A tympanal insect ear exploits a critical oscillator for active amplification and tuning

SummaryA dominant theme of acoustic communication is the partitioning of acoustic space into exclusive, species-specific niches to enable efficient information transfer. In insects, acoustic niche partitioning is achieved through auditory frequency filtering, brought about by the mechanical properties of their ears [1]. The tuning of the antennal ears of mosquitoes [2] and flies [3], however, arises from active amplification, a process similar to that at work in the mammalian cochlea [4]. Yet, the presence of active amplification in the other type of insect ears—tympanal ears—has remained uncertain [5]. Here we demonstrate the presence of active amplification and adaptive tuning in the tympanal ear of a phylogenetically basal insect, a tree cricket. We also show that the tree cricket exploits critical oscillator-like mechanics, enabling high auditory sensitivity and tuning to conspecific songs. These findings imply that sophisticated auditory mechanisms may have appeared even earlier in the evolution of hearing and acoustic communication than currently appreciated. Our findings also raise the possibility that frequency discrimination and directional hearing in tympanal systems may rely on physiological nonlinearities, in addition to mechanical properties, effectively lifting some of the physical constraints placed on insects by their small size [6] and prompting an extensive reexamination of invertebrate audition

Designing Intelligent Energy Efficient Scheduling Algorithm To Support Massive IoT Communication In LoRa Networks

We are about to enter a new world with sixth sense ability – “Network as a sensor -6G”. The driving force behind digital sensing abilities is IoT. Due to their capacity to work in high frequency, 6G devices have voracious energy demand. Hence there is a growing need to work on green solutions to support the underlying 6G network by making it more energy efficient. Low cost, low energy, and long-range communication capability make LoRa the most adopted and promising network for IoT devices. Since LoRaWAN uses ALOHA for multi-access of channels, collision management is an important task. Moreover, in massive IoT, due to the increased number of devices and their Adhoc transmissions, collision becomes and concern. Furthermore, in long-range communication, such as in forests, agriculture, and remote locations, the IoT devices need to be powered using a battery and cannot be attached to an energy grid. LoRaWAN originally has a star network wherein IoT devices communicated to a single gateway. Massive IoT causes increased traffic at a single gateway. To address Massive IoT issues of collision and gateway load handling, we have designed a reinforcement learning-based scheduling algorithm, a Deep Deterministic policy gradient algorithm with channel activity detection (CAD) to optimize the energy efficiency of LoRaWAN in cross-layer architecture in massive IoT with star topology. We also design a CAD-based simulator for evaluating any algorithms with channel sensing. We compare energy efficiency, packet delivery ratio, latency, and signal strength with existing state of art algorithms and prove that our proposed solution is efficient for massive IoT LoRaWAN with star topology

Transformed-linear models for time series extremes

2022 Summer.Includes bibliographical references.In order to capture the dependence in the upper tail of a time series, we develop nonnegative regularly-varying time series models that are constructed similarly to classical non-extreme ARMA models. Rather than fully characterizing tail dependence of the time series, we define the concept of weak tail stationarity which allows us to describe a regularly-varying time series through the tail pairwise dependence function (TPDF) which is a measure of pairwise extremal dependencies. We state consistency requirements among the finite-dimensional collections of the elements of a regularly-varying time series and show that the TPDF's value does not depend on the dimension being considered. So that our models take nonnegative values, we use transformed-linear operations. We show existence and stationarity of these models, and develop their properties such as the model TPDF's. Motivated by investigating conditions conducive to the spread of wildfires, we fit models to hourly windspeed data using a preliminary estimation method and find that the fitted transformed-linear models produce better estimates of upper tail quantities than traditional ARMA models or than classical linear regularly-varying models. The innovations algorithm is a classical recursive algorithm used in time series analysis. We develop an analogous transformed-linear innovations algorithm for our time series models that allows us to perform prediction which is fundamental to any time series analysis. The transformed-linear innovations algorithm also enables us to estimate parameters of the transformed-linear regularly-varying moving average models, thus providing a tool for modeling. We construct an inner product space of transformed-linear combinations of nonnegative regularly-varying random variables and prove its link to a Hilbert space which allows us to employ the projection theorem. We develop the transformed-linear innovations algorithm using the properties of the projection theorem. Turning our attention to the class of MA(∞) models, we talk about estimation and also show that this class of models is dense in the class of possible TPDFs. We also develop an extremes analogue of the classical Wold decomposition. Simulation study shows that our class of models provides adequate models for the GARCH and another model outside our class of models. The transformed-linear innovations algorithm gives us the best prediction and we also develop prediction intervals based on the geometry of regular variation. Simulation study shows that we obtain good coverage rates for prediction errors. We perform modeling and prediction for the hourly windspeed data by applying the innovations algorithm to the estimated TPDF

Joint Latency-Energy optimization scheme for Offloading in Mobile Edge computing environment based in Deep Reinforcement Learning

With the increasing number of mobile devices (MD), IoT devices, and computation-intensive tasks deployed on these devices, there is a need to increase the efficiency and speed of the deliverable. Due to inadequate resources, it is infeasible to compute all the tasks locally. Similarly, due to time constraints, it is not possible to compute the entire task at a remote site. Edge computing (EC) and cloud computing (CC) play the role of providing the resources to these devices on the fly. But a major drawback is increased delay and energy consumption due to transmission and offloading of computation tasks to these remote systems. There is a need to divide the task for computation at local sites, edge servers, and cloud servers to complete tasks with minimum delay and energy consumption. This paper proposes offloading strategy computation using Multi-Period Deep Deterministic Policy Gradient (MP-DDPG) algorithm based on Reinforcement Learning (RL) to optimize the latency caused and energy consumed. We formulate our problem as a Multi-period Markov Decision Process (MP-MDP). In this paper, we use the two-tier offloading architecture including more than one mobile device (MD), two EC-servers, and one CC-server as computation sites. Further, we also compare our proposed algorithm using one-tier architecture and one edge server with the Deep Deterministic Policy Gradient (DDPG) algorithm with similar architecture

GR-190 - DRL-FHSS: Dynamic Reinforcement Learning based Frequency Hopping Spread Spectrum Algorithm for Maximizing Data Rate and Minimizing Collision in Edge-enabled LoRaWAN

The Last decade saw new advances in LoRaWAN for IoT devices. Recent research in this field is as made it a game-changer for the IoT world. Lora, owing to its ability to establish a long-range communication using low power, has proved upper hand over its counterparts such as Wifi, Bluetooth, or cellular network. LoRa uses much lower bandwidth and as a result, network and power demands reduce. LoRa is suitable for short and periodic communications. But due to lower bandwidth and consequently lower transmission rates and low data volumes, it is not suitable for real-time communications. Frequency-hopping spread spectrum(FHSS) helps to rapidly switch frequencies and occupy larger spectral bands and increase overall data rate and throughput. It is observed that LoRaWAN defines 11 channels but uses only 3 channels frequently, thus keeping the other 8 channels underutilized. If all channels were utilized, the transmission rate would increase multifold. We have proposed a Dynamic approach, Reinforcement Learning-based Frequency Hopping Spread Spectrum (DRL-FHSS), a frequency hopping generation algorithm, to widen the occupancy of the spectral band. Our algorithm keeps a check on the frequency of used channels, overutilized and underutilized frequencies, and generates a strategy that allocates time slots and frequencies for all registered IoT devices in the network. DRL-FHSS learns its strategy based on the previous bandwidth utilization ratio. We introduce an edge-enabled LoRaWAN architecture that delegates the task of frequency hopping strategy generation to the edge server. Further, edge servers also notify transmitters and receivers in the network about the frequency hopping sequence. This also reduces the collision in the network since each transmitter is aware of busy frequencies in the network. Thus DRL-FHSS helps to improve throughput by increasing the transmission rate and lowering collision

GR-314 Reinforcement Learning based Offloading Scheme Computation to Optimize Latency-Energy in Collaborative Cloud Networks

Growing technologies like virtualization and artificial intelligence have become more popular on mobile devices. But lack of resources faced for processing these applications is still a major hurdle. Collaborative edge and cloud computing are one of the solutions to this problem. Remote servers have enough resources to support computation-heavy tasks and compute the results faster. But transmission time and energy are involved while offloading the computation to remote servers such as cloud and edge devices. There is a need to find an optimal offloading ratio for cloud as well as edge servers such that entire computation on remote as well as local can be achieved minimum energy consumption as well as minimum delay. We have proposed a multi-period deep deterministic policy gradient (MP-DDPG) algorithm to find an optimal offloading policy by partitioning the task and offloading it to the collaborative cloud and edge network to reduce energy consumption. Our results show that MP-DDPG achieves the minimum latency and energy consumption in the collaborative cloud network. We have compared our results with the existing DDPG-based approach and achieved about 65% speedup in terms of latency. Also, we observed energy consumption reduces with an increase in the number of edge servers

Temperature Evaluation of NoC Architectures and Dynamically Reconfigurable NoC

Advancements in the field of chip fabrication led to the integration of a large number of transistors in a small area, giving rise to the multi–core processor era. Massive multi–core processors facilitate innovation and research in the field of healthcare, defense, entertainment, meteorology and many others. Reduction in chip area and increase in the number of on–chip cores is accompanied by power and temperature issues. In high performance multi–core chips, power and heat are predominant constraints. High performance massive multicore systems suffer from thermal hotspots, exacerbating the problem of reliability in deep submicron technologies. High power consumption not only increases the chip temperature but also jeopardizes the integrity of the system. Hence, there is a need to explore holistic power and thermal optimization and management strategies for massive on–chip multi–core environments. In multi–core environments, the communication fabric plays a major role in deciding the efficiency of the system. In multi–core processor chips this communication infrastructure is predominantly a Network–on–Chip (NoC). Tradition NoC designs incorporate planar interconnects as a result these NoCs have long, multi–hop wireline links for data exchange. Due to the presence of multi–hop planar links such NoC architectures fall prey to high latency, significant power dissipation and temperature hotspots. Networks inspired from nature are envisioned as an enabling technology to achieve highly efficient and low power NoC designs. Adopting wireless technology in such architectures enhance their performance. Placement of wireless interconnects (WIs) alters the behavior of the network and hence a random deployment of WIs may not result in a thermally optimal solution. In such scenarios, the WIs being highly efficient would attract high traffic densities resulting in thermal hotspots. Hence, the location and utilization of the wireless links is a key factor in obtaining a thermal optimal highly efficient Network–on–chip. Optimization of the NoC framework alone is incapable of addressing the effects due to the runtime dynamics of the system. Minimal paths solely optimized for performance in the network may lead to excessive utilization of certain NoC components leading to thermal hotspots. Hence, architectural innovation in conjunction with suitable power and thermal management strategies is the key for designing high performance and energy–efficient multicore systems. This work contributes at exploring various wired and wireless NoC architectures that achieve best trade–offs between temperature, performance and energy–efficiency. It further proposes an adaptive routing scheme which factors in the thermal profile of the chip. The proposed routing mechanism dynamically reacts to the thermal profile of the chip and takes measures to avoid thermal hotspots, achieving a thermally efficient dynamically reconfigurable network on chip architecture