Detection of Link Failures and Autonomous Reconfiguration in WMNs

During their lifetime, multihop wireless mesh networks (WMNs) experience frequent link failures caused by channel interference, dynamic obstacles, and/or applications’ bandwidth demands. By reconfiguring these link failures ARS generates an effective reconfiguration plan that requires only local network configuration changes by exploiting channel, radio, and path diversity. ARS effectively identifies reconfiguration plans that satisfy QoS constraints. And ARS's online reconfigurability allows for real-time time failure detection and network reconfiguration. ARS is mainly evaluated in IEEE 802.11a networks. It's design goal is to reconfigure from network link failures accurately. Even then WMNs face some frequent link failures. By overcome these problems  we present Localized sElf-reconfiGuration algOrithms  (LEGO) to autonomously and effectively  recnfigure from wireless link failures. First, LEGO locally detects link failures. Second, it dynamically forms/deforms a local group for cooperative network reconfiguration among local mesh routers in a fully distributed manner. Next, LEGO intelligently generates a local network reconfiguration plan. Finally, by figuring local channel utilization and reconfiguration cost in its planning, LEGO maximizes the network’s ability to meet diverse links’ QoS demands. LEGO has been implemented on a Linux-based system and experimented on a real life test bed, demonstrating its effectiveness in recovering from link failures and its improvement of channel efficiency by up to 92%. Keywords - Self-Reconfigurable Networks, Multi-Radio Wireless Networks, IEEE 802.11, WLAN access points (AP)

On Power and Load Coupling in Cellular Networks for Energy Optimization

We consider the problem of minimization of sum transmission energy in cellular networks where coupling occurs between cells due to mutual interference. The coupling relation is characterized by the signal-to-interference-and-noise-ratio (SINR) coupling model. Both cell load and transmission power, where cell load measures the average level of resource usage in the cell, interact via the coupling model. The coupling is implicitly characterized with load and power as the variables of interest using two equivalent equations, namely, non-linear load coupling equation (NLCE) and non-linear power coupling equation (NPCE), respectively. By analyzing the NLCE and NPCE, we prove that operating at full load is optimal in minimizing sum energy, and provide an iterative power adjustment algorithm to obtain the corresponding optimal power solution with guaranteed convergence, where in each iteration a standard bisection search is employed. To obtain the algorithmic result, we use the properties of the so-called standard interference function; the proof is non-standard because the NPCE cannot even be expressed as a closed-form expression with power as the implicit variable of interest. We present numerical results illustrating the theoretical findings for a real-life and large-scale cellular network, showing the advantage of our solution compared to the conventional solution of deploying uniform power for base stations.Comment: Accepted for publication in IEEE Transactions on Wireless Communication

An Order-based Algorithm for Minimum Dominating Set with Application in Graph Mining

Dominating set is a set of vertices of a graph such that all other vertices have a neighbour in the dominating set. We propose a new order-based randomised local search (RLS$_o$) algorithm to solve minimum dominating set problem in large graphs. Experimental evaluation is presented for multiple types of problem instances. These instances include unit disk graphs, which represent a model of wireless networks, random scale-free networks, as well as samples from two social networks and real-world graphs studied in network science. Our experiments indicate that RLS$_o$ performs better than both a classical greedy approximation algorithm and two metaheuristic algorithms based on ant colony optimisation and local search. The order-based algorithm is able to find small dominating sets for graphs with tens of thousands of vertices. In addition, we propose a multi-start variant of RLS$_o$ that is suitable for solving the minimum weight dominating set problem. The application of RLS$_o$ in graph mining is also briefly demonstrated