Distributed Ledger Technology (DLT) has emerged as one of the most disruptive technologies in the last decade. It promises to change the way people do their business, track their products, and manage their personal data. Though the concept of DLT was first implemented in 2009 as Bitcoin, it has gained significant attention only in the past few years. During this time, different DLT enthusiasts and commercial companies have proposed and developed several DLT platforms. These platforms are usually categorized as public vs private, general purpose vs application specific and so on. As a growing number of people are interested to build DLT applications, it is important to understand their underlying architecture and capabilities in order to determine which DLT platform should be leveraged for a specific DLT application. In addition, the platforms need to be evaluated and critically analyzed to assess their applicability, resiliency and sustainability in the long run. In this paper, we have surveyed several leading DLT platforms and evaluated their capabilities based on a number of quantitative and qualitative criteria. The comparative analysis presented in this paper will help the DLT developers and architects to choose the best platform as per their requirement(s).

In recent times, Bitcoin has gained special attention both from industry and academia. The underlying technology that enables Bitcoin (or more generally crypto-currency) is called blockchain. At the core of the blockchain technology is a data structure that keeps record of the transactions in the network. The special feature that distinguishes it from existing technology is its immutability of the stored records. To achieve immutability, it uses consensus and cryptographic mechanisms. As the data is stored in distributed nodes this technology is also termed as "Distributed Ledger Technology (DLT)". As many researchers and practitioners are joining the hype of blockchain, some of them are raising the question about the fundamental difference between blockchain and traditional database and its real value or potential. In this paper, we present a critical analysis of both technologies based on a survey of the research literature where blockchain solutions are applied to various scenarios. Based on this analysis, we further develop a decision tree diagram that will help both practitioners and researchers to choose the appropriate technology for their use cases. Using our proposed decision tree we evaluate a sample of the existing works to see to what extent the blockchain solutions have been used appropriately in the relevant problem domains.

In an environmental context, the use of RFID (radio frequency identification) and load cell sensor technology can be employed for not only bringing down waste management costs, but also to facilitate automating and streamlining waste (e.g., garbage, recycling, and green) identification and weight measurement processes for designing smart waste management systems. In this paper, we outline a RFID and sensor model for designing a system in real-time waste management. An application of the architecture is described in the area of RFID and sensor based automatic waste identity, weight, and stolen bins identification system (WIWSBIS).

Due to millions of heterogeneous physical nodes, multiple-vendor and multi-tenant domains, and technologies etc., 5G has greatly expanded the threat landscape. Particularly from the high rate of traffic and ultra-low latency requirement of applications in 5G networks, the detection of the network traffic anomalies in real-time is critical. The conventional security approaches lack compatibility with modern network designs and are not much effective in 5G settings. We propose a two-stage network traffic anomaly detection system compatible with ETSI-NFV standard 5G architecture. Our architecture consists of two modules, i.e., (a) Dimensionality Reduction to compress the sample size at the edge of 5G networks and (b) Deep Neural Network classifier (DNN) that detects traffic anomalies. We have conducted our experiments using OMNET++ and ETSI-NFV (OSM MANO) 5G orchestration real platform deployed on AWS cloud systems. We have used the UNSW-NB15 data set and have shown that at dimensionality reduction factor of 81% the detection accuracy obtained is 98%. The proposal is compared with other recent approaches to show the overall merit of the architecture.