Local Gateway Research Articles

Internet-of-Things Traffic Analysis and Device Identification Based on Two-Stage Clustering in Smart Home Environments

Smart home environments, which consist of various Internet of Things (IoT) devices to support and improve our daily lives, are expected to be widely adopted in the near future. Owing to a lack of awareness regarding the risks associated with IoT devices and challenges in replacing or the updating their firmware, adequate security measures have not been implemented. Instead, IoT device identification methods based on traffic analysis have been proposed. Since conventional methods process and analyze traffic data simultaneously, bias in the occurrence rate of traffic patterns has a negative impact on the analysis results. Therefore, this paper proposes an IoT traffic analysis and device identification method based on two-stage clustering in smart home environments. In the first step, traffic patterns are extracted by clustering IoT traffic at a local gateway located in each smart home and subsequently sent to a cloud server. In the second step, the cloud server extracts common traffic units to represent IoT traffic by clustering the patterns obtained in the first step. Two-stage clustering can reduce the impact of data bias, because each cluster extracted in the first clustering is summarized as one value and used as a single data point in the second clustering, regardless of the occurrence rate of traffic patterns. Through the proposed two-stage clustering method, IoT traffic is transformed into time series vector data that consist of common unit patterns and can be identified based on time series representations. Experiments using public IoT traffic datasets indicated that the proposed method could identify 21 IoTs devices with an accuracy of 86.9%. Therefore, we can conclude that traffic analysis using two-stage clustering is effective for improving the clustering quality, device identification, and implementation in distributed environments.

Future Hybrid Technology for Pay TV Platform

The main objective of this study is to analyze the potentiality of new technology of TV broadcasting and video distribution systems and how to adapt to the new trend of video viewing experience and design a future generation TV for the Bangladesh Pay TV industry. This research is based on Bangladesh and the interest of Bangladesh's Pay-TV industry delves into a novel approach aimed at mitigating these challenges by harnessing terrestrial or mobile networks for video broadcasting, thereby upgrading the old-fashioned broadcasting system or diminishing reliance on conventional internet-based streaming for mobile. This study adapts to the latest practice and implementation of ATSC 3.0 Terrestrial broadcasting and video streaming technology enhancement and business success along with emerging 5G network feasibility. ATSC 3.0 terrestrial broadcasting for mobile users has a significant impact on view experience, and seamless video transmission without the usage of internet data, especially in rural areas of the country where the 4G network is very limited. Local Gateway Node will receive ATSC 3.0 live TV and locally added offline video or streaming apps injected through API to deliver household through private CDN. This study offers a comprehensive technical feasibility, benefits, and implications of this innovative approach, upgradation of terrestrial broadcasting system highlighting its potential to revolutionize video delivery to mobile users. This study analyzes the technology evolution and adaptability with the recent trend of video content viewing experience, the importance of this study is significant for Bangladesh's pay-TV market to secure the future business.

Real-time automatic integrated monitoring of barn environment and dairy cattle behaviour: Technical implementation and evaluation on three commercial farms

Due to increasing herd sizes and automation on dairy farms there is an important need for automated monitoring of cow production, health, and welfare. Despite much progress in automatic monitoring techniques, there is still a need to integrate data from multiple sources to create a comprehensive overview and accurate diagnosis of a cow’s state. To aid the technological development of data integration, a prototype of an open and customizable automatic system that integrates data from multiple sensors relating to barn environment and cow behaviour was developed. The system integrates data from sensors that measure barn climate (e.g., temperature, humidity, wind speed), air quality (e.g., CO2 concentration), water use and temperature, the moisture and temperature of the litter and cow behaviour (e.g., lying, eating, ruminating). An external weather system and video recording system are also included. The system’s architecture consists of four main elements: sensors, nodes, gateways, and backend. The data are recorded by sensors, then locally processed on custom-developed sensor nodes, and then transmitted via radio channels to local gateways that combine the data from multiple nodes and transmit them to distributed digital storage (“the cloud”) via a 3G/4G cellular network. On the cloud, the data are further processed and stored in a database. The data are then presented to the user continuously and in real time on a dashboard that can be accessed via the internet. In the design of the local wireless network, care was taken to avoid data packet collision and thus to minimize data loss. To test the system’s performance, the system was installed and operated on three commercial dairy cattle farms for one year. The system provided high data stability with minimal loss and outliers, showing that the system is reliable and suitable for long term application on commercial dairy farms. The system’s architecture, communication network, and data processing and visualization applications form an open framework for research and development purposes, allowing it to be customized and fine-tuned before being deployed as a management assistant on commercial dairy farms. Missing elements that should be added in the future are the integration of the data from the milking parlour and cow identification. Algorithms to integrate information from multiple sensors can be added to provide a comprehensive system that monitors all aspects related to cow welfare, health, and production automatically, remotely and in real time, thereby supporting farmers in important management decision-making.

Developing a firewater deluge monitoring and forecasting system based on GA-ARMA model

PurposeThe firewater deluge system (FDS) can provide water automatically through a deluge valve when a fire breaks out. However, there are many fire hazards caused by the abnormal operating state of the FDS. To monitor and predict the working state of the FDS, this paper aims to propose a firewater deluge monitoring and forecasting system using the Internet of Things (IoT) technology.Design/methodology/approachThe firewater deluge monitoring and forecasting system consists of three layers: the sensing layer, network layer and application layer. The firewater pressure obtained by the monitoring nodes was transmitted to the local gateway and then to the remote monitoring center. In the application layer, an autoregressive moving average (ARMA) model was put forward to forecast the firewater pressure. Furthermore, a genetic algorithm (GA) was proposed to perfect the order determination method of the ARMA model. Finally, a Web application was developed to display the real time and predicted working status of the FDS.FindingsThe predicted results show that the ARMA model improved by the GA (GA-ARMA) is significantly better than traditional ARMA models in terms of mean relative error, mean absolute error and mean square error. Moreover, the proposed system is demonstrated to be effective, and an early warning can be alerted to remind users of repairing abnormal FDS equipment ahead of fire dangers.Originality/valueThe proposed system cannot only be applied to the FDS of all buildings to avoid fire hazards by monitoring and predicting the working state of the FDS, but can also be widely used in other fields, such as environmental monitoring, intelligent logistics and intelligent transportation.

MidSiot: A Multistage Intrusion Detection System for Internet of Things

Internet of Things (IoT) has been thriving in recent years, playing an important role in a multitude of various domains, including industry 4.0, smart transportation, home automation, and healthcare. As a result, a massive number of IoT devices are deployed to collect data from our surrounding environment and transfer these data to other systems over the Internet. This may lead to cybersecurity threats, such as denial of service attacks, brute-force attacks, and unauthorized accesses. Unfortunately, many IoT devices lack solid security mechanisms and hardware security supports because of their limitations in computational capability. In addition, the heterogeneity of devices in IoT networks causes nontrivial challenges in detecting security threats. In this article, we present a collaborative intrusion detection system (IDS), namely, MidSiot, deployed at both Internet gateways and IoT local gateways. Our proposed IDS consists of three stages: (1) classifying the type of each IoT device in the IoT network; (2) differentiating between benign and malicious network traffic; and (3) identifying the type of attacks targeting IoT devices. The last two stages are handled by the Internet gateways, whereas the first stage is on the local gateway to leverage the computational resources from edge devices. The evaluation results on three popular IDS datasets (IoTID20, CIC-IDS-2017, and BOT-IoT) indicate our proposal could detect seven common cyberattacks targeting IoT devices with an average accuracy of 99.68% and outperforms state-of-the-art IDSs. This demonstrates that MidSiot could be an effective and practical IDS to protect IoT networks.

Realguard: A Lightweight Network Intrusion Detection System for IoT Gateways

Cyber security has become increasingly challenging due to the proliferation of the Internet of things (IoT), where a massive number of tiny, smart devices push trillion bytes of data to the Internet. However, these devices possess various security flaws resulting from the lack of defense mechanisms and hardware security support, therefore making them vulnerable to cyber attacks. In addition, IoT gateways provide very limited security features to detect such threats, especially the absence of intrusion detection methods powered by deep learning. Indeed, deep learning models require high computational power that exceeds the capacity of these gateways. In this paper, we introduce Realguard, an DNN-based network intrusion detection system (NIDS) directly operated on local gateways to protect IoT devices within the network. The superiority of our proposal is that it can accurately detect multiple cyber attacks in real time with a small computational footprint. This is achieved by a lightweight feature extraction mechanism and an efficient attack detection model powered by deep neural networks. Our evaluations on practical datasets indicate that Realguard could detect ten types of attacks (e.g., port scan, Botnet, and FTP-Patator) in real time with an average accuracy of 99.57%, whereas the best of our competitors is 98.85%. Furthermore, our proposal effectively operates on resource-constraint gateways (Raspberry PI) at a high packet processing rate reported about 10.600 packets per second.

Automatic Remote Farm Irrigation System with WSN and Weather Forecasting

The paper showcases the system used for automating agriculture using wireless sensor network (WSN) and weather prediction. WSN, is more efficient than IoT as it avoids connecting all the sensor nodes directly to Internet, thus reducing the traffic over Internet and energy consumption of the sensor network. The system consists of a clustered tree topology to increase the range of operation, connectivity and easily connect new nodes dynamically. The sensor nodes being the leaves, local gateways being the branches and the global gateway being the root node. The system is implemented using cost effective micro-controllers, robust communication modules and reliable data showcasing platforms. Our implementation uses weather prediction to minimize the water needed for irrigation. Thereby minimizing cost and increasing efficient usage of resources.

Relay Placement Algorithms for IoT Connectivity and Coverage in an Outdoor Heterogeneous Propagation Environment

A vast majority of the Internet of Things (IoT) devices will be connected in a topology where the edge-devices push data to a local gateway, which forwards the data to a cloud for further processing. In sizeable outdoor deployment regions, the edge-devices may experience poor connectivity due to their distant locations and limited transmission power. Repeaters or relays must be placed at a few locations to ensure reliable connectivity to either a gateway or another node in the network. A big challenge in achieving reliable connectivity and coverage is the outdoor propagation environment being heterogeneous. Engineers often deploy networks based on resource-intensive field visits, detailed surveys, measurements, initial test deployments, followed by fine-tuning. For scalability to large scale IoT deployments, automated network planning tools are essential. Such tools should predict connectivity based on the edge-device locations using available Geographical Information System (GIS) data, identify the need for relays/repeaters, and, if needed, suggest the number of relays needed with their locations. Furthermore, such tools should also be extended to suggest the minimum number and locations of base stations that maximise coverage. In this paper, we propose an automated network deployment framework using a black box received signal strength estimation oracle that provides signal strength estimates between candidate pairs of transceiver locations in a heterogeneous deployment region. Our proposed methodology uses either Ant Colony Optimisation (ACO) or Differential Evolution (DE) to identify the number and locations of relays for meeting specified quality of service constraints. We discuss adaptations of our techniques to handle scenarios with multiple gateways. Further, we show the effectiveness of these algorithms to find suitable candidate base station locations to provide coverage in a heterogeneous propagation environment that meets the specified quality of service constraints. We then demonstrate the effectiveness of our algorithms in two deployment regions.

Random Forest for Data Aggregation to Monitor and Predict COVID-19 Using Edge Networks

Medical sensors and distributed edge networks hold promise in advanced control and prediction of infectious diseases, such as COVID-19. Their integration can lower communication latency, bandwidth utilization, and energy consumption while achieving high application scalability and reliability. Existing edge-devices-enabled e-healthcare frameworks face two serious challenges that affect their success. First, real-time medical sensors generate similar or redundant readings used for disease prediction, mostly for non-COVID-19 patients, which increases data transmission latency and energy consumption. Second, predicting the risk level of COVID-19 patients using lightweight machine learning requires minimal training time to be useful. To address these challenges, we develop an edge-centric e-healthcare framework for online health data monitoring and analysis to predict the risk level of COVID-19 patients. In the proposed framework, a statistical data aggregation method (MEAN function) is deployed at local gateway devices to remove redundant data to minimize communication latency and energy usage. Geo-distributed edge servers are used to predict with 97 percent accuracy the risk level to each patient with Random Forest (RF) on the aggregated data. Finally, the efficiency of the RF algorithm is demonstrated with standard classification techniques.

COVID-19 and its impact on visitation and management at US national parks

PurposeNational Park Service (NPS) units generate a significant economic impact for states and local gateway communities across the USA. Utah is home to 13 NPS units with visitation accounting for 18% of the state's US$9.75bn tourism economy in 2018. Twelve NPS units, including five national parks, are located in Southern Utah, driving an economy that is heavily dependent on tourism. This paper examines the challenges and opportunities for visits to national parks post-COVID-19, generally and in the specific context of Southern Utah. Although the assumption is that visits to national parks will recover quickly, this paper will critically examine how visitation may change and what adaptive measures and alternative forms of unit management may be necessary.Design/methodology/approachBy adopting a holistic-inductive paradigm, this paper utilizes a descriptive case study approach. Data were collected across a variety of mediums focusing on interviews with key stakeholders in and around Southern Utah.FindingsThe results from this study highlight the various challenges faced in parks and gateway communities vis-à-vis changing patterns of visitation, adaptive measures and alternative forms of unit management necessary due to COVID-19 and their impact on the future management and marketing of national parks for touristic purposes.Originality/valueThis paper examines the impacts of COVID-19 on an often-neglected yet significant area within tourism, yielding implications for industry, visitors and destination communities.

Dwellers and Trespassers: Mononuclear Phagocytes at the Borders of the Central Nervous System.

The central nervous system (CNS) parenchyma is enclosed and protected by a multilayered system of cellular and acellular barriers, functionally separating glia and neurons from peripheral circulation and blood-borne immune cells. Populating these borders as dynamic observers, CNS-resident macrophages contribute to organ homeostasis. Upon autoimmune, traumatic or neurodegenerative inflammation, these phagocytes start playing additional roles as immune regulators contributing to disease evolution. At the same time, pathological CNS conditions drive the migration and recruitment of blood-borne monocyte-derived cells across distinct local gateways. This invasion process drastically increases border complexity and can lead to parenchymal infiltration of blood-borne phagocytes playing a direct role both in damage and in tissue repair. While recent studies and technical advancements have highlighted the extreme heterogeneity of these resident and CNS-invading cells, both the compartment-specific mechanism of invasion and the functional specification of intruding and resident cells remain unclear. This review illustrates the complexity of mononuclear phagocytes at CNS interfaces, indicating how further studies of CNS border dynamics are crucially needed to shed light on local and systemic regulation of CNS functions and dysfunctions.

G2F: A Secure User Authentication for Rapid Smart Home IoT Management

Internet-of-Things (IoT) devices are widely deployed nowadays. A large number of smart home IoT devices are hosted on a cloud server for easy management. Users can use their accounts to initiate operations and management on IoT devices through a cloud server, such as updating firmware and configuring devices. However, the cloud account may be hacked resulting in adversarial attacks to the hosted IoT devices. As a consequence, an adversary may perform malicious operations through the cloud remotely to the hosted IoT devices without user awareness. Motivated by this, in this article we propose gateway-based 2 factor authentication (G2F), a secure user authentication framework dedicated for a gateway based on the universal 2nd factor (U2F) protocol to enhance the security of IoT devices management. In G2F, the user authentication on the gateway is completed utilizing a hardware token that interacts with the local gateway node to guarantee the token owner’s presence. Furthermore, G2F can grant multiple simultaneous operations on IoT devices through just one user authentication. We implement a prototype to further evaluate the performance of G2F. Based on our realization on the commercial IoT server, i.e., Alibaba Cloud, G2F demonstrates the ability to protect against malicious attacks with high authentication efficiency.

Panop: Mimicry-Resistant ANN-Based Distributed NIDS for IoT Networks

Recently, using artificial neural networks (ANNs) for network intrusion detection systems (NIDSs) has drawn much attention from security researchers. The capability of ANNs to learn patterns from numerous data helps detect attacks on networked systems. Moreover, to effectively monitor a newly emerging networked system consisting of distributed subsystems, such as edge, Internet of Things (IoT), and fog, recent studies have proposed an ANN-based distributed NIDS , where multiple ANNs are deployed to local gateways. To meet the incessant demand for high accuracy, ANN-based NIDSs have become complicated and heavy. With local gateways being small and low-end, such ANNs cannot be executed. Some researchers have proposed optimized algorithms to balance detection accuracy and runtime performance to solve this problem. For example, Kitsune empirically proved its efficiency, but a recent study reveals that Kitsune has limitations. In particular, Kitsune fails at identifying host-oriented attacks, which pretend to be benign during packet delivery but incur malicious behavior on destination devices. Panop is a novel ANN-based NIDS for a distributed network system that aims to detect malicious packets, including host-oriented attacks, while remaining sufficiently lightweight to be executed by low-end devices. Thus, the Panop ANN is designed to comprehensively learn network and device behaviors related to packet transactions in an IoT network. According to the experiments, Panop can detect host-oriented and other attacks with reasonably high accuracy with little degradation in runtime performance compared to the state-of-the-art NIDS for distributed network environments.

SmartConnector: A Self-Powered IoT Solution to Ease Predictive Maintenance in Substations

The increased dependency on electricity of modern society makes reliability of power transmission systems a key point. This goal can be achieved by continuously monitoring power grid parameters, so possible failure modes can be predicted beforehand. The deployment of Internet of Things (IoT) solutions and new low-cost sensors eases this task. This paper presents a self-powered IoT solution for real-time monitoring of different parameters of a high-voltage substation connector. This new family of power connectors is called SmartConnector, which incorporates a thermal energy harvesting system powering a microcontroller that controls a transmitter, and several electronic sensors to measure the temperature, current and voltage drop across the connector to estimate the electrical contact resistance (ECR) of the connector. These measurements are sent remotely via a Bluetooth 5 wireless communication module to a local gateway, which transfers the measured data to a database server for storage and further analysis and visualization. Different experiments are presented to validate the feasibility of the SmartConnector in terms of measurement accuracy in comparison with a wired system. The proposed thermal energy harvesting is tested to calculate the optimum data transfer rate needed for extending the lifetime of the SmartConnector . A shielding solution for the electronics operating under this high voltage environment is also proposed.

Fog-based dynamic traffic light control system for improving public transport

Nowadays, traffic congestion is increasingly aggravating in almost every urban area and existing traffic light controllers cannot satisfy the rising demands efficiently to handle the traffic pressure. An intelligent transportation system (ITS) aims to provide major innovations for improving the performance of a public transport system including the traffic light control systems. In this paper, we propose a fog-based distributed architecture for a dynamic traffic light control system for ITS. In the proposed architecture, the local gateway collects real-time local traffic data using a wireless sensor network at each intersection. It also gets the neighboring traffic data from the distributed fog nodes. Using this local and global traffic data, the proposed Efficient Dynamic Traffic Light Control algorithm for Multiple (EDTLCM) intersections calculates the optimal green light sequence and duration (that maximizes the benefits), considering three objectives i) reducing the average waiting time ii) minimizing the fuel consumption and iii) maximizing the throughput. The simulation result shows that the proposed EDTLCM minimizes the waiting time, reduces fuel consumption and improves system throughput compared to the other dynamic strategies.

Machine Learning Analysis of Heart Rate Variability for the Detection of Seizures in Comatose Cardiac Arrest Survivors

Objective: Heart rate variability (HRV) reflects autonomous nervous system disturbance and is used for seizure prediction. The aim of this study was to develop a real-time, continuous physiological medical signal data acquisition system in seizure detection for intensive care unit (ICU). Methods: This prospective study was conducted in National Taiwan University Hospital from August 2018 to October 2019. This study included 20 patients who (a) had a sustained return of spontaneous circulation following out-of-hospital cardiac arrest, (b) were over 18 years old, (c) and were admitted to the emergency ICU for post-cardiac-arrest care. One-lead electrocardiography and bilateral two-channel electroencephalography recorders were synchronically used to conduct measurements for a maximum of 72 hours. The recorded data were wirelessly real-time transmitted by a proxy transmitting module through an access point and a local gateway. A system with a novel algorithm processed the signals and conducted feature extraction and supervised learning for seizure detection. Results: A total of 89 nonseizure and 83 seizure events were detected by the system. Seizure occurred in two-thirds of the patients assessed by intensivists and neurologists. Four HRV parameters, namely standard deviation of normal-to-normal R-wave intervals, high frequency, low frequency–high frequency ratio, and sample entropy, were determined as potential features for identifying seizures. The sensitivity and specificity of the developed system were 0.74 and 0.81, respectively, and the positive predictive value was 0.82. Conclusion: The developed system can be used to identify seizure events through HRV features. Significance: The current study achieved real-time seizure detection and overcame previous limitations on continuity and accessibility.

Balancing Security and Efficiency for Smart Metering Against Misbehaving Collectors

Smart grid enables two-way communications between smart meters and operation centers to collect real-time power consumption of customers to improve flexibility, reliability, and efficiency of the power system. It brings serious privacy issues to customers, since the meter readings possibly expose customers’ activities in the house. Data encryption can protect the readings, but lengthens the data size. Secure data aggregation improves communication efficiency and preserves customers’ privacy, while fails to support dynamic billing, or offer integrity protection against public collectors, which may be hacked in reality. In this paper, we define a new security model to formalize the misbehavior of collectors, in which the misbehaving collectors may launch pollution attacks to corrupt power consumption data. Under this model, we propose a novel privacy-preserving smart metering scheme to prevent pollution attacks for the balance of security and efficiency in smart grid. It achieves end-to-end security, data aggregation, and integrity protection against the misbehaving collectors, which act as local gateways to collect and aggregate usage data and forward to operation centers. As a result, the misbehaving collectors cannot access or corrupt power usage data of customers. In addition, we design a dynamic billing mechanism based on individual power consumption maintained on collectors with the verification of customers. Our analysis shows that the proposed scheme achieves secure smart metering and verifiable dynamic billing against misbehaving collectors with low computational and communication overhead.

On the Design of Low-Cost IoT Sensor Node for e-Health Environments

The proliferation of Internet of Things (IoT) devices for patient monitoring has gained much attention in clinical care performance, proficient chronic disease management, and home caregiving. This work presents the design of efficient medical IoT sensor nodes (SNs) in terms of low-cost, low power-consumption, and increased data accuracy based on open-source platforms. The method utilizes a Sensor Controller (SC) within the IoT SN, which is capable of performing medical checks supporting a broad coverage of medical uses. A communication protocol has been developed for data and command exchange among SC, local gateways, and physicians’ or patients’ mobile devices (tablets, smart phones). The SC supports moving average window (MAW) and principle component analysis (PCA) filtering algorithms to capture data from the attached low-cost body sensors of different sampling profiles. Significant extensions in SN’s portability is achieved through energy consumption minimization based on the idle time gaps between sensors’ activations. SN’s components are either deactivated or set to low activity operation during these idle intervals. A medical case study is presented and the evaluated results show that the proposed SN can be incorporated into e-health platforms since it achieves comparable accuracy to its certified and high-cost commercial counterparts.

Fog IoT for Health: A new Architecture for Patients and Elderly Monitoring.

The important increase of the elderly population and their desire to conduct an independent life, even when having medical diseases related to their age, requires the development of new technologies to ensure optimal living comfort for this population. In addition, another category of people, those who are patients with life-threatening problems, may benefit from preventive medical monitoring. In this paper, we present a Fog IoT Cloud-Based Health Monitoring System by using physiological and environmental signals allowing to provide contextual information in terms of Daily Living Activities. Our system enables healthcare providers to follow up health state and behavioral changes of elderly or alone people. Moreover, our system provides a monitoring rehabilitation and recovery processes of patients. Our Fog-IoT architecture consists of a wireless sensor network, a local gateway for data stored locally and quickly, and a Lambda cloud architecture for data processing and storage. The originality of our work resides in the graphical monitoring of new and recent patient data at local smart gateway level. This checkup gives the opportunity to the medical staff quick access to the data, and allows them to validate automatically the observed anomalies. Finally, if a telematic break occurs, the gateway continues to accumulate the data while conducting their analysis. Anonymized data are sent periodically from Smart Gateways to the cloud for archiving and for checkup by medical staff who follow up with patients.

Blood-Brain Delivery Methods Using Nanotechnology.

Pathologies of the brain, of which brain cancer, Alzheimer’s disease, Parkinson’s disease, stroke, and multiple sclerosis, are some of the most prevalent, and that presently are poorly treated due to the difficulties associated with drug development, administration, and targeting to the brain. The existence of the blood-brain barrier, a selective permeability system which acts as a local gateway against circulating foreign substances, represents the key challenge for the delivery of therapeutic agents to the brain. However, the development of nanotechnology-based approaches for brain delivery, such as nanoparticles, liposomes, dendrimers, micelles, and carbon nanotubes, might be the solution for improved brain therapies.