characterization of line of sight link availability in indoor visible light communication networks based on the behavior of human users

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GreeDi: Energy Efficient Routing Algorithm for Big Data on Cloud

The ever-increasing density in cloud computing parties, i.e. users, services, providers and data centres, has led to a significant exponential growth in: data produced and transferred among the cloud computing parties; network traffic; and the energy consumed by the cloud computing massive infrastructure, which is required to respond quickly and effectively to users requests. Transferring big data volume among the aforementioned parties requires a high bandwidth connection, which consumes larger amounts of energy than just processing and storing big data on cloud data centres, and hence producing high carbon dioxide emissions. This power consumption is highly significant when transferring big data into a data centre located relatively far from the users geographical location. Thus, it became high-necessity to locate the lowest energy consumption route between the user and the designated data centre, while making sure the users requirements, e.g. response time, are met. The main contribution of this paper is GreeDi, a network-based routing algorithm to find the most energy efficient path to the cloud data centre for processing and storing big data. The algorithm is, first, formalised by the situation calculus. The linear, goal and dynamic programming approaches used to model the algorithm. The algorithm is then evaluated against the baseline shortest path algorithm with minimum number of nodes traversed, using a real Italian ISP physical network topology

ABSTRACT Folding: A Method for Semantic Encoding of Error-Tolerant Data

Underwater acoustic sensor networks are characterized by both low link data rates, and very low data generation rates of the sensors. In this regime, Shannon capacity results, which presume long channel codes and an infinitely long information bitstream, are not directly applicable. Further, for scientific data collection, distortion and errors are tolerable at the semantic layer. For this regime, we formulate the problem of sending k successive source symbols using n successive modulation intervals, where n> k. We introduce “folding ” as a technique to map a k-dimensional source manifold into the n-dimensional modulation space, in order to minimize the average energy consumption per source symbol. We use an Archimedes ’ spiral, a helix, and Fermat’s spiral, as good foldings for low-dimensional mappings, and compute the energy consumption per source symbol under each

Automated MAC protocol generation under dynamic traffic conditions

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Multi-Channel Joint Forecasting-Scheduling for the Internet of Things

We develop a methodology for Multi-Channel Joint Forecasting-Scheduling (MC-JFS) targeted at solving the Medium Access Control (MAC) layer Massive Access Problem of Machine-to-Machine (M2M) communication in the presence of multiple channels, as found in Orthogonal Frequency Division Multiple Access (OFDMA) systems. In contrast with the existing schemes that merely react to current traffic demand, Joint Forecasting-Scheduling (JFS) forecasts the traffic generation pattern of each Internet of Things (IoT) device in the coverage area of an IoT Gateway and schedules the uplink transmissions of the IoT devices over multiple channels in advance, thus obviating contention, collision and handshaking, which are found in reactive protocols. In this paper, we present the general form of a deterministic scheduling optimization program for MC-JFS that maximizes the total number of bits that are delivered over multiple channels by the delay deadlines of the IoT applications. In order to enable real-time operation of the MC-JFS system, first, we design a heuristic, called Multi-Channel Look Ahead Priority based on Average Load (MC-LAPAL), that solves the general form of the scheduling problem. Second, for the special case of identical channels, we develop a reduction technique by virtue of which an optimal solution of the scheduling problem is computed in real time. We compare the network performance of our MC-JFS scheme against Multi-Channel Reservation-based Access Barring (MC-RAB) and Multi-Channel Enhanced Reservation-based Access Barring (MC-ERAB), both of which serve as benchmark reactive protocols. Our results show that MC-JFS outperforms both MC-RAB and MC-ERAB with respect to uplink cross-layer throughput and transmit energy consumption, and that MC-LAPAL provides high performance as an MC-JFS heuristic. Furthermore, we show that the computation time of MC-LAPAL scales approximately linearly with the number of IoT devices. This work serves as a foundation for building scalable JFS schemes at IoT Gateways in the near future.Project Support Commission of Yasar University within Scientific Research Project Scheduling Algorithms for Wireless Communication'' [BAP060]This work was supported by the Project Support Commission of Ya3ar University within the scope of the Scienti~c Research Project ``Scheduling Algorithms for Wireless Communication'' under Grant BAP060