A Hybrid Scheme based on Alternative Scalar Leader Election (HS-ASLE) for Redundant Data Minimization in Multi-event Occurrence Scenario for WMSNs

The current paper reports a hybrid approach namely “Hybrid Scheme based on Alternative Scalar Leader Election (HS-ASLE)” for camera sensor actuation in multi-event occurrence scenario. In the proposed approach, the whole monitored zone gets segregated into multiple virtual sub-compartments and in each of the sub-compartments, one and three scalar leaders are elected alternatively that behave as the representatives of scalars to report event information. During the event occurrence, the event information gets trapped through the scalar leaders in lieu of scalars and the leaders convey the event occurrence information to the respective camera sensors. Pervasive experiment and observation have been ordained to mark the impact of varying the number of deployed scalar sensors and camera sensors individually on various performance parameters in multi-event occurrence ambience. Further, the numerical outcomes attained in terms of number of cameras actuated, coverage ratio, redundance ratio and energy expenditure for camera activation proclaim the effectiveness of our proposed HS-ASLE over the other two existing approaches in literature. Moreover, it is marked that our proposed approach attains maximal event region coverage with least camera activation, least redundant data transmission and lowest energy expenditure for camera sensor actuation as compared to two other approaches, which justify the precedence of our proposition over the other existing approaches

Data Redundancy Reduction in Wireless Sensor Network

In Wireless Sensor Network, sensor nodes are randomly deployed where the sensor nodes are not situated faraway from each other. Thus, an overlapping area is generated due to intersection of their sensing ranges. If an event occurs within the overlapping area, all the sharing nodes sense the same event and produce redundant and correlated data. Data redundancy exhaust network resources and increase network overhead. Data aggregation and numerous data redundancy reduction algorithms are employed to solve this problem. This paper reviews modern data redundancy reduction used sleep schedule model to solve the redundancy. All proposed algorithms are classified on the basis of network coverage and similarity among sensory data which can be used in reducing redundancy in WSN effectively

Data redundancy reduction in wireless sensor network

In Wireless Sensor Network, sensor nodes are randomly deployed where the sensor nodes are not situated faraway from each other. Thus, an overlapping area is generated due to intersection of their sensing ranges. If an event occurs within the overlapping area, all the sharing nodes sense the same event and produce redundant and correlated data. Data redundancy exhaust network resources and increase network overhead. Data aggregation and numerous data redundancy reduction algorithms are employed to solve this problem. This paper reviews modern data redundancy reduction used sleep schedule model to solve the redundancy. All proposed algorithms are classified on the basis of network coverage and similarity among sensory data which can be used in reducing redundancy in WSN effectively

Saliency-directed prioritization of visual data in wireless surveillance networks

YesIn wireless visual sensor networks (WVSNs), streaming all imaging data is impractical due to resource constraints. Moreover, the sheer volume of surveillance videos inhibits the ability of analysts to extract actionable intelligence. In this work, an energy-efficient image prioritization framework is presented to cope with the fragility of traditional WVSNs. The proposed framework selects semantically relevant information before it is transmitted to a sink node. This is based on salient motion detection, which works on the principle of human cognitive processes. Each camera node estimates the background by a bootstrapping procedure, thus increasing the efficiency of salient motion detection. Based on the salient motion, each sensor node is classified as being high or low priority. This classification is dynamic, such that camera nodes toggle between high-priority and low-priority status depending on the coverage of the region of interest. High-priority camera nodes are allowed to access reliable radio channels to ensure the timely and reliable transmission of data. We compare the performance of this framework with other state-of-the-art methods for both single and multi-camera monitoring. The results demonstrate the usefulness of the proposed method in terms of salient event coverage and reduced computational and transmission costs, as well as in helping analysts find semantically relevant visual information.Supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2012904)

Towards Tactile Internet in Beyond 5G Era: Recent Advances, Current Issues and Future Directions

Tactile Internet (TI) is envisioned to create a paradigm shift from the content-oriented communications to steer/control-based communications by enabling real-time transmission of haptic information (i.e., touch, actuation, motion, vibration, surface texture) over Internet in addition to the conventional audiovisual and data traffics. This emerging TI technology, also considered as the next evolution phase of Internet of Things (IoT), is expected to create numerous opportunities for technology markets in a wide variety of applications ranging from teleoperation systems and Augmented/Virtual Reality (AR/VR) to automotive safety and eHealthcare towards addressing the complex problems of human society. However, the realization of TI over wireless media in the upcoming Fifth Generation (5G) and beyond networks creates various non-conventional communication challenges and stringent requirements in terms of ultra-low latency, ultra-high reliability, high data-rate connectivity, resource allocation, multiple access and quality-latency-rate tradeoff. To this end, this paper aims to provide a holistic view on wireless TI along with a thorough review of the existing state-of-the-art, to identify and analyze the involved technical issues, to highlight potential solutions and to propose future research directions. First, starting with the vision of TI and recent advances and a review of related survey/overview articles, we present a generalized framework for wireless TI in the Beyond 5G Era including a TI architecture, the main technical requirements, the key application areas and potential enabling technologies. Subsequently, we provide a comprehensive review of the existing TI works by broadly categorizing them into three main paradigms; namely, haptic communications, wireless AR/VR, and autonomous, intelligent and cooperative mobility systems. Next, potential enabling technologies across physical/Medium Access Control (MAC) and network layers are identified and discussed in detail. Also, security and privacy issues of TI applications are discussed along with some promising enablers. Finally, we present some open research challenges and recommend promising future research directions

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.publishedVersio

NASA Tech Briefs, August 2010

Topics covered include: Technology Focus: Mechanical Components; Electronics/Computers; Software; Materials; Mechanics/Machinery; Manufacturing; Bio-Medical; Physical Sciences; Information Sciences; and Books and Reports

Development of a smart weed detector and selective herbicide sprayer

Abstract: The fourth industrial revolution has brought about tremendous advancements in various sectors of the economy including the agricultural domain. Aimed at improving food production and alleviating poverty, these technological advancements through precision agriculture has ushered in optimized agricultural processes, real-time analysis and monitoring of agricultural data. The detrimental effects of applying agrochemicals in large or hard-to-reach farmlands and the need to treat a specific class of weed with a particular herbicide for effective weed elimination gave rise to the necessity of this research work...M.Ing. (Mechanical Engineering