Real-time Messages Research Articles

Flexoelectrically augmented triboelectrification enabled self-power wireless smart home control system

With the rapid development of the Internet of Things and artificial intelligence, smart home has emerged to fulfill the security, convenience, and energy-saving issues of modern life. Self-power, multifunctional, and sensitive sensors are one of the key components for the popularization of smart home systems. Herein, a flexoelectric mica crystal is shown to augment the finger touch-driven triboelectric output enough to operate a wireless and multichannel smart home controller. The smart home controller consists of concave mica films, a hand-grip-type vertical mouse, and an Arduino controller. Strain-gradient-dependent contact potential difference measurement and density functional theory calculation show that flexoelectric charge shifts the workfunction and concave bending reduces the effective electron mass in a mica. A portable hand-grip-type flexoelectrically augmented triboelectric nanogenerator (Flexo-TENG) demonstrates the wireless communication of message in real time and the selective operation of home appliances with various functions via a simple finger touch. This work provides important ingredients for the enhanced triboelectric output via flexoelectricity, and for the realization of a convenient, multifunctional, cost-effective, and adaptable smart home control system without batteries.

Seedlings Approval Request with Descriptive Analytics and Real-Time Messaging System for CENRO Bago

In response to the challenges faced by the City Environment and Natural Resources Office (CENRO) Bago in managing seedling requests, this study presents the development of a web-based and mobile application system named "VerdeQuest." This system seeks to modernize the existing seedling request process, providing enhanced accessibility, accuracy, and security. By replacing manual logbooks and electronic spreadsheets with a comprehensive database, VerdeQuest streamlines the entire transaction of seedling requests. It features user account management, request monitoring, seedling availability tracking, statistical reporting, real-time messaging, and automated notifications. The system not only improves efficiency for CENRO Bago but also empowers requesters, which include government agencies, non-government organizations, and schools, in accessing services related to seedling requests. The study employed a constructive research methodology and followed the Agile Scrum Methodology during system development. The project demonstrates the feasibility of VerdeQuest, with an estimated payback period of 1.98 years and a system evaluation result of 2.6. In conclusion, VerdeQuest represents a significant leap forward in seedling request management, offering efficiency, data security, and ease of use, thus benefiting both CENRO Bago and its clients. The system is recommended for implementation with standard hardware specifications, hosting licensing, and potential future developments for reference.

Blockchain-Assisted Privacy-Preserving Traffic Route Management Scheme for Fog-Based Vehicular Ad-Hoc Networks

Traffic route management is essential for reducing traffic jams and enhancing driving safety becauese of the growing number of vehicles and frequent occurrence of traffic accidents. However, in vehicular ad-hoc networks (VANETs), real-time messages are transmitted via wireless channels, which can result in security and privacy concerns. Existing proposals for traffic route management exist security vulnerabilities, as well as high calculation and communication costs. Encouraged by this fact, we design a lightweight traffic route management scheme for fog-based VANETs. In this scheme, vehicles utilize homomorphic encryption to encrypt their driving routes and then send the encrypted information to a fog node. The traffic management center (TMC) decrypts the received ciphertexts that are aggregated by the fog node and performs traffic management according to the decrypted data, without knowing individual route of each vehicle. Furthermore, blockchain is used in the scheme to conduct public keys management of vehicles. Our detailed security proof and analysis indicate that our proposal can meet the security objectives of VANETs. Further, to demonstrate the feasibility of the scheme, we also implement it in the Ethereum test network (i.e.,Rinkeby). Significantly, the performance analysis demonstrates that our proposal achieves a better performance than other relevant representative schemes.

Blockchain-assisted certificateless signcryption for vehicle-to-vehicle communication in VANETs

In vehicular ad-hoc networks (VANETs), real-time messages are used to guarantee transportation efficiency and passenger safety through the exchange among vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Therefore, the timely acceptance of safety messages is critical to establishing efficient and reliable communication in VANETs, which hinges on an efficient privacy-preserving authentication mechanism. However, these mechanisms face either security or performance problems. Although some state-of-the-art signcryption schemes meet the needs of security requirements of authentication, performance is not efficient in computational overhead for an increase of the bilinear pairing operations in batch unsigncryption verification. Therefore, we have designed an efficient and provably secure blockchain-assisted certificateless signcryption (BACLS) that provides a feasible security communication in theoretical analysis and experiments. The BACLS scheme ensures security against type-I and type-II attackers in regard to existential unforgeability against adaptively chosen message attacks (EUF-CMA) under the hardness assumption of the gap bilinear Diffie–Hellman problem and indistinguishability against adaptive chosen ciphertext attack (IND-CCA2) under the hardness assumption of the elliptic curve discrete logarithm problem in the random oracle model (ROM). The experiments demonstrate that the BACLS scheme significantly reduces unsigncryption computational costs and power consumption in contrast to state-of-the-art schemes, and promotes the development of a decentralized authentication system in VANETs.

CANASTA: Controller Area Network Authentication Schedulability Timing Analysis

The Controller Area Network (CAN) dominates in-vehicle networking systems in modern vehicles. CAN was designed with low-latency and reliability as key features. Authenticity of a CAN frame was not considered in the design, thus, most in-vehicle network nodes inherently trust received messages as coming from a legitimate source. As a result, it is trivial to program (or hack) a network node to spoof traffic. Authentication is challenging for CAN and related protocols, such as SAE J1939, due to limited frame sizes and high bus utilization. Adding a message authentication code (MAC) as a separate message can unduly stress the real-time delivery of safety-critical messages. Although this stressor is well-known, the impact of authentication protocols on real-time message delivery in CAN has not yet been thoroughly examined. In this paper, we provide the first comprehensive analysis of realtime schedulability analysis applied to authentication schemes for CAN, CAN Flexible Data-rate (CAN FD), and CAN extra long payload (CAN XL). We formulate the response time analysis for addition of MACs and periodic transmission of MACs, and we examine their impact on two case studies and through evaluation with randomized schedulability experiments over a wide range of message sets.

A Clustering-Based Routing Protocol Using Path Pattern Discovery Method to Minimize Delay in VANET

In vehicular ad hoc networks (VANETs), vehicle-to-vehicle (V2V) communications can link vehicles to each other, and vehicle-to-infrastructure (V2I) messaging and communications can link roadside infrastructure such as routers. The vehicles in these networks act as relays that transmit critical messages in the network. Due to the high-speed movement of vehicles on the road, real-time messaging and minimizing the delay in sending messages is one of the most important objectives of VANET developers. On the other hand, the high mobility of vehicles causes communication interruptions and decreases the data delivery rate in VANET. To overcome this issue, predicting the path of vehicles can play an important role in sending data from the source to the destination. When an accident occurs on the road, the messages that are sensed by the imbedded sensors in the vehicles need to be sent, and if they are sent by the vehicles that change their route, these messages will not be sent to the destination and the performance of the network will be disturbed. Previous methods in the literature for data transmission in intervehicular networks have focused more on reliability and trust, and little attention has been paid to the prediction of vehicle movement paths in these types of networks. Therefore, for fast and reliable data transmission in VANET, accurate prediction of vehicle movement and creation of movement patterns can be effective in message transmission delay and data delivery rate. In this paper, we present an approach using a combination of cluster-based routing protocols and pattern discovery methods to minimize latency in VANETs. The outline of the proposed method has four modules: primary data collection and analysis, primary data preparation and analysis, pattern extraction and vehicle route discovery, and vehicle clustering and data/information transmission routing. The simulation results show that the proposed method with a delivery rate of 88.56% has significantly improved compared to the previous methods in terms of package delivery rate. Also, the proposed method with a total delay of 24.566 ms has a shorter delay than the previous methods in terms of message sending delay in the network.

Wireless Smart Notice Board Using Dot Matrix

Abstract: Whether a firm is for profit or not, the information sharing component of information technology is a major factor in all types of businesses. Nowadays, all company models depend on and use the capacity of advertising to market their goods and services. The present demand of the modern business and information world is for digital advertising. International airports, metro train stations, shopping centres, bus stops, etc. all employ scrolling LED dot matrix displays. Led displays are a good way to show information, but making the message dynamic is challenging since users must adjust the message content to meet their particular needs. The user must connect the Led display to the computer in order to change the message content. The wireless based dot matrix display creates the wireless connection between individual mobile and Led display in the event that the user requires pairing the Led display with the computer in order to change the message content, which makes it difficult to place the display board anywhere due to the dedicated and complex setting up pattern. It provides the facility of real time message displaying means the user can change the message content that is to be display. The message is sent from a mobile device to the LED display, and the microcontroller handles all processing for scrolling the message's content on the LED display. The project's goal is to create a wireless scrolling message board that allows the user to update the information without using a connection to connect the display board to a computer or laptop. The user has the option of using Bluetooth or Wi-Fi to refresh the message content.

Vision-Based Work Zone Safety Alert System in a Connected Vehicle Environment

Work zones, being a critical component of roadway transportation systems, can benefit greatly from computer vision-enabled roadway infrastructures, specifically in connected vehicle (CV) environments. Connected infrastructures, such as roadside units (RSU) and on-board units (OBU), can greatly improve the environmental awareness and safety of CVs driving through a work zone. The contribution of this paper lies in developing a vision-based approach to generate work zone safety messages in real time, utilizing video streams from roadside monocular traffic cameras that can be used by CV work zone safety apps on mobile devices to reliably navigate through a work zone. A monocular traffic camera calibration method is proposed to establish the accurate mapping between the image plane and Global Position System (GPS) space. Real test scenarios show that our algorithm can precisely and effectively locate work zone boundaries from a monocular traffic camera in real time. We demonstrate the capabilities and features of our system through real-world experiments where the driver cannot see the work zone. End-to-end latency analysis reveals that the vision-based work zone safety warning system satisfies the active safety latency requirements. This vision-based work zone safety alert system ensures the safety of both the worker and the driver in a CV environment.

Grazing Sheep Behaviour Recognition Based on Improved YOLOV5

Fundamental sheep behaviours, for instance, walking, standing, and lying, can be closely associated with their physiological health. However, monitoring sheep in grazing land is complex as limited range, varied weather, and diverse outdoor lighting conditions, with the need to accurately recognise sheep behaviour in free range situations, are critical problems that must be addressed. This study proposes an enhanced sheep behaviour recognition algorithm based on the You Only Look Once Version 5 (YOLOV5) model. The algorithm investigates the effect of different shooting methodologies on sheep behaviour recognition and the model’s generalisation ability under different environmental conditions and, at the same time, provides an overview of the design for the real-time recognition system. The initial stage of the research involves the construction of sheep behaviour datasets using two shooting methods. Subsequently, the YOLOV5 model was executed, resulting in better performance on the corresponding datasets, with an average accuracy of over 90% for the three classifications. Next, cross-validation was employed to verify the model’s generalisation ability, and the results indicated the handheld camera-trained model had better generalisation ability. Furthermore, the enhanced YOLOV5 model with the addition of an attention mechanism module before feature extraction results displayed a mAP@0.5 of 91.8% which represented an increase of 1.7%. Lastly, a cloud-based structure was proposed with the Real-Time Messaging Protocol (RTMP) to push the video stream for real-time behaviour recognition to apply the model in a practical situation. Conclusively, this study proposes an improved YOLOV5 algorithm for sheep behaviour recognition in pasture scenarios. The model can effectively detect sheep’s daily behaviour for precision livestock management, promoting modern husbandry development.

Efficient Non-DHT-Based RC-Based Architecture for Fog Computing in Healthcare 4.0

Cloud-computing capabilities have revolutionized the remote processing of exploding volumes of healthcare data. However, cloud-based analytics capabilities are saddled with a lack of context-awareness and unnecessary access latency issues as data are processed and stored in remote servers. The emerging network infrastructure tier of fog computing can reduce expensive latency by bringing storage, processing, and networking closer to sensor nodes. Due to the growing variety of medical data and service types, there is a crucial need for efficient and secure architecture for sensor-based health-monitoring devices connected to fog nodes. In this paper, we present publish/subscribe and interest/resource-based non-DHT-based peer-to-peer (P2P) RC-based architecture for resource discovery. The publish/subscribe communication model provides a scalable way to handle large volumes of data and messages in real time, while allowing fine-grained access control to messages, thus enabling heightened security. Our two − level overlay network consists of (1) a transit ring containing group-heads representing a particular resource type, and (2) a completely connected group of peers. Our theoretical analysis shows that our search latency is independent of the number of peers. Additionally, the complexity of the intra-group data-lookup protocol is constant, and the complexity of the inter-group data lookup is O(n), where n is the total number of resource types present in the network. Overall, it therefore allows the system to handle large data throughput in a flexible, cost-effective, and secure way for medical IoT systems.

Redirection and Protocol Mechanisms in Content Delivery Network-Edge Servers for Adaptive Video Streaming

Redirection and protocol techniques are key components of the infrastructure for Content Delivery Networks (CDNs) that aid in the delivery of Multimedia Internet services to end-consumers. Redirection methods are used to route the user’s request to the nearest edge server, minimizing distance, improving delivery times, and lowering latency. Protocol mechanisms, such as HTTP Live Streaming (HLS), Dynamic Adaptive Streaming over HTTP (DASH), and Real-Time Messaging Protocol (RTMP), are used to deliver adaptive video streaming. These protocols are designed to transfer the adaptive streaming and provide high-quality video playback. They also allow the system to adjust the video quality based on the network conditions of the Quality of Service (QoS). Inadequate transmission protocols and poorly designed redirection algorithms are two major challenges that might degrade Cloud–CDN performance. These challenges lead to excessive latency, poor quality of service, and significant packet loss that have potential influences on the user experience. In this paper, firstly, three protocols are proposed by preparing a case study on selecting the optimal protocol for replicating adaptive video streaming content. Secondly, a redirection algorithm based on the Modified Cuckoo Search Algorithm (MCSA) is proposed to provide an accurate redirecting process of selected edge servers to end-users. Test results indicate that, when hybrid FASP/HTTP protocols were chosen (from original server to replicate server and to end-users), the delivery of adaptive video streaming segments was fast with lower latency. The average estimated time needed for replicating video content based on FASP is 25% better than that needed for File catalyst and Signiant protocols. Therefore, the Cuckoo search method presents more efficient results for selecting the optimal edge server for 100 servers, which is 0.296 s, compared to the conventional algorithm, which was 13 s.

Determining Real-Time Communication Feasibility in IoT Systems Supported by LoRaWAN.

LoRaWAN is a long range and low power protocol devised for connecting devices under the Internet of Things (IoT) paradigm. This protocol was not conceived to support real-time message delivery; therefore, it is not always feasible using it to support IoT solutions involving large wireless sensors networks and time constraint messaging, e.g., in early warning systems for natural hazards, remote monitoring of industrial machinery or autonomous control of transportation systems. This paper presents a model that provides certainty, at the design time of IoT systems, about the real-time communication capability of their supporting network. It allows solution designers: (1) to decide if developing or not a real-time IoT solution based on the feasibility of its communication infrastructure, and (2) to improve the communication infrastructure to try making real-time communication feasible using LoRaWAN.

All-in-one aerial image enhancement network for forest scenes.

Drone monitoring plays an irreplaceable and significant role in forest firefighting due to its characteristics of wide-range observation and real-time messaging. However, aerial images are often susceptible to different degradation problems before performing high-level visual tasks including but not limited to smoke detection, fire classification, and regional localization. Recently, the majority of image enhancement methods are centered around particular types of degradation, necessitating the memory unit to accommodate different models for distinct scenarios in practical applications. Furthermore, such a paradigm requires wasted computational and storage resources to determine the type of degradation, making it difficult to meet the real-time and lightweight requirements of real-world scenarios. In this paper, we propose an All-in-one Image Enhancement Network (AIENet) that can restore various degraded images in one network. Specifically, we design a new multi-scale receptive field image enhancement block, which can better reconstruct high-resolution details of target regions of different sizes. In particular, this plug-and-play module enables it to be embedded in any learning-based model. And it has better flexibility and generalization in practical applications. This paper takes three challenging image enhancement tasks encountered in drone monitoring as examples, whereby we conduct task-specific and all-in-one image enhancement experiments on a synthetic forest dataset. The results show that the proposed AIENet outperforms the state-of-the-art image enhancement algorithms quantitatively and qualitatively. Furthermore, extra experiments on high-level vision detection also show the promising performance of our method compared with some recent baselines.

RESEMBLE: A Real-Time Stack for Synchronized Mesh Mobile Bluetooth Low Energy Networks

Bluetooth Low Energy (BLE) is a wireless technology for low-power, low-cost and lowcomplexity short-range communications. On top of the BLE stack, the Bluetooth Mesh profile can be adopted to handle large networks with mesh topologies. BLE is a promising candidate for the implemention of Industrial Wireless Sensor Networks (IWSNs), thanks to its wide diffusion (e.g., on smartphones and tablets) and the lower cost of the devices compared to other wireless industrial communication technologies. However, neither the BLE nor the Bluetooth Mesh specifications can provide real-time messages with bounded delays. To overcome this limitation, this work proposes RESEMBLE, a real-time stack developed on top of BLE that is able to realize low-cost IWSNs over mesh topologies. RESEMBLE offers support to both real-time and non-real-time communications on the same network. Moreover, RESEMBLE provides clock synchronization, thus allowing for Time Division Multiple Access (TDMA) transmissions. The clock synchronization provided by RESEMBLE can be also exploited by the upper layers’ industrial applications to implement timecoordinated actions.

A Blockchain-Based Deep-Learning-Driven Architecture for Quality Routing in Wireless Sensor Networks

Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network delay and high energy consumption. In the WSNs, routing is performed using different routing protocols like low-energy adaptive clustering hierarchy (LEACH), heterogeneous gateway-based energy-aware multi-hop routing (HMGEAR), etc. In such protocols, some nodes in the network may perform malicious activities. Therefore, four deep learning (DL) techniques and a real-time message content validation (RMCV) scheme based on blockchain are used in the proposed network for the detection of malicious nodes (MNs). Moreover, to analyse the routing data in the WSN, DL models are trained on a state-of-the-art dataset generated from LEACH, known as WSN-DS 2016. The WSN contains three types of nodes: sensor nodes, cluster heads (CHs) and the base station (BS). The CHs after aggregating the data received from the sensor nodes, send it towards the BS. Furthermore, to overcome the single point of failure issue, a decentralized blockchain is deployed on CHs and BS. Additionally, MNs are removed from the network using RMCV and DL techniques. Moreover, legitimate nodes (LNs) are registered in the blockchain network using proof-of-authority consensus protocol. The protocol outperforms proof-of-work in terms of computational cost. Later, routing is performed between the LNs using different routing protocols and the results are compared with original LEACH and HMGEAR protocols. The results show that the accuracy of GRU is 97%, LSTM is 96%, CNN is 92% and ANN is 90%. Throughput, delay and the death of the first node are computed for LEACH, LEACH with DL, LEACH with RMCV, HMGEAR, HMGEAR with DL and HMGEAR with RMCV. Moreover, Oyente is used to perform the formal security analysis of the designed smart contract. The analysis shows that blockchain network is resilient against vulnerabilities.

Sustaining shopping momentum in retail malls using real-time messaging

Retail malls thrive not only on foot traffic they attract but also on buyers spending at multiple retailers in the mall. While the importance of shopping momentum, i.e., buyers continuing to shop, is known, few studies have examined how mobile promotions can incentivize buyers to sustain their shopping momentum in a mall. This study examines how a mall operator targets buyers in its loyalty program with a real-time message (RTM) to encourage them to sustain their shopping momentum and spend more at the next retailer. We use a quasi-experimental design and show that the RTM has a stronger effect on heavy than light buyers. Specifically, moderate and heavy buyers are more likely to respond favorably to RTMs by sustaining shopping momentum and increasing spending at the next retailer. In contrast, the RTM has no effect on shopping momentum of light buyers, and if they continue to shop, they lower their subsequent spending. The study has important implications for retailers using mobile applications to target buyers with relevant real-time promotions to achieve desired outcomes.

Affective Recommender System for Pet Social Network.

In this new era, it is no longer impossible to create a smart home environment around the household. Moreover, users are not limited to humans but also include pets such as dogs. Dogs need long-term close companionship with their owners; however, owners may occasionally need to be away from home for extended periods of time and can only monitor their dogs’ behaviors through home security cameras. Some dogs are sensitive and may develop separation anxiety, which can lead to disruptive behavior. Therefore, a novel smart home solution with an affective recommendation module is proposed by developing: (1) an application to predict the behavior of dogs and, (2) a communication platform using smartphones to connect with dog friends from different households. To predict the dogs’ behaviors, the dog emotion recognition and dog barking recognition methods are performed. The ResNet model and the sequential model are implemented to recognize dog emotions and dog barks. The weighted average is proposed to combine the prediction value of dog emotion and dog bark to improve the prediction output. Subsequently, the prediction output is forwarded to a recommendation module to respond to the dogs’ conditions. On the other hand, the Real-Time Messaging Protocol (RTMP) server is implemented as a platform to contact a dog’s friends on a list to interact with each other. Various tests were carried out and the proposed weighted average led to an improvement in the prediction accuracy. Additionally, the proposed communication platform using basic smartphones has successfully established the connection between dog friends.

Distribution of DDS-cerberus authenticated facial recognition streams

Successful missions in the field often rely upon communication technologies for tactics and coordination. One middleware used in securing these communication channels is Data Distribution Service (DDS) which employs a publish-subscribe model. However, researchers have found several security vulnerabilities in DDS implementations. DDS-Cerberus (DDS-C) is a security layer implemented into DDS to mitigate impersonation attacks using Kerberos authentication and ticketing. Even with the addition of DDS-C, the real-time message sending of DDS also needs to be upheld. This paper extends our previous work to analyze DDS-C’s impact on performance in a use case implementation. The use case covers an artificial intelligence (AI) scenario that connects edge sensors across a commercial network. Specifically, it characterizes how DDS-C performs between unmanned aerial vehicles (UAV), the cloud, and video streams for facial recognition. The experiments send a set number of video frames over the network using DDS to be processed by AI and displayed on a screen. An evaluation of network traffic using DDS-C revealed that it was not statistically significant compared to DDS for the majority of the configuration runs. The results demonstrate that DDS-C provides security benefits without significantly hindering the overall performance.

Real-Time Charging Risk Assessment for Electric Vehicles Based on Improved Broad BP-AHP

In the research of the electric vehicle charging safety evaluation model, the key problem is to determine the influencing factors of charging safety scientifically and ensure the accuracy and real-time performance of the model evaluation. This article takes the real-time charging message data of electric vehicles as the research object, analyzes and establishes the membership model of charging safety influencing factors, and proposes a real-time charging risk assessment method for electric vehicles based on the improved broad back-propagation and analytic hierarchy process (BBP-AHP) evaluation method. First, the characteristic membership degree of electric vehicle charging message data is calculated. Then, the improved broad BP neural network based on the compression factor is established to obtain the evaluation results of several experts, and the AHP model is optimized. Finally, the charging safety influencing factor system is constructed by the AHP to evaluate the charging safety of the real-time message. According to the accuracy comparison of the AHP model and the improved broad BP-AHP model, the experimental results show that the improved broad BP-AHP model can more accurately evaluate the safety risk of the electric vehicle real-time charging.

Kriper: A blockchain network with permissioned storage

Blockchain has been a revolution in the past few years. Beyond the new currencies that were created around different incarnations of the blockchain concept, there are many other contributions that provide interesting services as a data linked structure using a decentralized network that provide a high level of security. Companies have developed many projects to incorporate blockchain into their business logic pursuing to incorporate other related services as persistence of large volumes of data, privacy or anonymity of transactions, distributed data processing, security (confidentiality, integrity, and availability), document management or micro messages in real time. Nevertheless, as it will be discussed in this article, current blockchains do not meet the needs of companies in many aspects, leading to a scarce or superficial adoption. This article introduces Kriper, a blockchain that aims at meeting corporate world needs by responding with a community-based, open blockchain that may also be segregated and private for certain uses whereas it provides a permissioned distributed storage and micro message lightweight services.