Wireless sensor networks (WSNs) is an emerging technology in several application domains, ranging from urban surveillance to environmental and structural monitoring. Computational intelligence (CI) techniques are particularly suitable for enhancing these systems. However, when embedding CI into wireless sensors, severe hardware limitations must be taken into account. In this paper we investigate the possibility to perform an online, distributed optimization process within a WSN. Such a system might be used, for example, to implement advanced network features like distributed modelling, self-optimizing protocols, and anomaly detection, to name a few. The proposed approach, called DOWSN (distributed optimization for WSN) is an island-model infrastructure in which each node executes a simple, computationally cheap (both in terms of CPU and memory) optimization algorithm, and shares promising solutions with its neighbors. We perform extensive tests of different DOWSN configurations on a benchmark made up of 15 continuous optimization problems; we analyze the influence of the network parameters (number of nodes, inter-node communication period and probability of accepting incoming solutions) on the optimization performance. Finally, we profile energy and memory consumption of DOWSN to show the efficient usage of the limited hardware resources available on the sensor nodes.
One of the most recent trends in WSNs is the application of Genetic Programming (GP). Works like the aforementioned @cite_16 , @cite_1 and @cite_57 have started to investigate the possibility of generating, by means of genetic paradigms, the code running on motes. In particular, @cite_57 proposes a Distributed Genetic Programming Framework (DGPF) where various optimality criteria (such as energy consumption, memory usage and code size) are taken into account while generating the code for performing a given task. A similar framework, called Broadcast-Distributed Parallel Genetic Programming, is proposed in @cite_16 , where each node runs, independently, a lightweight GP process and asynchronously exchanges genetic material with its neighbors (island model). An island model distributed Genetic Programming engine, called Distributed Genetic Programming (IDGP), is also proposed in @cite_1 . One of the main conclusions of these studies is that, notwithstanding a potential risk of premature convergence, a framework combining GP and exchange of information endows the motes with robust self-adapting capabilities with regard to unpredicted changes of the local conditions and the surrounding environment.
@cite_57 @cite_16 @cite_1
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Wireless sensor networks (WSNs) are medium scale manifestations of a paintable or amorphous computing paradigm. WSNs are becoming increasingly important as they attain greater deployment. New techniques for evolutionary computing (EC) are needed to address these new computing models. This paper describes a novel effort to develop a variation of traditional parallel evolutionary computing models to enable their use in the wireless sensor network. The ability to compute evolutionary algorithms within the WSN has innumerable advantages including intelligent-sensing, resource-optimized communication strategies, intelligent-routing protocol design, novelty detection, etc. In this paper we develop a parallel evolutionary algorithm suitable for use in a WSN. We then describe the adaptations required to develop practicable implementations to effectively operate in resource constrained environments such as WSNs. Several adaptations including a novel representation scheme, an approximate fitness computation method and a sufficient statistics based data reduction technique. These adaptations lead to the development of a GP implementation that is usable on the low-power, small footprint architectures typical to wireless sensor motes. We demonstrate the utility of our formulations and validate the proposed ideas using the algorithm to compute symbolic regression problems. Wireless Sensor Actuator Networks (WSANs) extend wireless sensor networks through actuation capability. Designing robust logic for WSANs however is challenging since nodes can affect their environment which is already inherently complex and dynamic. Fixed (offline) logic does not have the ability to adapt to significant environmental changes and can fail under changed conditions. To address this challenge, we present In situ Distributed Genetic Programming (IDGP) as a framework for evolving logic post-deployment (online) and implement this framework on a physically deployed WSAN. To demonstrate the features of the framework including individual, cooperative and heterogeneous evolution, we apply it to two simple optimisation problems requiring sensing, communications and actuation. The experiments confirm that IDGP can evolve code to achieve a system wide objective function and is resilient to unexpected environmental changes.