Smart City Initiatives

Smart City is a wireless city. Investments in wireless information and communication technologies (ICT) fuel sustainable economic development and a high quality of life. Delivery of multimedia content over wireless networks became a significant challenge in modern IP networks. User’s mobility, diversity of fielded devices and ever increasing demand for high quality multimedia create a need for context driven media delivery within modern cities in order to support mobile applications. To that end LCI has developed a novel method which it named CAMRRI (Contextually Aware Media-centric Reconfigurable Radio Infrastructure) for creation of a contextually aware network overlay to manage available resources across plurality of participating nodes/networks available within a given city. The network overlay consists of intelligent access points, contextual information repository and autonomic management algorithms and instantiates a wireless Smart City infrastructure commonly called wireless Content Delivery Network (wCDN) which is capable of handling high temporal and spatial variations in the multimedia traffic. Goal of the wCDN overlay is to provide the highest level of resource utilization, which increases useful capacity of the underlying wireless system, and also to ensure required level of quality of service to the end users. The wCDN system exploits contextual intelligence for user and content profiling and for detection/prediction of overall traffic composition, network performance and content distribution. 

Industry experts are unanimous in their assessment that wCDN over proper cellular infrastructure is not the most viable option due to the associated technical and cost issues. Nevertheless, notion of mobile CDNs is important and offloading traffic to other networks such as WiFi has been identified as the most promising path forward.  LCI has been engaged in building  a wCDN solution featuring concept of contextual intelligence captured in the two patent applications recently filed by LCI. The proposed wCDN solution consists of three main building blocks:

1. Family of professional/industrial grade access points (CAMRRI_APs) with intelligent agents for context gathering and automatic reconfiguration. All CAMRRI_APs are based on the commercial-of-the-shelf (COTS) router board HW and open source SW stack which can be configured to work in one of the three possible modes. The components selected for CAMRRI_APs meet all necessary European radio regulatory specifications and are tested to operate over an extended range of environmental conditions. While CAMRRI_gap will provide possibility of virtually dividing it and supporting multiple VLANs over the same infrastructure, CAMRRI_bap and CAMRRI_map will be additionally capable of dynamically switching between single and multi-path routing operational modes under the control of our Autonomic intelligence system which gives them a competitive advantage relative to exploitation of wCDN functionality.
Status: Completed

2. Contextual data base (CAMRRI_DB) is used to store contextual parameters regarding wireless infrastructure (radio aspects, routes, links, temporal infrastructure overlays - ONs ), anonymized usage data (mobility patterns, preferred services, requests for content, used devices) and content information (length, number of chunks, type, genre, temporal and spatial popularity). These contextual data are collected by our ContextSense innovative middle-ware and subjected to proprietary ML algorithm to derive user mobility patterns and traffic profiles in terms of its temporal and spatial distribution in order to classify operating condition into easily recognizable patterns which are in turn used to trigger the need, or lack of it, for a wCDN overlay. Additionally, user friendly API interface will be developed opening CAMRRI_DB to third party developers.
Status: Ongoing

3. Autonomic management system (CAMRRI_AM) is based on ML algorithms and is responsible for decision making and contextual pattern derivation. Patterns relevant to wireless media delivery are derived on basis of supervised and unsupervised machine learning techniques of regression, regularization and clustering. CAMRRI_AM selects nodes to participate in the wCDN ON, then triggers and supervises necessary reconfiguration of these nodes. From business perspective, CAMRRI_AM system will be offered as an advance service available to wireless providers deploying LCI WiFi infrastructure.
Status: Ongoing

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