• Energy efficient resource allocation strategy in massive IoT for industrial 6G applications

      Mukherjee, Amrit; Goswami, Pratik; Khan, Mohammad Ayoub; Manman, Li; Yang, Lixia; Pillai, Prashant (Institute of Electrical and Electronics Engineers (IEEE), 2020-11-03)
      The birth of beyond 5G (B5G) and emerge of 6G has made personal and industrial operations more reliable, efficient, and profitable, accelerating the development of the next-generation Internet of Things (IoT). We know, one of the most important key performance indicators in 6G is smart network architecture, and in massive IoT applications, energy efficient ubiquity networks rely mainly on the intelligence and automation for industrial applications. This paper addresses the energy consumption problem with a massive IoT system model with dynamic network architecture or clustering using a multi-agent system (MAS) for industrial 6G applications. The work uses distributed artificial intelligence (DAI) to cluster the sensor nodes in the system to find the main node and predict its location. The work initially uses the back-propagation neural network (BPNN) and convolutional neural network (CNN), which are respectively introduced for optimization. Furthermore, the work analyze the correlation of mutual clusters to allocate resources to individual nodes in each cluster efficiently. The simulation results show that the proposed method reduces the waste of resources caused by redundant data, improves the energy efficiency of the whole network, along with information preservation.
    • Non-reciprocity compensation combined with turbo codes for secret key generation in vehicular ad hoc social IoT networks

      Epiphaniou, Gregory; Karadimas, Petros; Ismail, Dhouha Kbaier Ben; Al-Khateeb, Haider; Dehghantanha, Ali; Choo, Kim-Kwang Raymond (IEEE, 2017-10-18)
      The physical attributes of the dynamic vehicle-to-vehicle (V2V) propagation channel can be utilised for the generation of highly random and symmetric cryptographic keys. However, in a physical-layer key agreement scheme, non-reciprocity due to inherent channel noise and hardware impairments can propagate bit disagreements. This has to be addressed prior to the symmetric key generation which is inherently important in social Internet of Things (IoT) networks, including in adversarial settings (e.g. battlefields). In this paper, we parametrically incorporate temporal variability attributes, such as three-dimensional (3D) scattering and scatterers’ mobility. Accordingly, this is the first work to incorporate such features into the key generation process by combining non-reciprocity compensation with turbo codes. Preliminary results indicate a significant improvement when using Turbo Codes in bit mismatch rate (BMR) and key generation rate (KGR) in comparison to sample indexing techniques.