Biologically Inspired Networks: On Energy Efficient Event-Driven Data Collection for Wireless Sensor Networks

Biologically Inspired Networks: On Energy Efficient Event-Driven Data Collection for Wireless Sensor Networks

Mahdy Saedy (Department of Electrical & Computer Engineering Department, University of Texas at San Antonio, San Antonio, TX, USA)
DOI: 10.4018/jitn.2013010102
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Inspired by the nature, the interesting properties of natural organisms can be imitated in many different applications. The nature has been optimizing itself for billions of years and in many cases it simply introduces the most optimal approaches. On the other hand, the task of data collection and aggregation is critical in wireless sensor networks (WSN). The information packets should traverse the network towards the aggregating center i.e. sink node. The problem of finding the best path to route the data has been long under investigation. The authors propose a new method for collecting the data from sensor field based on a biological inspired method adopted from tubular network formation behavior of slime mold where biological organisms efficiently self-organize unreliable and dynamically changing topology, to compensate for the failure of individual components while not relying on explicit central coordination. They show that the emergent network exhibits a widely observed property in natural topologies called scale-free which explains a lot of inherent characteristics in living creatures. At the end the authors show that the data collection time for a biologically inspired network is shorter than uniform channel capacity scheduling.
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Considering the fact that the synchronization is a requirement to maintain the data consistency in wireless sensor networks (Saedy & Kelley, 2012a), the information packets should traverse the network towards the aggregating center i.e. sink node with the proper time stamp. Although there are many routing protocols developed to optimize the packet delivery, there is no algorithm that considers topology formation and packet delivery jointly optimized. Implementing such framework will be very time consuming and costly and it takes very long routing tables and introduces considerable latency in packet delivery. The idea of mimicking the way natural organisms operate and handle the network formation and delivery of say particles or food is introduces in this paper by studying the Biologic behavior of Physarum polycephalum. London and Tokyo train network are great examples of vast and high traffic networks. Engineers have been working on finding optimum paths to connect the cities. Surprisingly, the slime mold could solve this problem much faster and in many ways better than human. It begins with a single cell (very simple organism) and expands to a super organism based on where the food is and of course the minimum path. Longer paths (non-optimal paths) disappear due to lack of nutrients. The best paths are those who are closer to food and can carry more food to connected parts of the organism. The parts of the super organism that poorly have access to food die and the remaining organism will be a super organism that covers all food locations. The resulted topology dynamically ensures that at any given time the graph consisting of food locations remains a connected graph thus maintaining a robust network that connects all the nodes in an optimum way.

Physarum polycephalum is a slime mold which, during the plasmodium phase of its life cycle, consists of a large multinuclear cell body that can span tens of centimeters in length. During the course of this phase, the organism will consume organic matter to sustain itself. Nutrients are transported over short distances through diffusion and over large distances through self-assembled channels or tubes that form a dynamic, hierarchical network throughout the cell body (Nakagaki, Kobayashi, Nishiura, & Ueda, 2004). The network forms an essential component of the pseudopodia, extensions of the cell that seek new food sources. During the plasmodium phase, the slime mold will assemble and disassemble these tubes to link new food sources into its network and sustain itself by distributing nutrients through the organism. Fitness requires that resources be discovered in the environment and distributed efficiently throughout the organism, so physarum polycephalum is an excellent biological system to study when designing artificial resource distribution networks (Ben-Jacob & Cohen, 1998). Experimental observations have shown that slime mold will connect nutrient sources into a variety of final configurations including Steiner minimal trees, minimum spanning trees and other more redundant structures (Nakagaki, Kobayashi, Nishiura, & Ueda, 2004). Often, the tubes of a slime mold will be arranged in a geometry that balances efficiency (keeping the total tube length short) and robustness (having multiple paths in case of a tube being severed). These observations are entirely consistent with an algorithm lacking complete global information. A variety of modeling approaches have been applied to understanding slime mold. While it can be said that any living system is complex by necessity, often specific processes are dominated by a small number of mechanisms. Considerable research has focused on observed behavioral manifestations including search for food, avoid danger, and determine the shortest paths through mazes under certain circumstances (Ben-Jacob & Cohen, 1998; Nakagaki, Yamada, & Toth, 2000). The searching behavior of pseudopodia has also been modeled and analyzed (Hofer & Maini, 1997). More recently, Tero et al have studied a tube flow model using Poiseuille flow coupled to an adaptation term (Tero, Kobayashi, & Nakagaki, 2007). The remainder of the paper is organized as follows. We first describe a mathematical model of slime mold’s tube network formation behavior using Singular Potential in the next section. The overview of the designed protocol is presented afterwards. Followed by an explanation of the protocol in details. Simulation results are then presented and discussed . We then conclude the paper.

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