Wireless Sensor networks and the Internet of Things

It is estimated that mobile internet devices that can act as sensors will outnumber humans this year (2013), and by 2015, there will be about 15 billion internet-connected devices. Related applications are thriving in commercial, civic, and scientific operations that involve sensors, web, and services, leading by both academic societies and industry companies. It is commonly accepted that the next generation of internet is becoming the “Internet of Things (IoT)” which is a worldwide network of interconnected objects and their virtual representations uniquely addressable based on standard communication protocols. Identified by a unique address, any object including computers, mobile phones, RFID tagged devices, and especially Wireless Sensor Networks (WSN) will be able to dynamically join the network, collaborate, and cooperate efficiently to achieve different tasks. With all these objects in the world equipped with tiny identifying devices, daily life on earth would undergo a big transformation

Wireless sensor networks and the Internet of Things

El Internet de las cosas (IoT) percibe un mundo donde los dispositivos que lo conforman pueden ser identificados en el Internet y está creciendo a un ritmo acelerado con nuevos dispositivos que se van conectando. En este sentido, las redes de sensores inalámbricos juegan un papel importante para incrementar la ubicuidad de las redes con dispositivos inteligentes de bajo costo y fácil implementación, con estándares como IEEE 802.15.4 en la capa física, 6LoWPAN en la capa de red, y RPL como protocolo de enrutamiento, que se integran en el concepto de IoT para traer nuevas experiencias en las actividades de la vida diaria, como por ejemplo en aplicaciones para hogares y oficinas confortables, salud, vigilancia del medio ambiente y ciudades inteligentes. En el presente artículo se relacionará a la red de sensores inalámbricos con el Internet de las cosas a través de estándares y protocolos.The Internet of Things (IoT) perceives a world where the devices that make it up can be identified on the Internet and is growing at an accelerated pace with new devices that are connecting. In this sense, wireless sensor networks play an important role in increasing the ubiquity of networks with smart devices low cost and easy implementation, with standards such as IEEE 802.15.4 in the physical layer, 6LoWPAN in the network layer, and RPL as a routing protocol, which are integrated into the IoT concept to bring new experiences in the activities of the daily life, such as in applications for comfortable homes and offices, health, environmental monitoring and smart cities. In this article, the wireless sensor network will be related to the Internet of things through standards and protocols

Private inter-network routing for wireless sensor networks and the Internet of Things

As computing becomes increasingly pervasive, different heterogeneous networks are connected and integrated. This is especially true in the Internet of Things (IoT) and Wireless Sensor Networks (WSN) settings. However, as different networks managed by different parties and with different security requirements are integrated, security becomes a primary concern. WSN nodes, in particular, are often deployed "in the open", where a potential attacker can gain physical access to the device. As nodes can be deployed in hostile or difficult scenarios, such as military battlefields or disaster recovery settings, it is crucial to avoid escalation from successful attacks on a single node to the whole network, and from there to other connected networks. It is therefore crucial to secure the communication within the WSN, and in particular, maintain context information, such as the network topology and the location and identity of base stations (which collect data gathered by the sensors) private. In this paper, we propose a protocol achieving anonymous routing between different interconnected IoT or WSN networks, based on the Spatial Bloom Filter (SBF) data structure. The protocol enables communications between the nodes through the use of anonymous identifiers, thus hiding the location and identity of the nodes within the network. The proposed routing strategy preserves context privacy, and prevents adversaries from learning the network structure and topology, as routing information is encrypted using a homomorphic encryption scheme, and computed only in the encrypted domain. Preserving context privacy is crucial in preventing adversaries from gaining valuable network information from a successful attacks on a single node of the network, and reduces the potential for attack escalation

Reprogramming embedded systems at run-time

The dynamic re-programming of embedded systems is a long-standing problem in the field. With the advent of wireless sensor networks and the 'Internet of Things' it has now become necessary to be able to reprogram at run-time due to the difficulty of gaining access to such systems once deployed. The issues of power consumption, flexibility, and operating system protections are examined for a range of approaches, and a critical comparison is given. A combination of approaches is recommended for the implementation of real-world systems and areas where further work is required are highlighted.Postprin

Rate-Distortion Classification for Self-Tuning IoT Networks

Many future wireless sensor networks and the Internet of Things are expected to follow a software defined paradigm, where protocol parameters and behaviors will be dynamically tuned as a function of the signal statistics. New protocols will be then injected as a software as certain events occur. For instance, new data compressors could be (re)programmed on-the-fly as the monitored signal type or its statistical properties change. We consider a lossy compression scenario, where the application tolerates some distortion of the gathered signal in return for improved energy efficiency. To reap the full benefits of this paradigm, we discuss an automatic sensor profiling approach where the signal class, and in particular the corresponding rate-distortion curve, is automatically assessed using machine learning tools (namely, support vector machines and neural networks). We show that this curve can be reliably estimated on-the-fly through the computation of a small number (from ten to twenty) of statistical features on time windows of a few hundreds samples

Similarities and differences between Wireless Sensor Networks and the Internet of Things: Towards a clarifying position

Las redes de sensores inalámbricas (WSN) e Internet de las Cosas (IoT) son dos áreas de estudio que comparten entre sí ser una infraestructura de red autónoma, en la cual se interconectan objetos para medir variables físicas y dar solución a problemas en una variedad de escenarios de aplicación, como logística, industria, construcciones inteligentes, seguridad, agricultura, entre otros. Esta semejanza suscita una ambigüedad en el uso que la comunidad académica hace de los términos, WSN e IoT, y hace borrosa la línea de dónde pertenecen las contribuciones que se realizan en cada una de estas áreas. En consecuencia, el objetivo de este artículo es analizar la relación, similitud y diferencias entre WSN e IoT en torno a cinco temas: conceptos, requisitos generales, arquitecturas, aplicaciones y tratamiento de datos. A pesar de que WSN e IoT tienen un origen en común, sus enfoques son diferentes en varios aspectos que permiten aclarar la ambigüedad suscita entre la comunidad académica.Wireless Sensor Network (WSN) and Internet of Things (IoT) are two fields of study, which share, being an autonomous network infrastructure, where objects are interconnected to measure physical variables in scenarios such as logistics, industry, intelligent constructions, security, agriculture, among others. This similarity raises an ambiguity in the academic community's use of the terms WSN and IoT doing blurred the line of where belong the contributions that are made in each of these areas of study. Therefore, the purpose of this article is to analyze the relationship, similarity, and differences between WSN and IoT around five topics, namely: conceptual level, its general requirements and architectures, application construction and data processing. Although WSN and IoT have a common origin, their approaches are different in several ways that clarify the ambiguity arouses among the academic community

Integration of internet of things with wireless sensor network

The Internet of things (IoT) is a major source for technology solutions in many industries. The IoT can consider, Wireless Sensor Network (WSN) as the backbone network to reduce formation or advent of new technology. Integration of these would reduce the burden and form smart sensor node network with nodes given access to internet. WSN is already a major legacy system that has percolated into many industries. Thus by integration of IoT and WSN no huge paradigm shift is needed for the industries

Radio Frequency Energy Harvesting for Low Power Sensors

Wireless sensor networks and the internet of things are benefiting from recent advances in power consumption to implement intelligent control entities. Similar advances in battery technology have enabled these systems to become autonomous. Nevertheless, this approach is insufficient for modern applications. An alternative solution to power these sensors is to use the energy available in their environment, such as thermal, mechanical vibration, light or radio frequencies. However, sensors are frequently placed in an environment where power density is low. This study investigates energy harvesting from radio frequencies compared to other sources. After demonstrating the potential for collecting energy over a wide frequency band, a statistical study was carried out to determine the RF power density present in the urban environment and in rural areas. Multi-band RF harvester systems were designed to harvest energy in several frequency bands to show when multiple RF sources are available. The amount of energy harvested can be increased when the system is designed to operate over a wide frequency band. In this study, multiband RF energy harvester to power wireless sensors is produced using Advanced Design Software (ADS). According to the design outcomes the proposed energy harvesting scheme works better on the GSM900 and GSM1800 bands