IoT and Cooperation Platforms / Frameworks
O. Mazhelis and P. Tyrvainen, “A Framework for Evaluating Internet-of-Things Platforms: Application Provider Viewpoint,” in Proc. of the 2014 IEEE World Forum on Internet of Things (WF-IoT), March 2014.
The paper presents a framework for evaluating Internet of Things platforms in terms of maturity, availability, supporting features and services that can meet the requirements of application providers. A number of existing IoT Cloud platforms is also evaluated by the authors and based on the results of this initial analysis, none of these platforms today offers the comprehensive support. This can be seen as one of the reasons behind the slow adaption of the IoT platforms, and thus contributing to the slower-than-expected takeoff of the IoT ecosystem in general.
J. Mineraud, O. Mazhelis, X. Su, and S. Tarkoma, “A Gap Analysis of Internet-of-Things Platforms,” Computer Communications, vols. 89–90, pp. 5-16, September 2016.
The article evaluates a representative sample of existing Internet of Things platforms, both proprietary and open-source. The evaluation is completed by a gap analysis of the current IoT landscape with respect to features including the support of heterogeneous hardware, the capabilities of the platform for data management, the support of application developers, the extensibility of the different platforms for the formation of ecosystems, as well as the availability of dedicated marketplaces to the IoT. Based on the study results, an IoT marketplace would facilitate the growth of IoT apps and platforms, while data fusion, developer support and ecosystem formation are considered also crucial.
D. Miorandi, S. Sicari, F. D. Pellegrini, and I. Chlamtac, “Internet of Things: Vision, Applications and Research Challenges,” Ad Hoc Networks, vol. 10, no. 7, pp. 1497-1516, September 2012.
This paper is one of the early studies on the challenges of the Internet of Things and an overview of existing technologies that enable the IoT, both from the hardware (device) perspective and the communication with the Cloud and online services. The work also discusses a few of the potential visions for the reality of IoT.
J. Gubbi, R. Buyya, S. Marusic and M. Palaniswami, “Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645-1660, September 2013.
This paper gives an overview of the different visions and motivations behind the Internet of Things notion. It presents the various application domains for the IoT (healthcare, automation, smart cities, etc.) and discusses open challenges for the cloud-centric IoT platforms. It also presents a cloud-based implementation for the IoT that combines both private and public clouds depending on the use case and the requirements for data security and privacy.
R. Roman, P. Najera, and J. Lopez,“Securing the Internet of Things,” IEEE Computer, vol. 44, no. 9, pp. 51-58, September 2011.
The article presents a high-level overview of the security in the Internet of Things. The authors have covered security issues like privacy protection, identity and cryptography without going into technicalities, therefore even a reader with little familiarity with the subject should understand the presented matter. The article is a nice introduction to the problems of the security in the Internet of Things.
R. H. Weber,“Internet of Things – New Security and Privacy Challenges,” Computer Law & Security Review, vol. 26, no. 1, pp. 23-30, January 2010.
The paper describes security and privacy challenges in the Internet of Things from the legal perspective. The author presents various legal aspects that will be brought by the emergence of Internet of Things. Despite the fact that the article has been written for non-technical reader it is worth reading by technical readers for bringing more attention to legal issues during the design of technical solutions.
T. Heer, O. Garcia-Morchon, R. Hummen, S. L. Keoh, S. S. Kumar, and K. Wehrle,“Security Challenges in the IP-based Internet of Things,” Wireless Personal Communications, vol. 63, no. 3, pp. 527-542, December 2011.
This paper presents security challenges in the IP-based Internet of Things. The authors review architectural design for a secure IP-based IoT and its challenges where they have focused on the standard IP-based security protocols. They have described several security challenges like DoS resistance, end-to-end security and bootstrapping of a security domain. The article gives a nice overview of what has been done and what needs to be done in securing IP-based Internet of Things.
S. Raza, H. Shafagh, K. Hewage, R. Hummen, and T. Voigt,“Lithe: Lightweight Secure CoAP for the Internet of Things,” IEEE Sensors Journal, vol. 13, no. 10, pp. 3711-3720, October 2013.
The authors presented approach for Lightweight Secure CoAP for the Internet of Things where they have combined CoAP protocol with DTLS and subsequently showed the compression of the DTLS header for limiting the transmission overhead. The article gives good understanding of the transmission overhead problems and compression methods reducing them in the Internet of Things.
J.L. Hernandez-Ramos, M.P. Pawlowski, A.J. Jara, A. F. Skarmeta, and L. Ladid,“Toward a Lightweight Authentication and Authorization Framework for Smart Objects,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 4, pp. 690-702, April 2015.
The paper presents a framework for lightweight authentication and authorization in the Internet of Things. The authors have presented an overview of the authentication and authorization mechanisms for the Internet of Things and proposed framework that by combination of the EAP protocol for link-layer transmission and authentication, and DCapBAC for authorization, provides lightweight authentication and authorization in the Internet of Things.
A. J. Jara, D. Fernandez, P. Lopez, M.A. Zamora, and A. F Skarmeta, “Lightweight Mipv6 with Ipsec Support,” Mobile Information Systems, vol. 10, no. 1, pp. 37-77, 2014.
This paper presents the analysis of IPSec protocol for constrained environments (6LoWPAN networks) and an analysis of the impact of security in conjunction with mobility (MIPv6). Thereby, this paper introduces the basis for mobility and security in the Internet of Things, at the same time as evaluating and providing a deep analysis of the tradeoff between overhead (optimization of resources) and security.
M. Yannuzzi, R. Milito, R. Serral-Gracia, D. Montero, and M. Nemirovsky, “Key Ingredients in an IoT Recipe: Fog Computing, Cloud Computing, and More Fog Computing,” in Proc. of the 2014 IEEE 19th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), December 2014.
This paper examines some of the most promising and challenging scenarios in IoT, and shows why current compute and storage models confined to data centers will not be able to meet the requirements of many of the applications foreseen for those scenarios. The analysis is particularly centered on three interrelated requirements: 1) mobility; 2) reliable control and actuation; and 3) scalability, especially, in IoT scenarios that span large geographical areas and require real-time decisions based on data analytics. Based on the analysis, the authors expose the reasons why Fog Computing is the natural platform for IoT, and discuss the unavoidable interplay of the Fog and the Cloud in the coming years. In the process, the paper reviews some of the technologies that will require considerable advances in order to support the applications that the IoT market will demand.
F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog Computing and its Role in the Internet of Things,” in Proc. of the first edition of the MCC workshop on Mobile cloud computing, pp. 13-16, 2012.
Fog Computing extends the Cloud Computing paradigm to the edge of the network, thus enabling a new breed of applications and services. Defining characteristics of the Fog are: a) Low latency and location awareness; b) Wide-spread geographical distribution; c) c) Mobility; d) Very large number of nodes, e) Predominant role of wireless access, f) Strong presence of streaming and real time applications, g) Heterogeneity. This paper argues that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things (IoT) services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).
V. Stantchev, A. Barnawi, S. Ghulam, J. Schubert, and G. Tamm, ”Smart Items, Fog and Cloud Computing as Enablers of Servitization in Healthcare,” Sensors & Transducers, pp. 1726-5479, 185(2), February 2015.
This article argues that smart items and cloud computing can be powerful enablers of servitization as business trend. This is exemplified by an application scenario in healthcare that was developed in the context of the OpSIT-Project in Germany. It presents a three-level architecture for a smart healthcare infrastructure. The approach is based on a service-oriented architecture and extends established architectural approaches developed previously at our group. More specifically, it integrates a role model, a layered cloud computing architecture, as well as a fog-computing-informed paradigm in order to provide a viable architecture for healthcare and elderly-care applications. The integration of established paradigms is beneficial with respect to providing adequate quality of service and governance (e.g., data privacy and compliance). It has been verified by expert interviews with healthcare specialists and IT professionals.
I. Stojmenovic and S. Wen,“The Fog Computing Paradigm: Scenarios and Security Issues,” in Proc. of the 2014 Federated Conference on Computer Science and Information Systems (FedCSIS), September 2014.
Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. This article elaborates the motivation and advantages of Fog computing, and analyze its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks. It discusses the state-of-the-art of Fog computing and similar work under the same umbrella. Security and privacy issues are further disclosed according to current Fog computing paradigm. As an example, it studies a typical attack, man-in-the-middle attack, for the discussion of security in Fog computing. It investigates the stealthy features of this attack by examining its CPU and memory consumption on Fog device.
Complex Event Processing (CEP)
N. Stojanovic, L. Stojanovic, Y. Xu, and B. Stajic, “Mobile CEP in Real-Time Big Data Processing: Challenges and Opportunities,”in Proc. of the 8th ACM International Conference on Distributed Event-Based Systems, 2014.
This paper documents the experience gained during some funded projects targeting the difficult issue of mobility from the point of view of continuous event gathering and processing. The approach consider the way how highly unreliable environments can be controlled in a mix of streaming with semantics.
Y. Zhang and J.L. Chen,“A Hybrid Complex Event Service Based on IoT Resource Models,” in Proc. of the 2015 IEEE International Conference on Web Services, July 2015.
The rule defining task is error-prone and cumbersome. In this paper, therefore, a hybrid complex event service is proposed, which deals with not only discrete events but also continuous behavior computation based on IoT (Internet of Things) resource models. In order to satisfy the real-time constraints of processing IoT events, a divide-and-conquer principle is adopted, where we give a combination theorem such that different events can be processed on different IoT resources and then these processed results can be combined to derive complex events.
R. Mayer, B. Koldehofe, and K. Rothermel, “Predictable Low-Latency Event Detection with Parallel Complex Event Processing” IEEE Internet of Things Journal, vol. 2, no. 4, pp. 274-286, August 2015.
This paper proposes a pattern-sensitive partitioning model for data streams that is capable of achieving a high degree of parallelism in detecting event patterns, which formerly could only consistently be detected in a sequential manner or at a low parallelization degree. Moreover, the paper proposes methods to dynamically adapt the parallelization degree to limit the buffering imposed on event detection in the presence of dynamic changes to the workload.
C.Y. Ching, J. H. Fu; T. Sung, P. F. Wang, E. Jou, and M. W. Feng,“Complex Event Processing for the Internet of Things and its Applications,” 2014 IEEE International Conference on Automation Science and Engineering (CASE), August 2014.
This paper is valuable due to effective presentation of a use case, namely Smart Buildings and the value from CEP in this vertical market.
X. Jing, J. Zhang, and J.H. Li,“Semantic Complex Event Detection System of Express Delivery Business with Data Support from Multidimensional Space,” Applied Mechanics and Materials, vol. 484-485, pp.363-367, January 2014.
This paper proposes an automatic data acquisition framework to resolve such difficulty, which synthetically utilize intelligent internet of things (IoT), semantic web and complex event processing (CEP) technology. Was implemented a SCEP prototype system with the capability of real-time detecting complex business events on the goods sorting line, which adopts a detection method consisting of four stages.
IoT and Social Networks (Social IoT)
D. Guinard, M. Fischer, and V. Trifa, “Sharing Using Social Networks in a Composable Web of Things,” in Proc. of the 8th IEEE International Conference on Pervasive Computing and Communications Workshops, March 2010.
This paper presents a Web platform called Social Access Controller that allows users to share physical objects with people they know and trust by relying on existing social networks and their open APIs. The proposed concept is illustrated by introducing the Friends and Things (FAT) Web application, which is directly built upon the RESTful API of the Social Access Controller. FAT is a Web application that allows users to share and use shared smart things in a user-friendly manner. By starting from this, the authors also identify and briefly discuss some of the main challenges to face to build an ecosystem of shareable things.
L. Atzori, A. Iera, G. Morabito, and M. Nitti, “The Social Internet of Things (SIoT) - When Social Networks Meet the Internet of Things: Concept, Architecture and Network Characterization”, Computer Networks, vol. 56, no. 16, pp. 3594–3608, November 2012.
This paper focuses on the integration of social networking concepts into the Internet of Things and presents an extensive study on the novel paradigm of the “Social Internet of Things" (SIoT), introduced by the same authors in a previous paper. The authors provide the very first overview of the research activities aimed at the integration of Social Networks and the Internet of Things and contribute to the advancement of the state of the art (i) by identifying appropriate policies for the establishment and management of social relationships between objects in such a way that the resulting social network is navigable, (ii) by describing a possible architecture for the IoT that includes the functionality required to integrate things into a social network, and (iii) by studying the characteristics of the network structure of a SIoT.
A. Ortiz, D. Hussein, S. Park, S. Han, and N. Crespi, “The Cluster Between Internet of Things and Social Networks: Review and Research Challenges,” IEEE Internet of Things Journal, vol. 1, no. 3, pp. 206–215, June 2014.
This paper explores the Social Internet of Things (SIoT) paradigm by reviewing the research and development work conducted in this area, giving a general architecture description, and illustrating challenges and open research issues that must be solved to turn SIoT into reality. In this paper, social networking technologies are seen as a means to increase the pervasiveness and ubiquity of the computing systems by starting from the idea that (social) interaction among objects in our living places are necessary to improve the ICT system performance.
O. Voutyras, P.Bourelos, D. Kyriazis, and T. Varvarigou, “An Architecture Supporting Knowledge Flow in Social Internet of Things Systems”, in Proc. of the IEEE 10th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), October 2014.
This paper presents a social approach that is implemented within the framework of the EU COSMOS project, supporting knowledge flow between Things to provide a system that learns, observes and evaluates the usage and communication patterns and generates new knowledge. In their study, the authors follow the IoT-A reference model that supports real-virtual world integration by representing Things of the real world via their counterparts in the Cyberworld called Virtual Entities (VEs). In addition, they exploit the Social Internet of Things (SIoT) paradigm to allow their platform to define, monitor and exploit social relations and interactions between the VEs, and to use technologies and services from the domain of the social media. The authors conclude their research by stating that the social side of their platform improves the knowledge flow, which is of great importance for the constant evolution of the IoT systems, and introduces the concept of experience sharing between Things.
L. Atzori, A. Iera, and G. Morabito, “From “Smart Objects” to “Social Objects”: The Next Evolutionary Step of the Internet of Things,” IEEE Communications Magazine, vol. 52, no. 1, pp. 97–105, January 2014.
This paper investigate on the required evolution in the characteristics of the objects populating the Internet of Things in the view of the full deployment of the SIoT vision. Three stages of evolutions, that foresee increasing levels of social involvement of the IoT objects, are identified. In the first stage, objects can post information about their state in the social networks of humans. In the second stage, objects can interact at the application layer in social networks with humans and other objects. At the third stage, objects socially interact with each other to build an autonomous network. Following the description of main features and requirements of each generation of IoT objects ,with relevant main research and development achievements, the paper describes two use cases in which social relationships between objects can be fruitfully exploited. Finally, it highlights the main research issues, linked to the idea of an independent “social behavior of smart objects”, which still needs investigation.
K. M. Alam, M. Saini, and A. El Saddik, “Towards Social Internet of Vehicles: Concept, Architecture and Applications,” IEEE Access, vol. 3, pp. 343 – 357, 2015.
This paper proposes an IoT architecture for the Social Internet of Vehicles (SIoV), a vehicular instance of the Social IoT (SIoT), where vehicles are the key social entities in the envisioned machine-to-machine vehicular social networks. The authors identify the social structures of the SIoV components, their interactions and interrelations, which are inspired from the SIoT. The components of the Vehicular Area Network are mapped onto the IoT-A architecture reference model to offer better integration of SIoV with other IoT domains. Implementation details of the proposed system, related experimental analysis, and various application scenarios are also presented to assess the feasibility of the proposed solution. In the authors’ view, the SIoV has the potentials to become an integral part of Intelligent Transport Systems in the future smart cities.
Y. Kim and Y. Lee, “Automatic Generation of Social Relationships Between Internet of Things in Smart Home Using SDN-Based Home Cloud,” in Proc. of the 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 662-667, March 2015.
This paper gives an example of a possible use of the Social Internet of Things paradigm in a Smart Home scenario. It focuses on the issue of identifying the position of a fault occurring among various domains of a smart home environment by relying on suitably defined social relationships between IoT devices, IoT services, and IoT networks. The relationships are used to quickly discover IoT devices, services and resources by providing a distributed solution that results effective, efficient and reduces the burden on people. To prevent needless burdens on users, the basic idea is that the relationships are generated automatically by an SDN-based home cloud. The proposed mechanism is thought to bring benefits not only to users but also to home service providers to the purpose of smart home management.
IoT and Open Data
C. Bizer, T. Heath, and Tim Berners-Lee,“Linked Data - The Story So Far,” International Journal on Semantic Web and Information Systems, vol. 5, no. 3, pp. 1-22, 2009
This paper provides the basis and introduction of the Linked and Open Data.
D. L. Phuoc, M. Hauswirth, K. Taylor, A. Ayyagari, and D. D. Roure, "Linked Open Data in Sensor Data Mashups," in Proc. of the of the 2nd International Workshop on Semantic Sensor Networks (SSN09), in conjunction with ISWC 2009, vol. 522, CEUR, 2009.
This paper provides the introduction to the value of Open Data for IoT and its potential in the integration of multiple data sources into a solution.
P. Barnaghi, M. Bermudez-Edo, and R. Toenjes, "Challenges for Quality of Data in Smart Cities," Journal of Data and Information Quality, vol. 6, no. 2-3, July 2015.
This paper presents the challenges that noisy data sources are bringing about in cities are not explored yet. Since, we often work with ideal sets or already cleaned data that does not provide the same real world insight into working with big data in cities.
H. Jaakkola, T. Mäkinen, and A. Eteläaho, “Open Data: Opportunities and Challenges,” in Proc. of the 15th International Conference on Computer Systems and Technologies (CompSysTech '14), 2014.
Open data is often confused with a technology paradigm, instead it is a societal construct. This paper gives an introduction to open data from a societal perspective. It explains current challenges and opportunities and offers the reader a solid starting point to understanding open data initiatives.
J.Lanier, “Who Owns the Future?” Simon & Schuster, 2014.
The book provides a critical view of data and information flows. By convincing users to give away valuable information about themselves in exchange for free services, firms can accrue large amounts of data at virtually no cost. Lanier calls these firms “Siren Servers,” alluding to the Sirens of Ulysses. Instead of paying each individual for their contribution to the data pool, the Siren Servers concentrate wealth in the hands of the few who control the data centers.
O. Vermesan and P. Friess (Eds.),“Internet of Things Applications - From Research and Innovation to Market Deployment,” River Publishers, June 2014.
This book is a result of the IERC IERC-European Research Cluster on the Internet of Things. It is a relevant overview of the main innovation and results arising from different EU projects and collaborations with other research regions in the world. Relevant chapters are devoted to IoT and Energy and Smart Building deployment. The book also gives a broad overview of the main projects related to the deployment and research on new IoT areas related to Water and Energy Management.
S.D.T. Kelly, N.K. Suryadevara, and S.C. Mukhopadhyay, “Towards the Implementation of IoT for Environmental Condition Monitoring in Homes,” IEEE Sensors Journal, vol. 13, no. 10, pp. 3846-3853, October 2013.
This paper presents an effective implementation for Internet of Things used for monitoring regular domestic conditions by means of a low-cost ubiquitous sensing system. The authors investigate an integrated network architecture and the interconnecting mechanisms for the reliable measurement of parameters by smart sensors and transmission of data via the Internet, with a combination of pervasive distributed sensing units, information system for data aggregation, and reasoning and context awareness.
J. Pan, R. Jain, S. Paul, T. Vu, A. Saifullah, and M. Sha, “An Internet of Things Framework for Smart Energy in Buildings: Designs, Prototype, and Experiments,” IEEE Internet of Things Journal, vol. 2, no. 6, pp. 527-537, December 2015.
The paper presents an IoT experimental testbed for energy efficiency and building intelligence research, where the authors have monitored and collected one-year-long building energy usage data and then systematically evaluate and analyze them. The authors then build a proof-of-concept IoT network and control system prototype and carry out real-world experiments that demonstrate the effectiveness of the proposed solution. The work shows significant economic benefits in term of energy saving and the improvement of home/office network intelligence. Moreover, it has a huge social implication in terms of global sustainability.
Z.U. Shamszaman, L. Sanghong Lee, and C. Ilyoung, “WoO based User Centric Energy Management System in the Internet of Things,” in Proc. International Conference on Information Networking (ICOIN), February 2014.
This paper presents a Web-Of-Objects (WoO) based Energy Management System (WEMS) for providing a solution to efficiently manage energy consumption in a smart home environment. In their approach, the authors propose a service architecture system to support intelligent features through an objectification and virtualization of physical things. The proposed system is applied to a home environment, although can be applicable to other systems.
V. Moreno, B. Úbeda, A. Skarmeta, and M.A. Zamora, “How Can We Tackle Energy Efficiency in IoT Based Smart Buildings?,” Sensors, vol. 14, no. 6, pp. 9582-9614, June 2014.
Within this paper, the authors discuss the main parameters that should be considered for inclusion in any building energy management. The goal is to help designers select the most relevant parameters to control the energy consumption of buildings according to their context, selecting them as input data of the management system. The paper discusses the deployment of IoT sensors and actuators within three reference smart buildings with different contexts, involving the deployment of a proposed automation platform for energy monitoring. The first stages of this evaluation have already resulted in energy savings of about 23% in a real scenario.
M. Floeck, A. Papageorgiou, A. Schuelke, and S. JaeSeung, “Horizontal M2M Platforms Boost Vertical Industry: Effectiveness Study for Building Energy Management Systems,” in Proc. 2014 IEEE World Forum on Internet of Things (WF-IoT), pp. 15-20, March 2014.
This paper reviews machine-to-machine (M2M) platforms from vertical domains as well as M2M related standardizations. The authors argue that it is essential to develop standards-based horizontal service platforms in the domain of the M2M industry to achieve the M2M vision; i.e., connecting all different types of devices to communicate with one another. The paper performs an analysis centered on a building management M2M system (CAMPUS21) and two M2M standards-based (ETSI M2M & HGI) platforms, identifying incompatibilities in the data model. Dependencies on multiple vendors and restricted resource sharing are addressed. Finally, the authors suggest a required extension of horizontal M2M architecture in order to provide interconnection between proprietary M2M systems and standardized M2M systems.
A de Paola, M. Ortolani, G. Lo Re, G. Anastasi, and S. K. Das, “Intelligent Management Systems for Energy Efficiency in Buildings: A Survey,” ACM Computing Surveys (CSUR), vol. 47, no. 1, pp. 13:1-13:38, July 2014.
This informative survey provide a comprehensive structured and unifying treatment of the existing literature on intelligent energy management systems in buildings, with a distinct focus on available architectures and methodology supporting a vision transcending the well-established smart home vision, in favor of the novel Ambient Intelligence paradigm. The survey includes main architectural elements, such as basic sensory infrastructure, data processing engine, user interaction interface subsystem, and finally the actuation infrastructure necessary to transfer the planned modifications to the environment.
S. Sharad, P.B. Sivakumar, and V. Anantha Narayanan, “A Novel IoT-Based Energy Management System for Large Scale Data Centers,” in Proc. 2015 ACM Sixth International Conference on Future Energy Systems, pp. 313-318, July 2015.
The paper discusses how to make a smart-energy management system that is capable of collecting the data available and make decisions based on the energy consumption patterns. The authors propose a smart system that uses the Internet of Things to gather data and a machine learning algorithm for decision making to manage energy consumption in data centers to reduce operating costs and pollution.