Design and Deployment Tool for In-Building Wireless Sensor Networks

7th IEEE International Workshop on Performance and Management of Wireless and Mobile Networks

P2MNET 2011, Bonn, Germany

Design and Deployment Tool for In-Building Wireless Sensor Networks: a Performance Discussion Antony Guinard, Muhammad S. Aslam, Davide Pusceddu, Susan Rea, Alan McGibney, Dirk Pesch Nimbus Centre for Embedded Systems Research Cork Institute of Technology Cork, Ireland [email protected], [email protected], [email protected], [email protected], [email protected], [email protected]

indoor environments. The framework captures site specific constraints and identifies the optimal position for repeaters and gateways to ensure a reliable communications network. In order to provide practical results physical network deployments are rolled out. The case study evaluation compares the WSN design suggested by the deployment support tool against designs produced by following basic industry guidelines and a site survey of the deployment environment. The objective of the performance critique presented in this paper is to validate the need for formal design tools by demonstrating and quantifying the improvements over current WSN deployment approaches.

Abstract—The design and deployment of a wireless sensor network (WSN) for building automation applications is a complex operation that requires expert knowledge and experience. This paper presents an evaluation of a WSN deployment support framework for in-building wireless infrastructures. A case study consisting of a sample network deployment for environmental monitoring is used to investigate the need for such support tools. The network infrastructure design suggested by the deployment support tool is compared against designs done using basic planning guidelines and a design based on an extensive site survey and experience. It will be shown how the deployment support tools provide a WSN with a reduced infrastructure cost and improved sensing packet delivery ratio when compared to the designs using traditional approaches.

The remainder of this paper is structured as follows: Section II presents related work on WSN design considerations. Section III provides a description of the tool framework with the experimental methodology being presented in Section IV. The design evaluations are analyzed in Section V with a summary and the conclusions drawn from the findings of this work being presented in Section VI.

Keywords-wireless sensor networks; radio propagation; design; deployment, Planning Tools

I.

INTRODUCTION

The popularity of Wireless Sensor Networks (WSN) has grown dramatically in recent years, their low cost installation and flexibility makes them an excellent mechanism for capturing environmental data and interacting with the physical environment for both commercial and military applications [1]. While most of the early applications for WSNs have been focused in outdoor scenarios such as habitat monitoring, battlefield awareness, forest fire detection etc., the industrial and construction sectors are focusing their attention on harnessing WSN technology for optimized building performance.

II.

There are two main aspects that need to be considered when developing a wireless application for building deployments, the design of a reliable network and the management of that network post deployment. A. Network Infrastructure Design to support reliable communication The positioning and topology of wireless devices from the perspective of sensing, communications and network lifetime is an essential consideration pre-deployment to ensure a reliable sensing infrastructure. Current approaches to node placement are typically ad hoc and based on the designers experience. The commercially available tools are usually vender specific and generally based around site survey techniques where a test deployment is required prior to the final design. There is a lack of formal design methodologies particularly for in building wireless infrastructures to support monitoring and control. There are a number of commercially available propagation modeling software tools but are largely focused on the design of IEEE802.11 (WiFi) based wireless networks. There is a significant lack of commercial software tools to support the design of other wireless and embedded infrastructures such as wireless access control or environment monitoring. SpinWave Systems and Monnit are examples of two companies that

The widespread adoption of wireless sensing technology for large scale in building deployments is limited by concerns regarding network lifetime, reliability and robustness. As the majority of the wireless nodes are battery powered, available energy must be used prudently as there is a labor cost associated with replacing depleted batteries. The design of a WSN must ensure that a reliable communication channel is provided, failing to do so results in possible communications failure, data retransmissions and unnecessary power consumption. This paper presents an evaluation of a modeling framework and tools that is used to support the deployment of a WSN for The authors wish to acknowledge the support of Science Foundation Ireland under the 06-SRC-I1091 ITOBO project in funding work reported in this paper.

978-1-61284-928-7/11/$26.00 ©2011 IEEE

WIRELESS NETWORK DESIGN

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provide tool support with their wireless monitoring solutions. These tools typify the approaches currently undertaken by the providers of indoor wireless solutions and include site survey software, hardware positioning sensor and monitoring software for status monitoring. These methodologies require costly site surveys to evaluate the number and position of wireless devices to support specific applications. They do not provide any functionality to capture site specific requirements and automatically plan and optimize the wireless infrastructure.

performance models. The framework consists of a number of integrated model driven tools to support each phase of the design from requirements to operation. Fig. 1 shows the proposed model hierarchy consisting of a number of layers, requirements, design, deployment and application all of which consist of associated models.

There has been a number of research works investigating the node placement problem including [2]-[4], however these approaches lack the appropriate modeling and optimization algorithms and are tightly linked to their specific application. A number of simulation tools exist such as OPNET, OMNET and NS2 that can be utilized to provide a detailed simulation of the wireless systems. However they require significant expertise to configure and use them so are deemed to be excessive for the purpose of node placement optimization. As part of the model based methodology proposed in the next section, specification and system level simulation tools have been developed to provide the capability to formalize the deployment strategy, increase reliability, mitigate costly re-designs and reduce lead time to deployment.

Fig. 1. Wireless Infrastructure Design: Model Hierarchy

B. Management of WSNs post deployment The management of WSNs is a challenging task. In traditional networks the main objective of the network management is minimizing delay and providing detailed state information of the network. However in resource constrained sensor networks the primary objective is minimizing energy consumption whilst still providing management capabilities. Energy savings are usually achieved by reducing the required amount of communication between nodes. Network failures occur much more frequently in wireless sensor networks and are considered to be common events rather than exceptions. Management systems for WSNs have been developed from various perspectives such as resources control, fault detection, routing and traffic management. The authors in [5] provide an overview of commonly available management approaches and functionality of WSNs. Despite the large number and range of available WSN management systems, no single system provides a fully integrated solution that encompasses all WSN management functionalities. This is a difficult problem and may not have a practical solution in light of the extensive application space of WSN technology. However, in order to promote the use of wireless sensing technology for building monitoring and control applications the framework presented in Section 3 contains a building management specific WSN management system that focuses on lifecycle management of the wireless infrastructure from design to (re-)configuration. III.

The advantages of a model driven approach for building applications include having a better alignment with application requirements, abstraction from the complexity of design and implementation of sensor applications, a shorter design time through model reuse and code generation, improved adaptability to requirements changes and overall a higher quality system design through the use of formal design approaches. The following provides details on the various layers of the proposed model hierarchy as shown in Fig. 1. A. Requirements The first step of any system engineering is the definition of system requirements. For wireless building monitoring applications the essential component is a description of the building itself. Building models typically come in the forms of AutoCAD drawings or scaled images. When available, it is proposed to extract the environment description from Building Information Models (BIM). A BIM provides a standardized mechanism to share and exchange information about building data. BIM technology is used within the building construction and management sectors and is expected to gain widespread acceptance in the next few years [7]. Industry Foundation Classes (IFC) is an open specification of a BIM and is used to share and exchange BIMs in a neutral format among various software applications. The availability of IFC data models makes it an ideal method to gather the environment description before designing a WSN to support building management system. A parser has been developed in order to use IFC data to describe the deployment environment. Basic building entities such as walls, doors and windows are imported and associated with their material information. To support interoperability among the different models and tools a sensor model has been developed to capture sensor constraints. This model includes, information regarding the sensor type (e.g. Unit, range), node data (e.g. Frequency, Power) and sensor application

MODEL BASED METHODOLOGY

System engineers typically use models to get a better understanding of problems, develop candidate solutions and validate their design decisions [6]. To support the design of wireless infrastructures for building applications we propose a design framework which is underpinned by the use of various model categories typically used by system engineers including, requirements model, design models, physical models and

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requirements (e.g. reporting strategy, measurement interval) Future development aims to extend the IFC data model to encapsulate this sensor model, a topology model and other application requirements. Application requirements define constraints that are dictated by the building management application and which need to be supported by the WSN infrastructure. They define the goals of the design process and are used as design metrics once defined. These design constraints consist of

propagates between the wireless devices. This provides an input to link quality estimation and is a key input to the WSN design process. 2) Placement Optimisation For building applications such as a wireless building management system that uses sensors to monitor the environment, the positioning and sensing coverage of the devices can be critical to support the decision making process. If the data is inaccurate then this can have an adverse affect on the underlying application and performance of the building. For the correct positioning of sensors for monitoring applications general guidelines and considerations may be obtained from sensor manufacturer recommendations [11] and a comprehensive reference is available in ASHRAE Applications [12]. For example for temperature sensing wall mounted sensors are primarily affected by only the temperature within several inches of the sensor itself. Other areas in the space may have a temperature difference of several degrees hence the installation location greatly influences the ability to provide representative reading of current temperature conditions. Some considerations for temperature sensing include avoiding exterior wall-surfaces, solar radiation from windows and other heating and cooling sources such as radiators. The environment configuration plays an important role also especially the location of furniture and equipment (e.g. office copiers or other heat producing equipment). If there are temperature variations in the space multiple sensors will be required to enable improved estimation of current space conditions and improved control decisions. By using the model based approach with a definition of the building geometry and layout this can be combined with typical considerations to support the designer on placement of their sensors. However for a wireless scenario the designer must also consider communications aspects to reliably propagate the sensed data back to buildings data repository.

• Node demand zones: Sensors/actuators positions are tightly linked to their purpose within the environment. The demand zone (linked to an IFC Zone) defines the candidate position (or region) where sensing or actuating needs to take place. • Power source: most wireless devices are battery powered. However some key devices such as gateway nodes need to be line powered. Some actuator nodes and sensors, such as flow meters, need to be line powered as their operation requires high power consumption. The power source has a bearing on the topology and layout of network nodes and the overall system lifetime. • Sensing interval: the application requires sensor's data to be sent at regular interval. The sensing interval impacts how often sensors need to communicate and hence their energy consumption. • Transmission delay: timely delivery of sensing data and execution of actuation commands is ensured by respecting a transmission delay, required by the application. By encapsulating the above metrics into a complete requirements model it can be used by design tools to optimize the infrastructure configuration. They can also be utilized postdeployment to verify the design meets system requirements and user expectations. B. Design From the perspective of the wireless infrastructure the design phase consists of propagation modeling, communications evaluation and optimization. Therefore a software tool suite has been developed to support the design of the wireless infrastructure focused on in building applications [8][9]. The tools utilize the requirements models as outlined above to automatically optimize the number and more importantly the position of wireless devices as well as suggesting the expected network topology to support reliable communications for sensed data. It is envisaged in future work that the infrastructure design tools will be integrated into a single modeling framework covering all aspects of the building lifecycle for a complete design. The model based architecture will support this integration. The following elements describe the foundation of the tools capabilities, accurate propagation modeling and placement optimization.

As part of the framework the tool contains an optimization model to determine the best position and number of gateway or repeater devices. Based on specified measurement points (i.e. predefined senor locations) the tool identifies optimal positions for repeater and gateway devices for reliable networking. The objective of the tool is to provide a site-specific design that considers environmental constraints and application requirements. The foundation of the optimization approach used is presented in [8]. In the presented framework the optimization is carried out by an agent model, which is representative of a wireless device trying to find its best position within the environment by maximizing its personal utility function. The utility function of an agent provides a normalized equation that reflects the quality of the agent within the network design. The observations about network performance for building the multi-hop topology are the foundation for the utility function development. A detailed description of the optimization approach and utility function can be found in [9].

1) Propagation Modelling The RF propagation characteristics throughout the deployment environment are evaluated using a ray launching based propagation model known as the Motif Model [10]. The propagation model uses the building model and the different material types associated to objects to prediction how the signal

Existing simulation and modeling tools provide a user with the ability to manually position devices and evaluate various configurations of their proposed network. However it is the integration of an optimization and placement algorithm that enables a tool to automatically suggest an optimal

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configuration to the user’s site specific requirements that gives the proposed design tool an advantage over other approaches. This allows even users with little or no experience of indoor wireless deployments to create an optimal deployment plan.

Core also provides machine-2-machine (M2M) communication for distributed application accessing the network. The micro middleware is a master program based on parameters which can be modified easily through a GUI to change the functionality of the embedded devices which mitigates the need for software redeployment.

C. Deployment To push the boundaries for WSN design and management, approaches are needed that move beyond the traditional scope of a user simply deploying a WSN with a singular objective (i.e. monitoring the same point of interest over the lifetime of the WSN) to methods that support machine to machine communications for online reconfiguration and re-tasking. Traditionally expert knowledge is required to develop, deploy and manage WSNs as several operating systems and platforms are available each having different programming platforms and operating systems which makes it very expensive and time consuming to develop applications for these platforms. The use of models simplifies this and allows for the representation of a system in a manner that can be interpreted by standard application programming interfaces.

With the model description complete it is then dispatched into the OFM core using web services. The Core analyzes a model based on its knowledgebase which translates each tag in the model. Each tag is associated with a mapper class. The knowledgebase reads the tag and executes the specific mapper for each tag. These mappers can be developed in advance or can be written by user and deployed at runtime on the OFM Core. The OFM Core inducts the specific mappers at runtime. The purpose of this approach is to create a base for learning system where a user can teach the system to interpret OFM models by feeding information to Knowledge base and defining mappers to translate that information. The OFM Core Architecture runs on a J2EE compliant open source server “Glass Fish”.

To support WSN modeling it is proposed to use tag based system models. Unlike traditional mathematical models tag based models are more descriptive and using standardized machine to machine communications methodologies allows for exposure of WSN entities to other systems within the same sphere. For this work the tag based OSASIS oBIX [13] model format will be extended to support WSNs within the building automation context. OASIS (Organization for the Advancement of Structured Information Standards) oBIX (Open Building Information eXchange) is an industry-wide initiative to define XML- and Web services-based mechanisms for building control systems and acts as an integration interface to and between control systems and to a larger extent, between enterprises and building systems. Extending the oBIX model format to support WSNs underpins the integration of the WSN with the building control and management systems while providing real time access to sensory data at an enterprise level where web services are used to exchange information between building subsystems. To support model deployment within WSNs we have previously proposed the Open Middleware Framework – OFM [14] that operates across all levels of a WSN based on a service orientated architecture approach. OFM supports vertical scalability (scale middleware based on the device type i.e. sensing node, repeater and gateway), re-configurability, and management services while also enabling application distribution across the complete network. OFM consist of three key elements: Models, Core (Gateway middleware) and Micro middleware. Models are tag based oBIX compliant XML descriptions that define the network, e.g. a model can define the topology of the network, sensing system for a node (data to be sensed & sensing interval) or management functionality. OFM provides a standard set of model descriptions suitable for the deployment and configuration of WSNs in buildings which negates the need for expert programmers to deploy and configure applications/firmware on embedded devices. The Core is the central part of OFM and provides model interpretation to extract system information which is used to activate services within the network. The

D. Application The application layer of the proposed model driven framework consists of data storage and presentation models. This layer is used as an interface to the data gathering infrastructure with the building stakeholders. Data warehouses are used for bringing together selected data from multiple sources and storing it in a single repository for later querying and analysis. A data warehouse is also a collection of information as well as a supporting system. Data warehouses have distinguishing characteristics which are mainly intended to provide for Decision Support Systems (DSS) based applications. IV.

EXPERIMENTAL METHODOLOGY

The objective of the case study presented in this paper is to evaluate the quality of the WSN design produced using (i) a homogeneous placement strategy based on communications range, (ii) site survey and (iii) the WSN deployment support tool. The design evaluation is based on infrastructure requirements, monetary cost, data transmission cost in terms of packet overhead and data throughput (sensory data delivery ratio). When faced with the task of deploying a network an inexperienced network planner has two choices: develop a design following network planning guidelines or outsource the design process to a WSN design expert. The proposed WSN design tool offers an alternative which is intended to benefit both the novice and experienced network planner. The three different design approaches address the same design requirements. The objective of this paper is to identify the differences among the three designs in terms of network performance, efficiency and deployment cost. First the common design requirements are described. Then the three design methodologies and their characteristics are presented. Next, the node programming and deployment mechanism is described. Finally the data collection and analysis mechanism is described.

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this homogeneous placement strategy resulted in the design shown in Fig. 3a, with a total of 5 repeaters and 3 gateways identified as being necessary to support network connectivity.

A.

Design Requirements A wireless sensor network was required to be deployed across the first floor of the NIMBUS Centre for Embedded Systems Research building at Cork Institute of Technology in Ireland. This is a new building and in order to profile the thermal comfort settings throughout the offices located within this space an environmental engineer was consulted to identify the ideal measurement point locations at which to place sensors to record environmental readings. This resulted in 18 specific locations being identified where the sensor nodes are to be placed during the deployment. Sensors are required to send their readings every five minutes. Having fixed sensor positions satisfies the sensor coverage needs and results in a deterministic sensor node deployment. The design brief outlined is to plan a reliable communications network with a minimum number of repeaters and gateway devices for delivery of sensory data based on the deterministic sensing locations. The design should identify the locations at which additional repeater and gateway devices should be placed in order for each sensing node to have a communications path between itself and a gateway device. The network is said to be connected if for each sensing node there exists a communications path between it and a gateway. Sensors can forward data via other nodes towards a gateway and additional repeater nodes can be added. Considering these WSN design requirements three designs were defined, deployed and evaluated. The deployment environment with the fixed sensor positions is shown in Fig. 2. Each design approach is subsequently described.

Site Survey (SS): to support the premise of a heterogeneous communications model a site survey using the predefined senor node locations was undertaken to characterize the influence of interference coming from other radio sources and obstacles including people in the defined deployment area. The resulting design is presented in Fig. 3b which consists of 3 additional gateways and 1 repeater. Design Tool Support (DTS): The WSN Design Tool used to produce a WSN design is outlined in Section III and the output of this is presented in Fig. 3c which has 2 gateways and 2 repeaters.

a)

Homogeneous Placement Strategy

b)

Site Survey based Design

c)

Design Tool Support

Fig. 2. Defined Sensor Measurement Points

B. Design Methodologies Homogeneous Placement Strategy (HPS): Using the freely available range planning guidelines from EnOcean [15] a homogeneous sensor network is considered where the communication range for each sensor is the same and the communications model is idealized and based on a circle. This methodology consists of drawing circles with a radius of 10 meters around each sensor (in this case the sensing nodes) and this is taken as the communication range of each sensor. As part of the guidelines it is recommended that for a highly robust network, redundant radio receiver paths should be implemented though the inclusion of additional repeaters. The methodology also proposes that the deployment plan should be verified on site using site survey equipment and the deployment should be adjusted appropriately through the addition\removal of repeaters or gateways. For the purposes of this experiment a site survey was not undertaken for this design as the objective was to evaluate the initial design produced using the guidelines rather than the tuning of the design using a site survey. Using

Fig. 3. WSN Designs

As expected, due to the small scale area of the deployment environment all designs produced viable deployment plans. However, the homogeneous placement strategy has an excessive number of repeaters; this is driven by the guidelines provided which promotes an over-design to ensure robust communications. The site survey based design and the design tool both reduced the number of repeaters required with the design tool opting for 2 gateways and 2 repeaters and the site survey based design going for 3 gateways and 1 additional repeater (with 3 of the sensor nodes also acting as repeaters). During the optimization process once connectivity is satisfied a secondary goal for the design tool is to minimize

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infrastructure cost and as a result opts for a repeater (2F13) in the middle of the floor to keep monetary costs at a minimum. In a small environment it is expected that the design tool produces a design at least as good as the design based on a site survey and this premise is borne out in the evaluation presented in Section 5. The benefit of the tool becomes more apparent when applied to large scale environments, as the use of the tool can significantly reduce the design time as a site survey is not required. In a large environment it is best practice for even the most experienced network designer to carry out a site survey to evaluate connectivity either prior to or post deployment with the network infrastructure being adjusted accordingly.

deployment is identical. The recorded data is analyzed over one hour of network operation, starting thirty minutes after the complete network deployment. For each design the quality is assessed against the design brief of having reliable communications at minimal infrastructure cost (number of repeaters/gateways). This is assessed based on the data captured and the infrastructure requirements with an analysis of the relevant metrics presented next. A.

Sensing Packet Delivery Ratio The sensing packet delivery ratio metric represents the average delivery rate per deployment. Table I summarizes the results for each design. As can be seen in Table I the average delivery ratio is similar with little losses being experienced across all designs. The major difference is borne out in the infrastructure requirements and costs as shown in Tables IV and V and discussed later.

C. Node Platform and Implementation Nodes are deployed with pre-defined static route tables. This allows for the evaluation of the network behavior as expected by the network designs rather than the influence of the routing protocol. The sensor network that has been deployed during the experiments is based on the SunSPOT platform. The SunSPOT platform is a high end java based wireless sensor device. This is a development platform which allows rapid prototyping and deployment of WSN protocols. The radio chip used is the Chipcon CC2420 which is also used in several other sensor platforms such as the MICAz and TelosB motes from Crossbow Technology. As the radio communication is the main design criteria used the results provided by this experiment are applicable to other sensor platforms that use the same radio chip.

TABLE I.

Methodology

Average Sensing Packet Delivery Ratio 97.17% 97.68% 98.22%

HPS SS DTS

B. Ratio of Sensing versus Forwarding Packets The distribution of sensing and forwarding packet for each design is shown in Fig. 4. The data in Fig. 4 is summarized in Table II for the three designs. The novice design has added 5 additional nodes to the deployment that act as repeaters for other nodes. This results in an almost 60:40 split of sensor data packets and the number of packets forwarded by intermediate nodes, and in comparison to the experienced designer and the WSN deployment support tool there is approximately 12 to 20% more forwarding packets as seen in Table II. The design tool plan includes 2 dedicated repeaters and these act as forwarders for a total of 7 nodes giving on average a load of 3.5 nodes per repeater whereas in the expert design the 4 nodes that act as repeaters (including 1 dedicated repeater) forward for a total of 5 nodes giving an average of 1.25 nodes per repeater. The design tool loads its 2 repeaters with more traffic and so generates more forwarded packets than the 4 repeaters of the expert design but the higher traffic load on the design tool repeaters does not adversely affect the overall delivery ratio and in fact it is marginally higher as per Table I.

D. Data Collection Mechanism Sensor data is sent towards the gateway every five minutes where this is logged in a centralized database for subsequent processing. For this experimental case study, in addition to the sensory data, the node battery level and the local time of the sensor are incorporated in the packet header. In addition some routing information is added which includes the MAC address of the sender, the MAC addresses of the next hop and the gateway associated with this node. When a packet is received, by a sensor, gateway or repeater, the link quality information is captured and this includes the RSSI (received signal strength indicator), LQI (link quality indicator) and Correlation values. For multi-hop communication, the received sensor data, routing and link quality information are aggregated at each node in the path towards the gateway. This allows for the analysis of link quality and network topology over the lifetime of the network deployment. V.

AVERAGE SENSING PACKET DELIVERY RATIO

The expert design can be viewed as under utilizing its repeaters as it is nearly a 1:1 relationship of sensors and repeaters, while it does maintain a very high delivery ratio it does so with an over design as the design tool has shown that a lower number of repeaters is more than adequate.

EXPERIMENTAL RESULTS

The objective of the network deployment case study is to analyze the network operation during steady state operation. To do so a subset of the collected deployment’s data is used when analyzing the results. As nodes do not start operating at the same time, measurements obtained before all the nodes where started are omitted from the analysis. Similarly results obtained toward the end of the experiment are not included in the results. Data collected after some nodes have been switched off or died due to battery depletion, are not considered in the results analysis. To compare the three deployments under similar conditions the observation time and time of day for each

TABLE II.

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RATIO OF SENSING VERSUS FORWARDING PACKETS

HPS

Sensing Packets 59.04%

Forwarding Packets 40.96%

SS DTS

78.79% 71.29%

21.21% 28.71%

a)

Homogeneous Placement Strategy

b)

Site Survey based Design

design constraints would remain the same regardless of platform. For this experiment the infrastructure cost of each design is calculated based on the commercially available Crossbow mote platform. The price of a Crossbow Netbridge Gateway has been quoted as €410 and an additional €90 for each sensor. Regular sensor motes can be used as repeaters. The resulting infrastructure price for each design is shown in Table V. This suggests that as the design scales up to a larger deployment, the design becomes more difficult and significant cost savings can be achieved with the use of the WSN deployment support tool. Also consider that the novice designer spent approximately 4 hours in defining gateway and repeater locations, whereas the experienced designer and the design tool needed approximately the same time, between 3040 minutes. The potential time savings and associated labor costs associated with doing a deployment can be dramatically reduced when software support tools are employed. TABLE IV.

c)

DESIGN INFRASTRUCTURE REQUIREMENTS

HPS

Number of Gateways 3

Number of Sensors 18

Additional Repeaters 5

SS

3

18

1

DTS

2

18

2

Design Tool Support TABLE V.

INFRASTRUCTURE COST

Fig. 4. Packet distribution for each design per node Infrastructure Cost

C. Data Transmission Cost The data transmission cost of the three WSN designs is summarized in Table III. A high transmission cost means that more packets (i.e. retransmitted and forwarded packets) are required to send a single sensor data packet. The additional data can impact the performance and power consumption of the network. This is influenced by the number of repeaters in the individual designs, the novice design requires repeaters for 12 nodes and it has the highest cost. The experienced designer’s network has 4 repeaters and forwards for 5 nodes and finally the design tool has 2 repeaters that forward for 7 nodes. TABLE III.

HPS SS DTS

1.21 1.46

HPS

€3300

€0

SS DTS

€2940 €2620

€360 €680

VI.

CONCLUSION

A WSN design and deployment support framework has been presented and evaluated against traditional WSN design approaches. The use of the design tools led to a reduced infrastructure costs and improved network reliability with higher sensing packet reception rate. Although the deployment was small and across a single floor, the use of the WSN design framework demonstrated benefits which the authors believe would scale up to provide a significant advantage over traditional ‘ad-hoc’ approaches particularly for large scale deployments. Future work includes a large deployment, the evaluation of this and the consideration of the influence of design choices on network lifetime.

DATA TRANSMISSION COST Data Transmission Cost (# packets) 1.78

Cost Savings

D. Infrastructure Cost The differences in the number of devices between the three designs have a significant influence on the overall infrastructure cost. The characteristics of the three designs are summarized in Table IV.

REFERENCES [1] [2]

In order to give some indication of the infrastructure cost of each design it is proposed to calculate the price of hardware that is typically used in real WSN deployment. In terms of estimating the infrastructure cost of each design using SunSpot devices was not considered as it is a development platform which is not intended for real world deployments; however the

[3]

[4]

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IF Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci. “Wireless sensor networks: a survey.” Computer networks, 38 (4) 393–422, 2002. Yung-tsung Hou, Tzu-chen Lee, Chia-mei Chen, and Bingchiang Jeng. “Node Placement for Optimal Coverage in Sensor Networks”. Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC'06) - Volume 01 WS Jang and WM Healy. “Wireless sensor network performance metrics for building applications.” Elsevier Journal on Energy and Buildings, 2010. Johnstone, J. Nicholson, B. Shehzad, and J. Slipp. “Experiences from a wireless sensor network deployment in a petroleum environment”, In

[5] [6] [7] [8]

[9]

[10]

Proceedings of the international conference on Wireless communications and mobile computing, page 387. ACM, 2007. D. Georgoulas, K. Blow, “Wireless Sensor Network Management and Functionality: An Overview” Journal of Wireless Sensor Networks, doi:10.4236/wsn.2009.14032. L. Baker, P. Clemente, B. Cohen, L. Permenter, B. Purves, P. Salmon, “Foundational Concepts for Model Driven System Design” INCOSE Model Driven System Design Interest Group, 1997, White Paper G.V.R. Holness. “Building Information Modeling: Gaining Momentum.”, ASHRAE Journal, 50(6):28–41, 2008 Antony Guinard, Alan Mc Gibney, and Dirk Pesch. A Wireless Sensor Network Design Tool to Support Building Energy Management. In Proceedings of The First ACM Workshop On Embedded Sensing Systems For Energy-Efficiency In Buildings (BuildSys’09). ACM, 2009. Alan Mc Gibney, Antony Guinard and Dirk Pesch, Wi-Design: A Modelling and Optimization Tool for Wireless Embedded Systems in Buildings. In proceedings of 7th IEEE International Workshop on Performance and Management of Wireless and Mobile Networks, P2MNET 2011

[11] [12] [13] [14]

[15]

656

M. Klepal, P. Pechac, and Z. Hradecky. Prediction of Wide-Band Parameters of Mobile Propagation Channel. XXVIIth URSI General Assembly the International Union of Radio Science, Maastricht, page 459, 2002. Klaassen, C. J. (2001a), ‘Installing bas sensors properly’, HPAC Engineering August, 53–55. ASHRAE (2003b), Applications, ASRAE Publications, ASHRAE oBIX1-1 Specification Document http://www.oasisopen.org/committees/download.php/38212/oBIX-1-1-spec-wd06.pdf, Online (Last Accessed 28 March 2011) M. S. Asalm, E. O’Regan, S. Rea, D. Pesch, “Open Framework Middleware: An Experimental Middleware Design Concept for Wireless Sensor Networks”, in Proc. International Conference on Autonomic Computing, Proceedings of the 6th International Workshop on Managing ubiquitous communications and services ,Barcelona, Spain, 2009. EnOcean. Range Planning Guide. http://www.enocean.com/fileadmin/redaktion/pdf/white_paper/WP_RA NGE_PLANNING_Jun09_en.pdf