Enabling Grant-Free URLLC for AoI Minimization in RAN-Coordinated 5G Health Monitoring System

Age of information (AoI) is used to evaluate the performance of 5G health monitoring systems because stale data can be fatal for patients with serious illness. Recently, grant-free ultra-reliable and low latency communications (URLLC) have shown greater potential of minimizing AoI than conventional grant-based approaches; however, existing grant-free schedulers cannot provide guaranteed performance in 5G health monitoring systems because they involve two fundamental problems in time and frequency domains, namely the joint scheduling problem and physical resource block (PRB) allocation. In this study, we investigate two resource allocation problems for the first time, aiming to enable grant-free URLLC to minimize AoI in 5G health monitoring systems. Specifically, we propose two adaptive solutions based on an open radio access network-coordinated wireless system: 1) a joint scheduling algorithm and 2) an adaptive PRB allocation algorithm. To verify the effectiveness of the proposed solutions, we built a simulation environment similar to a real health monitoring system and captured the performance variations under realistic deployment scenarios

Design and Evaluation of Memory Efficient Data Structure Scheme for Energy Drainage Attacks in Wireless Sensor Networks

Wireless Sensor Networks (WSN) are deployed on a large scale and require protection from malicious energy drainage attacks, particularly those directed at the routing layer. The complexity increases during critical operations like cluster head selection where detection of such attacks is challenging. The dependency of WSN on batteries elevates the concern posed by these threats, making detection and isolation crucial, especially within the framework of energy-efficient clustering protocols such as Low Energy Adaptive Clustering Hierarchy (LEACH). Various approaches have been proposed in prior research to deal with such attacks. However, the use of memory-efficient data structures has yet to be effectively addressed. In this article, considering the limitations of WSN, we utilize memory-efficient data structures named Bloom filters, count-min (CM) sketch, and cellular automata (CA) to address abnormal energy drainage. A CA-based trust model is used to choose the legitimate node as the cluster head. CM sketch is used to control the frequency of a node selected as a cluster head, achieving fairness in the cluster head selection process, and Bloom filters maintain the record of malicious nodes blocked from participating in the communication or cluster head selection process. CA and trust functions collectively keep a record of neighbors' energy and their trust in the network. Grayhole, blackhole, and scheduling attacks are three well-known threats that lead to abnormal energy drainage in legitimate nodes. The proposed solution effectively detects and addresses abnormal energy drainage in WSN. Its impact is simulated and observed using ns2 IEEE 802.15.4 medium access control (MAC) and LEACH clustering protocols, specifically in the context of the mentioned attacks. The effectiveness of the proposed model was rigorously analysed, and it was observed that it reduces the energy consumption of WSN by approximately 16.66%, 48.33%, and 43.33% in the cases of grayhole, blackhole, and scheduling attacks, respectively. In terms of space/time complexity, its growth is linear O(n). The proposed solution also consumes 0.08-0.10 J more energy compared to the original LEACH as a cost of the solution, which is not more than 2% of the total initial energy. The tradeoff of implementing heightened security is worthwhile, as the proposed approach outperforms the original LEACH and related methods, effectively mitigating abnormal energy drainage in WSN and extending network lifetime, especially in challenging environments with persistent battery recharging challenges. INDEX TERMS WSN, LEACH, cellular automata, CM sketch, Bloom filter, energy drainage, blackhole, grayhole, and scheduling attacks, trust model

Forward-central two-particle correlations in p-Pb collisions at root s(NN)=5.02 TeV

Two-particle angular correlations between trigger particles in the forward pseudorapidity range (2.5 2GeV/c. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B. V.Peer reviewe

Event-shape engineering for inclusive spectra and elliptic flow in Pb-Pb collisions at root(NN)-N-S=2.76 TeV

Peer reviewe

Elliptic flow of muons from heavy-flavour hadron decays at forward rapidity in Pb-Pb collisions at root s(NN)=2.76TeV

The elliptic flow, v(2), of muons from heavy-flavour hadron decays at forward rapidity (2.5 <y <4) is measured in Pb-Pb collisions at root s(NN)= 2.76TeVwith the ALICE detector at the LHC. The scalar product, two- and four-particle Q cumulants and Lee-Yang zeros methods are used. The dependence of the v(2) of muons from heavy-flavour hadron decays on the collision centrality, in the range 0-40%, and on transverse momentum, p(T), is studied in the interval 3 <p(T)<10 GeV/c. A positive v(2) is observed with the scalar product and two-particle Q cumulants in semi-central collisions (10-20% and 20-40% centrality classes) for the p(T) interval from 3 to about 5GeV/c with a significance larger than 3 sigma, based on the combination of statistical and systematic uncertainties. The v(2) magnitude tends to decrease towards more central collisions and with increasing pT. It becomes compatible with zero in the interval 6 <p(T)<10 GeV/c. The results are compared to models describing the interaction of heavy quarks and open heavy-flavour hadrons with the high-density medium formed in high-energy heavy-ion collisions. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B.V.Peer reviewe

Pseudorapidity and transverse-momentum distributions of charged particles in proton-proton collisions at root s=13 TeV

The pseudorapidity (eta) and transverse-momentum (p(T)) distributions of charged particles produced in proton-proton collisions are measured at the centre-of-mass energy root s = 13 TeV. The pseudorapidity distribution in vertical bar eta vertical bar <1.8 is reported for inelastic events and for events with at least one charged particle in vertical bar eta vertical bar <1. The pseudorapidity density of charged particles produced in the pseudorapidity region vertical bar eta vertical bar <0.5 is 5.31 +/- 0.18 and 6.46 +/- 0.19 for the two event classes, respectively. The transverse-momentum distribution of charged particles is measured in the range 0.15 <p(T) <20 GeV/c and vertical bar eta vertical bar <0.8 for events with at least one charged particle in vertical bar eta vertical bar <1. The evolution of the transverse momentum spectra of charged particles is also investigated as a function of event multiplicity. The results are compared with calculations from PYTHIA and EPOS Monte Carlo generators. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

Design of Fuzzy Logic Based-temperature-aware Routing Protocol

Background: Rising temperatures in an implanted sensor node is harmful to human tissue; thus, a suitable approach must be considered in designing network protocols. Objective: To prevent high node temperatures, we present a new temperature-aware multi-hop routing protocol based on fuzzy logic with a low cost overhead. Results: Through comparative simulations, we verify that the proposed scheme can maintain node temperatures to an acceptable and balanced level. This leads to a higher packet delivery ratio than that in existing schemes. Conclusion: The fuzzy logic-based temperature routing protocol can contribute to avoiding excessive temperature rises in sensor nodes. In addition, the fuzzy logic input and decision rule are relevant for effectively computing the temperature variation

An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks

In wireless sensor networks, tree-based routing can achieve a low control overhead and high responsiveness by eliminating the path search and avoiding the use of extensive broadcast messages. However, existing approaches face difficulty in finding an optimal parent node, owing to conflicting performance metrics such as reliability, latency, and energy efficiency. To strike a balance between these multiple objectives, in this paper, we revisit a classic problem of finding an optimal parent node in a tree topology. Our key idea is to find the best parent node by utilizing empirical data about the network obtained through Q-learning. Specifically, we define a state space, action set, and reward function using multiple cognitive metrics, and then find the best parent node through trial and error. Simulation results demonstrate that the proposed solution can achieve better performance regarding end-to-end delay, packet delivery ratio, and energy consumption compared with existing approaches