Utilization of idle time slot in spectrum sensing under noise uncertainty

<p>Spectrum sensing in cognitive radio (CR) is a critical process as it directly influences the accuracy of detection. Noise uncertainty affects the reliability of detecting vacant holes in the spectrum, thus limiting the access of that spectrum by secondary users (SUs). In such uncertain environment; SUs sense the received power of a primary user (PU) independently with different measures of signal-to-noise ratio (SNR). Long sensing time serves in mitigating the effect of noise uncertainty, but on the cost of throughput performance of CR system. In this paper, the scheme of an asynchronous and crossed sensing-reporting is presented. The scheme reduces energy consumption during sensing process without affecting the detection accuracy. Exploiting the included idle time (𝑇𝑖) in sensing time slot; each SU collects power samples with higher SNR directly performs the reporting process to a fusion center (FC) consecutively. The FC terminates the sensing and reporting processes at a specific sensing time that corresponds to the lowest SNR (𝑆𝑁𝑅𝑤𝑎𝑙𝑙). Furthermore, this integrated scheme aims at optimizing the total frame duration (𝑇𝑓). Mathematical expressions of the scheme are obtained. Analytical results show the efficiency of the scheme in terms of energy saving and throughput increment under noise uncerainty.</p>

Estudio de la audiencia social en Twitter de los formatos de politainment en España. El caso de ‘El Intermedio’

This research delves into the television programs of political info- tainment (politainment) broadcast in 2018 in Spain and analyzes in a particular way the behavior of the infoshow ‘El Intermedio’, as well as the engagement of its social audience on the social network Twitter. For this, a content analysis methodology has been used on the tweets published by the program, the com- ments received and the reactions made by its social audience. The results of the investigation indicate that, as a whole, politainment programs, or those that include spectacular politics, and that are broadcast in the night time slot, have a greater number of followers on their Twitter profiles than those that are broad- cast in the morning, they also concentrate a higher accumulated monthly linear audience. Regarding the analysis of the tweets and comments of ‘El Intermedio’, the colloquial language and the critical and humorous intentionality are pre- dominant while the engagement obtained is scarce as it does not include only viralizing elements that reinforce its messages.

A TDMA-Based MAC Protocol for Mitigating Mobility-Caused Packet Collisions in Vehicular Ad Hoc Networks

An autonomous driving environment poses a very stringent requirement for the timely delivery of safety messages in vehicular ad hoc networks (VANETs). Time division multiple access (TDMA)-based medium access control (MAC) protocols are considered a promising solution because of their time-bound message delivery. However, in the event of mobility-caused packet collisions, they may experience an unpredicted and extended delay in delivering messages, which can cause catastrophic accidents. To solve this problem, a distributed TDMA-based MAC protocol with mobility-caused collision mitigation (MCCM-MAC) is presented in this paper. The protocol uses a novel mechanism to detect merging collisions and mitigates them by avoiding subsequent access collisions. One vehicle in the merging collisions retains the time slot, and the others release the slot. The common neighboring vehicles can timely suggest a suitable new time slot for the vacating vehicles, which can avoid access collisions between their packet transmissions. A tie-breakup mechanism is employed to avoid further access collisions. Simulation results show that the proposed protocol reduces packet loss more than the existing methods. Consequently, the average delay between the successfully delivered periodic messages is also reduced.

A Talk-Listen-Ack Beaconing Strategy for Neighbor Discovery Protocols in Wireless Sensor Networks

Neighbor discovery is a fundamental function for sensor networking. Sensor nodes discover each other by sending and receiving beacons. Although many time-slotted neighbor discovery protocols (NDPs) have been proposed, the theoretical discovery latency is measured by the number of time slots rather than the unit of time. Generally, the actual discovery latency of a NDP is proportional to its theoretical discovery latency and slot length, and inversely proportional to the discovery probability. Therefore, it is desired to increase discovery probability while reducing slot length. This task, however, is challenging because the slot length and the discovery probability are two conflicting factors, and they mainly depend on the beaconing strategy used. In this paper, we propose a new beaconing strategy, called talk-listen-ack beaconing (TLA). We analyze the discovery probability of TLA by using a fine-grained slot model. Further, we also analyze the discovery probability of TLA that uses random backoff mechanism to avoid persistent collisions. Simulation and experimental results show that, compared with the 2-Beacon approach that has been widely used in time-slotted NDPs, TLA can achieve a high discovery probability even in a short time slot. TLA is a generic beaconing strategy that can be applied to different slotted NDPs to reduce their discovery latency.

Uplink Spectrum Overlay Coverage Enhancement Algorithm in 5G Network

The imbalance between the uplink and downlink rates and coverage of the 5G network has led to limited vertical industry services. Aiming at breaking the imbalance between the uplink and downlink rates and improving the coverage of 5G network, a uplink coverage enhancement algorithm is designed from the aspects of networking mode, bandwidth, uplink and downlink subframe ratio, etc. It uses high- and low-frequency time-frequency joint scheduling to enable uplink full-time slot scheduling, thereby improving uplink coverage and rate. According to the actual test on the live network, the results show that the super-uplink algorithm can increase the near-point uplink rate by 15% to 30%, increase the uplink rate for indoor midpoint scenarios by 40% to 80%, and increase the uplink rate for outdoor and indoor weak spot scenarios by 100% to 400%.

Analysis of a Discrete-Time Queueing Model with Disasters

Queueing models with disasters can be used to evaluate the impact of a breakdown or a system reset in a service facility. In this paper, we consider a discrete-time single-server queueing system with general independent arrivals and general independent service times and we study the effect of the occurrence of disasters on the queueing behavior. Disasters occur independently from time slot to time slot according to a Bernoulli process and result in the simultaneous removal of all customers from the queueing system. General probability distributions are allowed for both the number of customer arrivals during a slot and the length of the service time of a customer (expressed in slots). Using a two-dimensional Markovian state description of the system, we obtain expressions for the probability, generating functions, the mean values, variances and tail probabilities of both the system content and the sojourn time of an arbitrary customer under a first-come-first-served policy. The customer loss probability due to a disaster occurrence is derived as well. Some numerical illustrations are given.

A throughput analysis of an energy-efficient spectrum sensing scheme for the cognitive radio-based Internet of things

Spectrum sensing in a cognitive radio network involves detecting when a primary user vacates their licensed spectrum, to enable secondary users to broadcast on the same band. Accurately sensing the absence of the primary user ensures maximum utilization of the licensed spectrum and is fundamental to building effective cognitive radio networks. In this paper, we address the issues of enhancing sensing gain, average throughput, energy consumption, and network lifetime in a cognitive radio-based Internet of things (CR-IoT) network using the non-sequential approach. As a solution, we propose a Dempster–Shafer theory-based throughput analysis of an energy-efficient spectrum sensing scheme for a heterogeneous CR-IoT network using the sequential approach, which utilizes firstly the signal-to-noise ratio (SNR) to evaluate the degree of reliability and secondly the time slot of reporting to merge as a flexible time slot of sensing to more efficiently assess spectrum sensing. Before a global decision is made on the basis of both the soft decision fusion rule like the Dempster–Shafer theory and hard decision fusion rule like the “n-out-of-k” rule at the fusion center, a flexible time slot of sensing is added to adjust its measuring result. Using the proposed Dempster–Shafer theory, evidence is aggregated during the time slot of reporting and then a global decision is made at the fusion center. In addition, the throughput of the proposed scheme using the sequential approach is analyzed based on both the soft decision fusion rule and hard decision fusion rule. Simulation results indicate that the new approach improves primary user sensing accuracy by $$13\%$$ 13 % over previous approaches, while concurrently increasing detection probability and decreasing false alarm probability. It also improves overall throughput, reduces energy consumption, prolongs expected lifetime, and reduces global error probability compared to the previous approaches under any condition [part of this paper was presented at the EuCAP2018 conference (Md. Sipon Miah et al. 2018)].

Optimising truck arrival management and number of service gates at container terminals

PurposeTruck appointment systems have been applied in critical container ports in the United States due to their potential to improve handling operations. This paper aims to develop a truck appointment system to optimise the total cost experiencing at the entrance of container terminals by managing truck arrivals and the number of service gates satisfying a given level of service.Design/methodology/approachThe approximation of Mt/G/nt queuing model is applied and integrated into a cost optimisation model to identify (1) the number of arrival trucks allowed at each time slot and (2) the number of service gates operating at each time slot that ensure the average waiting time is less than a designated time threshold. The optimisation model is solved by the Genetic Algorithm and tested with a case study. Its effectiveness is identified by comparing the model's outcomes with observed data and other recent studies.FindingsThe results indicate that the developed truck appointment system can provide more than threefold and twofold reductions of the total cost experiencing at the terminal entrance compared to the actual data and results from previous research, respectively.Originality/valueThe proposed approach provides applicably coordinated truck plans and operating service gates efficiently to decrease congestion, emission and expenses.