HMP: A Hybrid Monitoring Platform for Wireless Sensor Networks Evaluation

(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.[EN] Wireless sensor networks (WSNs), as an essential part of the deployment of the Internet of Things paradigm, require an adequate debugging and monitoring procedures to avoid errors in their operation. One of the best tools for WSN supervision is the so-called Monitoring Platforms that harvest information about the WSN operation in order to detect errors and evaluate performance. Monitoring platforms for the WSN can be hardware or software implemented, and, additionally, they can work in active or passive mode. Each approach has advantages and drawbacks. To benefit from their advantages and compensate their limitations, hybrid platforms combine different approaches. However, very few hybrid tools, with many restrictions, have been proposed. Most of them are designed for a specific implementation of WSN nodes; many of them are lack of a real implementation, and none of them provides an accurate solution to synchronization issues. This paper presents a hybrid monitoring platform for WSN, called HMP. This platform combines both hardware and software, active and passive monitoring approaches. This hybridization provides many interesting capabilities; HMP harvests the information both actively (directly from the sensor nodes) and passively (by means of messages captured from the WSN), causing a very low intrusion in the observed network. In addition, HMP is reusable; it may be applied to almost any WSN and includes a suitable trace synchronism procedure. Finally, HMP follows an open architecture that allows interoperability and layered development.This work was supported by the Agencia Estatal de Investigacion from the Spanish Ministerio de Economia, Industria y Competitividad, through the project Hacia el hospital inteligente: Investigacion en el diseno de una plataforma basada en Internet de las Cosas y su aplicacion en la mejora del cumplimiento de higiene de manos, under Grant DPI2016-80303-C2-1-P. The project covers the costs of publishing in open access.Navia-Mendoza, MR.; Campelo Rivadulla, JC.; Bonastre Pina, AM.; Capella Hernández, JV.; Ors Carot, R. (2019). HMP: A Hybrid Monitoring Platform for Wireless Sensor Networks Evaluation. IEEE Access. 7:87027-87041. https://doi.org/10.1109/ACCESS.2019.2925299S8702787041

A handheld diagnostic system for 6LoWPAN networks

The successful deployment of low-power wireless sensor networks (WSNs) in real application environments is a much broader exercise than just the simple instrumentation of the intended monitoring site. Many problems, from node malfunctions to connectivity issues, may arise during commissioning of these networks. These need to be corrected on the spot, often within limited time, to avoid undesired delays in commissioning and yet a fully functional system does not guarantee that no new problems will occur after leaving the site. In this paper we present the first ever (to our knowledge) implementation of a handheld diagnostic system for fast on-site commissioning of low-power IPv6 (6LoWPAN) WSNs as well as troubleshooting of network problems during and after deployment. This system can be used where traditional solutions are insufficient to ascertain the root causes of any problems encountered at no additional complexity in the implementation of the WSN. The embedded diagnosis capability in our system is based on a lightweight decision tree that distills the functioning of communication protocols in use by the network, with a major focus on interoperable IPv6 standards and protocols for low-power WSNs. To show the applicability of our system, we present a set of experiments based on results from a real deployment in a large construction site. Through these experiments, important performance insights are gained that can be used as guidelines for improvement of operation and maintenance of 6LoWPAN networks.This research has been funded by the EPSRC Innovation and Knowledge Centre for Smart Infrastructure and Construction project (EP/K000314/1). The authors wish to thank Costain-Skanska Joint Venture (CSJV) and our industrial partner Crossrail for allowing access and instrumentation of the Paddington site referenced in this paper

Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves

A Framework and Classification for Fault Detection Approaches in Wireless Sensor Networks with an Energy Efficiency Perspective

Wireless Sensor Networks (WSNs) are more and more considered a key enabling technology for the realisation of the Internet of Things (IoT) vision. With the long term goal of designing fault-tolerant IoT systems, this paper proposes a fault detection framework for WSNs with the perspective of energy efficiency to facilitate the design of fault detection methods and the evaluation of their energy efficiency. Following the same design principle of the fault detection framework, the paper proposes a classification for fault detection approaches. The classification is applied to a number of fault detection approaches for the comparison of several characteristics, namely, energy efficiency, correlation model, evaluation method, and detection accuracy. The design guidelines given in this paper aim at providing an insight into better design of energy-efficient detection approaches in resource-constraint WSNs

Dependence-Based Source Level Tracing and Replay for Networked Embedded Systems

Error detection and diagnosis for networked embedded systems remain challenging and tedious due to issues such as a large number of computing entities, hardware resource constraints, and non-deterministic behaviors. The run-time checking is often necessitated by the fact that the static verification fails whenever there exist conditions unknown prior to execution. Complexities in hardware, software and even the operating environments can also defeat the static analysis and simulations. Record-and-replay has long been proposed for distributed systems error diagnosis. Under this method, assertions are inserted in the target program for run-time error detection. At run-time, the violation of any asserted property triggers actions for reporting an error and saving an execution trace for error replay. This dissertation takes wireless sensor networks, a special but representative type of networked embedded systems, as an example to propose a dependence-based source-level tracing-and-replay methodology for detecting and reproducing errors. This work makes three main contributions towards making error detection and replay automatic. First, SensorC, a domain-specific language for wireless sensor networks, is proposed to specify properties at a high level. This property specification approach can be not only used in our record-replay methodology but also integrated with other verification analysis approaches, such as model checking. Second, a greedy heuristic method is developed to decompose global properties into a set of local ones with the goal of minimizing the communication traffic for state information exchanges. Each local property is checked by a certain sensor node. Third, a dependence-based multi-level method for memory-efficient tracing and replay is proposed. In the interest of portability across different hardware platforms, this method is implemented as a source-level tracing and replaying tool. To test our methodology, we have built different wireless sensor networks by using TelosB motes and Zolertia Z1 motes separately. The experiments\u27 results show that our work has made it possible to instrument several test programs on wireless sensor networks under the stringent program memory constraint, reduce the data transferring required for error detection, and find and diagnose realistic errors

A Passive Testing Approach for Protocols in Wireless Sensor Networks

Smart systems are today increasingly developed with the number of wireless sensor devices drastically increasing. They are implemented within several contexts throughout our environment. Thus, sensed data transported in ubiquitous systems are important, and the way to carry them must be efficient and reliable. For that purpose, several routing protocols have been proposed for wireless sensor networks (WSN). However, one stage that is often neglected before their deployment is the conformance testing process, a crucial and challenging step. Compared to active testing techniques commonly used in wired networks, passive approaches are more suitable to the WSN environment. While some works propose to specify the protocol with state models or to analyze them with simulators and emulators, we here propose a logic-based approach for formally specifying some functional requirements of a novel WSN routing protocol. We provide an algorithm to evaluate these properties on collected protocol execution traces. Further, we demonstrate the efficiency and suitability of our approach by its application into common WSN functional properties, as well as specific ones designed from our own routing protocol. We provide relevant testing verdicts through a real indoor testbed and the implementation of our protocol. Furthermore, the flexibility, genericity and practicability of our approach have been proven by the experimental results

A Reference Model for Monitoring IoT WSN-Based Applications

The Internet of Things (IoT) is, at this moment, one of the most promising technologies that has arisen for decades. Wireless Sensor Networks (WSNs) are one of the main pillars for many IoT applications, insofar as they require to obtain context-awareness information. The bibliography shows many difficulties in their real implementation that have prevented its massive deployment. Additionally, in IoT environments where data producers and data consumers are not directly related, compatibility and certification issues become fundamental. Both problems would profit from accurate knowledge of the internal behavior of WSNs that must be obtained by the utilization of appropriate tools. There are many ad-hoc proposals with no common structure or methodology, and intended to monitor a particular WSN. To overcome this problem, this paper proposes a structured three -layer reference model for WSN Monitoring Platforms (WSN-MP), which offers a standard environment for the design of new monitoring platforms to debug, verify and certify a WSN's behavior and performance, and applicable to every WSN. This model also allows the comparative analysis of the current proposals for monitoring the operation of WSNs. Following this methodology, it is possible to achieve a standardization of WSN-MP, promoting new research areas in order to solve the problems of each layer.This work was supported by the Technical University of Valencia Research Project SP20120889, the Ministerio de Economia y Competitividad by means of its project DPI2016-80303-C2-1-P and the Ministerio de Educacion, Cultura y Deporte as part of the program Campus de Excelencia Internacional UPV SP20140730 and UPV SP20150050.Capella Hernández, JV.; Campelo Rivadulla, JC.; Bonastre Pina, AM.; Ors Carot, R. (2016). A Reference Model for Monitoring IoT WSN-Based Applications. Sensors. 16(11):1-21. https://doi.org/10.3390/s16111816S121161

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things

In-band network telemetry in industrial wireless sensor networks

With the emergence of the Internet of Things (IoT) and Industry 4.0 concepts, industrial applications are going through a tremendous change that is imposing increasingly diverse and demanding network dynamics and requirements with a wider and more fine-grained scale. Therefore, there is a growing need for more flexible and reconfigurable industrial networking solutions complemented with powerful monitoring and management functionalities. In this sense, this paper presents a novel efficient network monitoring and telemetry solution for Industrial Wireless Sensor Networks mainly focusing on the 6TiSCH Network stack, a complete protocol stack for ultra-reliable ultra-low-power wireless mesh networks. The proposed monitoring solution creates a flexible and powerful in-band network telemetry design with minimized resource consumption and communication overhead while supporting a wide range of monitoring operations and strategies for dealing with various network scenarios and use cases. Besides, the technical capabilities and characteristics of the proposed solution are evaluated via a real-life implementation, practical and theoretical analysis. These experiments demonstrate that in-band telemetry can provide ultra-efficient network monitoring operations without any effect on the network behavior and performance, validating its suitability for Industrial Wireless Sensor Networks