1,377 research outputs found
Many-to-many data aggregation scheduling in wireless sensor networks with two sinks
Traditionally, wireless sensor networks (WSNs) have been deployed with a single sink. Due to the emergence of sophisticated applications, WSNs may require more than one sink. Moreover, deploying more than one sink may prolong the network lifetime and address fault tolerance issues. Several protocols have been proposed for WSNs with multiple sinks. However, most of them are routing protocols. Differently, our main contribution, in this paper, is the development of a distributed data aggregation scheduling (DAS) algorithm for WSNs with two sinks. We also propose a distributed energy-balancing algorithm to balance the energy consumption for the aggregators. The energy-balancing algorithm first forms trees rooted at nodes which are termed virtual sinks and then balances the number of children at a given level to level the energy consumption. Subsequently, the DAS algorithm takes the resulting balanced tree and assigns contiguous slots to sibling nodes, to avoid unnecessary energy waste due to frequent active-sleep transitions. We prove a number of theoretical results and the correctness of the algorithms. Through simulation and testbed experiments, we show the correctness and performance of our algorithms
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Optimal coverage multi-path scheduling scheme with multiple mobile sinks for WSNs
Wireless Sensor Networks (WSNs) are usually formed with many tiny sensors which are randomly deployed within sensing field for target monitoring. These sensors can transmit their monitored data to the sink in a multi-hop communication manner. However, the ‘hot spots’ problem will be caused since nodes near sink will consume more energy during forwarding. Recently, mobile sink based technology provides an alternative solution for the long-distance communication and sensor nodes only need to use single hop communication to the mobile sink during data transmission. Even though it is difficult to consider many network metrics such as sensor position, residual energy and coverage rate etc., it is still very important to schedule a reasonable moving trajectory for the mobile sink. In this paper, a novel trajectory scheduling method based on coverage rate for multiple mobile sinks (TSCR-M) is presented especially for large-scale WSNs. An improved particle swarm optimization (PSO) combined with mutation operator is introduced to search the parking positions with optimal coverage rate. Then the genetic algorithm (GA) is adopted to schedule the moving trajectory for multiple mobile sinks. Extensive simulations are performed to validate the performance of our proposed method
Multi-Channel Scheduling for Fast Convergecast in Wireless Sensor Networks
We explore the following fundamental question -
how fast can information be collected from a wireless sensor
network? We consider a number of design parameters such
as, power control, time and frequency scheduling, and routing.
There are essentially two factors that hinder efficient data
collection - interference and the half-duplex single-transceiver
radios. We show that while power control helps in reducing the
number of transmission slots to complete a convergecast under a
single frequency channel, scheduling transmissions on different
frequency channels is more efficient in mitigating the effects of
interference (empirically, 6 channels suffice for most 100-node
networks). With these observations, we define a receiver-based
channel assignment problem, and prove it to be NP-complete on
general graphs. We then introduce a greedy channel assignment
algorithm that efficiently eliminates interference, and compare
its performance with other existing schemes via simulations.
Once the interference is completely eliminated, we show that
with half-duplex single-transceiver radios the achievable schedule
length is lower-bounded by max(2nk − 1,N), where nk is the
maximum number of nodes on any subtree and N is the number
of nodes in the network. We modify an existing distributed time
slot assignment algorithm to achieve this bound when a suitable
balanced routing scheme is employed. Through extensive simulations,
we demonstrate that convergecast can be completed within
up to 50% less time slots, in 100-node networks, using multiple
channels as compared to that with single-channel communication.
Finally, we also demonstrate further improvements that are
possible when the sink is equipped with multiple transceivers
or when there are multiple sinks to collect data
On Modeling Geometric Joint Sink Mobility with Delay-Tolerant Cluster-less Wireless Sensor Networks
Moving Sink (MS) in Wireless Sensor Networks (WSNs) has appeared as a
blessing because it collects data directly from the nodes where the concept of
relay nodes is becomes obsolete. There are, however, a few challenges to be
taken care of, like data delay tolerance and trajectory of MS which is NP-hard.
In our proposed scheme, we divide the square field in small squares. Middle
point of the partitioned area is the sojourn location of the sink, and nodes
around MS are in its transmission range, which send directly the sensed data in
a delay-tolerant fashion. Two sinks are moving simultaneously; one inside and
having four sojourn locations and other in outer trajectory having twelve
sojourn locations. Introduction of the joint mobility enhances network life and
ultimately throughput. As the MS comes under the NP-hard problem, we convert it
into a geometric problem and define it as, Geometric Sink Movement (GSM). A set
of linear programming equations has also been given in support of GSM which
prolongs network life time
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
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Throughput optimization for data collection in wireless sensor networks
Wireless sensor networks are widely used in many application domains in recent years. Data collection is a fundamental function provided by wireless sensor networks. How to efficiently collect sensing data from all sensor nodes is critical to the performance of sensor networks. In this dissertation, we aim to study the theoretical limits of data collection in a TDMA-based sensor network in terms of possible and achievable maximum capacity. Various communication scenarios are considered in our analysis, such as with a single sink or multiple sinks, randomly-deployed or arbitrarily- deployed sensors, and different communication models. For both randomly-deployed and arbitrarily-deployed sensor networks, an efficient collection algorithm has been proposed under protocol interference model and physical interference model respec- tively. We can prove that its performance is within a constant factor of the optimal for both single sink and regularly-deployed multiple sinks cases. We also study the capacity bounds of data collection under a general graph model, where two nearby nodes may be unable to communicate due to barriers or path fading, and discuss per- formance implications. In addition, we further discuss the problem of data collection capacity under Gaussian channel model
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