Wireless Sensor Network Framework Design

Transcription

1 Wireless Sensor Network Framework Design Sheikh I Ahamed and Sanjay Vallecha Marquette University, Milwaukee, WI 53201, USA iq@mscs.mu.edu Abstract Recent advancement in electronics and wireless communications has enabled the development of low-cost sensor networks, which can be used in variety of applications like healthcare, defense, education, and traffic control. The sensor network consists of thousands of autonomous sensor nodes that are densely deployed and work for months without any human intervention. The sensor nodes are low cost, low battery powered, smaller size multifunctional devices, which are generally used to monitor a particular characteristic of a physical environment like temperature, light, humidity and collect data periodically at specified time interval. Due to physical characteristics, sensors cause lots of resource constraints on the sensor network like limited power, memory and computational capabilities, which causes design challenges in designing sensor networks in terms of type of data processing, amount of data, and storing the intermediate data.. The limited bandwidth and change in topology due to noise, position, and node malfunctioning causes challenges in terms of date transfer, routing of data and power consumption. This paper investigates various characteristics of sensor network; issues arise while designing sensor network and our approach for resolving these issues. The paper will then present our version of component based aspect oriented framework design that can be useful for designing various sensor network applications. Keywords: Sensor node, Wireless Network, Wireless sensor network 1 Introduction The recent advance in Micro-electro-mechanical system (MEMS) and wireless communication technology has enabled the development and deployment of a largescale, low power, inexpensive sensor network. The wireless sensor networks are networks of compact micro-sensors having wireless communication capabilities. Cheap and smart devices with multiple sensors onboard, networked through wireless connections are helping in sensing and controlling the environment of homes and cities. Smart disposable micro-sensors can be deployed on the ground, in the air, under water, on bodies, in vehicles, and inside buildings. A sensor network can provide access to information anytime, anywhere by collecting, processing, analyzing and disseminating data and thus participating in creating a smart environment. A system of networked sensors can detect and track personnel, chemical and biological threats, detect environmental hazards, monitor customer behavior surveillance, study and plan traffic route and congestion, and provide security in malls, homes, parking lots by wireless surveillance. A sensor node consists of a sensor unit, processing unit, communication device and power supply. A sensor unit can contain one or more sensors, which are generally used to gather information from the surrounding environment. The processing unit processes the data collected from the sensor node(s), performs some simple calculations on that data and sends only the required and partially processed data to other sensor nodes on the network using the wireless communication. Due to its characteristics, the sensor network puts communication, power, memory, computation, security and location constraints on the system [1], [3], [10], [15], [16], [17]. Apart from this, the issues like Node failure, topology changes, various communication issues, and mobility add new set of complexity to the network design. There is a need of designing a framework by which the sensor applications can be developed by hiding all the complexity of the sensor network from the developer of the sensor application. The framework should be flexible enough and provide multiple services to implement various functionalities of the sensor network. It should also take care of issues like efficient energy management and deployment, location determination, searching capabilities within the network, security and implement the same problem with different quality and resource requirements as desired by the application. The service can be local to the node or distributed over the network. The aspect-oriented approach will be used to implement distributed services. The middleware of the framework will isolate application from the internal implementation of sensor network hardware and software, and therefore hiding the complexity of message routing, handling of collision, retransmission of the data, and duplicate data handling. The middleware will allow various components to be plugged in providing or using special specific services of or from the sensor network. The

2 middleware will also provide the consistent interface to the application, so in event of low-level network changes, the application will not be affected by keeping the same interface. The remainder of the paper is structured as follows. In the next section, we discuss the various available sensor network projects, features and functionality provided by these projects. This section also describes the characteristics of wireless sensor network and criteria on which we shall design our wireless sensor network. The section 3 describes the applications of sensor network in different areas. The section 4 describes the characteristics of the sensor network and challenges in designing various features of sensor network. The section 5 describes our sensor network framework design. This section also concludes the paper. 2 Related Works There are few wireless sensor network projects, which are either developed or being developed and some of them have an overall goal to develop unobtrusive, deeply interconnected smart devices that can be attached to everyday items in order to support new functionality, novel interaction patterns and intelligent collaborative behavior. The research not only includes designing the smart hardware devices, but also the smart software that can be use to operate these devices. The following are few of the projects that are currently being developed: 2.1 TEA Project The Technology for Enabling Awareness (TEA) project [14], [20], [21] investigates several aspects of context awareness for small, mobile devices. The context sensitivity is achieved through hardware sensors and machine learning techniques, where device knows the context of the user and changes its behavior accordingly. TEA s goal is to develop an add-on component for mainstream mobile computing and communication devices, such as PDAs, laptops, and mobile phones. The add-on component will be context awareness enabled, which will not only covers where and when, but information on place (e.g. office, home, park), its environmental features (e.g. darkness, crowdedness), and on the owner's activity (e.g. writing, sleeping, walking). The TEA add-on is responsible for the continuous dynamic profiling of the 'user-placeactivity' (or context). The TEA uses one or more cues to calculate context (description). The hardware for the TEA named sensorboard is designed to generic, small and flexible and equipped with many different low-level sensors and a chip that digitizes the analog signals from the sensors at a certain rate. 2.2 Berkeley s Smart Dust Project Berkeley s Smart Dust project [9], [11], [18] explores the size and power consumption in autonomous sensor nodes. The smart dust node is a millimeter-scale node, which can contain requisite sensing, communication, and computing hardware along with power supply in few cubic millimeters volume. The smart dust contains a semiconductor laser diode, Micro Electro Mechanical Systems (MEMS) beam-steering mirror for active optical transmission, a MEMS corner-tube retroreflector for passive optical transmission, a optical receiver, signal processing and control circuitry and a power source based on thick film batteries and solar cells. The smart dust project uses most advance hardware technologies like battery power management, communication based on RF technology or optical transmission techniques. The smart dusts can be used in numerous civilian and military applications. The smart dust can be deployed over the region to record data for metrological, geophysical or planetary research. It is also useful where wired sensor are unusable or failed to take readings including instrumentation of semiconductor processing chambers, rotating machinery, wind tunnels and anechoic chambers. It can be deployed for stealthy monitoring of a hostile environment, detecting the passage of vehicles and other equipment, perimeter surveillance and detect the presence of chemical or biological agents on a battlefield. 2.3 Cornell s Cougar Project The Cougar project [5], [6], [19] uses a database approach to unite the seemingly conflicting requirements of scalability and flexibility in monitoring the physical world. It contains a new distributed data management layer that scales with the growth of sensor interconnectivity and computational power on the sensors. The Cougar system resides directly on the sensor nodes and creates the abstraction of a single processing node without centralizing data or computation. Rather than shipping all the relevant data to the gateway, it is stored in the network, processed locally and sends only the summarized query results. Also, instead of polling network regularly for detecting hazardous events incurring unnecessary communication, an event-based approach is used to respond to the event in timely manner. It also provides scalable, fault-tolerant, flexible data access and intelligent data reduction, and its design involves a confluence of novel research in database query processing, networking, algorithms, and distributed systems.

3 2.4 MIT s SLAM Project SLAM [2] is scalable location-aware computational network architecture. It integrates lots of software monitoring and control applications. These applications can get the data from millions of sensors with or without actuators and perform operations like traffic management, network monitoring. A tag is used to uniquely identify each physical region or object created using a cricket-equipped handheld device. The tag information is stored in a persistent storage along with other attributes. The SLAM uses the event mechanism for sensor data collection and notification. It also defines software handlers to connect sensor information and events. The scalable network consists of fixed and mobile sensor proxies, which are physically co-located with the objects and events they monitor, to integrate location, identity, and temporal information to form an event stream. Sensors and their proxies communicate using sensor-specific low-energy communication protocols. Applications are written as event handlers distributed across the network. SLAM provides support for dynamically distributing handlers across proxies and compute servers, routing events to handlers, and performing query processing operations. The overall framework design to create various sensor applications is missing from most of the research projects. The framework not only separates software functionality from the hardware, but also divides various functional and non-functional software functionality into smaller functional blocks based on the functionality provided by that wireless sensor network. 3 Sensor Network Applications The availability of low cost sensors and communication networks has resulted the development of many potential applications ranging from basic infrastructure application to highly complex industrial sensing. The sensor networks can consists of many different types of sensors such as temperature, acoustic, infrared that can be used to monitor a wide variety of environment conditions. The following are few examples: 3.1 Military Applications The sensor networks can be used in military [1], [8] command control, communications, computing, intelligence, reconnaissance and targeting system. The rapid development, self organization, fault tolerance and low cost of the sensor network make them useful in military application as sensor network are based on dense deployment of disposable nodes and are low cost, so the destruction of any node does not affect the overall functionality of the military Operation. 3.2 Environments and Habitat Monitoring The environment and habitat monitoring [13] uses sensor network where variables to be monitored are usually distributed over a large region. The environment sensor vegetation responses are used to study vegetation response to climatic trends and diseases and acoustic and imaging sensor can identify, track, and measure the population of birds and other species. 3.3 Health Applications The sensor networks can be used to provide interface [1], [4] for disabled, integrated patient monitoring, diagnostics, drug administration in hospitals, monitoring the movements and internal process of insects or other small animals, tele-monitoring of human physiological data and tracking and monitoring doctor and patients inside a hospital. 3.4 Traffic Control Applications The sensor networks can be used to for vehicle traffic monitoring [1], [4] and control using overhead or buried sensors to detect vehicles and control traffic lights. The sensors with embedded network capability can be deployed at every road intersection to detect and count vehicle traffic and estimate its speed. The sensor will communicate with other sensor nodes in the network and broadcast this information to other nodes. The human operator or automatic controller can query this information to generate control signal. 3.5 Home Applications The smart sensor nodes can be plugged in various appliances and electronic equipment like TV, refrigerator, and microwave. The sensors within these devices will allow them to talk to other sensor nodes internally as well as external nodes using satellite or Internet connection. This will allow users to control home devices locally and remotely more easily. 3.6 Robotics Applications The robots can either be used commercially or at home. More sophisticated robots are used in manufacturing plants and warehouses. The Carmakers also use automated machines to position car frames, bolt pieces together, and even do welds and priming. The robot can also be used in locating and disabling land mines, detecting chemical and biological weapons and handling service and repair tasks that are usually performed by astronauts on space walks. In the home applications [7], the robot can be used to vacuum the house, carry the load from one room to another room, home automation like light access, home security, education and hazard detection. The robot can operate home electronic

4 appliances or monitor household security. The ambulatory prototype robot is equipped with a wide range of functions, including telephone, camera, remote control, timer and surveillance equipment. 3.7 Automobile Applications The wireless sensor network can be used to monitor engine status, tire pressure and fluid levels. The sensor network can also be used to detect the motion of a moving object such as vehicles or human. Sensors can also be used in controlling the rate of combustion in a car engine. Now a day, few of the cars have sensors to detect a wide variety of processes enabling mechanics to plug cars into diagnostic computer equipment to identify the specific system failures for rapid and exacting repairs and maintenance. In future, vehicles will be able to see in the dark, to park them, to track pedestrians on the street, communicate with the cars behind them, or even tell their poor mortal drivers what possible danger is lurking around the next corner. 4 Sensor Network Characteristics and Challenges The figure 1 shows the typical sensor network sensor node not only uses the battery power to do the local data processing, but also uses battery power to transmit the data to other nodes. It also uses the power to route the data to other nodes, which it receives from other nodes. Since it is very difficult to recharge large number of nodes in the network, the sensor node should be energy efficient Small Memory The sensor node has a limited memory size to do the data processing and storing. Due to this, the large and complex data processing is done at the sensor node. Also, the large amount of data neither can not be passed from one node to other node nor can be stored at sensor node Low Cost Since the sensor network contains large numbers of sensor nodes, it is important to keep the cost of an individual sensor node as low as possible, so that overall cost of the sensor network can be justified Network Topology and Scalability The sensor network topology is changed very frequently due to dense population of sensor nodes in the network. The nodes can be added to the sensor network in mass or one by one. Once deployed, node can malfunction due to weather, low energy, blocking, change in position or wear and tear. These tasks result in change in the topology of sensor network Wireless Connection The sensor networks are wireless network due to the fact that the sensor nodes me be densely deployed in the location where no installed infrastructure for communications to take place. These nodes are linked by the wireless medium and communicate to each other using wireless communications channels like radio, infrared or optical medium. Fig 1: Typical Sensor Network 4.1 Characteristics The following are the various characteristics of a sensor network [1], [3], [10], [15], [16], [17]: Energy Efficient The wireless sensor node is a small micro-electronic device that is equipped with a limited power source, which is less than 0.5 Ah. The life of the sensor node is therefore fully dependent on the power source. The Distributed Sensing and Processing Each sensor node can sense and collect environment data, which causes data to be sensed and collected on distributed basis in the sensor network. The data collected at each sensor can be processed locally and transformed from one form to another form using various transform mechanisms. The data collected at the sensor node can be aggregated using aggregation functions, transformed into information and then can be sent to other nodes in the network Multi-hop Communication As the large numbers of sensor nodes are densely deployed in the sensor network, they are placed near to each other. The multi-hop communication is very useful

5 in this case as it is expected to consume less power as compared to single hop communication. The multi-hop transmission keeps transmission power level low and overcomes signal propagation effects experience in long distance wireless communication Fault Tolerant The sensor network should be fault tolerant and the failure of one or more sensor nodes due to lack of power, blocking or physical damage should not affect the overall task of the sensor network Hardware Design The components of a sensor node like sensing unit, power unit, processing unit, A to D converters should fit into small size module, which may be less than cubic centimeter, which is light enough to remain suspended in the air Collision and Congestion Since the sensor nodes are densely deployed in the network, this may cause collision and congestion while sending the data from one node to another node. The sensor, which is near or in the transmission range of each other, should not transmit the data simultaneously Querying Ability The sensor network should be able to handle data centric as well as address centric queries. It should be able to query a particular region containing multiple sensor nodes or an individual sensor node in the sensor network. 4.2 Challenges A sensor network design is influenced by many factors, which include fault tolerance; scalability; production costs; operating environment; sensor network topology; hardware constraints; transmission media; and power consumption. The sensor network physical characteristics should be able to handle failure, limited memory, battery and computational power of sensor nodes at any point of time. The sensor network also provides weaker service of quality due to resource limitations. The sensors provide data at the continuous stream and due to the fact that they have limited power and computational capabilities, there is need to reduce the data at each node by grouping similar looking data at each node end. The hardware design, communication protocol and application design as well as extending sensor network life and collecting data in intelligent way are also a big challenge. The other challenges includes change in network topology due to ad hoc deployment, collision and congestion due to densely populated sensors in the network and dynamic environment condition, which needs to be resolved while designing sensor network. The following describe various challenges of sensor network and our implementation of those functionalities: Query processing and Aggregation The query processing in the sensor network is not same as query processing in traditional database system as sensor nodes are streaming source of data [17]. They keep on sending or collecting the data on the regular time interval as defined at that node. The queries need to handle this real time environment data very efficiently as sample time bound data can not be stored temporarily on the disk or intermediate storage medium. The sensors do not collect the data at reliable rate and the sample taken may be garbled due to noise. The sensor node has very limited power and memory capacity so the query result produced at the node need to be conversed. The sensor nodes have computational capabilities and they can be able to compute queries as the data passed through them. As the sensor data collected from the nodes may contain noise, it is more appropriate to get the accurate results by fusing the data from several sensor nodes. The aggregation results are more useful in sensor application than just getting the data from the single sensor. Each sensor is capable of partially or fully aggregate the data itself. After doing the this aggregation, the data can be sent to some special sensor nodes assigned the role of further aggregating the data received from sensor nodes, cache, process and filter the data to meaningful information and then send these information to sink nodes. Dividing the aggregation role to multiple levels will provide low latency and low power consumption. Aggregation will not only increase the accuracy of the results but also compensate garbled data or missing data due to sensor node failing. The sensor nodes should be capable of doing SUM, AVE, MIN, MAX and COUNT functionality to perform the aggregation of the data. Doing aggregation at individual sensor node will reduce the data flow from one node to another node and thus reducing the traffic. The aggregate function may also provide HAVING and GROUP BY clauses to eliminate groups by a predicate. Another special requirement in case of sensor network is that sensor network is interested in monitoring the environment over a longer period of time, so the query should support time duration so that the query can run for that much duration periodically without reissuing query again and again. Another parameter to the queries can be sample rate at which the query should return the result within the time duration specified in duration.

6 Fig 2: Our approach of implementing Query Processing and Data aggregation multi-hop communication. Each dumb node will make sure that neighboring nodes in the network know its location. In order to make location determination faster, the average distance within the group is calculated based on the distance between nodes and then dividing it by number of hops traveled. Same calculations can be performed between sink nodes to determine the average distance. Any node, which is not able to determine it location uses average distance to convert hop count to distance. Addition and deletion of nodes can cause sink nodes to recalculate the average hop distance, which may not be a big issue in terms of power consumption due to the assumption that the additions and deletions are done in bulk and sink devices have the processing and power capabilities to perform this operation on regular basis. Mostly the query processing in the sensor network is done using polling mechanism by sending the query request from sink node to the individual sensor nodes and getting the continuous data from sensor node at varying time interval. The data sampling at the sensor node should also support events so that when that event occurs at that sensor node, the data sampling is affected at that node. Another nice feature of the query processing is to store some of the intermediate results either in the sensor nodes or in some of the designated nodes, so that they can be reused when required. Most often, these intermediate results will be complex and time consuming calculations performed by the sensor Location determination The sensors use location determination to determine its position in the sensor network. Generally, global positioning system can be used to keep track of the location of all the nodes in the network, but the GPS system works in the outdoor and requires high power consumption, which is difficult to implement, as the sensors in the network are low power devices. They have very limited transmission power capabilities, by which they can send the data to the selected neighboring nodes. Each node is responsible for knowing its neighbors and their location from this node. The overhead for storing location of each neighbor nodes can be very high if the sensor nodes are densely populated. In this case, sensor should be capable to determine the level of accuracy it wants to achieve depending on the energy level available. Our implementation proposes to group the nodes based on the geographical location. Each group will have at least one sink node, which is more powerful in term of memory and processing capabilities. The sink node will keep track of all the nodes in the group. It will keep on broadcasting its location in the group, which will be passed from one node to another node using Fig 3: Our approach of determining location Data Dissemination The data produced by the sensor node is routed through several sensor nodes before reaching to the destination node using multi-hop technique. The routing mechanism should use the shortest path algorithms to find the shortest path and reach to it destination in minimum amount of time. This will save energy at individual sensor node, as minimum number of nodes will be involved in routing data. Another challenge in routing is to handle duplicate data packets, which are sent by multiple sensor nodes as various nodes are involved in passing data from source to destination. Our implementation proposes to group the nodes based on the geographical location. Each location will have one or more sink nodes. The sink node(s) will then be responsible for handling the data in their own groups. Each node in the group needs to tell its location information to at least five nodes so that the forwarding information between nodes can be established. Once this is setup, sensor node can send the data to the sink nodes either on the continuous basis or on request depending on the application. The sink nodes in the

7 group are entry point for other sink nodes in other groups and sink nodes in outside group will talk to sink nodes in the group rather then requesting the data to individual sensor nodes in the group. Any data request from sink nodes outside this group will be sent to any sink node within group. If the sink node within group is already receiving data continuously from the sensor node, it can directly provide this information otherwise the query request will be passed to the appropriate sensor node using multi-hop technique, where the request will be passed from sink node to its neighboring nodes and from neighboring nodes to their neighboring node until it reached to the intended destination. The data from sensor node to sink node will be received in the reverse direction using multi-hop technique. synchronization scheme should be precise and energy efficient. Our approach assumes that each sensor node has its own clock, which is used, as primary source of time synchronization in the network. The sensor nodes are distributed in various logical groups depending on the location of sensor nodes in the sensor network. Each group has a much smarter node named sink node, which has much processing power than other nodes in the group. The sink node will be the master timekeeper and it will keep on broadcasting time synchronization message at regular interval. All the nodes in that group receive the time synchronization message from sink node. Each node will treat this message as reference time and then can use it to normalize its own timestamp Coverage Problem The coverage generally represents the quality of the service provided by a sensor network. The coverage problem suggests the way to find out the weak points in the sensor network and mechanism to improve the overall quality of the service by using various configuration schemes. The areas, which are of lower operability from the sensor nodes are said to be worst areas and areas, which provides high operability from sensor nodes and provide best coverage support. In order to get the best coverage out of the sensor network, the sensor network should use the optimistic location determination technique as mentioned before. our approach suggests using the location determination mechanism to calculate the optimistic distance between the nodes. Each node should ensure that its location is known to at least five neighboring nodes in the sensor network and the neighboring nodes in the network are within the range of average distance. The more critically area should be densely populated with sensor nodes, which will compensate the issue of node failures. The periodic maintenance is required to make sure that failed nodes are replaced on the regular basis Time Synchronization The time synchronization is important aspect of sensor network. The sensor network uses time synchronization to discard the duplicate messages that are coming from different sensors, but represent the same information. This is possible due to multi-hoping technique used to send the data. The accurate timestamps are required to debug any problem in sensor network by recording all time-based event handled by a particular node in the network. The energy efficient feature of sensor network node makes it difficult to handle time synchronization as nodes have finite battery resources and they should not use lots of energy in synchronizing time, so the time Fig 5: Our approach of implementing Time Synchronization in Sensor Network 5 Our Wireless Sensor Network Design Due to the sensor network characteristics like low cost, low memory availability, low power consumption, location dependency and fault tolerance, the important aspect of the sensor network design is to define the sensor network framework, which provides functionality to an individual node, set of nodes or entire sensor network. The sensor network framework should provide an environment in which Nodes can communicate with each other by transmitting and receiving the data from other nodes using a common protocol. Communication among nodes should be power efficient over the wireless medium. Collisions among the network should be detectable and if possible should be minimized. Data from one node should be reached to the destination node as soon as possible using the optimized routing algorithm.

8 The Data can be presented to the outside world in the presentable way as well as data from outside can be input to the network in free format. Low power consumption should be given higher priority Apart from the above features, the framework should be able to handle the challenges like query processing, efficient searching in the network, location determination, coverage, time synchronization. The framework should also be able to provide the intended security in the network depending on the application requirements. The features for the sensor network can be categorized into 2 types depending on the scope; 1) Features which are applicable to a particular node like low memory and power consumption, data processing etc. and 2) Features which are applicable to a group of nodes or the entire sensor network like searching, query processing at network level, location determination, security, time synchronization etc. These are the aspects of the sensor network, which can be defined with respect to a single node or group of nodes as they crosscut the structure of the overall system. All the nodes in the network will share these functionalities. We propose component-based framework architecture for the implementing individual sensor nodes functionalities. On the top of that, an Aspect oriented approach can be used to provide an efficient way to modularize crosscutting functionality of the sensor network. The component based framework approach will ease the software development process and provide simple, better organized, and efficient system configuration. The functionality at the sensor node can be in the form of component, which can be added, modified or removed from the sensor node depending on the hardware availability and application requirements. For example, the smart sensor node has more memory and processing capability, which makes it suitable for performing more powerful computations and thus providing more functionality at the node end. The functionality at the sensor network level can be implemented using aspect-oriented approach, which will provide easy tailoring of the system for a specific application by changing the behavior of the application by applying the aspects. This approach will help in defining the aspects of the system in natural and free language format. Once all the aspects of the system are defined, the aspects can be woven together and applied to the application at the time of running the application. The aspects will complement component-based design by allowing changing in the static objects to meet the new and growing requirements of the system. The new characteristics of the system can be implemented by changing the aspect of the system in which it is intended to work. Figure 6 shows the design of our sensor network framework: Figure 6: Our design of Sensor Network Framework The task of the sensor node is to sense the environment data, perform the processing of the data and then send the data over the network to the other nodes. The processing of data may contain easy or complex calculations along with data aggregation. The sensor node will have the interface to collect the data from the sensor. The sensor node interface will be flexible so that the data collection process can be configurable based on the application requirements like sampling rate, sample duration, data aggregation, and aggregation rules. The component-based design is the better choice to implement this interface. The data aggregation can be implemented in a separate component; input to this component would be the aggregation rules, based on which the data aggregation will be performed. The communication to the other nodes will be the part of the operating system running on the sensor node. As the component-based approach is used to implement various functionalities of the sensor nodes, the implementation details of functionalities will be hidden

9 from the user. The functionality of the each component will be exposed through its interface. The data collection process can be defined as periodic or event based depending on the application requirement. This will be one of the parameters that can be controlled by the sensor network application at higher level. For system with high performance requirement, event based sampling will be most probable choice. Also if the sample rate is less, more aggregation requirements will require to be implemented, otherwise system will be flooded with lots of data from the sample node and will cause network congestion. As data processing logic and aggregation logic is implemented as component, it is possible to configure each individual sensor node to processing and aggregation logic based on the requirements. The configuration at the sensor node can be changed at any point of time. If security of the data is concern for the network, a security component can be plugged in at the sensor node, which will encrypt data before passing it over the network. The destination node will decrypt the data based on the encryption rules applied at the time of sending the data. The middle layer will perform all application specific tasks like routing of data, location determination, queries, network maintenance services and time synchronization. This layer is middleware layer that will provide the interface between the sensor node and the application. The middleware will hide details of implementation of the sensor nodes software and hardware from applications. The middleware will provide functionalities to all the surrounding layers in the form of services. These services will be provided in the form of components, which can easily be plugged in to the sensor network without effecting the overall network configuration. The component providing a service or a set of services can be replaced with another component providing the same service or set of services more efficiently as required by the system provided that the component will keep on supporting the same old interface along with the new functionality. This feature of component will hide the implementation details of the service beneath interface. Also, it will be easy to provide the new services in the network by adding new components. Each component should have an event handler that will interpret the service request received from application (the layer above it) and send this request to appropriate method in the component. Also, all the changes in the plugged in component will be notified to the application through events if the application is configured to sign up for event changes. The objects will simultaneously interact with other objects in the system to provide system level services like security, querying, and synchronization, which means that multiple objects will be involved in the implementation of these services. The aspectual decomposition of the middleware will allow designing the functional component that is cross cut with real-time system aspects. The aspect-oriented approach will allow changing the requirement of the system by implementing the aspects in the system without changing basic functionality of the components predefined in the system. This approach will help to separate object level aspects from system level aspects and implement the aspects separately. These aspects will weaved together by the composition process known as weaving [12]. The weaving process provides the componentized implementation of the aspects. The aspected components are loosely coupled and by weaving different aspects into the code of the component, their internal behavior can be modified. The top layer will be the application layer, which will integrate various sensor applications in the sensor network. The sensor network applications can use the services provided by the middleware layer. As each service is implemented as component, it will provide configuration parameters or properties to configure the way in which application will want to use this service. Wireless devices like PDA can connect to the wireless sensor network at any location, use the services provided by the network and get the environment data collected by various sensor nodes in the network. The data collected at PDA can be displayed in various forms like charts, graphs. Depending on the functionality, few of the components in the system will also provide the event signup feature, which will allow the application to get updates from the sensor network. The application running on stationary devices like workstation terminal can sign up to a reader component, which will notify the application whenever there is change in sensor reading values or set of sensor readings. The signup process can also be configured so that the application can get the event notification based on the configuration specified. 6 Conclusion In this paper, we presented some of the design issues involved in the development of wireless sensor network applications. We listed various solutions for fixing those design issues and then presented our approach, which we think is the best to resolve this problem. It is evident from the discussion that wireless sensor network puts some strict requirements on the sensor network applications, which can best be resolved by using component-based system development for providing the component based services in the network. The cross cutting aspects of the sensor network like synchronization, location determination can be best resolved using aspect oriented design, which allows the developers to change the functionality of the components without changing the static structure of the

10 components. The aspects can be implemented on the top of the components and weaved together with aspect weaver to provide the intended functionality. The framework s flexibility, code usage, simplicity and less time to market are the main advantages of using our approach. We also proposed a new sensor network framework and we are currently implementing the code for components providing the basic services of the framework. We are also in the process of selecting the best aspect oriented programming language for implementing the cross cutting concerns. Some of the Aspect Oriented programming languages, which we are currently evaluating, is AspectJ, JAsCo, and AspectWerkz. The future work will involve the implementation of aspects in the sensor network for providing the network level services. 7 References [1] I.F. Akyldiz, W. Su, Y. Sankarasubramaniam, and E. Cairici, Wireless Sensor Networks: A Survey, Computer Networks, vol. 38, no. 4, pp , available at ets.pdf, 2002 [2] Hari Balakrishnan, Erik Demaine, Mike Stonebraker, and Seth Teller, ITR: Scalable Location-Aware Monitoring (SLAM) Systems, SLAM Systems, November 9, 2001 [3] Akyildiz, I.F., W. Su, Y. Sankarasubramaniam, and E. Cayirci, "A Survey on Sensor Networks," IEEE Communications Magazine, vol. 40, no. 8, pp , available at nsornets_ pdf, Aug [4] Chee-Yee Chong; Kumar, S.P., "Sensor networks: Evolution, opportunities, and challenges," Proc. IEEE, vol. 91, no. 8, Aug [5] Cornell Cougar Project, COUGAR, ugarmm/index.php [6] COUGAR: The Network is The Database, COUGAR [7] John Cutter, What Will Home Robots Actually Do?, Is There a Robot In Your Future, [8] Future Combat System (FCS), Future Combat System, [9] J. M. Kahn, R. H. Katz, K. S. J. Pister, "Next century challenges: mobile networking for Smart Dust," In International Conference on Mobile Computing and Networks (MobiCOM 99), Seattle, WA, pp Aug , [10] H. Karl and A. Willig, "A short survey of wireless sensor networks," Technical Report TKN , Telecommunication Networks Group, Technische Universität, Berlin, Oct [11] J. M. Kahn, R. H. Katz and K. S. J. Pister, "Mobile Networking for Smart Dust," ACM/IEEE Intl. Conf. on Mobile Computing and Networking (MobiCom 99), Seattle, WA, Aug , [12] Ramnivas Laddad, I want my AOP!, Part 1 Separate software concerns with aspect-oriented programming, Aspect Oriented Programming, aspect.html, 2002 [13] Alan Mainwaring, David Culler, Joseph Polastre, Robert Szewczyk, John Anderson, Wireless sensor networks for habitat monitoring, Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications (WSNA), Sept [14] Ubiquitous Computing, Telecooperation Office [15] S. Tilak, N. Abu-Ghazaleh, and W.Heinzelman, A Taxonomy of Wireless Micro-Sensor Network Models, ACM Mobile Computing and Communications Review (MC2R), vol. 6, no. 2, April [16] M. Tubaishat, and S. Madria, Sensor Networks: An Overview, IEEE Potentials, vol. 22, no. 2, pp , April, 2003 [17] Yong. Yao and Johannes Gehrke, Query Processing for Sensor Networks, In Proceedings of the First Biennial Conference on Innovative Data Systems Research (CIDR), pp , [18] K. S. J. Pister, J. M. Kahn, and B. E. Boser, "Smart Dust: Wireless Networks of Millimeter-Scale Sensor Nodes," Electronics Research Laboratory Research Summary, [19] Philippe Bonnet, J. E. Gehrke, and Praveen Seshadri, Towards Sensor Database Systems, Proceedings of the Second International Conference on Mobile Data Management, Hong Kong, pp. 3-14, January [20] A. Schmidt, M. Beigl, and H. W. Gellersen, "There is More to Context than Location," Comp. & Graphics J., vol. 23, no. 6, pp , available at t_cug_elsevier_ context-is-more-thanlocation.pdf, Dec [21] K. Van Laerhoven. "Teaching Contexts to Applications." In Proc. of the workshop Situated Interaction in Ubiquitous Computing, CHI 2000, The Hague, The Netherlands, pp. 5-8, 2000.