The proposed framework for enabling energy efficient semantic views is presented in Figure5. It mainly consists of four components: query aware sensing, probabilistic query dissemina- tion, correlated multi-query processing and location discovery. The correlated multi-query processing is further divided into two parts: the correlated multi-query processing at the base station and correlated data collection at the sensor nodes. Upon receiving a user se- mantic view, the base station uses query aware sensing scheduling to determine what sensors should sense data for the user semantic view. It then uses correlated multi-query process- ing to derive a new set of queries and SIVS and delivers these queries and SIVS to sensor nodes using probabilistic dissemination. After the relevant set of sensors are decided for the semantic view, sensors use correlated data collection to send their data back to the base station. Through the whole lifetime of the sensor network, the location discovery protocol is executed at sensor nodes periodically to determine sensors locations, and the end-to-end pairwise key establishment scheme is used to provide symmetric keys between sensors for secure message exchange.
In query aware sensing, the set of active sensors is dynamically adjusted to achieve the required level of coverage for the current set of semantic views. When new queries arrive at the base station, the base station first derives the level of coverage requirement, COV , from these queries. Then based on the current sensing scheduling, it computes a minimum set of sensors which must be additionally activated in order to provide the desired level of coverage using a greedy algorithm. These sensors are then added to the current set of active sensors. When the current sensing period ends, the current set of active sensors is updated from the set of queries which still need to be processed. The sensors do not sample data and turn off their sensing boards unless they are instructed by the base station to sense, rather than they are constantly sensing.
The queries in semantic views are also used at the same time by “correlated multi-query processing” at the base station. An estimation model is used to measure the size of shared data between two queries. Based on the estimation value, pairs of queries are selected in
Scheduling Sensing Existing COV COV Derivation Sensing Greedy Update COV SIVS Construction Query Rewriting Aggregation SIVS Update Existing SIVS New SIVS Q New Query Results Sensing Aware Q’ Queries
Correlated Multi−query Processing Query at base station Data Location of Sensors Query Dissemination Probabilistic Correlated Location Discovery Collection Data End−to−end Pairwise key Establishment
such a way that the estimated size of shared data among all these pairs is maximal. A shared intermediate view is constructed for each pair of queries, which captures the actual set of sensor data shared by these two queries. To ensure semantic correctness, the original queries are rewritten into a different set of queries such that the data for an original query is now divided into the set of sensors for the shared intermediate view and the rewritten query. The set of shared intermediate views is also dynamically updated when new queries arrive at the base station. As in query aware sensing, the set of shared intermediate views is cleared at the end of a sensing period and rebuilt at the beginning of the next sensing period.
These shared intermediate views, along with the rewritten queries, are delivered to sen- sor nodes in the network using probabilistic query dissemination. In probabilistic query dissemination, each sensor forwards a query with a certain probability. This probability is adapted to each sensor node’s local information, such as the additional area its forwarding can cover, or the additional number of sensor nodes its transmission can reach, or the number of messages with the same query it has already overheard.
After sensors receive the queries, they use correlated data collection to reduce the number of data transmissions for correlated queries. In correlated data collection, each sensor node stores its data to a proxy sensor node which is closer to the base station. A proxy node is established when the data at the sensor node is first acquired by a query. The node in the routing tree which first aggregates the value of a sensor node is the proxy sensor for the sensor node. After a proxy sensor has been established by a sensor node, any later query requesting its data shall retrieve the data from its proxy node. If the proxy node of a sensor fails, a new proxy node is established when the next query requests data from the sensor.
Sensors run the location discovery using Out-of-Range information with multi-lateration to compute their locations, which are used by query aware sensing to determine how to use the sensors to achieve a desired level of coverage and by sensors to check if its data is needed for queries with geographical constraints. In this scheme, some sensors are initially configured as reference nodes or anchor nodes. The scheme starts with the anchor nodes disseminating their positions to neighboring unknown sensor nodes. An unknown sensor node then measures its distance to each of the neighboring reference/anchor nodes respectively, assuming that the distance between two sensors can be estimated using methods such as
RSSI or ToA. If more than three neighbor nodes are reference nodes, an unknown node then estimates its own location using trilateration. In addition, the least square method is used to refine a sensor node’s location in an over determined system. Otherwise, the unknown sensor node sends messages to non-neighboring nodes to check if they can help to resolve its location using Out-of-Range information. It is shown that the out-of-range information, i.e. when the distance between two non-neighboring sensors is larger than a certain threshold value, can be used to resolve location ambiguities in many scenarios. Once the unknown sensor’s location is resolved, an unknown node becomes a reference node and disseminates its position to other unknown nodes in the network to enable the continuation of the location discovery process.
Furthermore, an end-to-end pairwise key establishment scheme based on key pre-distribution is used to set up symmetric keys between sensors. The scheme enhance the security of path keys by using multiple secure paths during key establishment. These symmetric keys are then used to protect message exchanges in semantic view processing against packet eavesdropping and traffic analysis by attackers.