We introduce the Selective-Awakening MAC (SA-MAC) protocol which is a synchronized duty-cycled protocol with pipelined scheduling for Linear Sensor Networks (LSNs). In the proposed protocol, nodes selectively awake depending on node density and traffic load conditions and on the state of the buffers of the receiving nodes. In order to characterize the performance of the proposed protocol, we present a Discrete-Time Markov Chain-based analysis that is validated through extensive discrete-event simulations. Our results show that SA-MAC significantly outperforms previous proposals in terms of energy consumption, throughput, and packet loss probability. This is particularly true under high node density and high traffic load conditions, which are expected to be common scenarios in the context of IoT applications. We also present an analysis by grade (i.e., the number of hops to the sink, which is located at one end of the LSN) that reveals that LSNs exhibit heterogeneous performance depending on the nodes’ grade. Such results can be used as a design guideline for future LSN implementations.
Linear topologies arise naturally in the context of Internet-of-Things (IoT) applications for smart cities, where the infrastructure itself commonly has a linear or semi-linear structure. This is the case of buildings, public transportation systems, road infrastructure, and utility distribution networks. Given the prevalence of this type of topologies, several Medium Access Control (MAC) protocols have been designed to take advantage of their particular properties. Unfortunately, most of them do not scale well as the node density and the distance in hops to the sink increases. The result is that packets generated many hops away from the sink tend to experience unacceptable high end-to-end delay and low delivery probabilities. This paper introduces HP-MAC, a synchronized duty-cycled MAC protocol for Linear Sensor Networks (LSNs) that assigns transmission priorities to nodes to avoid collisions, through the implementation of distributed elections based on hash functions. HP-MAC also implements a packet queuing scheme that acts as a mechanism to control the amount of network resources allocated to data flows generated at different distances to the sink. This way, packets can reach their destination with loss probability and end-to-end delay that do not depend on their distance to the sink. We use a Discrete-Time Markov Chain (DTMC) to model the performance of the proposed protocol. Numerical solutions of this model show that HP-MAC outperforms state-of-the-art representatives in terms of throughput, end-to-end delay, power consumption, and packet loss probability. These results are validated through extensive discrete-event simulations.INDEX TERMS Linear sensor networks (LSN), synchronized duty-cycled MAC protocol, collision-free MAC protocol, Markov chain, energy-efficient Internet of Things (IoT).
We describe a Peer-to-Peer (P2P) network that is designed to support Video on Demand (VoD) services. This network is based on a video-file sharing mechanism that classifies peers according to the window (segment of the file) that they are downloading. This classification easily allows identifying peers that are able to share windows among them, so one of our major contributions is the definition of a mechanism that could be implemented to efficiently distribute video content in future 5G networks. Considering that cooperation among peers can be insufficient to guarantee an appropriate system performance, we also propose that this network must be assisted by upload bandwidth from servers; since these resources represent an extra cost to the service provider, especially in mobile networks, we complement our work by defining a scheme that efficiently allocates them only to those peers that are in windows with resources scarcity (we called it prioritized windows distribution scheme). On the basis of a fluid model and a Markov chain, we also developed a methodology that allows us to select the system parameters values (e.g., windows sizes or minimum servers upload bandwidth) that satisfy a set of Quality of Experience (QoE) parameters.
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