This work considers the inpainting of missing data in an indoor field, such as geomagnetism and WiFi fingerprints. As opposed to typical image/video inpainting problems, this problem poses several new challenges. First, unlike images with rectangular shapes and fixed RGB channels, indoor geographic data are multi-channeled and highly influenced by building structures. Second, unlike natural objects with fixed shapes, each geographic field is distinct and geographic data are environmentsensitive, following complex physical laws. Consequently, learning from data in other fields is difficult. Third, such data may be obtained from manual surveys and crowdsourcing, which often results in weakly-labeled and noisy datasets. We model our field data as (i) manually surveyed labeled data with holes and (ii) crowdsourced weakly-labeled data without holes. We propose a two-level adversarial regularization inpainting model to conquer these challenges and validate our results with real field data.

When users give natural language instructions to service robots, positional information is often referenced relative to objects in the environment rather than absolute coordinates. However, humans naturally use relative references. For example, in“Go to the chair and pick up empty bottles”, where the positional reference is the chair, ambiguity arises when multiple similar objects co-exist in the environment or when the robot’s view is limited, resulting in multiple possible interpretations of the same command and affecting navigation decisions. To address this issue, we propose a two-level framework that integrates a large language model (LLM) and a vision-language model (VLM), allowing the robot to engage in multi-turn dialogues for spatial disambiguation. Our method first utilizes a VLM to map the semantic meanings of dialogues to a unique object ID in images and then further maps this object ID to a 3D depth map, enabling the robot to accurately determine its navigation target. To the best of our knowledge, this is the first work leveraging foundation models to address spatial ambiguity.

Physical mailboxes remain vital for receiving official documents and correspondence, particularly in residential and institutional settings. However, conventional mailboxes require manual checking, which is inefficient and prone to oversights, especially for time-critical letters, potentially leading to penalties or missed opportunities. Shared mailbox environments, such as those in apartment buildings or workplaces, further complicate access, and tracking. To address these issues, we demonstrate a multimodal smart mailbox system that integrates image recognition, optical character recognition, and large language model techniques. The system uses AIoT-based sensing and edge-cloud communication to automatically detect mail delivery, assess content type and urgency, and notify users in real time. Key features include: 1) smart sensing hardware with embedded control logic, 2) machine learning-based mail classification and prioritization, 3) prompt engineering to extract essential text via LLMs, and 4) mobile App and web interfaces with real-time alerts, history logs, and calendar reminders. Field experiments confirm the system's reliability and demonstrate accurate classification performance in real-world deployment scenarios.

WiFi-based indoor positioning has been extensively studied. A fundamental issue in such solutions is the collection of WiFi fingerprints. However, due to real-world constraints, collecting complete fingerprints at all intended locations is sometimes prohibited. This work considers the WiFi fingerprint inpainting problem. This problem differs from typical image/video inpainting problems in several aspects. Unlike RGB images, WiFi field maps come in any shape, and signal data may follow certain distributions. Therefore, it is difficult to forcefully fit them into a fixed-dimensional matrix, as done with processing images in RGB format. As soon as a map is changed, it also becomes difficult to adapt it to the same model due to scale issues. Furthermore, such models are significantly constrained in situations requiring outward inpainting. Fortunately, the spatial relationships of WiFi signals and the rich information provided among channels offer ample opportunities for this generative model to accomplish inpainting. Therefore, we designed this model to not only retain the characteristic of regression models in generating fingerprints of arbitrary shapes but also to accommodate the observational outcomes from densely deployed APs. This work makes two major contributions. Firstly, we delineate the distinctions between this problem and image inpainting, highlighting potential avenues for research. Secondly, we introduce novel generative inpainting models aimed at capturing both inter-AP and intra-AP correlations while preserving latent information. Additionally, we incorporate a specially designed adversarial discriminator to enhance the quality of inpainting outcomes.

Due to the COVID-19 epidemic, video conferencing has evolved as a new paradigm of communication and teamwork. However, private and personal information can be easily leaked through cameras during video conferencing. This includes leakage of a person's appearance as well as the contents in the background. This paper proposes a novel way of using online low-resolution thermal images as conditions to guide the synthesis of RGB images, bringing a promising solution for real-time video conferencing when privacy leakage is a concern. SPADE-SR [1] (Spatially-Adaptive De-normalization with Self Resampling), a variant of SPADE, is adopted to incorporate the spatial property of a thermal heatmap and the non-thermal property of a normal, privacy-free pre-recorded RGB image provided in a form of latent code. We create a PAIR-LRT-Human (LRT = Low-Resolution Thermal) dataset to validate our claims. The result enables a convenient way of video conferencing where users no longer need to groom themselves and tidy up backgrounds for a short meeting. Additionally, it allows a user to switch to a different appearance and background during a conference.

Generatinga detailed 4D medical image usually accompanies with prolonged examination time and increased radiation exposure risk. Modern deep learning solutions have exploited interpolation mechanisms to generate a complete 4D image with fewer 3D volumes. However, existing solutions focus more on 2D-slice information, thus missing the changes on the z-axis. This article tackles the 4D cardiac and lung image interpolation problem by synthesizing 3D volumes directly. Although heart and lung only account for a fraction of chest, they constantly undergo periodical motions of varying magnitudes in contrast to the rest of the chest volume, which is more stationary. This poses big challenges to existing models. In order to handle various magnitudes of motions, we propose a Multi-Pyramid Voxel Flows (MPVF) model that takes multiple multi-scale voxel flows into account. This renders our generation network rich information during interpolation, both globally and regionally. Focusing on periodic medical imaging, MPVF takes the maximal and the minimal phases of an organ motion cycle as inputs and can restore a 3D volume at any time point in between. MPVF is featured by a Bilateral Voxel Flow (BVF) module for generating multi-pyramid voxel flows in an unsupervised manner and a Pyramid Fusion (PyFu) module for fusing multiple pyramids of 3D volumes. The model is validated to outperform the state-of-the-art model in several indices with significantly less synthesis time.

Although indoor positioning has long been investigated by various means, its accuracy remains concern. Several recent studies have applied machine learning algorithms to explore wireless fidelity (WiFi)-based positioning. In this paper, we propose a novel deep learning model which concatenates an auto-encoder with a long short term memory (LSTM) network for the purpose of WiFi fingerprint positioning. We first employ an auto-encoder to extract representative latent codes of fingerprints. Such an extraction is proven to be more reliable than simply using a deep neural network to extract representative features since a latent code can be reverted back to its original input. Then, a sequence of latent codes are injected into an LSTM network to identify location. To assess the accuracy and effectiveness of our model, we perform extensive real-life experiments.

A highly efficient vehicle counting approach based on timespatial images with deep learning is proposed in this paper. Most vehicle counting solutions are based on frame-by frame object detection and tracking to calculate the number of cars that cross a counting line. However, these approaches incur a great deal of redundancy because they track vehicles in a large area though it matters only when vehicles cross the counting line. In this work, we use time-spatial images to focus only on the information happening along the counting lines, instead of whole images, to reduce redundancy. Due to the nature of time-spatial images, vehicle counting can be achieved by object detection in such images without frame-by-frame tracking. We propose Foreground Favorable Model to conquer occlusion, congestion, and lighting change problems and Cross-Image Object Linking to conquer the distortion problem of nearly static vehicles. We also present an automatic time-spatial image dataset generation flow and the first time-spatial image dataset, called DRIVE-TSI, for vehicle counting tasks. Our vehicle counting accuracy beats state-of-the-art solutions in accuracy and is proved to be much more efficient because it only focuses on a small number of pixels. Our model achieves a 97.95% counting accuracy at 2.91 ms per frame in day time urban scenarios.

With the maturity of 5G networks and V2X technologies, utilizing V2X to increase driving safety becomes feasible. Road side units (RSU) recognize dangerous events and notify the related vehicles using 5G V2X communications. The alert latency between RSUs and related vehicles may cause car accidents. Besides, the V2X emergency alarms are broadcasted by dipole antennas. Many unrelated vehicles will receive unnecessary alerts and distract drivers. This paper proposes an accurate vehicle-to-vehicle instant alert system (V2V-IAS) to detect potential dangerous events and alert target vehicles by directional antennas to reduce transmission delay and interference. In V2V-IAS, a vehicle can fuse camera information and cooperative awareness messages (CAM) messages from surrounding vehicles carried to predict dangerous events without relying on RSUs. Once an event is detected, the vehicle will send an alert to specific cars in real time using directional antennas to increase the transmission success rate and reduce disturbing unrelated vehicles. We also use two sets of Qualcomm MDM9150 to conduct real road tests and simulate multi-vehicle environments on Matlab to validate our idea. From experiments, directional antennas can reduce the possibility of packet collisions up to 21% and transmit at the same distance with less output power compared to dipole antennas.

Sensing data fusion is one of the most important technologies in autonomous driving. Its performance depends on advance communication technology. Cellular-Vehicle to Everything (C-V2X) initially defined as LTE V2X in 3GPP Release 14 is a solution for vehicle communication that includes Vehicle-to-Infrastructure (V2I), Vehicle-to-person (V2P), and Vehicle-to-Vehicle (V2V). Although 4G LTE and 5G provides high-speed transmission, packet loss and delay are still inevitable. Packet loss and delay affect the safety of autonomous driving, especially for the judgment of emergency. In this paper, we compare the accuracy of data fusion under different rate of packet loss and broadcast frequency on the simulated platform CALAR. And we propose a skill to improve accuracy. Experiments show that the proposed skill significantly alleviates the effect of communication packet loss and delay on the accuracy of V2X data fusion.

This paper introduces an innovative model which incorporates vehicle On-Board Unit (OBU) data and roadside video information to provide instant alert messages to drivers. We apply computer vision techniques to perform real-time danger event detection and to identify specific surrounding vehicles that should be alerted. Different from traditional broadcast-based alerting, we propose to send these instant alert messages to the target vehicles by unicast and geocast. To do so, an accurate method is required to analyze the spatial relation of vehicles. Also, to confine our alert messages to only those target vehicles, we rely on roadside cameras and apply a sensor fusion technique that can link a video object with its communication MAC address. Through this innovative idea, we integrate computer vision with 5G networks and enable transmitting instant alerts to precise vehicles without interfering irrelevant vehicles. How to incorporate our system with 3GPP V2X by setting proper transmission parameters is also addressed. To validate our idea, we present four common road danger events and show how our model works. To the best of our knowledge, this is the first work bringing computer vision to instant messaging.

Collecting surrounding vehicles' motion information is one of the key issues for accident prevention and autonomous driving. Although multi-vehicle simulation frameworks are widely provided, We need a platform that enable inter-vehicle V2X communications. In this work, based on the open source simulation platform, CARLA, we extend and implement several modules to build a V2X simulation framework. In the proposed framework, vehicles are allowed to share their profiles and sensory data through V2X communications. With the motion information of other vehicles, a car can thus make more intelligent decisions. To validate the effectiveness of the framework, we run simulations in variose scenarios. Each time, a primary vehicle is selected and then both its sensory data and received surrounding vehicles' information are output and recorded in a simulated dataset. It is shown that with the dataset and our multi-vehicle data fusion algorithm, the primary vehicle can visually see the driving status of surrounding cars, which can greatly help a vehicle to choose a better driving strategy. This work not only proposes a V2X communication-enabled multi-vehicle simulation framework based on CARLA, but also provides a low cost way to generate simulated V2X datasets.

This paper is to explore how to enhance the benefits of mobile sink data collection in the wireless sensor network (WSN). Mobile sink moves along a scheduled route to collect data. A shorten traveling salesman problem (TSP) route is explored in this study. Along the resultant route, energy efficiency is achieved for the mobile sink to collect data in a round. Finally, experimental results show that the total energy consumption of mobile sink consumed including the sensor communication and the sink movement distances compared to the existing work can be minimized.

The 3GPP Narrowband Internet of Thing (NB-IoT) is the promising technology that can provide multiple types of resource unit (RU) with a special repetition mechanism to improve the scheduling flexibility and transmission reliability. Since the IoT devices need to operate for a very long time, the energy consumption becomes a critical issue. In this paper, we study how to guarantee the quality of service (QoS) while minimizing the energy consumption for IoT devices. We first model the problem and then propose an energy-efficient scheme, which consists of two stages. The first stage tries to incur the lowest energy consumption of devices and satisfy their QoS requirement. The second stage determines the scheduling order to ensure the delay constraint while maintaining energy efficiency. Simulation results show that our scheme can serve more devices while saving their energy.

For next generation mobile communications, Cloud-RAN (C-RAN) is an emerging network architecture to provide broadband services. C-RAN separates computation entities, i.e., Baseband Units (BBUs), from base stations (BSs) and puts BBUs in a cloud located in a centralized network. With C-RAN, UEs can receive data from multiple collaborative cells and thus can leverage the dynamic point selection (DPS) technology to improve network efficiency. When user equipments (UEs) enter a hotspot and can not be served due to congestion, data offloading from the hotspot to its neighboring cells may take place to balance heterogeneous cells' loads. This work shows how to integrate such DPS offloading with the Discontinuous Reception (DRX) mechanism, which allows UEs to turn off their radio interfaces in a periodical manner. We address the resource allocation problem in heterogeneously-loaded C-RAN by optimizing UEs' energy consumption based on DRX while reserving sufficient bandwidths for UEs considering their quality-of-service (QoS) through DPS. We propose an offloading-based DPS scheduling scheme by exploiting not only maximal instantaneous throughputs but also minimal energy cost. Simulation results show that our scheme can improve throughput, resource utilization, and energy consumption as compared to existing schemes.

For 5G wireless communications, the 3GPP Narrowband Internet of Things (NB-IoT) is one of the most promising technologies, which provides multiple types of resource unit (RU) with a special repetition mechanism to improve the scheduling flexibility and enhance the coverage and transmission reliability. Besides, NB‐IoT supports different operation modes to reuse the spectrum of LTE and GSM, which can make use of bandwidth more efficiently. The IoT application grows rapidly; however, those massive IoT devices need to operate for a very long time. Thus, the energy consumption becomes a critical issue. Therefore, NB‐IoT provides discontinuous reception operation to save devices’ energy. But, how to further reduce the transmission energy while ensuring the required ultra‐reliability is still an open issue. In this paper, we study how to guarantee the reliable communication and satisfy the quality of service (QoS) while minimizing the energy consumption for IoT devices. We first model the problem as an optimization problem and prove it to be NP‐complete. Then, we propose an energy‐efficient, ultra‐reliable, and low‐complexity scheme, which consists of two phases. The first phase tries to optimize the default transmit configurations of devices which incur the lowest energy consumption and satisfy the QoS requirement. The second phase leverages a weighting strategy to balance the emergency and inflexibility for determining the scheduling order to ensure the delay constraint while maintaining energy efficiency. Extensive simulation results show that our scheme can serve more devices with guaranteed QoS while saving their energy effectively.

Machine-to-machine (M2M) communication is one of the key technologies to realize Internet of Things (IoT). Since IoT applications are mainly for smart sensing, such as metering, home surveillance, disaster detection, and e-health, their special sensing/uploading behaviors will result in periodic and/or event-driven small data transmissions, which may potentially decrease the radio resource efficiency. On the other hand, the widespread deployment of IoT raises the concurrent massive connectivity of IoT devices. How to solve these two problems is a critical issue. In this paper, we investigate an uplink resource allocation problem which considers the periodic, event-driven, and query-based IoT traffic behaviors over LTE-M. The proposed approach takes advantage of data aggregation and both spatial and temporal reuse. Our solution exploits long-term static scheduling for periodic data to ensure the latency and data rate, and employs short-term dynamic scheduling for event-driven, query-based data to improve transmission efficiency. Therefore, both small data and massive connectivity problems are relieved. Extensive simulation results show that the proposed scheme can improve resource efficiency and enlarge network capacity effectively.

For next generation wireless communications, Cloud-RAN (C-RAN) has become an emerging network architecture of mobile communications, which separates computation entities-Baseband Units (BBUs) from original base stations (BSs), and puts BBUs in the cloud then forms a centralized network architecture. With C-RAN architecture, user equipments (UEs) can receive data from multiple collaborative cells and thus can leverage dynamic point selection (DPS) technology to improve network efficiency. Note that since the UEs are powered by batteries, energy saving is always a critical issue under C-RAN architecture. In current standard of 3GPP LTE-A, it has defined Discontinuous Reception (DRX) mechanism to allow UEs to turn off their radio interfaces and go to sleep to save energy. However, how to save UEs' energy under DPS in C-RAN is still an open issue. Therefore, this paper addresses the resource allocation problem by asking how to optimize the energy conservation of UEs based on DRX while serving UEs as more as possible under the consideration of UEs' quality of service (QoS) in C-RAN with DPS. To solve this problem, we propose an energy-efficient DPS (EE-DPS) scheduling scheme. The key idea of our scheme is to serve the UEs in the intersection of cells continuously and allocate resource tightly to avoid additional wake-up intervals. Extensive simulation results show that our scheme can serve most number of UEs while achieving high throughput as well as lower energy consumption, compared to the existing schemes.

IoT (Internet of Things) and M2M (machine to machine) have attracted a lot of attention since more and more devices are expected to connect to the Internet for special purposes such as environment monitoring, home automation, industrial surveillance, and e-Health care. Currently, the OM2M (Open source platform for M2M communication) is a promising project which implements oneM2M and SmartM2M standards as an open-source platform for integrating various M2M services, applications, and devices. However, the individual data generated from those IoT/M2M devices is usually quite small, which incurs a lot of control overhead and thus decreases network performance significantly. Therefore, in this work we design and implement an OM2M 'plugin' that can aggregate small IoT data effectively. We will show how the plugin works and verify the effectiveness on the network bandwidth in this demonstration.

For next-generation wireless communications, the 3GPP Long Term Evolution-Advanced (LTE-A) is the most promising technology which provides transmission rate up to 1 Gbps and supports various broadband multimedia services, such as IPTV and Voice/Video-over-IP services. To reduce the energy consumption of user equipments (UEs), the LTE-A standard defines the Discontinuous Reception Mechanism (DRX) to allow UEs to turn off their radio interfaces and go to sleep when no data needs to be received. However, how to optimally configure DRX for UEs is still left as an open issue. In this paper, we address the DRX optimization problem for multicast services. This problem asks how to guarantee the quality of service (QoS) of the multicast streams under the Evolved Node B (eNB) while minimizing the UEs' wake-uptime. We prove this problem to be NP-complete and propose an energy-efficient heuristic. This heuristic consists of two phases. The first phase tries to aggregate the required bandwidth of the multicast streams for UEs to reduce their wake-up periods. The second phase further minimizes UEs' unnecessary wake-up periods by optimizing their DRX configurations. Extensive simulation results show that our scheduling is close to the optimum in most cases.

For M2M (Machine-to-Machine) machines in cellular networks, employing high transmission rates or transmitting in large power actually cost them much energy. This is harmful to the machines, especially they are operated by batteries. The Relay Node (RN) in Long-Term Evolution-Advanced (LTE-A) networks is used to enhance the coverage of high data rate and solve the coverage hole problem. Considering the limited energy nature of machines, connecting to the RN instead of the BS is a better choice for cell-edge machines. In this paper, we consider an uplink resource and power allocation problem for energy conservation in LTE-A relay networks. The objective is to minimize the total energy consumption of machines while guarantee their quality of service (QoS). We prove this uplink resource and power allocation problem to be NP-complete and develop an energy-conserved resource and power allocation method to solve the problem. Simulation results show that our algorithm can effectively reduce the energy consumption of machines and guarantee their required service qualities.

Most of the recent research on Object Tracking Sensor Networks has focused on collecting all data from the entire sensor network and placing it into the sink, which delivers the predicted locations to the corresponding nodes in order to predict an object's movement. This collection method affects the freshness of the data and creates latency in predicting movement patterns. In addition, due to the great amount of packets being sent and received, the sensor nodes' energy is quickly exhausted. Although this data collection method might result in a higher accuracy rate for prediction, it does not extend the lifetime of the sensor network. In this paper, a distributed method is proposed in using the network structure of convex polygons. These polygons are cooperated to find the trajectories of an object and then these trajectories are used to predict objects' movement. The proposed method, based on Trajectory-tree Construction, should reduce both the storage space of collected trajectories and the time spent on trajectory prediction analysis. Simulations show that the proposed method can reduce the energy consumption of the nodes and can extend the lifetime of the network in efficient.

With the design of data communications in mind, 3GPP LTE-Advanced is probably the most promising technology for next generation mobile communications. For mobile applications, continuous communications at the user equipments (UEs) over a long period of time, imposing stringent requirements on power saving. To manage power consumption, 3GPP LTE-Advanced has defined the Discontinuous Reception (DRX) mechanism to allow UEs to turn off their radio interfaces and go to sleep in various patterns. Existing literature has paid much attention to evaluate the performance of DRX; however, how to tune DRX parameters to optimize energy cost is still left open. This paper addresses the optimization problem of the DRX mechanism, by asking how to minimize the wake-up periods of the UEs while guarantee their QoS, especially on the aspects of traffic bit-rate, packet delay, and packet loss rate in mobile applications. Efficient schemes to optimize DRX parameters and schedule UEs' packets at the evolved Node B (eNB) are proposed. The key idea of these schemes is to analyze and balance the impacts between QoS parameters and DRX configurations. Simulation results show that our scheme can fully satisfy QoS requirements of the UEs while save considerable energy, compared to the existing schemes.

Power saving for mobile stations (MSs) is one of the most critical issues in IEEE 802.16j relay networks. To reduce power consumption of MSs, IEEE 802.16j borrows the sleep mode of 802.16e, but new parameters are introduced. Up to now, no previous work has addressed the sleep scheduling problem in IEEE 802.16j networks. Therefore, this paper proposes an energy-efficient, standard-compliant sleep scheduling scheme which involves relay stations (RSs) and realizes spatial reuse on RS transmissions to minimize energy consumption of MSs while guaranteeing their QoS. The main idea of the proposed scheme is to interleave the sleep patterns of MSs and exploit spatial reuse on MS-RS transmissions to reduce resource consumption while enlarging the available frame space. Comprehensive simulation has been conducted to verify the effeteness of our scheduling scheme. It shows that our scheme can serve more requests of MSs while increasing their sleep ratios.

The 3GPP LTE-A (Long Term Evolution-Advanced) is the most promising technology for next-generation wireless communications. It provides high transmission rate up to 1 Gbps and supports plentiful multimedia services, especially for those bandwidth required multicast type of services, such as IPTV and Voice/Video-over-IP services. However, when users activate more services at their user equipments (UEs), more energy is consumed. To save UEs' energy, the LTE-A standard defines the Discontinuous Reception Mechanism (DRX) to allow UEs turning off their radio interfaces and going to sleep to save energy when no data needs to be received. But, how to optimize DRX configurations for UEs is still left as an open issue. In this paper, we address the DRX optimization problem for multicast services, which asks how to guarantee the quality of service (QoS) of the multicast streams while minimize UEs' wake-up time. We propose an energy-efficient scheme to tackle this problem. The scheme tries to arrange the best multicast data reception orders to reduces UEs' wake-up periods while consider the resource collision avoidance. Simulation results show that the performance of the proposed scheme is effective even if the network is under saturated condition.

Among all the wireless localization techniques, the Wi-Fi pattern-matching scheme is one of the most widely used approaches, which estimates the user's location by comparing his/her device's received signal strength (RSS) against a pre-trained radio map on the fly. The pattern-matching solutions have an inherent drawback: the expensive calibration operation of war-driving. In order to reduce the calibration operation cost of war-driving, many solutions based on the community approach have been proposed [1]-[3], which collect the Wi-Fi training data from users' contributions. However, most works only consider the Wi-Fi patterns. For the heterogeneous training data, such as a radio map combined signals from both Wi-Fi and cellular networks, a method shows how to collect and exploit the data through an ongoing way is still missing. In this paper, our objective is to grow a heterogeneous radio map from barren to beautiful over a large area, such as a regional area or a national area. Based on the concept of community approach, we propose a geography-based method to combine the cellular and the Wi-Fi radio maps. We believe that our framework can provide a valuable solution for pattern-matching localization which shows how to effectively build a radio map and quickly estimate the user's location with acceptable distance errors.

In IEEE 802.16, power management at the Mobile Subscriber Station (MSS) side is always an important issue. The standard defines three types of power saving classes (PSCs). A PSC can bind one or multiple traffic flows. However, given multiple flows in an MSS, the standard does not define how to form PSCs, how to organize the cooperation of multiple PSCs to obtain better energy efficiency, and how to guarantee QoS of these flows. Given a set of flows and their QoS parameters, the objective of this paper is to define multiple PSCs and their listen-and-sleep-related parameters and packet-scheduling policy such that the unavailability intervals of the MSS can be maximized and the QoS of each flow can be guaranteed. To achieve this, we propose a novel fold-and-demultiplex method for an IEEE 802.16 network with PSCs of types I and II together with an earliest-next-bandwidth-first packet scheduler. Given a set of traffic flows in an MSS, the fold-and-demultiplex method first gives each flow a tentative PSC satisfying its bandwidth requirement. Then we fold them together into one long series so as to calculate the total bandwidth requirement. Finally, we demultiplex the series into multiple PSCs, each supporting one or multiple flows. It ends up with high energy efficiency of MSSs while meets flows' bandwidth requirements. Furthermore, our packet scheduler ensures that real-time flows' delay constraints can be met. To the best of our knowledge, this is the first result offering bounded packet delays under MSS's sleep-and-listen behaviors.

Power management is one of the most important issues in IEEE 802.16e wireless networks. In the standard, it defines three types of power saving classes (PSCs) for flows with different QoS characteristics. It allows a mobile device to turn off its wireless radio when all its PSCs are in sleep states. In this paper, we consider the scheduling of power saving classes of type II in an IEEE 802.16e network with a BS and multiple MSSs (mobile subscriber stations). Previous work proposes to enforce all MSSs to have the same sleep cycle, thus leading to higher energy cost for those MSSs with less strict delay bounds. We observe that if the sleep cycles of MSSs can be assigned according to their delay bounds, MSSs can significantly reduce their duty cycles. We propose an efficient tank-filling algorithm, which is standard-compliant and can allocate resources to MSSs according to their QoS characteristics with the least number of active frames. Simulation results verify that our algorithm incurs less power consumption and leads to higher bandwidth utilization than the previous schemes.

Power Saving Class (PSC) is an essential issue on IEEE 802.16--2009. In previous research, many algorithms had been proposed to reduce the consumption of power, but most of them only considered multiple connections in a Mobile Subscriber Station (MSS); in fact, it does not fit in with the situation of real world. On the contrary, others proposed algorithms considering the situation of multiple MSSs with multiple connections; nevertheless, it is difficult to increase the amount of MSSs. In this paper, we propose an efficient algorithm, which refers to both categories and avoids state transitions. When packet size is much smaller or delay bound is more loosening, the result shows that our scheduling algorithm can serve almost double multiple MSSs with multiple connections and still maintain high sleep ratio for energy efficiency.

By introducing the relay capability, the IEEE 802.16j standard is developed to improve the WiMAX performance. Under the transparent mode, existing studies aim at improving network throughput by increasing the transmission rates of mobile stations (MSs). However, we show that using higher rates will let MSs consume more energy. In the paper, we define an energy-conserved uplink resource allocation (EURA) problem in 802.16j networks under the transparent mode, which asks how to arrange the uplink resource to 1) satisfy MSs' requests and 2) minimize their energy consumption. Objective 1 is necessary while objective 2 should be achieved when objective 1 is met. The above bi-objective problem is especially important when the network is non-saturated. The EURA problem is NP-hard and we propose a heuristic with two key designs. First, we exploit relay stations to allow more concurrent uplink transmissions to fully use the frame space. Second, we reduce MSs' transmission powers by adjusting their rates and paths. Simulation results show that our heuristic can save up to 80% of MSs' energy as compared with existing work.

This work addresses the mini-slot scheduling problem in IEEE 802.16d wireless mesh networks (WMNs). A practical mini-slot scheduling needs to take into account following issues: the transmission overhead, the scheduling complexity, and the signaling overhead to notify the scheduling results to subscriber stations. We focus in a grid-based WMN, which is the most recommended topology due to its high capacity and connectivity. In this paper, we propose scheduling schemes featured by low complexity and low signaling overhead. The proposed schemes help find periodical and regular schedules, which can balance between transmission overhead and pipeline efficiency. They can achieve near-optimal transmission latencies. Simulation results show that our schemes outperform other schemes, especially when the network size is larger.

Power saving is a critical issue in the IEEE 802.16e broadband wireless networks. In order to reduce the power consumption, the IEEE 802.16e provides three types of power saving classes (PSCs) for the Mobile Subscriber Stations (MSSs), where each PSC may contain one or several connections. However, how to define a PSC and assign PSCs to connections are open issues in the standard. Focusing on real-time traffics, a single MSS and base station (BS) pair, previous studies use a single PSC of type II to manage the awake and sleep of the MSS. In this paper, two power saving class management algorithms based on multiple PSCs in the IEEE 802.16e are proposed. These two schemes define PSCs for real-time connections by referring to the delay bound and packet interarrival time of connections. Simulation results demonstrate that our schemes outperform the previous single PSC approach in both energy efficiency and resource utilization.

In wireless data transmission, the capacity of wireless links is typically limited. Since many applications exhibit temporal locality for data access, the cache mechanism can be built in a wireless terminal to effectively reduce the data access time. This paper studies the cache performance of the wireless terminal by considering a business-card application. We investigate the least-recently used replacement policy and two strongly consistent data access algorithms called poll-each-read and callback. An analytic model is proposed to derive the effective hit ratio of data access, which is used to validate against simulation experiments. Our study reports how the data access rate and the data update distribution affect the cache performance in a wireless terminal.