Ataul Bari

A genetic algorithm based approach for energy efficient routing in two-tiered sensor networks

Higher power relay nodes can be used as cluster heads in two-tiered sensor networks to achieve improved network lifetime. The relay nodes may form a network among themselves to route data towards the base station. In this model, the lifetime of a network is determined mainly by the lifetimes of these relay nodes. An energy-aware communication strategy can greatly extend the lifetime of such networks. However, integer linear program (ILP) formulations for optimal, energy-aware routing quickly become computationally intractable and are not suitable for practical networks. In this paper, we have proposed an efficient solution, based on a genetic algorithm (GA), for scheduling the data gathering of relay nodes, which can significantly extend the lifetime of a relay node network. For smaller networks, where the global optimum can be determined, our GA based approach is always able to find the optimal solution. Furthermore, our algorithm can easily handle large networks, where it leads to significant improvements compared to traditional routing schemes.

Clustering strategies for improving the lifetime of two-tiered sensor networks

In hierarchical sensor networks, sensor nodes are arranged in clusters, and higher-powered relay nodes can be used as cluster heads. Due to the limited transmission range and battery power of the nodes, it is important to develop techniques that minimize energy dissipation of the nodes, and thus extend the lifetime of network. Proper techniques for assigning sensor nodes to clusters have been shown to improve the lifetime of the network. In this paper, we have proposed two fast and efficient integer linear program (ILP) formulations for assigning sensor nodes to clusters in a two-tiered network, where the relay nodes are used as cluster heads. The first is for single hop routing and the second is a generalized formulation that can be used with any multi-hop routing strategy. The objective, in both cases, is to maximize the lifetime of the relay node network. Comparisons with existing heuristics for clustering demonstrate that our ILPs significantly extend the network lifetime and are fast enough to be used for practical networks with hundreds of sensor nodes.

Design of fault tolerant wireless sensor networks satisfying survivability and lifetime requirements

Sensor networks are deployed to accomplish certain specific missions over a period of time. It is essential that the network continues to operate, even if some of its nodes fail. It is also important that the network is able to support the mission for a minimum specified period of time. Hence, the design of a sensor network should not only provide some guarantees that all data from the sensor nodes are gathered at the base station, even in the presence of some faults, but should also allow the network to remain functional for a specified duration. This paper considers a two-tier, hierarchical sensor network architecture, where some relay nodes, provisioned with higher power and other capabilities, are used as cluster heads. Given a distribution of sensor nodes in a sensor network, finding the locations to place a minimum number of relay nodes such that, each sensor node is covered by at least one relay node, is known to be a computationally difficult problem. In addition, for successful and reliable data communication, the relay nodes network needs to be connected, as well as resilient to node failures. In this paper, a novel integrated Integer Linear Program (ILP) formulation is proposed, which, unlike existing techniques, not only finds a suitable placement strategy for the relay nodes, but also assigns the sensor nodes to the clusters and determines a load-balanced routing scheme. Therefore, in addition to the desired levels of fault tolerance for both the sensor nodes and the relay nodes, the proposed approach also meets specified performance guarantees with respect to network lifetime by limiting the maximum energy consumption of the relay nodes.

Challenges in the Smart Grid Applications: An Overview

The smart grid is expected to revolutionize existing electrical grid by allowing two-way communications to improve efficiency, reliability, economics, and sustainability of the generation, transmission, and distribution of electrical power. However, issues associated with communication and management must be addressed before full benefits of the smart grid can be achieved. Furthermore, how to maximize the use of network resources and available power, how to ensure reliability and security, and how to provide self-healing capability need to be considered in the design of smart grids. In this paper, some features of the smart grid have been discussed such as communications, demand response, and security. Microgrids and issues with integration of distributed energy sources are also considered.

Optimal Placement of Relay Nodes in Two-Tiered, Fault Tolerant Sensor Networks

Nodes in sensor networks are often prone to failure, particularly when deployed in hostile territories, where chances of damage/destruction are significantly higher. There is also the possibility for the loss of connectivity between nodes due to the inherent limitations of the wireless communication medium. Therefore, a sensor network should be designed in such a way that the network is able to continue to operate, even if some of the nodes/links in the network fail. The scalability and the lifetime of sensor networks are affected by the limited transmission range and the battery power of sensor nodes. Recently, relay nodes have been proposed for balanced data gathering, reduction of transmission range, connectivity and fault tolerance. In hierarchical sensor networks using relay nodes, sensor nodes are arranged in clusters and higher-powered relay nodes can be used as cluster heads. Finding the minimum number of such relay nodes, along with their locations, so that each sensor node can communicate with at least ks (ks = 1,2...) relay nodes and the relay node network is kr-connected (kr = 1,2...), is known to be a difficult problem. Some recent works in this area have proposed heuristic solutions for the the special cases of ks = 1 or 2 and kr = 1 or 2. In this paper, we have presented a generalized integer linear program (ILP) formulation capable of generating exact solutions for arbitrary values of ks and kr.

Maximizing the Lifetime of Two-Tiered Sensor Networks

Recent technological advances in the field of micro-electro-mechanical systems (MEMS) have made the development of multi-functional sensor nodes technically and economically feasible. The lifetime of sensor networks is still limited as individual sensor nodes are usually powered by battery. In the past few years, the use of relay nodes in sensor networks has been proposed in literature for balanced data gathering, reduction of transmission range, connectivity and fault tolerance. In hierarchical sensor networks, higher-powered relay nodes can also be used as cluster heads. These relay nodes may form a network among themselves and route data towards the base station. In such a sensor network, the lifetime of the network is directly related to the lifetime of these relay nodes. In this paper, we have proposed an ILP solution for scheduling the data gathering of relay nodes such that the lifetime of the relay node network is maximized. We have compared our formulation with the direct transmit energy model and shown that it can lead to significant improvements. We have also proposed a re-scheduling approach which can further extend the maximized lifetime of the network

New Techniques for Efficient Traffic Grooming in WDM Mesh Networks

Traffic grooming techniques are used to combine low-speed data streams onto high-speed lightpaths with the objective of minimizing the network cost, or maximizing the network throughput. In this paper, we first present an efficient integer linear program (ILP) formulation for traffic grooming on mesh WDM networks. Our formulation can be easily modified to implement different objective functions. Unlike previous formulations, our ILP formulation can be used for practical sized networks with several hundred requests. We then propose a second ILP for traffic grooming, with the simplifying assumption that RWA is not an issue. This second formulation is able to generate, in a reasonable time, grooming strategies, for networks with over 30 nodes, with hundreds and even thousands of low-speed data streams. Finally, we introduce a set of ILP formulations for traffic grooming, where the logical topology is specified. We have studied, using simulation, the time needed to determine grooming strategies, using the different ILP formulations.

Genetic Algorithm Based Approach for Extending the Lifetime of Two-Tiered Sensor Networks

In the past few years, the use of higher power relay nodes as cluster heads in two-tiered sensor networks have been proposed, to achieve various objectives including improved network lifetime. These relay nodes may form a network among themselves and route data towards the base station. In such a model, the lifetime of the network is determined mainly by the lifetime of these relay nodes, which, in turn, is directly affected by the data communication scheme. In this paper, we have proposed a genetic algorithm (GA) based solution for scheduling the data gathering of relay nodes that can significantly extend the lifetime of the relay node network. For smaller networks, where the global optimum can be determined, our GA based approach is always able to find the optimal solution. For larger networks, we have compared our approach with traditional routing schemes and shown that our method leads to significant improvements

Stable Logical Topologies for Survivable Traffic Grooming of Scheduled Demands

There has been considerable research interest in the area of traffic grooming for WDM mesh networks. The vast majority of the current work can be classified into one of two categories, either static grooming or dynamic grooming. In many situations, the individual traffic demands require bandwidth at certain predefined intervals, and resources allocated to nonoverlapping demands can be reused in time. In this paper, we propose a new traffic grooming technique that exploits knowledge of the connection holding times of traffic demands to lead to more efficient resource utilization. We consider wavelength-convertible networks as well as networks without any wavelength conversion capability and implement survivability using dedicated and shared path protection. Although individual demands may be short lived, it is desirable to have a logical topology that is relatively stable and not subject to frequent changes. Therefore, our objective is to design a stable logical topology that can accommodate a collection of low-speed traffic demands with specified setup and teardown times. Our approach results in lower equipment cost and significantly reduced overhead for connection setup/teardown. We present efficient integer linear program (ILP) formulations that address the complete traffic grooming problem, including logical topology design, routing and wavelength assignment, and routing of traffic demands over the selected topology. The primary focus of our ILP formulations is to minimize the resource requirements. However, it is possible to modify our formulations to maximize the throughput, if necessary.

Clustering Time-Series Gene Expression Data with Unequal Time Intervals

Clustering gene expression data given in terms of time-series is a challenging problem that imposes its own particular constraints, namely exchanging two or more time points is not possible as it would deliver quite different results, and also it would lead to erroneous biological conclusions. We have focused on issues related to clustering gene expression temporal profiles, and devised a novel algorithm for clustering gene temporal expression profile microarray data. The proposed clustering method introduces the concept of profile alignment which is achieved by minimizing the area between two aligned profiles. The overall pattern of expression in the time-series context is accomplished by applying agglomerative clustering combined with profile alignment, and finding the optimal number of clusters by means of a variant of a clustering index, which can effectively decide upon the optimal number of clusters for a given dataset. The effectiveness of the proposed approach is demonstrated on two well-known datasets, yeast and serum, and corroborated with a set of pre-clustered yeast genes, which show a very high classification accuracy of the proposed method, though it is an unsupervised scheme.