Mohammad Faisal Uddin

Smart Microgrids: Optimal Joint Scheduling for Electric Vehicles and Home Appliances

The integration of renewable energy sources and electrical vehicles (EVs) into microgrids is becoming a popular green approach. To reduce greenhouse gas emissions, several incentives are given to use renewable energy sources and EVs. By using EVs as electricity storage and renewable energy sources as distributed generators (DGs), microgrids become more reliable, stable, and cost-effective. In this paper, we propose an optimal centralized scheduling method to jointly control the electricity consumption of home appliances and plug-in EVs as well as to discharge the latter ones when they have excess energy, thereby increasing the reliability and stability of microgrids and giving lower electricity prices to customers. We mathematically formulate the scheduling method as a mixed integer linear programming (MILP) problem and solve it to optimality. We compare the optimal solution to that obtained from a scheduling framework, where EVs do not have discharge capabilities, decentralized charge control using game theory and to a solution obtained from a naive scheduling framework.

Cryptanalysis of Simple Substitution Ciphers Using Particle Swarm Optimization

We investigate the use of Particle Swarm Optimization (PSO) in automated cryptanalysis of classical simple substitution ciphers. Based on our experimental results, PSO-based attacks proved to be very effective on various sets of encoding keys.

Joint Relay Assignment and Power Allocation for Multicast Cooperative Networks

We investigate the joint problem of relay selection and optimal sharing of relay power in wireless cellular networks with multicast traffic. We first present a mixed Boolean-convex optimization model and solve this combinatorial problem optimally using branch and bound technique. We then show that obtaining the optimal solution is computationally infeasible for large network sizes and, unlike the case of unicast traffic, a water filling method does not yield near optimal solutions in multicast scenarios. We thus adopt an algorithm based on sequential fixing which substantially reduces the computation time and achieves near optimal solutions.

Joint Routing and Scheduling in WMNs with Variable-Width Spectrum Allocation

This paper addresses, in the context of wireless network design, the problem of optimally partitioning the spectrum into a set of nonoverlapping channels with nonuniform spectrum widths. While narrower bands split the total available spectrum into more nonoverlapping channels allowing more parallel concurrent transmissions, wider spectrum bands yield links with larger transport capacity. Thus, we model the combinatorially complex problem of joint routing, link scheduling, and variable-width channel allocation in both single and multirate multihop wireless networks as a mixed integer linear program, and present a solution framework using the column generation decomposition approach. Given the nature and complexity of the resulting dual subproblem, we propose heuristic methods for partitioning the spectrum and allocating resources to each active links, and hence obtain solutions for larger network instances. We present several numerical results and engineering insights suggesting both spectrum width and transmission rates as effective tunable knobs for combatting interference and promoting spatial reuse and thus achieving superior performance in multihop settings.

On the existence of (9,3,5,240) resilient functions

Using a heuristic search technique, several examples for 9-variable Boolean functions with nonlinearity 240, algebraic degree 5, and resiliency degree 3 were constructed. This construction affirmatively answers the open problem about the existence of such functions.

Optimal Flexible Spectrum Access in Wireless Networks with Software Defined Radios

We investigate the problem of flexible spectrum access in multihop wireless networks. We assume radios that are capable of transmitting on channels of contiguous frequency bands and which do not require any sophisticated processing. Because these radios can flexibly configure their transmissions anywhere in the available frequency band, the spectrum becomes vulnerable to fragmentation and interference. We consider the joint problem of routing, link scheduling and spectrum allocation where scheduling feasibility is considered under the physical interference (SINR) constraint. We present a primal-dual decomposition for this complex optimization problem based on column generation. We show that obtaining the optimal solution to this problem is computationally not feasible, except for very small networks. We thus adopt a two-fold method to circumvent the complexity while yielding practical solutions. First, we relax the SINR constraint and use a simplified graph-based model for the interference. Second, we use a simulated annealing (SA) approach to solve the dual subproblem. Our SA approach however is augmented with an SINR feasibility check. Our results confirm that the primal-dual decomposition method using SA substantially reduces the computation time and achieves near optimal solutions. The results also reveal that substantial improvement in network performance is obtained with flexible spectrum assignment which results from its capability of better managing the interference in the network.

Joint Routing, Scheduling and Variable-Width Channel Allocation for Multi-Hop WMNs

Recent research results have shown that channel width is an important control knob that can be easily adapted through software and can be used for achieving higher system throughput and better energy efficiency. In this paper we address the problem of joint routing and transmission scheduling in a multichannel wireless mesh network with variable-width channel allocation. While narrower bands split the total available spectrum into more non-overlapping channels allowing more parallel concurrent transmissions, wider bands increase the capacity of the communication links. We present a cross-layer problem formulation which incorporates multi-path routing and link layer scheduling. We model this combinatorial complex problem as a mixed integer linear program and present a primal-dual decomposition method for solving it. Our method has always shown to strike a good balance between often conflicting objectives to achieve higher system performance. Numerical results revealed that up to 57% improvement in network performance is obtained when variable-width channel assignment is used against the best fixed-width channel assignment for larger networks; this is due to the capability of the former in achieving a good balance between higher concurrency and better control of interference.

An Artificial Life Technique for the Cryptanalysis of Simple Substitution Ciphers

In this paper, we investigate the use of ant colony optimization (ACO) for automated cryptanalysis of classical simple substitution ciphers. Based on our experiments, ACO-based attacks proved to be very effective on various sets of encoding keys

Fair resource allocation and DF relay selection for multiuser OFDMA-based cooperative networks

We investigate the joint problem of power and subcarrier allocation with relay selection for a multi-user multi-relay cellular system with selective relaying. We first formulate the optimal problem using Boolean-convex optimization with the objective of improving the average network capacity while providing proportional rate fairness to all users. We show that except for very small network instances the model is computationally very complex to solve. Then, we present a sub-optimal model by decomposing the joint problem into two sub-problems. This model proves to be a simple one, however, the sub-optimal design of the first sub-problem restricts the model from achieving near optimal solutions. Therefore, to achieve near optimal solutions, we propose an iterative two step method where near-optimal solutions can be achieved by iterating between the two steps. Numerical results show that for smaller networks this two step iterative method obtains optimal solutions in much less time. We also show that a selective relaying technique always achieves better performance over the always relaying technique.

Joint optimal AF relay assignment and power allocation in wireless cooperative networks

We investigate the joint problem of optimal relay selection and power allocation in wireless cellular networks considering both unicast and multicast traffic. We first present mixed Boolean-convex optimization models for both unicast and multicast traffic scenarios to maximize the overall network capacity and solve these combinatorial problems optimally using the branch and bound technique. We then show that obtaining the optimal solution is computationally infeasible for large network sizes. For unicast, similar to [1], we present an efficient water-filling based technique to obtain near optimal solutions. We show that, unlike unicast traffic, water-filling does not yield near optimal solutions in multicast scenarios. We thus adopt an algorithm based on sequential fixing for the multicast case which substantially reduces the computation time and achieves near optimal solutions. Furthermore, in the above scenarios, we assume that the power levels are drawn from a continuous range. To make the proposed methods more practical, we also consider scenarios when the number of power levels is finite (i.e., discrete). We present optimal and sub-optimal methods for solving this optimization problem and compare their performance with previous methods.