International Journal of Mobile Network Design and Innovation (4 papers in press)

Regular Issues

• Fuzzy Dove Swarm Optimisation Based Mobile Sink Clustering Routing Protocol for Wireless Sensor Network  
  by K. Jayachandran, Govindaraju S 
  Abstract: Thousands of resource-constrained sensors work together in a wireless sensor network (WSN) to monitor their environment, gather data, and send it to distant servers for additional processing. Clustering is the most popular topology management technique in WSN, which groups nodes for administration and distributes task execution. The maintenance of energy efficiency in WSN is crucial. This paper introduces a new fuzzy dove swarm optimisation-mobile sink clustering routing (FDSO-MCR) method for the WSN model. The Cluster Head (CH) selection and mobile sink (MS) path identification techniques used by the FDSO algorithm are modelled after the foraging habits of doves in heterogeneous WSN. The nodes are clustered depending on the chosen CH from the FDSO method. It is proposed that several fitness criteria be investigated to ascertain the mobile path of the MS to BS. The MS sends all data to the Base Station using the best path. The suggested method is to collect data multi-hop with MS routing. The suggested algorithm is simulated using MATLABR2021a to compare to other methods. Simulations have used network lifetime, energy usage, BS packet receipts, packet delivery ratio (PDR), and end-to-end (E2E) Delay between routing techniques.
  Keywords: Wireless Sensor Network (WSN); Fuzzy Dove Swarm Optimization (FDSO); Mobile Sink Clustering Routing (MCR) protocol; Clustering; routing protocols; Mobile path selection; and Swarm Intelligence (SI).
  DOI: 10.1504/IJMNDI.2025.10069678
   
  
• A Guide to Indoor Localisation Using Different Wireless Technologies  
  by Leo John Baptist Andrews  
  Abstract: It's getting more and more important in school and business to be able to find your way around inside buildings as network technology and smart phones become more common. Wi-Fi technology has a lot of potential for use because it is widely available in general open spaces. The majority of the currently available methods of predicting locations based on a collection of annotated Wi-Fi observations make use of trilateration or machine learning techniques. In order to instruct the models, a mixed learning strategy must be developed. This approach combines controlled, unsupervised, and semi-supervised learning methodologies to maximise the value of the gathered data. Extensive trials indicate that our method allows the models to acquire useful information from unlabelled data with gradual gains. Furthermore, it has the potential to achieve impressive performance in terms of localization and navigation in an indoor environment that is complicated and contains obstacles.
  Keywords: Indoor localization; Wireless Technology; Decode Model for Indoor Localization; Indoor Navigation Task; Hyperparameter Optimization (HPO).
  DOI: 10.1504/IJMNDI.2025.10070020
   
  
• Comprehensive Guide to Indoor Localisation: Exploring Wireless Technologies  
  by Sankarsan Panda, Bharathi B, Aruna K.B., PeriyarSelvam K 
  Abstract: It's getting more and more important in school and business to be able to find your way around inside buildings as network technology and smart phones become more common. Wi-Fi technology has a lot of potential for use because it is widely available in general open spaces. The majority of the currently available methods of predicting locations based on a collection of annotated Wi-Fi observations make use of trilateration or machine learning techniques. In order to instruct the models, a mixed learning strategy must be developed. This approach combines controlled, unsupervised, and semi - supervised learning methodologies to maximise the value of the gathered data. Extensive trials indicate that our method allows the models to acquire useful information from unlabelled data with gradual gains. Furthermore, it has the potential to achieve impressive performance in terms of localisation and navigation in an indoor environment that is complicated and contains obstacles.
  Keywords: Indoor localization; Wireless Technology; Decode Model for Indoor Localization; Indoor Navigation Task; Hyperparameter Optimization (HPO).
  DOI: 10.1504/IJMNDI.2025.10070022
   
  
• Black Hole Attacks Recognition in Wireless Mobile ad hoc Networks (MANET)  
  by Rajasekar A, Kavitha V, Hima Vijayan, Sheshang Degadwala 
  Abstract: The properties of the mobile ad hoc network (MANET), such as the fast speed at which the network may be set up and the absence of the need for centralized administration, have led to the rising popularity of this network and its use in a variety of industries. One of the methods that is used to secure the network's safety is the implementation of intrusion detection systems, or IDSs. IDSs that are based on clustering have gained a lot of traction in this network owing to the benefits that they provide, such as adequate scalability. This article presents a novel approach for use in MANETs that uses the K-nearest neighbor (KNN) algorithm for clustering. The goal of this algorithm is to identify black hole attacks. The cluster head will be determined using fuzzy inference, taking into account its history and the amount of energy that is still available.
  Keywords: Black Hole Attack; Cooperative Black Hole Attack; Malicious Node; Packets.
  DOI: 10.1504/IJMNDI.2025.10070023