11,752 research outputs found
Quarantine region scheme to mitigate spam attacks in wireless sensor networks
The Quarantine Region Scheme (QRS) is introduced to defend against spam attacks in wireless sensor networks where malicious antinodes frequently generate dummy spam messages to be relayed toward the sink. The aim of the attacker is the exhaustion of the sensor node batteries and the extra delay caused by processing the spam messages. Network-wide message authentication may solve this problem with a cost of cryptographic operations to be performed over all messages. QRS is designed to reduce this cost by applying authentication only whenever and wherever necessary. In QRS, the nodes that detect a nearby spam attack assume themselves to be in a quarantine region. This detection is performed by intermittent authentication checks. Once quarantined, a node continuously applies authentication measures until the spam attack ceases. In the QRS scheme, there is a tradeoff between the resilience against spam attacks and the number of authentications. Our experiments show that, in the worst-case scenario that we considered, a not quarantined node catches 80 percent of the spam messages by authenticating only 50 percent of all messages that it processe
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
Multihop clustering algorithm for load balancing in wireless sensor networks
The paper presents a new cluster based routing algorithm that exploits the redundancy properties of the sensor networks in order to address the traditional problem of load balancing and energy efficiency in the WSNs.The algorithm makes use of the nodes in a sensor network of which area coverage is covered by the neighbours of the nodes and mark them as temporary cluster heads. The algorithm then forms two layers of multi hop communication. The bottom layer which involves intra cluster communication and the top layer which involves inter cluster communication involving the temporary cluster heads. Performance studies indicate that the proposed algorithm solves effectively the problem of load balancing and is also more efficient in terms of energy consumption from Leach and the enhanced version of Leach
Doped Fountain Coding for Minimum Delay Data Collection in Circular Networks
This paper studies decentralized, Fountain and network-coding based
strategies for facilitating data collection in circular wireless sensor
networks, which rely on the stochastic diversity of data storage. The goal is
to allow for a reduced delay collection by a data collector who accesses the
network at a random position and random time. Data dissemination is performed
by a set of relays which form a circular route to exchange source packets. The
storage nodes within the transmission range of the route's relays linearly
combine and store overheard relay transmissions using random decentralized
strategies. An intelligent data collector first collects a minimum set of coded
packets from a subset of storage nodes in its proximity, which might be
sufficient for recovering the original packets and, by using a message-passing
decoder, attempts recovering all original source packets from this set.
Whenever the decoder stalls, the source packet which restarts decoding is
polled/doped from its original source node. The random-walk-based analysis of
the decoding/doping process furnishes the collection delay analysis with a
prediction on the number of required doped packets. The number of doped packets
can be surprisingly small when employed with an Ideal Soliton code degree
distribution and, hence, the doping strategy may have the least collection
delay when the density of source nodes is sufficiently large. Furthermore, we
demonstrate that network coding makes dissemination more efficient at the
expense of a larger collection delay. Not surprisingly, a circular network
allows for a significantly more (analytically and otherwise) tractable
strategies relative to a network whose model is a random geometric graph
Energy Efficient and Reliable Wireless Sensor Networks - An Extension to IEEE 802.15.4e
Collecting sensor data in industrial environments from up to some tenth of
battery powered sensor nodes with sampling rates up to 100Hz requires energy
aware protocols, which avoid collisions and long listening phases. The IEEE
802.15.4 standard focuses on energy aware wireless sensor networks (WSNs) and
the Task Group 4e has published an amendment to fulfill up to 100 sensor value
transmissions per second per sensor node (Low Latency Deterministic Network
(LLDN) mode) to satisfy demands of factory automation. To improve the
reliability of the data collection in the star topology of the LLDN mode, we
propose a relay strategy, which can be performed within the LLDN schedule.
Furthermore we propose an extension of the star topology to collect data from
two-hop sensor nodes. The proposed Retransmission Mode enables power savings in
the sensor node of more than 33%, while reducing the packet loss by up to 50%.
To reach this performance, an optimum spatial distribution is necessary, which
is discussed in detail
LIS: Localization based on an intelligent distributed fuzzy system applied to a WSN
The localization of the sensor nodes is a fundamental problem in wireless sensor networks.
There are a lot of different kinds of solutions in the literature. Some of them use external
devices like GPS, while others use special hardware or implicit parameters in wireless
communications.
In applications like wildlife localization in a natural environment, where the power available
and the weight are big restrictions, the use of hungry energy devices like GPS or hardware
that add extra weight like mobile directional antenna is not a good solution.
Due to these reasons it would be better to use the localization’s implicit characteristics in
communications, such as connectivity, number of hops or RSSI. The measurement related
to these parameters are currently integrated in most radio devices. These measurement
techniques are based on the beacons’ transmissions between the devices.
In the current study, a novel tracking distributed method, called LIS, for localization of
the sensor nodes using moving devices in a network of static nodes, which have no additional
hardware requirements is proposed.
The position is obtained with the combination of two algorithms; one based on a local
node using a fuzzy system to obtain a partial solution and the other based on a centralized
method which merges all the partial solutions. The centralized algorithm is based on the
calculation of the centroid of the partial solutions.
Advantages of using fuzzy system versus the classical Centroid Localization (CL)
algorithm without fuzzy preprocessing are compared with an ad hoc simulator made for
testing localization algorithms.
With this simulator, it is demonstrated that the proposed method obtains less localization
errors and better accuracy than the centroid algorithm.Junta de Andalucía P07-TIC-0247
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
- …