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International Journal of Distributed Sensor Networks
Volume 2013 (2013), Article ID 696935, 2 pages
Perception, Reaction, and Cognition in Wireless Sensor Networks
1Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA
2LIUPPA Laboratory, University of Pau (UPPA), Pau, 64100 Bayonne, France
3School of Computer Science, University of Windsor, Windsor, ON, Canada N9B 3P4
4Department of Electrical and Electronic Engineering, Institut Teknologi Brunei, Bandar Seri Begawan 8610, Brunei Darussalam
5Department of Computer and Information Sciences, Universiti Teknologi Petronas, 31750 Tronoh, Perak, Malaysia
6Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China
Received 22 November 2012; Accepted 25 August 2013
Copyright © 2013 Shuai Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The past ten years have witnessed great developments of wireless sensor networks in both theory and application. The long been expected and advocated ubiquitous sensing is becoming increasingly popular and widely admitted with the great success of many applications of wireless sensor networks in environmental monitoring, precision agriculture, human health monitoring, and so forth. Correspondingly, theoretical foundations of wireless sensor networks, such as sensor positioning, time synchronization, communication protocols, data fusion, and operating systems, also have received intensive attentions. However, in current stage, most attentions in theory still regard wireless sensor networks as a means of data collection, instead of autonomous networks with self-decision making based on the collected data. The widely investigated type of wireless sensor network with perception but without reaction is in contrast to the autonomous network with perception, reaction, and cognition, which adapts itself to the monitored environment by exploiting information feedback (e.g., the collected data feedbacks to make adjustment of the electricity price for smart power grid network). Introducing reaction and cognition along with perception opens a door to transform wireless sensor network from a passive network for data collection to an adaptive and active network with self-intention, self-evolution, and self-intelligence and will open a new promising branch in the field of wireless sensor networks.
2. Major Topics around Perception, Reaction, and Cognition in Wireless Sensor Networks
With the capability of sensing, reacting, and thinking, a sensor node is increasingly like a live animal being and a fleet of them are connected together by information exchanging with others in the collection, which is in analogy to the social behaviors of animals. It is also in analogy to the skin of our human beings, which feels temperature with nerve endings in every area, makes decision with the nearby nerve cells, shrinks or constricts arterioles with the action of muscles, and thereby achieves the ability of regulating the body temperature and functions like a sensor network with perception, reaction, and cognition. From this perspective, biological systems or social behaviors of animals may give in-depth insight to the design of such a novel sensor network.
Integrating perception, reaction, and cognition into wireless sensor networks requires the effort of interdisciplinary researches, and this merging direction can be viewed from different perspectives. From the perspective of artificial intelligence, the sensor network with perception, reaction, and cognition can be treated as a network with interactive agents; from a system perspective, the sensor network with perception, reaction, and cognition is a feedback system involving nonlinear estimation and control with interdependence. The goal of sensor networks with perception, reaction, and cognition lies in optimizing an objective function under constraints. Optimum for a single sensor node or a portion of nodes does not necessarily imply global optimum. As cooperative nonlinear optimization, game theory may find applications in this scenario. As each sensor node in this network needs to adapt to a possibly variant environment, real-time signal processing, data fusion, and data mining are often required requirements and pose more challenges than conventional wireless sensor networks, to which real-time performance is often not crucial. From the view of communication, congestion control, routing, protocol designs, and so forth may encounter new challenges as integrating real-time sensing and control together inevitably introduces more information exchanging and possibly communication burdens.
We accepted 5 papers press from the submissions of 16 to this special issue. The overall acceptance rate is 31.25%. The papers cover specific problems such as routing algorithms, indoor localization, energy harvesting, wormhole detection, and circle fitting algorithms. Some of the topics are crucial in conventional wireless sensor networks and also play important roles in the sensor network with the capability of perception, reaction, and cognition integrated. Some of the topics are more likely encountered in the latter case. Inspired by the large and single-celled amoeboid organism, slime mold Physarum polycephalum, the authors of the paper “A novel Physarum-inspired routing protocol for wireless sensor networks” propose a novel Physarum-inspired routing protocol (P-iRP) to address the routing issue in wireless sensor networks with a low complexity . The paper “Circle fitting using a virtual source localization algorithm in wireless sensor networks” solves the circle fitting problem in wireless sensor networks by formulating the problem into the special source localization one and employing the multidimensional scaling (MDS) analysis. The paper “Indoor mobile localization in wireless sensor network under unknown NLOS errors” solves the nonline-of-sight (NLOS) propagation problem by proposing a likelihood matrix correction based mixed Kalman and H-infinity filter (LC-MKHF) method. Results show that the LC-MKHF algorithm has higher estimate accuracy in comparison with no-filter, Kalman filter, and H-infinity filter methods and is robust to the NLOS errors. The paper “Secure routing protocol using cross-layer design and energy harvesting in wireless sensor networks” proposes a secure routing protocol based on cross layer design and energy-harvesting mechanism. This algorithm ensures efficient use of energy and performs better in many scenarios and in hostile attack-prone environment; Wormhole attack is a severe threat to wireless sensor networks. The authors of the paper “MDS-based wormhole detection using local topology in wireless sensor networks” propose a novel approach to detect wormhole attacks by only local topology information without requiring special hardware devices or depending on rigorous assumptions on the network settings. Extensive simulations demonstrate the effectiveness and superior performance of the proposed approach.
The guest editors would like to thank all the authors for their contributions. Special thanks go to all reviewers for their great effort, timely responses, and constructive comments and suggestions. Shuai Li would like to share the words by Rabindranath Tagore with the readers “If you shed tears when you miss the sun, you also miss the stars.”
Mohammad Abdul Matin
Anang Hudaya M. Amin