575 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
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
A Survey on Energy-Efficient Strategies in Static Wireless Sensor Networks
A comprehensive analysis on the energy-efficient strategy in static Wireless Sensor Networks (WSNs) that are not equipped with any energy harvesting modules is conducted in this article. First, a novel generic mathematical definition of Energy Efficiency (EE) is proposed, which takes the acquisition rate of valid data, the total energy consumption, and the network lifetime of WSNs into consideration simultaneously. To the best of our knowledge, this is the first time that the EE of WSNs is mathematically defined. The energy consumption characteristics of each individual sensor node and the whole network are expounded at length. Accordingly, the concepts concerning EE, namely the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective, are proposed. Subsequently, the relevant energy-efficient strategies proposed from 2002 to 2019 are tracked and reviewed. Specifically, they respectively are classified into five categories: the Energy-Efficient Media Access Control protocol, the Mobile Node Assistance Scheme, the Energy-Efficient Clustering Scheme, the Energy-Efficient Routing Scheme, and the Compressive Sensing--based Scheme. A detailed elaboration on both of the basic principle and the evolution of them is made. Finally, further analysis on the categories is made and the related conclusion is drawn. To be specific, the interdependence among them, the relationships between each of them, and the Energy-Efficient Means, the Energy-Efficient Tier, and the Energy-Efficient Perspective are analyzed in detail. In addition, the specific applicable scenarios for each of them and the relevant statistical analysis are detailed. The proportion and the number of citations for each category are illustrated by the statistical chart. In addition, the existing opportunities and challenges facing WSNs in the context of the new computing paradigm and the feasible direction concerning EE in the future are pointed out
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
Emerging Communications for Wireless Sensor Networks
Wireless sensor networks are deployed in a rapidly increasing number of arenas, with uses ranging from healthcare monitoring to industrial and environmental safety, as well as new ubiquitous computing devices that are becoming ever more pervasive in our interconnected society. This book presents a range of exciting developments in software communication technologies including some novel applications, such as in high altitude systems, ground heat exchangers and body sensor networks. Authors from leading institutions on four continents present their latest findings in the spirit of exchanging information and stimulating discussion in the WSN community worldwide
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
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Uav-assisted data collection in wireless sensor networks: A comprehensive survey
Wireless sensor networks (WSNs) are usually deployed to different areas of interest to sense phenomena, process sensed data, and take actions accordingly. The networks are integrated with many advanced technologies to be able to fulfill their tasks that is becoming more and more complicated. These networks tend to connect to multimedia networks and to process huge data over long distances. Due to the limited resources of static sensor nodes, WSNs need to cooperate with mobile robots such as unmanned ground vehicles (UGVs), or unmanned aerial vehicles (UAVs) in their developments. The mobile devices show their maneuverability, computational and energystorage abilities to support WSNs in multimedia networks. This paper addresses a comprehensive survey of almost scenarios utilizing UAVs and UGVs with strogly emphasising on UAVs for data collection in WSNs. Either UGVs or UAVs can collect data from static sensor nodes in the monitoring fields. UAVs can either work alone to collect data or can cooperate with other UAVs to increase their coverage in their working fields. Different techniques to support the UAVs are addressed in this survey. Communication links, control algorithms, network structures and different mechanisms are provided and compared. Energy consumption or transportation cost for such scenarios are considered. Opening issues and challenges are provided and suggested for the future developments
Wireless Sensor Networks
The aim of this book is to present few important issues of WSNs, from the application, design and technology points of view. The book highlights power efficient design issues related to wireless sensor networks, the existing WSN applications, and discusses the research efforts being undertaken in this field which put the reader in good pace to be able to understand more advanced research and make a contribution in this field for themselves. It is believed that this book serves as a comprehensive reference for graduate and undergraduate senior students who seek to learn latest development in wireless sensor networks
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