Latency-constrained aspects of cellular Internet of Things (IoT) applications rely on Ultra-Reliable and Low Latency Communications (URLLC) which highlight research on satisfying strict deadlines. In this study, we address the problem of latency constrained communications with strict deadlines under average power constraint using Hybrid Multiple Access (MA) which consists of both Orthogonal MA (OMA) and power domain Non-Orthogonal MA (NOMA) as transmission scheme options. We aim to maximize the timely throughput, which represents the average number of successfully transmitted packets before deadline expiration, where expired packets still waiting in the buffer are dropped. We use Lyapunov stochastic optimization methods to develop a dynamic power assignment algorithm for minimizing the packet drop rate while satisfying time average power constraints. Numerical results show that Hybrid MA improves the timely throughput compared to conventional OMA by up to 46% and on the average by more than 21% while satisfying average power constraints.

Mobile edge computing (MEC) seems to be highly efficient to process the generated data from IoT devices by providing computational resources locating in close range to network edge. MEC can be promising in reduction of latency and consumption of energy from data transmissions from offloading computational tasks from IoT devices to nearby edge servers. In this paper, a computation offloading optimization algorithm is proposed which is based on deep deterministic policy gradient for realistic Aurelia X6 Pro unmanned aerial vehicle (UAV)-assisted MEC systems. The proposed algorithm optimizes the offloading decision for UAVs by taking task characteristics and the communication environment into consideration. The simulation yields outcomes indicating that the suggested algorithm can considerably enhance the competency of MEC systems.

Decentralized cryptocurrency systems, known as blockchains, have shown promise as an infrastructure for mutually distrustful parties to securely agree on transactions. Nevertheless, blockchain systems are constrained by the CAP Trilemma. Due to performance degradation, it is impossible to address this issue by improving simply the consensus layer or the network layer. To alleviate the CAP constraint in consortium blockchains, we propose a topological construction method to optimize the physical layer based on multi-dimensional hypercubes with excellent partition tolerance in probability. The basic topology has the advantage of solving the mismatch problem between the overlay network and the underlying network. It is further extended to hierarchical recursive topologies with more intermediate links or short links to balance the reliability requirement with the cost of building the physical network. We prove that the proposed hypercube topology has better partition tolerance than the regular rooted tree and ring lattice topologies, and effectively fits the upper-layer protocols at the consensus and network layers. As a result, combined with suitable transmission and consensus protocols that satisfy strong consistency and availability, the proposed topology-constructed blockchain can reach the CAP guarantee bound.

The process of estimating the number of individuals within a defined area, commonly referred to as people counting, is of paramount importance in the realm of safety, security and crisis management. It serves as a crucial tool for accurately monitoring crowd dynamics and facilitating well-informed decision-making during critical situations. In our current study, we place a special emphasis on the utilization of the WiFi fingerprint technique, leveraging probe request messages emitted by smart devices as a proxy for people counting. However, it is essential to recognize the evolving landscape of privacy regulations and the concerted efforts by major smart-device manufacturers to enhance user privacy, exemplified by the introduction of MAC addresses randomization techniques. In this context, we designed a crowd monitoring solution that exploits Bloom filters for ensuring a formal deniability, aligning with the stringent requirements set forth by regulations like the European GDPR [1] . Our proposed solution not only addresses the essential task of people counting but also incorporates advanced privacy-preserving mechanisms. Importantly, it seamlessly integrates with trajectory-based crowd monitoring, offering a comprehensive approach to managing crowds while respecting individual privacy rights.