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.
Often it is needed that a WSN is deployed according to some optimality criteria depending on the position of motes (e.g. metrics measuring motes distribution, spatial coverage, network energy consumption, etc.). In static WSNs (i.e. networks where the position of motes is fixed), this problem can be efficiently solved offline prior to the actual deployment, simulating the network with one of the several simulation tools available @cite_4 . Multi-objective optimization algorithms, such as Multi Objective Evolutionary Algorithms (MOEAs), can be used together with these tools to find optimal network configurations. Some studies on MOEAs applied to WSN deployment are reported in @cite_24 , while the proper MOEA parameter setting for this problem is investigated in @cite_43 . An interesting algorithmic case is DPSOSA, a combined offline online distributed hybrid algorithm presented in @cite_63 : in DPSOSA, Particle Swarm Optimization (PSO) performs offline the global search, while Simulated Annealing (SA) is executed on motes to apply local refinements and online adjustments.
@cite_24 @cite_43 @cite_4 @cite_63
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Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs. Wireless sensor networks (WSNs) consist of numerous sensor nodes with several possible configurations for each node. As there are a lot of nodes in a typical WSN, each with its own set of configurations, the number of configurations for the network as a whole is huge and the design space is extremely large. The configuration of a WSN has a strong effect on the quality of services of running applications and the performance of the WSN. Multi-objective evolutionary algorithms (EAs) are well suited to explore the trade-offs in a WSN design space. However, an EA has many configuration parameters in itself. This paper presents several guidelines for configuring a multi-objective EA for design space exploration, given a specification of the WSN to be configured and a time budget available for analysis. We demonstrate the effectiveness of these guidelines on a specific type of WSN that uses a gossip strategy for disseminating data over the network. Sensor networks face many problems that do not arise in other types of networks. Power constraints, limited hardware, decreased reliability, and a typically higher density and number of nodes than found in conventional networks are just a small portion of the problems that have to be considered when developing protocols for use in sensor networks. Simulation is often used to test new protocols that are being developed, as well as to compare old protocols. However, there is always a danger when using simulation in testing: the results are not necessarily going to be accurate or representative. To help overcome this, it is important to possess knowledge of the simulation tools available, along with their associated strengths and weaknesses. The goal of this paper is to aid developers in the selection of an appropriate simulation tool. Xue Wang *, Jun-Jie Ma , Sheng Wang and Dao-Wei Bi State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instruments, Tsinghua University, Beijing 100084, P. R. China; E-mails: mjj, wang_sheng00, bdw02 @mails.tsinghua.edu.cn. * Author to whom correspondence should be addressed; E-mail: wangxue@mail.tsinghua.edu.cn. Received: 25 April 2007 Accepted: 8 May 2007 Published: 10 May 2007 Abstract: The limited energy supply of wireless sensor networks poses a great challenge for the deployment of wireless sensor nodes. In this paper, we focus on energy-efficient coverage with distributed particle swarm optimization and simulated annealing. First, the energy-efficient coverage problem is formulated with sensing coverage and energy consumption models. We consider the network composed of stationary and mobile nodes. Second, coverage and energy metrics are presented to evaluate the coverage rate and energy consumption of a wireless sensor network, where a grid exclusion algorithm extracts the coverage state and Dijkstra’s algorithm calculates the lowest cost path for communication. Then, a hybrid algorithm optimizes the energy consumption, in which particle swarm optimization and simulated annealing are combined to find the optimal deployment solution in a distributed manner. Simulated annealing is performed on multiple wireless sensor nodes, results of which are employed to correct the local and global best solution of particle swarm optimization. Simulations of wireless sensor node deployment verify that coverage performance can be guaranteed, energy consumption of communication is conserved after deployment optimization and the optimization performance is boosted by the distributed algorithm. Moreover, it is demonstrated that energy efficiency of wireless sensor networks is enhanced by the proposed optimization algorithm in target tracking applications. Keywords: Wireless sensor network, deployment optimization, energy efficiency, particle swarm optimization, simulated annealing.