J. Sens. Actuator Netw., Volume 6, Issue 4 (December 2017) – 11 articles

Understanding home activities is important in social research to study aspects of home life, e.g., energy-related practices and assisted living arrangements. Common approaches to identifying which activities are being carried out in the home rely on self-reporting, either retrospectively (e.g., interviews, questionnaires, and surveys) or at the time of the activity (e.g., time use diaries). The use of digital sensors may provide an alternative means of observing activities in the home. For example, temperature, humidity and light sensors can report on the physical environment where activities occur, while energy monitors can report information on the electrical devices that are used to assist the activities. One may then be able to infer from the sensor data which activities are taking place. However, it is first necessary to calibrate the sensor data by matching it to activities identified from self-reports. The calibration involves identifying the features in the sensor data that correlate best with the self-reported activities. This in turn requires a good measure of the agreement between the activities detected from sensor-generated data and those recorded in self-reported data. To illustrate how this can be done, we conducted a trial in three single-occupancy households from which we collected data from a suite of sensors and from time use diaries completed by the occupants. For sensor-based activity recognition, we demonstrate the application of Hidden Markov Models with features extracted from mean-shift clustering and change points analysis. A correlation-based feature selection is also applied to reduce the computational cost. A method based on Levenshtein distance for measuring the agreement between the activities detected in the sensor data and that reported by the participants is demonstrated. We then discuss how the features derived from sensor data can be used in activity recognition and how they relate to activities recorded in time use diaries. Full article

Widely deploying sensors in the environment and embedding them in physical objects is a crucial step towards realizing smart and sustainable cities. To cope with rising resource demands and limited budgets, opportunistic networks (OppNets) offer a scalable backhaul option for collecting delay-tolerant data from sensors to gateways in order to enable efficient urban operations and services. While pervasive devices such as smartphones and tablets contribute significantly to the scalability of OppNets, closely following human movement patterns and social structure introduces network characteristics that pose routing challenges. Our study on the impact of these characteristics reveals that existing routing protocols subject a key set of devices to higher resource consumption, to which their users may respond by withdrawing participation. Unfortunately, existing solutions addressing this unfairness do not guarantee achievable throughput since they are not specifically designed for sensed data collection scenarios. Based on concepts derived from the study, we suggest design guidelines for adapting applicable routing protocols to sensed data collection scenarios. We also follow our design guidelines to propose the Fair Locality Aware Routing (FLARoute) technique. Evaluating FLARoute within an existing routing protocol confirms improved fairness and throughput under conditions that compromise the performance of existing solutions. Full article

One of the most critical issues in modern cities is transportation management. Issues that are encountered in this regard, such as traffic congestion, high accidents rates and air pollution etc., have pushed the use of Intelligent Transportation System (ITS) technologies in order to facilitate the traffic management. Seen in this perspective, this paper brings forward a road traffic management system based on wireless sensor networks; it introduces the functional and deployment architecture of the system and focuses on the analysis component that uses a new extension of batches Petri nets for modeling road traffic flow. A real world implementation of visualization and data analysis components were carried out. Full article

To address the ever increasing demand for wireless bandwidth, cognitive radio networks (CRNs) have been proposed to improve the efficiency of channel utilization. CRN permits unlicensed users to utilize the idle spectrum as long as it does not introduce interference to the primary users due to the Federal Communications Commission’s recent regulatory policies. In this paper, we first identify some required distinctive security and privacy features for CRNs focused on ECMA-392, which is the first industrial standard for personal or portable devices in the television white spaces. After that, we propose a delegation based user authentication framework as a basic security and privacy module with full consideration of the required features over CRNs. The proposed framework provides privacy preserving yet accountable security within the CRN entities. Security and privacy analyses show that the proposed framework supports unlinkability, context privacy, anonymity, no registration and conditional traceability, which are the required security and privacy aspects in CRNs. Full article

This paper presents a novel system based on the Internet of Things (IoT) to Human Activity Recognition (HAR) by monitoring vital signs remotely. We use machine learning algorithms to determine the activity done within four pre-established categories (lie, sit, walk and jog). Meanwhile, it is able to give feedback during and after the activity is performed, using a remote monitoring component with remote visualization and programmable alarms. This system was successfully implemented with a 95.83% success ratio. Full article

We live in an era where typical measures towards the mitigation of environmental degradation follow the identification and recording of natural parameters closely associated with it. In addition, current scientific knowledge on the one hand may be applied to minimize the environmental impact of anthropogenic activities, whereas informatics on the other, playing a key role in this ecosystem, do offer new ways of implementing complex scientific processes regarding the collection, aggregation and analysis of data concerning environmental parameters. Furthermore, another related aspect to consider is the fact that almost all relevant data recordings are influenced by their given spatial characteristics. Taking all aforementioned inputs into account, managing such a great amount of complex and remote data requires specific digital structures; these structures are typically deployed over the Web on an attempt to capitalize existing open software platforms and modern developments of hardware technology. In this paper we present an effort to provide a technical solution based on sensing devices that are based on the well-known Arduino platform and operate continuously for gathering and transmitting of environmental state information. Controls, user interface and extensions of the proposed project rely on the Android mobile device platform (both from the software and hardware side). Finally, a crucial novel aspect of our work is the fact that all herein gathered data carry spatial information, which is rather fundamental for the successful correlation between pollutants and their place of origin. The latter is implemented by an interactive Web GIS platform operating oversight in situ and on a timeline basis. Full article

With the advent of the Internet of Things (IoT) concept and its integration with the smart city sensing, smart connected health systems have appeared as integral components of the smart city services. Hard sensing-based data acquisition through wearables or invasive probes, coupled with soft sensing-based acquisition such as crowd-sensing results in hidden patterns in the aggregated sensor data. Recent research aims to address this challenge through many hidden perceptron layers in the conventional artificial neural networks, namely by deep learning. In this article, we review deep learning techniques that can be applied to sensed data to improve prediction and decision making in smart health services. Furthermore, we present a comparison and taxonomy of these methodologies based on types of sensors and sensed data. We further provide thorough discussions on the open issues and research challenges in each category. Full article

Mobile cloud computing (MCC) is becoming a popular mobile technology that aims to augment local resources of mobile devices, such as energy, computing, and storage, by using available cloud services and functionalities. The offloading process is one of the techniques used in MCC to enhance the capabilities of mobile devices by moving mobile data and computation-intensive operations to cloud platforms. Several techniques have been proposed to perform and improve the efficiency and effectiveness of the offloading process, such as multi-criteria decision analysis (MCDA). MCDA is a well-known concept that aims to select the best solution among several alternatives by evaluating multiple conflicting criteria, explicitly in decision making. However, as there are a variety of platforms and technologies in mobile cloud computing, it is still challenging for the offloading process to reach a satisfactory quality of service from the perspective of customers’ computational service requests. Thus, in this paper, we conduct a literature review that leads to a better understanding of the usability of the MCDA methods in the offloading operation that is strongly reliant on the mobile environment, network operators, and cloud services. Furthermore, we discuss the challenges and opportunities of these MCDA techniques for offloading research in mobile cloud computing. Finally, we recommend a set of future research directions in MCDA used for the mobile cloud offloading process. Full article

Localization is an important aspect in the field of wireless sensor networks (WSNs) that has developed significant research interest among academia and research community. Wireless sensor network is formed by a large number of tiny, low energy, limited processing capability and low-cost sensors that communicate with each other in ad-hoc fashion. The task of determining physical coordinates of sensor nodes in WSNs is known as localization or positioning and is a key factor in today’s communication systems to estimate the place of origin of events. As the requirement of the positioning accuracy for different applications varies, different localization methods are used in different applications and there are several challenges in some special scenarios such as forest fire detection. In this paper, we survey different measurement techniques and strategies for range based and range free localization with an emphasis on the latter. Further, we discuss different localization-based applications, where the estimation of the location information is crucial. Finally, a comprehensive discussion of the challenges such as accuracy, cost, complexity, and scalability are given. Full article

The ease of deployment and the auto-configuration capabilities of Wireless Sensor Networks (WSNs) make them very attractive in different domains like environmental, home automation or heath care applications. The use of multichannel communications in WSNs helps to improve the overall performance of the network. However, in heavy traffic scenarios, routing protocols should be adapted to allow load balancing and to avoid losing data packets due to congestion and queue overflow. In this paper, we present an Acknowledgement-Based Opportunistic Routing (ABORt) protocol designed for high data rate multichannel WSNs. It is a low overhead protocol that does not rely on synchronization for control traffic exchange during the operational phase of the network. ABORt is an opportunistic protocol that relies on link layer acknowledgements to disseminate routing metrics, which helps to reduce overhead. The performance of ABORt is evaluated using the Cooja simulator and the obtained results show that ABORt has a high packet delivery ratio with reduced packet end-to-end delay compared to two single channel routing protocols and two multichannel routing protocols that use number of hops and expected transmission count as routing metrics. Full article

In wireless sensor networks, building energy-efficient systems is one of the major challenges. In such networks, nodes are usually supplied by low power and small batteries. Many factors are involved in the energy consumption, and this issue may be considered as a cross-layer problem, from the hardware architecture to the application layer. This paper aims at presenting a hybrid solution for sensor networks based on two main aspects. The first one is the hardware architecture, where we present a prototype of a sensor node we designed. This node proved its efficiency in terms of energy consumption. The second aspect is related to the topology construction and presents a new topology control algorithm based on graph computing. Thus, our system consists of a real indoor application for temperature and humidity monitoring, applicable to home automation or industrial monitoring. We performed the experiments using a set of sensor nodes deployed over a building and proved the efficiency of the system in terms of energy consumption, network lifetime and data delivery. Full article