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Computational Intelligence in Wireless Sensor Networks: A Survey
- IEEE COMMUNICATIONS SURVEYS & TUTORIALS
, 2011
"... Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms o ..."
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Cited by 41 (0 self)
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Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.
SecureDAV: A secure data aggregation and verification protocol for sensor networks
- In Proceedings of the IEEE Global Telecommunications Conference
, 2004
"... Abstract — Sensor networks include nodes with limited computation and communication capabilities. One of the basic functions of sensor networks is to sense and transmit data to the end users. The resource constraints and security issues pose a challenge to information aggregation in large sensor net ..."
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Abstract — Sensor networks include nodes with limited computation and communication capabilities. One of the basic functions of sensor networks is to sense and transmit data to the end users. The resource constraints and security issues pose a challenge to information aggregation in large sensor networks. Bootstrapping keys is another challenge because public key cryptosystems are unsuitable for use in resource-constrained sensor networks. In this paper, we propose a solution by dividing the problem in two domains. First, we present a protocol for establishing cluster keys in sensor networks using verifiable secret sharing. We chose elliptic curve cryptosystems for security because of their smaller key size, faster computations and reductions in processing power. Second, we develop a Secure Data Aggregation and Verification (SecureDAV) protocol that ensures that the base station never accepts faulty aggregate readings. Integrity check of the readings is done using Merkle Hash Trees avoiding over-reliance on the cluster-heads. I.
An experimental study of routing and data aggregation in sensor networks.
- In LOCAN ’05,
, 2005
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PANEL: Position-based Aggregator Node Election in Wireless Sensor Networks
"... In this paper, we introduce PANEL, a position-based aggregator node election protocol for wireless sensor networks. The novelty of PANEL with respect to other aggregator node election protocols is that it supports asynchronous sensor network applications where the sensor readings are fetched by the ..."
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Cited by 20 (2 self)
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In this paper, we introduce PANEL, a position-based aggregator node election protocol for wireless sensor networks. The novelty of PANEL with respect to other aggregator node election protocols is that it supports asynchronous sensor network applications where the sensor readings are fetched by the base stations after some delay. In particular, the motivation for the design of PANEL was to support reliable and persistent data storage applications, such as TinyPEDS [13]. PANEL ensures load balancing, and it supports intraand inter-cluster routing allowing sensor to aggregator, aggregator to aggregator, base station to aggregator, and aggregator to base station communications. We also compare PANEL with HEED [42] in the simulation environment provided by TOSSIM, and show that, on the one hand, PANEL creates more cohesive clusters than HEED, and, on the other hand, that PANEL is more energy efficient than HEED.
Data Aggregation in Sensor Networks using Learning Automata
"... Abstract: One way to reduce energy consumption in wireless sensor networks is to reduce the number of packets being transmitted in the network. As sensor networks are usually deployed with a number of redundant nodes (to overcome the problem of node failures which is common in such networks), many n ..."
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Cited by 14 (7 self)
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Abstract: One way to reduce energy consumption in wireless sensor networks is to reduce the number of packets being transmitted in the network. As sensor networks are usually deployed with a number of redundant nodes (to overcome the problem of node failures which is common in such networks), many nodes may have almost the same information which can be aggregated in intermediate nodes, and hence reduce the number of transmitted packets. Aggregation ratio is maximized if data packets of all nodes having almost the same information are aggregated together. For this to occur, each node should forward its packets along a path on which maximum number of nodes with almost the same information as the information of the sending node exist. In many real scenarios, such a path has not been remained the same for the overall network lifetime and is changed from time to time. These changes may results from changes occurred in the environment in which the sensor network resides and usually cannot be predicted beforehand. In this paper, a learning automata based data aggregation method in sensor networks when the environment's changes can not be predicted beforehand will be proposed. In the proposed method, each node in the network is equipped with a learning automaton. These learning automata in the network collectively learn the path of aggregation with maximum aggregation ratio for each node for transmitting its packets toward the sink. To evaluate the performance of the proposed method computer simulations have been conducted and the results are compared with the results of three existing methods. The results have shown that the proposed method outperforms all these methods, especially when the environment is highly dynamic.
A learning automata based scheduling solution to the dynamic point coverage problem in wireless sensor networks. Computer Networks doi:10.1016/j
, 2010
"... The dynamic point coverage problem in wireless sensor networks is to detect some moving target points in the area of the network using as little sensor nodes as possible. One way to deal with this problem is to schedule sensor nodes in such a way that a node is activated only at the times a target p ..."
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Cited by 12 (6 self)
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The dynamic point coverage problem in wireless sensor networks is to detect some moving target points in the area of the network using as little sensor nodes as possible. One way to deal with this problem is to schedule sensor nodes in such a way that a node is activated only at the times a target point is in its sensing region. In this paper we propose SALA, a scheduling algorithm based on learning automata, to deal with the problem of dynamic point coverage. In SALA each node in the network is equipped with a set of learning automata. The learning automata residing in each node try to learn the maximum sleep duration for the node in such a way that the detection rate of target points by the node does not degrade dramatically. This is done using the information obtained about the movement patterns of target points while passing throughout the sensing region of the nodes. We consider two types of target points; events and moving objects. Events are assumed to occur periodically or based on a Poisson distribution and moving objects are assumed to have a static movement path which is repeated periodically with a randomly selected velocity. In order to show the performance of SALA, some experiments have been conducted. The experimental results show that SALA outperforms the existing methods such as LEACH, GAF, PEAS and PW in terms of energy consumption.
Energy Efficient Clustering Algorithms for Wireless Sensor Networks
- Proceeding of the IEEE International Conference on Communications Workshops
, 2008
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
P.R.Patil,”Data Aggregation in wireless sensor network”,
- IEEE International Conference on Computational Intelligence and Computing Research,
, 2010
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Energy-Efficient Clustering Method for Data Gathering
- in Sensor Networks,” in the Annual International Conference on Broadband Networks
, 2004
"... By deploying wireless sensor nodes and composing a sensor network, one can remotely obtain information about the behavior, conditions, and positions of entities in a region. Since sensor nodes operate on batteries, energyefficient mechanisms for gathering sensor data are indispensable to prolong the ..."
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Cited by 10 (2 self)
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By deploying wireless sensor nodes and composing a sensor network, one can remotely obtain information about the behavior, conditions, and positions of entities in a region. Since sensor nodes operate on batteries, energyefficient mechanisms for gathering sensor data are indispensable to prolong the lifetime of a sensor network as long as possible. A sensor node consumes energy: observing its surroundings, transmitting data, and receiving data. Cluster-based data gathering mechanisms have been proposed based on a model where energy consumption in data transmission is proportional to the square of the radius of the radio signal. In clustering sensor nodes, we need to consider that a cluster-head consumes more energy than the others when receiving data from cluster members, fusing data to reduce the size, and sending the aggregated data to a base station. In this paper, we proposed a novel clustering mechanism where clusters are organized in a distributed and energy-efficient way through local communication among neighboring sensor nodes. Through simulation experiments, we showed that our mechanism can gather data from more than 80 % of the sensor nodes longer than LEACH by over 25%. 1
Implementation and evaluation of a synchronization-based data gathering scheme for sensor networks
- in Proc. of IEEE International Conference on Communications, Wireless Networking (ICC 2005), Seoul, Korea
, 2005
"... Abstract- One can obtain information about a region by deploying a network of sensor nodes there. Since remotely deployed nodes are usually powered by batteries, an energy-efficient data gathering scheme is needed to prolong the lifetime of the sensor network. We proposed a novel scheme for periodic ..."
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Abstract- One can obtain information about a region by deploying a network of sensor nodes there. Since remotely deployed nodes are usually powered by batteries, an energy-efficient data gathering scheme is needed to prolong the lifetime of the sensor network. We proposed a novel scheme for periodic data gathering but evaluated in only in simulation experiments assuming ideal environments. In this paper, we evaluated the scheme experimentally in small networks consisting of commercial, offthe-shelf wireless sensor units. We also developed mechanisms to solve problems due to the instability of radio communications and demonstrated the effectiveness of these mechanisms experimentally. We confirmed that energy-efficient data gathering can be implemented by using our proposed scheme with several improvements and that synchronization can be established and maintained under unstable and changing conditions. Figure 1: Synchronization-based data gathering BS
