7,354 research outputs found
Analyzing Energy-efficiency and Route-selection of Multi-level Hierarchal Routing Protocols in WSNs
The advent and development in the field of Wireless Sensor Networks (WSNs) in
recent years has seen the growth of extremely small and low-cost sensors that
possess sensing, signal processing and wireless communication capabilities.
These sensors can be expended at a much lower cost and are capable of detecting
conditions such as temperature, sound, security or any other system. A good
protocol design should be able to scale well both in energy heterogeneous and
homogeneous environment, meet the demands of different application scenarios
and guarantee reliability. On this basis, we have compared six different
protocols of different scenarios which are presenting their own schemes of
energy minimizing, clustering and route selection in order to have more
effective communication. This research is motivated to have an insight that
which of the under consideration protocols suit well in which application and
can be a guide-line for the design of a more robust and efficient protocol.
MATLAB simulations are performed to analyze and compare the performance of
LEACH, multi-level hierarchal LEACH and multihop LEACH.Comment: NGWMN with 7th IEEE Inter- national Conference on Broadband and
Wireless Computing, Communication and Applications (BWCCA 2012), Victoria,
Canada, 201
A Coverage Monitoring algorithm based on Learning Automata for Wireless Sensor Networks
To cover a set of targets with known locations within an area with limited or
prohibited ground access using a wireless sensor network, one approach is to
deploy the sensors remotely, from an aircraft. In this approach, the lack of
precise sensor placement is compensated by redundant de-ployment of sensor
nodes. This redundancy can also be used for extending the lifetime of the
network, if a proper scheduling mechanism is available for scheduling the
active and sleep times of sensor nodes in such a way that each node is in
active mode only if it is required to. In this pa-per, we propose an efficient
scheduling method based on learning automata and we called it LAML, in which
each node is equipped with a learning automaton, which helps the node to select
its proper state (active or sleep), at any given time. To study the performance
of the proposed method, computer simulations are conducted. Results of these
simulations show that the pro-posed scheduling method can better prolong the
lifetime of the network in comparison to similar existing method
Coverage Protocols for Wireless Sensor Networks: Review and Future Directions
The coverage problem in wireless sensor networks (WSNs) can be generally
defined as a measure of how effectively a network field is monitored by its
sensor nodes. This problem has attracted a lot of interest over the years and
as a result, many coverage protocols were proposed. In this survey, we first
propose a taxonomy for classifying coverage protocols in WSNs. Then, we
classify the coverage protocols into three categories (i.e. coverage aware
deployment protocols, sleep scheduling protocols for flat networks, and
cluster-based sleep scheduling protocols) based on the network stage where the
coverage is optimized. For each category, relevant protocols are thoroughly
reviewed and classified based on the adopted coverage techniques. Finally, we
discuss open issues (and recommend future directions to resolve them)
associated with the design of realistic coverage protocols. Issues such as
realistic sensing models, realistic energy consumption models, realistic
connectivity models and sensor localization are covered
Formal Probabilistic Analysis of a Wireless Sensor Network for Forest Fire Detection
Wireless Sensor Networks (WSNs) have been widely explored for forest fire
detection, which is considered a fatal threat throughout the world. Energy
conservation of sensor nodes is one of the biggest challenges in this context
and random scheduling is frequently applied to overcome that. The performance
analysis of these random scheduling approaches is traditionally done by
paper-and-pencil proof methods or simulation. These traditional techniques
cannot ascertain 100% accuracy, and thus are not suitable for analyzing a
safety-critical application like forest fire detection using WSNs. In this
paper, we propose to overcome this limitation by applying formal probabilistic
analysis using theorem proving to verify scheduling performance of a real-world
WSN for forest fire detection using a k-set randomized algorithm as an energy
saving mechanism. In particular, we formally verify the expected values of
coverage intensity, the upper bound on the total number of disjoint subsets,
for a given coverage intensity, and the lower bound on the total number of
nodes.Comment: In Proceedings SCSS 2012, arXiv:1307.802
K-coverage in regular deterministic sensor deployments
An area is k-covered if every point of the area is covered by at least k sensors. K-coverage is necessary for many applications, such as intrusion detection, data gathering, and object tracking. It is also desirable in situations where a stronger environmental monitoring capability is desired, such as military applications. In this paper, we study the problem of k-coverage in deterministic homogeneous deployments of sensors. We examine the three regular sensor deployments - triangular, square and hexagonal deployments - for k-coverage of the deployment area, for k ≥ 1. We compare the three regular deployments in terms of sensor density. For each deployment, we compute an upper bound and a lower bound on the optimal distance of sensors from each other that ensure k-coverage of the area. We present the results for each k from 1 to 20 and show that the required number of sensors to k-cover the area using uniform random deployment is approximately 3-10 times higher than regular deployments
Push & Pull: autonomous deployment of mobile sensors for a complete coverage
Mobile sensor networks are important for several strategic applications
devoted to monitoring critical areas. In such hostile scenarios, sensors cannot
be deployed manually and are either sent from a safe location or dropped from
an aircraft. Mobile devices permit a dynamic deployment reconfiguration that
improves the coverage in terms of completeness and uniformity.
In this paper we propose a distributed algorithm for the autonomous
deployment of mobile sensors called Push&Pull. According to our proposal,
movement decisions are made by each sensor on the basis of locally available
information and do not require any prior knowledge of the operating conditions
or any manual tuning of key parameters.
We formally prove that, when a sufficient number of sensors are available,
our approach guarantees a complete and uniform coverage. Furthermore, we
demonstrate that the algorithm execution always terminates preventing movement
oscillations.
Numerous simulations show that our algorithm reaches a complete coverage
within reasonable time with moderate energy consumption, even when the target
area has irregular shapes. Performance comparisons between Push&Pull and one of
the most acknowledged algorithms show how the former one can efficiently reach
a more uniform and complete coverage under a wide range of working scenarios.Comment: Technical Report. This paper has been published on Wireless Networks,
Springer. Animations and the complete code of the proposed algorithm are
available for download at the address:
http://www.dsi.uniroma1.it/~novella/mobile_sensors
On the vulnerabilities of voronoi-based approaches to mobile sensor deployment
Mobile sensor networks are the most promising solution to cover an Area of Interest (AoI) in safety critical scenarios. Mobile devices can coordinate with each other according to a distributed deployment algorithm, without resorting to human supervision for device positioning and network configuration. In this paper, we focus on the vulnerabilities of the deployment algorithms based on Voronoi diagrams to coordinate mobile sensors and guide their movements. We give a geometric characterization of possible attack configurations, proving that a simple attack consisting of a barrier of few compromised sensors can severely reduce network coverage. On the basis of the above characterization, we propose two new secure deployment algorithms, named SecureVor and Secure Swap Deployment (SSD). These algorithms allow a sensor to detect compromised nodes by analyzing their movements, under different and complementary operative settings. We show that the proposed algorithms are effective in defeating a barrier attack, and both have guaranteed termination. We perform extensive simulations to study the performance of the two algorithms and compare them with the original approach. Results show that SecureVor and SSD have better robustness and flexibility and excellent coverage capabilities and deployment time, even in the presence of an attac
Movement-Efficient Sensor Deployment in Wireless Sensor Networks With Limited Communication Range.
We study a mobile wireless sensor network (MWSN) consisting of multiple
mobile sensors or robots. Three key factors in MWSNs, sensing quality, energy
consumption, and connectivity, have attracted plenty of attention, but the
interaction of these factors is not well studied. To take all the three factors
into consideration, we model the sensor deployment problem as a constrained
source coding problem. %, which can be applied to different coverage tasks,
such as area coverage, target coverage, and barrier coverage. Our goal is to
find an optimal sensor deployment (or relocation) to optimize the sensing
quality with a limited communication range and a specific network lifetime
constraint. We derive necessary conditions for the optimal sensor deployment in
both homogeneous and heterogeneous MWSNs. According to our derivation, some
sensors are idle in the optimal deployment of heterogeneous MWSNs. Using these
necessary conditions, we design both centralized and distributed algorithms to
provide a flexible and explicit trade-off between sensing uncertainty and
network lifetime. The proposed algorithms are successfully extended to more
applications, such as area coverage and target coverage, via properly selected
density functions. Simulation results show that our algorithms outperform the
existing relocation algorithms
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