Networked Embedded Systems for Enterprise Applications 

About: Networked Embedded Systems for Enterprise Applications is an academic conference. The conference publishes majorly in the area(s): Wireless sensor network & Network on a chip. Over the lifetime, 37 publications have been published by the conference receiving 771 citations.

SenaaS: An event-driven sensor virtualization approach for Internet of Things cloud

Abstract: In this paper, we propose an Internet of Things (IoT) virtualization framework to support connected objects sensor event processing and reasoning by providing a semantic overlay of underlying IoT cloud. The framework uses the sensor-as-aservice notion to expose IoT cloud's connected objects functional aspects in the form of web services. The framework uses an adapter oriented approach to address the issue of connectivity with various types of sensor nodes. We employ semantic enhanced access polices to ensure that only authorized parties can access the IoT framework services, which result in enhancing overall security of the proposed framework. Furthermore, the use of event-driven service oriented architecture (e-SOA) paradigm assists the framework to leverage the monitoring process by dynamically sensing and responding to different connected objects sensor events. We present our design principles, implementations, and demonstrate the development of IoT application with reasoning capability by using a green school motorcycle (GSMC) case study. Our exploration shows that amalgamation of e-SOA, semantic web technologies and virtualization paves the way to address the connectivity, security and monitoring issues of IoT domain.

Successful deployment of a Wireless Sensor Network for precision agriculture in Malawi

Abstract: This paper demonstrates how an Irrigation Management System (IMS) can practically be implemented by successfully deploying a Wireless Sensor Network (WSN). Specifically, the paper describes an IMS which was set up in Manja Township, City of Blantyre based on an advanced irrigation scheduling technique. Since the system had to be self-sustained in terms of power, which is a challenge for deployment in rural areas of developing countries like Malawi where grid power supply is scarce, we used solar Photovoltaic (PV) and rechargeable batteries to power all electrical devices in this system. The system incorporated a remote monitoring mechanism through a General Packet Radio Service (GPRS) modem to report soil temperature, soil moisture, WSN link performance and PV power levels. Irrigation valves were activated to water the field. Our preliminary results have revealed engineering weakness of deploying such a system. Nevertheless, the paper shows that it is possible to develop a robust, fully-automated, solar powered, and low cost IMS to suit the socio-economic conditions of small scale farmers in developing countries.

Extending sensor networks into the Cloud using Amazon Web Services

Abstract: Sensor networks provide a method of collecting environmental data for use in a variety of distributed applications. However, to date, limited support has been provided for the development of integrated environmental monitoring and modeling applications. Specifically, environmental dynamism makes it difficult to provide computational resources that are sufficient to deal with changing environmental conditions. This paper argues that the Cloud Computing model is a good fit with the dynamic computational requirements of environmental monitoring and modeling. We demonstrate that Amazon EC2 can meet the dynamic computational needs of environmental applications. We also demonstrate that EC2 can be integrated with existing sensor network technologies to offer an end-to-end environmental monitoring and modeling solution.

Q-learning based congestion-aware routing algorithm for on-chip network

Abstract: Network congestion can limit performance of NoC due to increased transmission latency and power consumption. Congestion-aware adaptive routing can greatly improve the network performance by balancing the traffic load over the network. In this paper, we present a reinforcement learning method, Q-learning, for NoC to alleviate congestion in the network. In the proposed method, local and nonlocal congestion information is propagated over the network utilizing learning packets. This learning approach results in better routing decisions due to up-to-date and more reliable congestion values. According to this congestion information, a path is chosen for a packet which is less congested. Experimental results with synthetic test cases demonstrate that the on-chip network utilizing the proposed method outperforms a conventional scheme, Dynamic XY, (28% for uniform traffic and 17% for hotspot traffic) with a 12% of area overhead.

Accurate passive RFID localization system for smart homes

Abstract: The smart home paradigm is a promising new trend of research aiming to propose an alternative to postpone the institutionalization of cognitively-impaired silver-aged people. These habitats are intended to provide security, guidance and direct support services to its resident. To be able to fulfill this important mission, a smart home system first has to identify the ongoing activities of its user by tracking, in real time, the position of the main daily living objects. Many researchers addressed this issue by proposing systems based on ultrasonic wave sensors, video cameras, and radio-frequency identification (RFID). However, because of its robustness and its low price, RFID constitutes the most viable technology for smart homes. Recently, several RFID localization algorithms have been developed, mainly for commercial and industrial uses, but they are not precise enough to be used in an assistive recognition context or they focus on active tags, which need batteries and are much more expensive. We present, in this paper, a new algorithmic approach for passive RFID localization in smart homes based on elliptical trilateration and fuzzy logic. This new algorithm has been implemented in a real smart home infrastructure and has been rigorously tested. We also analyze and compare the obtained results with the main existing approaches.