Convergence and monotonicity of the hormone levels in a hormone-based content delivery system

The practical significance of bio-inspired, self-organising methods is rapidly increasing due to their robustness, adaptability and capability of handling complex tasks in a dynamically changing environment. Our aim is to examine an artificial hormone system that was introduced in order to deliver multimedia content in dynamic networks. The artificial hormone algorithm proved to be an efficient approach to solve the problem during the experimental evaluations. In this paper we focus on the theoretical foundation of its goodness. We show that the hormone levels converge to a limit at each node in the typical cases. We form a series of theorems on convergence with different conditions which are built on each other by starting with a specific base case and then we consider more general, practically relevant cases. The theorems are proved by exploiting the analogy between the Markov chains and the artificial hormone system. We examine spatial and temporal monotonicity of the hormone levels as well and give sufficient conditions on monotonic increase

Analysis of an Artificial Hormone System

The increased complexity of modern networks and the increasingly dynamic access patterns in multimedia consumption have led to new challenges for content delivery. Dynamic networks and dynamic access patterns result in a complex system. To deliver content efficiently we introduced an artificial hormone system that is capable of handling the dynamics, is self-organizing, robust and adaptive. The content placement problem is NP complete and is closely related to several hard problems including edge-disjoint path routing, scheduling and the bin packing problem. The evaluation of self-organizing algorithms brings also a real challenge. For a first evaluation we created and ILP model of the problem. It is applied to get the exact optimum that serves as a bound in the evaluation of the solution algorithms. In this paper, we examine the convergence of the algorithm and found that the hormone levels converge to a limit at each node in the typical cases. We form a series of theorems on convergence with different conditions by starting with a specific base case and then we consider more general, practically relevant cases. The theorems can be proved by exploiting the analogy between the Markov chains and the artificial hormone system

Fault-Tolerant Certainty Grid

World modelling for mobile autonomous robot is usually a process that uses sensor data as input and provides a model of the robot's environment as output. In this paper we investigate on sensor fusion methods for robustness and fault tolerance. We evaluate three methods according to their performance, memory consumption, and required sensor configurations. Th

The Time-Triggered Sensor Fusion Model

The time-triggered sensor fusion model decom-poses a real time system into three levels, a node level, containing the sensors and the actuators, a cluster level that gathers measurements and per-forms sensor fusion, and an application level where an application program makes control decisions based on an environmental information provided by the cluster level. Because the application code is independent of the employed sensors, the system is open to sensor reconfigurations and reuse of the application code. Furthermore the model contains a hardware-independent application interface and a time-triggered smart transducer network. An application of the presented ideas is shown with a mobile robot controlled by a Time-Triggered Protocol network

Using Sensor Fusion in a Time-Triggered Network

Smart transducer technologies support the composability, configurability and maintainability of sensor networks. Sensor fusion techniques on the other hand offer a lot of advantages for systems that interact with their environment via a set of sensors. The combination of both leads to an effective system regarding cost, robustness, decomposability and maintainability. This paper examines architecture requirements that incorporate smart transducer networks with sensor fusion processing and a hardware-independent application interface. These requirements are compared to the properties of the Time-Triggered Architecture and the TTP/A protocol. An application of the presented ideas is shown with a mobile robot controlled by a Time-Triggered Protocol network