150 research outputs found
Aggregation Latency-Energy Tradeoff in Wireless Sensor Networks with Successive Inter- ference Cancellation
published_or_final_versio
Broadcast Scheduling in Interference Environment
Broadcast is a fundamental operation in wireless networks, and nai¨ve flooding is not practical, because it cannot deal
with interference. Scheduling is a good way of avoiding interference, but previous studies on broadcast scheduling algorithms all
assume highly theoretical models such as the unit disk graph model. In this work, we reinvestigate this problem by using the 2-Disk
and the signal-to-interference-plus-noise-ratio (SINR) models. We first design a constant approximation algorithm for the 2-Disk
model and then extend it to the SINR model. This result, to the best of our knowledge, is the first result on broadcast scheduling
algorithms in the SINR model
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
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
On the schedulability of deadline-constrained traffic in TDMA Wireless Mesh Networks
In this paper, we evaluate the schedulability of traffic with arbitrary end-to-end deadline constraints in Wireless Mesh Networks (WMNs). We formulate the problem as a mixed integer linear optimization problem, and show that, depending on the flow aggregation policy used in the network, the problem can be either convex or non-convex. We optimally solve the problem in both cases, and prove that the schedulability does depend on the aggregation policy. This allows us to derive rules of thumb to identify which policy improves the schedulability with a given traffic. Furthermore, we propose a heuristic solution strategy that allows good suboptimal solutions to the scheduling problem to be computed in relatively small times, comparable to those required for online admission control in relatively large WMNs
Optimal Schedules for Data Gathering in Wireless Sensor Networks
Wireless Sensor Networks (WSNs) are widely used for target monitoring: sensors monitor a set of targets, and forward the collected or aggregated data using multi-hop routing to the same location, called the sink. The resulting communication scheme is called ConvergeCast or Aggregated ConvergeCast.
Several researchers studied the ConvergeCast and the Aggregated ConvergeCast, as to produce the shortest possible schedule that conveys all the packets or a packet aggregation to the sink. Nearly all proposed methods proceed in two steps, first the routing, and then the scheduling of the packets along the routes defined in the first step.
The thesis is organized around four contributions. The first one is an improvement of the previous mathematical models that outputs (minimum-sized) multi-set of transmission configurations (TCs), in which a transmission configuration is defined as a set of links that can transmit concurrently. Our model allows the transmission of several packets per target, in both single-path and multi-path settings; we give two new heuristics for generating new improved transmission configurations. While such models go beyond the routing step, they do not specify an ordering over time of the configurations. Consequently, the second contribution consists of several algorithms, one exact and several heuristics, for ordering the configurations. Our results show that the approach of scheduling when restricted to a tree generated by the first contribution significantly outperforms the ordering of configurations of TC-approach for single-rate, single packet per sensor traffic patterns, but the TC approach gives better results for multi-rate traffic and when there are a large number of packets per sensor.
In the last two contributions, we propose an exact mathematical model that takes care, in a single phase, of the routing and the scheduling, for the ConvergeCast and the aggregated ConvergeCast problem. They both correspond to decomposition models in which not only we generate transmission configurations, but an ordering of them.
We performed extensive simulations on networks with up to 70 sensors for both ConvergeCast and Aggregated ConvergeCast, and compared our one phase results with one of the best heuristics in the literature
Compressive Data Gathering in Wireless Sensor Networks
The thesis focuses on collecting data from wireless sensors which are deployed randomly in a region. These sensors are widely used in applications ranging from tracking to the monitoring of environment, traffic and health among others. These energy constrained sensors, once deployed may receive little or no maintenance. Hence gathering data in the most energy efficient manner becomes critical for the longevity of wireless sensor networks (WSNs).
Recently, Compressive data gathering (CDG) has emerged as a useful method for collecting sensory data in WSN; this technique is able to reduce global scale communication cost without introducing intensive computation, and is capable of extending the lifetime of the entire sensor network by balancing the forwarding load across the network. This is particularly true due to the benefits obtained from in-network data compression. With CDG, the central unit, instead of receiving data from all sensors in the network, it may receive very few compressed or weighted sums from sensors, and eventually recovers the original data.
To prolong the lifetime of WSN, in this thesis, we present data gathering methods based on CDG. More specifically, we propose data gathering schemes using CDG by building up data aggregation trees from sensor nodes to a central unit (sink). Our problem aims at minimizing the number of links in the forwarding trees to minimize the number of overall transmissions. First, we mathematically formulate the problem and solve it using optimization program. Owing to its complexity, we present real-time algorithmic (centralized and decentralized) methods to efficiently solve the problem. We also explore the benefits one may obtain when jointly applying compressive data gathering with network coding in a wireless sensor network. Finally, and in the context of compressive data gathering, we study the problem of joint forwarding tree construction and scheduling under a realistic interference model, and propose some efficient distributed methods for solving it. We also present a primal dual decomposition method, using the theory of column generation, to solve this complex problem
An Efficient Communication Abstraction for Dense Wireless Networks
In this paper we study the problem of developing efficient distributed algorithms for dense wireless networks. For many problems in this setting, fast solutions must leverage the reality that radio signals fade with distance, which can be exploited to enable concurrent communication among multiple sender/receiver pairs. To simplify the development of these algorithms we describe a new communication abstraction called FadingMAC which exposes the benefits of this concurrent communication, but also hides the details of the underlying low-level radio signal behavior. This approach splits efforts between those who develop useful algorithms that run on the abstraction, and those who implement the abstraction in concrete low-level wireless models, or on real hardware.
After defining FadingMAC, we describe and analyze an efficient implementation of the abstraction in a standard low-level SINR-style network model. We then describe solutions to the following problems that run on the abstraction: max, min, sum, and mean computed over input values; process renaming; consensus and leader election; and optimal packet scheduling. Combining our abstraction implementation with these applications that run on the abstraction, we obtain near-optimal solutions to these problems in our low-level SINR model - significantly advancing the known results for distributed algorithms in this setting. Of equal importance to these concrete bounds, however, is the general idea advanced by this paper: as wireless networks become more dense, both theoreticians and practitioners must explore new communication abstractions that can help tame this density
Positioning and Scheduling of Wireless Sensor Networks - Models, Complexity, and Scalable Algorithms
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