Many wireless sensor network (WSN) applications rely on precise location or distance information. Despite the potentials of WSNs, efficient location prediction is one of the subsisting challenges. This paper presents novel prediction algorithms based on a Kriging interpolation technique. Given that each sensor is aware of its location only, the aims of this work are to accurately predict the temperature at uncovered areas and estimate positions of heat sources. By taking few measurements within the field of interest and by using Kriging interpolation to iteratively enhance predictions of temperature and location of heat sources in uncovered regions, degree of accuracy is significantly improved. Following a range of independent Monte Carlo runs in different experiments, it is shown through a comparative analysis that proposed algorithm delivers approximately 98% prediction accuracy.

The unprecedented growth in Augmented Reality (AR) has captured the focus of researchers and the industrial sector. The development of AR applications and their implementation in various domains is broadening. One of the advancements in the field of AR is Collaborative AR, which provides ample opportunities for the members of a team to work on a particular project remotely. The various activities carried out remotely, in a collaborative fashion, are based on the active interaction and transmission of data and applications across a communication channel that constitutes a mesh of frequently interacting applications, thus providing a real feeling of working together physically in the purportedly same demographic area. However, in the integration of different roles, remotely working in collaborative AR has a great chance of being intruded upon and manipulated. Consequently, the intrusion may explore novel vulnerabilities to various sensitive collaborative projects. One of the security concerns for collaborative and interconnected remote applications is to have pristine environments, where the participants of the collaborative AR can reliably trust each other during the execution of the various processes. This paper presents an integrity-aware CAR-Tourist (Collaborative Augmented reality for Tourism) framework wherein the unauthorized user’s access is denied and the remote participants of the network are provided with a secure environment through the state-of-the-art Blockchain architecture. This study further provides a use-case implementation of a tourism application. Each tourist has the chance to hire a remote guide for collaborative guidance over a blockchain-trusted network. Moreover, the proposed framework is lightweight, as the only necessary communication between the tourist and guide is recorded in the blockchain network. Each user has to register on a permission blockchain to be allowed to perform certain activities on our proposed CAR-Tourist framework. The decentralized Blockchain approach provides a consensus mechanism based on which not every participant is free to intrude on ongoing communication. Thus, through the proposed framework, all the participants in the collaborative Augmented Reality will have the essential trust of working remotely without external intrusion.

The Internet of Things (IoT) has revolutionized innovation to collect and store the information received from physical objects or sensors. The smart devices are linked to a repository that stores intelligent information executed by sensors on IoT-based smart objects. Now, the IoT is shifted from knowledge-based technologies to operational-based technologies. The IoT integrates sensors, smart devices, and a smart grid of implementations to deliver smart strategies. Nowadays, the IoT has been pondered to be an essential technology. The transmission of information to or from the cloud has recently been found to cause many network problems to include latency, power usage, security, privacy, etc. The distributed intelligence enables IoT to help the correct communication available at the correct time and correct place. Distributed Intelligence could strengthen the IoT in a variety of ways, including evaluating the integration of different big data or enhancing efficiency and distribution in huge IoT operations. While evaluating distributed intelligence in the IoT paradigm, the implementation of distributed intelligence services should take into consideration the transmission delay and bandwidth requirements of the network. In this article, the distributed intelligence at the Edge on IoT Networks, applications, opportunities, challenges and future scopes have been presented.

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The use of Wireless Sensors Network (WSN) is growing not only in popularity, but also in scope and application. Poised to shape the way we monitor the environment, wireless sensors has a lot of potentials waiting to be unlocked. Performance of WSN is largely determined by coverage and positioning of the sensor nodes. However, to be accepted as de-facto solution for monitoring different phenomena in our physical world, it is necessary to develop acceptable standards for its configuration and deployment. Motivated by this requirement, this paper investigates Kriging interpolation algorithm as an efficient location management system. With the aid of Kriging interpolation, it will be demonstrated later in this paper how targets of interest can be classified and monitored in a distributed manner for better accuracy. Effectiveness of this method on a large scale was established by applying it to temperature-based sensor network in a dynamic environment. Kriging interpolation sensor management is expected to improve coverage, reduce power consumption and enhance performance of sensor nodes and by extension the network.