Mid Sweden University

The Industrial Internet of Things (IIoT) has become a promising technology for the improvement of the productivity, efficiency, and intelligence of the manufacturing process. Industrial Wireless Sensor Networks (IWSNs) represent a main pillar of IIoT to support communications within the field network level. For several IIoT applications, IWSNs are defined by strict communication requirements in terms of latency and reliability to support the proper functioning of the industrial system and avoid production loss. However, there are many challenges in efficiently satisfying these requirements. The key challenges investigated in this thesis are related to the shortcomings of the existing IWSN standards to enable timely delivery of aperiodic critical data, support traffic differentiation, and maintain reliable end-to-end communications. The overall objective of this work is to improve the reliability and real-time communication at the field network level in IIoT applications, particularly in process automation scenarios. Specifically, the proposed solutions represent improvements within the data-link and network layers of the IWSN protocol stack. The work in this thesis introduces the following contributions. The first part of the thesis focuses on improving real-time delivery for critical traffic and enabling traffic differentiation for mixed-criticality systems. The contribution in this part comprises three approaches. The first approach introduces a deterministic priority-based channel access mechanism for emergency data in time- and mission-critical applications. The approach is based on a dynamic deadline-aware schedule to provide a delay-bounded performance for the unpredictable emergency traffic along with efficient channel utilization. In the second approach, a priority-based wireless fieldbus protocol is proposed to enable traffic differentiation in mixed-criticality systems, where each traffic flow is given a transmission priority according to its corresponding criticality level. The third approach presents an optimized retransmission scheme to maximize the probability that an emergency packet is successfully delivered within its deadline bound. The results of the proposed schemes prove their effectiveness in providing real-time delivery for critical traffic and efficient service differentiation for mixed-criticality systems. The second part of the thesis introduces a routing framework to improve the connectivity and the end-to-end communication reliability of 6TiSCH networks. The proposed solutions in this part are mainly designed on the basis of the standard Routing Protocol for Low-Power and Lossy Networks (RPL). The proposed framework comprises the following approaches: 1) a reliable mobility-aware routing scheme to support node connectivity and reliable routing in mobile 6TiSCH networks, 2) a congestion control and detection strategies to enhance packet delivery performance under imbalanced network and heavy load scenarios, 3) a hybrid multi-cast method to maintain downlink connectivity and mitigate routing memory limitations in large-scale 6TiSCH networks. The conducted performance evaluations prove the effectiveness of the proposed approaches to enhance network performance in terms of reliability and delay metrics. The proposed approaches manage to improve routing performance of 6TiSCH networks in terms of connectivity and end-to-end data delivery, which in turn improves the real-time communication in IIoT.

Nowadays, Wireless Sensor Networks (WSNs) have gained importance as aflexible, easier deployment/maintenance and cost-effective alternative to wired networks,e.g., Fieldbus and Wired-HART, in a wide-range of applications. Initially,WSNs were mostly designed for military and environmental monitoringapplications where energy efficiency is the main design goal. The nodes in the network were expected to have a long lifetime with minimum maintenance while providing best-effort data delivery which is acceptable in such scenarios. With recent advances in the industrial domain, WSNs have been subsequently extended to support industrial automation applications such as process automation and control scenarios. However, these emerging applications are characterized by stringent requirements regarding reliability and real-time communications that impose challenges in the design of Industrial Wireless Sensor Networks (IWSNs) to effectively support time- and mission-critical applications.

Typically, time- and mission-critical applications support different traffic categories ranging from relaxed requirements, such as monitoring traffic to firm requirements, such as critical safety and emergency traffic. The critical traffic is mostly acyclic in nature and occasionally occurs at unpredictable time instants. Once it is generated, it must be delivered within strict deadlines. Exceeding the delay bound could lead to system instability, economic loss, or even endanger human life in the working area. The situation becomes even more challenging when an emergency event triggers multiple sensor nodes to transmit critical traffic to the controller simultaneously. The unpredictability of the arrival of such a type of traffic introduces difficulties with regard to making a suitable scheduling that guarantees data delivery within deadline bounds. Existing industrial standards and related research work have thus far not presented a satisfactory solution to the issue. Therefore, providing deterministic and timely delivery for critical traffic and its prioritization over regular traffic is a vital research topic.

Motivated by the aforementioned challenges, this work aims to enable real-timecommunication for time- and mission-critical applications in IWSNs. In this context, improved Medium Access Control (MAC) protocols are proposed to enablea priority-based channel access that provides a timely delivery for acyclic critical traffic. The proposed framework starts with a stochastic modelling of the network delay performance under a priority-oriented transmission scheme, followed by two MAC approaches. The first approach proposes a random Clear Channel Assessment (CCA) mechanism to improve the transmission efficiency of acyclic control traffic that is generated occasionally as a result of observations of an established tendency, such as closed-loop supervisory traffic. A Discrete-Time Markov Chain (DTMC) model is provided to evaluate the performance of the proposed protocol analytically in terms of the expected delay and throughput. Numerical results show that the proposed random CCA mechanism improves the shared slots approach in WirelessHART in terms of delay and throughput along with better transmission reliability.

The second approach introduces a slot-stealing MAC protocol based on a dynamic deadline-aware scheduling to provide deterministic channel access in emergency and event-based situations, where multiple sensor nodes are triggered simultaneously to transmit time-critical data to the controller. The proposed protocol is evaluated mathematically to provide the worst-case delay bound for the time-critical traffic and the numerical results show that the proposed approach outperforms TDMA-based WSNs in terms of delay and channel utilization.

The emerging mission-critical Internet of Things (IoT) play a vital role in remote healthcare, haptic interaction, and industrial automation, where timely delivery of status updates is crucial. The Age of Information (AoI) metric is introduced as an effective criterion for evaluating the freshness of information received at the destination. A system design based solely on the optimization of the average AoI might not be adequate to capture the requirements of mission-critical applications, since averaging eliminates the effects of extreme events. In this paper, we introduce a Deep Reinforcement Learning (DRL)-based algorithm to improve AoI in mission-critical IoT applications. The objective is to minimize an AoI-based metric consisting of the weighted sum of the average AoI and the probability of exceeding an AoI threshold. We utilize the actor-critic method to train the algorithm to achieve optimized scheduling policy to solve the formulated problem. The performance of our proposed method is evaluated in a simulated setup and the results show a significant improvement in terms of the average AoI and the AoI violation probability compared to the related-work.

Industrial Wireless Sensor Networks (IWSNs) designedfor mission- and time-critical applications require timelyand deterministic data delivery within stringent deadline bounds.Exceeding delay limits for such applications can lead to system malfunction or ultimately dangerous situations that can threaten human safety. In this paper, we propose SS-MAC, an efficient slot stealing MAC protocol to guarantee predictable and timely channel access for time-critical data in IWSNs. In the proposed SS-MAC, aperiodic time-critical traffic opportunistically steals time slots assigned to periodic non-critical traffic. Additionally, a dynamic deadline-based scheduling is introduced to provide guaranteed channel access in emergency and event-based situations where multiple sensor nodes are triggered simultaneously to transmit time-critical data to the controller. The proposed protocol is evaluated mathematically to provide the worst-case delay bound for the time-critical traffic. Performance comparisons are carried out between the proposed SS-MAC and WirelessHARTstandard and they show that, for the time-critical traffic, theproposed SS-MAC can achieve, at least, a reduction of almost 30% in the worst-case delay with a significant channel utilization efficiency.

Industrial Wireless Sensor Networks (IWSNs) are emerged as flexible and cost-efficient alternatives to the traditional wired networks in various monitoring and control applications within the industrial domain. Low delay is a key feature of delay-sensitive applications as the data is typically valid for a short interval of time. If data arrives too late it is of limited use which may lead to performance drops or even system outages which can create significant economical losses. In this paper, we propose a decentralized optimization algorithm to minimize the End-to-End (E2E) delay of multi-hop IWSNs. Firstly, we formulate the optimization problem by considering the objective function as the network delay where the constraint is the stability criteria based on the total arrival rate and the total service rate. The objective function is proved to be strictly convex for the entire network, then a Decentralized Primal-Dual (DeP-D) algorithm is proposed based on the sub-gradient method to solve the formulated optimization problem. The performance of the proposed DeP-D is evaluated through simulations and compared with WirelessHART network and the results show that the proposed DeP-D can achieve at least 40% reduction in the average E2E delay.

In the perspective of the emerging Industrial Internet of things (IIoT), the 6TiSCH working group has been created with the main goal to integrate the capabilities of the IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) with the IPv6 protocol stack. In order to support time-critical applications in IIoT, reliable real-time communication is a key requirement. Specifically, aperiodic critical traffic, such as emergency alarms, must be reliably delivered to the DODAG root within strict deadline bounds to avoid system failure or safety-critical situations. Currently, there is no mechanism defined in the 6TiSCH architecture for timely and reliably handling of such traffic and its prioritization over the non-critical one. In this paper, we introduce REA-6TiSCH, a reliable emergency-aware communication scheme to support real-time communications of emergency alarms in 6TiSCH networks. In REA-6TiSCH, the aperiodic emergency traffic is opportunistically enabled to hijack transmission cells pre-assigned for the regular periodic traffic in the TSCH schedule. Moreover, we introduce a distributed optimization scheme to improve the probability that an emergency flow is delivered successfully within its deadline bound. To the best of our knowledge, this is the first approach to incorporate emergency alarms in 6TiSCH networks. We evaluate the performance of REA-6TiSCH through extensive simulations and the results show the effectiveness of our proposed method in handling emergency traffic compared to Orchestra scheme. Additionally, we discuss the applicability of REA-6TiSCH and provide guidelines for real implementation in 6TiSCH networks.

A fundamental challenge in Mission-Critical Internetof Things (MC-IoT) is to provide reliable and timely deliveryof the unpredictable critical traffic. In this paper, we propose an efficient prioritized Medium Access Control (MAC) protocol for Wireless Sensor Networks (WSNs) in MC-IoT control applications. The proposed protocol utilizes a random Clear Channel Assessment (CCA)-based channel access mechanism to handlethe simultaneous transmissions of critical data and to reduce thecollision probability between the contending nodes, which in turn decreases the transmission latency. We develop a Discrete-Time Markov Chain (DTMC) model to evaluate the performance of the proposed protocol analytically in terms of the expected delay and throughput. The obtained results show that the proposed protocolcan enhance the performance of the WirelessHART standard by 80% and 190% in terms of latency and throughput, respectively along with better transmission reliability.

Cognitive Radio (CR) is an intelligent technique for opportunistic access of idle resources. In CR, Spectrum sensing is one of its important key functionalities. It is used to sense the unused spectrumin an opportunistic manner. Energy detection constitutes a preferred approach for spectrum sensing in cognitive radio due to its simplicity and applicability. The conventional energy detection technique, which is based upon fixed threshold, is sensitive to noise uncertainty which is unavoidable in practical cases. This noise uncertainty gets the fixed threshold energy detector un-optimized in its performance. In this paper, an efficient energy detector is proposed for optimal CR performance. The proposed scheme is based upon a dynamic threshold energy detection algorithm, in which, the decision threshold is toggled between two levels based upon the average energy received from the primary user (PU) during a specified period of observation. Thresholds evaluations are based upon estimating the noise uncertainty factor. These thresholds are used to maximize the probability of detection (Pd) and minimize the probability of false alarm (Pfa). Theoretical analysis and simulation results show the effectiveness of the proposed scheme in comparison to the conventional energy detection method with less increase in complexity.

In hard decision cooperative spectrum sensing (CSS), each secondary user (SU) or simply cognitive radio user (CR) senses the primary user (PU) activity via a Sensing channel (S-channel) and forwards its own binary decision to a fusion center (FC) via a Reporting channel (R-channel) to make a final decision regarding PU existence. In practical scenarios, both S-channels and R-channels are contaminated with noise, fading and shadowing effects. Thus, the FC may receive faulty decisions from the CRs, which in turn degrades the overall sensing performance of the cognitive radio networks (CRNs). In this paper, an efficient hard decision CSS with two-stage censoring is proposed for boosting the sensing performance of CRNs against noise uncertainty inherent in the S-channels and erroneous inherent in the R-channels. In the first stage, CRs with low quality R-channels are censored by the FC, hence only CRs with high quality R-channels are selected for the next stage of censoring. In the second stage, the low confident CRs with high noise uncertainty factors of their S-channels are censored by the FC, i.e., the FC selects the candidate CRs with the highest quality R-channels and the lowest noisy S-channels. For boosting the sensing decisions made by the CRs, a double dynamic threshold (DDT) is utilized by each CR based on an estimated value of the noise uncertainty factor of its S-channel. The new detection and false alarm probabilities are evaluated mathematically for the proposed scheme. Moreover, numerical analysis is used to confirm the high potency of the proposed scheme over some existing hard decision CSS schemes.

Spectrum Sensing (SS) comprises the most important component in Cognitive Radio (CR) systems. Cooperative Spectrum Sensing (CSS) is proposed as an effective approach to improve detection performance in fading environments. This paper introduces an efficient energy detection based hard decision CSS algorithm to alleviate the noise uncertainty effect. In the proposed algorithm, the decision threshold is dynamically switched between two levels based on a prior prediction of the Primary User (PU) activity. The two threshold levels are evaluated using an estimated value of the noise uncertainty factor to maximize the probability of detection and minimize the probability of false alarm. The proposed algorithm is studied theoretically to deduce the enhanced detection and false alarm probabilities. Moreover, simulation analysis is used to confirm the theoretical claims and prove the high potency of the proposed scheme compared to the conventional CSS using different fusion rules.

Cognitive Radio (CR) is a regulated technique for opportunistic access of idle resources. In CR, Spectrum sensing is one of its important key functionalities. It is used to sense the unused spectrum in an opportunistic manner. Energy detection constitutes a preferred approach for spectrum sensing in cognitive radio due to its simplicity and applicability. The traditional energy detection technique, which is based upon fixed threshold, is sensitive to noise uncertainty which is unavoidable in practical cases. This noise uncertainty gets the fixed threshold energy detector un-optimized in its performance. In this paper, an efficient energy detector is proposed for optimal CR performance. The proposed scheme is a dynamic threshold energy detection algorithm, in which, two threshold levels are utilized based upon the average energy received from the primary user (PU) during a specified period of observation. Thresholds evaluations are based upon estimating the noise uncertainty factor. These thresholds are used to maximize the probability of detection (Pd) and minimize the probability of false alarm (Pfa). Theoretical analysis and simulation results show the effectiveness of the proposed scheme in comparison to the traditional energy detection method with less increase in complexity.

Spectrum Sensing (SS) constitutes the most critical task in Cognitive Radio ( CR) systems for Primary User (PU) detection. Cooperative Spectrum Sensing (CSS) is introduced to enhance the detection reliability of the PU in fading environments. In this paper, we propose a soft decision based CSS algorithm using energy detection by taking into account the noise uncertainty effect. In the proposed algorithm, two threshold levels are utilized based on predicting the current PU activity, which can be successfully expected using a simple successive averaging process with time. The two threshold levels are evaluated based on estimating the noise uncertainty factor. In addition, they are toggled in a dynamic manner to compensate the noise uncertainty effect and to increase the probability of detection and decrease the probability of false alarm. Theoretical analysis is performed on the proposed algorithm to evaluate its enhanced false alarm and detection probabilities over the conventional soft decision CSS using different combining schemes. In addition, simulation results show the high efficiency of the proposed scheme compared to the conventional soft decision CSS, with high computational complexity enhancements.

Cognitive radio (CR) is a potential strategy for opportunistic access of idle resources to solve the conflicts between spectrum scarcity and underutilization. Spectrum sensing (SS) constitutes the most critical part in CR systems since the CR needs to detect the presence of primary signals reliably and quickly. Energy detection (ED) based SS is considered as the most preferable SS technique due to its simplicity and applicability. However, it is influenced by the effect of noise uncertainty which highly degrades its sensing performance. Cooperative spectrum sensing (CSS) is also introduced to mitigate some sensing problems such as multipath fading, shadowing, and hidden node problems. In this paper, we propose an enhanced fusion center (FC) rule for soft decision CSS using ED, which highly alleviates the noise uncertainty effect and enhances the sensing performance of cognitive radio networks (CRNs). In the proposed fusion rule, to increase the probability of detection and decrease the probability of false alarm, two dynamic thresholds are utilized by the FC. These thresholds are toggled based on predicting the current activity of the primary user (PU), and their values are dynamically changed based on estimating the noise uncertainty factor of the collected energy measurements from the CRs. To effectively predict (estimate) the current PU activity (the noise uncertainty factor), simple successive averaging processes over the collected energy measurements (the estimated noise variances) are performed by the FC, respectively. Theoretical analysis is performed on the proposed fusion rule for soft decision CSS to evaluate its enhanced false alarm and detection probabilities using different data combining schemes. Performance evaluations are also investigated to confirm the theoretical claims and to prove the effectiveness of the proposed scheme over the conventional ED based soft decision CSS.

Industrial wireless sensor networks are becoming popular for critical monitoring and control applications in industrial automation systems. For such type of applications, providing reliable real-time performance regarding data delivery is considered as a fundamental challenge. The problem becomes more prominent with mixed-criticality systems, where different data flow with different levels of criticality (importance) coexist and characterized by different requirements regarding delay and reliability. In this paper, we propose a wireless fieldbus protocol to enable real-time communication and service differentiation for cluster-based mixed-criticality networks. A process monitoring scenario of plastic extrusion is used to define the protocol requirements and elaborate the working principle of the proposed work. In our proposed protocol, each data flow is scheduled for channel access based on its criticality level using a distributed prioritized medium access mechanism that ensures a guaranteed channel access for the most critical traffic over other traffic types. The performance of the proposed protocol is analyzed analytically using a discrete-time Markov chain model to evaluate the performance in terms of delay and throughput. Moreover, the extensive simulations are conducted to prove the analytical claims and different performance assessments are provided, which also demonstrate the effectiveness of the proposed approach compared with the related existing work.

Age of Information (AoI) and delay are crucial performance metrics for Industrial Internet of Things (IIoT) applications not only to perform seamless actuation and control actions but also to enable self-organized and re-configurable manufacturing systems. A challenging task in heterogeneous IIoT networks is to minimize the AoI while maintaining a predefined delay constraint. In this work, we consider a Device-to-Device (D2D)-based heterogeneous IIoT network that supports two types of traffic flows, namely AoI-sensitive flow and delay-sensitive flow. First, we introduce a distributed backlog-aware random access protocol that allows the AoI-sensitive nodes to opportunistically access the channel based on the queue occupancy of the delay-sensitive node. Then, we develop an analytical framework to evaluate the average delay and the average AoI, and formulate an optimization problem to minimize the AoI under a given delay constraint. Finally, we provide numerical results to demonstrate the impact of different network parameters on the performance in terms of the average delay and the average AoI. We also give numerical solutions of the optimal parameters that minimize the AoI subject to a defined delay constraint.

The Routing Protocol for Low power and lossy networks (RPL) has been introduced as the de-facto routing protocol for the Industrial Internet of Things (IIoT). In heavy load scenarios, particular parent nodes are likely prone to congestion, which in turn degrades the network performance, in terms of packet delivery and delay. Moreover, there is no explicit strategy in RPL to prioritize the transmission of different traffic types in heterogeneous 6TiSCH networks, each according to its criticality. In this paper, we address the aforementioned issues by introducing a congestion control and service differentiation strategies to support heterogeneous 6TiSCH networks in IIoT applications. First, we introduce a congestion control mechanism to achieve load balancing under heavy traffic scenarios. The congestion is detected through monitoring and sharing the status of the queue backlog among neighbor nodes. We define a new routing metric that considers the queue occupancy when selecting the new parent node in congestion situations. In addition, we design a multi-queue model to provide prioritized data transmission for critical data over the non-critical ones. Each traffic type is placed in a separate queue and scheduled for transmission based on the assigned queue priority, where critical data are always transmitted first. The performance of the proposed work is evaluated through extensive simulations and compared with existing work to demonstrate its effectiveness. The results show that our proposal achieves improved packet delivery and low queue losses under heavy load scenarios, as well as improved delay performance of critical traffic. 

In the context of Industrial Internet of Things (IIoT), the 6TiSCH working group has been created with the aim to enable IPv6 over the IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) mode. The Routing Protocol for Low power and lossy networks (RPL) is introduced as the de-facto routing protocol for 6TiSCH networks. However, RPL is primarily designed to handle moderate traffic loads, whereas, during specific events in industrial applications, high traffic rates cause congestion problems at particular intermediate nodes while other nodes are underutilized. Accordingly, packets are dropped due to buffer overflow, which in turn degrades the network performance in terms of packet loss and delay. In this paper, we introduce a congestion detection and control mechanism to reliably handle high traffic load in 6TiSCH networks. The proposed method comprises two parent selection mechanisms to adapt to dynamic traffic load in the network. Congestion is detected through monitoring of the queue backlog level of each node and new parent nodes are selected accordingly to balance the load in the network. Moreover, a new routing metric is defined that considers the queue occupancy while selecting the new parent node. Performance evaluations are carried out to prove the effectiveness of the proposed method and the results show that with a marginal increase in the average delay, our proposal improves the performance of the standard RPL under heavy traffic load conditions by at least 60% and 74% in terms of the packet delivery and queue loss, respectively.

The Routing Protocol for Low power and lossy network (RPL) is designed to support communication requirements in 6TiSCH networks in Industrial Internet of Things (IIoT) applications. RPL is mostly optimized for uplink communication, however, less attention is given to maintain connectivity for downlink communications. Supporting downlink communications is non-trivial task in process automation and control scenarios within the IIoT. RPL in its current definition is inefficient to support reliable downlink communications in terms of scalability and memory requirements leading to significant degradation in network performance. This paper introduces HyS-R, a Hybrid Subscription-Recovery method to maintain downlink connectivity and mitigate memory limitations in large-scale 6TiSCH networks. The proposed method is based on a relief group that is used as alternative route to unreachable destinations in the network. An intermediate node subscribes to the relief group when it fails to advertise a destination to its next-hop node. In addition, members of the relief group keep searching for alternative forwarders to keep the communication traffic to a minimum. Performance evaluations are carried out and the results demonstrate that the proposed HyS-R attains significant improvements in downlink communications compared to RPL storing and non-storing modes with a margin of energy cost.

Many emerging Industrial Internet of Things (IIoT) applications involve the use of mobile devices, such as sensors and robots in industrial automation scenarios. Movement of mobile sensor nodes causes intermittent connectivity which in turn deteriorates the network performance in terms of packet loss and delay. The Routing Protocol for Low Power and Lossy Networks (RPL) is introduced as the standard routing protocol for IIoT networks. Although RPL constitutes a reliable and energy-efficient solution for static networks, there is no mechanism defined in the RPL standard of how to support routing in mobile IIoT networks. This paper introduces RMA-RP, a reliable mobility-aware routing protocol to support mobile IIoT networks. RMA-RP utilizes a dynamic motion detection mechanism based on the link quality to cope with topology changes by updating next-hop nodes. Moreover, an adaptive timer is introduced to manage the transmission rate of control messages in order to decrease the network overhead and in turn the energy consumption. We evaluate the performance of RMA-RP through extensive simulations in comparison to existing works and the results demonstrate that RMA-RP has at least 17% higher packet delivery ratio and achieves a reduction of 34% and 51% in terms of delay and network overhead, respectively.

Priority-oriented packet transmission (PPT) has been a promising solution for transmitting time-critical packets in timely manner during emergency scenarios in Internet ofThings (IoT). In this paper, we develop two associated discrete time Markov chain (DTMC) models to analyze performance of the PPT in an IoT network. Using the proposed DTMC models, we investigate the effect of traffic prioritization interms of average packet delay for a synchronous medium access control (MAC) protocol. Furthermore, the results obtained from analytical models are validated via discrete-event simulations. Numerical results prove the accuracy of the models and reveal the behavior of priority based packet transmissions.