The potential of Bitcoin for money laundering and terrorist financing represents a significant challenge in law enforcement. In recent years, the use of privacy-improving CoinJoin transactions has grown significantly and helped criminal actors obfuscate Bitcoin money flows. In this study, we use unsupervised machine learning to analyze the complete Bitcoin user graph in order to identify suspicious actors potentially involved in illegal activities. In contrast to the existing studies, we introduce a novel set of features that we use to identify potential criminal activity more accurately. Furthermore, we apply our clustering algorithm to a CoinJoin-adjusted variant of the Bitcoin user graph, which enables us to analyze the network at a more detailed, user-centric level while still offering opportunities to address advanced privacy-enhancing techniques at a later stage. By comparing the results with our ground truth data set, we find that our improved clustering method is able to capture significantly more illicit activity within the most suspicious clusters. Finally, we find that users associated with illegal activities commonly have significant short paths to CoinJoin wallets and show tendencies toward outlier behavior. Our results have potential contributions to anti-money laundering efforts and combating the financing of terrorism and other illegal activities.

The ever-changing technological trends form the basis of smart cities. The integration of blockchain in a smart city architecture can solve the increasing economic and social problems that negatively impact human life. On one hand, the smart cities are effectively improving the living standards of the citizens. On the other hand, the data gathering, processing and storing requirements of such technologies raise various security and privacy issues. Therefore, we aim to investigate the threats related to the privacy and security of users in a blockchain-based smart city. We use a hybrid threat modeling technique to investigate user-centric threats in a smart city architecture. For the threat analysis, we leverage NetObjex as our smart city use case. Our threat assessment unveils six different types of threats that are critical to the safety and well-being of residents in a smart city.

Cloud servers provide services over the internet by using Virtual Machines (VMs). The power consumption of Physical Machines (PMs) needs to be considered, as VMs are running on physical machines. When a consumer sends request to the cloud, it takes time to respond because of distant location of cloud. Due to which delay and latency issue arises. Fog is introduced to overcome the peculiarities of cloud. In fog computing environment, the operational challenges for the research community are: reducing the energy consumption and load balancing. The energy consumption of the fog resources depends on the requests that are allocated to the set of VMs. This is a challenging task. In this paper, three layered architecture cloud, fog and consumer layer are proposed. The cloud and fog provide VMs to run the consumers' application quickly. The meta-heuristic algorithm that is: Genetic Algorithm (GA) is proposed and Binary Particle Swarm Optimization (BPSO) is implemented to balance the set of requests on VMs of cloud and fog. The proposed and implemented algorithm is compared with existing PSO and BAT algorithms to measure efficiency. The Closest Data Center (CDC), Optimize Response Time (ORT), Reconfigure Dynamically with Load (RDL) is implemented to optimize the Response Time (RT) and Processing Time (PT). These policies also decide which requests are allocated to which Data Center (DC). The proposed GA and implemented BPSO are use to minimize the computational cost and also decrease the RT and PT of DCs.

Forecasting of building energy consumption plays a key role in the energy management of the modern power system. However, the noise and randomness in the electricity load data makes it difficult to forecast accurate electricity load. In this paper, a novel scheme namely Empirical Mode Decomposition based Extreme Learning Machine (EMD-ELM) is proposed to forecast the electricity load consumption of a building. Randomness in the electric load data is removed using EMD, whereas, ELM is used to forecast the day, week and month ahead electricity load. To illustrate the usefulness of EMD-ELM, the performance is compared with the renowned neural networks namely Convolution Neural Network (CNN), Long Short Term Memory (LSTM) and ELM. The simulation results clearly indicate that EMD-ELM outperforms CNN, LSTM and ELM in forecasting the day, week and month ahead electricity load consumption of a building.

In this paper, we overcome the problem of energy holes in UWSNs while considering the unique characteristics of underwater communication. In proposed scheme we consider UWSNs where nodes are manually deployed according to the defined deployment pattern to satisfy our application requirements in terms of energy saving. We used mixed routing technique i.e. direct transmission and hop-by-hop transmission for energy balancing in continuous monitoring applications for UWSNs. Sensor nodes forward the total data traffic (generated plus received) periodically to the sink with calculated load weights using variable communication ranges. The transmission ranges for 1-hop, 2-hop and direct transmission to the sink are used for data transmission to achieve load balancing for balanced energy consumption of all sensor nodes in the network. We prove that our scheme outperforms the existing selected schemes in terms of network lifetime and energy conservation. We select an optimal result from the simulation results for different possible combinations of transmissions.