Cognitive strategies for reducing energy consumption in wireless sensor networks

El consumo energético de las Redes de Sensores Inalámbricas (WSNs por sus siglas en inglés) es un problema histórico que ha sido abordado desde diferentes niveles y visiones, ya que no solo afecta a la propia supervivencia de la red sino que el creciente uso de dispositivos inteligentes y el nuevo paradigma del Internet de las Cosas hace que las WSNs tengan cada vez una mayor influencia en la huella energética. Debido a la tendencia al alza en el uso de estas redes se añade un nuevo problema, la saturación espectral. Las WSNs operan habitualmente en bandas sin licencia como son las bandas Industrial, Científica y Médica (ISM por sus siglas en inglés). Estas bandas se comparten con otro tipo de redes como Wi-Fi o Bluetooth cuyo uso ha crecido exponencialmente en los últimos años. Para abordar este problema aparece el paradigma de la Radio Cognitiva (CR), una tecnología que permite el acceso oportunista al espectro. La introducción de capacidades cognitivas en las WSNs no solo permite optimizar su eficiencia espectral sino que también tiene un impacto positivo en parámetros como la calidad de servicio, la seguridad o el consumo energético. Sin embargo, por otra parte, este nuevo paradigma plantea algunos retos relacionados con el consumo energético. Concretamente, el sensado del espectro, la colaboración entre los nodos (que requiere comunicación adicional) y el cambio en los parámetros de transmisión aumentan el consumo respecto a las WSN clásicas. Teniendo en cuenta que la investigación en el campo del consumo energético ha sido ampliamente abordada puesto que se trata de una de sus principales limitaciones, asumimos que las nuevas estrategias deben surgir de las nuevas capacidades añadidas por las redes cognitivas. Por otro lado, a la hora de diseñar estrategias de optimización para CWSN hay que tener muy presentes las limitaciones de recursos de estas redes en cuanto a memoria, computación y consumo energético de los nodos. En esta tesis doctoral proponemos dos estrategias de reducción de consumo energético en CWSNs basadas en tres pilares fundamentales. El primero son las capacidades cognitivas añadidas a las WSNs que proporcionan la posibilidad de adaptar los parámetros de transmisión en función del espectro disponible. La segunda es la colaboración, como característica intrínseca de las CWSNs. Finalmente, el tercer pilar de este trabajo es teoría de juegos como algoritmo de soporte a la decisión, ampliamente utilizado en WSNs debido a su simplicidad. Como primer aporte de la tesis se presenta un análisis completo de las posibilidades introducidas por la radio cognitiva en materia de reducción de consumo para WSNs. Gracias a las conclusiones extraídas de este análisis, se han planteado las hipótesis de esta tesis relacionadas con la validez de usar capacidades cognitivas como herramienta para la reducción de consumo en CWSNs. Una vez presentada las hipótesis, pasamos a desarrollar las principales contribuciones de la tesis: las dos estrategias diseñadas para reducción de consumo basadas en teoría de juegos y CR. La primera de ellas hace uso de un juego no cooperativo que se juega mediante pares de jugadores. En la segunda estrategia, aunque el juego continúa siendo no cooperativo, se añade el concepto de colaboración. Para cada una de las estrategias se presenta el modelo del juego, el análisis formal de equilibrios y óptimos y la descripción de la estrategia completa donde se incluye la interacción entre nodos. Con el propósito de probar las estrategias mediante simulación e implementación en dispositivos reales hemos desarrollado un marco de pruebas compuesto por un simulador cognitivo y un banco de pruebas formado por nodos cognitivos capaces de comunicarse en tres bandas ISM desarrollados en el B105 Lab. Este marco de pruebas constituye otra de las aportaciones de la tesis que permitirá el avance en la investigación en el área de las CWSNs. Finalmente, se presentan y discuten los resultados derivados de la prueba de las estrategias desarrolladas. La primera estrategia proporciona ahorros de energía mayores al 65% comparados con una WSN sin capacidades cognitivas y alrededor del 25% si la comparamos con una estrategia cognitiva basada en el sensado periódico del espectro para el cambio de canal de acuerdo a un nivel de ruido fijado. Este algoritmo se comporta de forma similar independientemente del nivel de ruido siempre que éste sea espacialmente uniformemente. Esta estrategia, a pesar de su sencillez, nos asegura el comportamiento óptimo en cuanto a consumo energético debido a la utilización de teoría de juegos en la fase de diseño del comportamiento de los nodos. La estrategia colaborativa presenta mejoras respecto a la anterior en términos de protección frente al ruido en escenarios de ruido más complejos donde aporta una mejora del 50% comparada con la estrategia anterior. ABSTRACT Energy consumption in Wireless Sensor Networks (WSNs) is a known historical problem that has been addressed from different areas and on many levels. But this problem should not only be approached from the point of view of their own efficiency for survival. A major portion of communication traffic has migrated to mobile networks and systems. The increased use of smart devices and the introduction of the Internet of Things (IoT) give WSNs a great influence on the carbon footprint. Thus, optimizing the energy consumption of wireless networks could reduce their environmental impact considerably. In recent years, another problem has been added to the equation: spectrum saturation. Wireless Sensor Networks usually operate in unlicensed spectrum bands such as Industrial, Scientific, and Medical (ISM) bands shared with other networks (mainly Wi-Fi and Bluetooth). To address the efficient spectrum utilization problem, Cognitive Radio (CR) has emerged as the key technology that enables opportunistic access to the spectrum. Therefore, the introduction of cognitive capabilities to WSNs allows optimizing their spectral occupation. Cognitive Wireless Sensor Networks (CWSNs) do not only increase the reliability of communications, but they also have a positive impact on parameters such as the Quality of Service (QoS), network security, or energy consumption. These new opportunities introduced by CWSNs unveil a wide field in the energy consumption research area. However, this also implies some challenges. Specifically, the spectrum sensing stage, collaboration among devices (which requires extra communication), and changes in the transmission parameters increase the total energy consumption of the network. When designing CWSN optimization strategies, the fact that WSN nodes are very limited in terms of memory, computational power, or energy consumption has to be considered. Thus, light strategies that require a low computing capacity must be found. Since the field of energy conservation in WSNs has been widely explored, we assume that new strategies could emerge from the new opportunities presented by cognitive networks. In this PhD Thesis, we present two strategies for energy consumption reduction in CWSNs supported by three main pillars. The first pillar is that cognitive capabilities added to the WSN provide the ability to change the transmission parameters according to the spectrum. The second pillar is that the ability to collaborate is a basic characteristic of CWSNs. Finally, the third pillar for this work is the game theory as a decision-making algorithm, which has been widely used in WSNs due to its lightness and simplicity that make it valid to operate in CWSNs. For the development of these strategies, a complete analysis of the possibilities is first carried out by incorporating the cognitive abilities into the network. Once this analysis has been performed, we expose the hypotheses of this thesis related to the use of cognitive capabilities as a useful tool to reduce energy consumption in CWSNs. Once the analyses are exposed, we present the main contribution of this thesis: the two designed strategies for energy consumption reduction based on game theory and cognitive capabilities. The first one is based on a non-cooperative game played between two players in a simple and selfish way. In the second strategy, the concept of collaboration is introduced. Despite the fact that the game used is also a non-cooperative game, the decisions are taken through collaboration. For each strategy, we present the modeled game, the formal analysis of equilibrium and optimum, and the complete strategy describing the interaction between nodes. In order to test the strategies through simulation and implementation in real devices, we have developed a CWSN framework composed by a CWSN simulator based on Castalia and a testbed based on CWSN nodes able to communicate in three different ISM bands. We present and discuss the results derived by the energy optimization strategies. The first strategy brings energy improvement rates of over 65% compared to WSN without cognitive techniques. It also brings energy improvement rates of over 25% compared with sensing strategies for changing channels based on a decision threshold. We have also seen that the algorithm behaves similarly even with significant variations in the level of noise while working in a uniform noise scenario. The collaborative strategy presents improvements respecting the previous strategy in terms of noise protection when the noise scheme is more complex where this strategy shows improvement rates of over 50%

PUE attack detection in CWSNs using anomaly detection techniques

Cognitive wireless sensor network (CWSN) is a new paradigm, integrating cognitive features in traditional wireless sensor networks (WSNs) to mitigate important problems such as spectrum occupancy. Security in cognitive wireless sensor networks is an important problem since these kinds of networks manage critical applications and data. The specific constraints of WSN make the problem even more critical, and effective solutions have not yet been implemented. Primary user emulation (PUE) attack is the most studied specific attack deriving from new cognitive features. This work discusses a new approach, based on anomaly behavior detection and collaboration, to detect the primary user emulation attack in CWSN scenarios. Two non-parametric algorithms, suitable for low-resource networks like CWSNs, have been used in this work: the cumulative sum and data clustering algorithms. The comparison is based on some characteristics such as detection delay, learning time, scalability, resources, and scenario dependency. The algorithms have been tested using a cognitive simulator that provides important results in this area. Both algorithms have shown to be valid in order to detect PUE attacks, reaching a detection rate of 99% and less than 1% of false positives using collaboration

Controlling the degradation of wireless sensor networks

With the fast expansion of Wireless Sensor Networks (WSNs) and the increasing emergence of new scenarios and applications, extending their lifetime is crucial. Usually, WSN developers use generic algorithms and deployment arrangements without considering the specific needs of their network's application. Taking this application into account can result in a significant enhancement of performance, both in terms of increasing the lifetime and improving the quality of service (QoS). Furthermore, most WSN developers do not consider the final behavior of the network when nodes are nearly depleted and resources are scarce. In this paper we introduce the concept of the controlled degradation of the network, to refer to the strategies aimed at managing this deterioration process. The existing definitions of the network lifetime do not normally consider the specific purpose or application for which the WSN is intended. Thus, they are not suited to describe and test controlled degradation strategies. Consequently, we propose a new formal and comprehensive definition for the network lifetime. Finally, this work presents a proof of concept that confirms our statements and reinforces the potential of this research line

PUE attack detection in CWSN using collaboration and learning behavior

Cognitive Wireless Sensor Network (CWSN) is a new paradigm which integrates cognitive features in traditional Wireless Sensor Networks (WSNs) to mitigate important problems such as spectrum occupancy. Security in Cognitive Wireless Sensor Networks is an important problem because these kinds of networks manage critical applications and data. Moreover, the specific constraints of WSN make the problem even more critical. However, effective solutions have not been implemented yet. Among the specific attacks derived from new cognitive features, the one most studied is the Primary User Emulation (PUE) attack. This paper discusses a new approach, based on anomaly behavior detection and collaboration, to detect the PUE attack in CWSN scenarios. A nonparametric CUSUM algorithm, suitable for low resource networks like CWSN, has been used in this work. The algorithm has been tested using a cognitive simulator that brings important results in this area. For example, the result shows that the number of collaborative nodes is the most important parameter in order to improve the PUE attack detection rates. If the 20% of the nodes collaborates, the PUE detection reaches the 98% with less than 1% of false positives

Evaluation, energy optimization, and spectrum analysis of an artificial noise technique to improve CWSN security

This paper presents the security evaluation, energy consumption optimization, and spectrum scarcity analysis of artificial noise techniques to increase physical-layer security in Cognitive Wireless Sensor Networks (CWSNs). These techniques introduce noise into the spectrum in order to hide real information. Nevertheless, they directly affect two important parameters in Cognitive Wireless Sensor Networks (CWSNs), energy consumption and spectrum utilization. Both are affected because the number of packets transmitted by the network and the active period of the nodes increase. Security evaluation demonstrates that these techniques are effective against eavesdropper attacks, but also optimization allows for the implementation of these approaches in low-resource networks such as Cognitive Wireless Sensor Networks. In this work, the scenario is formally modeled and the optimization according to the simulation results and the impact analysis over the frequency spectrum are presented

A game theory based strategy for reducing energy consumption in cognitive WSN

Wireless sensor networks (WSNs) are one of the most important users of wireless communication technologies in the coming years and some challenges in this area must be addressed for their complete development. Energy consumption and spectrum availability are two of the most severe constraints of WSNs due to their intrinsic nature. The introduction of cognitive capabilities into these networks has arisen to face the issue of spectrum scarcity but could be used to face energy challenges too due to their new range of communication possibilities. In this paper a new strategy based on game theory for cognitive WSNs is discussed. The presented strategy improves energy consumption by taking advantage of the new change-communication-channel capability. Based on game theory, the strategy decides when to change the transmission channel depending on the behavior of the rest of the network nodes. The strategy presented is lightweight but still has higher energy saving rates as compared to noncognitive networks and even to other strategies based on scheduled spectrum sensing. Simulations are presented for several scenarios that demonstrate energy saving rates of around 65% as compared to WSNs without cognitive techniques

Cognitive test-bed for wireless sensor networks

Cognitive Wireless Sensor Networks are an emerging technology with a vast potential to avoid traditional wireless problems such as reliability, interferences and spectrum scarcity in Wireless Sensor Networks. Cognitive Wireless Sensor Networks test-beds are an important tool for future developments, protocol strategy testing and algorithm optimization in real scenarios. A new cognitive test-bed for Cognitive Wireless Sensor Networks is presented in this paper. This work in progress includes both the design of a cognitive simulator for networks with a high number of nodes and the implementation of a new platform with three wireless interfaces and a cognitive software for extracting real data. Finally, as a future work, a remote programmable system and the planning for the physical deployment of the nodes at the university building is presented

A security scheme for wireless sensor networks

Security is critical for wireless sensor networks (WSN)deployed in hostile environments since many types of attacks could reduce the trust on the global functioning of any WSN. Many solutions have been proposed to secure communications for WSNs and most of them rely on a centralized component which behaves as a certificate authority. We propose in this paper a distributed solution able to ensure authentication of nodes at any time without having any on-line access to a certificate authority. Each node will be equipped with a Trusted Platform Module (TPM) which is able to store keys with security. Each node will have its own public key and private key pair in the TPM and a certificate of the public key. The certificate is issued off-line when setting-up the node. When a node communicates with another, it has to sign the message with its own private key (done securely by the TPM) and sends the message, the signature and the certificate of the public key. The evaluation of the solution has been done using simulation and the overhead added by integrating authentication does not exceed 15% of energy consumption

Cognitive wireless sensor network platform for cooperative communications

Nowadays, Wireless Ad Hoc Sensor Networks (WAHSNs), specially limited in energy and resources, are subject to development constraints and difficulties such as the increasing RF spectrum saturation at the unlicensed bands. Cognitive Wireless Sensor Networks (CWSNs), leaning on a cooperative communication model, develop new strategies to mitigate the inefficient use of the spectrum that WAHSNs face. However, few and poorly featured platforms allow their study due to their early research stage. This paper presents a versatile platform that brings together cognitive properties into WAHSNs. It combines hardware and software modules as an entire instrument to investigate CWSNs. The hardware fits WAHSN requirements in terms of size, cost, features, and energy. It allows communication over three different RF bands, becoming the only cognitive platform for WAHSNs with this capability. In addition, its modular and scalable design is widely adaptable to almost any WAHSN application. Significant features such as radio interface (RI) agility or energy consumption have been proven throughout different performance tests

Validação intercultural em português de um questionário para avaliar a síndrome visual do computador em trabalhadores expostos a dispositivos digitais

Purpose: As digital devices are increasingly used at work, valid and reliable tools are needed to assess their effect on visual health. This study aimed to translate, cross-culturally adapt, and validate the Computer Vision Syndrome Questionnaire (CVS-Q©) into Portuguese. Methods: A 5-phase process was followed: direct translation, synthesis of translation, back-translation, consolidation by an expert committee, and pretest. To run the pretest, a cross-sectional pilot study was conducted with 26 participants who completed the prefinal Portuguese version of the CVS-Q© and were asked about difficulties, comprehensibility, and suggestions to improve the questionnaire. To evaluate the reliability and validity of the Portuguese version of the CVS-Q©, a cross-sectional validation study was performed in a different sample (280 workers). Results: In the pretest, 96.2% had no difficulty in completing it, and 84.0% valued it as clear and understandable. CVS-Q© in Portuguese (Questionário da Síndrome Visual do Computador, CVS-Q PT©) was then obtained. Validation revealed the scale’s good internal consistency (Cronbach’s alpha=0.793), good temporal stability (intraclass correlation coefficient=0.847; 95% CI 0.764-0.902, kappa=0.839), adequate sensitivity and specificity (78.5% and 70.7%, respectively), good discriminant capacity (area under the curve=0.832; 95% CI 0.784-0.879), and adequate convergent validity with the ocular surface disease index (Spearman correlation coefficient=0.728, p<0.001). The factor analysis provided a single factor accounting for 37.7% of the explained common variance. A worker who scored ≥7 points would have computer vision syndrome. Conclusions: CVS-Q PT© can be considered an intuitive and easy-to-understand tool with good psychometric properties to measure computer vision syndrome in Portuguese workers exposed to digital devices. This questionnaire will assist in making decisions on preventive measures, interventions, and treatment and comparing exposed populations in different Portuguese-speaking countries.Objetivos: À medida que a utilização de equipamentos digitais no emprego aumenta, a avaliação do seu efeito na saúde visual necessita de ferramentas válidas e robustas. Este estudo teve como objetivo traduzir, adaptar culturalmente e validar para português o Questionário da Síndrome Visual do Computador (CVS-Q©). Métodos: O procedimento foi realizado em 5 fases: tradução direta, síntese da tradução, tradução inversa, consolidação por um painel de especialistas, e pré-teste. Para fazer o pré-teste foi realizado um estudo piloto transversal aplicado a uma amostra de 26 participantes que completaram a versão pré-final da versão portuguesa do CVS-Q©, questionando por dificuldades, compreensão e sugestões de melhoria do questionário. Para avaliar a confiança e validade da versão portuguesa do CVS-Q© foi realizado um estudo transversal de validação em uma amostra diferente (280 funcionários). Resultados: No préteste, 96.2% dos participantes não apresentaram dificuldades no preenchimento do questionário, enquanto 84.0% indicaram que era claro e compreensível. Obteve-se, então, o CVS-Q© em português (Questionário da Síndrome Visual do Computador, CVS-Q PT©). A sua validação revelou uma boa consistência interna da sua escala (Cronbach’s alpha=0.793), boa estabilidade tem poral (coeficiente de correlação interclasse=0.847; 95% CI 0.764-0.902, kappa=0.839), sensibilidades e especificidades adequadas (78.5% e 70.7%, respetivamente), boa capacidade de discriminação (área abaixo da curva=0.832; 95% CI 0.784-0.879), e uma adequada validade da convergência com o índice de doença da superfície ocular (ocular surface disease index - OSDI; coeficiente de correlação de Spearman=0.728, p<0.001). A análise fatorial revelou um único fator responsável por explicar a variância comum em 37.7%. Um funcionário com uma pontuação ≥7 pontos sofria de síndrome visual do computador. Conclusão: O CVS-Q PT© pode ser considerada uma ferramenta intuitiva, de fácil interpretação e com boas pro priedades psicométricas para avaliar a síndrome visual do computador em funcionários portugueses expostos a ecrãs digitais. Este questionário facilitará as decisões sobre medidas preventivas, intervenções e tratamento, e a comparação entre as populações expostas em diferentes países de língua portuguesa.This work was supported by the Portuguese Foundation for Science and Technology in the framework of the Strategic Funding UIDB/04650/2020. The authors thank the Vice-Rectorate of Research of the University of Alicante for the pre-doctoral training contract for the first author (UAFPU2019-08). This article will form part of the first author’s doctoral thesis