The IETF, concerned with the evolution of the Internet architecture, nowadays also looks into industrial automation processes. The contributions of a variety of IETF activities, initiated during the last ten years, enable now the replacement of proprietary standards by an open standardized protocol stack. This stack, denoted in the following as 6TiSCH-stack, is tailored for industrial internet of things (IIoTs). The suitability of 6TiSCH-stack for Industry 4.0 is yet to explore. In this paper, we identify four challenges that, in our opinion, may delay or hinder its adoption. As a prime example of that, we focus on the initial 6TiSCH-network formation, highlighting the shortcomings of the default procedure and introducing our current work for a fast and reliable formation of dense network.

The Industrial Internet of Things (IIoT) will leverage on wireless network technologies to integrate in a seamless manner Cyber-Physical Systems into existing information systems. In this context, the 6TiSCH architecture, proposed by IETF, represents the current leading standardization effort to enable timed and reliable data communication within IPv6 networks for industrial applications. In wireless networks, Link Quality Estimation (LQE) is a crucial task to select the best routes for data forwarding, regardless of unpredictable time varying conditions. Although, many solutions for LQE have been proposed in literature, the majority of them are not designed specifically for 6TiSCH networks. In this paper, we analyze the performance of existing LQE strategies on 6TiSCH networks. First, we run a set of simulations to measure the performance of one existing LQE strategy in 6TiSCH. Our simulations show that such strategy can result in measurements with low accuracy due to the 6TiSCH default timeslot allocation strategy. Consequently, we propose an extension of the 6TiSCH Minimal Configuration that allocates specific timeslots for the transmission of probing messages to mitigate the problem. The proposed methodology is demonstrated to effectively reduce the LQE error.

The cornerstone of cognitive systems is environment awareness which enables agile and adaptive use of channel resources. Whitespace prediction based on learning the statistics of the wireless traffic has proven to be a powerful tool to achieve such awareness. In this paper, we propose a novel HiddenMarkov Model (HMM) based spectrum learning and prediction approach which accurately estimates the exact length of the whitespace in WiFi channels within the shared industrial scientific medical (ISM) bands. We show that extending the number of hidden states and formulating the prediction problem as a maximum likelihood (ML) classification leads to a substantial increase in the prediction horizon compared to classical approaches that predict the immediate (short-term) future. We verify the proposed algorithm through simulations which utilize a model for WiFi traffic based on extensive measurement campaigns.

An open standardized protocol stack proposed by IETF is nowadays emerging in industrial wireless communication. Main building blocks are TSCH at MAC layer and RPL as routing protocol. This standard architecture is able to replace proprietary technology and to guarantee a timely, reliable and energy efficient communication. However, IETF can not offer a one-size-fits-all solution. Therefore, implementers of industrial IoT have to correctly set the parameters of the protocols in this stack, adapting it to the application requirements and on the physical topology. This paper focuses on the network formation procedure proposed by IETF 6TiSCH working group, a mandatory phase before nodes may transmit any sensed data. We evaluate through simulations the impact of TSCH- and RPL-parameters on the duration of and the energy consumed for the network formation process. We describe how to avoid an unsuccessful network formation and we give guidelines for an appropriate parameter setting, depending on typical network topologies.

The development of cognitive wireless technologies is a key enabler of automatic coexistence of industrial communication applications in the industrial scientific medical (ISM) bands. This is crucial for achieving the flexibility required in the so-called Industrie 4.0. In this work we present a single channel cognitive medium access control (MAC) protocol for wireless industrial communication in highly dynamic shared environments. The protocol design utilizes traffic models based on measurements of industrial wireless traffic in the 2.4 GHz ISM band. The proposed protocol enables optimum spectrum use and throughput by equipping nodes with predictive channel access. We address the problem of predictive channel access where cognitive networks are 1) either aware only of non-cognitive networks or 2) aware of other cognitive networks as well due to continuous online learning. The proposed protocol supports service differentiation, i.e. critical industrial applications could be assigned higher priorities when accessing the channel in order to fulfill their strict delay requirements. We also develop a highly accurate theoretical framework for predictive channel access and validate the proposed framework through extensive simulations. Simulation results show optimal throughput and spectrum use and a significant improvement on WiFi's Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA).

In this paper we provide a performance analysis framework for wireless industrial networks by deriving a service curve and a bound on the delay violation probability. For this purpose we use the (min,x) stochastic network calculus as well as a recently presented recursive formula for an end-to-end delay bound of wireless heterogeneous networks. The derived results are mapped to WirelessHART networks used in process automation and were validated via simulations. In addition to WirelessHART, our results can be applied to any wireless network whose physical layer conforms the IEEE 802.15.4 standard, while its MAC protocol incorporates TDMA and channel hopping, like e.g. ISA100.11a or TSCH-based networks. The provided delay analysis is especially useful during the network design phase, offering further research potential towards optimal routing and power management in QoS-constrained wireless industrial networks.

With an increasing wireless sensor network (WSN) application complexity, more alternatives for the WSN design arose. This complexity is also the reason why it is difficult to asses how and at which price in terms of money or decreased quality of service, design alternatives increase the system performance. In this work we therefore introduce a concept for quickly answering likewise questions, namely the task-based resource consumption modeling. It is the heart of the framework Tuontu which allows to easily estimate if an application is feasible in a given WSN deployment and which performance is to expect.

Cognitive radio (CR) is a key enabler of wireless in industrial applications especially for those with strict quality-of-service (QoS) requirements. The cornerstone of CR is spectrum occupancy prediction that enables agile and proactive spectrum access and efficient utilization of spectral resources. Hidden Markov Models (HMM) provide powerful and flexible tools for statistical spectrum prediction. In this paper we introduce a HMM-based spectrum prediction algorithm for industrial applications that accurately predicts multiple slots in the future. Traditional HMM prediction approaches use two hidden states enabling the prediction of only one step ahead in the future. This one step is most often not enough due to internal hardware delays that render it outdated. We show in this work that extending the number of hidden states and formulating the prediction problem as a maximum likelihood (ML) classification approach enables a prediction span of multiple slots in the future even with fine spectrum sensing resolution. We verify the suitability of our approach to industrial wireless through extensive simulations that utilize a realistic measurement-based traffic model specifically tailored for industrial automotive settings.

IEEE 802.15.4 proposes the advantage of a standardized low power low data rate communication stack and is therefore also an option for deploying low power wireless sensor networks (WSNs). Most studies of 802.15.4 based WSNs concentrate on the operational phase and neglected the initial startup phase. This bears however also potentials for energy savings, as the 802.15.4 association procedure has to be executed to make the network operational and is not optimized for low power networks. In this study, we point out directions how to perform the association in a self organizing and energy saving way.

Large wireless sensor network deployments used for environmental monitoring or cargo tracking, require energy efficient mesh topologies. This implies duty cycling of sensor nodes to be coordinated with the routing protocol. Staying in the context of ZigBee, we simulate the combination of the sleep enabled non-beaconed mode of 802.15.4 and AODV routing and compare the duty cycling effects of synchronized and unsynchronized sleep scheduling. We consider two different link layer feedback schemes for AODV, denoted as regular and smooth AODV.

Realistic traffic modeling plays a key role in efficient Dynamic Spectrum Access (DSA) which is considered as enabler for the employment of wireless technologies in critical industrial automation applications (IAA). The majority of models of spectrum usage are not suitable for this specific use case as they are based on measurement campaigns conducted in urban or controlled laboratory environments. In this work we present a time-domain traffic model for industrial communication in the 2.4 GHz industrial, scientific, medical (ISM) band based on measurements in an industrial automotive production site. As DSA is usually implemented on Software Defined Radios (SDR), our measurement campaign is based on SDR platforms rather than sophisticated spectrum analyzers. We show through the estimation of the Hurst parameter that industrial wireless traffic possesses inherent self-similarity that could be exploited for efficient DSA. We also show that wireless traffic could be modeled as a semi-Markov model with channel on and off durations Log-normally and Pareto distributed, respectively. We finally estimate the parameters of the derived models using Maximum Likelihood estimation.

In applications with strict requirements regarding reliability and real time capability such as factory and process automation, it is critical to detect sources of interference that might corrupt data packets leading to retransmissions and delays. To tackle this issue we propose a method for measuring and quantifying radio interference using higher-order statistics. Unlike traditional energy detectors that suffer from several shortcomings in noisy environments, higher-order statistics are robust and do not suffer from threshold uncertainties. We present results of experimental measurements as well as simulations for detecting mutual interference using a normalized fourth-order moment named the kurtosis. The proposed method is independent of the received power and is efficient for detecting low-level interference compared to energy detectors. Results show that not only the presence of interference could be detected but also its strength. As a use case, the proposed method has been applied to Bluetooth mutual interference in this work. In can however be extended to other standards and scenarios.

In the course of increasing the production flexibility and dynamics, the replacement of wired industrial monitoring and control systems by wireless solutions is ongoing. To offer a similar degree of reliability as wired solutions, the IEEE 802.15.4e, targeting factory automation, defines the Time Slotted Channel Hopping (TSCH) mechanism. It appears promising to reach a timely, reliable and energy efficient communication. In particular, the TSCH link allocation mechanism, which cares for transmission scheduling in time and frequency, plays a crucial role. In this paper we model and analyze a network monitoring solution for TSCH-based wireless sensor networks, which supplies the link scheduling algorithm with the necessary information about network health status. For distributing the information, we investigate the piggybacking principle and define a new information container for monitoring purposes in the MAC frame. We show the results of our preliminary evaluation for a centralized and a distributed approach in cluster-tree networks in terms of control plane overhead.

In factory automation where reliable low-delay communication is essential to maintain the required Quality of Service (QoS), efficient medium access schemes play quite an important role. Therefore we propose a centralized cognitive medium access method that utilizes prediction of whitespaces to avoid collisions as well as improve utilization of transmission opportunities. Our simulation results show that under idealistic assumptions, CSMA extended by prediction outperforms the classical CSMA/CA by far. We demonstrate that even if the prediction is erroneous to some extent, the proposed algorithm (CSMA/PCA) shows remarkable improvements in terms of successful channel access, average queuing time per packet, as well as efficiency of spectrum utilization.

The paper is devoted to modeling wireless mesh networks (WMN) through mixed-integer programming (MIP) formulations that allow to precisely characterize the link data rate capacity and transmission scheduling using the notion of time slots. Such MIP models are formulated for several cases of the modulation and coding schemes (MCS) assignment. We present a general way of solving the max–min fairness (MMF) traffic objective for WMN using the formulated capacity models. Thus the paper combines WMN radio link modeling with a non-standard way of dealing with uncertain traffic, a combination that has not, to our knowledge, been treated so far by exact optimization models. We discuss several ways, including a method based on the so called compatible or independent sets, of solving the arising MIP problems. We also present an extensive numerical study that illustrates the running time efficiency of different solution approaches, and the influence of the MCS selection options and the number of time slots on traffic performance of a WMN. Exact joint optimization modeling of the WMN capacity and the MMF traffic objectives forms the main contribution of the paper.

Developed societies have a high level of preparedness for natural or man-made disasters. But such incidents cannot be completely prevented, and when an incident like an earthquake or an accident in a chemical or nuclear plant hits a populated area, rescue teams need to be employed. In such situations it is a necessity for rescue teams to get a quick overview of the situation in order to identify possible locations of victims that need to be rescued and dangerous locations that need to be secured. Rescue forces must operate quickly in order to save lives, and they often need to operate in dangerous environments. Hence, robot-supported systems are increasingly used to support and accelerate search operations. The objective of the SENEKA concept is to network the various robots and sensor systems used by first responders in order to make the search for victims and survivors more quick and efficient. SENEKA targets the integration of the robot-sensor network into the operation procedures of the rescue teams. The aim of this paper is to inform on the goals and first research results of the ongoing joint research project SENEKA.

Wireless mesh networks (WMNs) are a convenient type of Internet access networks, because they are self-configuring and self-healing wireless multi-hop networks. This is in particular true for IEEE 802.11 based WMNs as they cover large areas, are easy and cheap to setup, and provide sufficient capacity. Despite these advantages, WMNs have not yet entered the mass market. One of the reasons for this is, that many existing WLAN mesh networks use their own specific combination of higher layer protocols and IEEE 802.11 MAC extensions what makes it hard to characterize their performance and to provide interoperability. The recently published IEEE 802.11s-2011 standard addresses this issue by introducing standardized MAC layer mechanisms for WLAN mesh networking. One of these mechanisms is the IEEE 802.11s intra-mesh congestion control (IMCC) framework which addresses one of the key challenges in contention-based WMNs, namely congestion on the wireless medium. In this paper we quantify the benefits of this framework by introducing and evaluating three IEEE 802.11s-compliant IMCC algorithms. An extensive simulation study reveals that in dependence on the different levels of complexity of the algorithms-node-specific (TCC), link-specific (LSCC), or path-specific (PSCC)-the network performance in terms of throughput and fairness can be significantly improved.

Over the last decade, Quality of Experience (QoE) has become a new, central paradigm for understanding the quality of networks and services.In particular, the concept has attracted the interest of communication network and service providers, since being able to guarantee good QoE to customers provides an opportunity for differentiation.In this paper we investigate the potential as well as the implementation challenges of QoE management in the Internet.Using YouTube video streaming service as example, we discuss the different elements that are required for the realization of the paradigm-shift towards truly user-centric network orchestration.To this end, we elaborate QoE management requirements for two complementary network scenarios (wireless mesh Internet access networks vs. global Internet delivery) and provide a QoE model for YouTube taking into account impairments like stalling and initial delay.We present two YouTube QoE monitoring approaches operating on the network and the end user level.Finally, we demonstrate how QoE can be dynamically optimized in both network scenarios with two exemplary concepts, AquareYoum and FoG, respectively.Our results show how QoE management can truly improve the user experience while at the same time increase the efficiency of network resource allocation.