Smart Collaborative Mobile System for Taking Care of Disabled and Elderly People

Official statistics data show that in many countries the population is aging. In addition, there are several illnesses and disabilities that also affect a small sector of the population. In recent years, researchers and medical foundations are working in order to develop systems based on new technologies and enhance the quality of life of them. One of the cheapest ways is to take advantage of the features provided by the smartphones. Nowadays, the development of reduced size smartphones, but with high processing capacity, has increased dramatically. We can take profit of the sensors placed in smartphones in order to monitor disabled and elderly people. In this paper, we propose a smart collaborative system based on the sensors embedded in mobile devices, which permit us to monitor the status of a person based on what is happening in the environment, but comparing and taking decisions based on what is happening to its neighbors. The proposed protocol for the mobile ad hoc network and the smart system algorithm are described in detail. We provide some measurements showing the decisions taken for several common cases and we also show the performance of our proposal when there is a medium size group of disabled or elderly people. Our proposal can also be applied to take care of children in several situations.This work has been partially supported by the Instituto de Telecomunicacoes, Next Generation Networks and Applications Group (NetGNA), Portugal, and by National Funding from the FCT - Fundacao para a Ciencia e a Tecnologia through the PEst-OE/EEI/LA0008/2011 Project.Sendra Compte, S.; Granell Romero, E.; Lloret, J.; Rodrigues, JJPC. (2014). Smart Collaborative Mobile System for Taking Care of Disabled and Elderly People. Mobile Networks and Applications. 19(3):287-302. doi:10.1007/s11036-013-0445-zS287302193Cisco Systems Inc. “Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2010–2015.” White Paper, February 1, 2011Pereira O, Caldeira J, Rodrigues J (2011) Body sensor network mobile solutions for biofeedback monitoring. J Mob Netw Appl 16(6):713–732Google. Galaxy nexus (2012). Available: http://www.google.com/nexus/E. Commission. “Demography report 2010.” Eurostat, the Statistical Office of the European Union, 2010. At http://ec.europa.eu/social/BlobServlet?docId=6824&langId=enThomas KE, Stevens JA, Sarmiento K, Wald MM (2008) Fall-related traumatic brain injury deaths and hospitalizations among older adults—United States, 2005. J Saf Res 39(3):269–272Fortino G, Giannantonio R, Gravina R, Kuryloski P, Jafari R, (2013) Enabling effective programming and flexible management of efficient body sensor network applications. IEEE Trans Hum Mach Syst 43(1):115–133Bellifemine F, Fortino G, Giannantonio R, Gravina R, Guerrieri A, Sgroi M (2011) SPINE: a domain-specific framework for rapid prototyping of WBSN applications. Softw Pract Exper 41(3):237–265Macias E, Lloret J, Suarez A, Garcia M (2012) Architecture and protocol of a semantic system designed for video tagging with sensor data in mobile devices. Sensors 12(2):2062–2087Sendra S, Granell E, Lloret J, Rodrigues JJPC. Smart Collaborative System Using the Sensors of Mobile Devices for Monitoring Disabled and Elderly People, 3rd IEEE International Workshop on Smart Communications in Network Technologies, Ottawa, Canada, June 11, 2012Lane N, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell A (2010) A survey of mobile phone sensing. IEEE Commun Mag 48(9):140–150Muldoon C, OHare G, OGrady M (2006) Collaborative agent tuning: Performance enhancement on mobile devices Engineering Societies in the Agents World VI, Lecture Notes in Computer Science, Volume 3963/2006, pp 241–258Turner H, White J, Thompson C, Zienkiewicz K, Campbell S, Schmidt DC (2009) Building Mobile Sensor Networks Using Smartphones and Web Services: Ramifications and Development Challenges, Handbook of Research on Mobility and Computing, Hershey, PA. Available: http://lsrg.cs.wustl.edu/~schmidt/PDF/new-ww-mobile-computing.pdfKansal A, Goraczko M, Zhao F. Building a sensor network of mobile phones, 6th International Conference on Information Processing in Sensor Networks. Cambridge, Massachusetts, USA, April 24–27, 2007 pp 547–548Plaza I, Martín L, Martin S, Medrano C (2011) Mobile applications in an aging society: status and trends. J Syst Softw 84(11):1977–1988Camarinha-Matos L, Afsarmanesh H. Telecare: Collaborative virtual elderly support communities, 1st Workshop on Tele-Care and Collaborative Virtual Communities in Elderly Care, Porto, Portugal, 13 April, 2004Chen B, Pompili D (2011) Transmission of patient vital signs using wireless body area networks. J Mob Netw Appl 16(6):663–682Dai J, Bai X, Yang Z, Shen Z, Xuan D (2010) Mobile phone-based pervasive fall detection. Pers Ubiquit Comput 14(7):633–643Martin P, Sánchez MA, Álvarez L, Alonso V, Bajo J. Multiagent system for detecting elderly people falls through mobile devices, International Symposium on Ambient Intelligence (ISAmI’11), Salamanca (Spain) 6–8 April 2011Fahmi PN, Viet V, Deok-Jai C. “Semi-supervised fall detection algorithm using fall indicators in smartphone.” Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication, 2012, pp 122Sánchez M, Martín P, Álvarez L, Alonso V, Zato C, Pedrero A, Bajo J (2011) A New Adaptive Algorithm for Detecting Falls through Mobile Devices, Trends in Practical Applications of Agents and Multiagent Systems, pp 17–24Fahim M, Fatima I, Lee S, Lee YK. Daily Life Activity Tracking Application for Smart Homes using Android Smartphone, 14th International Conference on Advanced Communication Technology, Yongin, South Korea, 19–22 February 2012, pp 241–245Kaluža B, Mirchevska V, Dovgan E, Luštrek M, Gams M (2010) An agent-based approach to care in independent living, Ambient Intelligence, Lecture Notes in Computer Science, vol. 6439, pp 177–186Costa A, Barbosa G, Melo T, Novais P (2011) Using mobile systems to monitor an ambulatory patient. In: International Symposium on Distributed Computing and Artificial Intelligence, Advances in Intelligent and Soft Computing, vol. 91, pp 337–344Olfati-Saber R, Fax J, Murray R (2007) Consensus and cooperation in networked multi-agent systems. Proc IEEE 95(1):215–233Arcelus A, Jones MH, Goubran R, Knoefel F (2007) Integration of smart home technologies in a health monitoring system for the elderly, 21st International Conference on Advanced Information Networking and Applications Workshops, vol. 2, pp 820–825Kahmen H, Faig W (1988) Surveying. Walter de Gruyter & Co, New YorkSol LM870 mobile phone features. Available at: http://es.made-in-china.com/co_runrise/product_Dual-SIM-Card-Dual-Standby-GPS-Temperature-UV-Sensor-Pedometer-Sunrise-LM870-Mobile-Phone_hesighyiy.htmlSTLM20 temperature sensor features. Datashhet available at: http://www.st.com/internet/com/TECHNICAL_RESOURCES/TECHNICAL_LITERATURE/DATASHEET/CD00119601.pdfSendra S, Lloret J, Garcia M, Toledo JF (2011) Power saving and energy optimization techniques for wireless sensor networks. J Commun 6(6):439–459Matlab Website. Available at: www.mathworks.com/products/matlabPal A (2010) Localization algorithms in wireless sensor networks: current approaches and future challenges. Netw Protocol Algorithm 2(1):45–74Garcia M, Boronat F, Tomás J, Lloret J (2009) The development of two systems for indoor wireless sensors self-location. Ad Hoc Sensor Wirel Netw 8(3–4):235–258Lloret J, Tomás J, Garcia M, Cánovas A (2009) A hybrid stochastic approach for self-location of wireless sensors in indoor environments. Sensors 9(5):3695–3712Garcia M, Sendra S, Turro C, Lloret J (2011) User’s macro and micro-mobility study using WLANs in a university campus. Int J Adv Internet Technol 4(1&2):37–46Lloret J, Tomas J, Canovas A, Bellver I. GeoWiFi: A Geopositioning System Based on WiFi Networks, The Seventh International Conference on Networking and Services (ICNS 2011), Venice (Italy), May 6–10, 2011Yu W, Su X, Hansen J (2012) A smartphone design approach to user communication interface for administering storage system network. Netw Protoc Algorithm 4(4):126–15

Human-mobility-based sensor context-aware routing protocol for delay-tolerant data gathering in multi-sink cell-phone-based sensor networks

Ubiquitous use of cell phones encourages development of novel applications with sensors embedded in cell phones. The collection of information generated by these devices is a challenging task considering volatile topologies and energy-based scarce resources. Further, the data delivery to the sink is delay tolerant. Mobility of cell phones is opportunistically exploited for forwarding sensor generated data towards the sink. Human mobility model shows truncated power law distribution of flight length, pause time, and intercontact time. The power law behavior of inter-contact time often discourages routing of data using naive forwarding schemes. This work exploits the flight length and the pause time distributions of human mobility to design a better and efficient routing strategy. We propose a Human-Mobility-based Sensor Context-Aware Routing protocol (HMSCAR), which exploits human mobility patterns to smartly forward data towards the sink basically comprised of wi-fi hot spots or cellular base stations. The simulation results show that HMSCAR significantly outperforms the SCAR, SFR, and GRAD-MOB on the aspects of delivery ratio and time delay. A multi-sink scenario and single-copy replication scheme is assumed

Team-level programming of drone sensor networks

Autonomous drones are a powerful new breed of mobile sensing platform that can greatly extend the capabilities of traditional sensing systems. Unfortunately, it is still non-trivial to coordinate multiple drones to perform a task collaboratively. We present a novel programming model called team-level programming that can express collaborative sensing tasks without exposing the complexity of managing multiple drones, such as concurrent programming, parallel execution, scaling, and failure recovering. We create the Voltron programming system to explore the concept of team-level programming in active sensing applications. Voltron offers programming constructs to create the illusion of a simple sequential execution model while still maximizing opportunities to dynamically re-task the drones as needed. We implement Voltron by targeting a popular aerial drone platform, and evaluate the resulting system using a combination of real deployments, user studies, and emulation. Our results indicate that Voltron enables simpler code and produces marginal overhead in terms of CPU, memory, and network utilization. In addition, it greatly facilitates implementing correct and complete collaborative drone applications, compared to existing drone programming systems

Recommendations to Harmonize Travel Behaviour Analysis

Among several other efforts to identify data needs and to harmonize travel surveys in Europe, this report aims to define recommendations to collect and report travel data with the identification of main data needs and gaps, and with the analysis of alternative sources of information and new data collection techniques. Based on the findings of the previous tasks and a stakeholder workshop in OPTIMISM project, and after a brief review of past studies in the same direction, this report starts from a list of variables which are needed for policy making but are unavailable/insufficient in the context of existing data collection methodologies especially with respect to NTS. The report then, explores alternative sources of information, potential use of modern data collection techniques (mainly ICT applications such as GPS and smart phone technologies) and options to merge them with NTS data. Finally, it discusses recommendations for a Europe-wide travel survey considering the current data needs for policy making in transportation. The research has been conducted under the OPTIMISM project which was received funding from the European Union's Seventh Framework Programme (FP7/2007-2013), grant agreement n° 284892. The report has been produced as the OPTIMISM project deliverable 2.3: Recommendations to Harmonize Travel Behaviour Analysis.JRC.J.1-Economics of Climate Change, Energy and Transpor

Smart vehicle navigation system using hidden Markov model and RFID technology

The road transport of dangerous goods has been the subject of research with increasing frequency in recent years. Global positioning system (GPS) based vehicle location devices are used to track vehicles in transit. However, this tracking technology suffers from inaccuracy and other limitations. In addition, real-time tracking of vehicles through areas shielded from GPS satellites is difficult. In this paper, the authors have addressed the implementation of a smart vehicle navigation system capable of using radio frequency identification based on information about navigation paths. For prediction of paths and accurate determination of navigation paths in advance, predictive algorithms have been used based on the hidden Markov model. At the core of the system there is an existing field programmable gate array board and hardware for collection of navigation data. A communication protocol and a database to store the driver’s habit data have been designed. From the experimental results obtained, an accurate navigation path prediction is consistently achieved by the system. In addition, once-off disturbances to the driver habits have been filtered out successfully.http://link.springer.com/journal/112772017-10-31hb2017Electrical, Electronic and Computer Engineerin

Supporting Learning with Wireless Sensor Data

In this article, learning is studied in in situ applications that involve sensors. The main questions are how to conceptualize experiential learning involving sensors and what kinds of learning applications using sensors already exist or could be designed. It is claimed that experiential learning, context information and sensor data supports twenty first century learning. The concepts of context, technology-mediated experiences, shared felt experiences and experiential learning theory will be used to describe a framework for sensor-based mobile learning environments. Several scenarios and case examples using sensors and sensor data will be presented, and they will be analyzed using the framework. Finally, the article contributes to the discussion concerning the role of technology-mediated learning experiences and collective sensor data in developing twenty first century learning by characterizing what kinds of skills and competences are supported in learning situations that involve sensors.In this article learning is studied in situations that involve sensors. The main questions are how to conceptualize experiential learning involving sensors and what kinds of learning applications using sensors already exist or could be designed. It is claimed that experiential learning, context information and sensor data supports 21st century learning. The concepts of context, technology-mediated experiences, shared felt experiences, and experiential learning theory will be used to describe a framework for sensor based mobile learning environments. Several scenarios and case examples using sensors and sensor data will be presented and they will be analyzed using the framework. Finally, the article contributes to the discussion concerning the role of technology-mediated learning experiences and collective sensor data in developing 21st century learning by characterizing what kinds of skills and competences are supported in learning situations that involve sensors.Peer reviewe

Location reliability and gamification mechanisms for mobile crowd sensing

People-centric sensing with smart phones can be used for large scale sensing of the physical world by leveraging the sensors on the phones. This new type of sensing can be a scalable and cost-effective alternative to deploying static wireless sensor networks for dense sensing coverage across large areas. However, mobile people-centric sensing has two main issues: 1) Data reliability in sensed data and 2) Incentives for participants. To study these issues, this dissertation designs and develops McSense, a mobile crowd sensing system which provides monetary and social incentives to users. This dissertation proposes and evaluates two protocols for location reliability as a step toward achieving data reliability in sensed data, namely, ILR (Improving Location Reliability) and LINK (Location authentication through Immediate Neighbors Knowledge). ILR is a scheme which improves the location reliability of mobile crowd sensed data with minimal human efforts based on location validation using photo tasks and expanding the trust to nearby data points using periodic Bluetooth scanning. LINK is a location authentication protocol working independent of wireless carriers, in which nearby users help authenticate each other’s location claims using Bluetooth communication. The results of experiments done on Android phones show that the proposed protocols are capable of detecting a significant percentage of the malicious users claiming false location. Furthermore, simulations with the LINK protocol demonstrate that LINK can effectively thwart a number of colluding user attacks. This dissertation also proposes a mobile sensing game which helps collect crowd sensing data by incentivizing smart phone users to play sensing games on their phones. We design and implement a first person shooter sensing game, “Alien vs. Mobile User”, which employs techniques to attract users to unpopular regions. The user study results show that mobile gaming can be a successful alternative to micro-payments for fast and efficient area coverage in crowd sensing. It is observed that the proposed game design succeeds in achieving good player engagement