Abstract-Duty-cycle MAC protocols have been proposed to meet the demanding energy requirements of wireless sensor networks. Although existing duty-cycle MAC protocols such as S-MAC are power efficient, they introduce significant end-to-end delivery latency and provide poor traffic contention handling. In this paper, we present a new duty-cycle MAC protocol, called RMAC (the Routing enhanced MAC protocol), that exploits crosslayer routing information in order to avoid these problems without sacrificing energy efficiency. In RMAC, a setup control frame can travel across multiple hops and schedule the upcoming data packet delivery along that route. Each intermediate relaying node for the data packet along these hops sleeps and intelligently wakes up at a scheduled time, so that its upstream node can send the data packet to it and it can immediately forward the data packet to its downstream node. When wireless medium contention occurs, RMAC moves contention traffic away from the busy area by delivering data packets over multiple hops in a single cycle, helping to reduce the contention in the area quickly. Our simulation results in ns-2 show that RMAC achieves significant improvement in end-to-end delivery latency over S-MAC and can handle traffic contention much more efficiently than S-MAC, without sacrificing energy efficiency or network throughput.
A highly chemoselective reduction of aromatic nitro compounds to the corresponding amino derivatives has been achieved by a combination of copper nanoparticles and ammonium formate in ethylene glycol at 120 degrees C. The reductions are successfully carried out in presence of a wide variety of other reducible functional groups in the molecule, such as Cl, I, OCH2Ph, NHCH2Ph, COR, COOR, CN, etc. The reactions are very clean and high yielding.
Recent advances in technology have made low cost, low power wireless sensors a reality. Clock synchronization is an important service in any distributed system, including sensor network systems. Applications of clock synchronization in sensor networks include data integration in sensors, sensor reading fusion, TDMA medium access scheduling, and power mode energy saving. However, for a number of reasons, standard clock synchronization protocols are unsuitable for direct application in sensor networks.In this paper, we introduce the concept of adaptive clock synchronization based on the need of the application and the resource constraint in the sensor networks. We describe a probabilistic method for clock synchronization that uses the higher precision of receiver-to-receiver synchronization, as described in Reference Broadcast Synchronization (RBS) protocol. This deterministic protocol is extended to provide a probabilistic bound on the accuracy of the clock synchronization, allowing for a tradeoff between accuracy and resource requirement. Expressions to convert service specifications (maximum clock synchronization error and confidence probability) to actual protocol parameters (minimum number of messages and synchronization overhead) are derived. Further, we extend this protocol for maintaining clock synchronization in a multihop network.
In a multimodal optimization task, the main purpose is to find multiple optimal solutions (global and local), so that the user can have better knowledge about different optimal solutions in the search space and as and when needed, the current solution may be switched to another suitable optimum solution. To this end, evolutionary optimization algorithms (EA) stand as viable methodologies mainly due to their ability to find and capture multiple solutions within a population in a single simulation run. With the preselection method suggested in 1970, there has been a steady suggestion of new algorithms. Most of these methodologies employed a niching scheme in an existing single-objective evolutionary algorithm framework so that similar solutions in a population are deemphasized in order to focus and maintain multiple distant yet near-optimal solutions. In this paper, we use a completely different strategy in which the single-objective multimodal optimization problem is converted into a suitable bi-objective optimization problem so that all optimal solutions become members of the resulting weak Pareto-optimal set. With the modified definitions of domination and different formulations of an artificially created additional objective function, we present successful results on problems with as large as 500 optima. Most past multimodal EA studies considered problems having only a few variables. In this paper, we have solved up to 16-variable test problems having as many as 48 optimal solutions and for the first time suggested multimodal constrained test problems which are scalable in terms of number of optima, constraints, and variables. The concept of using bi-objective optimization for solving single-objective multimodal optimization problems seems novel and interesting, and more importantly opens up further avenues for research and application.
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.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.