Lossy Links Research Articles

DistressNet-NG: A Resilient Data Storage and Sharing Framework for Mobile Edge Computing in Cyber-Physical Systems

Mobile Edge Computing (MEC) has been gaining a major interest for use in Cyber-Physical Systems (CPS) for Disaster Response and Tactical applications. These CPS generate a very large amount of mission-critical and personal data that require resilient and secure storage and sharing. In this article, we present the design, implementation, and evaluation of a framework for resilient data storage and sharing for MEC in CPS targeting the aforementioned applications. Our framework is built on the resiliency of three main components: EdgeKeeper, which ensures resilient coordination of the framework’s components; RSock, which provides resilient communication among CPS’s nodes; and R-Drive/R-Share which, leveraging EdgeKeeper and RSock, provides resilient data storage and sharing. EdgeKeeper employs a set of replicas and a consensus protocol for storing critical meta-data and ensuring fast reorganization of the CPS; RSock decides an optimal degree for replicating data that is communicated over lossy links. R-Drive employs an adaptive erasure-coded and encrypted resilient data storage; R-Share, leveraging RSock provides resilient peer-to-peer data sharing. We implemented our proposed framework on rapidly deployable systems (e.g., manpacks, testMobile Edge Clouds) and on Android devices, and integrated it with existing MEC applications. Performance evaluation results from three real-world deployments show that our framework provides resilient data storage and sharing in MEC for CPS.

Towards Reliable Space-Ground Integrated Networks: From System-Level Design to Implementation

The integration of space and terrestrial networks results in a promising future network architecture, which exploits the high data rate and low latency of terrestrial networks as well as the wide-range coverage of satellite networks. However, spaceground integrated networks (SGINs) face some unprecedented challenges, including the rapidly fluctuating network topology, limited resources, intermittent connections, lossy links, asymmetric bandwidth allocation, and so on. The traditional endto- end (E2E) transport protocols designed for reliable terrestrial networks exhibit inherent limitations in the context of dynamic SGIN. A promising solution is to decouple the E2E perfect reception confirmation into hop-by-hop acknowledgments, which results in prompt packet loss recovery and high transmission resilience in the face of high-dynamic topologies. Hence, in this paper we propose a reliable cache-enabled transport system, which enhances the efficiency of reliable hop-by-hop transmission, and supports multi-orbit breakpoint transmission, while at the same time facilitates the reliability of intra-satellite communication with a novel transport protocol. In addition to unveiling a compelling system-level design, we demonstrate the benefits of the proposed reliable transport system (RTS) in a real SGIN prototype relying on satellites having onboard processing capability. Our experimental results validate the feasibility of the proposed RTS in the face of lossy links, intermittent connections and intra-satellite transmission failure. Moreover, one of the satellites has been launched in April 2021, while the other two will be launched to perform on-orbit test.

Enhanced Distributed Consensus Filtering Over Lossy Wireless Sensor Networks With Application to Target Speed Tracking

This paper presents a novel way of distributed consensus filtering with enhanced robustness over wireless sensor networks, where bounded-power disturbances related to modeling uncertainty and white Gaussian noises are included. Sensors are assumed to receive local observations and transmit them to their neighbors through lossy links. A distributed consensus filtering scheme, consisting of an optimal filter, a separated damping term and a consensus tuning parameter, is considered through exchanging prior estimates among neighboring nodes, to achieve the cohesiveness among local estimates together with robust optimal estimation performance. A sufficient and necessary condition is conducted for determining the consensus parameter, leading to a non-conservative design way. Finally, to show the effectiveness of the presented results an experiment of target speed tracking is included.

Barycentric coordinate-based distributed localization for wireless sensor networks subject to random lossy links

Distributed localization in wireless sensor networks is essential for verifying the positions of the sensor nodes deployed in the sensing field based on information interactions. However, the information transmitted from the controller to the actuator for each sensor node may be lost owing to communication noise, channel interference, or network congestion. With this in mind, this paper aims at investigating the distributed localization for wireless sensor networks subject to random lossy links. The barycentric coordinate, which can be determined by the distance measurements between node pairs in the network, is used as the basic scheme for range-based localization. First, based on the characterization of random link loss in wireless sensor networks with a Bernoulli random variable, a distributed iterative localization algorithm is proposed. And then, the global convergence of the proposed localization algorithm which ensures accurate localization is theoretically proved by using the convergence of sub-stochastic matrices’ product. Finally, numerical examples are conducted to illustrate the effectiveness of the proposed localization algorithm.

COFlood: Concurrent Opportunistic Flooding in Asynchronous Duty Cycle Networks

For energy constraint wireless IoT nodes, their radios usually operate in duty cycle mode. With low maintenance and negotiation cost, asynchronous duty cycle radio management is widely adopted. To achieve fast network flooding is challenging in asynchronous duty cycle networks. Recently, concurrent flooding, which allows a set of nodes (called concurrent senders ) to immediately broadcast the received packet without any backoff, is a promising approach to improve the flooding speed. We observe that selecting either large or small number of concurrent senders cannot achieve the optimal flooding speed in different deployments. There is a tradeoff between the degradation of concurrent broadcast efficiency and the missing of early receiving chance. In this article, we propose COFlood (Concurrent Opportunistic Flooding), a practical and efficient concurrent flooding protocol in asynchronous duty cycle networks. First, COFlood constructs a concurrent flooding tree in distributed manner. The non-leaf nodes are selected as concurrent senders and they can cover the entire network while reserving the most capacity of concurrent broadcast for later added opportunistic concurrent senders. Moreover, we find that exploiting both early wake-up nodes and long lossy links can speed up the concurrent flooding tree-based network flooding by increasing the early receiving chances. Then, COFlood develops a lightweight method to select the nodes that meet the conditions of these two opportunities as opportunistic concurrent senders. We implement COFlood in TinyOS and evaluate it on two real testbeds. In comparison with state-of-the-art concurrent flooding protocol, completion time and energy consumption can be reduced by up to 35.3% and 26.6%.

Emergence of Bipartite Flocking Behavior for Cucker-Smale Model in Presence of Lossy Links

This work investigates the issue of discrete-time bipartite flocking control for Cucker-Smale model subject to cooperation-competition interations and lossy links. The data packets loss from the controller to the actuator are simulated as a Bernoulli distribution process. The cooperation and competition among individuals are described as a signed digraph. Based on the products convergence approach of infinite sub-stochastic matrices, a algebraic condition which is related to initial states, the topological structure and the successful information transmission rate is established, so as to achieve bipartite flocking. At last, the theoretical result is exemplied through numerical simulations.

Packet transport for maritime communications: A streaming coded UDP approach

The maritime communication network (MCN) plays an important role in the 6th generation (6G) system development. In MCNs, packet transport over long-distance lossy links will be ubiquitous. Transmission control protocol (TCP), the dominant transport protocol in the past decades, have had performance issues in such links. In this paper, we propose a novel transport approach which uses user datagram protocol (UDP) along with a simple yet effective bandwidth estimator for congestion control, and with a proactive packet-level forward erasure correction (FEC) code called streaming code to provide low-delay loss recovery without data retransmissions at all. We show that the approach can effectively address two issues of the state-of-the-art TCP variants in the long-distance lossy links, namely 1) the low bandwidth utilization caused by the slow increase of the congestion window (CWND) due to long round-trip time (RTT) and the frequent CWND drop due to random and congestion losses, and 2) the high end-to-end in-order delivery delay when re-transmissions are incurred to recover lost packets. In addition, we show that the scheme's goodput has good smoothness and short-term intra-protocol fairness properties, which are beneficial for multimedia streaming and interactive applications that are prominent parts of today's wireless traffic.

Learning-Based FEC for Non-Terrestrial Networks With Delayed Feedback

Packet-level forward erasure correction (FEC) is effective for achieving low-latency transmissions in non-terrestrial networks (NTNs), which often contain lossy links with long propagation delay. This letter considers the problem of dynamically sending FEC coded packets based on delayed feedback to achieve fast in-order data delivery without retransmissions. We show that the problem can be formulated using reinforcement learning (RL) by tracking history feedback and system state. With carefully devised state space and reward signal, the RL scheme is shown to be effective for achieving high and smooth in-order goodput with low delay. For example, smooth goodput can be achieved at a FEC code rate of more than 90% of the capacity with the delay of all the packets being lower than 1.5 round-trip time, which would be incurred by a single packet loss if using retransmissions.

Optimal proactive monitor placement & scheduling for IoT networks

This work is fulfilled in the context of the optimized monitoring of Internet of Things (IoT) networks. IoT networks are faulty; Things are resource-constrained in terms of energy and computational capabilities; they are also connected via lossy links. For IoT systems performing a critical mission, it is crucial to ensure connectivity, availability, and network reliability, which requires proactive network monitoring. The idea is to oversee the network state and functioning of the nodes and links; to ensure the early detection of faults and decrease node-unreachability times. It is imperative to minimize the resulting monitoring energy consumption to allow the IoT network to perform its functions. Furthermore, to realize the integration of the monitoring mechanism with IoT services, this latter should work in tandem with the IoT standardized protocols, especially the IPv6 for Low-power Wireless Personal Area Networks (6LoWPAN) and the Routing Protocol for Low-power and lossy networks (RPL). In this paper, an optimized, proactive, passive monitoring system is proposed for IoT networks. The proposition ensures the optimal placement of monitoring nodes (monitors). Leveraging the graph built by RPL for routing (the DODAG), minimal sets of monitors are optimally placed to cover a given domain. The monitoring activity is optimally scheduled between several subsets of nodes to prolong longevity while minimizing the energy consumption for monitoring, communication, and state transitions. Our proposition provides the exact solution to the defined monitoring placement and scheduling problem via a Binary Integer Program. The model serves as a benchmark for the performance evaluation of contemporary models. Experimentation is designed using network instances of different topologies. Results demonstrate the proposed model’s effectiveness in realizing full monitoring coverage with minimum energy consumption and communication overhead and a balanced distributed monitoring role.

The limits of multiplexing quantum and classical channels: Case study of a 2.5 GHz discrete variable quantum key distribution system

Network integration of quantum key distribution is crucial for its future widespread deployment due to the high cost of using optical fibers dedicated for the quantum channel only. We studied the performance of a system running a simplified BB84 protocol at 2.5 GHz repetition rate, operating in the original wavelength band, the short O-band, when multiplexed with communication channels in the conventional wavelength band, and the short C-band. Our system could successfully generate secret keys over a single-mode fiber with a length of 95.5 km and with co-propagating classical signals at a launch power of 8.9 dBm. Furthermore, we discuss the performance of an ideal system under the same conditions, showing the limits of what is possible with a discrete variable system in the O-band. We also considered a short and lossy link with 51 km optical fiber resembling a real link in a metropolitan area network. In this scenario, we could exchange a secret key with a launch power up to 16.7 dBm in the classical channels.

Proactive fault-tolerant wireless mesh networks for mission-critical control systems

Although wireless networks are becoming a fundamental infrastructure for various control applications, they are inherently exposed to network faults such as lossy links and node failures in environments such as mining, outdoor monitoring, and chemical process control. In this paper, we propose a proactive fault-tolerant mechanism to protect the wireless network against temporal faults without any explicit network state information for mission-critical control systems. Specifically, the proposed mechanism optimizes the multiple routing paths, link scheduling, and traffic generation rate such that it meets the control stability demands even if it experiences multiple link faults and node faults. The proactive network relies on a constrained optimization problem, where the objective function is the network robustness, and the main constraints are the set of the traffic demand, link, and routing layer requirements. To analyze the robustness, we propose a novel performance metric called stability margin ratio, based on the network performance and the stability boundary. Our numerical and experimental performance evaluation shows that the traffic generation rate and the delay of wireless networks are found as critical as the network reliability to guarantee the stability of control systems. Furthermore, the proposed proactive network provides more robust performance than practical state-of-the-art solutions while maintaining high energy efficiency.

An overview of DoSL Attacks in IoT Networks

Many methodologies has been discovered to lower the power consumption in the devices, which is connected to the network in recent years. Many medium access control protocols have been used for low power lossy networks (LLNs).The major goal of introducing this methods is to improve the energy efficiency and to increase the fastness of the communication in the network or the data transmission. The Low power lossy networks is used in many real time scenarios nowadays. These methods use battery-powered devices, which makes transmission easier over lossy links. A particular node generally switches off radio interfaces when no traffic is sent. These devices are made to keep the radio interfaces in ON thus it makes exhausting of batteries causing Denial of sleep attacks. Here, we are going to use time division and channel hoping techniques to get a impact on jamming attacks. We will look on the impacts of such attacks by the ideas got through attacker and to which level the protection allows jamming attacks at upper layers.

Energy efficient data correlation aware opportunistic routing protocol for wireless sensor networks

In this paper, we study the design of data correlation aware opportunistic routing protocol for wireless sensor networks (WSNs) constituent of lossy links for improved network performance. We focus on a scenario where only M < N sensor nodes are required to report their readings, where N is the number of sensor nodes in the network. We formulate the problem of selecting M such reporting sensors among the N nodes as a mutual information maximization problem such that minimal information loss is resulted. Due to the NP-hardness of this issue, we present a greedy informative sensor selection algorithm. To reduce the transmission redundancy and improve the network lifetime performance, we design an energy efficient correlation aware hop-by-hop opportunistic routing protocol EECAR, which takes the following factors into account: spatial correlation characteristics among sensor readings, broadcast nature of wireless channels, and residual battery energy of sensor nodes. EECAR requires very limited routing information to be kept at nodes in the network. Extensive simulation results demonstrate the effectiveness of our greedy informative sensor selection algorithm and also show that EECAR outperforms existing work in terms of network lifetime and data gathering.

Jointly Optimizing Throughput and Content Delivery Cost Over Lossy Cache Networks

Cache optimization, i.e., determining the optimal content placement and routing paths, is essential for obtaining high performance of cache-enabled networks. This paper studies the problem of optimizing system throughput and content delivery cost over cache networks with lossy links (i.e., ICN-based wireless IoT systems), where content is divided into packet-level chunks, and packets may be lost in transmission. We first propose a new performance metric - the expected overall content routing cost for satisfied requests (RCS), for better characterizing content delivery cost under packet losses. RCS at the same time possesses the attractive mathematical property of super-modularity. We then formulate an optimization problem for the task through jointly optimizing content caching and request routing, and analyze it under fixed-routing scenario. The formulated problem is NP-hard and we prove it is reducible to the one of minimizing content routing cost without packet losses. We establish rules for the reduction, and leverage existing efficient algorithm to solve the problem. We also propose a potential-based online algorithm that is simple and adaptive to traffic changes and packet losses. The effectiveness of our mechanism is validated through extensive simulations over a wide array of network topologies.

Adaptive Decrease Window for BALIA (ADW-BALIA): Congestion Control Algorithm for Throughput Improvement in Nonshared Bottlenecks

The main design goals of the multipath transmission control protocol (MPTCP) are to improve the throughput and share a common bottleneck link fairly with a single-path transmission control protocol (TCP). The existing MPTCP congestion control algorithms achieve the goal of fairness with single-path TCP flows in a shared bottleneck, but they cannot maximize the throughput in nonshared bottlenecks, where multiple subflows traverse different bottleneck links. This is because the MPTCP is designed not to exceed the throughput of a single-path TCP competing in the bottleneck. Therefore, we believe that MPTCP congestion control should have different congestion window control mechanisms, depending on the bottleneck type. In this paper, we propose an adaptive decrease window (ADW) balanced linked adaptation (BALIA) congestion control algorithm that adaptively adjusts the congestion window decrease in order to achieve better throughput in nonshared bottlenecks while maintaining fairness with the single-path TCP flows in shared bottlenecks. The ADW-BALIA algorithm detects shared and nonshared bottlenecks based on delay fluctuations and it uses different congestion window decrease methods for the two types of bottleneck. When the delay fluctuations of the MPTCP subflows are similar, the ADW-BALIA algorithm behaves the same as the existing BALIA congestion control algorithm. If the delay fluctuations are dissimilar, then the ADW-BALIA algorithm adaptively modulates the congestion window reduction. We implement the ADW-BALIA algorithm in the Linux kernel and perform an emulation experiment that is based on various topologies. ADW-BALIA improves the aggregate MPTCP throughput by 20% in the nonshared bottleneck scenario, while maintaining fairness with the single-path TCP in the shared bottleneck scenario. Even in a triple bottleneck topology, where both types of bottlenecks exist together, the throughput increases significantly. We confirmed that the ADW-BALIA algorithm works stably for different delay paths, in competition with CUBIC flows, and with lossy links.

A Green Routing Protocol with Wireless Power Transfer for Internet of Things

The usually constrained resources and lossy links scenarios of Internet of Things (IoT) applications require specific protocol suite, as the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). Due to its flexibility, RPL can support efficiently vertical applications such as environmental monitoring, smart city and Industry 4.0. In this paper, we propose a new Objective Function (OF) for RPL based on a composite metric considering jointly the residual energy of a node (parent) together with the energy that a neighbor node (child) can transfer to the parent according to the Wireless Power Transfer (WPT) concept. Specifically, we consider simultaneous wireless information and power transfer (SWIPT) technique, which enables both the energy harvesting and information decoding from the same radio frequency (RF) signal, in order to influence the selection of the best path according to the proposed energy efficient metric in RPL. Performance evaluation on a realistic scenario pointed out a remarkable energy saving to prolong the network lifetime, by selecting the best path toward the sink node, with respect to the OFs usually considered in the literature.

Leader–Follower Flocking for Discrete-Time Cucker–Smale Models With Lossy Links and General Weight Functions

In this article, we investigate leader–follower flocking for the Cucker–Smale model with lossy links and general weight functions. Here, the loss phenomenon of the control packages from the controller to the actuator is characterized by a Bernoulli stochastic variable and the edge weights of the interaction network are determined by a general update rule based on the distance between agents. A method based on the products of substochastic matrices is developed to analyze the flocking behavior with lossy links. By means of this method, a sufficient condition which depends on the agents’ initial states, the topology structure, the successful information transmission rate and the weight function is established. Compared with the previous Cucker–Smale flocking results, which are only applicable to positive and decreasing weight functions with specific forms, our result is more general and can be applied to arbitrary positive and decreasing weight functions with nonzero lower bounds. Finally, our result is illustrated through a simulation example.

Throughput Maximization for full-duplex two-way relay with finite buffers

&lt;div&gt;Optimal queueing control of multi-hop networks remains a challenging problem, e&lt;/div&gt;&lt;div&gt;specially in two-way relaying systems, even in the most straightforward scenarios.&lt;/div&gt;&lt;div&gt;In this paper, we explore two-way relaying having a full-duplex decode-and-forward&lt;/div&gt;&lt;div&gt;relay with two fifinite buffers. Principally, we propose a novel concept based on the&lt;/div&gt;&lt;div&gt;multi-agent reinforcement learning (that maximizes the cumulative network through&lt;/div&gt;&lt;div&gt;put) based on the combination of the buffer states and the lossy links; a decision is&lt;/div&gt;&lt;div&gt;generated as to whether it can transmit, receive or even simultaneously receive and&lt;/div&gt;&lt;div&gt;transmit information. Towards this objective, chieflfly, based on the queue state transi&lt;/div&gt;&lt;div&gt;tion and the lossy links, an analytic Markov decision process is proposed to analyze&lt;/div&gt;&lt;div&gt;this scheme, and the throughput and queueing delay are derived. Our numerical results&lt;/div&gt;&lt;div&gt;reveal exciting insights. First, artifificial intelligence based on reinforcement learning&lt;/div&gt;&lt;div&gt;is optimal when the length of the buffer is superior to a certain threshold. Second, we&lt;/div&gt;&lt;div&gt;demonstrate that reinforcement learning can boost transmission effificiency and prevent&lt;/div&gt;&lt;div&gt;buffer overflflow.&lt;/div&gt;

Field-Trial Experiments of an IoT-Based Fiber Networks Control and Management-Plane Early Disaster Recovery via Narrow-Band and Lossy Links System (FRENLL)

Field-Trial Experiments of an IoT-Based Fiber Networks Control and Management-Plane Early Disaster Recovery via Narrow-Band and Lossy Links System (FRENLL)

Dynamic Sparse Coded Multi-Hop Transmissions Using Reinforcement Learning

Packet transmissions over multi-hop lossy links are important in the future space-air-ground integrated networks. This letter considers sparse coded transmissions with reduced complexity compared to the well-known random linear network coding, which is known to be efficient in multi-hop lossy links. We propose a reinforcement learning framework for dynamically adjusting coding parameters on-line to improve the performance based on decoder feedback. A key advantage of the dynamic scheme is that it does not require a prior knowledge of the environment nor an analysis model. Extensive evaluation shows that it adapts well in scenarios where link conditions are unknown and/or changing, and achieves performance close to that of the optimal fixed schemes found by exhaustive search.