POWER: Planning and Deployment Platform for Wireless Sensor Networks *

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1 POWER: Planning and Deployment Platform for Wireless Sensor Networks * Jinghao Li 1, Yuebin Bai 1, Haixing Ji 1, Jihong Ma 2, Yong Tian 1 and Depei Qian 1 1 School of Computer, Beihang University, Beijing, , China 2 Handan Polytechnic College, Handan , China lijinghao@cse.buaa.edu.cn Abstract Impeded by the bottleneck of the information collection in the information chain, researchers are more and more interested in the development of wireless sensor networks; the applications of wireless sensor networks are growing too. But less attention are paid to wireless sensor networks planning. To resolve deployment challenges, reduce the deployment risk, we propose to build the planning and deployment platform for wireless sensor networks. POWER is a software environment for planning and deploying wireless sensor networks applications into actual environment. POWER has three main parts, network deployment, simulation, and performance evaluation and optimization. Simulation is the foundation of the POWER. Lifetime is the most important performance evaluation metrics. The target of the POWER is to supply an integrated and optimal deployment solution for an actual application. 1. Introduction With the requirement of the wireless sensor networks (WSNs) growing, the applications of the WSNs get more and more. Currently, WSNs have been widely used in the fields like habitat monitoring, health-care, smart home, industries, and military [1, 2]. When users deploy the above mentioned WSN applications into an actual environment, we found that there are various challenges and problems; thereby we propose the planning and deployment platform for WSNs, namely POWER. And, the goal of the POWER is to: support WSNs deployment solution reduce the deployment cost and improve the efficiency of deployment afford the whole network performance evaluation test new routing protocols and MAC protocols accelerate the WSNs practicality There are limited literature on planning and deployment for WSNs, but a lot on the WSN simulator, which is the base of POWER. J-Sim [3], Ns- 2 [4] and OPNET [5] are the popular widely adopted network simulators. SENS [6] and EmStar [7] are application-oriented simulators. TOSSIM [8] is a discrete-event simulator for TinyOS. In this paper we propose the problems of planning actual applications into environment, and give our solution to solve these problems, which is to build the POWER. Moreover, we also introduce the implementation framework of POWER prototype, which consists of three main parts, network deployment, simulation, and performance evaluation and optimization. The framework is based on the opensource J-Sim simulator. This paper is organized as the following. Section 2 describes the design considerations. Section 3 presents the POWER framework. Finally, Section 4 concludes this paper and presents the future work. 2. Design Considerations 2.1. The Problems To understand the requirements when users deploy sensors into actual environment, these key problems can be divided into following five parts. A. How to place sensor nodes The placement is divided into two kinds, predetermined or stochastically. In a pre-determined network, sensors can be placed according to the user s request (i.e. manually). In a more realistic WSN environment, sensors are placed in a stochastically * Project and project supported by National Natural Science Foundation of China.

2 fashion. For example, a common method to distribute sensor nodes is to scatter them from the plane. B. The connectivity of WSN Suppose that every node has a fixed radio range. The network topology is seen as a mathematic graph, the target is to studying the graph s connectivity, i.e. to determine whether all the sensor nodes can deliver packets to SINK nodes. In addition, the relationship between the number of the sensor nodes and the connectivity; the radio range of the sensor node and the connectivity should also be studied. In this way, this can help users to choose optimal sensor nodes and save the cost. From above motioned, we found that POWER should contain relative functions to resolve these problems. But these problems are not independence, they are often interrelated. We have to circulatory implement these operations. Figure 1 describes this circulatory process. C. The coverage of WSN Coverage which by definition is how well a sensing field is monitored or tracked by sensors [9] is another key point. Generally, the coverage validation can divide into two kinds. One is to confirm how well some key field is monitored. Another one is in some application specific scenarios, WSN require a predetermined sensor nodes density in whole areas. All the two kinds would require extra sensors to be placed, and also need to modify MAC or routing protocol to deal with the heavy traffic. D. How to collect the data Until now, several kinds of routing protocols and MAC protocols have been studied. They all base on different applications background. So, in order to maximize performance in application specific WSN, one must not only take into account the routing protocol but also the MAC protocol. The choice of MAC protocol or routing protocol can help to determine the node and the application specific WSN parameter. Moreover, whether the WSN needs the data aggregation, including average, minimum, or maximum data, should also be taken into account. E. How to evaluate the WSN Having discussed possible questions and choices, one must also determine the appropriate performance metrics. The key metrics for evaluating sensor network protocols as a whole are lifetime, coverage, latency, fault-tolerance, scalability, and security [2, 10]. Due to the character of the WSN, these metrics are often interrelated Our Solution Fig. 1. The implement process of POWER In figure 1, the first step of POWER is pre-placement. it manually or stochastically places some sensor nodes into the virtual environment, and become an integrated network topology graph. Of course, it should initialize some WSN parameter, including the nodes parameter, MAC protocol, and the routing protocol etc. Except these, it also accomplishes coverage validation and connectivity validation. The coverage validation is to determine whether the WSN satisfies the density requirement and whether the number of sensor nodes is enough. The connectivity validation is to determine whether the WSN is connective. Simulation and Evaluation accomplishes the network simulation and performance evaluation. When the evaluation is done, where should be modify to get optimal performance is the purpose of the optimization. Maybe the performance bottleneck is the unfitted protocol, unfitted placement, even the unfitted nodes. When modified scheme is determined, the process goes back to the pre-placement to modify placement or to set models over again. The whole process is a circulation and the target is to get optimal deployment solution. The major aim of the POWER is to rebuild the actual WSN application in the virtual environment, and supplies a deployment solution. Thus simulation is the foundation of POWER. We should build several kinds of models to ensure the accuracy of the simulation. Just like in the traditional network architecture, the bottom

3 three layers of the sensor network architecture are mainly concerned by POWER, which are shown in the Figure 2. By combining these models, POWER can set up a virtual network according to the actual network. course, it should have a drag-and-draw graphics user interface (GUI) to help user to operate the network topology. Moreover, the network topology structure should also guarantee the requirement of the coverage and connectivity. Coverage validation helps users to determine whether the number of the nodes is enough, whether the nodes density and coverage are satisfied, where should be added nodes, and where can be reduced nodes. Connectivity validation is to determine the network topology graph connectivity Simulation Fig. 2. The main type of models 3. Overview of the Proposed Framework This section describes the conceptual framework design of POWER prototype. According to the above mentioned deployment problems and the implementation process, we found POWER should contain three main parts. And the key part of POWER is the simulation. The framework of POWER is shown in Figure 3. In the following sections, we will describe these parts in details. To support WSN deployment solution for a real application, it is needed to simulate the WSN and carry out the quantitative analysis of the WSN. So simulation is the fundamental component of the architecture. J-Sim is chosen as the simulator for POWER. J-Sim is an open-source, component-based compositional network simulation environment that is developed entirely in Java. J-Sim was chosen due to its looselycoupled, component-based programming model, as well as its completed Sensor Network packets [12, 13]. J-Sim includes several kinds of WSN models, such as radio models, energy models, MAC protocol models, and routing protocol models. But not all models are suited for special WSN simulation. To improve the accuracy of simulation, POWER should add some new special WSN models, which is the most important part of this platform. The extensions are shown as follows. A. Protocol models Fig. 3. The framework of POWER 3.1. Network Deployment The network deployment aims to find an optimal placement solution. It contains three main parts, preplacement, coverage validation and connectivity validation. The pre-placement finishes the placement of the sensor nodes. Users can manually or stochastically place each node. Now, quite a lot literature has studies it; in [11], the author proposes three typical types of stochastic sensor placement. Of Just as figure 3 shows, POWER adds new MAC protocols; they are IEEE , TDMA, S-MAC, etc. POWER also adds some new special WSN routing protocols according to different network characteristics [10]. They are One-Hop, Multi-Hop, and Hierarchical (LEACH) [14]. Other well known routing protocols, for example SPEED [15], may be added later. B. Radio models All the previous preparation work does not help POWER support environment factor (i.e. assume that there are no obstructions in the environment), so the radio propagation model is needed to ensure the accuracy of simulations. Radio propagation models attempt to predict the received signal strength at a given distance from the transmitter. If the strength is more than a threshold, the sensor begins to receive the packet. In addition, there are three main phenomena

4 that affect wireless communication which should be taken into account. A standard model used to simulate a clear, unobstructed line-to-sight path between two nodes is the Friss free-space equation [10]. More accurate radio model also will be added later. C. Energy models The most important factor in a WSN application is the energy, as well as the energy model in the architecture. J-Sim energy model is too simple, which should be extended. The energy consumption of a sensor node is divided into three parts: CPU energy consumption, sense energy consumption, and radio energy consumption. Thanks to the development of the micro-electro-mechanical systems, the sense energy consumption and the CPU energy consumption is very little. So in the framework, the CPU energy consumption and the radio energy consumption are mainly considered. A sensor s CPU can be in one of the following four states: active, idle, sleep and off. Similarly, a sensor s radio also has four states: transmit, receive, idle, and sleep. There are two methods to calculate the energy consumption. The first method is to assign every state a constant value. When there is transferring into a different state, relevant value will be subtracted from the sensor remnant energy with relevant value. The second method is to calculate energy according to some equations. For example in the radio transmit state, energy consumption is in association with the distant between the two nodes. The longer the distant, the more the energy consumption is. This is much flexible than the first one Performance Evaluation and Optimization The evaluation part focuses on the evaluations of the virtual WSN. The appropriate performance metrics should be determined initially. POWER evaluates the WSN by following performance metrics: lifetime, latency, scalability, and reliability. They are used to determine which protocols as a whole provides the most efficient paradigm. Lifetime is the most crucial indicator of the usefulness of a WSN. Supposing that lifetime is the time between sensor nodes begins to sense and the time SINK nodes can get packet from any sensor nodes. Latency was the travel time between the packet creation at a sensor node and the received time at the SINK node. In some disaster forecast applications, the WSN must update the critical information in time. Hence keeping the delay time as short as possible is a must. Reliability means, any protocol that is chosen must be robust. The dropped packets and error packets should be as few as possible. The dropped package can be calculated by comparing the number of packets that were sent by sensor nodes and the number of packets that were received by SINK nodes. Scalability for WSN means the performance is independent of the number of sensor nodes. It can be calculated by comparing the performance with different amount of sensor nodes. Other performance metrics, including security and fault-tolerance, are not easy to scale. They will not be taken into account for now. Optimization works on the optimization of the virtual network. According to the evaluation result, we can find the bottleneck of the performance. Therefore, we can correct the problem to eliminate or avoid the bottleneck. Possible solutions include the changing of the types of sensor nodes, protocols, or some nodes position, etc. Of course, we can validate the modification by re-simulate the new WSN according to the implementation process of POWER. 4. Conclusions POWER is a software environment to plan the deployment of the WSN applications. Its target is to resolve the deployment problems, reduce the deployment risk, and get an optimal deployment solution. In this paper, we analyze the deployment challenges, and then propose the planning and deployment platform for WSN, namely POWER, to resolve these problems. Moreover, we introduce the implement framework of the POWER prototype. It offers an integrated process for planning an actual deployment. Recently, according to the framework, we have built a prototype of POWER. It only has two types of routing protocol, two types of MAC protocol, an energy model and a simple radio model. However it proves that POWER is credible and useful. The more accurate models the proposed models are; the more accuracy the POWER result would be. Therefore, in future work, new models that POWER needs would be investigated, such as environment models, obstacle models, new special protocols, and new radio models etc. Except these, we also should take into account about other deployment problems to improve the reliable of POWER. 5. References [1] Deborah Estrin, Ramesh Govindan, John Heidemann, and Satish Kumar.: Next century challenges: scalable

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