Abstract IoT video applications such as public transportation, traffic monitoring, parking and surveillance systems in smart cities collect and transmit video over multi-hop wireless network fabrics which are known to be less reliable and responsive as their wired counterparts. While multipath routing has emerged as an effective strategy to help meet IoT video transmission’s, one of the main challenges current multipath routing approaches face is to be able to select routes that meet the requirements of the driving video applications in a timely manner. To fill this gap, we recently proposed FITPATH, an adaptive multipath selection mechanism for IoT video transmission that uses a heuristic-based iterative optimization approach to estimate in real time the conditions of the underlying network while accounting for the different bitrate requirements of the application video flows. To provide a complete understanding of FITPATH’s performance, limitations, and applicability in realistic IoT video transmission scenarios, this work presents a comprehensive experimental evaluation of FITPATH under a wide range of conditions. We demonstrate that FITPATH consistently outperforms existing multipath selection mechanisms in terms of both end-user Quality of Experience (QoE) and overall network performance. We also evaluate the performance and convergence of FITPATH’s heuristic, showing that it quickly generates feasible solutions and incrementally improves QoE as the algorithm iterates. Furthermore, we propose practical deployment strategies for FITPATH under both centralized and decentralized network control plane architectures. Additionally, we analyze the impact of control messages and network dynamics on video quality in decentralized network control plane environments.

This paper presents a HaLow wireless simulator, which provides a simulated environment based on the IEEE 802.11ah standard. The simulator allows generating metrics to evaluate network performance considering groups generated by the Restricted Access Window (RAW) mechanism. Users can also provide grouping configurations through input files for testing with custom grouping algorithms. Evaluation results show that the simulator scales efficiently while maintaining low memory footprint and execution time, even in large-scale network scenarios with 1,000 stations.

Video applications in the Internet of Things benefit from multipath routing strategies to meet Quality of Service requirements. Although several multipath selection mechanisms have been proposed, changes in topology and/or in video services requirements can impact video quality. This work presents a wireless network architecture that enables dynamic routing, adapting these changes to new path solutions that provide a better Quality of Experience for the users. Simulation results demonstrated that the proposed architecture is capable of recovering video quality in case of degradation due to topology change, as well as also confirming that the presence of control flows, essential for maintaining the topology, reduces the final video quality.

Connected and digital electricity substations based on IEC–61850 standards enable novel applications. On the other hand, such connectivity also creates an extended attack surface. Therefore, Intrusion Detection Systems (IDSs) have become an essential component of safeguarding substations from malicious activities. However, in contrast to traditional information technology systems, there is a serious lack of realistic data for training, testing, and evaluating IDSs in smart grid scenarios. Many existing substation IDSs rely on datasets from other contexts or on proprietary datasets that do not allow reproducibility, validation, or performance comparison with competing algorithms. To address this issue, we propose the Efficacious Reproducer Engine for Network Operations (ERENO) synthetic traffic generation framework based on the IEC–61850 standard specifications. As an additional contribution, and as a proof-of-concept, we create and make available a suite of realistic IEC–61850 datasets that model 8 use cases, namely traffic for 7 common attacks and one for normal network traffic. Based on those datasets, we further evaluate how enriched features combining raw data from the substation can significantly improve intrusion detection performance. Our results suggest that it can improve F1-Score up to 47.22% for masquerade attacks.

Mobile devices’ popularization has brought several new applications to communication networks. As we move into an increasingly denser scenario, problems such as collisions between transmissions and unbalanced load become more pronounced. Moreover, while station-based handoff is inefficient to reduce these issues, network-wide handover decisions might provide better network resource management. This paper proposes LEAF, an access point virtualization solution based on Software Defined Networking to enable station (STA) handover conducted by the network, based on a global scope. Unlike other solutions in the literature, our proposal fully supports multichannel migrations through the IEEE 802.11h Channel Switch Announcement without restricting the channel utilization by the access points. To demonstrate the feasibility of such an approach, we present experimental data regarding the behavior of several different devices in face of this mechanism. We also evaluate our complete virtualization solution, which reveals that the handoff of STAs did not lead to significant packet losses or delays in STAs’ connections, while providing a foundation to improve network’s self-management and flexibility, allowing association control and load balancing tasks to be executed on top of our solution.

For Internet of Things devices, one of the main power demands comes from the radio interfaces. Hence, duty cycling, i.e., activating and deactivating the radio, becomes essential to energy savings. In asynchronous scenarios, scheduled-based duty cycle methods stand out for their low deployment cost. Recently, the literature reports many studies on asymmetric scheduled-based methods — i.e., different nodes operating under different duty cycles. In this work we propose an extension of this concept: the heterogeneous duty cycling. It allows nodes to operate under schedules generated by distinct methods, resulting in a wider range of duty cycle choices and also a better coexistence between devices from different manufactures. In particular, we study which combinations of methods present the rotation closure property, and what are the average and maximum latencies for those pairs. We also show that heterogeneous duty cycling can improve performance if schedules are properly selected.

The 2.5 GHz band is allocated to licensed systems of the LTE. Due to the propagation characteristics in this band, the signal is severely degraded by walls and other similar obstacles, making coverage in indoor environments difficult. While this is an issue for the purpose of providing LTE indoor coverage, it may also produce spectrum opportunities for secondary users. In this paper, we introduce a design of a cognitive module for secondary radios to operate on this band alongside the primary LTE users. This module leverages both statistical and Machine Learning methods to detect idle channel periods and to estimate for how long the secondary user may use the band. Based on real data of LTE channel usage in indoor environments, we propose a mathematical model for the length of idle periods and show that a small set of narrow-band energy sensors is enough to detect transmission opportunities.

Evaluating wireless links is a common task for many control mechanisms. However, the inherent variability of those estimates negatively impacts network performance. To reduce this variability, the Hypothesis Test Estimator (HTE) was recently developed as an alternative to the commonly employed moving averages. Performance analyses carried out in recent works found that HTE returns more stable estimates at the cost of a typically larger average estimate error. This work uses numerical simulations to complement the previous analyses, but now under the perspective of convergence time –i.e., how long it takes for actual changes in the link quality to be reflected in the estimates. Our results indicate that HTE has, in general, a better convergence time than the moving averages. They also show that further improving HTE's convergence time is not trivial, as simple variations of the method that aim to improve convergence do not result in significant gains.

Cyber-Physical Systems (CPS) are the basis for the world’s critical infrastructure and, thus, have the potential to significantly impact human lives in the near future. In recent years, there has been an increasing demand for connectivity in CPS, which has brought to attention the issue of cybersecurity. Aside from traditional information systems threats, CPS face new challenges due to the heterogeneity of devices and protocols, as well as its strong reliability requirements. In this work, we provide a brief overview of the CPS architecture and applications and describe the security challenges in the three CPS layers of perception, transmission, and application. Besides, we discuss how feature selection (FS) may improve intrusion detection performance. In particular, we evaluate how metaheuristic approaches can improve classification performance in CPS perception, transmission, and application layers. Our results reveal that (i) Greedy Randomized Adaptive Search Procedure (GRASP) outperforms traditional filter-based methods, and (ii) using the proposed enhanced approaches in GRASP construction and local search phases can enhance the average F1-Score of five classifier algorithms.

With the development of wireless technologies and the proliferation of sensors, Wireless Sensor Networks (WSNs) have been widely used in many applications. However, their energy constraints make energy reduction one of the main project concerns. Among the several solutions in the literature, schedule-based asynchronous duty cycle methods are the simplest because they do not require synchronization mechanisms, protocols nor specific hardware, while facilitating the entry of new nodes. However, scenarios with sporadic events can benefit from nodes operating on different schedules. This work evaluates asymmetric asynchronous duty cycle schedules for possible use in such scenarios. Using numerical simulations, we show that, for some asymmetrical pairs of schedules, the overlap of active slots is guaranteed which enables the communication between the nodes. Also, depending on the choice of the pairs of schedules, an asymmetric setup allows a reduction in the Neighbor Discovery Time in relation to the symmetric methods.

The Smart Grid is a high availability system that requires fault recovery protocols to minimize downtime. In addition, recovery, in most cases, must be made within certain temporal constraints. The IEC 62493-3 standard defines the Parallel Redundancy Protocol (PRP) and High-availability Seamless Redundancy (HSR) as protocols for dealing with link layer communication failures in electrical substations. There are variations that perform the same functionality handling packets at the IP layer, such as iPRP. Those protocols promise zero-time recovery by proactively sending duplicate packets across distinct and independent paths. In this paper, those fault recovery protocols are analyzed by means of simulations. Our results show that, in realistic setups, recovery time is never actually zero and that, under certain conditions, this time can exceed the temporal requirements that some protection applications demand for proper operation.

Association instability is a common phenomenon in dense networks. The decision of whether or not to perform a handoff between access points in an infrastructured IEEE 802.11 network is taken exclusively by the wireless client stations. Even without mobility, static client devices may decide to migrate to another access point with the goal of improving performance. However, the criteria used to perform handoffs are not defined by the IEEE 802.11 standard and, thus, are dependent on specific vendor implementations. In this paper, we use data from a real large scale network and run experiments to demonstrate that such implementations are commonly deficient, resulting in high levels of association instability in dense environments. By analyzing the implementation used by the most common devices, we were able to conclude that this instability, known as the “ping-pong effect”, results from the direct usage of RSSI samples which are highly variable. Finally, we analyze the behavior of RSSI in indoor environments showing that its time series presents multimodal distribution. We argue that the findings presented in this study can help develop more stable handoff algorithms for dense wireless networks.

-In Wireless Sensor Networks, nodes typically employ batteries that cannot be easily recharged or replaced. Hence, optimizing energy consumption is a major concern. Among the several proposals in the literature, schedule-based asynchronous duty cycle methods are the simplest because they do not require mechanisms, protocols or specific hardware for clock synchronization between nodes. These solutions, however, result in high latency for multi-hop communication. In this work, we show how existing asynchronous mechanisms can benefit from a low level of synchronism, with resolution of slots. Assuming this possibility, we show through mathematical models, numerical and network simulations that the use of specific offsets between clocks of neighboring nodes, according to their distances to the sink node, significantly reduces latency.

Wireless mesh networks are multi-hop wireless networks which are useful in situations where there is little or no network infrastructure. Recently, those networks have been proposed for scenarios of Smart Grids and Internet of Things. The variability in routing metrics, which causes frequent changes in path choice, is a challenge in these networks. In this article, we propose a method, called Hypothesis Testing Estimator (HTE), for estimating the frame delivery probability of a link based on the concept of hypothesis testing. In this method, the estimate is fixed until the link behavior in the recent past significantly deviates from the expectation. The method was evaluated using simulations whose results show a significant reduction in the variability of the link quality estimates.

The inherent complexity of Wireless Mesh Networks (WMNs) makes management and configuration tasks difficult, specially for fault detection and diagnosis. In addition, manual inspections are extremely costly and require a highly skilled workforce, thus becoming impractical as the problem scales. To address this issue, this paper proposes a solution that makes use of machine learning techniques for automated fault detection and diagnosis (FDD) on solar-powered Wireless Mesh Networks (WMNs). We have used the Knowledge Discovery in Databases (KDD) methodology and a pre-defined dictionary of failures based on our previous experience with the deployment of WMNs. Thereafter, the problem was solved as a pattern classification problem. Several classification algorithms were evaluated, such as Naive Bayes, Support Vector Machine (SVM), Decision Table, k-Nearest Neighbors (k-NN) and C4.5. The SVM presented the best results, achieving a 90.59% overall accuracy during training and over 85% in validation tests.

The rapid growth of wireless networking technologies, the emergence of several new devices that offer or need Internet interconnection, and a pent-up demand for wide band access, especially away from the big cities, are hampered by the problem of the frequency spectrum exhaustion for telecommunications services. A more efficient use of the spectrum passes through solutions, such as the improvement and deployment of radios with cognitive ability. In this context, the problem of neighbor discovery extends not only for the initial blind rendezvous, but also for the maintenance of periodical encounters of neighbors after such initial encounter. At this stage, it will be necessary for a node that has already found a peer to interrupt its data communication, so that nodes can become aware of changes in their surroundings and the network can support the addition of new nodes. The contribution of this paper is the creation of asynchronous, distributed and robust schedules to guarantee multiple continuous rendezvous and communication opportunities between two or more cognitive radios using control channels, employing frequency hopping with new sequences and mappings based on combinatorial design theory.

This paper proposes LIBR, a routing protocol for Wireless Mesh Networks specially designed for linear topologies, i.e., topologies in which nodes are placed following a well-defined line-based pattern. This kind of topology may arise, for example, in power transmission lines, pipelines, roads, and railway lines. By exploring the intrinsic characteristics of these linear topologies, LIBR can achieve good performance and scalability, while being simple and incurring in low overhead for the network. Even employing a heuristic next hop selection procedure, in our simulation results, we found that LIBR was able improve network goodput in typical linear topologies up to 22%, when compared to traditional ad hoc routing protocols. Our analysis suggests that this improvement comes from a number of reasons, including better sharing of network resources (such as nodes' queues and the wireless medium itself). Finally, due to its heuristic nature, LIBR results in lower levels of control overhead, increasing the network energetic efficiency in 14% in our simulations.

Smart Grid represents the evolution of the current electrical power system. It is designed to meet the challenge of increasing demands for energy by fully integrating the electrical power grid with data communication networks. One of the main challenges faced by this kind of network is to fulfill reliability and resilience requirements in order to meet various types of services and applications. Wireless mesh networks can provide scalability and resilience to this communication network, but there are issues that need to be addressed before it can be used in practical smart grids. One of these issues relates to the robustness of the network when it faces gateway failures. In this situation, communication to smart meters may be unavailable for a considerable amount of time, which is prohibitive for many types of applications. In this sense, we present DDSA, an algorithm for dynamic selection of gateways in a multihoming smart grid network. The algorithm uses a probabilistic approach for choosing gateways, prioritizing those with the most reliable paths. Results indicate that DDSA increases network robustness and resilience in the presence of gateway failures compared to existing algorithms for dynamic gateway selection.

A basic property of wireless networks is the half duplex nature of their links. However, previous works have already shown, with real prototypes of full duplex radios, to be possible for a node to transmit and receive packets at the same time. Nevertheless, the gains that result from such techniques are not straightforward. In this work we analyze how frequently full duplex transmissions can occur in typical wireless network scenarios. Simulations were conducted in both infra-structured and multihop networks with several traffic patterns, and results show potential gains up to 99.96% and 36.93% in the infra-structured and multihop cases, respectively.

This paper analyzes the process of sending a coded packet in the wireless medium. Understanding this basic network coding operation is fundamental for the recent work on coding aware routing. Unlike the transmission of a native (not coded) packet, a coded packet can be partially received, i.e., received by only a subset of its intended receivers. The literature on this subject has relied on strong assumptions to simplify the models for evaluating routes. Specifically, most works adopt the hypothesis of independence between the events of reception by different receivers. In this paper we present theoretical arguments and experimental results that show this hypothesis is not valid in practical cases. To overcome this issue, we present a simple, yet effective, method for estimating the joint reception probability for a coded packet. We also discuss the mechanisms that are used in the literature to implement the operation of transmitting a coded packet to a group of receivers and conclude that they are still inefficient. As a result of this analyses, we propose a new mechanism that maximizes the joint reception probability of the packet. Our experimental results show that this novel mechanism outperforms other commonly used techniques.

This paper presents MARA, a joint mechanism for automatic rate selection and route quality evaluation in wireless mesh networks. This mechanism targets at avoiding the problems of lack of synchronization between metric and rate selection decisions and inaccurate link quality estimates, common to main existing proposals of multihop wireless routing metrics and automatic rate adaptation. In this proposal, the statistics collected by the routing protocol are used by the rate adaptation algorithm to compute the best rate for each wireless link. This coordinated decision aims at providing better routing and rate choices. In addition to the basic MARA algorithm, two variations are proposed: MARA-P and MARA-RP. The first considers the size of each packet in the transmission rate decision. The second variation considers the packet size also for the routing choices. For evaluation purposes, experiments were conducted on both real and simulated environments. In these experiments, MARA was compared to a number of rate adaptation algorithms and routing metrics. Results from both environments indicate that MARA may lead to an overall network performance improvement.

This paper discusses wireless mesh network topology visualization tools and their requirements. It presents an interactive tool for visualizing mesh network topologies. The proposed tool uses the SVG — Scalable Vector Graphics — web standard, which is based on XML, to build the network map. In a wireless mesh network, link quality varies quite often, therefore the visualization tool dynamically displays the quality metric of each link, in addition to presenting router configuration information and other network statistics. This work also introduces a configuration tool designed to adapt the topology view to different wireless mesh environments.