In an effort to curb the potential losses associated with the event of security bridge, admitting the uneven bandwidth support that characterizes most developing smart cities, we propose a neural inspired Multimodal Security Management System (MSMS) that is bandwidth-tolerant. The proposed system leverages on a Next-Generation Network (NGN) architecture in catering for the challenges associated with the provisioning of ubiquitous broadband access for IoT support in a heterogeneous morphological network environment. In order to evaluate the MSMS, we simulated the proposed cloud-based system on a Next Generation Network (NGN) architecture which utilizes Internet Protocol/Multi-Protocol Label Switching (IP/MPLS) as transport technique in a Long Term Evolution (LTE) backbone infrastructure. We then compare its performance over a competitive alternative transport technique: "Internet Protocol Asynchronous Transfer Mode (IP/ATM)". Thus, we further evaluated the MEMS on the latter architecture. While, our proposed system is able to capture both textual, aural, and visual information of individuals in security vulnerable environments via installed smart microphones and cameras, it is also able to integrate this information's in predicting security threats. When compared with the popular Security Management System (SMS) "ShotSpotter", results show that our proposed system outperforms the ShotSpotter system by 0.87 and 0.45 in terms of efficiency and response time respectively. Finally, simulation of our proposed system on an IP/MPLS transport schemes shows that the former outperforms the latter with respect to overall network bandwidth utilization and average traffic loss in the ratio of 0.098 and 0.087 respectively.In an effort to curb the potential losses associated with the event of security bridge, admitting the uneven bandwidth support that characterizes most developing smart cities, we propose a neural inspired Multimodal Security Management System (MSMS) that is bandwidth-tolerant. The proposed system leverages on a Next-Generation Network (NGN) architecture in catering for the challenges associated with the provisioning of ubiquitous broadband access for IoT support in a heterogeneous morphological network environment. In order to evaluate the MSMS, we simulated the proposed cloud-based system on a Next Generation Network (NGN) architecture which utilizes Internet Protocol/Multi-Protocol Label Switching (IP/MPLS) as transport technique in a Long Term Evolution (LTE) backbone infrastructure. We then compare its performance over a competitive alternative transport technique: "Internet Protocol Asynchronous Transfer Mode (IP/ATM)". Thus, we further evaluated the MEMS on the latter architecture. While, our proposed system is able to capture both textual, aural, and visual information of individuals in security vulnerable environments via installed smart microphones and cameras, it is also able to integrate this information's in predicting security threats. When compared with the popular Security Management System (SMS) "ShotSpotter", results show that our proposed system outperforms the ShotSpotter system by 0.87 and 0.45 in terms of efficiency and response time respectively. Finally, simulation of our proposed system on an IP/MPLS transport schemes shows that the former outperforms the latter with respect to overall network bandwidth utilization and average traffic loss in the ratio of 0.098 and 0.087 respectively.

Dogo Eustace

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IoT Based Security Management Framework for Heterogeneous Network Environment