TRCs, with taste at the periphery proposed to be encoded via labeled lines [i.e., a sweet line, a sour line, a bitter line, etc. (21)]. Given that Car4 is specifically tethered to the surface of sour-sensing cells, and thus ideally poised to provide a highly localized acid signal to the sour TRCs, we reasoned that carbonation might be sensed through activation of the sour-labeled line. A prediction of this postulate is that prevention of sour cell activation should eliminate CO2 detection, even in the presence of wild-type Car4 function. To test this hypothesis, we engineered animals in which the activation of nerve fibers innervating sour-sensing cells was blocked by preventing neurotransmitter release from the PKD2L1-expressing TRCs. In essence, we transgenically targeted expression of tetanus toxin light chain [TeNT, an endopeptidase that removes an essential component of the synaptic machinery (34–36)] to sour-sensing TRCs, and then monitored the physiological responses of these mice to sweet, sour, bitter, salty, umami and CO2 stimulation. As predicted, taste responses to sour stimuli were selectively and completely abolished, whereas responses to sweet, bitter, salty and umami tastants remained unaltered (Fig. 4 and fig. S5). However, these animals also displayed a complete loss of taste responses to CO2 even though they still expressed Car4 on the surface of PKD2L1 cells. Together, these results implicate the extracellular generation of protons, rather than intracellular acidification (15), as the primary signal that mediates the taste of CO2, and demonstrate that sour cells not only provide the membrane anchor for Car4 but also serve as the cellular sensors for carbonation. Why do animals need CO2 sensing? CO2 detection could have evolved as a mechanism to recognize CO2-producing sources (18, 37)—for instance, to avoid fermenting foods. This view would be consistent with the recent discovery of a specialized CO2 taste detection in insects where it mediates robust innate taste behaviors (38). Alternatively, Car4 may be important to maintain the pH balance within taste buds, and might gratuitously function as a detector for carbonation only as an accidental consequence. Although CO2 activates the sour-sensing cells, it does not simply taste sour to humans. CO2 (like acid) acts not only on the taste system but also in other orosensory pathways, including robust stimulation of the somatosensory system (17, 22); thus, the final percept of carbonation is likely to be a combination of multiple sensory inputs. Nonetheless, the “fizz” and “tingle” of heavily carbonated water is often likened to mild acid stimulation of the tongue, and in some cultures seltzer is even named for its salient sour taste (e.g., saurer Sprudel or Sauerwasser).

http://www.nedsahin.com/wp-content/files/sahin_etal_science2009_ICE_Broca_3-stage.pdf

Sequential Processing of Lexical, Grammatical, and Phonological Information Within Broca's Area

The 21st century has seen rapid changes in technology, industry, and social patterns. Most industries have moved towards automation, and human intervention has decreased, which has led to a revolution in industries, named the fourth industrial revolution (Industry 4.0). Industry 4.0 or the fourth industrial revolution (IR 4.0) relies heavily on the Internet of Things (IoT) and wireless sensor networks (WSN). IoT and WSN are used in various control systems, including environmental monitoring, home automation, and chemical/biological attack detection. IoT devices and applications are used to process extracted data from WSN devices and transmit them to remote locations. This systematic literature review offers a wide range of information on Industry 4.0, finds research gaps, and recommends future directions. Seven research questions are addressed in this article: (i) What are the contributions of WSN in IR 4.0? (ii) What are the contributions of IoT in IR 4.0? (iii) What are the types of WSN coverage areas for IR 4.0? (iv) What are the major types of network intruders in WSN and IoT systems? (v) What are the prominent network security attacks in WSN and IoT? (vi) What are the significant issues in IoT and WSN frameworks? and (vii) What are the limitations and research gaps in the existing work? This study mainly focuses on research solutions and new techniques to automate Industry 4.0. In this research, we analyzed over 130 articles from 2014 until 2021. This paper covers several aspects of Industry 4.0, from the designing phase to security needs, from the deployment stage to the classification of the network, the difficulties, challenges, and future directions.

https://www.mdpi.com/1424-8220/22/6/2087/pdf?version=1646828497

Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review

Wireless sensor networks (WSNs) have gained wide attention from researchers in the last few years because it has a vital role in countless applications. The main function of WSN is to process extracted data and to transmit it to remote locations. A large number of sensor nodes are deployed in the monitoring area. Therefore, deploying the minimum number of nodes that maintain full coverage and connectivity is of immense importance for research. Hence, coverage and connectivity issues, besides maximizing the network lifetime, represented the main concern to be considered in this paper. The key point of this paper is to classify different coverage techniques in WSNs into three main parts: coverage based on classical deployment techniques, coverage based on meta-heuristic techniques, and coverage based on self-scheduling techniques. Moreover, multiple comparisons among these techniques are provided considering their advantages and disadvantages. Additionally, performance metrics that must be considered in WSNs and comparison among different WSNs simulators are provided. Finally, open research issues, as well as recommendations for researchers, are discussed.

/pdf/deployment-techniques-in-wireless-sensor-networks-coverage-33sqyeldy1.pdf

Deployment Techniques in Wireless Sensor Networks, Coverage and Connectivity: A Survey

Recently, many researchers have paid attention to wireless sensor networks (WSNs) due to their ability to encourage the innovation of the IT industry. Although WSN provides dynamically scalable solutions with various smart applications, the growing need to maximize the area coverage with decreasing the percentage of deployed sensor nodes is still required. Random deployment is preferable for large areas that require a maximal number of nodes but result in coverage holes. As a result, mobile nodes are used to reduce coverage holes and maximize area coverage. The main objective of this study is to present an Improved Dynamic Deployment Technique based-on Genetic Algorithm (IDDT-GA) to maximize the area coverage with the lowest number of nodes as well as minimizing overlapping area between neighboring nodes. A two-point crossover novel is introduced to demonstrate the notation of variable-length encoding. Simulation results reveal that the superiority of the proposed IDDT-GA compared with other state-of-the-art techniques. IDDT-GA has better coverage rates with 9.69% and a minimum overlapping ratio with 35.43% compared to deployment based on Harmony Search (HS). Also, IDDT-GA has minimized the network cost by 13% and 7.44% than Immune Algorithm (IA) and Whale Optimization Algorithm (WOA) respectively. Besides, it confirms its stability with 83.04% compared to maximizing coverage with WOA.

An improved dynamic deployment technique based-on genetic algorithm (IDDT-GA) for maximizing coverage in wireless sensor networks

Coverage is a key metric in evaluating the monitoring capacity and quality of services in wireless sensor networks. The energy consumption of self-contained sensors is also a challenging problem for energy-efficient use while still achieving better coverage performance. Although techniques have been developed to mitigate the problem of area coverage, particularly together with efficient clustering methods, none focuses intensively on the sensor activation stage, which is used to maintain coverage while optimizing energy usage. In this research, we thus propose a cover set to find the minimum set of sensors that completely cover the sensing ranges within an interest area as a criterion for sensor activation. Our main goal is to select an optimal number of active sensors considering residual energy and the cover set and to keep alive the important sensors for the sensing coverage task as long as possible. Additionally, this research proposes an area coverage-aware clustering protocol (ACACP) with energy consumption optimization with respect to the activation sensor, network clustering, and multi-hop communication to improve overall network lifetime while preserving coverage. Throughout the intensive simulation, given a diversity of deployments with scalability concern, the results demonstrate the effectiveness of ACACP when compared with other competitive approaches such as ECDC and DECAR, including state-of-the-art clustering protocols such as LEACH, in terms of coverage ratio and overall network lifetime.

A novel energy-efficient clustering protocol with area coverage awareness for wireless sensor networks

In recent years, Wireless Sensor Networks (WSNs) have gained much attention because of its varying applications from catastrophic region to industrial and household region. In few applications, sensors are deployed in extreme environmental conditions. Hence, node access is not possible in that scenario. Therefore, a large number of sensor nodes are deployed in the target field so that node replacement problem is eliminated. Also, coverage is a very important parameter because it measures how effectively a target field is monitored by the sensor network. This paper focuses on the coverage issue in wireless sensor networks. Initially, three different types of coverage issues are discussed. Then, full coverage issue is examined by considering different points such as node type, deployment type, relation of communication range to sensing range, strategy used to detect full coverage and positioning based/independent algorithms. Some applications of wireless sensor networks are given. Finally, research challenges in the field of area coverage are discussed.

A Survey on Area Coverage in Wireless Sensor Networks

Decoding Imagined Speech and Computer Control using Brain Waves.

In recent years, wireless sensor networks (WSN's) have gained much attention due to its various applications in military, environmental monitoring, industries and in many others. All these applications require some target field to be monitored by a group of sensor nodes. Hence, coverage becomes an important issue in WSN's. This paper focuses on full coverage issue of WSN's. Based on the idea of some existing and derived theorems, Position and Hop-count Assisted (PHA) algorithm is proposed. This algorithm provides full coverage of the target field, maintains network connectivity and tries to minimize the number of working sensor nodes. Algorithm works for communication range less than root three times of sensing range and it can be extended for arbitrary relation between communication range and sensing range. By using hop-count value, three-connectivity in the network is maintained. Also, neighbors information is used to create logical tree structure which can be utilized in routing, redundant data removal and in other areas. Simulation results show that PHA algorithm outperforms layered diffusion-based coverage control algorithm by providing better area coverage and activating fewer nodes.

Position and hop-count assisted full coverage control in dense sensor networks

5G promises to be revolutionary from the perspective of offering unprecedented bandwidth to the end user. In this pursuit, the limited spectrum of the wireless overlay, further constricted by distance, requires strong support from a backhaul network. Such a strong backhaul network should provide large bandwidth pipes with near proximity to the end user at low costs and using pragmatic technology. Naturally, an optical solution is best suited to meet the voluminous bandwidth requirements of the backhaul. This optical solution has to cater to dynamic bandwidth needs among base stations and provide rapid and smart provisioning to meet the requirement of new features such as Coordinated Multipoint (CoMP) among base stations, and yet be low cost. To this end, we propose the use of light-trails (LTs), which are a generalization of a lightpath that provides for dynamic bandwidth communication, sub-wavelength optical grooming and optical layer multicasting using contemporary optics. We observe that each feature of a LT, i.e., dynamic provisioning, optical multicast, and sub-wavelength grooming is strongly desired by the 5G backhaul, implying a clear mapping between them. We investigate the usage of light-trails as a 5G backhaul solution. We discuss the proposed architecture of light-trails for micro cells, pico cells, and femto cells. We present the LT design problem for 5G backhaul as a constrained optimization problem. We also present a mechanism to achieve coordinated multipoint using light-trails. A dynamic virtual topology growth algorithm for the backhaul is also presented and extensively evaluated. The light-trails approach is compared to other approaches, the use of an SDN controller is discussed and significant performance benefits are observed.

Light-Trail Design for 5G Backhaul: Architecture, SDN Impact and Coordinated Multipoint

This work explores the possibility of decoding Imagined Speech (IS) signals which can be used to create a new design of Human-Computer Interface (HCI). Since the underlying process generating EEG signals is unknown, various feature extraction methods, along with different neural network (NN) models, are used to approximate data distribution and classify IS signals. Based on the experimental results, feed-forward NN model with ensemble and covariance matrix transformed features showed the highest performance in comparison to other existing methods. For comparison, three publicly available datasets were used. We report a mean classification accuracy of 80% between rest and imagined state, 96% and 80% for decoding long and short words on two datasets. These results show that it is possible to differentiate brain signals (generated during rest state) from the IS brain signals. Based on the experimental results, we suggest that the word length and complexity can be used to decode IS signals with high accuracy, and a BCI system can be designed with IS signals for computer interaction. These ideas, and results give direction for the development of a commercial level IS based BCI system, which can be used for human-computer interaction in daily life.

Abhiram Singh

Papers

A Survey on Area Coverage in Wireless Sensor Networks

Decoding Imagined Speech and Computer Control using Brain Waves.

Position and hop-count assisted full coverage control in dense sensor networks

Light-Trail Design for 5G Backhaul: Architecture, SDN Impact and Coordinated Multipoint

Interpreting Imagined Speech Waves with Machine Learning techniques.