A Cooperative Cache Management Scheme for IEEE802.15.4 based Wireless Sensor Networks

Wireless Sensor Networks (WSNs) based on the IEEE 802.15.4 MAC and PHY layer standards is a recent trend in the market. It has gained tremendous attention due to its low energy consumption characteristics and low data rates. However, for larger networks minimizing energy consumption is still an issue because of the dissemination of large overheads throughout the network. This consumption of energy can be reduced by incorporating a novel cooperative caching scheme to minimize overheads and to serve data with minimal latency and thereby reduce the energy consumption. This paper explores the possibilities to enhance the energy efficiency by incorporating a cooperative caching strategy

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

The realm of cloud computing has revolutionized access to cloud resources and their utilization and applications over the Internet. However, deploying cloud computing for delay critical applications and reducing the delay in access to the resources are challenging. The Mobile Edge Computing (MEC) paradigm is one of the effective solutions, which brings the cloud computing services to the proximity of the edge network and leverages the available resources. This paper presents a survey of the latest and state-of-the-art algorithms, techniques, and concepts of MEC. The proposed work is unique in that the most novel algorithms are considered, which are not considered by the existing surveys. Moreover, the chosen novel literature of the existing researchers is classified in terms of performance metrics by describing the realms of promising performance and the regions where the margin of improvement exists for future investigation for the future researchers. This also eases the choice of a particular algorithm for a particular application. As compared to the existing surveys, the bibliometric overview is provided, which is further helpful for the researchers, engineers, and scientists for a thorough insight, application selection, and future consideration for improvement. In addition, applications related to the MEC platform are presented. Open research challenges, future directions, and lessons learned in area of the MEC are provided for further future investigation

An Energy Efficient and Cost Reduction based Hybridization Scheme for Mobile Ad-hoc Networks (MANET) over the Internet of Things (IoT)

Wireless networks are viewed as the best-used network and specifically Portable Specially Appointed Organizations (MANETs) have tracked down numerous applications for its information transmission progressively. The plan issues in this organization are to confine the utilization of energy while communicating data and give security to the hubs. Soa protocol needs to be energy efficient to avoid network failures. Thereby this paper brings an effective energy efficient to optimize LEAR and make it energy efficient. The energy-mindfulness element is added to the LEAR guiding convention in this work using the Binary Particle Swarm Optimization method (BPSO). The recommended method selects programmes taking into account course length in addition to the programme level of energy when predicting the future. To get good results, the steered challenge is first designed using LEAR. The next step is to choose a route that enhances the weighting capability of the study hours and programming power used.This MANET has been secured using the cryptographic method known as AES.According to experimental findings, the proposed hybrid version outperformed other cutting-edge models

Energy efficiency in content delivery networks

The increasing popularity of bandwidth-intensive video Internet services has positioned Content Distribution Networks (CDNs) in the limelight as the emerging provider platforms for video delivery. The goal of CDNs is to maximise the availability of content in the network while maintaining the quality of experience expected by users. This is a challenging task due to the scattered nature of video content sources and destinations. Furthermore, the high energy consumption associated with content distribution calls for developing energy-efficient solutions able to cater for the future Internet. This thesis addresses the problem of content placement and update while considering energy consumption in CDNs. First, this work contributed a new energy-efficient caching scheme that stores the most popular content at the edge of the core network and optimises the size of cached content to minimise energy usage. It takes into account the trend of daily traffic and recommends putting inactive segments of caches in sleep-mode during off-peak hours. Our results showed that power minimisation is achieved by deploying switch-off capable caches, and the trend of active cache segments over the time of day follows the trend of traffic. Second, the study explores different content popularity distributions and determines their influence on power consumption. The distribution of content popularity dictates the resultant cache hit ratio achieved by storing a certain number of videos. Therefore, it directly influences the power consumption of the cache. The evaluation results indicated that under video services where the popularity of content is very diverse, the optimum solution is to store the few most popular videos in caches. In contrast, when video popularities are similar, the most power efficient scheme is either to cache the whole library or to avoid caching completely depending on the size of the video library. Third, this thesis contributed an evaluation of the power consumption of the network under real world TV data and considering standard and high definition TV programmes. We proposed a cache replacement algorithm based on the predictable nature of TV viewings. The time-driven proactive cache replacement algorithm replaces cache contents several times a day to minimise power consumption. The algorithm achieves major power savings on top of the power reductions introduced by caching. CDNs are expected to continue to be the backbone for Internet video applications. This work has shown that storing the right amount of popular videos in core caches reduces from 42% to 72% of network power consumption considering a range of content popularity distributions. Maintaining up-to-date cache contents reduces up to 48% and 86% of power consumption considering fixed and sleep-mode capable caches, respectively. Reducing the energy consumption of CDNs provides a valuable contribution for future green video delivery

Edge Computing for Internet of Things

The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond