The rapidly growing IPTV market has resulted in increased traffic volumes raising concerns over Internet energy consumption. In this paper, we explore the dynamics of TV viewing behavior and program popularity in order to devise a strategy to minimize energy usage. We evaluate the impact of our strategy by calculating the power consumption of IPTV delivered over an IP-over-WDM network, considering both standard definition and high definition TV. Caches are used to reduce energy consumption by storing the most popular programs at nodes closer to the end user. We then use our knowledge of viewing behaviors to generate a time-driven content replacement strategy to maximize cache hit ratios and minimize energy use. We develop a mixed integer linear programming (MILP) model to evaluate the power consumption of the network while performing time-driven content replacements on caches and validate the results by simulation. Finally, we extend our model to perform content replacements on caches with sleepmode capabilities which can save power by reducing their size. Our results show that time-based content replacements with such variable caches increase cache hit ratios and so reduces the overall power consumption by up to 86% compared to no caching. Our findings also show that more power savings are achieved for high definition TV compared to standard definition TV, so this strategy will be beneficial in the long term. Index Terms-Cachecontent replacement, core network energy efficiency, HDTV, IP over WDM, TV program popularity. Manuscript ). J. M. H. Elmirghani is with the School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9JT, U.K., and also with the in 2002 and the M.Sc. degree (with distinction) in mobile computing from the University of Bradford, Bradford, U.K., in 2006. She is currently working toward the Ph.D. degree in energy efficient content delivery networks at the

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This paper evaluates the effect of content popularity distribution on the power consumption of cache-based services in an IP over WDM network. We consider the Zipf, Bimodal, Pareto and Equal Popularity distributions for content popularity. The results reveal that using our MILP model, reductions in power consumption under the Zipf, Pareto, Bimodal and Equal Popularity distributions are up to 47%, 71%, 72% and 71%, respectively. We also study the impact of the sizes of media files and the impact of the power efficiency of caches on the network power consumption. The results confirm that under the Zipf and Equal Popularity distributions the power consumption rises as the average size of media files and cache power consumption increases. Under the Bimodal and Pareto distributions, the average cache size in the network is not affected by the increase in media file sizes.

Internet of Things (IoT) is a system that connects computing devices including phones, sensor, mechanical machines and other items over a network allowing them to exchange data and perform tasks without human interaction. It has altered the means by which connected devices interact and communicate, facilitating smarter and more convenient applications. As a result of their technological specifications, these devices are considered power constrained, as they lack battery sources. Therefore, they require a suitable underlying technology that considers power constraints and fulfills other IoT application requirements including long-range communication and low cost. Low Power Wide Area Network (LPWAN) is a communication technology that is gaining increasing popularity, as it provides low-rate long-range communication suitable for IoT applications. This research uses simulation to evaluate the performance of LoRa and Sigfox as the leading LPWAN technologies. The simulation evaluates collision, packet error rate and spectrum using different values for channel bandwidth. It also examines the impact of the number of IoT devices on network performance. Results reveal that Sigfox has less collisions and packet error rate compared to LoRa. They also show that in general, increasing the number of devices leads to increasing collision and packet error rate. Utilizing larger bandwidth provides more available slots and therefore reducing collision.

The increasing demand for video services has made video caching a necessity to decrease download times and reduce Internet traffic. In addition, it is very important to store the right content at the right time in caches to make effective use of caching. An informative decision has to be made as to which videos are to be evicted from the cache in case of cache saturation. Therefore, the best cache replacement algorithm is the algorithm which dynamically selects a suitable subset of videos for caching, and maximizes the cache hit ratio by attempting to cache the videos which are most likely to be referenced in the future. In this paper we study the most popular cache replacement algorithms (OPT, CC, QC, LRU-2, LRU, LFU and FIFO) which are currently used in video caching. We use simulations to evaluate and compare these algorithms using video popularities that follow a Zipf distribution. We consider different cache sizes and video request rates. Our results show that the CC algorithm achieves the largest hit ratio and performs well even under small cache sizes. On the other hand, the FIFO algorithm has the smallest hit ratio among all algorithms.

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