High-resolution data streams-such as images, Li-DAR point-clouds-are common in robotic communication, yet Robot Operating System 2 (ROS 2) struggles to transmit these streams due to increased average latency. On lossy wireless links, the default DDS communication stack in ROS 2 suffers significant performance degradation. This paper presents the first comprehensive network-layer analysis of ROS 2’s DDS stack operating over wireless links with large payloads. We analyze three network bottlenecks that emerge during large-payload data transfers and presents DDS-level optimizations for each one. The proposed solutions are exposed through an XML-based QoS configuration interface, allowing them to be easily tuned. Experiments demonstrate that our approach transmits large-payload data successfully while maintaining lower latency than existing methods.

After its initial release in 2007, the robot operating system (ROS) has been widely adopted as an open-source robotics middleware suite. In 2017, ROS 2 is introduced to offer enhanced performance, and it has since become the de facto standard for robot software development. In this paper, we propose an analytical latency model for the data distribution service (DDS) in ROS 2. DDS operates at the application layer on top of the user datagram protocol (UDP). In ROS 2, retransmissions for reliable data delivery are handled at the application layer, resulting in latency characteristics different from those of the transmission control protocol (TCP). We derive a closed-form analytical model to characterize the latency of DDS reliable data delivery in ROS 2, taking into account key parameters such as the packet delivery ratio, the data period, and the heartbeat period in DDS. Our extensive empirical study shows that the proposed model matches well with the empirical data, with an average error of 6.88 % across 35 different scenarios.

The integration of Cyber-Physical Systems (CPS) and AI presents both opportunities and challenges. AI operates on the principle that “good things happen probabilistically,” while CPS adheres to the principle that “all bad things must not happen,” requiring uncertainty-awareness. Furthermore, the difference between AI’s resource accessibility assumption and CPS’s resource limitations highlights the need for resource-awareness. We introduce Cyber-Physical AI (CPAI), an interdisciplinary sub-field of AI and CPS research, to address these constraints. To the best of our knowledge, CPAI is the first research domain on CPS-AI integration. We propose a 3D classification schema of CPAI: Constraint (C), Purpose (P), and Approach (A). We also systematize the CPS-AI integration process into three phases and nine steps. By analyzing 104 studies, we highlight nine key challenges and insights from a CPAI perspective. CPAI aims to unify fragmented studies and provide guidance for reliable and resource-efficient integration of AI as a component of CPS.

Most of the existing studies on analyzing the productivity of serial production lines focus on cases where the coefficient of variation ($CV$) for both uptime and downtime is less than 1. Hardly any result is available when$CV>1$, i.e., uptime and downtime of machines exhibit high variability. The improvement of the production lines with high variable uptime and downtime depends on heuristic trial and error due to the lack of analysis method. This article suggests a neural network that approximates the throughput of serial production lines from machine and buffer parameters. Four neural network architectures (multilayer perceptron, recurrent neural network, long short-term memory (LSTM), and gated recurrent unit) are compared to determine the most effective architecture for the throughput approximation task. Training data are obtained from discrete-event simulations, encompassing a wide range of parameters. The results indicate that the LSTM model outperforms the other architecture considered. Furthermore, we present bottleneck identification and continuous improvement scenarios utilizing the model.

Multijob production (MJP) is a class of flexible manufacturing systems, which produces different products within the same production system. MJP is widely used in product assembly, and efficient MJP scheduling is crucial for productivity. Most of the existing MJP scheduling methods are inefficient for multijob serial lines with practical constraints. We propose a deep reinforcement learning (DRL)-driven scheduling framework for multijob serial lines by properly considering the practical constraints of identical machines, finite buffers, machine breakdown, and delayed reward. We analyze the starvation and the blockage time, and derive a DRL-driven scheduling strategy to reduce the blockage time and balance the loads. We validate the proposed framework by using real-world factory data collected over six months from a tier-one vendor of a world top-three automobile company. Our case study shows that the proposed scheduling framework improves the average throughput by 24.2% compared with the conventional approach.

In the Industrial Internet of Things (IIoT), energy efficiency is critical for effective management of physical systems. To achieve stable control of IIoT with minimal energy consumption, it is essential to co-design the controller and the wireless network. In this paper, we present a novel reinforcement learning (RL) approach called the Learning-enabled Self-triggered Wireless Networked-Control System (LS-WNCS). LS-WNCS learns complex interdependence between control and network systems, generating near-optimal control commands and sampling periods simultaneously to minimize energy consumption and maximize control performance. Compared with conventional RL algorithms, LS-WNCS reduces network energy consumption by up to 66% while maintaining a high level of control performance.

Recently, deep reinforcement learning (DRL) has been employed in flexible job-shop scheduling problems (FJSP) to minimize makespan within flexible manufacturing systems (FMS). In practice, numerous modern enterprises are incor-porating automated guided vehicles (AGV) into their FMS implementations. However, existing DRL-based FJSP solutions do not account for transportation constraints. To tackle this practical issue, we propose a novel graph-based DRL method, called Heterogeneous Job Scheduler (HJS), which interprets the environment status using the graph structure and then training the DRL model based on graph embeddings. Our findings indicate that the proposed approach surpasses conventional dispatching rules and existing DRL-based methods in terms of makespan, running time, and generalization performance.

Wireless technologies, such as WirelessHART, are being adopted in industrial wireless sensor–actuator networks (IWSAN), which are required to provide reliable quality of control (QoC). This article focuses on adaptively selecting the best network path for reliable QoC in the IWSAN. The main challenge is estimating the time-varying packet delivery ratio (PDR) of each path. The IWSAN path selection problem in a multi-armed bandit (MAB) framework is formulated. A novel algorithm criticality-aware adaptive path learning (CAPL) is proposed, which determines the criticality of each packet according to the degree of QoC degradation if it is lost. The key novelty of CAPL is that it simultaneously considers the fundamental exploration–exploitation trade-off in MAB and QoC in the IWSAN. CAPL uses low-criticality packets for exploration to measure the PDR so that it can minimize the impact of exploration on QoC degradation. CAPL with extensive simulation and empirical studies for DC motor position control is validated.

Automated measurement of the machine reliability parameters for a production system enables a continuous update of the mathematical model of the system, which can be used for various analyses toward productivity improvement. However, the continuous update may be impeded by some machines of which automated parameter measurements are out of order. Such a situation has been observed, for instance, when some of the machines in the line cannot save log files or Internet of Things devices that measure these machines stop functioning. In this context, this article addresses the problem of estimating the reliability parameters of those machines while avoiding a direct manual measurement (by humans) of uptime and downtime. It turns out that those parameters can be computed using buffer-related data of the neighboring machines along with the system information. With this, a continuous update of the model is possible even though some machines stop recording their status in an automated manner. The method is indirect as opposed to direct manual measurement. The results are derived for synchronous serial production lines with Bernoulli and also exponential reliability characteristics. Our simulation studies verify the accuracy of the proposed estimation methods.

Cyber-physical attacks exploit intrinsic natures of physical systems and can severely damage cyber-physical systems (CPSs) without being detected by the conventional anomaly detector. In this article, based on software-defined networking, we propose a holistic resilient CPS framework that can detect, isolate, and recover from cyber-physical attacks in real time. To show the effectiveness of the proposed framework, we focus on the pole-dynamics attack (PDA), a newly reported stealthy sensor attack that can make the physical system unstable. We develop an efficient detection algorithm for PDA and embed it into the proposed framework. By implementing a testbed, we validate that the proposed framework guarantees resilience of CPS against the PDA.

This study investigates the synergy between centralized and decentralized (i.e., ad hoc) data scheduling in vehicular ad hoc networks (VANETs) for offloading and balancing the workloads of roadside units (RSU) in bidirectional road scenarios. In the centralized scheduling, an RSU schedules data dissemination using a hybrid of infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communications. Specifically, RSUs cooperate with each other by transferring unserved requests, and each RSU schedules the data services based on its locally received requests and the transferred requests. In the decentralized scheduling, vehicles driving in opposite directions share the cached data items via V2V communication when out of the coverage of RSU. The ad hoc scheduling can be benefited from the cooperation among RSUs since the chance for V2V data sharing could be enhanced when considering transferred requests into scheduling at each RSU. We formulate a hybrid of centralized and ad hoc data scheduling (HCA) problem, aiming at best exploiting the synergistic effects of I2V and V2V communication based on centralized data broadcast and ad hoc data sharing. On this basis, we propose an RSU Cooperation-based Adaptive Scheduling (RCAS) algorithm that consists of three mechanisms, including a centralized scheduling mechanism at each RSU, an ad hoc scheduling mechanism for vehicles, and a cluster management mechanism. Finally, we build the simulation model and give a comprehensive performance evaluation, which demonstrates the superiority of the proposed solution under a variety of circumstances.

Cyber-physical system (CPS) provides a well-organized integration betweencommunication,computation, andcontrol(3C) technologies. CPS has been widely used in the vehicular networks and it requires to discover multimodal data from the physical system to make appropriate decisions and actions, for example, congestion warnings, applying brakes, adjusting speed limits, etc. Information discovery and availability at individual network elements is one of the fundamental foundations of CPS. In this paper, we proposed two multimodal network information discovery schemes for vehicular CPS using the Named Data Networking (NDN). One of the proposed schemes simply modifies the pull-based NDN communication mechanism to discover multimodal multi-hop data from the network and the other scheme uses the Interest broadcast suppression (IBS) mechanism. The proposed Interest broadcast suppression scheme adapts the holding time technique to defer the Interest forwarding and its computation involves the hop-count, distance, and other network parameters. Simulation results show that the proposed schemes discover about 172 and 162 percent more multimodal information from approximately 283 and 210 percent more network area by suppressing approximately 50 percent of the Interest broadcast storm in highway and the urban traffic scenarios, respectively.

Sharding is a promising solution to achieving scalability within the blockchain network. A sharded blockchain network consists of a beacon chain and several committees powered by the participants (i.e., validators) through the Proof-of-Stake (PoS) consensus protocol. Efficient and scalable as it can be, the sharded blockchain based on PoS is vulnerable to discouragement attack. A discouragement attack occurs when malicious validators censor messages to discourage validators from participating in the network. Furthermore, no rate-limiting validator rotation (enter/exit quickly) makes it more challenging to detect such an attack. In this paper, considering the undetermined rotation and the discouragement attack, we render the beacon chain an intermediary, allowing the beacon chain to interact with validators and the cyber-insurer, aiming to encourage the validators' stable rotation through insurance compensation. Specifically, we utilize a two-stage hierarchical game-based model to formulate the complicated interactions under the cyber insurance framework. In the first stage, the beacon chain develops compensatory strategies according to the insurer's profile. In the second stage, the beacon chain designs a series of contracts for validators, including insurance items, compensatory strategies, and rotation requirements. Consequently, the proposed scheme incentivizes validators to remain online by transferring risk to the cyber insurer and enables the sharded blockchain network to weaken the attack's impact through validators' stable rotation. This paper presents closed-form solutions for the proposed model, in which the beacon chain and the cyber insurer can gain maximized profits. The simulations demonstrate the feasibility and superiority of the proposed model.

As the basis of the most existing blockchain networks, Proof of Work (PoW) consensus protocol highly relies on the computational resources, and thus causing a huge waste of energy. Proof of Stake (PoS) is the alternative to relieve the PoW dilemma. However, it is also under threat, i.e., discouragement attack, which is a way to bring down the blockchain networks without any effective defense against it. To prevent the discouragement attack, the founders of Ethereum argue that the system should set a withdraw delay instead of allowing the validators entry/exit quickly. But how to determine the delay is still an open question. In this paper, we adopt the cyber insurance idea and propose the insurance contract to help determine the withdraw delay, as well as the insurance claim to relieve the loss of victims. Specifically, instead of requiring the insurance premium from the validators, the cyber insurer first signs the contract with the blockchain representative (e.g., beacon chain). Then the blockchain representative would sign a series of contracts with the validators. By such design, the validators can obtain the insurance claim without paying the premium, while the blockchain networks can keep the validators staying online to resist the discouragement attack. Finally, through the simulations, we demonstrate that the proposed model is capable of providing adaptive insurance contracts for the different validators and keeping the profits of the blockchain network and the cyber insurer.

A traffic monitoring system (TMS) is an integral part of Intelligent Transportation Systems (ITS). It is an essential tool for traffic analysis and planning. One of the biggest challenges is, however, the high cost especially in covering the huge rural road network. In this paper, we propose to address the problem by developing a novel TMS called DeepWiTraffic. DeepWiTraffic is a low-cost, portable, and non-intrusive solution that is built only with two WiFi transceivers. It exploits the unique WiFi Channel State Information (CSI) of passing vehicles to perform detection and classification of vehicles. Spatial and temporal correlations of CSI amplitude and phase data are identified and analyzed using a machine learning technique to classify vehicles into five different types: motorcycles, passenger vehicles, SUVs, pickup trucks, and large trucks. A large amount of CSI data and ground-truth video data are collected over a month period from a real-world two-lane rural roadway to validate the effectiveness of DeepWiTraffic. The results validate that DeepWiTraffic is an effective TMS with the average detection accuracy of 99.4% and the average classification accuracy of 91.1% in comparison with state-of-the-art non-intrusive TMSs.

A cyber–physical system (CPS) consists of physical devices and operations that are closely controlled and monitored by computational processes. This concrete connection involves the real-time actuation of physical devices, real-time sensing of physical quantities, and modeling and control of the overall system. A CPS may be connected to the Internet of Things (IoT) and, if so, should be considered in that context; the IoT is essential to realize a vision of future CPSs, where numerous devices are connected over the Internet, allowing them to collect information about the real world in real time, and share it with other systems and physical devices.

A cyber-physical system (CPS) is an entanglement of physical and computing systems by real-time information exchange through networking, which can be considered as real-time IoT because of end-to-end real-time performance guarantee. Most societal infrastructures, such as transportation systems, smart power grid, smart factory, and smart buildings, are key application domains of CPS. Though there have been extensive studies on infrastructures from the perspective of cyber security, insufficient research has been conducted from a practical viewpoint of cyber-physical security. In this paper, we focus on train control systems as one of the critical infrastructures. We fully investigate the emerging de facto standard of train control systems, communication-based train control (CBTC). We analyze the cyber-physical vulnerability of CBTC and discover that a man-in-the-middle attack combined with knowledge on train signaling can cause train collisions in CBTC. To resolve the issue, we propose a countermeasure for resiliency of CBTC. By implementing a realistic CBTC testbed, we validate our analysis. To the best of our knowledge, this is the first in-depth empirical study on cyber-physical vulnerability of CBTC systems.

During the past two decades, we have witnessed a tremendous development in Vehicular networks, while exploring emerging communication technologies such as vehicular cyber-physical systems (VCPS). Basically, VCPS requires multimodal data from the physical system to take appropriate decision and actions, for example, the congestion warnings, applying brakes, adjusting speed limits, etc. However, there are multiple systems interconnected in the VCPS with different communication capabilities and data communication between those systems that lead us to a challenging task. In this paper, we consider named data networking (NDN) as a promising solution to enhance the reachability of Data among multi-hop VCPS. NDN offers a simple pull-based content communication in the network with multiple interfaces and also supports heterogeneity in terms of communications technologies. The proposed NDN forwarding scheme enables vehicles to send one Interest (request) to collect multiple instances of the Data from different content sources in the network. Simulation results show that the proposed scheme can collect information from many nodes that are at longer distance from the information requesting nodes.

As various wireless devices share the same frequencies in the unlicensed 2.4-GHz industrial scientific medical band, frequency sharing has become a challenging issue in the heterogeneous network. Many Wi-Fi applications increase network traffic and lead to the significant performance loss of other protocol devices including ZigBee for critical missions (e.g., medical devices) in the same band. In this paper, we propose a coexistence solution of the guide busy tone (GBT), providing reliable communications to the ZigBee network under Wi-Fi interference, and present fairness criteria in the tradeoff relation between Wi-Fi and ZigBee with GBT. The proposed GBT design, consisting of a GBT signaler and a busy tone canceller, reserves a channel for ZigBee through the full-duplex technique under heavy Wi-Fi traffic. Our experimental evaluation shows that the packet delivery ratio of the ZigBee network can be improved up to nearly 100% under the saturated Wi-Fi traffic by using GBT, which is scalable for the multinode case as well.

Within a macrocell with a large coverage area, multiple small cells are deployed such that each small cell base station (SBS) supports wireless service demands from user equipments (UEs). Each UE can be simultaneously served by multiple SBSs for quality of service (QoS) enhancement. When there exist hotspot areas with a number of UEs, the SBSs near the hotspot areas may experience a higher resource utilisation level than those outside of the hotspot areas, resulting in a shortage of available resources. The authors propose a robust resource‐utilisation‐based coordinated transmission for heterogeneous networks with a locally different level of traffic demands. In the utilisation‐based coordinated transmission, low‐utilisation SBSs with a small number of UEs are selected to serve a newly joining UE because they have more capacity to serve requests with bursty traffic demand. They further formulate the selection of cooperative SBSs as a robust optimisation problem in order to ensure that UEs have sufficiently high signal‐to‐interference‐plus‐noise ratios, even with channel estimation inaccuracy and strong interference from non‐cooperative SBSs. The simulation results indicate that the proposed method guarantees robust and efficient service performance in heterogeneous small cell networks.

Thermal control is critical for real-time systems as overheated processors can result in serious performance degradation or even system breakdown due to hardware throttling. The major challenges in thermal control for real-time systems are (i) the need to enforce both real-time and thermal constraints; (ii) uncertain system dynamics; and (iii) thermal sensor noise. Previous studies have resolved the first two, but the practical issue of sensor noise has not been properly addressed yet. In this paper, we introduce a novel thermal control algorithm that can appropriately handle thermal sensor noise. Our key observation is that even a small zero-mean sensor noise can induce a significant steady-state error between the target and the actual temperature of a processor. This steady-state error is contrary to our intuition that zero-mean sensor noise induces zero-mean fluctuations. We show that an intuitive attempt to resolve this unusual situation is not effective at all. By a rigorous approach, we analyze the underlying mechanism and quantify the noised-induced error in a closed form in terms of noise statistics and system parameters. Based on our analysis, we propose a simple and effective solution for eliminating the error and maintaining the desired processor temperature. Through extensive simulations, we show the advantages of our proposed algorithm, referred to as Thermal Control under Utilization Bound with Virtual Saturation (TCUB-VS).

New features are often added incrementally to avionics systems to minimize the need for redesign and recertification. However, it then becomes necessary to check that the timing constraints of existing as well as new applications are met. We facilitate these checks by introducing a new data switch that bounds the latency of end-to-end communications across a network. This switch runs a clock-driven switching algorithm that is throughput-optimal with a bounded worst-case delay for all feasible traffic. We propose associated heuristics that determine whether the timing constraints of an integrated modular avionics (IMA) system network that uses this switch are met, even if new features have caused traffic to increase, and then search for alternative network configurations if necessary. Virtual integration is used to make a combined analysis of the worst-case delay in the network and the local buses of individual computing modules. This analysis considers the shared network topology, local hardware architectures, and specified IMA configurations. Our approach can be used by a system architect as an effective method for quickly determining which possible system architectures should be pursued to meet timing constraints, and it allows the cascading effects of changes to be tracked and managed. We demonstrate how these heuristics work through an example in which changes are made to an environmental monitoring facility within an avionics system that uses our switch.

The reliability of control in cyber-physical systems (CPSs) heavily depends on the network-induced delay. The problem of obtaining a maximum allowable delay bound has been widely studied in the networked control systems (NCS) area. Once the delay bound is derived, the remaining question is how to make a network satisfy the bound. In this paper, we propose a robust path selection algorithm, which exploits multipath diversity for providing robust network performance against intrinsic randomness in delay. Our path selection algorithm gives the required paths for any given robustness level parameterized by the reliability violation probability. Based on extensive experimental results with our testbed, we empirically show that the proposed scheme can provide the required network quality of service (QoS) for system robustness.

In this paper, we propose an adaptive tuning scheme of IEEE 802.11 WLAN for healthcare applications. Our proposed method can significantly improve medical-grade quality of service (QoS) and network performance at the same time. Though the conventional IEEE 802.11e protocol supports a certain level of QoS, it does not provide medical-grade QoS due to its relative priority among different traffic classes. Our proposed scheme adaptively tunes the arbitrary inter frame space number (AIFSN) of the IEEE 802.11e protocol for enhancing the overall network performance while providing the required medical-grade QoS. In our healthcare scenario, we consider the following three medical traffic categories: medical alarm, real-time electrocardiogram (ECG) transmission, and TCP connection. Our simulation results show that the proposed scheme improves the performance of low-priority TCP traffic while protecting high-priority medical alarms from lower priority traffic.

No abstract available.

In order to reduce the overhead of legacy WLANs, the IEEE 802.11n standard defines two aggregation schemes, i.e., A-MSDU and A-MPDU. In general, A-MPDU outperforms A-MSDU due to its selective retransmission capability. However, A-MPDU has a fundamental restriction on the minimum separation in time between the start of two consecutive subframes carried on the same A-MPDU. If such a gap is smaller than the minimum MPDU start spacing of the receiver, the sender should insert additional padding, thus resulting in throughput degradation. The main contribution of this paper is that we provide an adaptive aggregation scheme in which the sender conveys A-MSDUs within A-MPDUs in an adaptive manner, in order to resolve this potential problem in A-MPDU. Our analytical and simulation results demonstrate that the proposed scheme improves throughput performance over A-MPDU and A-MSDU by up to 280% and 19%, respectively.

In real-time networked control systems, the end-to-end path delays on the communication network should be bounded in order to satisfy the stringent timing constraints of physical system. While the packet delivery on a single path could be significantly affected by network congestion levels, transmitting duplicate packets along multiple paths can overcome fluctuations of the time delay on a specific path by exploiting the diversity of multiple paths. As such, we propose a multi-path selection scheme that selects an appropriate number of multiple paths while minimizing the network traffic overhead due to multiple paths.

Self-organizing network (SON), which is an essential technology of future radio networks, is proposed in the 3GPP Long Term Evolution (LTE) specification as a usage case. In this paper, we focus on user equipment (UE)'s power control considering interference mitigation and retransmission constraints especially for physical random access channel (PRACH) of LTE. When a UE initially accesses an evolved-NodeB (eNB), it sends a preamble with an initial access level, and raises the power by a fixed step whenever the preamble was not detected by the eNB. We present an analysis and simulation to study the tradeoff relationship between inter-cell interference and retransmission according to the PRACH power setting, i.e., the initial target received power and power ramping step, assuming Rayleigh fading channels. Furthermore, we propose an algorithm tracing the optimal power setting that minimizes the resource wastage of the cell in consideration and its adjacent cells satisfying the constraint of retransmission and failure probability bound.

With the advance of hardware technology, more features are incrementally added to already existing networked systems. Avionics has a stronger tendency to use preexisting applications due to its complexity and scale. As resource sharing becomes intense among the network and the computing modules, it has become a difficult task for the system designer to make confident architectural decisions even for incremental changes. Providing a tailored environment to model and analyze incremental changes requires a combination of software tools and hardware support. We have built a virtual integration tool called ASIIST which can provide a worst-case end-to-end latency of data that is sent through a network and the internal bus architecture of the end-systems. Also, we have devised a new real-time switching algorithm which guarantees the worst-case network delay of preexisting network traffic under feasible conditions. With the real-time switch support, ASIIST can provide an early modularized analysis of the end-to-end latency to make architectural design choices and incremental changes easier for the user.

New features are often added incrementally to avionics systems. This avoids redesign and recertification but still requires verifying the timing constraints of both new and existing applications. We introduce a new switch that facilitates this verification by bounding the latency of end-to-end communication across a network. Our clock-driven real-time switching algorithm is throughput-optimal with a bounded worst-case delay for all feasible traffic. Associated heuristics can verify whether the timing constraints of an avionics network are met, after new features have caused traffic to increase, and then search for alternative network configurations if necessary. We show how these heuristics cope with changes to an example environmental monitoring architecture within an avionics system that incorporates our switch. Our approach to analysis can be used to determine, quickly but rigorously, which system architecture meet timing constraints, and it allows the system architect to manage the cascading effects of component changes in a comprehensive manner.

In telecardiology, electrocardiogram (ECG) signals from a patient are acquired by sensors and transmitted in real time to medical personnel across a wireless network. The use of IEEE 802.11 wireless LANs (WLANs), which are already deployed in many hospitals, can provide ubiquitous connectivity and thus allow cardiology patients greater mobility. However, engineering issues, including the error-prone nature of wireless channels and the unpredictable delay and jitter due to the nondeterministic nature of access to the wireless medium, need to be addressed before telecardiology can be safely realized. We propose a medical-grade WLAN architecture for remote ECG monitoring, which employs the point-coordination function (PCF) for medium access control and Reed-Solomon coding for error control. Realistic simulations with uncompressed two-lead ECG data from the MIT-BIH arrhythmia database demonstrate reliable wireless ECG monitoring; the reliability of ECG transmission exceeds 99.99% with the initial buffering delay of only 2.4 s.

The authors propose an adaptive frequency hopping (AFH) algorithm, entitled robust adaptive frequency hopping (RAFH), for providing increased reliability of a wireless medical telemetry system (WMTS) under coexistence environment with non-medical devices. The conventional AFH scheme classifies channels into ‘good’ or ‘bad’ according to the threshold-based on–off decision by packet error rate (PER) measurement, and only uses good channels with a uniform hop probability. Unlike the conventional AFH scheme, RAFH is a novel technique, which solves a constrained entropy maximisation problem and assigns every channel a different hop probability as a decreasing function of the measured PER. The key novelty of RAFH over existing AFH schemes is that it reflects the relative channel condition by assigning non-uniform hop probabilities. By adopting constrained entropy maximisation, RAFH not only improves the average PER, but also reduces the PER fluctuation over time under a dynamic interference environment, both of which increase the reliability of WMTS. Through extensive simulation, we show that RAFH outperforms basic frequency hopping (FH) and the conventional AFH with respect to the PER under various scenarios of dynamic interference.

The recent development of high-speed data transmission over wireless cellular networks has enabled the delivery of multimedia broadcasting services to mobile users. These services involve a range of interactions among different system components, including the wireless channel, the network, and mobile devices, making it crucial for the service provider to verify the model, design, and behavior of a new service before it is deployed. However, previous studies have largely relied on network simulations or scaled experiments, and there has been little work on the sort of unified framework for quality-of-service (QoS) assessment, which considers the interactions between components, that we propose in this paper. Accurate models of the wireless channel, the network, and the data processing that takes place on an embedded system of a mobile client, are integrated within our framework, and allow us to predict several key system metrics and the quality of the video stream as it is perceived by users. Furthermore, different models of system components can be easily plugged in to extend this framework. As an example application, we analyze the performance of the process of decoding scalable videos on ARM-based mobile embedded systems in CDMA2000 wireless cellular networks.

Transmit power and carrier sense threshold are key MAC/PHY parameters in carrier sense multiple access (CSMA) wireless networks. Transmit power control has been extensively studied in the context of topology control. However, the effect of carrier sense threshold on topology control has not been properly investigated in spite of its crucial role. Our key motivation is that the performance of a topology-controlled network may become worse than that of a network without any topology control unless carrier sense threshold is properly chosen. In order to remedy this deficiency of conventional topology control, we present a framework on how to incorporate physical carrier sense into topology control. We identify that joint control of transmit power and carrier sense threshold can be efficiently divided into topology control and carrier sense adaptation. We devise a distributed carrier sense update algorithm (DCUA), by which each node drives its carrier sense threshold toward a desirable operating point in a fully distributed manner. We derive a sufficient condition for the convergence of DCUA. To demonstrate the utility of integrating physical carrier sense into topology control, we equip a localized topology control algorithm, LMST, with the capability of DCUA. Simulation studies show that LMST-DCUA significantly outperforms LMST and the standard CSMA protocol.

In multihop wireless networks, packets of a flow originating from a source node are relayed by intermediate nodes (relay nodes) and travel towards their destination along a multihop wireless path. Since the traffic forwarding capability of each node varies according to its level of contention, ideally, a node should not transmit more packets to its relay node than the corresponding relay node can forward. Instead, each node should yield its channel access opportunity to its neighbor nodes so that all the nodes can evenly share the channel and have similar forwarding capabilities. In this manner, nodes can utilize the wireless channel effectively, and further increase the end-to-end throughput of a multihop path. We propose a fully distributed contention window adaptation (CWA) mechanism, which adjusts the channel access probability depending on the difference between the incoming and outgoing traffic at each node, in order to equate the traffic forwarding capabilities among all the nodes in the path. We implement the proposed adaptive contention algorithm on Madwifi Linux kernel driver for Wi-Fi interface with Atheros chipset and carry out an empirical study in our division building. The experiment results demonstrate how the proposed mechanism can improve end-to-end throughput performance in the multihop wireless networks.

The performance of carrier sense multiple access (CSMA) wireless networks heavily depends on the level of spatial reuse, i.e., how many concurrent transmissions are allowed. Spatial reuse is primarily determined by physical carrier sense, and a key parameter for physical carrier sense is the carrier sense threshold. Our focus is on how to control the carrier sense threshold for improving network performance. We present a noncooperative game-theoretic framework, which leads to a fully distributed algorithm for tuning the carrier sense threshold. We introduce a utility function of each node, which is a nondecreasing concave function of the carrier sense threshold. A pricing function is further introduced to mitigate severe interference among nodes. The cost function is defined as the difference between the pricing and the utility functions. We prove that the noncooperative carrier sense game admits a unique Nash equilibrium (NE) under some technical conditions.We derive sufficient conditions that ensure the convergence of the synchronous and asynchronous update algorithms. Based on the analysis, we propose a fully distributed algorithm, entitled noncooperative carrier sense update algorithm (NCUA). Our simulation study indicates that NCUA outperforms standard CSMA with respect to the per-node throughput by 10-50%.

In wireless sensor networks, scheduling the sleep duration of each node is one of the key elements for controlling critical performance metrics such as energy consumption and latency. Since the wakeup interval is a primary parameter for determining the sleeping schedule, how to tune the wakeup interval is crucial for the overall network performance. In this paper, we present an effective framework for tuning asynchronous wakeup intervals of IEEE 802.15.4 sensor networks from the energy consumption viewpoint. First, we derive an energy consumption model of each node as an explicit function of the wakeup interval, and empirically validate the derived model. Second, based on the proposed model, we formulate the problem of tuning the wakeup interval with the following two objectives: to minimize total energy consumption and to maximize network lifetime. We show that these two problems can be optimally solved by an iterative algorithm with global information by virtue of the convexity of the problem structure. Finally, as practical solutions, we further propose heuristic optimization algorithms that only exploit local information. In order to develop heuristic algorithms, we propose two broadcasting schemes, which are entitled as maximum wakeup interval broadcasting and efficient local maximum broadcasting. These broadcasting algorithms enable nodes in the network to have heterogeneous wakeup intervals.

The virtual rate control (VRC) algorithm has been proposed for active queue management (AQM) in TCP networks. This algorithm uses an adaptive rate control instead of queue length control in order to respond quickly to traffic change with high utilization and small loss. By introducing the notion of virtual target rate, the VRC algorithm can maintain an input rate around the target rate, while attempting to regulate the queue length. In this paper, we analyze the stability of the VRC algorithm in a linearized model. From the results of our analysis, we provide a design guideline for the system to remain stable. We show the validity of our analysis and the effectiveness of the VRC algorithm compared to RED, PI, REM and AVQ algorithms through ns-2 simulations.

In Internet audio applications, delay and delay jitter affect mostly the applications' quality of service. Since packet delays are different and changing over time, the receiver needs to buffer some amount of packets before playout. Therefore, the amount of buffered packets and the timing of playout are very important for the performance of applications. We adopt an autoregressive (AR) model for estimation of packet delay and deploy a robust identification algorithm for adjustment of the parameters of the AR process. In our preliminary experiments, this robust algorithm leads to better performance when the noise is correlated and/or non-stationary, and also it is robust to model uncertainties.