Content Delivery Latency of Caching Strategies for Information-Centric IoT

In-network caching is a central aspect of Information-Centric Networking (ICN). It enables the rapid distribution of content across the network, alleviating strain on content producers and reducing content delivery latencies. ICN has emerged as a promising candidate for use in the Internet of Things (IoT). However, IoT devices operate under severe constraints, most notably limited memory. This means that nodes cannot indiscriminately cache all content; instead, there is a need for a caching strategy that decides what content to cache. Furthermore, many applications in the IoT space are timesensitive; therefore, finding a caching strategy that minimises the latency between content request and delivery is desirable. In this paper, we evaluate a number of ICN caching strategies in regards to latency and hop count reduction using IoT devices in a physical testbed. We find that the topology of the network, and thus the routing algorithm used to generate forwarding information, has a significant impact on the performance of a given caching strategy. To the best of our knowledge, this is the first study that focuses on latency effects in ICN-IoT caching while using real IoT hardware, and the first to explicitly discuss the link between routing algorithm, network topology, and caching effects.Comment: 10 pages, 9 figures, journal pape

Renewables powered cellular networks: Energy field modeling and network coverage

Powering radio access networks using renewables, such as wind and solar power, promises dramatic reduction in the network operation cost and the network carbon footprints. However, the spatial variation of the energy field can lead to fluctuations in power supplied to the network and thereby affects its coverage. This warrants research on quantifying the aforementioned negative effect and designing countermeasure techniques, motivating the current work. First, a novel energy field model is presented, in which fixed maximum energy intensity γ occurs at Poisson distributed locations, called energy centers. The intensities fall off from the centers following an exponential decay function of squared distance and the energy intensity at an arbitrary location is given by the decayed intensity from the nearest energy center. The product between the energy center density and the exponential rate of the decay function, denoted as ψ, is shown to determine the energy field distribution. Next, the paper considers a cellular downlink network powered by harvesting energy from the energy field and analyzes its network coverage. For the case of harvesters deployed at the same sites as base stations (BSs), as γ increases, the mobile outage probability is shown to scale as (cγ-πψ+p), where p is the outage probability corresponding to a flat energy field and $c$ is a constant. Subsequently, a simple scheme is proposed for counteracting the energy randomness by spatial averaging. Specifically, distributed harvesters are deployed in clusters and the generated energy from the same cluster is aggregated and then redistributed to BSs. As the cluster size increases, the power supplied to each BS is shown to converge to a constant proportional to the number of harvesters per BS. Several additional issues are investigated in this paper, including regulation of the power transmission loss in energy aggregation and extensions of the energy field model. © 2002-2012 IEEE.published_or_final_versio

Renewable Powered Cellular Networks: Energy Field Modeling and Network Coverage

Powering radio access networks using renewables, such as wind and solar power, promises dramatic reduction in the network operation cost and the network carbon footprints. However, the spatial variation of the energy field can lead to fluctuations in power supplied to the network and thereby affects its coverage. This warrants research on quantifying the aforementioned negative effect and countermeasure techniques, motivating the current work. First, a novel energy field model is presented, in which fixed maximum energy intensity $\gamma$ occurs at Poisson distributed locations, called energy centers. The intensities fall off from the centers following an exponential decay function of squared distance and the energy intensity at an arbitrary location is given by the decayed intensity from the nearest energy center. The product between the energy center density and the exponential rate of the decay function, denoted as $\psi$, is shown to determine the energy field distribution. Next, the paper considers a cellular downlink network powered by harvesting energy from the energy field and analyzes its network coverage. For the case of harvesters deployed at the same sites as base stations (BSs), as $\gamma$ increases, the mobile outage probability is shown to scale as $(c \gamma^{-\pi\psi}+p)$, where $p$ is the outage probability corresponding to a flat energy field and $c$ a constant. Subsequently, a simple scheme is proposed for counteracting the energy randomness by spatial averaging. Specifically, distributed harvesters are deployed in clusters and the generated energy from the same cluster is aggregated and then redistributed to BSs. As the cluster size increases, the power supplied to each BS is shown to converge to a constant proportional to the number of harvesters per BS.Comment: double-column, 13 pages; to appear in IEEE Transactions on Wireless Communication

Secure Wireless Communications Based on Compressive Sensing: A Survey

IEEE Compressive sensing (CS) has become a popular signal processing technique and has extensive applications in numerous fields such as wireless communications, image processing, magnetic resonance imaging, remote sensing imaging, and anology to information conversion, since it can realize simultaneous sampling and compression. In the information security field, secure CS has received much attention due to the fact that CS can be regarded as a cryptosystem to attain simultaneous sampling, compression and encryption when maintaining the secret measurement matrix. Considering that there are increasing works focusing on secure wireless communications based on CS in recent years, we produce a detailed review for the state-of-the-art in this paper. To be specific, the survey proceeds with two phases. The first phase reviews the security aspects of CS according to different types of random measurement matrices such as Gaussian matrix, circulant matrix, and other special random matrices, which establishes theoretical foundations for applications in secure wireless communications. The second phase reviews the applications of secure CS depending on communication scenarios such as wireless wiretap channel, wireless sensor network, internet of things, crowdsensing, smart grid, and wireless body area networks. Finally, some concluding remarks are given