Energy conservation in Wireless Sensor Networks Sagnik Bhattacharya, Tarek Abdelzaher University of Virginia, Department of Computer Science School of Engineering and Applied Science, Charlottesville, VA Tiny Sensor NodesTowards efficient Wireless Sensor Networks A Sensor Network Applications National Science Foundation Office of Naval Research DARPA ITO In recent years, wireless sensor networks have emerged as a new fast-growing application domain for distributed computing. The vision of sensor networks presents new and unique challenges arising from the highly constrained resources of individual sensor-equipped nodes, and the large scale of the overall network. Power is identified as the most expensive resource in a sensor network. In most cases, such networks are meant for one- time use, i.e., once the battery dies, the node dies too. Hence, maximizing network lifetime by conserving power is a matter of great importance. Our approach is to develop middleware to conserve energy and give the base-stations the abstractions of a network virtual machine embedded in the network. 4 Mhz, 8 bit MCU (Amtel), 512 bytes RAM, 8K ROM 900 Mhz radio (RF Monolithics) ft. range Data Placement and Replication Middleware Middleware for Sensor Networks MAC / Wireless Broadcast communication Data Placement / Replication In-network aggregation / Group Consensus Sensor Interface Power Management Middleware Application Location ServiceLocation-aware Routing The sensor nodes run on a battery which limits their lifetime. Experimental results have shown that wireless packet communication consumes most of the energy of a mode. Hence reducing the amount of communication shall result in increased network lifetime. The middleware allows the sensor nodes to conserve energy in an aggregated and distributed manner. The main attributes of the middleware are: Energy Conservation, Scalability, Portability, Consistency, Reduced Communication Overhead, Decentralized computation and Resource Allocation. Event detection by in-network aggregation Inspired by web caching and multicast technologies, our data placement middleware creates and places replicas of requested data so that communication is minimized. According to the request rates and the update rates of the data a hierarchy of replicas are created such that the depth of the hierarchy adapts with change in request and update rates. i - Base-station - Level-i copy - Sensor In general, when the environment is placid and the update rate is low, more copies are created, and when the environment is volatile and the update rate is high, consolidation takes place and the number of copies is reduced. In general the depth of the copy tree is equal to R max / R update. Routing / Location service Data Placement Middleware Application Copy/Sensor data (at sensor) Copy table (at sensor) Cache Copy table Redirect table (at base- station) Message Handlers The data placement middleware causes the location-directed queries from the application to be redirected to the copy locations using the redirect table. These redirect queries get served by the copies which are stored in the cache at certain locations. A tree of replicas of the sensor data is formed, and in general a base-station with a request rate R gets served by a level-  R / R update  copy. Whenever an update occurs it gets propagated along the copy tree. Experimental Results Our experimental results (using ns-2 simulator) show that our data placement strategy provides considerable energy savings and is quite close to the optimal omniscient multicast for the case when the request rates are greater than the update rate, but does not show the degradation in performance shown by omniscient multicast when the update rate becomes greater. It gracefully consolidates the copies as the update rate increases, until it reaches a point when no copies are created. When an event such as an explosion occurs, individual sensor values like temperature, light etc, by themselves may not be of interest to the applications, but taken together, the macro-information (i.e., explosion) is much more relevant. We use dynamically-programmable rules, distributed arbitrarily in the sensor network to identify and triangulate the location of the events. Event Cluster Formation Single Value Aggregation Rule Evaluation Data Aggregation Example of a rule: (Temperature>500) and (Light>100) => Explosion. If (T= 600) and (L=200) then Explosion = true and confidence = ( )/500 + ( )/100 = 1.2 At this step, clusters are formed dynamically within the region where the sensors pick up abnormal values, and a cluster leader is elected. No node can belong to more than one cluster. At this stage all the sensors of a single type (e.g.., temperature) come to a consensus so that each node in a cluster has exactly the same value for different types of sensors. At this stage, clusters evaluate the rules among themselves. Each node may not have the necessary rule which evaluates to true given the data, but eventually the identified event and confidence is transmitted to the cluster leader. In the final stage, the cluster leaders communicate amongst themselves and use the confidence values of the individual clusters to pinpoint the location of the events. The final data about the event type and location are propagated to the base- stations