A Review on Edge Connectivity Framework for Handheld Computing

The Internet of Things(IoT) such as the use of robots, sensors, actuators, electronic signalization and a variety of other internet-enabled physical devices may provide for new advanced smart applications to be used in construction in the very near future. Such applications require real-time responses and are therefore time-critical. Therefore, in order to support collaboration, control, monitoring, supply management, safety and other construction processes, they have to meet dependability requirements, including requirements for high Quality of Service (QoS). Dependability and high QoS can be achieved by using adequate number and quality of computing resources, such as processing, memory and networking elements, geographically close to the smart environments for handheld device computing (HDC).

Multi-Layered Security Model for Hadoop Environment

In this article, a novel security model for the Hadoop environment has been developed to enhance security credentials of handheld systems. The proposed system deals with enabling Hadoop security in terms of a dataset and a user which is willing to access the content inside the Hadoop system. It deals with security in terms of three different features: encryption, confidentiality and authentication. The significance of the proposed model is it provides protection against malicious intent which allows only valid content into the Big data system; it enables authenticated users and people to enter into the system and make the dataset more secure; and if authentication is enhanced, then authorization can be easily gained in the Hadoop system which provides access control and access rights to resource which the user is willing to perform its function or operation. This model is implemented, and the performance has been validated using existing security variants.

Defensive Mechanism Against DDoS Attack to Preserve Resource Availability for IoT Applications

The major challenge of Internet of Things (IoT) generated data is its hypervisor level vulnerabilities. Malicious VM deployment and termination are so simple due to its multitenant shared nature and distributed elastic cloud features. These features enable the attackers to launch Distributed Denial of Service attacks to degrade cloud server performance. Attack detection techniques are applied to the VMs that are used by malicious tenants to hold the cloud resources by launching DDoS attacks at data center subnets. Traditional dataflow-based attack detection methods rely on the similarities of incoming requests which consist of IP and TCP header information flows. The proposed approach classifies the status patterns of malicious VMs and ideal VMs to identify the attackers. In this article, information theory is used to calculate the entropy value of the malicious virtual machines for detecting attack behaviors. Experimental results prove that the proposed system works well against DDoS attacks in IoT applications.

A Method of Identifying User Identity Based on Username Features

Users typically register to use multiple web applications; there are many duplicate user identities in the Internet. The identification and integration of duplicate user identities are of great importance in the commercial field and network security field. As a result of privacy considerations, the personal information provided on the internet is usually incomplete or partially false. Considering that the username can reflect the user's personality or habit, is easy to obtain and does not involve the privacy problem, this article proposes a method to determine the identity of the user only by the username. First, the problem of user identity is described formally, and then the username features are divided into two categories: intuitive feature and contrastive feature, and the username probability distribution is analysed quantitatively. This article proposes a method of identity whether a given username belongs to the same user. Finally, a method is proposed to retrieve other usernames that may belong to the user in the username candidate set when a single user name is given.

Malicious Data Stream Identification to Improve the Resource Elasticity of Handheld Edge Computing System

This article lights the need for the identification of resource elasticity in handheld edge computing systems and its related issues. Under a few developing application situations, for example, in urban areas, operational checking of huge foundations, wearable help, and the Internet of Things, nonstop information streams must be prepared under short postponements. A few arrangements, including various programming motors, have been created for handling unbounded information streams in an adaptive and productive way. As of late, designs have been proposed to utilize edge processing for information stream handling. This article reviews the cutting-edge stream preparing motors and systems for misusing asset versatility which highlights distributed computing in stream preparation. Asset flexibility takes into consideration an application or administration to scale out/in as per fluctuating requests. Flexibility turns out to be much more difficult in conveyed conditions involving edge and distributed computing assets. Device security is one of the real difficulties for fruitful execution of the Internet of Things and fog figuring conditions in the current IT space. Specialists and information technology (IT) associations have investigated numerous answers for shield frameworks from unauthenticated device assaults. Fog registering utilizes organize devices (e.g. switch, switch and center) for dormancy mindful handling of gathered information utilizing IoT. This article concludes with the various process for improvising the resource elasticity of handheld devices for leading the communication to the next stage of computing.

Image Visual Improvement on Handheld Devices Using Linear Mapping Function

In the current scenario, handheld devices play a major role in the human life. Handheld devices become an essential kit, not only acting as a conduit for social media, but also in medicine. Several new opportunities for the different applications of mobile image processing exist, such as to improve the visual quality, and image recognition. Captured images do not provide an effective visualization due to the poor specifications of the device camera, low light, poor sensing features, etc. In this article, an adaptive histogram equalization for contrast enhancement using a linear mapping function scheme is proposed to improve the images. The image from the mobile device is fed into a contrast improvement phase. The intensity value of each pixel is processed to improve the image visuals. The pixel density value is measured and according to it, the low-density value is changed. Hence, the image is tuned finely to yield better results.

A Distributed Least-Squares Algorithm in Wireless Sensor Networks With Unknown and Limited Communications

In this article, the authors propose a new distributed least-squares algorithm to address the sensor fusion problem in using wireless sensor networks (WSN) to monitor the behaviors of large-scale multiagent systems. Under a mild assumption on network observability, that is, each sensor can take the measurements of a limited number of agents but the complete multiagent systems are covered under the union of all sensors in the network, the proposed algorithm achieves the estimation consensus if local information exchange can be performed among sensors. The proposed distributed least-squares algorithm can handle the directed communication network by explicitly estimating the left eigenvector corresponding to the largest eigenvalue of the sensing/communication matrix. The convergence of the proposed algorithm is analyzed, and simulation results are provided to further illustrate its effectiveness.

A Cloud-Based Incentive Mechanism for Sensing in Mobile Sensor Networks

Due to proliferation of smart cities and other smart services, extensive data collection needs to be accomplished by mobile sensor networks (MSNs). However, sensing and data collection are voluntary tasks for many MSN users. For example, drivers are not required to report traffic condition although their vehicles with advanced sensors have easy access to critical information. Therefore, incentive mechanisms are needed to recruit sensing users (SUs). Incentive mechanisms proposed for traditional MSNs cannot be applied directly due to limited information of SU used for recruitment. In this article, the authors propose a novel cloud-based MSN model that consists of three parties, including data request party, cloud-based platform and SUs. To better utilize information of SUs, a data quality model is proposed to measure the credit level of SUs. The proposed SU recruitment strategy takes into consideration social connections of users. According to the strategy, SUs are divided into two separate levels. Moreover, the authors propose an incentive mechanism using a Stackelberg game theoretical approach to achieve the maximum utility of each recruited SU. The simulation results demonstrate that the proposed incentive mechanism can recruit SUs more efficiently while providing data quality guarantee.

WSN Lifetime and Reliability Analysis From the Death Criterion Perspective

Wireless Sensor Networks (WSNs) are widely used in numerous critical applications, and require the network to have a prolonged lifetime and high tolerance to failures. However, the battery-operated sensor nodes used in WSNs cause the network to be resource-constrained. On the one hand, there is a continuous urge to efficiently exploit the WSN energy, and hence, prolong the network lifetime. On the other hand, WSN node failures are not only attributed to battery drain. Node failures can be caused by hardware or software malfunctioning. In this article, the authors assess the impact of the death criterion on the network lifetime and reliability. It is related how the data from the different sensors are aggregated to the death criterion. Additionally, the impact of the number of sensing cycles per network master on the network lifetime and energy efficiency for the different considered death criteria. The effect of the network master selection process on the energy efficiency is also examined. Finally, the impact of the death criterion on the reliability of the WSN is evaluated.

A Study of Recursive Techniques for Robust Identification of Time-Varying Electrical Equivalent Circuit Models of Li-Ion Batteries

This article presents results of a comparative study of recursive techniques for robust identification of time-varying electrical equivalent circuit models of li-ion batteries. Two such methods are studied, a direct continuous time system identification method and an indirect discrete time technique. The results of this study indicate that although both methods work equally well for identification of time-invariant circuit models from clean voltage-current data, the direct continuous time method outperforms indirect discrete time technique for identification of time-varying circuit models. Similar conclusions can also be drawn for identification of equivalent circuit models in the presence of noise and/or unmodeled dynamics.