International Journal of Communication Systems

Goel, Anu; Bhatia, Richa; Upadhya, Abhijeet

doi: 10.1002/dac.5881pmid: N/A

The present research work aims to investigate the reliability of the mixed free space optical (FSO)/radio frequency (RF) decode‐and‐forward (DF) relaying system where the satellite intends to communicate with the ground station through the unmanned aerial vehicles (UAV) as the relay node. Moreover, it has been considered that a second UAV interferes with the intended UAV. The operation of the UAVs has been represented considering the small scale fading, path loss, 3‐D location, and probability of maintaining line‐of‐sight (LoS) and non line of sight (NLoS) links, while FSO link undergoes Malaga distributed turbulence. Using the aforementioned model, the closed form expressions for the outage probability and bit error rate (BER) have been derived. The exact expressions have been extended to obtain the high signal‐to‐noise ratio (SNR) results for the outage probability and BER. The analytical expressions have been numerically evaluated and the results obtained through these expressions have been verified using the Monte Carlo simulations. More importantly, long short‐term memory (LSTM)‐based deep learning model has been trained for prediction of outage probability. The model is trained offline and then utilized to predict the values of the outage probability in online mode with mean square error (MSE) and root mean square error (RMSE) of MSE = −45.02 and RMSE = −18.34 dB.

Zanbouri, Kouros; Darbandi, Mehdi; Nassr, Mohammad; Heidari, Arash; Navimipour, Nima Jafari; Yalcın, Senay

doi: 10.1002/dac.5886pmid: N/A

The latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain‐based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain‐based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain‐based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision‐making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain‐based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations.

Cavalcanti, David; Rosa, Nelson

doi: 10.1002/dac.5887pmid: N/A

Middleware has become an essential element in the construction of distributed Internet of Things (IoT) applications. While it plays a central role in hiding the complexities of distribution, middleware systems have also been responsible for dealing with the uncertainties in IoT environments, such as changes during operation (e.g., inaccuracies in sensor data collection) and fluctuations in resource availability, for example, the battery. These uncertainties demand attention as they can result in application failures or, even worse, jeopardize the safety of applications. Existing middleware systems are being enhanced with self‐adaptive capabilities to address these uncertainties. It means they can make runtime adjustments to the middleware and applications (built atop them) without complete shutdowns. Despite the variety of available adaptive solutions, IoT applications often face uncertainties, each requiring a distinct adaptive action. For instance, the need to fine‐tune a thing's workload due to battery consumption is a common challenge. Furthermore, these applications are susceptible to changes occurring at various layers, presenting a complex challenge of managing them simultaneously. This paper introduces Middleware Extendify (MEx), a solution for building and executing IoT adaptive middleware systems. MEx simplifies the implementation of middleware and provides an underlying environment that executes the middleware and supports a range of adaptation mechanisms. This approach ensures that the middleware meets the evolving demands of applications and copes with changes at runtime. The evaluation of MEx encompasses different adaptive middleware implementations to measure the impact of the proposed adaptation mechanisms. The results indicate that adaptation comes with acceptable performance costs while offering the ability to fine‐tune middleware functionality or align IoT applications more effectively.

Abbood, Iman Kadhum; Idrees, Ali Kadhum

doi: 10.1002/dac.5880pmid: N/A

Wireless Multimedia Sensor Networks (WMSNs) are composed of a large number of sensor nodes that are distributed in a region to collect and transmit data. Video transmission is one of the most important applications of WMSNs because it can provide critical information about monitored areas. WMSNs face challenges related to energy consumption, bandwidth usage, and network congestion related to huge amounts of data collected by sensors. To tackle this problem, this paper proposes the Distributed Video Transmission Reduction Approach for Energy Saving in WMSN (DiViTRA). The method involves two phases: sensing and transmission phases. DiViTRA achieves frame rate adaptation to reduce the number of captured video frames and save energy during the sensing phase. In the transmission phase, three effective techniques, ORB (Oriented FAST and Rotated BRIEF), Brute‐Force (BF) Matcher, and Grid‐based Motion Statistics (GMS) are applied to decide whether to transmit the current captured frame or remove it and adjust the frame capturing rate of the video sensor accordingly. In the case of frame transmission, the DiViTRA approach compresses the frame using two data reduction approaches: PCA (Principal Component Analysis) and Huffman encoding. Through simulations, DiViTRA demonstrates a 12% reduction in energy consumption, and 71% is a ratio of reduction in sent frames while preserving stream quality. The approach has been validated in scenarios involving critical events, showcasing its efficacy in maintaining data integrity during transmission.

Srinivasarao, K.; Sharma, Priyank; Surendar, M.

doi: 10.1002/dac.5879pmid: N/A

Intelligent reflecting surface (IRS) is evolved as a one of key technology by enabling a reconfigurable, intelligent, and low power for the sixth‐generation (6G) wireless communication. In this research, an IRS‐assisted NOMA network is explored over η−μ fading channel, where IRS is placed on top of the base station (BS). IRS aids in fine‐tuning the phase of the incoming signal from BS in a meticulous way, which improves the performance of the system. The statistical channel modeling of downlink IRS‐NOMA system is proposed and validated with Monte Carlo (MC) simulation. Also, analytical expressions of OP and average sum‐rate are derived for kth user in IRS‐NOMA system over η−μ fading channel. Furthermore, the influence of performance factors such as number of reflecting elements (M), power allocation factor, and imperfect successive interference cancelation (I‐SIC) on OP are examined. Simulation results reveal that the IRS‐NOMA system experiences less outage compared to IRS‐OMA and conventional relaying techniques.

Mekatohti, Vamsi kiran; B, Nithya

doi: 10.1002/dac.5892pmid: N/A

Wireless body area network (WBAN) is a potential low‐cost technology for privacy‐sensitive telemedicine and e‐health monitoring and services. However, it faces limited protocol and physical resource support challenges, which can result in packet transfer difficulties. In particular, WBAN requires an emergency‐aware technology that ensures a promising quality of service (QoS). One significant issue affecting QoS and energy efficiency in WBAN is congestion. Effective congestion control techniques are essential for achieving proper load balancing. To address these challenges, we propose a congestion severity aware rate control (CSRR) algorithm that enhances packet transmission rate by reducing packet losses and retransmissions. The CSRR algorithm incorporates a fuzzy controller to predict congestion rates based on runtime metrics. To regulate congestion window growth in different algorithm phases, we introduce sequences such as the Fibonacci retracement sequence, knight's move sequence, and the binary logarithm of the Nth primorial sequence to regulate congestion window growth in the different phases of the proposed algorithm. We mathematically analyze the proposed CSRR algorithm using a Markov model. The simulation results demonstrate the superiority of our algorithm compared to existing approaches. Specifically, our algorithm achieves significant optimizations in terms of throughput (52.92%), packet loss (38.11%), delay (37.23%), and remaining energy (36.86%) when compared to existing algorithms.

doi: 10.1002/dac.5540pmid: N/A

No abstract is available for this article.

Saini, Shalu; Singh, Manjeet

doi: 10.1002/dac.5889pmid: N/A

Wireless sensor networks consist of several autonomous nodes that are outfitted with sensors, radio, processors, memory storage, and power sources. These nodes track, sense, and send data using radio. While establishing a network, the two most essential characteristics are coverage and connectivity. For better connectivity and a longer network life, it's important to make the coverage area as big as possible with the fewest number of sensor nodes. The goal of this research is to make a connected sensor network that uses less energy and can be used in situations where the sensors need to be placed in the best way to make the network last as long as possible. The probabilistic sensing model is used, and improved network lifetime is the goal of the research work by using problem‐specific intelligent optimization techniques like BAT, ACO, and JOA to maximize the coverage area with respect to energy and points of interest. This work introduces a novel approach that optimizes both coverage and connectivity. The modified binary bat algorithm overcomes computational complexities and local optima observed in existing methods. Uniquely, it models the three states of each sensor node and includes innovative features like a greedy initialization and a weighted cost function for improving network efficiency. After investigation, it was analyzed that the proposed solution significantly improves network lifetime by over 10% to 12% compared to existing methods like JOA and ACO. The proposed approach converges faster and performs more efficiently.

Periyamuthaiah, Sakthivel; Vembu, Sumathy

doi: 10.1002/dac.5883pmid: N/A

Wireless body area networks (WBANs) are essential for medical applications, especially in remote health monitoring, as they transmit crucial and time‐sensitive data collected by nodes positioned around or within the body. However, the coexistence of WBANs with wireless channels can degrade performance due to interference. This study introduces OIM‐DLCAM, an optimal interference mitigation scheme for WBANs, which utilizes a deep learning‐based channel access method. OIM‐DLCAM addresses interference through the multiobjective Hungarian optimization (MOHO) algorithm, considering design constraints such as node transmission power, packet delivery ratio, and interference range. Additionally, it employs a deep probabilistic neural network‐based channel access method (DPNN‐CAM) to effectively mitigate interference by making decisions regarding contention window size, frame length, and buffer size. The proposed OIM‐DLCAM scheme ensures fairness between users while enhancing system performance. Simulation results from both static and dynamic sensor node scenarios demonstrate its effectiveness under various conditions, showcasing its potential to improve WBAN performance in medical applications. The simulations reveal that OIM‐DLCAM outperforms existing state‐of‐the‐art schemes across various scenarios, with efficiency gains of up to 86.187%, 72.452%, and 47.954% for WBAN node density, mobility, and packet arrival rate, respectively. Moreover, it significantly reduces the average end‐to‐end delay and packet drop rate while improving throughput and packet delivery ratio compared with existing schemes. Additionally, comparisons with industry standards, such as the IEEE 802.15.4e norm, validate the suitability of OIM‐DLCAM for cofounded WBANs.

John, Shemin T.; Sarkar, Pradip; Davis, Robin

doi: 10.1002/dac.5888pmid: N/A

Low‐Power Wide‐Area Network technologies, such as LoRa, are gaining popularity in the agricultural sector for field deployment. The crucial factors in these devices are their range and power efficiency. The energy consumption of a LoRa wireless sensor network is predominantly affected by transmission parameters like carrier frequency, bandwidth, transmit power, spreading factor, and coding rate. Incorrect chosen transmission parameters can lead to a reduction in the battery life of end nodes, requiring frequent battery replacements—a situation undesirable for field deployment. This study introduces a machine learning deployment in the form of a web application designed to monitor the energy consumption of end nodes in LoRa wireless sensor networks. The research initially employs 12 regression models, including Linear, Random Forest, K‐Nearest Neighbours, Decision Tree, Support Vector, Lasso, Ridge, AdaBoost, Gradient Boost, XGBoost, CatBoost, and LightGBM models. The findings of the study reveal that the LightGBM model surpasses other models in accurately predicting the energy consumption of Internet of Things (IoT) nodes, leading to its selection for the web application. This machine learning web application can be implemented in a programmable Long Range Wide Area Network (LoRaWAN) gateway to effectively monitor the energy consumption of IoT end nodes in the agricultural sector.