Chien-Chung Shen

Slime mold inspired routing protocols for wireless sensor networks

Many biological systems are composed of unreliable components which self-organize effectively into systems that achieve a balance between efficiency and robustness. One such example is the true slime mold Physarum polycephalum which is an amoeba-like organism that seeks and connects food sources and efficiently distributes nutrients throughout its cell body. The distribution of nutrients is accomplished by a self-assembled resource distribution network of small tubes with varying diameter which can evolve with changing environmental conditions without any global control. In this paper, we exploit two different mechanisms of the slime mold’s tubular network formation process via laboratory experiments and mathematical behavior modeling to design two corresponding localized routing protocols for wireless sensor networks (WSNs) that take both efficiency and robustness into account. In the first mechanism of path growth, slime mold explores its immediate surroundings to discover and connect new food sources during its growth cycle. We adapt this mechanism for a path growth routing protocol by treating data sources and sinks as singular potentials to establish routes from the sinks to all the data sources. The second mechanism of path evolution is the temporal evolution of existing tubes through nonlinear feedback in order to distribute nutrients efficiently throughout the organism. Specifically, the diameters of tubes carrying large fluxes of nutrients grow to expand their capacities, and tubes that are not used decline and disappear entirely. We adapt the tube dynamics of the slime mold for a path evolution routing protocol. In our protocol, we identify one key adaptation parameter to adjust the tradeoff between efficiency and robustness of network routes. Through extensive realistic network simulations and ideal closed form or numerical computations, we validate the effectiveness of both protocols, as well as the efficiency and robustness of the resulting network connectivity.

Slime mold inspired path formation protocol for wireless sensor networks

Many biological systems are composed of unreliable components which self-organize efficiently into systems that can tackle complex problems. One such example is the true slimemold Physarum polycephalum which is an amoeba-like organism that seeks food sources and efficiently distributes nutrients throughout its cell body. The distribution of nutrients is accomplished by a self-assembled resource distribution network of small tubes with varying diameter which can evolve with changing environmental conditions without any global control. In this paper, we use a phenomenological model for the tube evolution in slime mold and map it to a path formation protocol for wireless sensor networks. By selecting certain evolution parameters in the protocol, the network may evolve toward single paths connecting data sources to a data sink. In other parameter regimes, the protocol may evolve toward multiple redundant paths. We present detailed analysis of a small model network. A thorough understanding of the simple network leads to design insights into appropriate parameter selection. We also validate the design via simulation of large-scale realistic wireless sensor networks using the QualNet network simulator.

Modeling, analysis and simulation of ant-based network routing protocols

Using the metaphor of swarm intelligence, ant-based routing protocols deploy control packets that behave like ants to discover and optimize routes between pairs of nodes. These ant-based routing protocols provide an elegant, scalable solution to the routing problem for both wired and mobile ad hoc networks. The routing problem is highly nonlinear because the control packets alter the local routing tables as they are routed through the network. We mathematically map the local rules by which the routing tables are altered to the dynamics of the entire networks. Using dynamical systems theory, we map local protocol rules to full network performance, which helps us understand the impact of protocol parameters on network performance. In this paper, we systematically derive and analyze global models for simple ant-based routing protocols using both pheromone deposition and evaporation. In particular, we develop a stochastic model by modeling the probability density of ants over the network. The model is validated by comparing equilibrium pheromone levels produced by the global analysis to results obtained from simulation studies. We use both a Matlab simulation with ideal communications and a QualNet simulation with realistic communication models. Using these analytic and computational methods, we map out a complete phase diagram of network behavior over a small multipath network. We show the existence of both stable and unstable (inaccessible) routing solutions having varying properties of efficiency and redundancy depending upon the routing parameters. Finally, we apply these techniques to a larger 50-node network and show that the design principles acquired from studying the small model network extend to larger networks.

Slime Mold Inspired Protocol for Wireless Sensor Networks

The phenomenon of self-organization is pervasive in nature,where biological organisms efficiently self-organize unreliable and dynamically changing components to develop a wide diversity of functions. In addition, these biological organisms enjoy the desirable properties of robustness to the failure of individual components, adaptivity to changing conditions, and the lack of reliance on explicit central coordination. In this work, we seek inspiration from the study of the tubular network formation behavior of slime mold to design a localized protocol to connect sensors to sink(s) that balances efficiency and robustness in wireless sensor networks (WSN). Extensive simulations have been conducted to validate the effectiveness of the protocol, as well as the efficiency and robustness of the resulting network connectivity.

Autonomous navigation of wireless robot swarms with covert leaders

The integration of advanced computation, wireless communication, and control technologies has facilitated the creation of autonomous robot swarms for many civil and military applications. In nature, animals that travel in groups often rely on social interactions among group members to make movement decisions. In many cases, few individuals within the group have pertinent knowledge about the destination and/or migration routes. In this paper, we adapt a swarm model developed for animal groups to study the unique problems associated with covert leadership in the context of wireless robot swarms. We term this problem autonomous navigation with covert leaders. In this covert leadership problem, only a small subset of robots in a robot swarm possess extra information that guides their movement, and both this information and the identities of those individuals possessing this information remain covert (to minimize the chance of being compromised). We describe a distributed navigation algorithm, where each robot locally makes its movement decision solely based on one-hop information collected via wireless communications. The effectiveness and merits of the described navigation algorithm are demonstrated through extensive simulations.

Naturally Adaptive Protocol for Wireless Sensor Networks Based on Slime Mold

Many biological systems are composed of unreliable and noisy components self-organizing efficiently into systems that can solve complex problems. One such example is the true slime mold {\em Physarum polycephalum} which is an amoeba-like organism that seeks food sources and efficiently distributes nutrients throughout the cell body. The cell body can grow to be centimeters in size. The distribution of nutrients is accomplished by a self-assembled resource distribution network of small tubes of varying diameter which can evolve with changing environmental conditions without any global control. In this paper, we use a phenomenological model for tube evolution in slime mold and map it to a path formation protocol for wireless sensor networks. By selecting certain evolution parameters in the protocol, the network will evolve toward single paths connecting data sources to a data sink. In other parameter regimes, the protocol will evolve toward multiple redundant paths. We present detailed analysis of a small model network. A thorough understanding of a simple network leads to design insights into appropriate parameter selection, and also validates the design via large-scale simulation of realistic wireless sensor networks using the QualNet network simulator.

Slime mold inspired coordinations for wireless sensor and actor networks

We adapt the tubular network formation behavior of slime mold to design coordination protocols for wireless sensor and actor networks.

Modeling and Analyzing Large Swarms with Covert Leaders

In this paper, we analyze general models of large swarms with covert leaders. A covert leader is an individual who acts on additional information but is treated like all other individuals in the swarm. We concentrate our efforts on behavior driven by three-zone swarming, and present a new nonlinear model in which a leader will respond more strongly to additional information when the swarm is less dense. Conversely, leaders in denser regions behave more like followers. Linear stability analysis shows that the growth or decay of perturbations in an infinite, uniform swarm depends on the strength of attraction relative to repulsion and orientation. Understanding general systems like this has a wide range of applications in ecology, sociology and wireless robotics.

Swarm interpolation using an approximate chebyshev distribution

In this paper, we describe a novel swarming framework that guides autonomous mobile sensors into a flexible arrangement to interpolate values of a field in an unknown region. The algorithm is devised so that the sensor distribution will behave like a Chebyshev distribution, which can be optimal for certain ideal geometries. The framework is designed to dynamically adjust to changes in the region of interest, and operates well with very little a priori knowledge of the given region. For comparison, we interpolate a variety of nontrivial fields using a standard swarming algorithm that produces a uniform distribution and our new algorithm. We find that our new algorithm interpolates fields with greater accuracy.

Tracking Time-Dependent Scalar Fields with Swarms of Mobile Sensors

In previous work, we introduced a novel swarming interpolation framework and validated its effectiveness on static fields. In this paper, we show that a slightly revised version of this framework is able to track fields that translate, rotate, or expand over time, enabling interpolation of both static and dynamic fields. Our framework can be used to control autonomous mobile sensors into flexible spatial arrangements in order to interpolate values of a field in an unknown region. The key advantage to this framework is that the stable sensor distribution can be chosen to resemble a Chebyshev distribution, which can be optimal for certain ideal geometries.