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RPL-Routing Protocol for Low-Power and Lossy Networks (usually pronounced "ripple")-is the de facto standard for IoT networks. However, it neglects to exploit IoT devices' full capacity to optimize their transmission power, mainly because it is quite challenging to do so in parallel with the routing strategy, given the dynamic nature of wireless links and the typically constrained resources of IoT devices. Adapting the transmission power requires dynamically assessing many parameters, such as the probability of packet collisions, energy consumption, the number of hops, and interference. This paper introduces Adaptive Control of Transmission Power for RPL (ACTOR) for the dynamic optimization of transmission power. ACTOR aims to improve throughput in dense networks by passively exploring different transmission power levels. The classic solutions of bandit theory, including the Upper Confidence Bound (UCB) and Discounted UCB, accelerate the convergence of the exploration and guarantee its optimality. ACTOR is also enhanced via mechanisms to blacklist undesirable transmission power levels and stabilize the topology of parent-child negotiations. The results of the experiments conducted on our 40-node, 12-node testbed demonstrate that ACTOR achieves a higher packet delivery ratio by almost 20%, reduces the transmission power of nodes by up to 10 dBm, and maintains a stable topology with significantly fewer parent switches compared to the standard RPL and the selected benchmarks. These findings are consistent with simulations conducted across 7 different scenarios, where improvements in end-to-end delay, packet delivery, and energy consumption were observed by up to 50%.

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., https://www.mdu.se/en/malardalen-university/research/research-projects/greener-intelligent-energy-management-in-connected-construction-sites

Internet-of-Things (IoT) applications are envisaged to evolve to support mobility of devices while providing quality of service in the system. To keep the connectivity of the constrained nodes upon topological changes, it is of vital importance to enhance the standard protocol stack, including the Routing Protocol for Lossy Low-power Networks (RPL), with accurate and real-time control decisions. We argue that devising a centralized mobility management solution based on a lightweight Software Defined Networking (SDN) controller provides seamless handoff with reasonable communication overhead. A centralized controller can exploit its global view of the network, computation capacity, and flexibility, to predict and significantly improve the responsiveness of the network. This approach requires the controller to be fed with the required input and to get involved in the distributed operation of the standard RPL. We present SDMob, which is a lightweight SDN-based mobility management architecture that integrates an external controller within a constrained IoT network. SDMob lifts the burden of computation-intensive filtering algorithms away from the resource-constrained nodes to achieve seamless handoffs upon nodes’ mobility. The current work extends our previous work, by supporting multiple mobile nodes, networks with a high density of anchors, and varying hop-distance from the controller, as well as harsh and realistic mobility patterns. Through analytical modeling and simulations, we show that SDMob outperforms the baseline RPL and the state-of-the-art ARMOR in terms of packet delivery ratio and end-to-end delay, with an adjustable and tolerable overhead. With SDMob, the network provides close to 100% packet delivery ratio (PDR) for a limited number of mobile nodes, and maintains sub-meter accuracy in localization under random mobility patterns and varying network topologies.

Accurate and timely mobility support in Internet of Things (IoT) applications is a challenging issue, considering the inherent scarce resources of IoT devices. However, the computational, memory and communication burden may be pushed into more "muscled" Software Defined Network (SDN) controllers. A centralised controller can exploit its global view of the network to predict and support seamless handovers. However, it requires the controller to be enhanced with extra link quality information. In this work, we present SDMob, an SDN-based mobility management solution that lifts the burden of computation intensive filtering algorithms from resource constrained nodes and achieves accurate and fast handovers upon nodes' mobility under Routing Protocol for Lossy Low-power Networks (RPL) and IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). We show that SDMob improves the baseline RPL and the state-of-the-art mRPL in terms of packet delivery ratio leveraging more reliable routing and applying Particle filter and variations of Kalman filter on radio signal strength data enables more accurate localization for complex real world trajectories., MobiFog: mobility management in Fog-assisted IoT networks, Health5G: Future eHealth powered by 5G, FlexiHealth: flexible softwarized networks for digital healthcare, GREENER: Intelligent energy management in connected construction sites

Heterogeneous networks (hetnets) is an interconnectionof distinctive networking paradigms to enable widerreachability and greater collaborations. In large Internet-of-Things (IoT) applications, many wireless networks are spatiallyco-located and intertwined forming hetnets; for instance, healthmonitoring devices utilising ZigBee or IEEE 802.15.4 co-exist in2.4 GHz spectrum alongside Wi-Fi devices. Interoperability ornon-obtrusive operations are required among the disjoint domainsto achieve operational efficiency in overall IoT ecosystem.Specifically, network interoperability in hetnets assure desiredreachability, resource orchestration and network quality. Inthis work, we have modelled and implemented a simulationenvironment for hetnets to support different schemes of networkinteroperability under distributed and centralised managementof network. The implementation has been evaluated for networkscalability and reliability to replicate large IoT hetnets. Byevaluating against increasing number of nodes in the hetnet,the mean latency under distributed management is improved by100-fold with the centralised management. Similar observationscould also be made for throughput and packet loss rate.

Routing Protocol for Low-Power and Lossy Networks(RPL) as the most widely used routing protocol for constrained Internet of Things (IoT) devices optimizes the number of routing states that nodes maintain to minimize resource consumption.Given that the routes are optimized for data collection, this leads to selecting sub-optimal routes, particularly in case of east-westor ”transversal” traffic. Additionally, RPL neglects interactions with a central entity in the network for monitoring or managing routes and enabling more flexibility and responsiveness to the system.In this paper, we present RPL with Route Projection (RPL-RP)that enables collecting siblings’ relations at the root node in order to inject routing states to the routers. This backward-compatible RPL extension still favors collection-based traffic patterns but it enriches the way routing protocol handles other flow directions.We address different advantages of RPL-RP in contrast to standard RPL and evaluate its overhead and improvements in terms of end-to-end delay, control overhead and packet delivery ratio. Overall, RPL-RP halves the end-to-end delay and increases network reliability by 5% while increasing network overhead by only 3%

There are many initiatives and technologies working towards implementing factories of the future. One consensus is that the classical hierarchical automation system design needs to be flattened while supporting the functionality of both Operation Technology (OT) and Information Technology (IT) within the same network infrastructure. To achieve the goal of IT/OT convergence in process automation, an evolutionary transition is preferred. Challenges are foreseen during the transition, mainly caused by the traditional automation architecture, and the main challenge is to identify the gap between the current and future network architectures. To address the challenges, in this paper, we describe one desired future scenario for process automation and carry out traffic measurements from a pulp and paper mill. The measured traffic is further analyzed, which reveals representative traffic characteristics in the process automation. Finally, the key challenges and future directions towards a system architecture for factories of the future are presented.

ESS-H - Embedded Sensor Systems for Health Research Profile, Health5G: Future eHealth powered by 5G

Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML community due to its wide range of applications. HAR is one of the most helpful technology tools to support the elderly’s daily life and to help people suffering from cognitive disorders, Parkinson’s disease, dementia, etc. It is also very useful in areas such as transportation, robotics and sports. Deep learning (DL) is a branch of ML based on complex Artificial Neural Networks (ANNs) that has demonstrated a high level of accuracy and performance in HAR. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DL models widely used in the recent years to address the HAR problem. The purpose of this paper is to investigate the effectiveness of their integration in recognizing daily activities, e.g., walking. We analyze four hybrid models that integrate CNNs with four powerful RNNs, i.e., LSTMs, BiLSTMs, GRUs and BiGRUs. The outcomes of our experiments on the PAMAP2 dataset indicate that our proposed hybrid models achieve an outstanding level of performance with respect to several indicative measures, e.g., F-score, accuracy, sensitivity, and specificity. © 2020 by the authors. Licensee MDPI, Basel, Switzerland., Export Date: 21 October 2020; Letter; Correspondence Address: Abbaspour, S.; School of Innovation, Design, and Engineering, Mälardalen University, Engineering Department, University of Qom, Engineering Department, University of QomSweden; email: saedeh.abbaspour@mdh.se; Funding text 1: Funding: This research was funded by ESS-H plus project grant number [16871].

Mobility is becoming a challenging issue in upcoming IoT applications, where it is crucial to employ mobile entities. Patients with sensors attached to their body in health monitoring application, AGVs in industrial monitoring and factory automation applications, cars with several sensing devices in vehicular applications are a few examples of use cases with the need for mobile nodes. In parallel, Fog computing has revolutionized network architecture, while enabling local processing of measurements, and reducing bandwidth overhead, which results in a more reliable system and real-time support. However, mobility management is a missing framework within the mobile IoT networks with Fog computing architecture. This paper provides a simple and generic seamless handoff model, dubbed as MobiFog, where it addresses the handoff mechanism with zero delay, while providing high reliability., Future factories in the Cloud, MobiFog: mobility management in Fog-assisted IoT networks, Health5G: Future eHealth powered by 5G, FlexiHealth: flexible softwarized networks for digital healthcare

It has become widely accepted that the Internet of Things (IoT) devices and technologies are the key enablers for many emerging applications including remote health monitoring. Various physiological sensing devices have been designed and equipped with different radio technologies. The choice of radio hardware plays an important role on the overall performance of the system since it imposes some limitations on the delivered quality of service. Hence, it is critical to properly evaluate the embedded radio technology based on the application requirements. In this paper, we perform extensive experiments on Shimmer physiological sensors that is one of the leading providers of wearable wireless sensor products powered by Bluetooth classic radio. Shimmer sensors are designed and used for monitoring various human health information such as temperature, heart rate, movement, etc. We review and investigate different scenarios in which Shimmer devices are used by medical practitioners to monitor the ECG signal and the movement of a human. This study shows that the Shimmer device can provide reliable data delivery by using a specific configuration. For instance, employing a maximum number of seven Shimmer devices attached on a body at home environment within the range of at most 5 m and with the sampling rate of 512 Hz would result in a reasonable quality of service, while varying these parameters may degrade the overall performance. Mobility of human body, noisy environment, and higher packet transmission rates are some examples that will reduce the system quality. © Copyright 2019 IADIS Press All rights reserved., ESS-H - Embedded Sensor Systems for Health Research Profile, MobiFog: mobility management in Fog-assisted IoT networks, Health5G: Future eHealth powered by 5G, FlexiHealth: flexible softwarized networks for digital healthcare

Nowadays, the Industrial Internet of Things (IIoT) has the potential to be implemented in factories and supply chains to improve manufacturing efficiency. It is becoming more common to use mobile robots in such factories for further improvements. Adding data collection capability to the mobile robots would realize the mobile sink deployment in future factories. As it is important to reduce the deployment cost, we are aiming at a network with minimum number of mobile sinks while ensuring network reliability and timeliness. In this paper, we analytically model a given trajectory for the motion of mobile sinks and the routing of mobile sinks along the trajectory in an IIoT system. We introduce an optimization problem in the form of Integer Linear Programming (ILP) to specify the minimum number of required mobile sinks to reduce deployment cost of an IIoT system, and also to identify the routing of multiple mobile sinks along a given trajectory. The proposed ILP model can be solved by several existing off-the-shelf ILP-solvers., QC 20180614

ESS-H - Embedded Sensor Systems for Health Research Profile

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%.

ESS-H - Embedded Sensor Systems for Health Research Profile, READY - Research Environment for Advancing Low Latency Internet, Future factories in the Cloud

ESS-H - Embedded Sensor Systems for Health Research Profile, ecare@home

There is an increasing demand in designing heterogeneous networks, where various low-power wireless radios such as ZigBee and Bluetooth devices coexist, while sensing devices may generate traffic with different intervals. In this work, we devise an architecture of a cross layer controller, encompassing a software defined radio at the data plane to manage radio access, and a software defined networking at the control plane to manage data flow, while considering the application requirements. The overall architecture aims at simplifying system management and network re-configuration, while providing better network performance., ESS-H - Embedded Sensor Systems for Health Research Profile, READY - Research Environment for Advancing Low Latency Internet, ecare@home, Future factories in the Cloud

Ensuring interoperability in the future Internet of Things applications can be a challenging task, especially in mission-critical applications such as Health Monitoring Systems. Existing low-power wireless network architectures are designed in isolated networks, and ensure a satisfying level of performance in homogeneous networks. However, with co-existence of different low-power networks, the interoperability related problems arise. To bridge this gap in this paper, we study various protocol stacks (i.e., Bluetooth, Bluetooth Low Energy, IEEE 802.15.4, ZigBee, 6LoWPAN and IEEE 802.15.6), and explain their specific features. Furthermore, we provide a generic protocol stack design that facilitates multiple radios with different protocol stacks, regardless of being IP-based or non-IP-based networks. We see this approach as a possibility to enhance network performance in terms of reliability, timeliness, and security, while providing higher levels of scalability and connectivity.

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%., READY - Research Environment for Advancing Low Latency Internet