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
1967549527 2148027941 1998837010 2126071695
The Internet is different from traditional parallel computing environments, and Distributed Genetic Algorithms (DGAs) for the Internet need to be designed to address these differences. This paper presents a framework for Internet island model DGAs that uses adaptation methods to maintain efficiency and robustness in a volatile and dynamic run-time environment. The applicability of the methods is demonstrated on benchmark tests, and a real-world optimization problem in VLSI design. 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. Genetic Programming (GP) is an evolutionary method for generating tree structural programs. Normal subtree crossover in GP randomly selects a crossover point in each parental tree, and offspring are created by exchanging the selected subtrees. In the normal crossover, it is difficult to control the global and local search because the similarity between the subtrees is not considered. In this paper, we propose a new crossover operation based on the semantic distance between the subtrees. We call this operation Semantic Control Crossover. By using the Semantic Control Crossover, the global search can be performed in the early stage of search, and the search property can be shifted to the local search as the search proceeds. As the results of experiments, the Semantic Control Crossover showed better performance than the conventional crossover. Genetic programming is combined with program analysis methods to repair bugs in off-the-shelf legacy C programs. Fitness is defined using negative test cases that exercise the bug to be repaired and positive test cases that encode program requirements. Once a successful repair is discovered, structural differencing algorithms and delta debugging methods are used to minimize its size. Several modifications to the GP technique contribute to its success: (1) genetic operations are localized to the nodes along the execution path of the negative test case; (2) high-level statements are represented as single nodes in the program tree; (3) genetic operators use existing code in other parts of the program, so new code does not need to be invented. The paper describes the method, reviews earlier experiments that repaired 11 bugs in over 60,000 lines of code, reports results on new bug repairs, and describes experiments that analyze the performance and efficacy of the evolutionary components of the algorithm.