This paper considers a novel problem in rechargeable Wireless Sensor Networks (WSNs): given a set of locations with one or more targets, determine the minimum number of sensor nodes to deploy in order to ensure a given coverage quality. This problem is significant as sensor nodes are often used to monitor one or more valuable assets or critical infrastructure. We formulate the problem as an Integer Linear Program (ILP) and use it to compute the minimum number of sensor nodes required to monitor targets in small-scale WSNs. For large-scale WSNs, we relax the integer variables of the ILP and devise three approximation algorithms: Greedy Round Node Placement (GRNP), Target Protection Node Placement (TPNP) and Energy Efficient Node Placement (EENP). We prove the worst case performance bound of these algorithms. We also conducted simulation to compare these algorithms against the optimal solution produced by the ILP. Our results show that the solution computed by EENP is within one percentage point from the optimal solution.

Backscatter communication can potentially find wide IoT applications because of its negligible energy consumption. Traditional backscatter communication relies on either dedicated Radio Frequency (RF) sources, such as RF identification readers and power beacons, or ambient RF sources, e.g., TV and cellular signals. In this paper, we study a backscatter-assisted wirelessly powered IoT system where devices can backscatter when nearby devices are actively transmitting via RF. Our objective is to determine the transmission schedule for all devices that maximizes the minimum system throughput. We formulate such a scheduling problem as a linear program for both linear and random networks. A key step in the formulation is to identify groups of devices that can simultaneously backscatter without causing interference. Simulation results show that the max-min system throughput in linear and random networks can be increased by 46 and 180 times, respectively, by using the proposed backscatter-assisted schedule as compared with the traditional Time Division Multiple Access (TDMA).

In recent years, money laundering crimes on blockchain, especially on Ethereum, have become increasingly rampant, resulting in substantial losses. The unique features of money laundering on Ethereum, such as decentralization and pseudonymity, pose new challenges for Ethereum anti-money laundering. Specifically, the existence of dense and extensive laundering gangs and intricate multilayered laundering pathways makes it exceptionally challenging for regulators to identify suspicious accounts and trace money flows. To address this issue, we propose an innovative DenseFlow framework that effectively identifies and traces money laundering activities by finding dense subgraphs and applying the maximum flow idea. We conduct multiple experiments on four datasets from Ethereum to validate the effectiveness of our approach. The precision of our DenseFlow is 16.34% higher than the start-of-the-art comparison methods on average, highlighting its distinctive contribution to tackling money laundering issues on blockchain.

A fundamental issue in blockchain systems is their scalability in terms of data storage, computation, communication, and security. To resolve this issue, a promising research direction is coding theory, which is widely used for distributed storage, recovery from erasures or channel errors and/or to reduce communication cost. To this end, this article provides the first comprehensive survey of approaches that employ coding theory to scale blockchain systems. It shows how the use of coded symbols or shards allow participants to only store a fraction of the total blockchain, protect against malicious nodes or erasures, ensure data availability in order to promote transparency, and scale the security of sharded blockchains. Further, coded symbols help reduce communication cost when disseminating blocks, which help bootstrap new nodes and speed up consensus of blocks. For each category of solutions, we highlight problems and issues that motivated their designs and use of coding. Moreover, we provide a qualitative analysis of their storage, communication, and computation costs.

Energy Harvesting Wireless Sensor Networks (EHWSNs) form the foundation of Internet of Things (IoTs) systems. Energy harvesting nodes can be deployed strategically to monitor one or more targets such as a valuable asset. However, as these nodes rely on ambient energy sources such as solar, they experience random energy arrivals. Consequently, they may exhaust their harvested energy whilst monitoring a target. To this end, network operators require a robust solution that ensures all targets are monitored continuously over some time period with a given probability. In this paper, we consider three novel robust coverage requirements; each must hold with probability ( $1-\epsilon$ ), where $\epsilon$ is the probability of failures. First, sensor nodes must not expend more than their total harvested energy over $T$ time slots. Second, the energy expenditure of each sensor node must not exceed the energy harvested in each slot. We formulate chance-constrained stochastic programs that incorporate these requirements and solve them using the Sample Average Approximation (SAA) method. We confirm via extensive simulation studies that our programs are capable of computing sensor nodes activation times that meet a given coverage failure probability.

Sparse Code Multiple Access (SCMA) is an attractive Non-orthogonal Multiple Access (NOMA) technology. An effective SCMA codebook design metrics is maximizing minimum Euclidean distance between superimposed codewords. However, the number of superimposed codewords will significantly expand with the growing number of resources and users, making the max-min problem impractical. To this end, an efficient suboptimal SCMA codebook design method is proposed for large source-user scale. We first project multi-dimensional superimposed codewords to resources to reduce the number of superimposed codewords in the constellation. Then we formulate the max-min optimization as a nonconvex quadratically constrained quadratic programming (QCQP) problem and solve it by Semidefinite Relaxation (SDR). Simulation result shows that the method, which can dramatically reduce computational complexity, is able to solve large scale codebook design problem while incurring little loss in performance compared with the existing codebook design method.