Internet Of Things Wireless Sensor Research Articles

StateOS : A Memory Sufficient Hybrid Operating System for IOT Devices

The proliferation of Internet of Things (IoT) technology has spurred the need for efficient operating systems tailored to the constraints of IoT devices. This paper presents StateOS, a hybrid operating system designed to meet the demands of sensor-based IoT devices and wireless sensors. StateOS combines the memory efficiency of event-driven systems with the clarity of control flow provided by multithreaded approaches. By implementing a microkernel architecture, cross-layer network protocol design, and a hybrid task scheduler, StateOS offers real-time capabilities while minimizing memory footprint. Macro-based task APIs and state machine-based visual programming facilitate cooperative threaded programming, making StateOS suitable for both experienced developers and novices. The paper provides an in-depth overview of StateOS's architecture, kernel functionalities, code examples, implementation on various platforms, performance evaluation, and concludes by highlighting its potential as a valuable tool for modern IoT application development. Keywords — Hybrid operating system (OS), Internet of Things (IoT) and Memory efficiency.

Smart Water Quality Monitoring with IoT Wireless Sensor Networks.

Traditional laboratory-based water quality monitoring and testing approaches are soon to be outdated, mainly because of the need for real-time feedback and immediate responses to emergencies. The more recent wireless sensor network (WSN)-based techniques are evolving to alleviate the problems of monitoring, coverage, and energy management, among others. The inclusion of the Internet of Things (IoT) in WSN techniques can further lead to their improvement in delivering, in real time, effective and efficient water-monitoring systems, reaping from the benefits of IoT wireless systems. However, they still suffer from the inability to deliver accurate real-time data, a lack of reconfigurability, the need to be deployed in ad hoc harsh environments, and their limited acceptability within industry. Electronic sensors are required for them to be effectively incorporated into the IoT WSN water-quality-monitoring system. Very few electronic sensors exist for parameter measurement. This necessitates the incorporation of artificial intelligence (AI) sensory techniques for smart water-quality-monitoring systems for indicators without actual electronic sensors by relating with available sensor data. This approach is in its infancy and is still not yet accepted nor standardized by the industry. This work presents a smart water-quality-monitoring framework featuring an intelligent IoT WSN monitoring system. The system uses AI sensors for indicators without electronic sensors, as the design of electronic sensors is lagging behind monitoring systems. In particular, machine learning algorithms are used to predict E. coli concentrations in water. Six different machine learning models (ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor) are used on a sourced dataset. From the results, the best-performing model on average during testing was the AdaBoost regressor (a MAE¯ of 14.37 counts/100 mL), and the worst-performing model was stochastic gradient boosting (a MAE¯ of 42.27 counts/100 mL). The development and application of such a system is not trivial. The best-performing water parameter set (Set A) contained pH, conductivity, chloride, turbidity, nitrates, and chlorophyll.

Farmers’ Toolkit: Deep Learning in Weed Detection and Precision Crop & Fertilizer Recommendations

Agriculture is widely recognized as a significant and indispensable occupation on a global scale. The current imperative is to optimize agricultural practices and progressively transition towards smart agriculture. The Internet of Things (IoT) technology has dramatically enhanced people’s daily lives via diverse applications across several domains. Previous studies have yet to effectively incorporate Artificial Intelligence (AI) with sensor technology to provide comprehensive guidance to agricultural practitioners, hindering their ability to achieve good outcomes. This research offers Farmers’ Toolkit with four layers: sensor, network, service, and application. This toolkit aims to facilitate the implementation of a smart farming system while effectively managing energy resources. With a specific emphasis on the application layer, the toolkit uses a deep learning methodology to construct a fertilizer recommendation system that aligns with the expert’s perspective. This study utilizes IoT devices and Wireless Sensor Network (WSN) methods to enhance the efficiency and speed of recommending appropriate crops to farmers. The recommendation process considers several criteria: temperature, yearly precipitation, land area, prior crop history, and available resources. The identification of undesirable vegetation on agricultural fields, namely the detection of weeds, is carried out using drone technology equipped with frame-capturing capabilities and advanced deep-learning algorithms. The findings demonstrate an accuracy rate of 94%, precision rate of 92%, recall rate of 96%, and F1 score of 94%. The toolkit for farmers alleviates physical labor and time expended on various agricultural tasks while enhancing overall land productivity, mitigating potential crop failures in specific soil conditions, and minimizing crop damage inflicted by weeds.

LEACH-D: A low-energy, low-delay data transmission method for industrial internet of things wireless sensors

In recent years, the Internet of Things (IoT) has experienced extensive adoption in industrial environments, healthcare, smart cities, and more, playing a vital role in these domains. Within IoT-based systems, wireless sensor networks (WSNs) have emerged as a crucial method for collecting peripheral environmental data within industries, owing to their self-organizational attributes. Nevertheless, the enormous volume of heterogeneous data from various sensing devices presents many challenges for IoT-enabled WSNs, encompassing high transmission delay times (TD) and excessive battery energy consumption (EC). To address these challenges, it is imperative to prioritize efficiency and optimize energy utilization. Moreover, enhancing energy efficiency within the Industrial Internet of Things (IIoT) realm hinges significantly on factors such as data transmission modes and the allocation of cluster head nodes. Numerous researchers have proposed algorithms to minimize transmission time and energy consumption, specifically focusing on industrial environments. This paper introduces an inventive clustering-based data transmission algorithm for IIoT, LEACH-D, to enhance efficiency. The LEACH-D algorithm improves the transmission task duration while maintaining consistent battery energy consumption. It also seeks to elevate performance in metrics such as average transmission time during the first node death (FND). Numerous experimental results provide strong evidence that the algorithm introduced in this paper has effectively reduced the average transmission time by remarkable percentages: 51.32%, 12.12%, 12.96%, and 5.42%, while simultaneously increasing the number of FND rounds by significant margins: 222.43%, 36.63%, 33.72%, and 7.81%, respectively. These improvements stand in stark contrast to the performance of existing algorithms, including FREE_MODE, LEACH, EE-LEACH, and ETH-LEACH.

Optimized Low-Powered Wide Area Network within Internet of Things

The Internet of Things (IoT) is rapidly becoming an integral part of everyday life. LPWANs have been introduced to support the billions of internet-connected devices and the data they produce. LPWANs are capable of providing reliable connectivity even in low-density areas and devices consuming a low amount of energy. The exponential increase in the use of IoT applications across the globe will continue to generate more and more data traffic within the IoT network. Hence, it will increase device battery usage that may reduce the battery life expectancy limits. Thus, End Devices (EDs) within the IoT network in the near future will rise up to billions of devices operating in public, industry, and personal networks, generating a necessity for more correct and reliable energy conservation technology. This prompted the research work on an optimized low-powered wide area network within IoT. This paper focuses on three different strategies: LoRa power consumption model design, simulation of IoT wireless sensor networks, and implementation of SF allocation across the wireless sensor network and results analysis. The experiment has been carried out in various stages: firstly running a simulation over a wireless sensor network without optimization using MATLAB Simulink and obtaining the following result of 6.3997e-17 joules power consumption. Secondly, the authors test the network with power optimization using particle swarm optimization algorithms and obtained a better result of 2.5230e-17 joules. The LoRa energy consumption is reduced by 60%. Lastly, different simulation tests of LoRaWAN protocols with respect to throughput, packet loss, delay, data transmission, buffer size, and network density. The results presented on the graph showed that the proposed model outperforms the existing models. Hence, appropriate spreading factor allocation has increased the power efficiency of LoRa end device battery.

StateOS: A Memory-Efficient Hybrid Operating System for IoT Devices

The increasing significance of operating systems (OSs) in the development of the internet of things (IoT) has emerged in the last decade. An event-driven OS is memory efficient and suitable for resource-constrained IoT devices and wireless sensors, although the program’s control flow, which is determined by events, is not always obvious. A multithreaded OS with sequential control flow is often considered clearer. However, this approach is memory-consuming. A hybrid OS seeks to combine the strengths of the event-driven approach with multithreaded approach. An event-driven cooperative threaded OS represents a hybrid approach that supports concurrency by explicitly yielding control to another thread. Although this approach is memory efficient, as cooperative threads are not preemptive, it may not provide sufficient real-time performance. This article proposes a memory-efficient hybrid OS, called StateOS, for resource-constrained IoT devices. It is an event-driven cooperative threaded OS with partial real-time performance. StateOS implements a hybrid task scheduler that combines two cooperative threaded subsystems as kernel processes on a priority-based preemptive scheduler. This approach provides adequate real-time performance for IoT devices at a low memory cost.

Energy-Efficient Dynamic Directional Modulation With Electrically Small Antennas

This letter proposes a compact and energy-efficient directional modulation scheme for small and power constrained devices (e.g., Internet of Things wireless sensors). The scheme uses a uniform circular array of monopole antennas, with a single radiofrequency chain and antenna active at a time. The antennas can be located in close proximity, offering significant size reduction. Furthermore, the scheme was demonstrated to operate successfully with an array of 0.6 λ diameter. The system does not generate additional artificial noise, limiting interference to other systems. Finally, the antenna switching sequence can be randomly generated and without fine-tuned synchronization with the transmitter.

An Ambient RF Powered Wireless Sensor System

The development of an ambient radio frequency (RF) powered Internet of Things (IoT) wireless sensor system is reported. The system integrates all major components including multiple antennas, rectifiers, a power management unit (PMU), a microcontroller unit (MCU) with RF transceiver module and sensing unit. A novelty of this applied engineering innovation is the design and use of multi-band dual-polarized multiple antennas to handle the low ambient RF power density found in the everyday environment. Our prototype demonstrates that it is possible to sense temperature, pressure and humidity and wirelessly transmit these signals to a central server with a duty cycle of approximately 15 minutes, when the background ambient RF signals are as low as -28.6 dBm at 950 MHz. The proposed system can address the important issue of energy supply in the development of IoT providing an alternative energy source to batteries or solar cells. Ambient RF powered IoT devices do not require periodic replacement of their batteries or to be placed in visible regions which makes them suitable for wall, ceiling and floor applications. Our ambient RF powered IoT wireless sensor system provides concrete proof, through demonstration, that harnessing ambient RF energy for powering IoT sensor systems is feasible.

Secure and Privacy-Preserved Data Collection for IoT Wireless Sensors

The captured data from smart devices via Internet of Things (IoT) wireless sensors are vulnerable to numerous online and offline attacks and unauthorized accesses, hence, some digital signature and encryption solutions have been designed to ensure public verifiability, data integrity, and confidentiality. However, there are still some issues to be addressed. For example, the data source is revealed to the public due to the public verifiability of digital signatures, in which authentication is transferrable. Moreover, computation of these data can only be done after decryption, restricting outsourced computation, such as a computing facility from a cloud. The best approach of private computation, which supports outsourced computation, is based on homomorphic encryption. However, significant computational overhead is a concern. To deal with these issues, in this article, we propose an efficient and provably secure scheme based on designated-verifier proofs, deniable authentication and homomorphic encryption for secure and lightweight data collection, batch verification, and data analysis in the privacy-preserved IoT wireless sensors applications. The main contribution of our work is the privacy-preserved IoT wireless sensors system along with a novel deniable authenticated homomorphic encryption scheme that can securely aggregate data from IoT wireless sensors for secure outsourced applications. To prove the security and efficiency of our proposed scheme, we provide formal security analysis and performance comparisons for IoT wireless sensors.

Energy Harvesting Mechanisms in a Smart City—A Review

The issue of how to power the deployed Internet of Things (IoT) nodes with ubiquitous and long lasting energy in order to ensure uninterruptible optimisation of smart cities is of utmost concern. This among other challenges has continued to gear efforts toward energy harvesting research. With the outbreak of COVID-19 pandemic and the lockdown that nearly paralysed activities of everyday living in many nations of the world, option of human remote interaction to enforce social distancing became imperative. Hence, the world is witnessing a renewed awareness of the importance of IoT devices, as integral components of smart city, especially for the essence of survival in the face of lockdown. Energy harvesting is a possible solution that could enable IoT nodes to scavenge self-sustaining energy from environmental ambient sources. In this paper, we have reviewed most sources within city that energy could be harvested from, as reported by researchers in literature. In addition, we have submitted that energy sources can be application specific, such that, since there are many free sources in the city as presented in this review, energy should be scavenged within close proximity of need for various IoT devices or wireless sensor networks (WSNs), for smart city automation.

Trust aggregation authentication protocol using machine learning for IoT wireless sensor networks

Security is a huge concern in the Internet of Things (IoT)-based Wireless Sensor Networks (WSNs). Authentication becomes critical in a highly secured environment and reliable communication, specifically trustworthiness. In security, nonrepudiation refers to a service, which provides proof of the origin of data and the integrity of the data. Hence, this factor is to be considered in an IoT environment. This work focuses on the proposed Trust Aggregation Authentication Protocol based on the Machine Learning technique (TAAPML). The total trust value is derived for each device from behavior and data trust values by the internet gateways. In the authentication phase, if either the trust value is less than a standard threshold value or the authentication token is invalid, then gateways omit that node. The trust threshold value is adaptively calculated by using a technique called Support Vector Machine (SVM) over the collected traffic data. TAAPML technique performance is evaluated with respect to Packet Delivery Ratio, Delay, Residual Energy, and Computational Overhead.

The Impact of Memory-Efficient Bots on IoT-WSN Botnet Propagation

By successfully infecting certain numbers of nodes in an Internet of Things (IoT) platform, botmaster often relies on specific bots (infected nodes) to scan and gather information about the target nodes for onward propagation of the attack. Therefore, the bot’s processing capability to scan and executes the botmaster’s instructions relied on its memory capability. Hence, the bot’s memory efficiency determines the selection or the abandonment of the bot by the botmaster during the propagation of the attack. To defend against IoT botnets attack, there is need to accurately analyze the dynamic characteristics of the botnet propagation in a network. Although conventional IoT botnet propagation models considered certain characteristics of IoT wireless sensor networks (IoT-WSN) in analyzing the propagation time and the size of the botnet infection, the models do not consider the impact of memory-efficient bots on the size of the botnet infection. As such they are not applicable to defend the network against them. To complement this gap, this study proposed an IoT- Susceptible-Infectious-Abandon (IoT-SIA) model to analyze the impact of the memory-efficient bots on the size of the botnet infection. Consequently, based on the numerical simulation results, the memory-efficient bots have shown a direct impact on the size of the botnet infection and the propagation time of the attack.

Game theory based adaptive transmit power control algorithm for IoT wireless sensor networks

Objectives: To design a transmission power control (TPC) algorithm for the Internet of Things (IoT) based Wireless Sensor Networks (WSN) such that the energy cost is reduced and the expected throughput is increased. Method: In this study, a Game theory based adaptive transmit power control (GT-ATPC) algorithm for IoT-WSN is proposed. In this algorithm, for each sender, the initial transmission power level (TPL) is determined from the difference of Received Signal Strength (RSS) measurements at the base station (BS). The TPL is adaptively adjusted by means of Game theory. For each sender, the expected throughput, energy cost and connectivity values are estimated from which a utility function is derived. Findings: By simulation results, it has been shown that GT-ATPC attains 37% higher throughput with 57% reduced energy cost and 30% lesser overhead when compared with GTS-PM approach. Novelty: The TPL of a node is adjusted based on the estimated values of utility function such that the throughput and connectivity is high and energy cost is less. Keywords: Internet of Things (IoT); wireless sensor network (WSN); transmission power; Energy cost; game theory; utility function

Research on vibration monitoring and fault diagnosis of rotating machinery based on internet of things technology

Abstract Recently, researchers are investing more fervently in fault diagnosis area of electrical machines. The users and manufacturers of these various efforts are strong to contain diagnostic features in software for improving reliability and scalability. Internet of Things (IoT) has grown immensely and contributing for the development of recent technological advancements in industries, medical and various environmental applications. It provides efficient processing power through cloud, and presents various new opportunities for industrial automation by implementing IoT and industrial wireless sensor networks. The process of regular monitoring enables early detection of machine faults and hence beneficial for Industrial automation by providing efficient process control. The performance of fault detection and its classification by implementing machine-learning algorithms highly dependent on the amount of features involved. The accuracy of classification will adversely affect by the dimensionality features increment. To address these problems, the proposed work presents the extraction of relevant features based on oriented sport vector machine (FO-SVM). The proposed algorithm is capable for extracting the most relevant feature set and hence presenting the accurate classification of faults accordingly. The extraction of most relevant features before the process of classification results in higher classification accuracy. Moreover it is observed that the lesser dimensionality of propose process consumes less time and more suitable for cloud. The experimental analysis based on the implementation of proposed approach provides and solution for the monitoring of machine condition and prediction of fault accurately based on cloud platform using industrial wireless sensor networks and IoT service.

SDCN

Building an open global sensing layer is critical for the Internet of Things (IoT). In this article, we present a Sensory Data-Centric Networking (SDCN) architecture for inter-networking two main networked sensing systems in IoT—wireless sensor networks and mobile sensing networks. Specifically, the proposed SDCN is a systematic solution including NDNs for sensor nodes in the Zigbee network, NDNm for mobilephones in the Wi-Fi network, and NDNg for gateways. Considering the sensing requirement of IoT, we first design a novel Spatio-Temporal 16 Tree (ST16T) naming scheme associated with the scope-matching method. Based on the naming scheme, we further propose the related discovery methods, network switching mechanism, forwarding, and routing strategies according to the features of large-scale sensing and resource-constrained environment. A proof-of-concept prototype is implemented and further is deployed on our campus (BUPT) and the Great Wall (Shaanxi, China) for Environment Monitoring Project. Several experiments are conducted on the deployed platform. The experimental results show that SDCN outperforms the state-of-the-arts and gains a great performance improvement in terms of energy consumption, data collection efficiency, memory footprint, and time delay.

A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities

Nowadays, there is a growing trend in smart cities. Therefore, the Internet of Things (IoT) enabled Underwater and Wireless Sensor Networks (I-UWSN) are mostly used for monitoring and exploring the environment with the help of smart technology, such as smart cities. The acoustic medium is used in underwater communication and radio frequency is mostly used for wireless sensor networks to make communication more reliable. Therefore, some challenging tasks still exist in I-UWSN, i.e., selection of multiple nodes’ reliable paths towards the sink nodes; and efficient topology of the network. In this research, the novel routing protocol, namely Time Based Reliable Link (TBRL), for dynamic topology is proposed to support smart city. TBRL works in three phases. In the first phase, it discovers the topology of each node in network area using a topology discovery algorithm. In the second phase, the reliability of each established link has been determined while using two nodes reliable model for a smart environment. This reliability model reduces the chances of horizontal and higher depth level communication between nodes and selects next reliable forwarders. In the third phase, all paths are examined and the most reliable path is selected to send data packets. TBRL is simulated with the help of a network simulator tool (NS-2 AquaSim). The TBRL is compared with other well known routing protocols, i.e., Depth Based Routing (DBR) and Reliable Energy-efficient Routing Protocol (R-ERR), to check the performance in terms of end to end delay, packet delivery ratio, and energy consumption of a network. Furthermore, the reliability of TBRL is compared with 2H-ACK and 3H-RM. The simulation results proved that TBRL performs approximately 15% better as compared to DBR and 10% better as compared to R-ERR in terms of aforementioned performance metrics.

Characterization of challenges in asymmetric nonlinear vibration energy harvesters subjected to realistic excitation

With an explosion of the internet of things (IoT), vibration energy harvesting provides an environmentally friendly solution to replace consumable batteries in powering IoT wireless sensors. Yet, when implemented in practice, ambient vibration input energy is much less periodic than assumptions adopted in previous studies. This becomes especially important given that asymmetries are inevitable in nonlinear device platforms. This research sheds light on these complex challenges of practical vibration energy harvesting by developing and exploring an analytical model based on equivalent linearization. The modeling approach provides an opportunity to understand influences of asymmetry, nonlinearity, and combined excitation response on the DC power delivery of energy harvesters. In the analytical model, a weighted Gaussian joint distribution is utilized to approximate the influences caused by the random excitation. Combined with numerical and experimental validation, the analysis indicates that with the increase of stochastic base acceleration, two outcomes are possible. A first outcome involves an enhancement of DC power by way of triggering large amplitude nonlinear oscillations. A second outcome corresponds to a loss of high power delivery since the noise interferes with the attainment of the snap-through dynamic. Either reducing asymmetry or increasing harmonic excitation component is found to be favorable to induce the power-enhancing dynamics and inhibit the occurrence of the second case. Although with the simplified Gaussian distribution, the analytical framework cannot reproduce exact details of the dynamic responses in every case, the results show that the statistical trends of the analysis are overall borne out in simulation and experiment. This indicates the new modeling of this research may help guide attention to design and deployment techniques for nonlinear vibration energy harvesters in practical combined excitation environments, where limitations on precise manufacture or placement may introduce structural asymmetry.

Efficient fault-tolerant routing in IoT wireless sensor networks based on path graph flow modeling with Marchenko–Pastur distribution (EFT-PMD)

In the internet of things (IoT) based wireless sensor network (WSN), the nodes are scattered to segregate the rapt data in the relevant field of application. In general, sensor nodes of IoT possess heterogeneous property and display cluster-based routing to transmit the data as it is considered as an efficient routing method. When one or more cluster heads (CHs) fail, the sensed data of sensor nodes that are currently serving cannot be forwarded by the faulty CHs. Consequently, data of the IoT application will not be sufficiently sensed by the sink node (gateway). As a result, information processing of this field will be affected profusely. This paper focuses on the development of a paired cluster fault-tolerant disjoint path routing in a path graph and a novel approach to solve this dilemma in polynomial time of the degree of the graph. The objective of this proposed IoT–WSN architecture is to diminish the latency, end-to-end delay as well as energy consumption and thereby improving the performance in terms of throughput and packet delivery ratio. The performance of this proposed method in IoT–WSN network is measured and affirmed using benchmark network simulator.

Distributed Key Management to Secure IoT Wireless Sensor Networks in Smart-Agro.

With the deepening of the research and development in the field of embedded devices, the paradigm of the Internet of things (IoT) is gaining momentum. Its technology’s widespread applications increasing the number of connected devices constantly. IoT is built on sensor networks, which are enabling a new variety of solutions for applications in several fields (health, industry, defense, agrifood and agro sectors, etc.). Wireless communications are indispensable for taking full advantage of sensor networks but implies new requirements in the security and privacy of communications. Security in wireless sensor networks (WSNs) is a major challenge for extending IoT applications, in particular those related to the smart-agro. Moreover, limitations on processing capabilities of sensor nodes, and power consumption have made the encryption techniques devised for conventional networks not feasible. In such scenario, symmetric-key ciphers are preferred for key management in WSN; key distribution is therefore an issue. In this work, we provide a concrete implementation of a novel scalable group distributed key management method and a protocol for securing communications in IoT systems used in the smart agro sector, based on elliptic curve cryptography, to ensure that information exchange between layers of the IoT framework is not affected by sensor faults or intentional attacks. In this sense, each sensor node executes an initial key agreement, which is done through every member’s public information in just two rounds and uses some authenticating information that avoids external intrusions. Further rekeying operations require just a single message and provide backward and forward security.

A novel method of IoT wireless sensor nodes management based on OID technology

With the continuous development of Internet of Things (IoT) technologies, massive wireless sensor nodes are deployed to construct several local wireless sensor networks over the world, which bring a great challenge to enormous wireless sensor nodes management. In this paper, a mechanism of sensor node managed object identifier (OID) is designed to merge classic SNMP MIB managed object identifier with sensor node identifier. Then, the framework of classic SNMP manager and agent is modified and optimised. Finally, a method for sensor nodes remote managing, which adapts to heterogeneous IoT network such as Internet linked with industry wireless network etc., is proposed to achieve hierarchical addressing and unified management of massive sensor nodes. The experimental result demonstrates that the proposed method satisfies the unified managing demand of a tremendous amount of sensor nodes in large-scale IoT network scenario by configuring and controlling sensor nodes.