Summary
This paper deals with the problem of sensor node localization in the presence of uncertainty in anchor node location. Aqueous environments are prone to adverse effects of underwater currents. This adversity causes non‐negligible mobility to the anchor nodes deployed under water. Localization in the presence of uncertainty in the anchor node location is quite challenging. Also, the authors consider the ray‐bending property of underwater medium due to depth dependent sound speed, to furnish the accurate position estimate of the target node. Standard ray equations are used to model the path followed by acoustic rays in water. Maximum likelihood estimation is proposed to estimate the location of target node with uncertainty in anchor node positions and is compared with the scheme with exact knowledge of anchor node positions, and the results are reported. Monte Carlo simulation is used to assess the performance of the proposed method. Also, the Cramer‐Rao lower bound with uncertainty in anchor nodes is derived and described. Simulation results of the proposed algorithm outperform the existing algorithm with known anchor location by up to 49.4%, and hence, accuracy is improved in the proposed method.
Cognitive communication is an effective solution to the spectrum scarcity issues in wireless networks. The underwater sensor networks are prone to large propagation delays which result in the fundamental limitation on introducing cognitive aspects in underwater scenario. This letter explores the fundamental limitation of using cognitive communication in large propagation delay underwater networks. This work proposes a method to find the optimal position of the secondary user, to minimize the interference to primary users, in an underwater cognitive acoustic network. The proposed method also considers the effect of channel randomness which is modeled using the log-normal shadowing model. The method can also be used to select and schedule the secondary user transmissions, from a set of secondary users, such that the interruption time to the primary users is minimized.
Summary
Optimal scheduling is essential to minimize the time wastage and maximize throughput in high propagation delay networks such as in underwater and satellite communication. Understanding the drawbacks of synchronous scheduling, this paper addresses an asynchronous optimal scheduling problem to minimize the time wastage during the transmission. The proposed scheduling problem is analyzed in both broadcast and non‐broadcast networks, which is highly applicable in high propagation delay networks. In broadcast networks, the proposed scheduling method reduces to a graph‐theoretic model that is shown to be equivalent to the classic algorithmic asymmetric traveling salesman problem (TSP) which is NP‐Hard. Although it is NP‐Hard, the TSP is well‐investigated with many available methods to find the best solution for up to tens of thousands of nodes. In non‐broadcast networks, the optimal solution to the scheduling problem considers the possibility of parallel transmission, which is optimized using graph coloring algorithm. The groups obtained through graph coloring are solved using Asymmetric Traveling Salesman algorithm to obtain the optimal schedule. The proposed method efficiently solves the scheduling problem for networks of practical size.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.