Smart Nodes Research Articles

A smart WSNs node with sensor computing and unsupervised One-Class SVM classifier for machine fault detection

This paper proposed a novel machine fault detection method based on a smart WSNs node with sensor computing and unsupervised learning. Firstly, sensor computing mode, which achieves machine fault detection on the WSNs node, has been employed to address the limitations of raw data transmission mode, such as huge payload transmission data and node energy consumption. Secondly, a fault classifier based on unsupervised one-class support vector machine (OC-SVM) is designed to overcome the drawbacks of supervised learning, like the requirement of myriad labeled samples for model training. Finally, a smart WSNs node with sensor computing and an unsupervised OC-SVM classifier is developed and fabricated as test beds. A set of experiments has been conducted to verify the proposed method and smart node. The results show that they can reduce 99% of payload transmission data and about 5% of node energy consumption while maintaining acceptable detection accuracy (above 99.2%).

Unveiling the Power of the Internet of Things: Exploring Services, Applications, and Overcoming Challenges

The Internet of Things (IoT) has transcended its futuristic perception and become an omnipresent reality. Its pervasive nature encompasses devices, sensors, clouds, big data, and business interactions. This revolutionary concept amalgamates traditional embedded systems with wireless microsensors, automation-driven control systems, and other elements to establish a vast infrastructure. The integration of wireless communication, micro electro mechanical devices, and the Internet has given rise to novel IoT applications. The IoT is essentially a network of interconnected objects accessible through the Internet, each object uniquely identifiable. The advent of IPv6, superseding IPv4, plays a pivotal role in expanding the address space for IoT development. The primary objective of IoT applications is to imbue objects with intelligence, eliminating the need for human intervention. However, the proliferation of smart nodes and the exponential data generated by each node present new challenges pertaining to data privacy, scalability, security, manageability, and other critical issues, which we delve into in this comprehensive exploration

Enhancing local energy sharing reliability within peer-to-peer prosumer communities: A cellular automata and deep learning approach

This study introduces a significant advancement in peer-to-peer (P2P) energy trading systems within smart grids, addressing a crucial gap in existing research by incorporating optimal energy storage capacities to accommodate varying energy demands resulting from lifestyle changes. Through a two-level optimization approach, aimed at maximizing self consumption and optimizing energy flow within the grid, we propose a novel energy management strategy. Our contribution lies in the introduction of a new layer of deep learning and rules control, forming a self-energy sharing system for each prosumer. This architecture, termed the smart node, integrates deep learning techniques, to predict and customize energy services through dynamic adjustment of lower and upper bounds of battery capacities. Additionally, we leverage cellular automaton (CA) approaches to establish sustainable consensus among P2P network users, enhancing the adaptability and efficiency of the energy management system. The results show that the proposed algorithm could reduce the energy consumed by the P2P community from the utility by around 20% and maximize the collective self-consumption by around 8% compared to conventional energy trading in microgrids.

Introducing Security Mechanisms in OpenFog-Compliant Smart Buildings

Designing smart building IoT applications is a complex task. It requires efficiently integrating a broad number of heterogeneous, low-resource devices that adopt lightweight strategies. IoT frameworks, especially if they are standard-based, may help designers to scaffold the applications. OpenFog, established as IEEE 1934 standard, promotes the use of free open source (FOS) technologies and has been identified for use in smart buildings. However, smart building systems may present vulnerabilities, which can put their integrity at risk. Adopting state-of-the-art security mechanisms in this domain is critical but not trivial. It complicates the design and operation of the applications, increasing the cost of the deployed systems. In addition, difficulties may arise in finding qualified cybersecurity personnel. OpenFog identifies the security requirements of the applications, although it does not describe clearly how to implement them. This article presents a scalable architecture, based on the OpenFog reference architecture, to provide security by design in buildings of different sizes. It adopts FOS technologies over low-cost IoT devices. Moreover, it presents guidelines to help developers create secure applications, even if they are not security experts. It also proposes a selection of technologies in different layers to achieve the security dimensions defined in the X.805 ITU-T recommendation. A proof-of-concept Indoor Environment Quality (IEQ) system, based on low-cost smart nodes, was deployed in the Faculty of Engineering of Vitoria-Gasteiz to illustrate the implementation of the presented approach. The operation of the IEQ system was analyzed using software tools frequently used to find vulnerabilities in IoT applications. The use of state-of-the-art security mechanisms such as encryption, certificates, protocol selection and network partitioning/configuration in the OpenFog-based architecture improves smart building security.

SNS: Smart Node Selection for Scalable Traffic Engineering in Segment Routing Networks

SNS: Smart Node Selection for Scalable Traffic Engineering in Segment Routing Networks

Energy efficient gateway based routing with maximized node coverage in a UAV assisted wireless sensor network.

Ad-hoc wireless sensor networks face challenges of optimized node deployment for maximizing coverage and efficiently routing data to control centers in post disaster events. These challenges impact the outcome for extending the lifetime of wireless sensor networks. This study presents a uav assisted reactive zone based EHGR (energy efficient hierarchical gateway routing protocol) that is deployed in a situation where the natural calamity has caused communication and infrastructure damage to a major portion of the sensor network. EHGR is a hybrid multi layer routing protocol for large heterogeneous sensor nodes (smart nodes, basic nodes, user handheld devices etc.) EHGR is tailored to meet two important concerns for a disaster hit wsn ie. optimized deployment and energy efficient routing. The first part of EGHR focuses on maximized coverage during node deployments. Maximized coverage is an important aspect to be considered during the event of disaster since most of the nodes loose coverage and are detached from the wireless sensor network. The first part of EHGR uses state of the art game theory approach to build a model that maximizes the coverage of nodes during the deployment phase from all participating entities i.e. nodes and uavs. Rather than fixing the cluster head as is the case in traditional cluster-based approaches EHGR uses the energy centroid nodes. Energy centroid nodes evolve on the basis of aggregated energy of the zone. This approach is superior to simply electing cluster head nodes on the basis of some probability function. The nodes that fail to achieve any successful outcome from the game theory matching model fail to get any association. These nodes will use multi hop d2d relay approach to reach the energy centroid nodes. Gateway relay nodes used with the game theory approach during the deployment of the uav assisted wsn improves the overall coverage by 25% against traditional leach based hierarchical approaches. Once the optimum deployment phase is completed the routing phase is initiated. Aggregated data is sent by the energy centroid nodes from the ECN nodes to the servicing micro controller enabled un manned aerial vehicles. The routing process places partial burden of zone formation and data transmission to the control center for each phase on the servicing uavs. Energy centroid nodes engage only in the data aggregation process and transmission of data to servicing uav. Servicing-uavs reduce energy dissipated of the entire zone which result in gradual decrease of energy for the zone thus increasing the network lifetime. Node deployment phase and the routing phase of EHGR utilize the computations provide by the mirco controller enabled unmanned aerial vehicles such that the computationally intensive calculations are offloaded to the servicing uav. Experiment results indicate an increase in the first dead node report, half dead node report, and last dead node report. Network lifetime is extended to approximately 1800 rounds which is an increase by ratio of 100% against the traditional leach approach and increase by 50% percent against the latest approaches as highlighted in the literature.

An innovative approach for dynamic key dependent S-Box to enhance security of IoT systems

The Internet of Things (IoT) foresees pervasive and connected smart nodes that interact independently while offering varied services. Today, IoT nodes are a treasure house of data and hence security has become a major concern in IoT networks. Conventional cryptography faces many challenges during implementation in IoT devices. These challenges can be tackled by lightweight cryptography which is highly secure with less resource requirements. The vital characteristic of security is fulfilled by one out of 6 inner structures such as Substitution-Permutation Network, Feistel Network, General Feistel Network, Add-Rotate-XOR, Non Linear-Feedback Shift Register and Hybrid. PRESENT, which is a NIST approved lightweight cryptographic algorithm with SPN structure uses fixed or static S-Box. This permits attackers to explore S-Box and identify weaknesses of the algorithm. Key dependent approaches are better because it impedes an attacker from any offline analyses of attacks on the S-Box. Additionally, this is susceptible to linear and differential attacks. The static S Box does not possess superior diffusion and does not have a good rate of avalanche effect. So, the solution is to create a robust dynamic key dependent algorithm with nonlinear S-Boxes to secure the communication between IoT devices and platform. Security analysis is done on Non-linearity, Differential and Linear Cryptanalysis and Avalanche characteristics, besides, encryption of IoT data to demonstrate its aptness for cold chain security applications.© 2017 Elsevier Inc. All rights reserved.

From Radio to In-Pipe Acoustic Communication for Smart Water Networks in Urban Environments: Design Challenges and Future Trends

The smart management of water resources is an increasingly important topic in today’s society. In this context, the paradigm of Smart Water Grids (SWGs) aims at a constant monitoring through a network of smart nodes deployed over the water distribution infrastructure. This facilitates a continuous assessment of water quality and the state of health of the pipeline infrastructure, enabling early detection of leaks and water contamination. Acoustic-wave-based technology has arisen as a viable communication technique among the nodes of the network. Such technology can be suitable for replacing traditional wireless networks in SWGs, as the acoustic channel is intrinsically embedded in the water supply network. However, the fluid-filled pipe is one of the most challenging media for data communication. Existing works proposing in-pipe acoustic communication systems are promising, but a comparison between the different implementations and their performance has not yet been reported. This paper reviews existing works dealing with acoustic-based communication networks in real large-scale urban water supply networks. For this purpose, an overview of the characteristics, trends and design challenges of existing works is provided in the present work as a guideline for future research.

Design of a Novel Edge-Centric Cloud Architecture for m-Learning Performance Effectiveness by Leveraging Distributed Computing Paradigms’ Potentials

This article aims to design a novel edge-centric hierarchical cloud architecture to optimize mobile learning (m-learning) performance during learners executing computation-intensive learning applications. This research adopts the potential of distributed computing paradigms, that is, mobile edge, for improving the effectiveness of m-learning performance in higher education. Edge computing enables computing at the network’s edge and effectively avoids latencies while processing learners’ computational requests. The envisioned architecture was designed on the ETSI MEC ISG protocols and deployed on the university mobile cloud infrastructure. Additionally, a use case was designed, focusing the edge computing’s latency-avoiding ability, and executing it in a real-time environment involving sixteen students from an academic course. The execution results validated the architecture’s contribution, such as tasks executed in the local server, optimized learner privacy, reduced latencies, instant access, lowered bandwidth consumption, and continued tasks’ execution despite the failure of smart nodes. The result influences user acceptance and attracts designers to extend the architecture base focusing on machine learning algorithms (for learning analytics) and blockchain (to prevent malicious attacks) to improve the effectiveness of learning management system performance.

Study on trust evaluation and service selection for Service-Oriented E-Commerce systems in IoT environments

Trust-based service management in IoT systems, a promising research direction provides an intelligent solution for the identification of appropriate and trustworthy service providers from a huge of number smart nodes providing similar services. An efficient trust-based service ranking scheme (TBSRS) is proposed. The novelty of our design lies in providing accurate trust evaluation and service ranking in the absence of assessment data or presence of untrustworthiness recommendations. TBSRS also addresses the issue about the trustworthiness evaluation of a new service. Service providers are ranking based on the binary fruit fly optimization algorithm in TBSRS. The experimental results demonstrate desirable convergence, accuracy, and resiliency properties of TBSRS, and also demonstrate that TBSRS outperforms Adaptive IoT Trust and IoT-HiTrust in terms of the precision of service selection and other a few performance metrics.

Eternal-thing 2.0: Analog-Trojan-resilient Ripple-less Solar Harvesting System for Sustainable IoT

Recently, harvesting natural energy is gaining more attention than other conventional approaches for sustainable IoT. System on chip power requirement for the internet of things (IoT) and generating higher voltages on chip is a massive challenge for on-chip peripherals and systems. In this article, an on-chip reliable energy-harvesting system (EHS) is designed for IoT with an inductor-free methodology. The control section monitors the computational load and the recharging of the battery/super-capacitor. An efficient maximum power point tracking algorithm is also used to avoid quiescent power consumption. The reliability of the proposed EHS is improved by using an aging tolerant ring oscillator. The effect of Trojan on the performance of energy-harvesting system is analyzed, and proper detection and mitigation mechanism is proposed. Finally, the proposed ripple mitigation techniques further improves the performance of the aging sensor. The proposed EHS is designed and simulated in CMOS 90-nm technology. The output voltage is in the range of 3–3.55 V with an input 1–1.5 V with a power throughput of 0–22 μW. The EHS consumes power under the ultra-low-power requirements of IoT smart nodes.

Game-Theory-Based Multimode Routing Protocol for Internet of Things

Various routing protocols have been proposed for ad hoc networks such as the Internet of Things (IoT). Most of the routing protocols introduced for IoT are specific to applications and networks. In the current literature, it is essential to configure all the network nodes with a single proposed protocol. Moreover, it is also possible for a single IoT network to consist of different kinds of nodes. Two or more IoT networks can also be connected to create a bigger heterogeneous network. Such networks may need various routing protocols with some gateway nodes installed. The role of gateway nodes should not be limited to the interconnection of different nodes. In this paper, a multi-mode hybrid routing mechanism is proposed that can be installed on all or a limited number of nodes in a heterogenous IoT network. The nodes configured with the proposed protocols are termed smart nodes. These nodes can be used to connect multiple IoT networks into one. Furthermore, a game-theory-based model is proposed that is used for intercommunication among the smart nodes to gain optimal efficiency. Various performance matrices are assessed under different network scenarios. The simulation results show that the proposed mechanism outperforms in broader heterogeneous IoT networks with diverse nodes.

Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence

Information and communication technology has many promising benefits including improvement the traffic network capacity, efficiency, and stability. However, to date, most of the improvements in signal management and interactions in connected vehicle environments focus solely on the vehicular side. This has led to a massive gap for non-motorized users and vulnerable road users. Specifically, deficit perception capability, inconsistent dissemination, obsolescent acquisition techniques, and ignorance of equality make the current experience of the active non-motorized users inconvenient and risky, especially for those with disabilities. To serve the users in an unbiased and automated way, a novel cooperated signal phase and timing (SPaT) services infrastructure — Vision Enhanced Non-motorized Users Services (VENUS) smart node is proposed. With customized up-to-date computer vision algorithms and artificial intelligence pipelines on the edge, VENUS smart node can collect necessary active-user information (including location, class, pose direction and mobility status), and generate directional crossing request for every pedestrian and cyclist in real time. Meanwhile, the improved communication system makes the VENUS node a reliable information hub to share the SPaT messages and carry interactions to/from the signal controller, connected vehicles and user personal information devices (i.e., cell phones, wearable devices) through various protocols. Based on extensive experimentation, 1076 testing users from six intersections, the VENUS sensing achieves 90.24% accuracy on directional-aware crossing trigger generation and 89.87% accuracy on mobility status estimation for normal users and four types of disabled persons. Furthermore, the VENUS smart node is fully compatible with the connected vehicles environment, and improves the signal system at low cost, mainly due to its flexibility and adaptability with existing infrastructure. The VENUS smart node is the first connected infrastructure architecture that integrates traffic sensing, data processing and information dissemination together for the self-operating indistinguishable signal services based on edge computing.

Virtual CANBUS and Ethernet Switching in Future Smart Cars Using Hybrid Architecture

Smart cars have gained much attention in recent years due to the introduction of several safety and convenience features. In this paper, we propose a virtual CANBUS architecture that will improve the safety and data processing in future smart cars with the hybrid use of Ethernet technology deployed in conjunction with a CANBUS system to take advantage of the virtualization, speed, and quality of data processing. Data will be routed intelligently across the dual data paths of the traditional CANBUS and the Ethernet. The virtualized nature, with the help of a series of smart nodes and network traffic analyzers, will allocate the needed resources at the right time during the execution of different processes. This enables the possibility of routing data traffic over both Ethernet and CANBUS connections. The architecture is backward compatible with older vehicles and therefore takes advantage of the existing CANBUS system. The proposed architecture ensures that different segments are isolated from each other so that a breakdown in a segment does not bring down the entire system. The experimental results demonstrate the benefits of the proposed solution, which is to switch between two data pathways depending on the traffic loads. While the CANBUS is sufficient with low-bandwidth data, the Ethernet will create a better performance with high-bandwidth processes. The virtualized environment creates virtual topologies among communicating nodes, greatly simplifying the network management and enhancing the data traffic performance as the bandwidth requirement and the number of processors in future smart cars continue to scale.

Effective proactive routing protocol using smart nodes system

Small Power Restricted Unit (PRU) platform known as the Wireless Sensor Network (WSN) to monitor a Large Region of Interest (ROI) and send data to the Base Station (BS). Accurately capturing the ROI and communicating observed information to the BS over the longest period is indeed the main problem facing WSN. Despite the latest introduction of many power routing algorithms in regular monitoring applications, the variable environment and complex environment for WSN applications end up creating these procedures as an important task. This study Degree Restricted Tree (DRE) nodes for such networks, including a BS outside of the ROI in a homogeneous pre-emptive WSN. The optimal degree of a node with low DRT energy consumption is determined because the degree of a node affects the network lifespan of these forms of connections. To provide an equitable distribution of the burden in terms of transmission power, this study then suggests a Joint Decentralized Antenna (JDA) algorithm which is based on several antenna theories. With an optimum node density and DRT base, JDA is made for frequent surveillance systems with real-time applications. The results validate our research, which emphasizes that the network throughput of DRT is doubled when utilizing optimum node angles as opposed to certain other node degrees. Additionally, it has been demonstrated that introducing JDA into DRT with ideal network density increases the network's latency thus eliminating the proportion between the unstable period and the lifetime of the network in halves. Additionally, it displays a 25% improvement in network lifespan and the lowest rate of node loss when compared to the existing system ensuring that halves of nodes are still alive just a few rounds even before the lifetime of the network expires.

Resilient Deployment of Smart Nodes for Improving Confident Information Coverage in 5G IoT

The development of 5G has brought new opportunities for the application of Internet of Things (IoT). The integration of 5G and IoT technologies promote high availability, resilience, and reliability of the network infrastructures. IoT deployment optimization is the core issue of 5G IoT. Traditionally, IoT node deployment methods mostly used disk coverage model or probabilistic detection coverage model, which only utilizes the sensing capability of a single IoT node, which results in higher deployment costs. In this article, we study the network resilience of coverage estimation error and solve the coverage problem of resilient deployment of smart nodes in 5G IoT. The coverage formulation in the deployment optimization method is defined based on the confident information coverage (CIC). In order to obtain the optimal deployment with a given coverage quality and with a given budget, the mixed-integer linear programming models (CICILP-COST) and (CICILP-ERROR) are proposed based on the CIC model. After analyzing the model complexity, the proposed models are solved by the variable relaxation algorithm (CICVR-COST) and dichotomous search algorithm (CICDS-ERROR), respectively. Simulations on air pollution datasets in Lyon, France, show that the proposed model yields a lower cost optimal deployment than existing peer schemes.

Smart Framework for Automatic Control of Home Appliances Using IoT

With the advent of the 21st century, comfort and efficiency started becoming a part of a common man’s life. Virtual assistants like Amazon Alexa and mobile applications like Google Assistant have tremendously helped in making Home Automation Systems more effortless and orderly. Efforts are still being made for better intra-connection of our homes. Home automation has created a great impact in the field of the Internet Of Things. In this paper, we have used the pre-existing technology of Alexa Echo to make Home Automation more cost-effective by enabling non-smart devices to react to the virtual assistant. In this framework, we proposed a smart node (relay) that can remotely control the connected electrical gadget using text/voice commands. The status can be monitored and controlled by the Alexa app remotely without the need for physical voice commands to Alexa. Further, room ambience, as well as emotion-based lighting, can be provided.

Security Threats to Internet of Things : A Survey

Internet of things (IoT) is an emerging technology in the present era. The term IoT refers to as an interconnection of several smart nodes through some heterogeneous link for the purpose of data communication. Some particular protocols control the entire communication in IoT. Due to plenitude of devices, it becomes a huge task to check the loyalty status of each node which is going to be a part of IoT environment. These nodes sometimes get involved in some malicious activities which may cause critical threats to this environment. These anonymous activities may include some attack on the working or security of IoT. In this uncongenial circumstance we need a strong security measurement to countermeasure these attacks. Innumerable efforts have been made to improve the security of IoT. This paper is an effort to make a glance of some of these security schemes

Additive manufacturing (3D Printing)-applied construction: Smart node system for an irregular building façade

This paper presents a proposed node system for an actual building project, named the Galleria Department Store (GDS). The exterior of the building was designed as an irregular shape of curtain wall, and it had a challenge that the existing methods could not address the requirement. The GDS curtain wall comprises different flat glass shapes connected by varying deflection angles. This structural requirement caused unprecedented challenges, such as the vertex deflection phenomenon.The proposed method, named the Smart Node system, was developed and applied to the GDS project to address this challenge. Additive manufacturing (AM) technology was used in production, showing a novel method for irregular façade construction. The originality of this study is to accomplish a new construction method using the AM technology, and also it covers the quantified effect of this application.One of the key achievements is that the developed Smart Note system performed with 99.4% construction accuracy, where only 10 out of 1652 glass sheets required reworks or corrections. In addition, the analysis found that the length of the vertical span was identified as one of the most critical factors to prevent construction error(s). When the length of the section is less than 9.8 m, no construction issues were observed.The main contribution of this study lies in the accuracy level of construction delivery, which is 647 node and 1632 glass panel productions and installations. The novelty of this study also includes the qualitative description of practical trials and errors from carrying out an AM-applied construction of GDS's exterior façade.

Monitoring and Control System for Energy Management Using Internet of Things

Abstract: The use of technology has become an essential part of improving lifestyle, work efficiency, and a catalyst for economic growth. The benefit of the Internet of Things (IoT) and connected nodes has been on a steep incline in recent years. This paper aims to research, build, test and implement a low-cost energy monitoring and control system using IoT devices. Electrical appliances (e.g., air conditioning units and overhead lighting) can be controlled and monitored using IoT technology from any place in the world. In order to accomplish this goal, a complete front-end to back-end system that includes a smart device application (iOS platform), a cloud-based database, an Application Programming Interface (API), and a hardware development is proposed. A small programmable specialized computing device (e.g., Raspberry Pi) for preliminary testing. This smart node was chosen due to familiarity, and its capabilities, such as general purpose pins and built-in Wi-Fi chip. The end goal is to observe energy efficiency by monitoring and controlling air conditioning appliances and standard overhead lighting units. These smart IoT devices allow for the usage energy data from each unit to be collected and stored in a Cloud-based database that can be analysed and reported for energy conservation and analysis. Keywords: Internet of Things (IoT), Energy Monitoring, Control System, Security.