International Journal of Ad Hoc and Ubiquitous Computing (15 papers in press)

Regular Issues

• Intelligent Omni-Surfaces (IOS) with Hybrid Solar and Wind Energy Harvesting  
  by Faisal Alanazi 
  Abstract: This paper studies the throughput of intelligent omni-surfaces (IOS) when the source recovers power from the sun and wind to broadcast packets to two users Ut and Ur. Ut and Ur are located in the transmit and reflect spaces of IOS. The recovered power from the sun depends on radiation intensity that has a Gaussian distribution. IOS is an excellent candidate for 6G communications as it offers significant performance enhancement. However, IOS using the wind or the solar powers was not yet studied. The harvesting process is optimised to maximise the throughput. IOS with 64 elements offers 48 dB gain.
  Keywords: IOS; Solar; wind energy harvesting; SINR; Rayleigh channels.
  DOI: 10.1504/IJAHUC.2024.10066964
   
  
• Energy Aware Handoff Management for Cluster based D2D Communications  
  by Poulomi Mukherjee, Swarnabh Paul, Tanmay De 
  Abstract: In device-to-device (D2D) multicasting, network service continuity can be disrupted due to the mobility of multicasting users, potentially resulting in a lower multicasting rate and increased energy dissipation by the serving cluster heads (CHs). To ensure seamless communication for mobile multicasting users, this paper addresses an energy-efficient handoff strategy for 5G D2D multicasting. The proposed approach identifies victim users based on the energy dissipation of their current CH and determines the target destination accordingly. An integer programming-based mathematical model is presented, along with a suitable greedy algorithm. The proposed handoff technique demonstrates over 90% service coverage with a high fairness index, and achieves more than double the energy savings compared to other methods. A detailed quantitative and qualitative performance evaluation is provided, demonstrating that our approach results in minimal energy dissipation of 0.3 Joules and achieves a higher multicasting rate of 8 Mbps, even under conditions of high user density.
  Keywords: 5G D2D; Cluster Head; Energy Efficiency; Handoff Management.
  DOI: 10.1504/IJAHUC.2024.10066966
   
  
• Hybrid Recommendation System Based on Human-Computer Interaction Technology and Smart Tourism  
  by Jianan Yan, Meiqi Wang, Ruixiang Xue 
  Abstract: With the development of information technology, people's demand for personalised and intelligent tourism experiences is increasing, and traditional recommendation systems can no longer meet this demand. This article proposes a design based on human-computer interaction technology and the concept of smart tourism, aiming to enhance users’ personalised travel experience. Firstly, the study collects user preference and demand data through human-computer interaction, analyses the data using collaborative filtering algorithms, and then recommends personalised travel routes for users based on their interest characteristics, travel routes, and transportation modes. The experimental results show that the hybrid collaborative filtering algorithm proposed in this paper outperforms a single collaborative filtering algorithm in terms of recommendation accuracy, recall, coverage, and popularity, achieving the best recommendation performance at a k value of 50. Therefore, human-computer interaction technology and smart tourism concepts can help users achieve more comprehensive, accurate, and personalised travel recommendations.
  Keywords: tourism routes; personalised recommendations; smart tourism; human-computer interaction technology; collaborative filtering.
  DOI: 10.1504/IJAHUC.2025.10067090
   
  
• Investigation of Pixel Neighbourhood for Prediction Error Based Reversible Data Hiding using Neural Networks  
  by Sabhapathy Myakal, Rajarshi Pal, .Nekuri Naveen 
  Abstract: Success of a prediction error based reversible data hiding (RDH) technique depends on a good pixel value predictor. A pixel value predictor predicts a pixel value with the help of its neighbouring pixel values. Considered neighbourhood for pixel value prediction influences the performance of such predictor. Varieties of pixel neighbourhoods have been considered in literature. This paper presents a unique work of its kind to explore the effect of various pixel neighbourhoods. In order to perform this comparative analysis, a neural network is used here to predict a pixel value from a neighbourhood. The best neighbourhood for the pixel value prediction task is determined from the reported experimental observations. The selected neighbourhood is used for pixel value prediction and subsequent RDH scheme. It is observed from the experimental results that the proposed neural network based adaptive RDH scheme with the selected neighbourhood outperforms majority of the state-of-the-art RDH techniques.
  Keywords: reversible data hiding; pixel neighborhood; pixel value prediction; prediction error; multi-layer perceptron.
  DOI: 10.1504/IJAHUC.2024.10067149
   
  
• Multimodal Fusion of Different Medical Image Modalities using Optimised Hybrid Network  
  by Tanima Ghosh, Jayanthi N 
  Abstract: Image fusion leverages the strengths of various imaging modalities to create a more complete and informative picture of medical conditions, which leads to better identification and treatment. Accordingly, this paper implements a new multimodel image fusion approach, named pelican optimisation algorithm-based DenseNet and ResidualNet (POA+Dense-ResNet) for multimodel image fusion. Here, the POA is used to train the Dense-ResNet, which is the combination of ResidualNet and DenseNet. The input images from different modalities are pre-processed and then the transformation of the spatial domain to the spectral domain is done by dual-tree complex wavelet transform (DTCWT). These transformed images are segmented by edge-attention guidance network (ET-Net). Then, the fusion is done by the POA+Dense-ResNet. The POA+Dense-ResNet achieved minimum root mean square error (RMSE), mean square error (MSE), and maximum peak signal to noise ratio (PSNR) of 0.650, 0.423, and 53.525 dB.
  Keywords: ResidualNet; edge-attention guidance network; pelican optimisation algorithm; DenseNet; dual-tree complex wavelet transform; mean square error; MSE; peak signal to noise ratio; PSNR.
  DOI: 10.1504/IJAHUC.2024.10067151
   
  
• A Power-Awareness Routing Protocol for Sustainable Low-Power Wireless Networks: FPGA vs. Microcontroller Implementation  
  by Aparna Telgote, Sudhakar S. Mande 
  Abstract: Wireless networks play a vital role in our daily lives; however, addressing the energy efficiency challenge in low-power wireless networks requires innovative strategies. This research explores deploying a centralised power-aware routing protocol (CPARP) on field programmable gate arrays (FPGAs), presenting a promising alternative to traditional microcontroller-based devices. The CPARP enhances power efficiency by strategically selecting data routing paths based on metrics such as transceiver power, delay, and link quality. Comparative analysis shows that the FPGA-based approach achieves up to 47% power savings, reducing current draw from 80 mA to 20 mA while maintaining network performance metrics like latency and packet delivery success rates. Real-time testing reveals that individual microcontroller-based nodes consume only 101 mW. This study not only demonstrates the practical applications and benefits of CPARP but also advocates for exploring alternative hardware platforms. The insights gained contribute to the advancement of wireless network technologies, promoting sustainability and energy efficiency.
  Keywords: FPGA; ESP32; Spartan 6; Zigbee S2C; X CTU.
  DOI: 10.1504/IJAHUC.2024.10067198
   
  
• Integrating Utility and Interest to Recommend Healthcare Interventions for Online Users  
  by Pei Yin, Xu-Chen Fang, Zeng-yue Luo 
  Abstract: There is a critical need for online healthcare communities to recommend interventions that satisfy users' interests and improve their health. This paper proposes a recommendation algorithm based on representation learning of user interests and healthcare needs using a multi-task learning architecture. The algorithm learns user interest representations through an attention mechanism and assesses the expected treatment effects of recommended interventions via a deep neural network. An auxiliary loss function evaluates the intervention's utility for the target user, combining this with the user's interest representation to generate recommendations. Offline experiments using authentic data from online health communities substantiate the model's efficacy, exploring hyper-parameter influences and conducting ablation experiments to assess model components. The results demonstrate that the proposed algorithm outperforms baseline methods, effectively recommending interventions that help users improve their healthcare conditions and encourage their continued participation in online communities.
  Keywords: healthcare intervention recommendation; multi-task learning architecture; representation learning; attention mechanism; users’ interest; treatments’ utility.
  DOI: 10.1504/IJAHUC.2025.10067297
   
  
• An Inter-Slice Mobility with Data Offloading Scheme Using Hierarchical-Tunnelling-Based Proxy Mobile IPv6 for 5G Networks  
  by Yuh-Shyan Chen, Chih-Shun Hsu, Wei-Ming Wu 
  Abstract: Session continuity across different slices is critical for inter-slice mobility in 5G networks. To minimise service disruption latency, the MIPV6-RR/BU>Pv1-U protocol was proposed for session continuity. However, this approach results in higher resource utilization overhead compared to the 3GPP standard scheme. In this paper, we introduce a new inter-slice mobility scheme for 5G networks using the hierarchical-tunnelling- based Proxy Mobile IPv6 (HTISDO) protocol. This scheme combines the existing Proxy Mobile IPv6 protocol with an inter-slice mobility protocol and addresses data offloading to facilitate network-initiated inter-slice mobility for load balancing. The main contribution of our HTISDO scheme is to alleviate the problem of frequent access to the 5G core network for requesting new network slices and re-establishing sessions, thereby enhancing service continuity. Experimental results show that our HTISDO scheme reduces the average transmission delay by approximately 10.6% and improves the average throughput by about 12.5% compared to existing inter-slice mobility schemes.
  Keywords: Network Slicing; inter-slice mobility; hierarchical-tunneling; 5G core network; data offloading; PMIPv6; transmission delay.
  DOI: 10.1504/IJAHUC.2024.10067586
   
  
• Mobility-Aware Optical Random Waypoint and Transfer Learning-Based Load Balancing  
  by Arunkumar R, B. Thanasekhar 
  Abstract: In recent years, a hopeful model of hybrid networks based on light fidelity (LiFi) and wireless fidelity (WiFi) named hybrid LiFi-WiFi networks (HLWNets) has been introduced. To address this issue, an innovative approach sewing training inspired optimisation_transfer learning (STIO_TL) is introduced for AP selection in the handover process. Initially, a system model of HLWNets is developed and the mobility-aware optical RWP is designed for the handover process. In the handover process, location is predicted every time using the deep recurrent neural network (DRNN). Afterwards, the AP selection is done by the proposed STIO_TL and is processed by several parameters. The proposed STIO_TL is the integration of the sewing circle inspired optimisation algorithm (STBO) and training-based optimisation algorithm (CIOA). Additionally, the effectiveness of the proposed STIO_TL is evaluated based on the evaluation metrics, like delay, handover occurrence, energy efficiency, and network throughput of 0.111 mS, 6.086, 0.099 Mbits/joules and 0.913 Mbps respectively.
  Keywords: sewing training inspired optimisation; deep recurrent neural network; DRNN; transfer learning; circle inspired optimisation algorithm; multipath transmission control protocol.
  DOI: 10.1504/IJAHUC.2024.10067596
   
  
• Leveraging Constitutive Artificial Neural Networks for Plant Leaf Disease Detection  
  by Kaavya Kanagaraj, Madhumitha Kulandaivel, F.H. Shajin, Salini Prabhakaran 
  Abstract: The appearance of new diseases in plant leaf is a significant threat to global food security and agricultural production. Therefore, a plant leaf disease detection and constitutive artificial neural network (PLDD-CANN) is proposed in this paper to provide developments in deep learning. After segmenting the image using the adaptive convex clustering (ACC) technique, the features are extracted using the fast Fourier and continuous wavelet (FFCWT) transformations. Constitutive artificial neural network (CANN) is considered to classify the input image as normal or virus, such as yellow leaf curl virus, septoria leaf spot, two-spotted spider mite, bacterial spot, target spot, leaf mould, mosaic virus, early blight, late blight. PLDD-CANN attains 26.75%, 25.83%, 27.46% better accuracy analysed with existing models, like improved CNN strategy for tomato plant leaf infection detection (CNN-PLDD), tomato leaf ailments finding for agro-base industries (FRCNN-PLDD).
  Keywords: plant leaf infection; plant village dataset; image processing; adaptive convex clustering; ACC; constitutive artificial neural network; CANN.
  DOI: 10.1504/IJAHUC.2024.10067780
   
  
• Performance Analysis of the Contention Based Access Periods of the IEEE 802.11ad Hybrid MAC based on Pareto Arrivals  
  by Sangeetha R. G, Hemanth C, Venkatesh T. G 
  Abstract: IEEE 802.11ad is a Wireless LAN standard that operates in the 60 GHz range. In this article, we undertake a simulation-based performance evaluation of the IEEE 802.11ad, hybrid MAC protocol's Contention-Based Access Periods (CBAP) under Pareto arrivals. In this article we evaluate the performance in terms of throughput and average latency. Because of the protocol's hybrid nature, CBAP packet transfers must be deferred when the protocol transitions from CBAP to a contention-free phase. We demonstrate that (i) deferring packet transmission and (ii) the CBAP bandwidth allocation method have a substantial impact on the system performance. We also highlight the difference in performance that is achieved when a heavy-tailed traffic model like Pareto is used as against the traditional Poisson model.
  Keywords: IEEE 802.11ad; Hybrid MAC; Pareto arrivals; throughput.
  DOI: 10.1504/IJAHUC.2024.10067923
   
  
• A Three-Stage Approach using Deep Learning for Automated Vehicle Smart Parking with Licence Plate Recognition  
  by Shaunak Gupta, Pushkar Garg, Abhinav Aggarwal, Gaurav Goyal, Kanu Goel 
  Abstract: Urbanization and increasing vehicular density have amplified challenges in conventional parking systems, necessitating innovative solutions for effective parking management. Common challenges include the absence of real-time information on available parking slots, limited data analysis tools for space utilization, reliance on manual processes, and the inadequate incorporation of automation at entry and exit of vehicles. The proposed smart parking approach endeavors to mitigate these challenges by integrating advanced technologies to enable efficient parking slot detection, comprehensive data analysis, and seamless automation. It integrates automation at entry and exit points, streamlining the parking process. Leveraging advanced computer vision and sensor technologies, the system provides real-time identification of empty parking slots, enhancing overall space utilization. The system also incorporates accident tracking mechanisms to enhance safety within parking facilities. The research paper also presents a novel accident detection model attaining a commendable accuracy of 94.5% .
  Keywords: Artificial intelligence;Smart Parking; Deep Learning; Automated Number Plate Recognition(ANPR); Pre-trained models; Convolutional Neural Network (CNN).
  DOI: 10.1504/IJAHUC.2024.10067924
   
  
• FPGA-Based Reduction in Complexity of FFT Twiddle Factor Butterfly with Embedded Cordic Module  
  by Priya Mule, Sudhakar S. Mande 
  Abstract: In conventional FFT, techniques to reduce twiddle factor complex multiplication have become a research hotspot topic. Cordic FFT architecture overcomes the existence of multiplier blocks, which raise hardware complexity costs, increase power consumption, and lower maximum operating clock frequency. An iterative-based Cordic architecture with a compensated barrel shifting network to provide gain k = 0.6 is proposed. This algorithm substitutes sine and cosine convolution factors with repeated KORDIC convolutions, allowing for reduced ROM. A High-speed FFT processor comprising of twiddle factor WN is replaced with an embedded Cordic module on FPGA. It is observed that Cordic FFT eliminates the use of 20 DSP components thereby reducing computational complexity at the maximum achievable speed of 75MHZ. Output is shown successfully on the Artix7 board with a minimal error of approx.0.25% and an accuracy of 99.75%. Latency and throughput issues are improved in this. Static power in cordic FFT is approximately 114mw.
  Keywords: cordic;embedded; twiddle factor; Computation; complexity.
  
  
• Secure Transmission and Authentication Protocol in IoT with Deep-Q-Net-Key Updation  
  by Ramesh K, Sheema D, Balasubramani S, Vijaya G 
  Abstract: This research proposes a secure transmission and authentication protocol named deep Q Net_Key updation (DQN_KeyUp) in the internet of things (IoT) for providing security to medical data. Here, multiple entities, like as IoT user, IoT Server, and IoT data provider are used and security is provided using processes, such as authentication, data transmission, verification, and dynamic key updation. Moreover, user verification is established using a finger vein-based biometric modality. In addition to this, the proposed DQN_KeyUp uses security operations, like encryption, hashing, secret keys, Kronecker product, and session passwords, wherein the secret key is generated using the deep Q network (DQN). The assessment of the DQN_KeyUp based on computational time, validation time, and memory shows that the DQN_KeyUp is effective in attaining minimal values of computational time of 0.067 S, validation time of 0.155 S, and memory of 0.154 MB.
  Keywords: internet of things; IoT; device-to-device communication; authentication; dynamic key updation; deep Q network; DQN.
  
  
• Optimising Power Management in Wireless Sensor Networks Using Machine Learning: an Experimental Study on Energy Efficiency  
  by Mohammed Amine Zafrane, Ahmed Ramzi Houalef, Miloud Benchehima 
  Abstract: Wireless sensor networks (WSNs) have emerged as essential components across various fields. Comprising small, self-sustaining devices known as "Nodes," they play a critical role in data collection and analysis. However, ensuring optimal longevity without compromising data collection timeliness is a fundamental challenge. Regular data aggregation tasks, while essential, consume substantial energy resources. Furthermore, constraints in computation power, storage capacity, and energy supply pose significant design challenges within the Wireless Sensor Network domain. In pursuit of optimizing energy efficiency and extending the operational lifetime of nodes through artificial intelligence, we have developed a prototype for data collection to create a comprehensive dataset. Our approach leverages both current and precedent measurements, triggering data transmission only in the presence of significant changes. This intelligent strategy minimizes unnecessary communication and conserves energy resources. Based on the measurements presented in this paper, using a 3400 mAh battery, traditional methods achieve a lifetime of 191 hours,
  Keywords: Artificial intelligent; WSN; Power optimization; data acquisition.