Many basic key distribution schemes specifically tuned to wireless sensor networks have been proposed in the literature. Recently, several researchers have proposed schemes in which they have used group-based deployment models and assumed predeployment knowledge of the expected locations of nodes. They have shown that these schemes achieve better performance than the basic schemes, in terms of connectivity, resilience against node capture and storage requirements. But in many situations expected locations of nodes are not available. In this paper we propose a solution which uses the basic scheme, but does not use group-based deployment model and predeployment knowledge of the locations of nodes, and yet performs better than schemes which make the aforementioned assumptions. In our scheme, groups are formed after deployment of sensor nodes, on the basis of their physical locations, and the nodes sample keys from disjoint key pools. Compromise of a node affects secure links with other nodes that are part of its group only. Because of this reason, our scheme performs better than the basic schemes and the schemes using predeployment knowledge, in terms of connectivity, storage requirement, and security. Moreover, the post-deployment key generation process completes sooner than in schemes like LEAP+.
All the location-based schemes which depend on the knowledge of expected locations of nodes perform well, but they are all prone to estimation errors in the expected positions of the nodes. So other schemes which do not assume predeployment knowledge of the expected locations of the nodes have been proposed. In @cite_1 , Liu et.al. proposed a scheme which does not use expected locations of the nodes but still uses group-based deployment. This scheme proposes a framework, and any basic scheme like random key distribution or polynomial-based scheme can be used with this framework. The authors showed that basic schemes used with their proposed framework perform better than when used alone.
@cite_1
2156914580 1785573601 2052897720 1872902815
Many key pre-distribution techniques have been developed recently to establish pairwise keys for wireless sensor networks. To further improve these schemes, researchers have proposed to take advantage of sensors' expected locations to help pre-distributing keying materials. However, it is usually very difficult, and sometimes impossible, to guarantee the knowledge of sensors' expected locations. In order to remove the dependency on expected locations, this paper proposes a practical deployment model, where sensor nodes are deployed in groups, and the nodes in the same group are close to each other after the deployment. Based on this model, the paper develops a novel group-based key pre-distribution framework, which can be combined with any of existing key pre-distribution techniques. A distinguishing property of this framework is that it does not require the knowledge of sensors' expected locations and greatly simplifies the deployment of sensor networks. The analysis also shows that the framework can substantially improve the security as well as the performance of existing key pre-distribution techniques. A distributed mobility management scheme using randomized database groups (RDG) is proposed and analyzed for ad-hoc networks. In the proposed scheme, location databases are stored in the network nodes, comprising a virtual backbone within the flat network architecture. Upon location update or call arrival, a mobile's location information is written to or read from, respectively, a group of randomly chosen databases. Compared with a centralized scheme (such as the home location register) with fixed associations, this scheme is more suitable for ad-hoc networks, where the connectivity of the nodes with the rest of the network can be intermittent and sporadic, and the databases are relatively unstable. The expected cost due to call loss and location updates using this scheme is analyzed in the presence of database disconnections. Based on the expected cost, we present the numerical determination and approximation of the optimal total location database number, the optimal database access group size, and the optimal location update frequency, under different network stability, traffic, and mobility conditions. Numerical results show that the RDG scheme provides an robust and efficient approach to ad-hoc mobility management. In cellular networks, an important practical issue is how to limit the handoff dropping probability efficiently. One possible approach is to perform dynamic bandwidth reservation based on mobility predictions. With the rapid advances in mobile positioning technology, and the widespread availability of digital road maps previously designed for navigational devices, we propose a predictive bandwidth reservation scheme built upon these timely opportunities. In contrast to the common practice of utilizing only incoming handoff predictions at each cell to compute the reservations, our scheme is more efficient as it innovatively utilizes both incoming and outgoing handoff predictions; it can meet the same target handoff dropping probability by blocking fewer new calls. The individual base stations are responsible for the computations, which are shown to be simple enough to be performed in real-time. We evaluate the scheme via simulation, along with five other schemes for comparison. Simulation results show that those schemes that rely on positioning information are significantly more efficient than those that do not. Our scheme's additional use of the road topology information further improves upon this advantage, bringing the efficiency closer to the bound set by a benchmark scheme that assumes perfect knowledge about future handoffs. Evolving networks of ad-hoc wireless sensing nodes rely heavily on the ability to establish position information. The algorithms presented herein rely on range measurements between pairs of nodes and the a priori coordinates of sparsely located anchor nodes. Clusters of nodes surrounding anchor nodes cooperatively establish confident position estimates through assumptions, checks, and iterative refinements. Once established, these positions are propagated to more distant nodes, allowing the entire network to create an accurate map of itself. Major obstacles include overcoming inaccuracies in range measurements as great as spl plusmn 50 , as well as the development of initial guesses for node locations in clusters with few or no anchor nodes. Solutions to these problems are presented and discussed, using position error as the primary metric. Algorithms are compared according to position error, scalability, and communication and computational requirements. Early simulations yield average position errors of 5 in the presence of both range and initial position inaccuracies.