489 research outputs found
Joint Routing and STDMA-based Scheduling to Minimize Delays in Grid Wireless Sensor Networks
In this report, we study the issue of delay optimization and energy
efficiency in grid wireless sensor networks (WSNs). We focus on STDMA (Spatial
Reuse TDMA)) scheduling, where a predefined cycle is repeated, and where each
node has fixed transmission opportunities during specific slots (defined by
colors). We assume a STDMA algorithm that takes advantage of the regularity of
grid topology to also provide a spatially periodic coloring ("tiling" of the
same color pattern). In this setting, the key challenges are: 1) minimizing the
average routing delay by ordering the slots in the cycle 2) being energy
efficient. Our work follows two directions: first, the baseline performance is
evaluated when nothing specific is done and the colors are randomly ordered in
the STDMA cycle. Then, we propose a solution, ORCHID that deliberately
constructs an efficient STDMA schedule. It proceeds in two steps. In the first
step, ORCHID starts form a colored grid and builds a hierarchical routing based
on these colors. In the second step, ORCHID builds a color ordering, by
considering jointly both routing and scheduling so as to ensure that any node
will reach a sink in a single STDMA cycle. We study the performance of these
solutions by means of simulations and modeling. Results show the excellent
performance of ORCHID in terms of delays and energy compared to a shortest path
routing that uses the delay as a heuristic. We also present the adaptation of
ORCHID to general networks under the SINR interference model
A Case for Time Slotted Channel Hopping for ICN in the IoT
Recent proposals to simplify the operation of the IoT include the use of
Information Centric Networking (ICN) paradigms. While this is promising,
several challenges remain. In this paper, our core contributions (a) leverage
ICN communication patterns to dynamically optimize the use of TSCH (Time
Slotted Channel Hopping), a wireless link layer technology increasingly popular
in the IoT, and (b) make IoT-style routing adaptive to names, resources, and
traffic patterns throughout the network--both without cross-layering. Through a
series of experiments on the FIT IoT-LAB interconnecting typical IoT hardware,
we find that our approach is fully robust against wireless interference, and
almost halves the energy consumed for transmission when compared to CSMA. Most
importantly, our adaptive scheduling prevents the time-slotted MAC layer from
sacrificing throughput and delay
Aggregation Scheduling Algorithms in Wireless Sensor Networks
In Wireless Sensor Networks which consist of tiny
wireless sensor nodes with limited battery power, one of the most
fundamental applications is data aggregation which collects nearby
environmental conditions and aggregates the data to a designated
destination, called a sink node. Important issues concerning the
data aggregation are time efficiency and energy consumption due
to its limited energy, and therefore, the related problem, named
Minimum Latency Aggregation Scheduling (MLAS), has been the
focus of many researchers. Its objective is to compute the minimum
latency schedule, that is, to compute a schedule with the minimum
number of timeslots, such that the sink node can receive the
aggregated data from all the other nodes without any collision or
interference. For the problem, the two interference models, the graph
model and the more realistic physical interference model known as
Signal-to-Interference-Noise-Ratio (SINR), have been adopted with
different power models, uniform-power and non-uniform power (with
power control or without power control), and different antenna
models, omni-directional antenna and directional antenna models.
In this survey article, as the problem has proven to be NP-hard,
we present and compare several state-of-the-art approximation
algorithms in various models on the basis of latency as its
performance measure
Aggregation Latency-Energy Tradeoff in Wireless Sensor Networks with Successive Inter- ference Cancellation
published_or_final_versio
On the Data Gathering Capacity and Latency in Wireless
In this paper, we investigate the fundamental properties of data gathering in wirelesssensor networks, in terms of both transport capacity and latency. We consider a scenarioin which s(n) out of n total network nodes have to deliver data to a set of d(n) sink nodesat a constant rate f(n; s(n); d(n)). The goal is to characterize the maximum achievablerate, and the latency in data delivery. We present a simple data gathering scheme thatachieves asymptotically optimal data gathering capacity and latency with arbitrary net-work deployments when d(n) = 1, and for most scaling regimes of s(n) and d(n) whend(n) > 1 in case of square grid and random node deployments. Differently from mostprevious work, our results and the presented data gathering scheme do not sacrifice en-ergy efficiency to the need of maximizing capacity and minimizing latency. Finally, weconsider the effects of a simple form of data aggregation on data gathering performance,and show that capacity can be increased of a factor f(n) with respect to the case of nodata aggregation, where f(n) is the node density. To the best of our knowledge, theones presented in this paper are the first results showing that asymptotically optimal datagathering capacity and latency can be achieved in arbitrary networks in an energy efficientway
Message and time efficient multi-broadcast schemes
We consider message and time efficient broadcasting and multi-broadcasting in
wireless ad-hoc networks, where a subset of nodes, each with a unique rumor,
wish to broadcast their rumors to all destinations while minimizing the total
number of transmissions and total time until all rumors arrive to their
destination. Under centralized settings, we introduce a novel approximation
algorithm that provides almost optimal results with respect to the number of
transmissions and total time, separately. Later on, we show how to efficiently
implement this algorithm under distributed settings, where the nodes have only
local information about their surroundings. In addition, we show multiple
approximation techniques based on the network collision detection capabilities
and explain how to calibrate the algorithms' parameters to produce optimal
results for time and messages.Comment: In Proceedings FOMC 2013, arXiv:1310.459
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