Salimur Choudhury

Energy efficient cellular automaton based algorithms for mobile wireless sensor networks

We design new cellular automaton based algorithms to improve coverage in a network with mobile sensors. The algorithms can be useful in applications where sensors are initially deployed in one place and need to disperse to the environment autonomously, or in situations where in certain areas sensors may be destroyed (e.g. due to a natural disaster), and the sensors need to use their mobility in order to restore coverage. We propose a cellular automaton model that divides the neighborhood of a cell into four (North West, North East, South West and South East) quadrants and the sensors try to find out the directions where they can move to increase the coverage. We compared our model with a previous model for different initial configurations and have found that our model reaches a comparable coverage more quickly. Our algorithms use two parameter values to guide the movements of the sensors. Especially with the best choices of the parameter values, our algorithms require the sensors to make considerably fewer atomic movements than the earlier algorithm. For mobile sensor networks, energy consumption is largely determined by the amount of movement, and minimizing movement will increase the life time of the network.

An approximation algorithm for max k-uncut with capacity constraints

Given an edge-weighted graph the problem is to partition the vertices of the graph into k partitions of prescribed sizes such that the total weight of the edges within partitions are maximized. This problem is NP-complete for all k ≥ 2. We give a factor approximation where d is the ratio of the largest size and the smallest size in the partition. When the number of partitions k is 2, we give an approximation algorithm with performance ratio .

A cellular automaton model for connectivity preserving deployment of mobile wireless sensors

We propose a cellular automaton based local algorithm to reposition mobile sensors of a wireless sensors network. Our main goal is to maximize the total coverage of the network while maintaining the connectivity among the sensors. In most of the applications, it is not feasible to deploy mobile sensors using a global algorithm. Typically, the sensors are initially densely deployed and use their mobility to increase the coverage of the network. Our algorithm uses very limited local information to compute the final positions of the sensors. In many applications, maximizing the coverage is not the only objective; the sensors also need to communicate with each other. Therefore, maintaining connectivity when the sensors disperse is an important goal, and our algorithm achieves this as well. We perform different simulations on different starting configurations. For some configurations, the optimal solution is arrived at, while for others a near optimal solution is obtained.

Cellular automaton based motion planning algorithms for mobile sensor networks

We develop a set of probabilistic and deterministic cellular automaton based algorithms for an optimization problem of mobile wireless sensor networks (MWSN). We consider a scenario where the sensors are initially randomly distributed and the mobile sensors need to disperse autonomously to both maximize coverage of the network and to maintain connectivity. We perform extensive simulations of both deterministic and randomized variants of the algorithm and argue that randomized algorithms have better overall performance. Cellular automaton algorithms rely only on local information about the network and, hence, they can be used in practice for MWSN problems. On the other hand, locality of the algorithm implies that maintaining connectivity becomes a non-trivial problem.

Cellular automaton-based algorithms for the dispersion of mobile wireless sensor networks

Due to the advent of sensor technology and its applications, mobile wireless sensor networks (MWSNs) have gained a significant amount of research interest. In a typical MWSN, sensors can move within the network. We develop a set of probabilistic and deterministic cellular automaton (CA)-based algorithms for motion planning problems in MWSNs. First, we consider a scenario where a group of sensors are deployed and they need to disperse in order to maximise the area covered by the network. In this variant of the problem we do not explicitly consider that the sensors should maintain the connectivity of the network while they move. Second, we consider a scenario where the sensors are initially randomly distributed and they need to disperse autonomously to both maximise the coverage of the network and maintain its connectivity. We carry out extensive simulations of both deterministic and randomised variants of the algorithms. For the first variant of the problem we compare our algorithms with one previous algorithm and find that our algorithm yields better network coverage than the earlier algorithm. We also find that probabilistic algorithms have better overall performance for the second variant. CA algorithms rely only on local information about the network and, hence, they can be used in practice for MWSN problems. On the other hand, locality of the algorithm implies that maintaining connectivity becomes a non-trivial problem.

CARRE: Cellular automaton based redundant readers elimination in RFID networks

Redundant readers elimination is one of the fundamental optimization research problems in RFID networks. The problem is NP-hard and can be solved approximately using best known centralized set cover algorithms. However, either distributed or localized solutions for this problem are much more realistic and useful in practice. Different distributed and a few local algorithms are known in the literature. In this paper, we propose a cellular automaton based local algorithm for the redundant readers elimination optimization problem. To the best of our knowledge, this is the first cellular automaton based algorithm (that is, a strictly local algorithm) to solve this problem. We compare the performance of our algorithm with other local algorithms and establish that our algorithm gives much better results. We also compare our algorithm with the best known centralized approximation algorithm and find very competitive results even though our algorithm is a local one.

An optimal resource allocation algorithm for D2D communication underlaying cellular networks

In a device to device (D2D) communication underlaying cellular network, total system sum rate can be improved if cellular user equipments (UEs) and D2D pairs share resource blocks (RBs). We consider such an optimization problem where the objective is to maximize the total sum rate of the system while sharing RBs among cellular UEs and D2D pairs and maintaining some quality of service (QoS) requirements. Most of the existing algorithms consider that sharing can only improve the sum rate. However, some sharing can also decrease the sum rate. Considering this observation, we design an optimal algorithm based on weighted bipartite matching which avoids such sharing and maximize the total system sum rate. We prove that our algorithm is optimal and validate the results through simulations which shows that our algorithm outperforms other existing heuristics in terms of maximizing system sum rate. Our algorithm also performs better in terms of total interference introduced through the sharing of resource blocks among cellular equipments and D2D pairs.

A two-phase auction-based fair resource allocation for underlaying D2D communications

Interference coordination while sharing cellular radio resources with Device-to-Device (D2D) pairs needs to be done in the short LTE scheduling period of 1 ms. In this paper, we propose a fast, two-phase auction based, fair and interference aware resource allocation algorithm (TAFIRA) for underlaying D2D communication. TAFIRA can be used to minimize the interference both at the evolved Node B (eNB) and the receiver of the D2D pairs while simultaneously maintaining a target system sum rate and ensuring fair allocation of cellular resources among D2D pairs. We compare TAFIRA with a MInimum Knapsack based Interference Resource Allocation algorithm (MIKIRA) and a random allocation technique. The time complexity of TAFIRA on average is O(m2n), where m and n are the number of D2D pairs and number of cellular users respectively. Our simulation results how that, TAFIRA obtains a much better system sum rate while incurring very little increased interference at the eNB and the D2D receivers when compared with MIKIRA and the random approach. TAFIRA also shows much more fairness in allocating cellular resources among the D2D pairs when compared with MIKIRA.

A Local Search Algorithm for Resource Allocation for Underlaying Device-to-Device Communications

Resource allocation for Device-to-Device (D2D) communication underlaying cellular network poses new challenges in terms of interference while at the same time provides increased system sum rate. In this paper, we propose a local search based resource allocation algorithm (LORA) for allocating resource blocks to D2D devices that are shared with Long Term Evolution (LTE) cellular users. We first formulate the problem of downlink resource block (RB) allocation to D2D users from cellular users as a computationally expensive mixed integer nonlinear programming (MINLP) problem. However, as the optimal solution of an MINLP can take exponential time to compute, we propose a local search based algorithm to compute a locally optimal solution based on an initial feasible solution. We compare the obtained system sum rate from this local search algorithm with a well-known greedy heuristic based resource allocation algorithm and a random resource allocation algorithm. The simulation results show that LORA achieves an overall better system sum rate compared to the other algorithms for RB allocation while maintaining the signal quality at the cellular users and the D2D receivers.

A near optimal interference minimization resource allocation algorithm for D2D communication

Interference minimization while maintaining a target system sum rate by sharing resources among cellular User Equipments (UEs) and Device-to-Device pairs (D2D) is an important research question in Long Term Evolution (LTE). We propose a two phase resource allocation algorithm for this research problem. In the first phase, we use the bipartite matching algorithm to minimize the interference which also avoids matching that can decrease the total system sum rate. In some cases, after the first phase, the solution may not be the optimal one. Therefore, in the second phase, we use a local search algorithm to improve the solution. We compare our algorithm with two other existing algorithms (MIKIRA and TAFIRA) which address the same research problem. We prove that MIKIRA fails to provide feasible solutions in most of the cases. We also show that the performance ratio of TAFIRA can be unbounded in the worst case. Moreover, in some cases, TAFIRA can not provide any solution of the problem where solutions exist. We prove that, our algorithm always provides the solution whenever it exists. We perform extensive simulations of the algorithms and find that in all cases, our solution is either optimal or very close to the optimal.