Handwritten signature verification has emerged as the predominant authentication method for paper-based transactions in critical sectors including financial services, legal documentation, and banking operations. While offline approaches utilizing static signature images remain vulnerable to forgery attacks, online methods demonstrate greater reliability by incorporating dynamic biometric features captured during the signing process. The existing methods primarily capture the signature trajectory with built-in sensors of wearable devices, which are constrained by dominant-hand limitations, resulting in poor usability. Besides, relying solely on single-dimensional features renders the system vulnerable. Therefore, in this article, we propose a signature verification prototype FDsign, which performs feature fusion of pressure sensor and IMU sensors that are embedded in a smart pen to accurately capture users’ multi-dimensional signature personality information, which greatly improves the system security. To address the challenges of multi-modal signal fusion, few-shot learning and recognition accuracy, FDsign proposes DiffRNN-ClassNet, a framework that integrates a diffusion model with Bi-LSTM. Extensive experiments validate that FDsign achieves 97.1% authentication accuracy across diverse signing environments while maintaining security in diverse operational scenarios.

Serverless edge computing seamlessly integrates edge computing with serverless computing, not only overcoming the limitations of resource-constrained edge nodes but also alleviating the high latency associated with cloud response. Due to the elastic scalability of serverless computing platforms, the cold start of latency-sensitive serverless functions (SFs) has become a significant challenge. Traditional strategies, such as resource reservation and prewarming, often suffer from low resource utilization. Meanwhile, offloading-based approaches simplify the problem by assuming a fixed high cold start delay cost, which is unsuitable for heterogeneous serverless edge computing scenarios. This paper proposes a Cold-Start aware offloading by double-dueling-DQN (CSODQN) model for SFs in a cloud-edge-device serverless computing system. The model creates an instance warming pool for SFs to enable reuse and allocates edge service node resources based on the priority of user and SFs, achieving multi-objective offloading optimization that considers cold starts. Our goal is to balance the frequency of cold start and resource utilization. To address the partially observable offloading optimization problem among agents, we employ a multi-agent deep reinforcement learning approach. By introducing an priority of action based sampling strategy, we accelerate the convergence of learning for each agent. Simulation results demonstrate that our method improves task success rates, reduces average task latency and cold start occurrences, and enhances resource utilization. Our approach alleviates the frequency of cold starts without excessively consuming system resources and costs, achieving long-term optimization of service quality, device energy consumption, and expenses.

Blood pressure is a critical indicator of human health, making convenient and easy-to-use monitoring methods a significant research focus. Existing blood pressure monitoring techniques often rely on additional sensors or devices, imposing both usage and economic burdens on users. To tackle this problem, we propose MetaBP, a blood pressure monitoring system that leverages the camera on smart devices. The basic idea is to record a video when the fingertip covers the camera to capture the tiny skin color changes caused by the heartbeat and extract pulse signals from the video frames. Furthermore, MetaBP employs a meta-learning approach, enabling the model to quickly adapt to the blood pressure monitoring task with minimal data and develop personalized models. By leveraging meta-learning, the model can efficiently learn the unique features of an individual’s physiological signals, providing accurate and personalized blood pressure estimation even with limited training samples. To obtain accurate measurements, we use a variational mode decomposition (VMD) method to reduce signal noise, ensuring that the pulse signals are both reliable and precise. To evaluate the robustness of MetaBP, we conduct experiments with 30 participants and implement MetaBP on commercial devices with camera parameters. The results demonstrate that MetaBP can accurately estimate systolic and diastolic blood pressure, with mean errors of 1.37 and 0.82 mmHg and standard deviations of 7.39 and 5.83 mmHg. Furthermore, our results demonstrate the feasibility of using widely available smart device cameras for reliable blood pressure monitoring, offering a practical solution for continuous health tracking.

The integration of biometric-based user authentication into wearable devices has become increasingly important for protecting users' private information and property. In this paper, we propose a two-factor authentication mechanism, PressHeart, which utilizes widely-used Photoplethysmography (PPG) sensors embedded in wearable devices. Our observations reveal that PPG sensors can implicitly measure excitation press signals when the users press the skin or the device, which implies individual wearing and behavioral habits that can serve as reliable factors for user authentication. For better separating the press signals from PPG signals and extracting sufficient signals for user authentication, we introduce two adaptive segmentation methods and a specific feature set for feature extraction in PressHeart. To validate the performance of Press Heart, we develop a prototype with a PPG sensor and conduct experiments involving 14 participants. The experiment results demonstrate that PressHeart can achieve an average of 94.9 % accuracy with high authentication efficiency and security.

Unmanned surface vessel (USV) has been widely used in various fields due to its autonomous advantages, and path planning is a crucial technology for autonomy. However, using global path planning alone cannot avoid moving obstacles, while using local path planning alone may lead to falling into local minima and fail to reach the target. Therefore, this article proposed the dynamic target artificial potential field (DTAPF) method which use a dynamic point that follows the global path generated by the A* algorithm as the target point of the artificial potential field (APF). In addition, in order to improve response time and safety of unmanned surface vessel (USV) navigation of the traditional centralized path planning methods, we proposed an edge computing architecture for global path planning and an offset guidance method to avoid moving obstacles while confirming to the collision regulations (CORLEGs). The experimental results show that, using the method proposed in this article, USV can reach the target in an environment with moving obstacles with high probability (about 99.4%), and compared to the traditional APF algorithm, our method can reduce collision probability by 71% with almost no increase in average path length and average navigation time. Besides, our architecture has much lower computing delay than local computing, and also lower than cloud computing.

In recent years, with the widening applications of the Internet of Things (IoT), more and more perception services (e.g., air quality indicator services, road traffic congestion monitoring services, etc) with different arguments (e.g., data type, source location, creator, etc) will be deployed by dedicated IT infrastructure service providers for constructing customized IoT systems with low cost by subscription. So it is an indispensable step to check whether the required perception services with specified arguments have been available for the constructing IoT through discovery method to reduce the redundancy of service deployment. However, it is a challenging problem to design efficient (i.e., achieving high accuracy and low response delay with low overhead), highly robust, and trustworthy mechanisms for discovering perception services on resource-constrained IoT devices. To solve this problem, we proposed a distributed service discovery method, named VSA-SD, based on the Vector Symbolic Architecture (VSA). This method employs hyperdimensional vectors to describe services in a distributed manner, and measures the degree of service matching by calculating the Hamming distance, thereby achieving service discovery. We implemented VSA-SD in NBUFlow, which is an IoT task construction and offloading test platform, and evaluated its performance through comprehensive experiments. Results show that VSA-SD outperforms the centralized, hybrid, and other distributed service discovery mechanisms in terms of accuracy, response delay, overhead, robustness, trustability, interoperability, and mobility.

In recent years, wireless networks and applications have grown rapidly and converged across a wide variety of scenarios. More and more applications require wireless networks for high bandwidth and low latency. However, due to the attenuated propagation of wireless signals, bandwidth changes rapidly in a short period. TCP fails to work properly in such an environment and suffers from low network link utilization and high latency. To solve above problems, this paper proposes a receiver-driven congestion control framework, named NUiVa. NUiVa decouples the congestion avoidance phase of sender side congestion control and implements it on the receiver side. In addition, NUiVa uses one-way delay to detect network congestion and controls the sending rate of senders via the receiving window field in the packet header. We confirm that the throughput degradation caused by network flips can be mitigated by NUiVa. And the throughput of data transmission can be further improved by the design of the receiver’s algorithm. The evaluation results show that NUiVa improves the throughput of TCP stream by 10 to 23 percent in most cases and reduces the queuing delay by an average of 29 percent.

Verifying the user identity of wearable devices is crucial for system security, especially before sensitive operations like making financial payments. A PPG-based two-factor authentication can be a promising solution with widely deployed PPG (Photoplethysmography) sensors within wearable devices. Our observations find PPG readings reveal a significant relevance to the user’s hand motions, i.e., gestures, while the user’s heartbeat characteristics and wearing habits are also implicitly related, which can be utilized for user authentication. In this paper, we design G-PPG, a gesture-related PPG-based two-factor authentication mechanism that can non-intrusively validate the user’s identity. In G-PPG, gesture detection and segmentation and a specific feature set are proposed for accurate gesture-related PPG characteristic extraction. Moreover, an adaptive update scheme is proposed for the high accuracy of long-term authentication. Our experiments among 15 participants demonstrate that G-PPG can achieve a 90% accuracy in the long-term study.

Currently, indoor localization technology and indoor location-based services are becoming increasingly important in the area of mobile and ubiquitous computing. However, the design of an indoor location-based system confronts two challenges: 1) achieving high-precision location recognition and 2) identifying what indoor objects actually are (which is called semantic labeling). In this article, we propose AtLAS, an activity-based indoor localization and semantic labeling mechanism. The key idea is that some objects in an indoor environment, such as doors and toilets, determine predictable human behaviors in small areas, which can be reflected in unique sensor readings. AtLAS leverages this idea to determine a user's accurate location by identifying users' activities. Furthermore, we leverage the topological structure of indoor objects to mine the semantic knowledge and label the objects through gained knowledge automatically. To the best of our knowledge, AtLAS is the first attempt to build a system that leverages users' activities to conduct a high-precision indoor localization and semantic labeling system for the case of residences. The experimental results show that AtLAS can achieve a median localization accuracy of 0.57 m, and the system can localize the landmarks with a median accuracy of 0.43 m on average without 5% worst errors. AtLAS can label the objects semantically with a 5.7% false-positive rate and a 5.8% false-negative rate on average.

Internet of Things (IoT) systems are usually built with entity services, which are those abstracting functionalities of sensing and executing devices in the physical space. As requirements of sensing or controlling the physical space can be varied with different systems, entity services are supposed to be easily adapted to meet such dynamicity. To ease updating and modification of entity services, although a software-defined network approach has been applied in building IoT systems, entity services developed with the same software architecture as traditional services on the Internet have an inherited problem in adaptability. In order to solve the problem, we abstract the functionalities of an entity service in social, cyber, and physical spaces into application model, sense-execute model, and physical model, respectively, and propose a physical model driven software architecture (PMDA) for guiding design of entity services. To ease development of entity services, we also propose a formal development method of entity services (fDES) to transform the abstracted models of PMDA into implementable software modules. Besides, to reduce maintenance cost of entity services when adapting them to different requirements from the social space, we propose a formal maintenance method of entity service (fMES). The correctness of fDES and fMES is verified by a case study, and their effectiveness in reducing cost of developing and maintaining IoT systems composed of large-scale frequently changed entity services is proved by analysis.

Mobile Broad Band (MBB) networks and Software-Defined Networking (SDN) are expected to strongly characterize the future evolution of global communications envisioned by the Fifth Generation mobile networks (5G). Although SDN has seen adoption and wide experimentation in data-center networks, its benefits and challenges in MBB has not received comparable coverage. In this work we experiment with a state-of-art SDN-based approach for passive monitoring available bandwidth and throughput with an OpenFlow switch in the mobile node. We evaluate the approach on a real-world commercial 4G network (leveraging the MONROE platform), considering two deployments (with an SDN controller local to the mobile node, and a remote one, whose control messages traverse the radio access network) and compare the results of the experiments against analogous deployments in a fully-wired testbed. For both the local and remote deployments, different polling periods, in different traffic conditions, are considered. Results show that, while further research is needed to investigate the variability of the relative error (standard deviation ranges between 1.21 and 8.65% in the worst case), its mean is very low, confirming the feasibility of the proposed estimation approach.

The high-throughput, low-latency, and reliable data delivery are fundamental demands of many networked applications, e.g. BitTorrent and Skype. But the inappropriate congestion control of TCPs, caused by the reactive and coarse- grained congestion feedbacks, brings the low link utilization, high queuing delay and frequent packet loss in high bandwidth-delay product network. To mitigate this issue, TCP variants have been developed. Thereinto, the load factor based congestion control (LFCC), e.g. VCP, BMCC, have shown the powerful capabilities to achieve better performances in terms of high link utilization, low persistent queue length, negligible packet loss, and fairness. However, due to the conservative increase and synchronized feedbacks, LFCC faces the slow convergence of the link utilization and inter-flow fairness. This could incur the large flow completion time of new-coming flows indirectly. To solve the issue of existing LFCCs, an asynchronous congestion control based on hybrid feedbacks, called HFCC, is proposed to achieve the faster convergence while keeping the features of LFCCs in this paper. Specifically, HFCC decreases the congestion window when the bottleneck link is in the high-load region and the flow rate exceeds the fair share of the bottleneck bandwidth, or the bottleneck link is in overload region. Otherwise, HFCC increases the congestion window. Note that an overlay coding method is developed in HFCC. To reduce the flow completion time, HFCC adopts an available bandwidth estimation method to speed up the data delivery in low-load region. The simulation results indicate that HFCC has the better performance and faster convergence than VCP, MLCP, and BMCC.

Multiple-input multiple-output (MIMO) provides high throughput for WiFi networks, but it also leads to high overhead due to channel state information (CSI) feedback. Based on experiment measurements, this paper shows that MIMO has different feedback requirements when the receiver is rotating compared with when the receiver is in other mobility scenarios. Experiments of four popular Android games show that device rotation accounts for around 50% of the running time for these games, which implies that rotation-awareness could improve WiFi efficiency significantly for these games. We propose rotation-aware WiFi (RoFi) channel feedback to eliminate unnecessary CSI feedback while maintaining high throughput. We show the failure of existing mobility-aware methods, including CSI similarity, time-of-flight (ToF), and compression noise, in distinguishing the mobility status of rotation and mobile. RoFi calculates power delay profile (PDP) similarity for rotation detection and performs feedback compression and rate selection accordingly. To deal with false rotation detection and status transition between rotation and static, RoFi uses the power of the strongest path, which is calculated from PDP, to further refine CSI feedback when necessary. The RoFi design is compatible with legacy 802.11 protocols and is easy to be deployed on existing WiFi systems. Evaluation results show that RoFi reduces 25%-40% overhead with negligible signal-to-noise ratio decrease in rotation scenarios. RoFi also consumes 29%-69% less energy compared with state-of-the-art feedback compression and rate selection algorithms.

Concurrent MAC protocols can improve channel usage of wireless sensor networks (WSNs), and provide a high-performance infrastructure for data intensive applications. Most of the existing concurrent MAC protocols are based on proactively constructed physical interference models, i.e., PRR-SINR models (PSM). However, it incurs relatively high bandwidth and energy overheads to construct PSM for WSNs. In this paper, we propose NoPSM, which does not take PSM as base to determine transmission concurrency. Instead, the base of NoPSM is reactively constructed interference relationships by passively analyzing overlapping relationships among time logs of block data transmissions and corresponding reception status of each packet in blocks. In this way, NoPSM has two salient features. First, NoPSM is able to construct interference relationships among nodes quickly and accurately along with block data transmissions without needs of network downtime. Second, based on the constructed interference relationships, NoPSM can make decisions of transmission concurrency with a comprehensive criterion, which not only estimates quality of any active links after initiating a new link, but also estimates throughput improvement gained from concurrent transmissions. NoPSM has been implemented in Tinyos-2.1 and extensively evaluated in TOSSIM. Experimental results show that NoPSM improves system throughput by up to 60 percent compared with a traditional CSMA protocol, which cannot exploit potential transmission concurrency. Moreover, NoPSM can gain up to 55 percent throughput improvement as compared to an existing reactive concurrent MAC.

With rapid development of smart hardwares and networking protocols, more and more IoT sensors are becoming publicly accessible through the Internet. Many semantic enhanced IoT sensors store the captured events in their description files, making the build of a generic IoT search engine possible. Crawling the events captured by these sensors is a fundamental step towards building this IoT search engine. However, this step faces a challenge due to sensors' sleep behavior and limited energy supply. Using traditional web access strategy for IoT application may cause unpredictable latency in receiving events with low power efficiency. In this paper, firstly the issue how to crawl newly captured events from periodically sleeping sensors is formulated as a schedule problem, which can be solved by constrained optimization. We take expected latency as the optimization object, as this indicates whether the wanted events can be gathered by crawlers in time. Then a sleep-aware schedule method, named EasiCrawl, is proposed for achieving near-optimal expected latency in receiving events. Finally, EasiCrawl is evaluated by simulations and a case study with real-world data from Xively. The simulation results show that EasiCrawl has lower latency than the periodic and greedy crawl strategy.

Sensing/actuating ingredients are software running on smart devices which are widely deployed in physical spaces and integrated into our daily life. An Internet of Things (IoT) application can be composed of multiple sets of sensing/actuating ingredients and each ingredient can satisfy specific requirement of users by processing specific physical data collected from physical world or committing an action. However, users' requirements for functionalities of ingredients and overall goals of IoT applications are diverse and may change. The application diversity leads to a rise in complexity as well as the cost of developing and maintaining the system software of an IoT application. In this work, we aim to reduce the design complexity and development cost of IoT application software. To achieve this goal, we use the software architecture PMDA which was proposed in our previous work. Here, we present a component-based formal methodology, namely FMDA, to transform the components of PMDA into sets of software modules as sensing/actuating functionality required. At the same time, we propose a connection mechanism, namely FM-CA, which can match and establish the interconnection between any two sensing/actuating ingredients as required. The behavior of FMDA and FMCA have been investigated. The evaluation results show that FMCA is more effective in reducing the cost if the requirements changing frequently or the failure probability of matching the required sensing/actuating ingredients is small.

With rapid development of the Internet of Things, more and more smart devices are deployed in the physical space.A physical application is composed by several smart devices which provide physical data.The physical applications need appropriate physical information processing systems to process the related data.However, the physical applications are dynamic because of the ever-changing demands in the IoT.So it is necessary to design an evolution mechanism for the dynamic physical applications to find appropriate physical information processing systems.We first analyze the changing types of dynamic physical applications.Then we conclude three relationships between the dynamic physical applications and physical information processing systems.In order to verify the correctness of the evolution mechanism, we use Communication Sequential Process to formalize the evolution mechanism and use Process Analysis Toolkit to verify deadlock-free, divergence-free and nonterminating of the evolution mechanism.

Traditional wireless sensor networks (WSNs) can be integrated into Internet and be regarded as its sensing infrastructure, which supports development and running of multiple third-party applications simultaneously. Therefore, due to constrained resource of sensor nodes, it is necessary to establish a runtime framework to improve sensor sharing efficiency for concurrent third-party applications. This paper presents EasiCAE, a concurrent applications runtime framework, to enhance sensor sharing efficiency greatly by incorporating task allocation with redundancy elimination. In brief, EasiCAE decompose the applications into tasks and distributes tasks to the sensors which will bring the least energy to run them. EasiCAE has three salient features. Firstly, we define task-sensor correlation to indicate how many samplings of a sensor can be shared with the new task. Secondly, EasiCAE reduces energy consumption by assigning tasks to a sensor with higher task-sensor correlation. Finally, a light-weight merging algorithm is proposed to eliminate redundant samplings for the assigned sensors. Experimental results show that EasiCAE reduces energy consumption by 31% to 79% compared with existing methods, while introducing tolerable overheads. We also evaluate EasiCAE with various influencing parameters, showing that the performance of EasiCAE increases stably as the network scale and the number of concurrent applications increases.

It is well known that there are two kinds of causes, namely channel-errors and collisions, which lead to high probability of packet losses and errors in wireless networks. The ability of discriminating the above two causes provides many opportunities for implementing high efficient networking protocols in wireless sensor networks (WSNs). This paper presents EasiPLED, a discriminator that can accurately and timely predict these two causes. EasiPLED has three salient features. First, it investigates F-BER patterns and statistic characteristics of RSSI in different indoor environments through extensive experimental studies. F-BER is the Frame-level Bit Error Rate measured at the receiver side by a coarse-grained method without incurring any overhead. An adaptive RSSI estimator based on error-based filter is proposed to mitigate effects of noise on RSSI readings for successfully received packets. Second, EasiPLED designs an off-line dominant-factor classifier using machine learning method. The classifier takes a combination of F-BER and RSSI features as input and outputs the probability of dominant causes of failed transmissions. Finally, it presents a lightweight on-line discriminator which diagnoses the root cause of a packet loss or error when it occurs at the receiver side. Experimental results show that EasiPLED achieves an accuracy by up to 95.4%. We evaluate the effectiveness of EasiPLED by applying it to link-layer retransmission scheme, which yields a reduction of single-hop transmission delay by up to 47%, and provides high packet delivery ratios as compared to the existing retransmission methods.

This paper focuses on the design of high-throughput MAC for data-intensive sensor networks with unpredictable traffic. We propose Log A, a reactive concurrent MAC protocol that increases transmission concurrency based on time log analysis. Each node reactively and passively learns the interference relationship by analyzing the start transmission time and the end transmission time of packet blocks. Then, the learned interference relationship is exploited to improve the probability of beneficial concurrent transmissions of nodes that are within the interference range of each other. Log A has two salient features. First, it is passive and does not need network downtime to build interference relationship. Second, it is reactive and works only when traffic are generated. Log A has been implemented in Tinyos-2.1 and extensively evaluated in TOSSIM, the simulator of sensor networks. Experimental result shows that Log A outperforms the traditional CSMA protocol and an existing reactive concurrent MAC in terms of throughput, delivery latency, and energy consumption.

Rate adaptation, which adapts transmission bit rate according to current wireless link conditions, is a fundamental mechanism used by link-layer protocols to improve the performance of 802.11 wireless access networks in terms of throughput. However, rate adaptation faces to severe challenges due to more and more congested and dynamic wireless links. In this paper, we design a hybrid rate adaptation scheme, called EasiRA, for 802.11 mobile wireless access networks. It has following three features. First, it combines the sensor-hints and protocol-hints information together to estimate current link status. Second, EasiRA exploits environmental signal strength information obtained by a 802.15.4-based radio to help distinguish the causes of packet losses and adjust the thresholds of the protocol-hints. Finally, EasiRA uses both random and deterministic rate increase or decrease schemes to combat the dynamic and unpredictable characteristics of wireless links. Simulation results show that EasiRA consistently outperforms the existing rate adaptation schemes, namely CARA, Minstrel and RapidSample, particularly in relatively high dynamic scenario.

Traditional Chinese Pulse Diagnosis is a convenient and noninvasive method for disease diagnosis and healthcare. We have designed and implemented a Chinese wrist-pulse retrieval system based on the principle of Traditional Chinese Pulse Diagnosis (TCPD), called EasiCPRS. It is designed to be small in size, low in cost, with flexibility in deployment, and simplicity in operation. The contributions of this work are: 1. The wrist-pulse at "cun, guan and chi"points over the radial artery are obtained by applying a moderate and adjustable taking pressure during wrist-pulse retrieval. 2. A wrist-pulse signal conditioning circuit and a robust external taking pressure control algorithm are designed to overcome low signal-to-noise ratio (SNR). 3. A lightweight algorithm for wrist-pulse feature extraction is achieved on a resource-constrained platform to economize energy and bandwidth.

Nowadays, the harmful blue-green algae blooms on lakes or streams threaten the daily life of millions of people in China. In this paper, we demonstrate the sensor network system we built on Lake Tai for the surveillance and cleanup of the algae blooms which is at work in Wuxi City, Jiangsu Province. We designed the sensor device and algorithm to monitor the algae bloom and estimate the bloom area. When the bloom area goes beyond the threshold, the salvaging boats are automatically dispatched to the scene to clean up the bloom for recycling utilization according to a mechanism in a scalable coordination fashion. The dispatching mechanism includes to consider the locations of salvaging boats, the number of available salvaging boats and the facility status in eight algae harvesting factories around the lake. The system also balances workloads of factories to achieve an overall high working efficiency. We develop a GIS-based management website for the end user to monitor the running of the whole system. All the sensing stations, the real time performance of the algae harvesting factory, the locations of salvaging boats and the automatically generated cleanup schedule are displayed in the map.

Motivated by the highly dynamic topology in wireless sensor networks with asynchronous duty cycle, and its impact on reliable data delivery, we propose a light-weight opportunistic forwarding (LWOF) scheme. Differing from other recently proposed schemes, LWOF neither employs historical network information nor a contention process to select a forwarder prior to data transmission. It takes advantage of the preamble in low power listening (LPL) media access control (MAC) protocols and dual-channel communication to remove the overhead of making a forwarding decision. Along with LWOF, we propose an energy efficient MAC protocol (LWMAC) with a shortened preamble, to exploit the non-deterministic characteristics of opportunistic forwarding. The preamble length in LWMAC is a function of the node density and sleep duration. Simulation results show that LWOF, along with LWMAC, can provide reliable service of data delivery with less energy consumption.

Energy efficient routing and power control techniques in wireless ad hoc networks have drawn considerable research interests recently. In this paper, we address the problem of energy efficient reliable routing for wireless ad hoc networks in the presence of unreliable communication links or devices or lossy wireless link layers by integrating the power control techniques into the energy efficient routing. We consider both the case when the link layer implements a perfect reliability and the case when the reliability is implemented through the transport layer, e.g., TCP. We study the energy efficient unicast and multicast when the links are unreliable. Subsequently, we study how to perform power control (thus, controlling the reliability of each communication link) such that the unicast routings use the least power when the communication links are unreliable, while the power used by multicast is close to optimum. Extensive simulations have been conducted to study the power consumption, the end-to-end delay, and the network throughput of our proposed protocols compared with existing protocols.

In this paper we propose a distributed call admission control protocol (DCAC) to provide bandwidth and delay guaranteed quality of service (QoS) in multi- hop wireless mesh networks, by exploiting the multi-channel multi-radio (mc-mr) feature. We propose a distributed link scheduling algorithm to give the bandwidth with minimal one hop delay, and a routing metric for route setup. To the best of our knowledge, this is the first distributed protocol that embeds (mc-mr) feature into time division medium access (TDMA) to do QoS call admission in wireless mesh networks. Extensive simulations show that our protocol significantly improves network performance on supporting QoS flows compared with some widely used protocols.

Energy efficient routings and power control techniques in wireless networks have drawn considerable research interests recently. In this paper, we address the problem of energy efficient reliable routing in wireless networks in the presence of unreliable communication links or devices or lossy wireless link layers by integrating the power control techniques into the energy efficient routing. We study both the case when the link layer implements a perfect reliability and the case when the reliability is implemented through the transport layer, e.g., TCP. We study the energy efficient unicast when the links are unreliable. Subsequently, we study how to perform power control (thus, controlling the reliability of each communication link) such that the unicast routings use the least power when the communication links are unreliable. We presented both centralized algorithms and distributed algorithms for all the questions we studied. We conducted extensive simulations to study the power consumption, the end-to-end delay, and the network throughput of our protocols compared with existing protocols