An Energy Balanced Dynamic Topology Control Algorithm for Improved Network Lifetime

In wireless sensor networks, a few sensor nodes end up being vulnerable to potentially rapid depletion of the battery reserves due to either their central location or just the traffic patterns generated by the application. Traditional energy management strategies, such as those which use topology control algorithms, reduce the energy consumed at each node to the minimum necessary. In this paper, we use a different approach that balances the energy consumption at each of the nodes, thus increasing the functional lifetime of the network. We propose a new distributed dynamic topology control algorithm called Energy Balanced Topology Control (EBTC) which considers the actual energy consumed for each transmission and reception to achieve the goal of an increased functional lifetime. We analyze the algorithm's computational and communication complexity and show that it is equivalent or lower in complexity to other dynamic topology control algorithms. Using an empirical model of energy consumption, we show that the EBTC algorithm increases the lifetime of a wireless sensor network by over 40% compared to the best of previously known algorithms

A Review of Interference Reduction in Wireless Networks Using Graph Coloring Methods

The interference imposes a significant negative impact on the performance of wireless networks. With the continuous deployment of larger and more sophisticated wireless networks, reducing interference in such networks is quickly being focused upon as a problem in today's world. In this paper we analyze the interference reduction problem from a graph theoretical viewpoint. A graph coloring methods are exploited to model the interference reduction problem. However, additional constraints to graph coloring scenarios that account for various networking conditions result in additional complexity to standard graph coloring. This paper reviews a variety of algorithmic solutions for specific network topologies.Comment: 10 pages, 5 figure

Radio Co-location Aware Channel Assignments for Interference Mitigation in Wireless Mesh Networks

Designing high performance channel assignment schemes to harness the potential of multi-radio multi-channel deployments in wireless mesh networks (WMNs) is an active research domain. A pragmatic channel assignment approach strives to maximize network capacity by restraining the endemic interference and mitigating its adverse impact on network performance. Interference prevalent in WMNs is multi-faceted, radio co-location interference (RCI) being a crucial aspect that is seldom addressed in research endeavors. In this effort, we propose a set of intelligent channel assignment algorithms, which focus primarily on alleviating the RCI. These graph theoretic schemes are structurally inspired by the spatio-statistical characteristics of interference. We present the theoretical design foundations for each of the proposed algorithms, and demonstrate their potential to significantly enhance network capacity in comparison to some well-known existing schemes. We also demonstrate the adverse impact of radio co- location interference on the network, and the efficacy of the proposed schemes in successfully mitigating it. The experimental results to validate the proposed theoretical notions were obtained by running an exhaustive set of ns-3 simulations in IEEE 802.11g/n environments.Comment: Accepted @ ICACCI-201

Wireless Scheduling with Power Control

We consider the scheduling of arbitrary wireless links in the physical model of interference to minimize the time for satisfying all requests. We study here the combined problem of scheduling and power control, where we seek both an assignment of power settings and a partition of the links so that each set satisfies the signal-to-interference-plus-noise (SINR) constraints. We give an algorithm that attains an approximation ratio of $O(\log n \cdot \log\log \Delta)$, where $n$ is the number of links and $\Delta$ is the ratio between the longest and the shortest link length. Under the natural assumption that lengths are represented in binary, this gives the first approximation ratio that is polylogarithmic in the size of the input. The algorithm has the desirable property of using an oblivious power assignment, where the power assigned to a sender depends only on the length of the link. We give evidence that this dependence on $\Delta$ is unavoidable, showing that any reasonably-behaving oblivious power assignment results in a $\Omega(\log\log \Delta)$-approximation. These results hold also for the (weighted) capacity problem of finding a maximum (weighted) subset of links that can be scheduled in a single time slot. In addition, we obtain improved approximation for a bidirectional variant of the scheduling problem, give partial answers to questions about the utility of graphs for modeling physical interference, and generalize the setting from the standard 2-dimensional Euclidean plane to doubling metrics. Finally, we explore the utility of graph models in capturing wireless interference.Comment: Revised full versio

Cognitive radio-enabled Internet of Vehicles (IoVs): a cooperative spectrum sensing and allocation for vehicular communication

Internet of Things (IoTs) era is expected to empower all aspects of Intelligent Transportation System (ITS) to improve transport safety and reduce road accidents. US Federal Communication Commission (FCC) officially allocated 75MHz spectrum in the 5.9GHz band to support vehicular communication which many studies have found insufficient. In this paper, we studied the application of Cognitive Radio (CR) technology to IoVs in order to increase the spectrum resource opportunities available for vehicular communication, especially when the officially allocated 75MHz spectrum in 5.9GHz band is not enough due to high demands as a result of increasing number of connected vehicles as already foreseen in the near era of IoTs. We proposed a novel CR Assisted Vehicular NETwork (CRAVNET) framework which empowers CR enabled vehicles to make opportunistic usage of licensed spectrum bands on the highways. We also developed a novel co-operative three-state spectrum sensing and allocation model which makes CR vehicular secondary units (SUs) aware of additional spectrum resources opportunities on their current and future positions and applies optimal sensing node allocation algorithm to guarantee timely acquisition of the available channels within a limited sensing time. The results of the theoretical analyses and simulation experiments have demonstrated that the proposed model can significantly improve the performance of a cooperative spectrum sensing and provide vehicles with additional spectrum opportunities without harmful interference against the Primary Users (PUs) activities