Internet Of Things Wireless Sensor Networks Research Articles

Hybrid optimization‐based topology construction and DRNN‐based prediction method for data reduction in IoT

SummaryThe Internet of Things (IoT) acts as a prevalent networking setup that plays a vital role in everyday activities due to the increased services provided through uniform data collection. In this research paper, a hybrid optimization approach for the construction of heterogeneous multi‐hop IoT wireless sensor network (WSN) network topology and data aggregation and reduction is performed using a deep learning model. Initially, the IoT network is stimulated and the network topology is constructed using Namib Beetle Spotted Hyena Optimization (NBSHO) by considering different network parameters and encoding solutions. Moreover, the data aggregation and reduction in the IoT network are performed using a Deep Recurrent Neural Network (DRNN)‐based prediction model. In addition, the performance improvement of the designed NBSHO + DRNN approach is validated. Here, the designed NBSHO + DRNN method achieved a packet delivery ratio (PDR) of 0.469, energy of 0.367 J, prediction error of 0.237, and delay of 0.595 s.

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Traditional laboratory-based water quality monitoring and testing approaches are soon to be outdated, mainly because of the need for real-time feedback and immediate responses to emergencies.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.

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.

A multi-gateway authentication and key-agreement scheme on wireless sensor networks for IoT

The Internet of Things (IoT) is designed to let anything connect to the Internet, and the things can be people, computers, and things. On the IoT, the Wireless Sensor Network (WSN) plays an important role because it can be used in many applications such as smart home, intelligent transportation, and intelligent disaster prevention. Since the WSN transmits data in the wireless way, the security problem is a concerning issue in this field. On the WSN, an authentication and key-agreement scheme can let the sensors authenticate to each other and share a common key to encrypt the data. Thus, it can be used to solve the security problem of WSNs. In this paper, I propose a new multi-gateway authentication and key-agreement scheme on WSN for IoT. The proposed scheme adopts a new multi-gateway structure, and thus it allows users and sensors to join in different areas of WSN dynamically. According to the performance analysis, the execution time of the proposed scheme is only 24Th, where Th is the execution time of a one-way hash function. In conclusion, the proposed scheme is more efficient than the related works on the WSN for IoT applications.

Efficient and Secured Mechanisms for Data Link in IoT WSNs: A Literature Review

The Internet of things (IoT) and wireless sensor networks (WSNs) have been rapidly and tremendously developing recently as computing technologies have brought about a significant revolution. Their applications and implementations can be found all around us, either individually or collaboratively. WSN plays a leading role in developing the general flexibility of industrial resources in terms of increasing productivity in the IoT. The critical principle of the IoT is to make existing businesses sufficiently intelligent to recognize the need for significant fault mitigation and short-cycle adaptation to improve effectiveness and financial profits. This article presents efficiently applied security protocols at the data link layer for WSN and IoT-based frameworks. It outlines the importance of WSN–IoT applications as well as the architecture of WSN in the IoT. Our primary aim is to highlight the research issues and limitations of WSNs related to the IoT. The fundamental goal of this work is to emphasize a suggested architecture linked to WSN–IoT to enhance energy and power consumption, mobility, information transmission, QoS, and security, as well as to present practical solutions to data link layer difficulties for the future using machine learning. Moreover, we present data link layer protocol issues, attacks, limitations, and research gaps for WSN frameworks based on the recent work conducted on the data link layer concerning WSN applications. Current significant issues and challenges pertain to flow control, quality of service (QoS), security, and performance. In the context of the literature, less work has been undertaken concerning the data link layer in WSN and its relation to improved network performance.

Guest Editorial Special Issue on Computational Intelligence for IoT-based Human Activity Recognition

The papers in this special section focus on computational intelligence for Internet-of-Things-based human activity recognition (HAR). HAR benefits numerous real-world applications. For instance, it can be adopted in a healthcare service system to monitor the rehabilitation processes of patients. Another important application of HAR is in security and surveillance which require to analyze human behaviors and detect anomalies in specific areas. Finally, in human computer interface, recognizing human activities can be used to control robots and play virtual reality games. Many Internet of Things (IoT) sensors have been utilized for human activity recognition, such as wearable sensors, smartphones, radio frequency (RF) sensors. Owing to the rapid development of wireless sensor networks in IoT, a large amount of data have been collected for the recognition of human activities with different types of sensors. Conventional computational intelligence algorithms, such as shallow neural networks, require to manually extract some representative features from large and noisy sensory data, which may hinder their performance in real-world applications. Alternatively, the more advanced computational intelligence algorithms of deep neural networks have achieved great successes in many challenging research areas, such as image recognition and natural language processing. The key merit of the deep neural networks is the ability to automatically learn more accurate representative features from massive amount of data, without going through the manual and time-consuming feature extraction process.

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

In this modern age, Internet of Things (IoT) and Wireless Sensor Network (WSN) as its derivatives have become one of the most popular and important technological advancements. In IoT, all things and services in the real world are digitalized and it continues to grow exponentially every year. This growth in number of IoT device in the end has created a tremendous amount of data and new data services such as big data systems. These new technologies can be managed to produce additional value to the existing business model. It also can provide a forecasting service and is capable to produce decision-making support using computational intelligence methods. In this survey paper, we provide detailed research activities concerning Computational Intelligence methods application in IoT WSN. To build a good understanding, in this paper we also present various challenges and issues for Computational Intelligence in IoT WSN. In the last presentation, we discuss the future direction of Computational Intelligence applications in IoT WSN such as Self-Organizing Network (dynamic network) concept.

Internet of things based wireless sensor network: a review

Recently, Internet of Things (IoT) technologies are developing technology with a variety of applications. The Internet of Things (IoTs) is defined as a network of ordinary objects such as Internet TVs, smartphones, actuators and sensors that are smartly connected together to enable new types of communication between people and things as well as between things themselves. Wireless sensor networks (WSNs) play an important part in Internet of Things (IoT) technology. A contribution to wireless sensor networks and IoT applications is wireless sensor nodes’ construction with high-speed CPUs and low-power radio links. The IoT-based wireless Sensor network (WSN) is a game-changing smart monitoring solution. ZigBee standard is an important wireless sensor network (WSN) and Internet of Things (IoT) communication protocol in order to facilitate low-power, low-cost IoT applications and to handle numerous network topologies. This paper presented a review on the energy efficient and routing topologies of ZigBee WSN, applications of IoT enabled Wireless Sensor Network as well IoT WSN security challenges.

Efficient topology discovery protocol using IT-SDN for software-defined wireless sensor network

The internet of things (IoT) and wireless sensor networks (WSNs) are two promising technologies for supporting new services and applications. However, because IoT and WSN entail interaction and transaction of device topology discovery protocols, WSN facing some challenges. Topology discovery (TD) is important for WSN in IoT since sensor nodes (SNs) are the main points of this network. Networking these SN (IoT-devices) has a few difficulties such as energy and bandwidth consumption, and data storage issues resulting from frequent interactions and transactions between devices. Because these challenges cannot be resolved by one solution, we focused on reducing the energy consumption associated with a WSN. We proposed software-defined networking (SDN) model to tackle energy-efficiency issue in a WSN setup by integrating SDN and WSN, which gave rise to a more robust system, software-defined wireless sensor network (SDWSN). In this direction, we proposed an improved fuzzy-logic-based strategy, fuzzy topology discovery protocol (FTDP). It used IT-SDN (an SDN-based WSN framework) to increase the network’s lifepan due to its low energy consumption. The link layer discovery protocol (LLDP) to build the SDN controller’s topology. The system’s performance was presented and compared, demonstrating that with an effective SDWSN discovery policy, energy efficiency is achievable.

Cryptanalysis of authentication protocol for WSN in IoT based electric vehicle environment

The 21st century is of IoT (Internet of Things) wherein models like smart home, smart environment, smart device, smart vehicle, etc. playing its vital role. Among these, the smart vehicle making the key focus in research due to its requirements like low cost, less maintenance, etc. Indeed, Electrical Vehicle (EV) is best suit for the smart vehicle as it works on the same principle. Today’s EV are self sustained as well as environment friendly having the inbuilt WSN (Wireless Sensor Network) as one of the key component. In WSN, there is need of authentication in order to identify the communicating devices. In years till now, many schemes based on one-factor (identity), two-factor (identity and text password), three-factor (identity, text password and biometric password), etc. have been proposed and analyzed. Recently, in 2021, Xue et al. proposed the novel scheme (i.e. case-1 and case-2) for the WSN in IoT based framework using multi gateway model and claimed that it is secure against various attacks. Thus, in this research we do the cryptanalysis of the Xue et al.’s scheme (case-1) and proved that it is insecure against various attacks.

Piezoelectric Energy Harvester for IoT Sensor Devices

Limited battery power is a major challenge for wireless sensor network (WSN) in internet of things (IoT) applications, especially in hard-to-reach places that require periodic battery replacement. The energy harvesting application is intended as an alternative to maintain network lifetime by utilizing environmental energy. The proposed method utilized piezoelectricity to convert vibration or pressure energy into electrical energy through a modular piezoelectric energy harvesting design used to supply energy to sensor nodes in WSN. The module design consisted of several piezoelectric elements, of which each had a different character in generating energy. A bridge diode was connected to each element to reduce the feedback effect of other elements when pressure was exerted. The energy produced by the piezoelectric is an impulse so that the capacitor was used to quickly store the energy. The proposed module produced 7.436 μJ for each step and 297.4 μJ of total energy with pressure of a 45 kg load 40 times with specific experiments installed under each step. The energy could supply WSN nodes in IoT application with a simple energy harvesting system. This paper presents a procedure for measuring the energy harvested from a commonly available piezoelectric buzzer. The specific configurations of the piezoelectric and the experiment setups will be explained. Therefore, the output energy characteristics will be understood. In the end, the potentially harvested energy can be estimated. Therefore, the configuration of IoT WSN could be planned.

A Survey on Enhanced Energy Efficient Routing Protocol of WSN for Internet of Things

Wireless Sensor Networks (WSNs) are a resource-constrained network class recognized as a major energy consumer. Wireless sensor technologies are used in many commercialized industrial automation processes and other real-world applications. The WSN protocol is well-suited to harsh situations where deployment is difficult or impossible, such as the battlefield, a toxic chemical plant, the cloud, fog computing, and the Internet of Things, but not in a high-temperature network infrastructure environment. WSNs have introduced various Energy-Efficient Routing Protocols based on network (NW) organization and protocols in recent years. Various WSN routing options for energy efficiency are explored in this work. The WSN Energy Efficient Routing Protocol is compared to other routing systems. We also compare and investigate better WSN routing algorithms for cloud computing, fog computing, and the Internet of Things.

An energy aware grouping memetic algorithm to schedule the sensing activity in WSNs-based IoT for smart cities

Wireless Sensor Networks (WSNs) are the main component in the Internet of Things (IoT) and smart cities to sense our environment, gather essential and meaningful data, and forward it to the base station (BS). Nowadays, IoT utilizes WSN as a necessary platform for sensing and communication of the data. One of the main strategies to minimize consumption of energy in a WSN with highly dense sensors is to maximize sleeping sensors at each time. This way implies to schedule the activity of sensors, i.e. determining when a sensor node is kept idle (sleep mode) and when a sensor node is activated to sense environment (active mode). Due to heterogeneity of the sensor nodes in WSNs-based IoT for smart cities, one approach for scheduling the sensing activity is clustering the sensors into K mutually different subsets, so that every subset of sensors alone can cover all targets of the network. In this case, we can solve finding the maximum number of sensor subsets, or equivalently sensor covers problem, by conversion it to SET K-COVER problem. In this paper, an energy aware Grouping Memetic Algorithm (GMA) is proposed for solving the SET K-COVER problem. The proposed GMA varies in four general ways from any of the other evolutionary algorithms for solving the SET K-COVER problem. First, to transform solution structures linked to the SET K-COVER problem into chromosome genes, a new encoding scheme was being used. Second, specific genetic operators appropriate for the chromosomes are used, based on the encoding used. Third, to direct the search process in the solution space of the SET K-COVER problem, a novel proper fitness function is proposed. Fourth, a local improvement algorithm which uses a sensor dominance rule is proposed. This study, carried out detailed experiments of different numbers of targets and different numbers of sensors in WSNs to assess the proposed algorithm. In several instances of the problem, the experiments demonstrate that the algorithm works considerably better in terms of solution quality than many other heuristics and evolutionary algorithms and significantly outperformed the evolutionary algorithms in terms of runtime, suggesting the usefulness of the proposed algorithm to increase the lifetime of WSN in IoT and smart cities.

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.

Tree Structure Based Routing Algorithm Wih Mobile Gateway for IOT-WSN


 
 
 
 ABSTRACT
 The Internet of Things, often known as IoT, is a sector that is seeing significant expansion due to the fact that billions of objects are now linked to the internet. The most effective way to transmit data from one device to another is one of the most significant obstacles presented by the Internet of Things. In this article, we present a Tree Structure Based Routing Algorithm with Mobile Gateway for Internet of Things-Wireless Sensor Networks (WSN), the goal of which is to increase the efficiency of routing data in Internet of Things-Wireless Sensor Networks (WSN). The algorithm that has been presented makes use of a tree-based structure to route data and integrates a mobile gateway in order to increase network coverage and minimise the amount of energy that is used. In terms of network longevity, packet delivery ratio, and energy efficiency, the results of the simulation show that our suggested method surpasses current routing algorithms.
 
 
 

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

A Novel Hybrid Optimization for Cluster‐Based Routing Protocol in Information-Centric Wireless Sensor Networks for IoT Based Mobile Edge Computing

In present days, the utilization of mobile edge computing (MEC) and Internet of Things (IoT) in mobile networks offers a bottleneck in the evolving technological requirements. Wireless Sensors Network (WSN) become an important component of the IoT and is the major source of big data. In IoT enabled WSN, a massive amount of data collection generated from a resource-limited network is a tedious process, posing several challenging issues. Traditional networking protocols offer unfeasible mechanisms for large-scaled networks and might be applied to IoT platform without any modifications. Information-Centric Networking (ICN) is a revolutionary archetype which that can resolve those big data gathering challenges. Employing the ICN architecture for resource-limited WSN enabled IoT networks may additionally enhance the data access mechanism, reliability challenges in case of a mobility event, and maximum delay under multihop communication. In this view, this paper proposes an IoT enabled cluster based routing (CBR) protocol for information centric wireless sensor networks (ICWSN), named CBR-ICWSN. The proposed model undergoes a black widow optimization (BWO) based clustering technique to select the optimal set of cluster heads (CHs) effectively. Besides, the CBR-ICWSN technique involves an oppositional artificial bee colony (OABC) based routing process for optimal selection of paths. A series of simulations take place to verify the performance of the CBR-ICWSN technique and the results are examined under several aspects. The experimental outcome of the CBR-ICWSN technique has outperformed the compared methods interms of network lifetime and energy efficiency.