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.
Wireless sensor network (WSN) is a rapidly evolving technological platform with tremendous and novel applications. Recent advances in WSN have led to many new protocols specifically designed for them where energy awareness (i.e. long lived wireless network) is an essential consideration. Most of the attention, however, has been given to the routing protocols since they might differ depending on the application and network architecture. As routing approach with hierarchical structure is realized to successfully provide energy efficient solution, various heuristic clustering algorithms have been proposed. As an attractive WSN routing protocol, LEACH has been widely accepted for its energy efficiency and simplicity. Also, the discipline of meta-heuristics Evolutionary Algorithms (EAs) has been utilized by several researchers to tackle cluster-based routing problem in WSN. These biologically inspired routing mechanisms, e.g., HCR, have proved beneficial in prolonging the WSN lifetime, but unfortunately at the expense of decreasing the stability period of WSN. This is most probably due to the abstract modeling of the EA's clustering fitness function. The aim of this paper is to alleviate the undesirable behavior of the EA when dealing with clustered routing problem in WSN by formulating a new fitness function that incorporates two clustering aspects, viz. cohesion and separation error. Simulation over 20 random heterogeneous WSNs shows that our evolutionary based clustered routing protocol (ERP) always prolongs the network lifetime, preserves more energy as compared to the results obtained using the current heuristics such as LEACH, SEP, and HCR protocols. Additionally, we found that ERP outperforms LEACH and HCR in prolonging the stability period, comparable to SEP performance for heterogeneous networks with 10 extra heterogeneity but requires further heterogeneous-aware modification in the presence of 20 of node heterogeneity. A new two-stage optimization model for mobile agent routing in the wireless sensor network is presented as a integer linear programming problem based on the virtual connection topology sub graph. In the model, the solution is presented by two subpaths instead of the closed path which is usually applied in many path search methods to keep balance between computation cost and accuracy. A hybrid technique of genetic algorithm (GA) integrated with discrete particle swarm optimization (PSO), GAPSO is designed to solve the problem. Simulation experiments with different sizes and distributions of nodes show the effectiveness of the new model and GAPSO algorithm. In recent years, wireless sensor networks have been attracting considerable research attention for a wide range of applications, but they still present significant network communication challenges, involving essentially the use of large numbers of resource-constrained nodes operating unattended and exposed to potential local failures. In order to maximize the network lifespan, in this paper, genetical swarm optimization (GSO) is applied, a class of hybrid evolutionary techniques developed in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches; particle swarm optimization (PSO) and genetic algorithms (GA). This procedure is here implemented to optimize the communication energy consumption in a wireless network by selecting the optimal multihop routing schemes, with a suitable hybridization of different routing criteria, confirming itself as a flexible and useful tool for engineering applications. Wireless Sensor Networks consisting of nodes with limited power are deployed to gather useful information from the field. In WSNs it is critical to collect the information in an energy efficient manner. Ant Colony Optimization, a swarm intelligence based optimization technique, is widely used in network routing. A novel routing approach using an Ant Colony Optimization algorithm is proposed for Wireless Sensor Networks consisting of stable nodes. Illustrative examples, detailed descriptions and comparative performance test results of the proposed approach are included. The approach is also implemented to a small sized hardware component as a router chip. Simulation results show that proposed algorithm provides promising solutions allowing node designers to efficiently operate routing tasks.