Organization-Aware Routing in Mission Critical Networks

Abstract-In a large mission critical wireless ad hoc network, heterogeneous nodes with different capabilities and nodes from different organizations may be deployed within the same geographical area. This is typical for many mission critical applications such as military networks. For such applications, on one hand, a node from one organization may collaborate with the nodes from a different organization for better survivability and efficiency. On the other hand, it is important to keep crossorganization data transfer at the lowest possible level for minimal disruption of the traffic within each organization and for security reasons. We propose a novel routing metric that both maximizes the benefits from the collaboration of heterogeneous nodes and takes the organizational constraints into account. Furthermore, we propose a novel routing protocol that may accommodate this new organization-aware metric, while backward compatible with the most popular routing protocols for general MANET, such as the optimized link state routing protocol (OLSR). Simulation results demonstrate that the proposed method provides the much needed survivability and efficiency in battlefield environment while keeping the cross-organization data transfer at a low level

Approximating minimum size weakly-connected dominating sets for clustering mobile ad hoc networks

We present a series of approximation algorithms for finding a small weakly-connected dominating set (WCDS) in a given graph to be used in clustering mobile ad hoc networks.The structure of a graph can be simplified using WCDS’s and made more succinct for routing in ad hoc networks.The theoretical performance ratio of these algorithms is O(ln ∆) compared to the minimum size WCDS, where ∆ is the maximum degree of the input graph.The first two algorithms are based on the centralized approximation algorithms of Guha and Khuller [14] for finding small connected dominating sets (CDS’s).The main contribution of this work is a completely distributed algorithm for finding small WCDS’s and the performance of this algorithm is shown to be very close to that of the centralized approach.Comparisons between our work and some previous work (CDS-based) are also given in terms of the size of resultant dominating sets and graph connectivity degradation

Localized Distributed Sensor Deployment via Co-evolutionary Computation

Abstract—Sensor placement of a wireless sensor network has a significant impact on the network performance, such as sensing coverage, communication costs and connectivity. Depending on the application domains, the deployment method of sensor networks may vary. In autonomous sensor networks, the deployment is typically realized via cooperative movement among the nodes themselves. In this paper, we propose a sensor deployment protocol, LODICO, to optimize the sensor placement with arbitrary initial positions. In essence, it is a distributed evolutionary algorithm executed cooperatively among all sensor nodes via local information exchange. The simulation results show that LODICO can provide a very high coverage rate in a short convergence time. I

Adaptive MAC scheduling using channel state diversity for wireless networks

Abstract—Head-of-line blocking problem compromises the throughput of multi-hop wireless networks. FIFO scheduling in the current IEEE802.11 MAC causes this problem when the network is highly loaded. One solution is to increase the RTS success rate by extending the RTS frame to MRTS (multicast RTS), so that multiple receivers could be checked simultaneously. There is a tradeoff for the length of the MRTSframe receiver list, since longer lists increase transmission success rate, but shorter lists impose lower control overhead. We present an adaptive learning process that observes the dynamic channel-state diversity among the candidate receivers. By maximizing the receiver diversity, we can achieve high transmission success rate using short receiver lists. This is supported by our simulation results. Keywords- 802.11 MAC; scheduling; head-of-line blocking I