Review of Optimization Problems in Wireless Sensor Networks

International audienc

A multiobjective Tabu framework for the optimization and evaluation of wireless systems

This chapter will focus on the multiobjective formulation of an optimization problem and highlight the assets of a multiobjective Tabu implementation for such problems. An illustration of a specific Multiobjective Tabu heuristic (referred to as MO Tabu in the following) will be given for 2 particular problems arising in wireless systems. The first problem addresses the planning of access points for a WLAN network with some Quality of Service requirements and the second one provides an evaluation mean to assess the performance evaluation of a wireless sensor network. The chapter will begin with an overview of multiobjective (MO) optimization featuring the definitions and concepts of the domain (e.g. Dominance, Pareto front,...) and the main MO search heuristics available so far. We will then emphasize on the definition of a problem as a multiobjective optimization problem and illustrate it by the two examples from the field of wireless networking. The next part will focus on MO Tabu, a Tabu-inspired multiobjective heuristic and describe its assets compared to other MO heuristics. The last part of the chapter will show the results obtained with this MO Tabu strategy on the 2 wireless networks related problems. Conclusion on the use of Tabu as a multiobjective heuristic will be drawn based on the results presented so far

Energy-aware sink node localization algorithm for wireless sensor networks

Wireless sensor networks (WSNs) are a family of wireless networks that usually operate with irreplaceable batteries. The energy sources limitation raises the need for designing specific protocols to prolong the operational lifetime of such networks. These protocols are responsible for messages exchanging through the wireless communications medium from the sensors to the base station (sink node). Therefore, the determination of the optimal location of the sink node becomes crucial to assure both the prolongation of the network’s operation and the quality of the provided services. This paper proposes a novel algorithm based on a Particle Swarm Optimization (PSO) approach for designing an energy-aware topology control protocol. The deliverable of the algorithm is the optimal sink node location within a deployment area. The proposed objective function is based on a number of topology control protocol’s characteristics such as numbers of neighbors per node, the nodes’ residual energy, and how they are far from the center of the deployment area. The simulation results show that the proposed algorithm reveals significant effectiveness to both topology construction and maintenance phases of a topology control protocol in terms of the number of active nodes, the topology construction time, the number of topology reconstructions, and the operational network’s lifetime.Web of Scienceart. ID 81035

Computational Intelligence Algorithms for Optimisation of Wireless Sensor Networks

Recent studies have tended towards incorporating Computation Intelligence, which is a large umbrella for all Machine Learning and Metaheuristic approaches into wireless sensor network (WSN) applications for enhanced and intuitive performance. Meta-heuristic optimisation techniques are used for solving several WSN issues such as energy minimisation, coverage, routing, scheduling and so on. This research designs and develops highly intelligent WSNs that can provide the core requirement of energy efficiency and reliability. To meet these requirements, two major decisions were carried out at the sink node or base station. The first decision involves the use of supervised and unsupervised machine learning algorithms to achieve an accurate decision at the sink node. This thesis presents a new hybrid approach for event (fire) detection system using k-means clustering on aggregated fire data to form two class labels (fire and non-fire). The resulting data outputs are trained and tested by the Feed Forward Neural Network, Naive Bayes, and Decision Trees classifier. This hybrid approach was found to significantly improve fire detection performance against the use of only the classifiers. The second decision employs a metaheuristic approach to optimise the solution of WSNs clustering problem. Two metaheuristic-based protocols namely the Dynamic Local Search Algorithm for Clustering Hierarchy (DLSACH) and Heuristics Algorithm for Clustering Hierarchy (HACH) are proposed to achieve an evenly balanced energy and minimise the net residual energy of each sensor nodes. This thesis proved that the two protocols outperforms state-of-the-art protocols such as LEACH, TCAC and SEECH in terms of network lifetime and maintains a favourable performance even under different energy heterogeneity settings

An enhanced evolutionary algorithm for requested coverage in wireless sensor networks

Wireless sensor nodes with specific and new sensing capabilities and application requirements have affected the behaviour of wireless sensor networks and created problems. Placement of the nodes in an application area is a wellknown problem in the field. In addition, high per-node cost as well as need to produce a requested coverage and guaranteed connectivity features is a must in some applications. Conventional deployments and methods of modelling the behaviour of coverage and connectivity cannot satisfy the application needs and increase the network lifetime. Thus, the research designed and developed an effective node deployment evaluation parameter, produced a more efficient node deployment algorithm to reduce cost, and proposed an evolutionary algorithm to increase network lifetime while optimising deployment cost in relation to the requested coverage scheme. This research presents Accumulative Path Reception Rate (APRR) as a new method to evaluate node connectivity in a network. APRR, a node deployment evaluation parameter was used as the quality of routing path from a sensing node to sink node to evaluate the quality of a network deployment strategy. Simulation results showed that the behaviour of the network is close to the prediction of the APRR. Besides that, a discrete imperialist competitive algorithm, an extension of the Imperialist Competitive Algorithm (ICA) evolutionary algorithm was used to produce a network deployment plan according to the requested event detection probability with a more efficient APRR. It was used to reduce deployment cost in comparison to the use of Multi-Objective Evolutionary Algorithm (MOEA) and Multi-Objective Deployment Algorithm (MODA) algorithms. Finally, a Repulsion Force and Bottleneck Handling (RFBH) evolutionary-based algorithm was proposed to prepare a higher APRR and increase network lifetime as well as reduce deployment cost. Experimental results from simulations showed that the lifetime and communication quality of the output network strategies have proven the accuracy of the RFBH algorithm performance

A Trapezoidal Fuzzy Membership Genetic Algorithm (TFMGA) for Energy and Network Lifetime Maximization under Coverage Constrained Problems in Heterogeneous Wireless Sensor Networks

Network lifetime maximization of Wireless Heterogeneous Wireless Sensor Networks (HWSNs) is a difficult problem. Though many methods have been introduced and developed in the recent works to solve network lifetime maximization. However, in HWSNs, the energy efficiency of sensor nodes becomes also a very difficult issue. On the other hand target coverage problem have been also becoming most important and difficult problem. In this paper, new Markov Chain Monte Carlo (MCMC) is introduced which solves the energy efficiency of sensor nodes in HWSN. At initially graph model is modeled to represent HWSNs with each vertex representing the assignment of a sensor nodes in a subset. At the same time, Trapezoidal Fuzzy Membership Genetic Algorithm (TFMGA) is proposed to maximize the number of Disjoint Connected Covers (DCC) and K-Coverage (KC) known as TFMGA-MDCCKC. Based on gene and chromosome information from the TFMGA, the gene seeks an optimal path on the construction graph model that maximizes the MDCCKC. In TFMGA gene thus focuses on finding one more connected covers and avoids creating subsets particularly. A local search procedure is designed to TFMGA thus increases the search efficiency. The proposed TFMGA-MDCCKC approach has been applied to a variety of HWSNs. The results show that the TFMGA-MDCCKC approach is efficient and successful in finding optimal results for maximizing the lifetime of HWSNs. Experimental results show that proposed TFMGA-MDCCKC approach performs better than Bacteria Foraging Optimization (BFO) based approach, Ant Colony Optimization (ACO) method and the performance of the TFMGA-MDCCKC approach is closer to the energy-conserving strategy

Mécanismes optimisés de planification des états des capteurs pour la maximisation de la durée de vie dans les réseaux de capteurs sans fil.

RÉSUMÉ Depuis leur création, les réseaux de communication sans l ont connu un engouement fulgurant qui ne cesse de croître au sein des communautés scientiques et industrielles. Ainsi, le paradigme sans l a vu naître, au cours de son évolution, diverses architectures dérivées, telles que les réseaux cellulaires, les réseaux locaux sans l, les réseaux WiMax, etc. Durant la dernière décennie, un nouveau type de réseau sans l a suscité un grand intérêt auprès de la communauté scientique, il s'agit des réseaux ad hoc et des réseaux de capteurs sans l (RCSF). Ce nouveau type de réseau se distingue des réseaux sans l classiques par l'absence d'infrastructure (ou structure) préétablie et par la versatilité de ses n÷uds (i.e., ces derniers peuvent s'ajouter au réseau ou en disparaître d'une manière assez aléatoire). Un RCSF est composé d'un ensemble d'unités de traitements embarquées, appelées capteurs, communiquant via des liens sans l et dont la fonction principale est la collecte de paramètres relatifs à l'environnement qui les entoure, telles que la tempé- rature, la pression ou la présence d'objets. Les RCSF trouvent leur application dans diverses activités de la société, tels les processus industriels, les applications militaires de surveillance, l'observation et le suivi d'habitat, etc. À cause de la nature intrinsèque de leur fonctionnalité, les capteurs ont une contrainte principale : leur source d'énergie est limitée et presque jamais renouvelable. Ceci place l'optimisation de l'énergie consommée par les capteurs et la maximisation de la durée de vie des RCSF au centre des dés posés par ces réseaux.----------ABSTRACT Since their creation, wireless communication networks have witnessed a huge success that continues to grow in scientic and industrial communities. During its evolution the wireless paradigm has given birth to various derivative architectures, such as cellular, WiMax and wireless local area networks. During the last decade, a new type of wireless networks has stirred up great interest within the scientic community ; it consists in mobile ad hoc networks (MANETS) and wireless sensor networks (WSN). This new type of networks diers from conventional wireless networks by the absence of predetermined infrastructure (or structure) and by the versatileness of its nodes (i.e., any node can join the network or leave it in a pretty random manner). A WSN consists of a set of embedded processing units, called sensors, communicating via wireless links, whose main function is the collection of parameters related to the surrounding environment, such as temperature, pressure or the presence/motion of objects. WSN are expected to have many applications in various elds, such as industrial processes, military surveillance, observation and monitoring of habitat, etc. Because of the intrinsic nature of their intended applications, sensors have a major constraint : their energy source is usually limited and hardly renewable. This turns energy optimization and network lifetime maximization in WSN into real challenges. In this thesis, we address the problem of maximizing the network lifetime of WSN through optimal sensor state planning, with a view to minimizing the total dissipated energy while ensuring a fair balance of energy consumption over all sensors. This involves such techniques as cluster formation and switch-o of some sensors, while conforming to the application-related constraints, such as total coverage of the area monitored by the sensors and the presence of a routing topology composed of cluster heads

Cognitive Security Framework For Heterogeneous Sensor Network Using Swarm Intelligence

Rapid development of sensor technology has led to applications ranging from academic to military in a short time span. These tiny sensors are deployed in environments where security for data or hardware cannot be guaranteed. Due to resource constraints, traditional security schemes cannot be directly applied. Unfortunately, due to minimal or no communication security schemes, the data, link and the sensor node can be easily tampered by intruder attacks. This dissertation presents a security framework applied to a sensor network that can be managed by a cohesive sensor manager. A simple framework that can support security based on situation assessment is best suited for chaotic and harsh environments. The objective of this research is designing an evolutionary algorithm with controllable parameters to solve existing and new security threats in a heterogeneous communication network. An in-depth analysis of the different threats and the security measures applied considering the resource constrained network is explored. Any framework works best, if the correlated or orthogonal performance parameters are carefully considered based on system goals and functions. Hence, a trade-off between the different performance parameters based on weights from partially ordered sets is applied to satisfy application specific requirements and security measures. The proposed novel framework controls heterogeneous sensor network requirements,and balance the resources optimally and efficiently while communicating securely using a multi-objection function. In addition, the framework can measure the affect of single or combined denial of service attacks and also predict new attacks under both cooperative and non-cooperative sensor nodes. The cognitive intuition of the framework is evaluated under different simulated real time scenarios such as Health-care monitoring, Emergency Responder, VANET, Biometric security access system, and Battlefield monitoring. The proposed three-tiered Cognitive Security Framework is capable of performing situation assessment and performs the appropriate security measures to maintain reliability and security of the system. The first tier of the proposed framework, a crosslayer cognitive security protocol defends the communication link between nodes during denial-of-Service attacks by re-routing data through secure nodes. The cognitive nature of the protocol balances resources and security making optimal decisions to obtain reachable and reliable solutions. The versatility and robustness of the protocol is justified by the results obtained in simulating health-care and emergency responder applications under Sybil and Wormhole attacks. The protocol considers metrics from each layer of the network model to obtain an optimal and feasible resource efficient solution. In the second tier, the emergent behavior of the protocol is further extended to mine information from the nodes to defend the network against denial-of-service attack using Bayesian models. The jammer attack is considered the most vulnerable attack, and therefore simulated vehicular ad-hoc network is experimented with varied types of jammer. Classification of the jammer under various attack scenarios is formulated to predict the genuineness of the attacks on the sensor nodes using receiver operating characteristics. In addition to detecting the jammer attack, a simple technique of locating the jammer under cooperative nodes is implemented. This feature enables the network in isolating the jammer or the reputation of node is affected, thus removing the malicious node from participating in future routes. Finally, a intrusion detection system using `bait\u27 architecture is analyzed where resources is traded-off for the sake of security due to sensitivity of the application. The architecture strategically enables ant agents to detect and track the intruders threateningthe network. The proposed framework is evaluated based on accuracy and speed of intrusion detection before the network is compromised. This process of detecting the intrusion earlier helps learn future attacks, but also serves as a defense countermeasure. The simulated scenarios of this dissertation show that Cognitive Security Framework isbest suited for both homogeneous and heterogeneous sensor networks