Wireless Sensor Network Devices Research Articles

Enhancement of Tiny Encryption Algorithm for Resource-Constrained WSNs Using Four Connected Additive Fibonacci Generators

The usage of wireless sensor networks (WSN) is widespread in industries where data security is crucial. Due to the energy and computational limits of WSN, the cryptographic protocols designed for it must be as computationally cheap as feasible. The components of these protocols, such as the random number generator, are subject to the same constraints. For such resource-constrained devices, several lightweight encryption techniques have been created. One of the most efficient lightweight ciphers is Tiny Encryption Algorithm (TEA). TEA uses a few lines of source code that are based on Feistel. It is however susceptible to attacks utilizing equivalent and related key attacks. In order to address TEA’s key vulnerabilities, a modification is suggested in this paper that focuses on key creation. Four connected Additive Fibonacci Generators (AFGs) make up the structure, which addresses the security vulnerability by using a unique key each round. Performance evaluation was assessed using three statistical tests: avalanche effect, randomness analysis, and completeness testing. Through experimental results, ATEA outperforms TEA by an average of 51.68 % to 47.51 % for the avalanche effect, and 51.95 % to 48.36 % for the completeness test, and satisfies all the NIST requirements. Results of advanced security measurements indicate that, ATEA can be used to secure WSN devices.

Security and Privacy of Wearable Wireless Sensors in Healthcare: A Systematic Review

Wearable Wireless Sensor Network (WWSN) devices are widely used in healthcare to monitor health data. However, when WWSN users transmit their data to healthcare professionals or third parties over wireless connections, they face privacy and security vulnerabilities. This paper aims to identify the unsolved privacy and security challenges in wearable sensor devices in healthcare, especially the aspects overlooked by previous research. The main research question is: What are the unsolved privacy and security challenges in wearable sensor devices in healthcare, and what are their implications for users and healthcare professionals? This systematic review employs specific keywords to search for relevant publications on bibliographic databases, including Google Scholar, Scopus, IEEE Xplore, and Web of Science. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) charts helped in screening and summarising the selected papers. The results highlight the critical areas that can make WWSNs vulnerable to security attacks. The findings examine the security and privacy issues of wearable sensor devices in cloud computing, fog computing, the Internet of Things (IoT) and the like. Many studies examine IoT due to its privacy and security challenges, especially regarding handling extensive data, using public channels, deploying advanced technologies, managing sharing policies alongside the growing number of wireless devices, and protecting data from hackers. These challenges seriously threaten the confidentiality, integrity, and availability of health data transmitted by WWSN users to healthcare professionals or third parties in cloud-based environments and IoT and are exacerbated by limited resources. The significant findings thereby focus on unresolved areas in IoT. This paper aims to safeguard against cyber-attacks on healthcare and increase users' adoption rate of WWSN devices.

CNN and Attention-Based Joint Source Channel Coding for Semantic Communications in WSNs

Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost-effectiveness, and ease of deployment. The rapid advancement of 5G technology and mobile edge computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices. However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission. In response to the aforementioned challenges, this paper proposes a semantic communication solution. Specifically, considering the limited computational and storage resources of WSN devices, we propose a flexible Attention-based Adaptive Coding (AAC) module. This module integrates window and channel attention mechanisms, dynamically adjusts semantic information in response to the current channel state, and facilitates adaptation of a single model across various Signal-to-Noise Ratio (SNR) environments. Furthermore, to validate the effectiveness of this approach, the paper introduces an end-to-end Joint Source Channel Coding (JSCC) scheme for image semantic communication, employing the AAC module. Experimental results demonstrate that the proposed scheme surpasses existing deep JSCC schemes across datasets of varying resolutions; furthermore, they validate the efficacy of the proposed AAC module, which is capable of dynamically adjusting critical information according to the current channel state. This enables the model to be trained over a range of SNRs and obtain better results.

Internet of Things experimental platform for real-time water monitoring: a case study of the Araranguá River estuary

The Internet of Things (IoT) is a technology that can be employed to monitor several sectors, mostly precision agriculture, where environmental data can be used to better manage resources, reduce costs, and improve the use of natural resources. This work proposes the application of cloud-based IoT for monitoring irrigation water in rice fields. Southern Brazil was chosen as a case study because it suffers from water pollution problems due to urban activities, coal mining, and the inflow of saline water into the Araranguá River estuary. All these factors can compromise the water quality for irrigation. The presence of salt in irrigation water can compromise rice growth, making its monitoring essential for successful cultivation. Currently, the rice farmer needs to travel to the riverbank to assess the water quality using a manual sensor. This procedure demands time and resources and is not always efficient. The technology helps indicate whether the water is of quality to irrigate crops, helping the farmers decide whether to use that water to irrigate their fields. The solution is based on low-cost wireless sensor network devices, with subsequent transmission of data from the gateway using the network mobile phone cloud. Data collected were also accessed by the smartphone through a mobile application. This system was implemented for testing in the Araranguá River estuary, southern Santa Catarina, Brazil. Also, it appears as an option for farmers who need to monitor the quality of the water channel of the crop, thus avoiding the loss and reduction in crop productivity because the system can be continuously monitored and show notifications to the user. The highlights of this work were prototyping a complete solution to help the farmer using a smartphone application.

Wearable Wireless Sensor Network for Mitigating COVID-19 Transmission Through Physical Distancing

The global community is currently facing a critical situation with the widespread transmission of the Covid-19 virus. The rapid increase in Covid-19 cases has necessitated immediate action and treatment. As the World Health Organization (WHO) recommended, maintaining a minimum distance of one meter between individuals is an effective preventive measure to avoid transmission through respiratory droplets. To address this issue, this paper introduces a cost-effective design and implementation of a wearable wireless sensor network using the ESP32 microcontroller and Bluetooth Low-Energy (BLE) technology that promotes physical distancing and contact tracing to mitigate the spread of COVID-19. In this research, we have determined that the safe distance between individuals is 1.5 meters. Proximity testing has indicated that an RSSI value of -62 dBm corresponds to a distance below 1.5 meters. Hence, we set -62 dBm as the distance threshold between wearable devices to ensure safe physical distancing. These wearable wireless sensor network devices are intended to assist individuals in maintaining a safe distance from others, thereby reducing the risk of COVID-19 transmission.

Energy harvesting for devices in wireless sensor networks: A Review

Recent years have witnessed several technological breakthroughs in wireless sensor networks (WSN), yet energy continues to be an indispensable resource despite these advancements. The amount of energy that is available in a WSN has a direct bearing on how well it functions, how well it performs, and how long it will continue to operate. Because of the limitations imposed on them by cost and size, sensor nodes almost always come outfitted with a constrained amount of energy. As a direct consequence of this, their batteries will need to be replaced at regular intervals. However, the replacement is only sometimes a viable alternative; in fact, there are some situations in which it is unlikely to be achievable and entirely improbable. Because of this, there is an urgent need for more feasible solutions, which include energy harvesting or wireless energy transfer, as well as the creation of power at the sensor nodes themselves or their delivery of power to them. These options are among the options that are now available. This study intends to accomplish the following three primary goals: In the first step of this process, we will investigate prospective renewable energy resources and information on their qualities and uses in wireless sensor networks (WSN). Second, this study examines various methods for charging batteries and the various ways each of these methods might be applied to wireless sensor networks (WSN).

Evaluation of Correlation between Temperature of IoT Microcontroller Devices and Blockchain Energy Consumption in Wireless Sensor Networks.

Blockchain technology is an information security solution that operates on a distributed ledger system. Blockchain technology has considerable potential for securing Internet of Things (IoT) low-powered devices. However, the integration of IoT and blockchain technologies raises a number of research issues. One of the most important is the energy consumption of different blockchain algorithms. Because IoT devices are typically low-powered battery-powered devices, the energy consumption of any blockchain node must be kept low. IoT end nodes are typically low-powered devices expected to survive for extended periods without battery replacement. Energy consumption of blockchain algorithms is an important consideration in any application that combines both technologies, as some blockchain algorithms are infeasible because they consume large amounts of energy, causing the IoT device to reach high temperatures and potentially damaging the hardware; they are also a possible fire hazard. In this paper, we examine the temperatures reached in devices used to process blockchain algorithms, and the energy consumption of three commonly used blockchain algorithms running on low-powered microcontrollers communicating in a wireless sensor network. We found temperatures of IoT devices and energy consumption were highly correlated with the temperatures reached. The results indicate that device temperatures reached 80 °C. This work will contribute to developing energy-efficient blockchain-based IoT sensor networks.

Enhancing Security in ZigBee Wireless Sensor Networks: A New Approach and Mutual Authentication Scheme for D2D Communication.

The latest version of ZigBee offers improvements in various aspects, including its low power consumption, flexibility, and cost-effective deployment. However, the challenges persist, as the upgraded protocol continues to suffer from a wide range of security weaknesses. Constrained wireless sensor network devices cannot use standard security protocols such as asymmetric cryptography mechanisms, which are resource-intensive and unsuitable for wireless sensor networks. ZigBee uses the Advanced Encryption Standard (AES), which is the best recommended symmetric key block cipher for securing data of sensitive networks and applications. However, AES is expected to be vulnerable to some attacks in the near future. Moreover, symmetric cryptosystems have key management and authentication issues. To address these concerns in wireless sensor networks, particularly in ZigBee communications, in this paper, we propose a mutual authentication scheme that can dynamically update the secret key value of device-to-trust center (D2TC) and device-to-device (D2D) communications. In addition, the suggested solution improves the cryptographic strength of ZigBee communications by improving the encryption process of a regular AES without the need for asymmetric cryptography. To achieve that, we use a secure one-way hash function operation when D2TC and D2D mutually authenticate each other, along with bitwise exclusive OR operations to enhance cryptography. Once authentication is accomplished, the ZigBee-based participants can mutually agree upon a shared session key and exchange a secure value. This secure value is then integrated with the sensed data from the devices and utilized as input for regular AES encryption. By adopting this technique, the encrypted data gains robust protection against potential cryptanalysis attacks. Finally, a comparative analysis is conducted to illustrate how the proposed scheme effectively maintains efficiency in comparison to eight competitive schemes. This analysis evaluates the scheme's performance across various factors, including security features, communication, and computational cost.

Hybrid Muddy Soil Fish Optimization-Based Energy Aware Routing in IoT-Assisted Wireless Sensor Networks

The seamless operation of interconnected smart devices in wireless sensor networks (WSN) and the Internet of Things (IoT) needs continuously accessible end-to-end routes. However, the sensor node (SN) relies on a limited power source and tends to cause disconnection in multi-hop routes because of a power shortage in the WSN, eventually leading to the inefficiency of the total IoT network. Furthermore, the density of available SNs affects the existence of feasible routes and the level of path multiplicity in the WSN. Thus, an effective routing model is predictable to extend the lifetime of WSN by adaptively choosing the better route for the data transfers between interconnected IoT devices. This study develops a Hybrid Muddy Soil Fish Optimization-based Energy Aware Routing Scheme (HMSFO-EARS) for IoT-assisted WSN. The presented HMSFO-EARS technique majorly focuses on the identification of optimal routes for data transmission in the IoT-assisted WSN. To accomplish this, the presented HMSFO-EARS technique involves the integration of the MSFO algorithm with the Adaptive β-Hill Climbing (ABHC) concept. Moreover, the presented HMSFO-EARS technique derives a fitness function for maximizing the lifespan and minimizing energy consumption. To demonstrate the enhanced performance of the HMSFO-EARS technique, a series of experiments was performed. The simulation results indicate the better performance of the HMSFO-EARS algorithm over other recent approaches with reduced energy consumption, less delay, high throughput, and extended network lifetime.

Stochastic Modeling and Analysis of the Energy Consumption of Wireless Sensor Networks

Energy management in Wireless Sensor Networks (WSN) is a challenging problem that calls for careful modeling and analysis. It is shown in the paper that the problem can be more precisely characterized by calculating the probability distribution function (PDF) and the maximum and minimum values of the energy consumed by the network. As a result, this paper provides a novel approach for modeling and assessing the probability range of energy expended by WSN nodes. The steps that were taken for developing this project were: (1) an initial investigation into the power consumption of WSN devices; (2) the proposal of a stochastic model for consumption; (3) the collection of data using the LoRaWANSim simulator; and (4) the interpretation and comparison of simulation with theoretical results. The examination of the suggested method with a discrete-event simulator and the resulting mathematical expressions provide a deeper insight into the energy consumption patterns of WSNs.

An Analysis into Physical and Virtual Power Draw Characteristics of Embedded Wireless Sensor Network Devices under DoS and RPL-Based Attacks.

Currently, within the world, cybercrime is becoming increasingly rampant-often targeting civil infrastructure like power stations and other critical systems. A trend that is being noticed with these attacks is their increased use of embedded devices in denial-of-service (DoS) attacks. This creates a substantial risk to systems and infrastructures worldwide. Threats to embedded devices can be significant, and network stability and reliability can suffer, mainly through the risk of battery draining or complete system hang. This paper investigates such consequences through simulations of excessive loads, by staging attacks on embedded devices. Experimentation within Contiki OS focused on loads placed on physical and virtualised wireless sensor network (WSN) embedded devices by launching DoS attacks and by exploiting the Routing Protocol for Low Power and Lossy Networks (RPL). Results from these experiments were based on the metric of power draw, mainly the percentage increase over baseline and the pattern of it. The physical study relied on the output of the inline power analyser and the virtual study relied on the output of a Cooja plugin called PowerTracker. This involved experiments on both physical and virtual devices, and analysis of the power draws characteristics of WSN devices with a focus on embedded Linux platforms and Contiki OS. Experimental results provide evidence that peak power draining occurs with a malicious-node-to-sensor device ratio of 13-to-1. Results show a decline in power usage with a more expansive 16-sensor network after modelling and simulating a growing sensor network within the Cooja simulator.

Real-Time Auto Calibration for Heterogeneous Wireless Sensor Networks

The constant technological advances bring new devices to the market every day. Due to this, heterogeneous Wireless Sensor Network (WSN) are common scenarios in many applications. Neural Network (NN) based models may implement particular features provided by sensor hardware to collect surrounding environment information. Thus, a sensor can provide a specific group of features while others do not. In this perspective, it may be required, for some sensors in a WSN, to be trained and have their data manually categorized, which does not scale, particularly for large WSN setups. In light of this problem, this paper proposes a Real-time (RT) auto-calibration framework to allow WSN devices to collaborate in the training process to enable new uncalibrated devices to join the network. The method does not need previous knowledge about sensor features. Also, the proposal is validated by practical experiments evaluating its accuracy in image classification. The provided experimental results demonstrate the feasibility of the proposed method.

A Rule-Based Approach for Grey Hole Attack Prediction in Wireless Sensor Networks

The Wireless Sensor Networks (WSN) are vulnerable to assaults due to the fact that the devices connected to them have a reliable connection to the internet. A malicious node acts as the controller and uses a grey hole attack to get the data from all of the other nodes in the network. Additionally, the nodes are discarding and modifying the data packets according to the requirements of the system. The assault modifies the fundamental concept of the WSNs, which is that different devices should communicate with one another. In the proposed system, there is a fuzzy idea offered for the purpose of preventing the grey hole attack from making effective communication among the WSN devices. The currently available model is unable to recognise the myriad of different kinds of attacks. The fuzzy engine identified suspicious actions by utilising the rules that were generated to make a prediction about the malicious node that would halt the process. Experiments conducted using simulation are used to determine delay, accuracy, energy consumption, throughput, and the ratio of packets successfully delivered. It stands in contrast to the model that was suggested, as well as the methodologies that are currently being used, and analogue behavioural modelling. In comparison to the existing method, the proposed model achieves an accuracy rate of 45 percent, a packet delivery ratio of 79 percent, and a reduction in energy usage of around 35.6 percent. These results from the simulation demonstrate that the fuzzy grey detection technique that was presented has the potential to increase the network’s capability of detecting grey hole assaults.

Computer-Aided Diagnosis of Muscle Mass through Antenna as a Sensor

Wireless body area network (WBAN) incorporates a wireless sensor network and wearable devices in miniature size. In this paper, a dual-band microstrip patch (DBMSP) antenna as a sensor with a modified split ring resonator (SRR) and defective ground structure (DGS) is proposed for muscle mass measurement and prediction. Modified SRR on the ground plane forms a defected ground structure (DGS) for back radiation reduction and suits muscle mass measurement. The proposed dual-band microstrip patch antenna resonates at 5.2 GHz and 8.4 GHz, with impedance bandwidth of about 0.9 GHz and 1.89 GHz, input reflection coefficient is about -21.12 dB and -14.5 dB, respectively. This DBMSP antenna has an efficiency of 99.9%, with a negligible amount of specific absorption rate (SAR). From the proposed DBMSP antenna sensor, muscle mass is predicted from human muscle. The proposed antenna is fixed on the ventral surface of the forearm and biceps. DBMSP antenna sensor detects electromagnetic energy from muscle tissues under radiating near-field conditions. The muscle tissue signal is acquired through the proposed DBMSP antenna. The acquired antenna process with nondecimated wavelet transform (NDWT) and discrete wavelet transform (DWT) algorithms for noise reduction. Further, early prediction of muscle mass prevents humans from lack of protein and oxygen levels in the blood and avoids major issues in human health. The proposed DBMSP antenna-based muscle mass measurement achieves 89% accuracy when compared with laboratory measurement.

A multi-path secure routing for the detection of node capturing attack in wireless sensor network

Over the past few years, the devices in Wireless Sensor Networks (WSN) are growing exponentially due to the emergence of many sophisticated applications. This tremendous growth leads to serious security challenges, and the devices of WSN should be protected from various attacks. WSN can be configured dynamically without fixed infrastructure and the devices can be talked with one another in an ad-hoc manner. Due to the dynamic nature of WSN, routing is considered as the challenging task that should be performed efficiently with robust routing mechanism. Even though many routing schemes have been emerged for WSN, they are not well scalable in very large-scale environment. This work introduces multi path routing strategy, and the routing will be selected based on trusted nodes. First, the trusted nodes are identified using trusted metrics of each node in the network. These metrics are calculated based on the threshold value of nodes. Then, secure routing is established by isolating node capturing attacks from the path. The performance of the work is analyzed in terms of packet loss, computational time and throughput. The paper compares the performance with the state-of-the-art routing schemes such as EMBTR (Enhanced Multi Attribute Based Attack Resistance), TSRM (Trust based secure routing model), and TARF (Trust-aware routing framework for WSNs). The outcome of the simulation shows that the proposed scheme outperforms the other state-of-the-work in terms of computational cost, throughput, and delay.

ISM 2.4 GHz Band Antenna Model for RF Energy Harvesting Systems

In today’s world many technological devices can operate with very low power levels. Despite this situation, these devices need to be fed with a sustainable energy source and constantly charged. This low level power need can be attained from ambient electromagnetic waves from any radio frequency sources. Radio frequency energy harvesting systems can be offered to feed these type of devices and these systems have been used in many areas in recent years. In this study, an antenna designment that can be used in RF energy harvesting systems is emphasized. Within the scope of this study, an RF energy harvesting antenna operating at 2.4 GHz frequency has been modeled. The electromagnetic performance and antenna fundamental parameters of the RF energy harvesting antenna are numerically calculated using a commercial 3D based on a transmission line matrix (TLM) and the finite integration technique (FIT). The physical extents of the antenna are 35 x 28 x 1.6 mm. The proposed RF energy harvesting antenna has 2.1 dBi directivty and %98.1 radiation efficiency performance parameters. The antenna proposed here can be have usage areas such as an RF energy harvesting antenna for low-power medical devices, self sustainable wireless devices in Internet of Things (IoT), Wireless Body Sensor Network (WBSN) devices.

Energy Efficient Routing Design for Target Tracking in Wireless Sensor Network

The wireless sensor networks (WSNs) provides an advance way for connection of various applications. Most of the applications use the wireless sensor network to transmit the information from the device to the main base station. The main operation of the wireless sensor network is to first sense the data using the sensor, then collect the data and finally transmit to the required base station. The data is transmitted in a timely manner such that the other wireless sensor network transmitting the information should not have any problem. During the transmission of the data the main objective of the wireless sensor network device is to provide reliability to the information which is being sent with less latency and to reduce the energy consumption in order to increase the lifespan of the wireless sensor network. Therefore, this paper presents an Energy Efficient Routing for the Target Tracking (EER-TT) in the wireless sensor network which provides reliability, less latency and reduces the energy consumption during the transmission of the information to the base station. Proposed model also provides a cluster selection method for the routing of the wireless sensor network devices. The results attained show that our model EER-TT shows better results when compared with the existing routing-based models.

Energy Efficient Low Latency Routing Design for Target Tracking Applications of Wireless Sensor Network

Target tracking is the greatest important applications in Wireless Sensor Networks (WSNs). The wireless sensor network applications have been increasing since the IoT has been established. Most of the applications have various kind of sensors to transmit the information from one source to another. The basic operation of a wireless sensor network is to sense the data, collect the data and transmit the data from time to time whenever the base station requires the data for evaluation. Improving the reliability, performance for the collection of the data is the main role of the wireless sensor device. Moreover, the objective of the wireless sensor network device is to minimize the latency and improve the energy efficiency in order to provide more reliability is a major performance metric for provisioning WSNs. In this paper, we have presented an Energy Efficient Low Latency Routing (EELLR) design for Target Tracking (TT) Applications of Wireless Sensor Network. This model provides reliability and has a better performance in terms of communication overhead, energy efficiency and packet processing latency reduction when compared with the existing routing-based models.

An Effective Wireless Sensor Network Routing Protocol Based on Particle Swarm Optimization Algorithm

Improving wireless communication and artificial intelligence technologies by using Internet of Things (Itoh) paradigm has been contributed in developing a wide range of different applications. However, the exponential growth of smart phones and Internet of Things (IoT) devices in wireless sensor networks (WSNs) is becoming an emerging challenge that adds some limitations on Quality of Service (QoS) requirements. End-to-end latency, energy consumption, and packet loss during transmission are the main QoS requirements that could be affected by increasing the number of IoT applications connected through WSNs. To address these limitations, an effective routing protocol needs to be designed for boosting the performance of WSNs and QoS metrics. In this paper, an optimization approach using Particle Swarm Optimization (PSO) algorithm is proposed to develop a multipath protocol, called a Particle Swarm Optimization Routing Protocol (MPSORP). The MPSORP is used for WSN-based IoT applications with a large volume of traffic loads and unfairness in network flow. For evaluating the developed protocol, an experiment is conducted using NS-2 simulator with different configurations and parameters. Furthermore, the performance of MPSORP is compared with AODV and DSDV routing protocols. The experimental results of this comparison demonstrated that the proposed approach achieves several advantages such as saving energy, low end-to-end delay, high packet delivery ratio, high throughput, and low normalization load.

Broadcast signcryption scheme based on certificateless in wireless sensor network

In order to satisfy the confidentiality and integrity of the communication link data in the wireless sensor networks, and solve the key escrow problem of the identity-based signcryption scheme, a certificateless broadcast signcryption scheme is proposed. The scheme has dynamically adjusting authorization set without updating private key. In the encryption stage, since part of the operation is outsourced to a trusted third party to relieve the computational burden of the broadcaster, the scheme achieves lightweight broadcast encryption, which is suitable for resource-constrained devices in wireless sensor networks. The partial key is hidden in the secret value sequence to disguise the recipient’s identity and enhance the security of ciphertext. Lastly, we make the comparisons of the proposed work with the existing works in terms of functions and computational cost. In addition, performance evaluation shows that the scheme has higher efficiency in the signcryption and unsigncryption phase and is more suitable for practical broadcast service applications.