated infrastructure and localization.

The exponential growth in a number of the smart devices connected to the Internet of Things (IoT) in recent years has resulted in a massive increase in the volume of user generated content. Edge computing has been proposed to reduce the communication latency and improve the security of the user data by moving the computation and storage functions of these services closer to the users. However, the transmission delay between the devices and the edge server has a critical effect on the performance of the data offloading service since most IoT applications are delay-intolerant. Accordingly, the present study presents the use of measurement-based selection to determine the server with the shortest delay. Overall, the results show that irrespective of the scale of the networks, this kind of method helps to select the server with a reasonable cost.

As mobility is an important key to many applications, this work proposes a location-less model to represent mobility that is used to quantify correlations between mobility traces collected by built-in sensors on smartphones. We analyze the mobility correlations from two aspects: co-direction relationship and co-movement relationship . The former is to quantify the similarity of macroscopic moving directions between mobility traces, whereas the latter is to quantify the similarity of their microscopic vibrations. To verify the merits of the two proposed metrics, an exemplary use case, termed co-mobility detection , is considered to determine if two mobile devices share the same journey on the same mobile entity (e.g., carried by the same person). Comprehensive experiments with diverse combinations of mobility traces are conducted in three different environments with different density of Wi-Fi networks. The experimental results indicate that the proposed metrics can effectively evaluate both the coarse-grained similarity of moving directions and the fine-grained similarity of movement variations along mobility traces. The accuracy of the co-mobility detection algorithm can achieve 90% on average for mobility traces with a duration of 70 s.

Multipath mobile data offloading technologies allow user equipments (UEs) to aggregate cellular network resources and WiFi network resources in order to increase the total effective bandwidth. The IETF recently proposed Multiple TCP (MPTCP) as an extension of the standard TCP protocol to enable UEs to deliver data streams across multiple connections simultaneously. By combining multipath mobile data offloading with MPTCP, operators have the ability to adjust the cellular bandwidth assigned to the users in accordance with their WiFi connection status, thereby saving valuable LTE resources while simultaneously ensuring the quality of service (QoS) of the transmission. Accordingly, this study proposes a dynamic bandwidth reallocation scheme which reduces the cellular bandwidth allocated to a UE when WiFi access is available and reassigns the saved LTE bandwidth to other UEs with no WiFi connection. The effectiveness of the proposed scheme is demonstrated by means of numerical simulations. It is shown that the proposed method successfully reduces both the deadline miss ratio of deadline assurance services and the amount of idle LTE bandwidth.

With the proliferation of mobile devices in recent years, the volume of global mobile data traffic has grown exponentially. As a result, effective approaches for mobile data offloading are urgently required. In the past few years, two main Wi-Fi offloading techniques, namely, opportunistic offloading and delayed offloading, have been widely discussed. A promising L4-approach known as multipath transmission control protocol has recently been standardized, which allows a single transport connection to use multiple paths simultaneously over multiple interfaces, which then calls for a new Wi-Fi offloading model that considers discontinuous Wi-Fi connection periods. This paper presents an analytical model for multipath Wi-Fi offloading in deriving the aggregate offloading time via an alternative path for the use of multipath offloading. Then, the respective offloading volume and ratio are computed. Our theoretical results are supported by extensive simulations. We show how this model further assists the cellular operators to make better decision in resource allocation. Compared with opportunistic offloading, multipath offloading results in a significantly improved performance in the support for a guaranteed minimum bit rate.

In Long Term Evolution-Advanced (LTE-A) network, Machine Type Communications (MTC) allows machines to transfer data with almost no human intervention. In MTC, power saving is one of the most important issues. In the meantime, the 3rd Generation Partnership Project (3GPP) proposes Discontinuous Reception (DRX) mechanism to allow user equipments (UEs) to receive data only at specified time slots and turn off the radio module at other time slots. Unfortunately, current DRX mechanism is designed for normal usage of mobile users, not for MTC. In this paper, we proposes Optimistic DRX (ODRX) mechanism to allow more sleep periods for MTC devices. To allow more sleep periods, ODRX considers to release the RRC connection and re-establish the connection when the MTC device is paged. We propose analytical and simulation models for ODRX, and compare with the standard DRX through simulation experiments. The results show that, compared to standard DRX, significant extra power can be saved by sacrificing little extra wake up latency.

In 3GPP Machine Type Communication (MTC), devices are responsible for periodically collecting data to the MTC server through cellular network. As currently discussed in 3GPP TR 22.368, one of the major open problems in the field of MTC is to improve performance with devices having low mobility. Traditional location tracking in 3GPP is based on tracking area list (TAL). This kind of tracking schemes may include some cells that the device probably not resides, which consequently lead to high paging cost for the MTC device trigger operation. This paper proposes a small scale tracking scheme based on cell-based policy for low mobility device. Our study indicates that the cell- based policy can significantly reduce the paging cost compared with TA-based policy with low mobility MTC devices.

Reduction of traveling time of ambulance is very important to the safety of users of the transportation system. Vehicular messaging can be used to improve upon emergency response efforts in traffic accidents. This paper realizes an application for emergency message (EM) transmission for rescue services. A dispatched ambulance might occasionally get stuck in a huge traffic and delayed its arrival. The EM, sent via vehicular network, can be used to inform drivers of the traveling ambulance. The recipients of EM could kindly avoid occupying the engaging route (ER) that the ambulance is about to take, such that the medical services can be delivered as quickly as possible. We propose Emergency Messaging for Ambulance Traveling (EM4AT) to smartly reduce transmission cost of EM and to shorten traveling time of ambulance. We develop the simulation to estimate the advised traffic and travel time on ER. It shows that approximately 80% of drivers can receive EM while only 50~60% of the relevant road links are involved for EM transmission.

DTN is an intermittently connected wireless ad hoc network with long and variable delay caused by the traditional store-carry-forward message dissemination over disconnected clusters. To reduce the end-to-end delay and increase reliability, this work proposes a Social-Based Broadcasting message dissemination protocol which incorporates long range communication with broadcasting. To increase reliability, we propose a rebroadcast node selection mechanism to increase the number of received nodes. To evaluate the performance of our proposed method, we conduct extensive simulation to investigate message dissemination efficiency, including penetration rate and reachability, of the proposed method and the greedy epidemic routing method in a large mobile network. The extensive simulation results demonstrate that our proposed method is practical, scalable and cost-effective for message dissemination in DTNs.

In a drive-thru Internet vehicular network, vehicles can communicate with access points (APs) for the Internet services. When a vehicle leaves the AP's transmission range, it encounters intermittent connectivity and unreliable connection. Using vehicle-to-vehicle (V2V) relay can loosen the limitation of transmission coverage. However, the harsh conditions of wireless channel and the rapid change of vehicle topology remain the challenge for relay selection. The relay candidate with a better channel quality can forward the packet with a higher data rate. Therefore, the MAC layer mechanisms are needed to exploit the multi-rate capability in the PHY layer. This paper utilizes the channel state information to exploit good channel quality for the relay link between the sender and the receiver. The objective is to increase the opportunity of channel access for the relay candidate with better channel quality. Considering various issues, such as the collision probability, packet delivery ratio, and delivery delay, we propose an analytical model to dynamically configure the window size for channel contention. Numerical results show that the proposed method outperforms the basic DCF and distance-based DCF.

In a Vehicular Ad Hoc Network (VANET), the wireless Collision Avoidance (CA) system issues warnings to drivers before they reach a potentially dangerous zone on the road. This paper proposes an analytical model for evaluating the performance of emergency messaging via wireless CA systems. First, we utilize the dichotomized headway model, the braking model, and Greenberg's logarithmic model to generate vehicular mobility traces for analysis. Second, we derive the probability of a rear-end collision between two vehicles that travel in the same direction when a sudden event occurs. Third, we quantify the probability of vehicles failing to receive the emergency message. Numerical results from the model show that the number of car crashes per accident is much higher when a wireless CA system is not used. We also find it interesting that the number of car crashes is not directly proportional to the vehicle density when the vehicular mobility traces follow the speed-density relationship offered by Greenberg's logarithmic model. By integrating flow theory into VANET analysis, our model provides useful insights for future intelligent transportation.

Different from unicast data delivery through dedicated channels, next generation Multicast and Broadcast Service (MBS) delivers data with higher bandwidth efficiency through multicasting. When a group of online users receive the same MBS content, the average MBS network cost per user can be significantly reduced. However, traditional charging methods are unable to account for the variation of network resource. To address this shortfall, this paper develops an innovative concept in MBS charging to realize the goal that “the more users join an MBS session, the more everyone saves.” Specifically, we propose a multi-tariff charging method for MBS with a competitive advantage that attracts more users to subscribe to MBS services. Analysis and numerical results demonstrate that operators can alleviate customer concerns about user credit consumption with dynamic tariff while remaining profitable in MBS. These features in our multi-tariff charging method can create a “win-win” situation for both operators and subscribers.

To take on challenges of communication in sparse VANET, we propose a Store-Carry-Broadcast (SCB) scheme to assist message dissemination by broadcasting over a specific road segment instead of a single vehicle. In the SCB scheme, an opposite vehicle helps to disseminate the safety messages to oncoming vehicles traveling on the reverse lane by broadcasting. Compare with the well-known store-carry-forward broadcasting in VANET, the SCB scheme consumes much less network bandwidth in terms of the number of broadcasting performed. Extensive simulation results are presented to demonstrate that when the traffic density is low or when the DSCR penetration rate is low, our proposed SCB scheme significantly reduced both broadcast overhead as well as delivery delay in sparse VANET.

ABSTRACT The Evolved Packet System (EPS) is standardized to support a common IP‐based core network with heterogeneous radio accesses in the latest 3GPP release. In the EPS, the Policy and Charging Control (PCC) framework provides dynamic policy control combined with real‐time charging management. Long core network signaling for PCC degrades session continuity performance during handover. In this paper, we investigate the handover for an EPS session. Based on the results, we propose a proactive scheme along with a user mobility tracking model to improve session continuity. More specifically, our proposed scheme attempts to reduce PCC reauthorization signaling delay during the handover procedure. Then, we formulate a Markov process to model the user mobility pattern and to derive the session continuity probability. Numerical results show that our scheme can significantly keep the session continuity at a user acceptable level even when the mobility pattern is high. We also provide guidelines for operators to set up the network configuration parameters according to various traffic conditions. Copyright © 2011 John Wiley & Sons, Ltd.

3GPP has standardized Multimedia Broadcast Multicast Service (MBMS) to deliver multimedia services with high bandwidth efficiency through IP multicasting technology. In this paper, we present the implementation details of an MBMS test-bed with the standardized Policy and Charging Control (PCC) to realize the dynamic charging plan for multicast services. Specifically, we illustrate the detailed message flow and the design block diagram with 3GPP policy and charging control. The corresponding performance metrics (e.g., signaling delays) have been measured through our test-bed.

Due to the limited budget and high maintenance cost, it is difficult to deploy a large amount of roadside units (such as 802.11 base stations) to cover all road sections. Through a sparse network infrastructure, vehicles experience long download delay for large-size videos. In this paper, we proposed a Video Frame Sharing Mechanism (VFSM) to improve the performance of download sessions. We also investigate how to prevent selfish nodes in VFSM. Through VFSM, users can download video frames from RSUs (Road Side Units) or share video frames with neighboring vehicles by V2V communications in a fair manner. As a result, the video downloading can be speeded up and the video quality can be substantially improved. The extensive simulation results show that the our proposed protocol is practical.

Both zero-forcing (ZF) and maximum-likelihood (ML) criteria are widely used for signal detection in multiple-input multiple-output (MIMO) spatial multiplexing systems. In this paper, we propose a special formulation to combine these two detection algorithms for the N × N scenario, which can provide a good compromise between the error-rate performance and computational complexity. Specifically, the detection is decomposed into some ZF stages, and at each of these stages a fraction of the data streams is nulled to zero. After that, an ML stage is performed utilizing the idea of ML with reduced dimension. We show that the resultant bit-error rate (BER) performance of this hybrid ZF-ML detection can be much better than that of the ZF detection, and the total number of floating-point operations (flops) can be comparable with that of the conventional ZF calculation.

Due to the limited budget and high maintenance cost, it is difficult to deploy a large amount of roadside units (such as 802.11 base stations) to cover the complicated road areas. This paper addresses an important topic on frame sharing since vehicles often need to download large size data such as video clips regarding local news. It is important to fairly use V2V communications at places where an infrastructure is not immediately available or cannot be directly used. We aim to improve local video downloading performance by integrating V2V and V2I communications. The proposed approach, ZFEP/SD, is able to exploit user requests locality and to initiate video frames sharing among neighboring vehicles. We also consider the case of selfish users, e.g., those not willing to share frames with others. Numerical results show that our ZFEP performs better than a pure infrastructure-based download, and that ZFEP/SD ensures that cooperative nodes always get better service than every type of selfish nodes.

For cooperative communications, the decode-and-forward (DF) protocol can give promising error-rate performance provided that the relay node has the decoding capability. In this study, the authors consider the DF cooperation for cellular uplink communications, in which the source node is equipped with a single antenna, and both the relay and destination nodes are equipped with multiple antennas. For this case, the authors analyse the exact symbol-error-rate (SER) for M-quadrature amplitude modulation (QAM) signals over Nakagami-m fading channels. To avoid the cumbersome integral calculation, the authors derive an asymptotic approximation for the SER, which lets them not only gain fast understanding of the improvement from cooperative diversity, but also have more insight into how the channel characteristics and the number of antennas affect the SER performance. Specifically, to reduce the SER, the authors find that: first, good condition of the relay–destination channels is more critical than good condition of the source–relay channels; second, large number of antennas at the relay node is more critical than large number of antennas at the destination node. All these results are not exploited and discussed before. Simulations are finally presented to demonstrate the correctness of the authors' analysis and perception for the considered cooperation scenario.

Short message service (SMS) is the most popular mobile data service today. In Taiwan, a subscriber sends more than 200 short messages per year on average. The huge demand for SMS significantly increases network traffic, and it is essential that mobile operators should provide efficient SMS delivery mechanism. In this paper, we study the short message retransmission policies and derive some facts about these policies. Then, we propose an analytic model to investigate the short message retransmission performance. The analytic model is validated against simulation experiments. We also collect SMS statistics from a commercial mobile telecommunications network. Our study indicates that the performance trends for the analytic/simulation models and the measured data are consistent.

In this letter, we examine the symbol-error-rate (SER) performance of decode-and-forward (DF) cooperative communications. Our focus is on the scenario in which multiple dual-hop relays are employed and the channel environment is described by Nakagami-m fading. Cooperative diversity is observed from the derived error-rate expressions, and some insight into how channel conditions in the relay nodes affect the SER performance is obtained. In addition, with the knowledge of the partial channel state information (CSI) at the transmitting sides, we derive an optimal power allocation scheme to further enhance the SER performance. Simulation results are finally presented to demonstrate the correctness of our analysis and perception for the considered DF protocol.

During an online charging general packet radio service (GPRS) session, a number of mid-session events, such as changes of quality of service (QoS), could dynamically affect the rating of the in-progress service. When such events occur, the GPRS support node needs to re-authorize the granted credit units with the online charging system (OCS). This paper proposes a threshold-based scheme that utilizes a threshold parameter delta to reduce the signaling traffic for the credit re-authorization procedure. By selecting an appropriate delta value, the signaling overhead in the OCS can be significantly reduced while the inaccuracy of the credit information insignificantly increases. The mobile operator can choose appropriate parameter values in the threshold-based scheme based on our study.

In 3GPP Release 6, the IP-based Online Charging System (OCS) is defined to support online charging for General Packet Radio Service (GPRS) and IP Multimedia core network Subsystem (IMS) services. During an online charging GPRS session, a number of mid-session events, such as Quality of Service (QoS) could dynamically affect the rating of the in-progress service. When such events occur, the GPRS support node needs to re-authorize the granted credit units with the OCS. If the change of QoS occurs frequently,the signaling load incurred by the re-authorization procedure increases heavily. In this paper, we propose a threshold-based scheme with parameter δ to reduce the traffic signaling for credit reauthorization procedure. By selecting an appropriate δ value, our study indicates that the signaling overhead in the OCS can be significantlyreduced while the inaccuracy of the credit informationinsignificantly increases. Based on our model, the mobile operator can choose appropriate parameter values in the threshold-based scheme for various traffic conditions.

The Universal Mobile Telecommunications System (UMTS) provides high bandwidth packet data services to mobile users. To support mobility management, an MM context is established for every mobile station in its corresponding Serving GPRS Support Node (SGSN). When the SGSN fails, all MM contexts in the SGSN are corrupted. These MM contexts must be recovered or data delivery to the mobile stations will fail. This paper describes a broadcast approach that allows the MS to detect lost MM context in SGSN and therefore speeds up the process for SGSN recovery. We propose an analytic model to evaluate the performance of the broadcast approach. The analytic model is validated against simulation experiments. Based on our study, the network operator can select the appropriate parameter values in the broadcast approach for various traffic conditions.

The IP multimedia core network subsystem (IMS) provides real-time multimedia services for Universal Mobile Telecommunications System (UMTS). Through recharge threshold-based credit reservation (RTCR) mechanism, prepaid IMS services can be supported by the online charging system (OCS) in UMTS. In RTCR, when the remaining amount of prepaid credit is below a threshold, the OCS reminds the user to recharge the prepaid account. It is essential to choose an appropriate recharge threshold to reduce the probability that the in-progress service sessions are forced-terminated. An analytic model is developed to investigate the performance of RTCR for the OCS. Based on our study, the network operator can select the appropriate parameter values for various traffic conditions.

This paper studies checkpointing and failure restoration of mobility database for Universal Mobile Telecommunications System (UMTS). By utilizing per-user checkpointing technique, individual home location register (HLR) records are saved into non-volatile backup storage form time to time. When a failure occurs, the backup record is restored back to the mobility database. We consider three per-user checkpoint schemes for the HLR. An analytic model is developed to investigate these schemes in terms of the probability that a HLR backup record is obsolete and the expected checkpoint interval. This model is validated against simulation experiments. Our study provides guidelines for selecting an appropriate checkpoint scheme and parameters for various traffic conditions

In Universal Mobile Telecommunications System (UMTS), the extension of GPRS tunneling protocol called GTP' is utilized to transfer the charging data records (CDRs) from GPRS support nodes (GSNs) to charging gateways (CGs). To ensure that the mobile operator receives the charging information, availability for the GTP' transmission is essential. One important issue on GTP' availability is connection failure detection. It is desirable to select appropriate parameter values to avoid false failure detections (e.g., temporary network congestions) while to detect the true failures quickly. We propose an analytic model to compute the false failure detection probability and the expected true failure detection time. Based on our study, the network operator can select the appropriate parameter values for various traffic conditions to reduce the probability of false failure detection and/or true failure detection time.