Efficient building management with IP-based wireless sensor network

Existing Building/Energy Management Systems (BMS/EMS) fail to convey holistic performance to the building manager. A 20% reduction in energy consumption can be achieved by efficiently operated buildings compared with current practice. However, in the majority of buildings, occupant comfort and energy consumption analysis is primarily restricted by available sensor and meter data. Installation of a continuous monitoring process can significantly improve the building systems’ performance. We present WSN-BMDS, an IP-based wireless sensor network building monitoring and diagnostic system. The main focus of WSN-BMDS is to obtain much higher degree of information about the building operation then current BMSs are able to provide. Our system integrates a heterogeneous set of wireless sensor nodes with IEEE 802.11 backbone routers and the Global Sensor Network (GSN) web server. Sensing data is stored in a database at the back office via UDP protocol and can be access over the Internet using GSN. Through this demonstration, we show that WSN-BMDS provides accurate measurements of air-temperature, air-humidity, light, and energy consumption for particular rooms in our target building. Our interactive graphical user interface provides a user-friendly environment showing live network topology, monitor network statistics, and run-time management actions on the network. We also demonstrate actuation by changing the artificial light level in one of the rooms

Adaptive TrimTree: Green Data Center Networks through Resource Consolidation, Selective Connectedness and Energy Proportional Computing

A data center is a facility with a group of networked servers used by an organization for storage, management and dissemination of its data. The increase in data center energy consumption over the past several years is staggering, therefore efforts are being initiated to achieve energy efficiency of various components of data centers. One of the main reasons data centers have high energy inefficiency is largely due to the fact that most organizations run their data centers at full capacity 24/7. This results into a number of servers and switches being underutilized or even unutilized, yet working and consuming electricity around the clock. In this paper, we present Adaptive TrimTree; a mechanism that employs a combination of resource consolidation, selective connectedness and energy proportional computing for optimizing energy consumption in a Data Center Network (DCN). Adaptive TrimTree adopts a simple traffic-and-topology-based heuristic to find a minimum power network subset called ‘active network subset’ that satisfies the existing network traffic conditions while switching off the residual unused network components. A ‘passive network subset’ is also identified for redundancy which consists of links and switches that can be required in future and this subset is toggled to sleep state. An energy proportional computing technique is applied to the active network subset for adapting link data rates to workload thus maximizing energy optimization. We have compared our proposed mechanism with fat-tree topology and ElasticTree; a scheme based on resource consolidation. Our simulation results show that our mechanism saves 50%–70% more energy as compared to fat-tree and 19.6% as compared to ElasticTree, with minimal impact on packet loss percentage and delay. Additionally, our mechanism copes better with traffic anomalies and surges due to passive network provision

Change Detection Algorithms for Surveillance in Visual IoT: A Comparative Study Visual Internet of Things

International audienceThe VIoT (Visual Internet of Things) connects virtual information world with real world objects using sensors and pervasive computing. For video surveillance in VIoT, ChD (Change Detection) is a critical component. ChD algorithms identify regions of change in multiple images of the same scene recorded at different time intervals for video surveillance. This paper presents performance comparison of histogram thresholding and classification ChD algorithms using quantitative measures for video surveillance in VIoT based on salient features of datasets. The thresholding algorithms Otsu, Kapur, Rosin and classification methods k-means, EM (Expectation Maximization) were simulated in MATLAB using diverse datasets. For performance evaluation, the quantitative measures used include OSR (Overall Success Rate), YC (Yule's Coefficient) and JC (Jaccard's Coefficient), execution time and memory consumption. Experimental results showed that Kapur's algorithm performed better for both indoor and outdoor environments with illumination changes, shadowing and medium to fast moving objects. However, it reflected degraded performance for small object size with minor changes. Otsu algorithm showed better results for indoor environments with slow to medium changes and nomadic object mobility. k-means showed good results in indoor environment with small object size producing slow change, no shadowing and scarce illumination changes

Change Detection Algorithms for Surveillance in Visual IoT: A Comparative Study

The VIoT (Visual Internet of Things) connects virtual information world with real world objects using sensors and pervasive computing. For video surveillance in VIoT, ChD (Change Detection) is a critical component. ChD algorithms identify regions of change in multiple images of the same scene recorded at different time intervals for video surveillance. This paper presents performance comparison of histogram thresholding and classification ChD algorithms using quantitative measures for video surveillance in VIoT based on salient features of datasets. The thresholding algorithms Otsu, Kapur, Rosin and classification methods k-means, EM (Expectation Maximization) were simulated in MATLAB using diverse datasets. For performance evaluation, the quantitative measures used include OSR (Overall Success Rate), YC (Yule’s Coefficient) and JC (Jaccard’s Coefficient), execution time and memory consumption. Experimental results showed that Kapur’s algorithm performed better for both indoor and outdoor environments with illumination changes, shadowing and medium to fast moving objects. However, it reflected degraded performance for small object size with minor changes. Otsu algorithm showed better results for indoor environments with slow to medium changes and nomadic object mobility. k-means showed good results in indoor environment with small object size producing slow change, no shadowing and scarce illumination changes