Data Highways

The design of efficient routing algorithms is an important issue in dense ad hoc wireless networks. Previous theoretical work has shown that benefits can be achieved through the creation of a set of data “highways” that carry packets across the network, from source(s) to sink(s). Current approaches to the design of these highways however require a–priori knowledge of the global network topology, with consequent communications burden and scalability issues, particularly with regard to reconfiguration after node failures. In this chapter, we describe a bio–inspired approach to generating these data highways through a distributed reaction–diffusion model that uses localized convolution with activation–inhibition filters. The result is the distributed emergence of data highways that can be tuned to provide appropriate highway separation and connection to data sinks. In this chapter, we present the underlying models, algorithms, and protocols for generating data highways in a dense wireless sensor network. The proposed methods are validated through extensive simulations performed using OMNeT++.

Autonomously Evolving Communication Protocols

While traditional telecommunication still relies on rigid, highly regulated, and highly controlled communication protocols, with the emergence of new forms of networks (mobile ad hoc and delay-tolerant networks, lacking central infrastructure and strict regulations) bio-inspired communication protocols have also found their way to success. In this chapter we introduce a nontraditional way of creating and shaping communication protocols, through an autonomous machine intelligence model, built upon on-line evolutionary methods such as natural selection and genetic programming. Creating a genetic programming language and a selection mechanism for multi-hop broadcast protocols in ad hoc networks, we show that this kind of approach can outperform traditional ones under given circumstances, offering a powerful alternative in the future.

Application of Genetic Algorithms for Optimization of Anycast Routing in Delay and Disruption Tolerant Networks

Delay and disruption tolerant networks (DTNs) have the capacity of providing data communication to remote and rural areas where current networking technology does not work well. In such challenging areas characterized by long duration partition, routing is a common problem. Anycast routing can be used for many applications in DTNs, and it is useful when nodes wish to send messages to at least one, and preferably only one, of the members in an anycast destination group. In this chapter, an anycast routing algorithm for DTNs based on genetic algorithms (GAs) is presented and analyzed. The GA is applied to find the appropriate combination of each path to comply with the delivery needs of the group of anycast sessions simultaneously. The routing algorithm based on GAs under consideration uses the concept of subpopulation to produce the next generation of the population, a limited number of solutions to be evaluated, and yields minimum delay in achieving a specified rate of delivery. Simulation results show that the studied GA-based anycast routing algorithm can produce good results.

Neural Networks in Cognitive Science

In this chapter we give a brief overview of the biological and technical background of artificial neural networks as are used in cognitive modelling and in technical applications. This will be complemented by three instructive case studies which demonstrate the use of different neural networks in cognitive modelling.

Robust Network Services with Distributed Code Rewriting

Nature does not know the concept of a dedicated controlling instance; instead, “control” is an emergent phenomenon. This is in stark contrast with computer networking where protocol control loops are (seemingly) in charge: while the functional aspect of a networking service can be well mastered, the dynamic behavior is still difficult to understand and even control. In this chapter, we present a methodology how to design distributed software systems that are dynamically stable and robust in execution. It is based on continuously replicating a system’s own code base in order to thwart unreliable execution and even accidental code changes. The crucial part is to design the system such that it regulates its own replication. This can be achieved by an execution environment inspired by chemistry to which we add the concept of self-rewriting programs (Quines). With a link load balancing example we show how to exploit competition and cooperation in a self-rewriting service implementation.

Organic Network Control

In this chapter, we present the Organic Network Control (ONC) architecture, which is based on a three-layered Observer/Controller-Architecture and the usage of Evolutionary Algorithms. Without touching the internal behavior of the protocol itself, this approach allows for the automatic adaptation of protocol parameters towards a changing environment at runtime. Based on the background of related work, we will first describe the generic ONC architecture, followed by a step by step description of how to apply this concept to an existing system. Two examples are explained of how ONC can be applied to existing protocols and what effect this application has on the system’s performance. Finally, the chapter concludes with an outline of current and future work and a summary of the concept.

Network Energy Driven Wireless Sensor Networks

Scarcity of energy in tiny battery-powered wireless sensor nodes have led to a tremendous amount of research thrust at all protocol levels in wireless networks. Despite efficient design of the underlying communication protocols, limited battery energy primarily restricts the usage of nodes and hence the lifetime of the network. As a result, although there has been a lot of promise of pervasive networking via sensors, limited energy of the nodes has been a major bottleneck to deployment feasibility and cost of such a network. With this view, alongside many innovative network communication protocol research to increase nodal as well as network lifetime, there have been significant ongoing efforts on how to impart energy to the depleted batteries on-line. In this chapter, we propose to apply the lessons learnt from our surrounding nature and practices of the living world to realize network energy operated field sensors. We show that, although the regular communicating nodes may not benefit from network energy harvesting, by modifying the carrier sensing principle in a hierarchical network setting, the low power consuming field nodes can extend their lifetimes, or even the scavenged RF energy can be sufficient for the uninterrupted processing and transmission activities of the field nodes.

TCP Symbiosis

In this chapter, we introduce a robust, self-adaptive and scalable congestion control mechanism for TCP. We change the window size of a TCP connection according to the information of the physical and available bandwidths of the end-to-end network path. The bandwidth information is obtained by an inline network measurement technique. We also borrowed algorithms from biophysics to update the window size: the logistic growth model and the Lotka-Volterra competition model. The greatest advantage of using these models is that we can refer previous discussions and results for various characteristics of the mathematical models, including scalability, convergence, fairness and stability in these models. Through mathematical analysis and simulation experiments, we compare the proposed mechanism with traditional TCP Reno, HighSpeed TCP, Scalable TCP and FAST TCP, and exhibit its effectiveness in terms of scalability to the network bandwidth and delay, convergence time, fairness among competing connections, and stability.

Congestion Control in Wireless Sensor Networks based on the Lotka Volterra Competition Model

Next generation communication networks are moving towards autonomous wireless infrastructures, as for example, Wireless Sensor Networks (WSNs) that are capable of working unattended under dynamically changing conditions. Over the last few years, WSNs are being developed towards a large number of multimedia streaming applications, e.g., video surveillance, traffic control systems, health monitoring, and industrial process control. However, WSNs face important limitations in terms of energy, memory and computational power. The uncontrolled use of limited resources in conjunction with the unpredictable nature of WSNs in terms of traffic load injection, wireless link capacity fluctuations and topology modifications (e.g. due to node failures) may lead to congestion. Congestion can cause deterioration of the offered quality of service (QoS). This study proposes a bio-inspired congestion control approach for WSNs streaming applications that necessitate controlled performance with graceful degradation. The proposed approach prevents congestion by regulating the rate of each traffic flow based on the Lotka-Volterra competition model. Performance evaluations reveal that the proposed approach achieves adaptability to changing traffic loads, scalability and fairness among flows, while providing graceful performance degradation as the offered load increases.

From Local Growth to Global Optimization in Insect Built Networks

Social insect colonies build large net-like systems: gallery and trail networks. Many such networks appear to show near-optimal performance. Focusing on the network system inside termite nests we address the question how simple agents with probabilistic behaviour can control and optimize the growth of a structure with size several magnitude orders above their perceptual range. We identify two major classes of mechanisms: (i) purely local mechanisms, which involve the arrangement of simple motifs according to predetermined rules of behaviour and (ii) local estimation of global quantities, where sizes, lengths, and numbers are estimated from densities, concentrations, and traffic. Theoretical considerations suggest that purely local mechanisms work better during early network formation and are less likely to fall into local optima. On the contrary, estimation of global properties is only possible on functional networks and is more likely to work through pruning. This latter mechanism may contribute to restore network functionalities following unpredicted changes of external conditions or network topology. An analysis of the network properties of Cubitermes termite nests supports the role of both classes of mechanisms, possibly in interplay with environmental conditions acting as a template.