Forthcoming articles

International Journal of Mobile Network Design and Innovation

International Journal of Mobile Network Design and Innovation (IJMNDI)

These articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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

Regular Issues

  • Performance Analysis of WiFi Networks Based on Sporadic Traffic Model Using NS3   Order a copy of this article
    by Adel Agamy, Abdelmageed Ali, Ahmed Mohamed 
    Abstract: Wireless Local Area Networks (WLAN) are widely used in malls, universities and stadiums due to its high data rate and cheap cost. The unpredictable exponential demand of wireless devices such as phones, digital devices and Internet of Thing(IoT) sensors leads to a huge demand onWi-Fi access (IEEE802.11 standard) from various domains.Wi-Fi experiences a lot of challenges in its design and implementation due to its popularity and the large demand. Todays multimedia streaming represents the most significant part of Wi-Fi traffic besides other types of traffic. The effect of the interactive multimedia traffic on the performance of the WLAN has a significant impact on the design and implantation of Wi-Fi networks. We investigated the impact of sporadic traffic on the behavior of WLANs using NS3. The Wi-Fi performance metrics such as end-to-end delay, Jitter and throughput were analyzed using two different types of traffic distributions (Exponential and Pareto) based on the N-burst analytical traffic model. The results show the negative impact of heavy-tailed applications on the performance Wi-Fi networks. Also the results of low utilization load and high utilization load show that the effect of heavy-tailed application is not significant in high utilized networks.
    Keywords: Wireless Access Networks; WiFi; Traffic Modeling; Performance modeling;.

  • Machine Learning based Cell Association for mMTC 5G Communication Networks   Order a copy of this article
    by Siddhant Ray, Budhaditya Bhattacharyya 
    Abstract: With the advent of 5G communication networks, the number of devices on the core 5G network significantly increases. A 5G network is a cloud native, massively connected IoT platform with a huge number of devices hosted on thernnetwork as compared to prior generation networks. Previously known Machine Type Communication (MTC), it is now known as massive Machine Type Communication (mMTC) and plays a pivotal role in the new network scenario with a larger pool of devices. As ultra-low latency is the key metric in developing 5G communication, a proper cell association scheme is now required to meet the load and traffic needs of the new network, as compared to the earlier cell association schemes which were based only on the Reference Signal Received Power (RSRP). The eNodeB with the highest RSRP may not always be optimal for cell association to provide the lowest latency. This paper proposes an unsupervised machine learning algorithm, namely Hidden Markov Model (HMM) learning on the networks telemetry data, which is used to learn network parameters and select the best eNodeB for cell association, with the objective of ultimate ultralow latency. The proposed model uses an HMM learning followed by decoding for selecting the optimal cell for association.
    Keywords: Machine Type Communication (MTC); HMM learning; Viterbi decoding; channel availability.

  • Binary Search Algorithm to Predict the Time of Next Link Change for Wireless Mobile Networks   Order a copy of this article
    by Natarajan Meghanathan 
    Abstract: We propose a binary search algorithm of logarithmic time complexity to predict the time for next link change in a wireless mobile network (WMN). The search space for the algorithm ranges from the current time instant (initial left index) to the last time instant of the simulation (initial right index). The algorithm always maintains an invariant that the WMN has not undergone any link change at the time instant corresponding to the left index and has undergone at least one link change at the time instant corresponding to the right index. The algorithm goes through a sequence of iterations (the search space reduces by half in each iteration) until the difference between the right index and left index is within a termination threshold (?). We also show that the predicted times of next link changes follow an exponential distribution, especially for lower values of the termination threshold.
    Keywords: Wireless Mobile Networks; Binary Search; Link Change; Simulations; Exponential Distribution; Goodness-of-fit Test.

  • A Novel Beaconless Routing Algorithm Based on EBGR Algorithm for Wireless Sensor Networks   Order a copy of this article
    by Fateme Ganje, Azar Mahmoodzadeh 
    Abstract: The purpose of EBGR algorithm is to reduce power consumption in the network. But it cannot select the best relay node in all conditions. Also, in the EBGR algorithm, the relay nodes send their data after receiving them, which creates an overhead in the network and decrease spectral efficiency in the network. We introduce an improved method to improve the performance of the EBGR algorithm. In our proposed method, we have divided the space into several subspaces. Each node prioritizes the data transmission to the node that is located beneath its own space and along the source-to-destination direction. In order to reduce the slag on the grid, we have also considered adaptive delay. The advantage of this method is that if the network is ideal, the data arrives with the least delay and if the network has a lot of overhead, the delay rate will also increase.
    Keywords: Wireless sensor network; Beaconless Routing Algorithm; EBGR algorithm; adaptive delay.