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We present UBI-AMI v2 for holistic real-time monitoring of building energy consumption with two types of wireless sensors. A Mains sensor monitors the aggregate load at the root of the load tree and the individual loads of up to 30 circuit breakers using analog Hall sensors. A Socket sensor monitors the load of the connected individual appliances....
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... Socket sensor (Figure 2) is a plug, that is connected be- tween a wall socket and the appliances to be measured and controlled. It measures energy usage and additionally local illumination and temperature. ...
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Citations
... The author started the work towards this thesis by integration of process migration and introduced. This event had significant effect on this thesis as the framework was then adopted for the UBI-AMI v2 application (Leppänen et al. 2012). However, this WSN was not used in further evaluation in this thesis, but as a software development platform. ...
The Internet of Things vision proposes a global platform in an unforeseen scale for distributed applications that rely on data provided by interconnected resource-constrained things. In such large-scale systems, centralized control of system operation by a single component through vertical interactions becomes unfeasible. Ideally, decentralized control in the proximity of things enables to take into account the local dynamic resource availability and environmental characteristics that are used to optimize the application execution. To realize decentralization, capabilities for horizontal interactions, that complement the vertical interactions, and for opportunistic participation of things are needed.
This thesis explores mobile agent technology to implement distributed Internet of Things applications that benefit from both vertical and horizontal interactions of the application components. First, a resource-oriented reactive mobile agent architecture and a software framework are presented. The framework facilitates RESTful interactions between agents and other system components and provides a REST-based interface to build opportunistic agent-based applications. Two agent platforms are presented that integrate resource-constrained things into the framework as mobile agent hosts. Second, mobile agent based mobile crowdsensing and edge computing applications are evaluated with large-scale simulations and real-world experiments. The results show that energy consumption is decreased in the participating things, in comparison with the existing approaches, by agent-based in-network data processing and control of the thing operation.
This thesis makes a valuable contribution by enabling mobile agent operations in a heterogeneous set of resource-constrained things. The presented empirical evidence shows how mobile agent technology increases energy efficiency in distributed application execution. The presented mobile agent architecture and software framework potentially accelerate the utilization of mobile agent technology in the Internet of Things.
... The MAR application was run in a Web browser in a Android tablet that was connected to the Internet over Wi-Fi. We utilized a WSN node with a power consumption monitoring sensor [17], communicating atop 6LoWPAN in 868Mhz frequency band, as an IoT device. The WSN node was connected to the Internet via a WSN router. ...
Augmented reality (AR) is a promising technology for building applications in an Internet of Things (IoT) environ-ment, utilized for visualizing information provided by IoT devices. In this paper, we enable Web-based mobile AR applications with mobile agents in a resource-oriented IoT system architecture. We present an adaptable mobile agent composition that contains the data representation logic and mappings between AR applications and system resources. Thus, mobile agents and AR application-specific data structures are exposed as global system resources. System resource linkages are considered between real-world objects and their virtual representations for mobile agent-based AR applications. The agent composition also complies with the REST principles for resource access and control system-wide. This allows dynamic runtime adaptation and dealing with the device and resource heterogeneity, thus eliminating the need for application-specific communication protocols. Moreover, we utilize a Web-based mobile AR application framework, running completely in a Web browser, which facilitates straightforward AR application development. Lastly, a proof-of-concept mobile AR application is implemented, where a coffee maker with a visual tag is connected to a low-power resource-constrained wireless sensor network node as an IoT device. A mobile agent is injected into the IoT environment to expose state changes of the coffee maker. Through the visual tag, AR applications are able to visualize the state changes of the coffee maker in their user interface.
... Code repository within the proxy hosts the agent's task codes, then accessible as system resources. The static WSN nodes [4] communicate with CoAP atop 6LoW- PAN protocol stack with IEEE 802.15.4 radios on 868 MHz. As the mobile nodes, we utilize Android smartphones with integrated sensors, Samsung Galaxy S III, communicating with HTTP over IEEE 802.11 on 2.4 Ghz band. ...
... The agent is injected into the system as depicted with dotted line. Here the task code is precompiled IntelHEX binary for the static node and Python for the mobile nodes [3, 4]. The agent migrates as depicted by the solid lines from node to node based on the resource segment. ...
We demonstrate interoperable mobile agents for language- and platform-independent wireless sensor network programming, with low-power resource-constrained embedded devices as static nodes and Android-based smartphones as mobile
nodes, over disparate networks: 6LoWPAN and Wi-Fi. Representational state transfer architectural principles are applied in agent composition, control and migration and exposing the system resources to the Web: the devices, agents,
sensor data, tasks and data processing results. The adaptable agent composition includes the task code in any programming language, the agent migrates according to a resource list and the state, i.e. the intermediate task result, represents the agent as a system resource. Mobile agents are then utilized in two tasks: first to collect light sensor data cooperatively in location, saving the mobile node battery whenever possible, and secondly the magnetometer sensor data with the scanned Wi-Fi access points' signal strength is used to detect groups of mobile nodes moving in the same direction in real-time.
... We utilized Android 4.0 smartphones, Samsung Galaxy S III, as general-purpose IoT devices communicating with HTTP over Wi-Fi in 2.4 GHz band. On the other end, we have a real-world IP-based WSN atop 6LoWPAN in 868 MHz band [14]. The WSN node platforms are ATmega 1284P and 2560 8-bit microcontroller-based embedded devices, running in 18 MHz with 8 or 16 Kb of RAM. ...
... The nodes communicate by CoAP. We have described the system architecture, inFigure 2, in our earlier work1314, which is generally based on the framework in IETF CoRE Working Group [11] and on continuations as the abstraction of the mobile agent. We utilized the resource directory (RD) in [15] as a name server to store the system resource descriptions. ...
We demonstrate interoperable mobile agents to integrate Internet of Things and wireless sensor networks with resource-constrained low-power embedded networked devices. We introduce adaptable composition for the mobile agent, complying with the Representational State Transfer principles, which are then used for agent migration, controlling the agent and exposing the data, system resources, tasks and services, to the Web. We gather requirements for the system and heterogeneous networked devices and present an application programming interface to enable mobile agents in these systems. The agents are demonstrated in a real-world prototype with smartphones and embedded networked devices, where we utilize a proxy component to expose system resources to the Web for human-machine interactions. Spanning over disparate networks and protocols, the proxy translates messages including the agent composition, between HTTP and Constrained Application Protocol. Lastly, we suggest an evaluation method for the agent communication and migration costs, considering the different types of system resources and utilization.
... Thus, a resource directory is fundamental. On one hand, most of the devices in M2M applications are constrained devices (e.g., having limited memory, and battery-powered [5]). On the other hand, these constrained devices use different communication protocols (e.g., smartphone use HTTP and wireless sensors use Constrained Application Protocol (CoAP) [6]), which increases the complexity of the resource directory. ...
... However, in most of studies, the resource directory was not designed for constrained devices (e.g. wireless sensor with 8 KB RAM [5]). For example, Teranishi [23] presented the PIAX for building wireless sensor overlay. ...
... The prototype is deployed in the Gumstix OveroTM Earth board [28]. The constrained wireless sensors are using Atmel 1284P microcontroller-based embedded devices with 8KB of RAM running in 18MHz [5]. The sensor communicates with the DRD4M using CoAP messages over 6LoWPAN. ...
Abstract—Machine-to-Machine (M2M) communications
emerge to achieve ubiquitous communications among all the
devices. One challenging problem in M2M applications is to
discover the resources provided by the devices efficiently. This
challenge arises from two perspectives: (1) a large number of
heterogeneous devices co-exist and most of them are constrained
devices (e.g., limited processing capability), (2) different
communication protocols are utilized to get access to resources
provided by the devices (e.g., humidity information provided by
a sensor). This paper proposes distributed resource directory
architecture for M2M applications, referred to as DRD4M. The
DRD4M supports heterogeneous devices using HTTP and CoAP
messages for resource registration and lookup. This enables the
interoperability among heterogeneous devices and resources
access from Internet. In the DRD4M, a resource registration
component is designed to register resources. A resource lookup
component with caching functionality is designed to process
resource lookup (with filters). A P2P overlay is introduced in
DRD4M to distribute workload and avoid single point of failure.
A real prototype is implemented and is verified with a simple
demo application. Preliminary performance evaluation in terms
of response time of looking up resource descriptions is provided.
Future evaluation plan is discussed
... One challenge in M2M applications is to discover the resources provided by these huge number of heterogeneous devices efficiently. The challenge arises from two perspectives: (1) most devices are constrained devices (e.g., embedded sensor with 8KB RAM [4]), (2) different communication protocols are utilized by these devices (e.g., smartphone use HTTP and wireless sensors use Constrained Application Protocol (CoAP) [5]). In addition, direct discovery of some resources is not feasible when the nodes are sleeping or having intermittent connection in constrained network [6]. ...
The CoRE Working Group has proposed a resource directory (RD) in the centralized resource discovery of the constrained application protocol (CoAP). This directory contains a description for resources held in the servers, in which the servers register their resources in the RD while the clients are allowed to perform a lookup operation on these resources, and ask for any resource using single request. In order of making the resource directory fully updated, all nodes are periodically sending update messages (after fixed period of time) to the RD contains the latest status of their information, these update messages will result in extra signaling overhead and consume more power from node’s battery, this will drain the batteries and reduce the overall network lifetime. We have proposed a dynamic tuning technique to adjust the update message interval in CoAP, the proposed algorithm is depending on the battery level of the sending node, in which the update interval increased as battery level decreased. The results of experiments show that the dynamic approach can extend the network lifetime by 25%.
Internet of Things (IoT) is to connect objects of different application fields, functionality and technology. These objects are entirely addressable and use standard communication protocol. Intelligent agents are used to integrate Internet of Things with heterogeneous low-power embedded resource-constrained networked devices. This paper discusses with the implemented real world scenario of smart autonomous patient management with the assistance of semantic technology in IoT. It uses the Smart Semantic framework using domain ontologies to encapsulate the processed information from sensor networks. This embedded Agent based Semantic Internet of Things in healthcare (ASIOTH) system is having semantic logic and semantic value based Information to make the system as smart and intelligent.
This paper aims at explaining in detail the technology drivers behind the IoT and health care with the information on data modeling, data mapping of existing IoT data into different other associated system data, workflow or the process flow behind the technical operations of the remote device coordination, the architecture of network, middleware, databases, application services. The challenges and the associated solution in this field are discussed with the use case.
