Low-power wireless Internet of Things (IoT) devices employ Time Slotted Channel Hopping (TSCH) Medium Access Control to achieve predictable timing behaviour. TSCH aims at collision-free scheduling by exploiting diversity over time (slots) and frequency (channels). However, existing works on performance and worst-case analysis are based on deterministic models, which lead to rather pessimistic non-realistic results, i.e. tools for probabilistic performance analysis of TSCH schedulers are still lacking. In this context, we devised a Stochastic Network Calculus model that enables to calculate end-to-end delays for specific traffic flows and (deadline) violation probability, building on Moment Generating Functions. We instantiate this SNC model and provide bounds for three widely used TSCH schedulers, namely Minimal Scheduling Function, Orchestra, and a custom collision-free scheduler, with different parameters such as radio duty-cycle, radio link quality, and traffic arrival rate. We demonstrate that our proposed model closely follows the simulation results, under different network scenarios.

Applications of the Internet of Things (IoT), particularly within Industrial IoT, impose stricter reliability and efficiency requirements on low-power wireless technologies. This has driven the creation of new medium access protocols, such as Time Slotted Channel Hopping (TSCH). Recently, autonomous schedulers, which manage wireless links without node negotiation, are gaining popularity due to their lightweight and reliable operation. However, challenges arise with node mobility and dynamic traffic, as current schedulers use a static allocation method. To overcome this gap, we propose Forte, a hybrid scheduler that combines autonomous scheduling for basic connectivity with a centralized on-demand scheduler that allocates extra timeslots and frequency channels so that nodes adapt to the dynamic requirements. The centralized module formulates a Lyapunov optimization to guarantee queue stability while minimizing negotiation overhead and nodes’ duty-cycles. Forte outperforms the state-of-the-art by reducing packet end-to-end delay and increasing packet delivery ratio, all with minimal duty-cycle increase.

Future mines are expected to be operated by increasingly autonomous construction equipment, requiring dependable intercommunication between control centers, human operators, and construction machines such as excavators, drill rigs, and scrapers. Achieving stable, reliable and timely communications in such harsh and ever-changing environments is quite challenging. However, the changes in the three-dimensional (3D) topography of the mine are mostly predictable and scheduled through mine planning methods, which consequently can be used for radio communications network planning, namely to dimension, orientate and locate Base Station (BS) antennas in the mine field. In this context, we consider BSs to exist in the form of fixed cells or Cell on Wheel (CoW). The former is deployed in fixed locations throughout a long-term mine operation, while the latter is expected to be moved based on the changes in the topology of the terrain. We present an optimization framework that builds on an evolutionary algorithm to plan private 5G networks based on a given mine plan, featuring both fixed and movable base stations. We assess how the changing terrain affects the wireless coverage on the mine’s surface and demonstrate that, in certain scenarios, CoWs improve the average Signal-to-Interference & Noise Ratio (SINR) by 1 to 10 dB.

There is a growing interest in increasing the flexibility and the mobility of industrial infrastructures to support novel industrial applications. To support this change, industrial networks must provide real-time guarantees while integrating a great variety of traffic over a cohesive network infrastructure. Specifically, there is special interest in the integration of wired and wireless communications in the industrial domain. We present a solution that integrates a Time-Sensitive Networking Ethernet network with a low-power 6TiSCH IoT network and discuss the implementation alternatives and their implications on delay and resource consumption. The main goal is to experimentally identify the research challenges and necessary enhancements for interoperability between constrained battery-driven wireless communication with the time sensitive wired networks.

Many industrial real-time applications in various domains, e.g., automotive, industrial automation, industrial IoT, and industry 4.0, require ultra-low end-to-end network latency, often in the order of 10 milliseconds or less. The IEEE 802.1 time-sensitive networking (TSN) is a set of standards that supports the required low-latency wired communication with ultra-low jitter. The flexibility of such a wired connection can be increased if it is integrated with a mobile wireless network. The fifth generation of cellular networks (5G) is capable of supporting the required levels of network latency with the Ultra-Reliable Low Latency Communication (URLLC) service. To fully utilize the potential of these two technologies (TSN and 5G) in industrial applications, seamless integration of the TSN wired-based network with the 5G wireless-based network is needed. In this article, we provide a comprehensive and well-structured snapshot of the existing research on TSN-5G integration. In this regard, we present the planning, execution, and analysis results of the systematic review. We also identify the trends, technical characteristics, and potential gaps in the state of the art, thus highlighting future research directions in the integration of TSN and 5G communication technologies. We notice that 73% of the primary studies address the time synchronization in the integration of TSN and 5G technologies, introducing approaches with an accuracy starting from the levels of hundred nanoseconds to one microsecond. Majority of primary studies aim at optimizing communication latency in their approach, which is a key quality attribute in automotive and industrial automation applications today.

Integrating wired Ethernet networks, such as Time-Sensitive Networks (TSN), to 5G cellular network requires a flow management technique to efficiently map TSN traffic to 5G Quality-of-Service (QoS) flows. The 3GPP Release 16 provides a set of predefined QoS characteristics, such as priority level, packet delay budget, and maximum data burst volume, which can be used for the 5G QoS flows. Within this context, mapping TSN traffic flows to 5G QoS flows in an integrated TSN-5G network is of paramount importance as the mapping can significantly impact on the end-to-end QoS in the integrated network. In this paper, we present a novel and efficient mapping algorithm to map different TSN traffic flows to 5G QoS flows. To the best of our knowledge, this is the first QoS-aware mapping algorithm based on the application constraints used to exchange flows between TSN and 5G network domains. We evaluate the proposed mapping algorithm on synthetic scenarios with random sets of constraints on deadline, jitter, bandwidth, and packet loss rate. The evaluation results show that the proposed mapping algorithm can fulfill over 90% of the applications’ constraints.

Time Sensitive Networking (TSN) is a set of IEEE standards based on switched Ethernet that aim at meeting high-bandwidth and low-latency requirements in wired communication. TSN implementations typically do not support integration of wireless networks, which limits their applicability to many industrial applications that need both wired and wire-less communication. The development of 5G and its promised Ultra-Reliable and Low-Latency Communication (URLLC) in-tegrated with TSN would offer a promising solution to meet the bandwidth, latency and reliability requirements in these industrial applications. In order to support such an integration, we propose a technique to translate the traffic between TSN and 5G communication technologies. As a proof of concept, we implement the translation technique in a well-known TSN simulator, namely NeSTiNg, that is based on the OMNeT ++ tool. Furthermore, we evaluate the proposed technique using an automotive industrial use case.

Data gathering using mobile sink (MS) based on rendezvous points (RPs) is a need in several Internet of Things (IoT) applications. However, devising energy-efficient and reliable tour planning strategies for MS is a challenging issue, considering that sensors have finite buffer space and disparate sensing rates. This is even more challenging in delay-tolerant networks, where it is more desirable to select the shortest traveling path. There exist several algorithms on MS scheduling, which are based on hierarchical protocols for data forwarding and data collection. These algorithms are lacking efficient tradeoff between the Quality-of-Service (QoS) requirements in terms of energy efficiency, reliability, and computational cost. Besides, these algorithms have shown high packet losses while jointly performing MS tour planning and buffer overflow management. To address these limitations, we propose EE-MSWSN, an energy-efficient MS wireless sensor network that reliably collects data by implementing efficient buffer management. It forms novel clustered tree-based structures to cover all the network, and select each RP based on 1) hop count; 2) number of accumulated data in each clustered tree; and 3) distance to the stationary sink. The extensive simulation results verify that the EE-MSWSN minimizes tour length for various network configurations and incurs less energy consumption while reliably gathering data without packet losses as compared with existing protocols.

Heterogeneous networks (hetnets) is an interconnection of distinctive networking paradigms to enable wider reachability and greater collaborations. In large Internet-of-Things (IoT) applications, many wireless networks are spatially co-located and intertwined forming hetnets; for instance, health monitoring devices utilising ZigBee or IEEE 802.15.4 co-exist in 2.4 GHz spectrum alongside Wi-Fi devices. Interoperability or non-obtrusive operations are required among the disjoint domains to achieve operational efficiency in overall IoT ecosystem. Specifically, network interoperability in hetnets assure desired reachability, resource orchestration and network quality. In this work, we have modelled and implemented a simulation environment for hetnets to support different schemes of network interoperability under distributed and centralised management of network. The implementation has been evaluated for network scalability and reliability to replicate large IoT hetnets. By evaluating against increasing number of nodes in the hetnet, the mean latency under distributed management is improved by 100-fold with the centralised management. Similar observations could also be made for throughput and packet loss rate.

Fog computing offers a wide range of service levels including low bandwidth usage, low response time, support of heterogeneous applications, and high energy efficiency. Therefore, real-time embedded applications could potentially benefit from Fog infrastructure. However, providing high system utilization is an important challenge of Fog computing especially for processing embedded applications. In addition, although Fog computing extends cloud computing by providing more energy efficiency, it still suffers from remarkable energy consumption, which is a limitation for embedded systems. To overcome the above limitations, in this paper, we propose SoFA, a Spark-oriented Fog architecture that leverages Spark functionalities to provide higher system utilization, energy efficiency and scalability. Compared to the common Fog computing platforms where edge devices are only responsible for processing data received from their IoT nodes, SoFA leverages the remaining processing capacity of all other edge devices. To attain this purpose, SoFA provides a distributed processing paradigm by the help of Spark to utilize the whole processing capacity of all the available edge devices leading to increase energy efficiency and system utilization. In other words, SoFA proposes a near-sensor processing solution in which the edge devices act as the Fog nodes. In addition, SoFA provides scalability by taking advantage of Spark functionalities. According to the experimental results, SoFA is a power-efficient and scalable solution desirable for embedded platforms by providing up to 3.1x energy efficiency for the Word-Count benchmark compared to the common Fog processing platform.

Future industrial networks are envisioned to support a broad set of applications and services with diverse communication requirements that are difficult to meet adopting a single communication technology. Software-Defined Networking and network virtualization techniques represent two promising innovations and abstractions to improve the network management and to support scalable network control functions, flexible resource allocation, and changes in the network traffic.This paper draws a research direction towards software-defined management of heterogeneous Industry 4.0 communication networks, proposing a software-defined architecture that is able to support a multitude of diverse wired and wireless communication technologies, while meeting the requirements of diverse applications. The paper also provides an implementation roadmap, discussing how to extend the EmPOWER framework to fulfill the intended target, pointing at design challenges and solutions.

Summary Recently, a growing trend has emerged toward using Internet of Things (IoT) in the context of industrial systems, which is referred to as industrial IoT. To deal with the time‐critical requirements of industrial applications, it is necessary to consider reliability and timeliness during the design of an industrial IoT system. Through the separation of the control plane and the data plane, software‐defined networking provides control units (controllers) coexisting with sink nodes, efficiently coping with network dynamics during run‐time. It is of paramount importance to select a proper number of these devices (i.e., software‐defined networking controllers and sink nodes) and locate them wisely in a network to reduce deployment cost. In this paper, we optimize the type and location of sinks and controllers in the network, subject to reliability and timeliness as the prominent performance requirements in time‐critical IoT systems through ensuring that each sensor node is covered by a certain number of sinks and controllers. We propose PACSA‐MSCP, an algorithm hybridizing a parallel version of the max‐min ant system with simulated annealing for multiple‐sink/controller placement. We evaluate the proposed algorithm through extensive experiments. The performance is compared against several well‐known methods, and it is shown that our approach outperforms those methods by lowering the total deployment cost by up to 19%. Moreover, the deviation from the optimal solution achieved by CPLEX is shown to be less than 2.7%.

Supporting mobility in wireless sensor networks is one of the major requirements for future Internet of Things (IoT) applications. This work focuses on optimizing the objective function of Routing Protocol for Low-power and Lossy Networks (RPL) in mobile applications. RPL routing is the most common standard routing protocol designed for IoT applications. We optimized RPL objective function by combining several RPL parameters, such as (i) Expected Transmission Count (ETX), (ii) number of hops, and (iii) average Received Signal Strength Indicator (RSSI) as inputs in a fuzzy logic model. These parameters are more influenced in mobile applications. We applied the fuzzy decision to the mRPL (a hand-off enabled RPL mechanism). We fine-tuned the weighting scheme by running extensive simulations to achieve reliable data communication. We found that the fuzzy-based hand-off approach provides high reliability by successfully delivering nearly 100% of data packets, while achieving a very short hand-off delay.

The fast development of sensing devices and radios enables more powerful and flexible remote health monitoring systems. Considering the future vision of the Internet of Things (IoT), many requirements and challenges rise to the design and implementation of such systems. Bridging the gap between sensor nodes on the human body and the Internet becomes a challenging task in terms of reliable communications. Additionally, the systems will not only have to provide functionality, but also be highly secure. In this paper, we provide a survey on existing communication protocols and security issues related to pervasive health monitoring, describing their limitations, challenges, and possible solutions. We propose a generic protocol stack design as a first step toward handling interoperability in heterogeneous low-power wireless body area networks.

Wireless communication will play an increasingly important role in future factory automation and process control, where the presence of mobile autonomous devices is expected to grow. However, wireless links are prone to errors due to shadowing and multi-path fading, which is even more severe in dynamic environments. These problems can be attenuated by using a mesh backbone to which mobile node connect to, using a hand-off algorithm. This solution is particularly important under real-time requirements typically found in factory automation. In this paper, we devise the Double-Threshold Hand-off (DoTHa) algorithm, a novel hand-off mechanism that triggers a hand-off in various environmental conditions. As a case study, we carry out the tele-operation of a mobile robot through a wireless mesh network in an indoor setting, using a wireless chain network protocol (WICKPro-SRT) that supports soft real-time traffic. We empirically compared DoTHa with two existing hand-off algorithms based on single and double hysteresis margin. The results revealed that DoTHa achieves Data Delivery Ratio (DDR) close to 100% whereas the single hysteresis-based hand-off suffers from frequent disconnections, dropping DDR to 88%. The double hysteresis-based hand-off shows higher ping-pong effect than DoTHa, doubling the number of hand-offs in some scenarios.

Congestion control is a challenging issue in wireless sensor networks with limited channel bandwidth. Thus, many protocols have been designed to provide a distributed traffic control during packet forwarding. However, all these approaches are applied to single-hop communication networks, ignoring the multi-hop restrictions. In this work, we take advantage of software defined networking paradigm by devising a controller node in such a way that it collects all the necessary information from wireless sensor network nodes. Thus, based on hop count and local traffic information, controller decides for possible flow path changes to evenly distribute the traffic. The evaluations revealed that the SDN-TAP outperforms conventional routing protocols by reducing packet loss rate up to 46%.

Industrial applications have been shifting towards wireless multi-hop networks in recent years due to their lower cost of deployment and reconfiguration compared with their wired counterparts. These wireless networks usually must support real-time communication to meet the application requirements. For this reason, Wireless Mesh Networks (WMNs) are potential candidates for industrial applications as they support a fixed infrastructure of static nodes for relaying packets. To meet the application demands, we modify the wireless chain network protocol (WICKPro) to support soft real-time traffic in WMNs with chain topologies over IEEE 802.11. We employ tele-operation of mobile robots as our case study, and perform extensive simulation and laboratory experiments. We show that the data delivery ratio is increased up to 42% in a scenario with 7 nodes, when the maximum end-to-end delay tolerated by the application is doubled. This is particularly suited to soft real-time applications that can trade longer delays by higher reliability. Moreover, when compared with a distributed priority-based token-passing protocol (RT-WMP), the lower overhead of WICKPro allows, in an error-free scenario, obtaining a throughput improvement of 33.42% on average.

Hand-off (or hand-over), the process where mobile nodes select the best access point available to transfer data, has been well studied in wireless networks. The performance of a hand-off process depends on the specific characteristics of the wireless links. In the case of low-power wireless networks, hand-off decisions must be carefully taken by considering the unique properties of inexpensive low-power radios. This paper addresses the design, implementation and evaluation of smart-HOP, a hand-off mechanism tailored for low-power wireless networks. This work has three main contributions. First, it formulates the hard hand-off process for low-power networks (such as typical wireless sensor networks - WSNs) with a probabilistic model, to investigate the impact of the most relevant channel parameters through an analytical approach. Second, it confirms the probabilistic model through simulation and further elaborates on the impact of several hand-off parameters. Third, it fine-tunes the most relevant hand-off parameters via an extended set of experiments, in a realistic experimental scenario. The evaluation shows that smart-HOP performs well in the transitional region while achieving more than 98 percent relative delivery ratio and hand-off delays in the order of a few tens of a milliseconds.