Jason C. Tillett

Optimal topologies for wireless sensor networks

Since untethered sensor nodes operate on battery, and because they must communicate through a multi-hop network, it is vital to optimally configure the transmit power of the nodes both to conserve power and optimize spatial reuse of a shared channel. Current topology control algorithms try to minimize radio power while ensuring connectivity of the network. We propose that another important metric for a sensor network topology will involve consideration of hidden nodes and asymmetric links. Minimizing the number of hidden nodes and asymmetric links at the expense of increasing the transmit power of a subset of the nodes may in fact increase the longevity of the sensor network. In this paper we explore a distributed evolutionary approach to optimizing this new metric. Inspiration from the Particle Swarm Optimization technique motivates a distributed version of the algorithm. We generate topologies with fewer hidden nodes and asymmetric links than a comparable algorithm and present some results that indicate that our topologies deliver more data and last longer.

An energy-efficient approach to securing tree-zone-based sensor networks

Large-scale ad hoc sensor networks (ASN), when deployed among mobile patients, can provide a dynamic data query architecture to allow the medical specialists to monitor patients at any place. We propose a low-energy, distributed, concentric-zone-based data query mechanism that has the advantages of both proactive and reactive ad hoc routing algorithms to collect medical results from large-scale mobile patients for medical specialists. In order to secure that tree-zone-based ASN, we suggest the using of key-chain to predistribute keys in each sensor. Then we further secure data fusion based on our telemedicine hierarchical architecture. We also propose a scalable global session-key generation mechanism in our tree-zone-based sensor networks.

An evolutionary algorithmic approach to learning a Bayesian network from complete data

Discovering relationships between variables is crucial for interpreting data from large databases. Relationships between variables can be modeled using a Bayesian network. The challenge of learning a Bayesian network from a complete dataset grows exponentially with the number of variables in the database and the number of states in each variable. It therefore becomes important to identify promising heuristics for exploring the space of possible networks. This paper utilizes an evolutionary algorithmic approach, Particle Swarm Optimization (PSO) to perform this search. A fundamental problem with a search for a Bayesian network is that of handling cyclic networks, which are not allowed. This paper explores the PSO approach, handling cyclic networks in two different ways. Results of network extraction for the well-studied ALARM network are presented for PSO simulations where cycles are broken heuristically at each step of the optimization and where networks with cycles are allowed to exist as candidate solutions, but are assigned a poor fitness. The results of the two approaches are compared and it is found that allowing cyclic networks to exist in the particle swarm of candidate solutions can dramatically reduce the number of objective function evaluations required to converge to a target fitness value.

Application of particle swarm techniques in sensor network configuration

A decentralized version of particle swarm optimization called the distributed particle swarm optimization (DPSO) approach is formulated and applied to the generation of sensor network configurations or topologies so that the deleterious effects of hidden nodes and asymmetric links on the performance of wireless sensor networks are minimized. Three different topology generation schemes, COMPOW, Cone-Based and the DPSO--based schemes are examined using ns-2. Simulations are executed by varying the node density and traffic rates. Results contrasting heterogeneous vs. homogeneous power reveal that an important metric for a sensor network topology may involve consideration of hidden nodes and asymmetric links, and demonstrate the effect of spatial reuse on the potency of topology generators.

A Distributed evolutionary algorithmic approach to the least-cost connected constrained sub-graph and power control problem

When wireless sensors are capable of variable transmit power and are battery powered, it is important to select the appropriate transmit power level for the node. Lowering the transmit power of the sensor nodes imposes a natural clustering on the network and has been shown to improve throughput of the network. However, a common transmit power level is not appropriate for inhomogeneous networks. A possible fitness-based approach, motivated by an evolutionary optimization technique, Particle Swarm Optimization (PSO) is proposed and extended in a novel way to determine the appropriate transmit power of each sensor node. A distributed version of PSO is developed and explored using experimental fitness to achieve an approximation of least-cost connectivity.

Robust recruitment near the edge of chaos and an application to mine sweeping

The field of robotics is in rapid development. As robots become cheaper to build, new applications involving many robots systems can be envisioned. One reason for using many robots is to achieve robustness. Having many robots, however, does not ensure robustness. A control strategy and robot behaviors must be engineered to incorporate robustness into the system. Swarm intelligence based approaches are popular for developing optimal and robust control strategies for systems of robots. Here we analyze the behavior of a swarm of robots modeled after a swarm of ants, where tasks are spatially distributed in the environment and robots/ants are recruited through short-range recruitment. For ants that move probabilistically in response to the short range signal and who adjust their probabilities such that they are near a phase change boundary, or edge-of-chaos, in the mean field analysis of their motions, we find a significant improvement in the robustness of the system.

A distributed evolutionary algorithmic approach to the coverage problem for submersible sensors

Untethered, underwater sensors, deployed for event detection and tracking and operating in an autonomous mode will be required to self-assemble into a configuration, which optimizes their coverage, effectively minimizing the probability that an event in the target area goes undetected. This organized, cooperative, and autonomous, spreading-out of the sensors is complicated due to sensors localized communication. A given sensor will not in general have position and velocity information for all sensors, but only for those in its communication area. A possible approach to this problem, motivated by an evolutionary optimization technique, Particle Swarm Optimization (PSO) is proposed and extended in a novel way. A distributed version of PSO is developed. A distributed version of PSO is explored using experimental fitness to address the coverage problem in a two dimensional area.

Evolutionary algorithms for the edge biconnectivity augmentation problem

A graph is edge-biconnected if it requires the removal of at least two edges to disconnect it. Assume that we have weighted graph that is not biconnected, and an additional set of augmentation edges. The (NP-hard) edge biconnectivity augmentation problem is to select a minimal subset of the augmentation edges, whose inclusion will cause the graph to be biconnected. This paper explores the application of particle swarm optimization and genetic algorithms for this problem.

Cooperation of decision-theoretic agents in the context of multi-agent systems

Multi-agent based solutions to problems, whether they are software agents or physical robots, are attractive because they are robust and scalable. Fundamental aspects of designing multi-agent systems involve modeling the intelligence of the agents and modeling their interactions. The intelligences of agents modeled here are encoded in Bayesian representations of their world. The agents interact only by observing others and moving in such a way so as to probabilistically maximize their internalized goal or utility. Using this multi-agent framework, a three agent herding is explored.