Maulin Patel

Energy efficient sensor, relay and base station placements for coverage, connectivity and routing

We consider a wireless sensor network made of sensor nodes capable of sensing and communication, relay nodes capable of communication, and base stations responsible for collecting data generated by sensor nodes, to be deployed in sensor field. We address the problem of placing the sensor nodes, relay nodes and base stations in the sensor field such that (i) each point of interest in the sensor field is covered by a subset of sensors of desired cardinality (ii) the resulting sensor network is connected and (iii) the sensor network has sufficient bandwidth. We propose several deployment strategies to determine optimal placements of sensor nodes, relay nodes and base stations for guaranteed coverage, connectivity, bandwidth and robustness. We study several different objectives such as minimizing the number of sensor nodes deployed, minimizing the total cost, minimizing the energy consumption, maximizing the network lifetime and maximizing the network utilization. The placement problems for reliable as well as unreliable/probabilistic detection models are formulated as integer linear programs (ILPs). The practicality, effectiveness and performance of the proposed strategies are illustrated through simulations.

A Full Duplex Multi-channel MAC Protocol for Multi-hop Cognitive Radio Networks

Cognitive radio (CR) offers a new mechanism for flexible usage of radio spectrum. This paper presents a full-duplex multi-channel MAC protocol designed for CR enabled multi-hop networks. Each node is equipped with at least two transceivers, one for transmitting and another for receiving. A node selects an unused frequency band as its home channel (HCh) and tunes its receiver to its HCh. When a node j has a packet to transmit to its neighbor i, node j tunes its transmitter to the HCh of node i and sends the packet(s) to node i using CSMA/CA scheme of IEEE 802.11 DCF mode. The protocol has two flavors: (a) With a common control channel which requires 3 transceivers and (b) without the control channel. Simulation studies show that the proposed protocol outperforms IEEE 802.11 DCF mode by a factor of 20

A Network-flow based Integral Optimal Algorithm for Lexicographic Maximum Lifetime Routing in Wireless Sensor Networks

Longevity is one the key design goals in the wireless sensor networks. In many applications, longevity of all the sensor nodes in the network is equally important. Therefore, it is imperative to design network protocols that would keep the maximum number of nodes alive for longest possible duration. Towards this goal we study the lexicographic maximum lifetime (Lex-max-life) routing scheme. The objective of Lex-max-life routing is to maximize the time until the lirst set of sensor nodes deplete their battery energies (among all the nodes), then maximize the depletion time for the second set of sensor nodes if the depletion time for the first set of nodes is as long as possible, and then maximize the depletion time for the third set of nodes if the depletion times for the first and second set of nodes are as long as possible and so on. In our previous work by M. Patel et al. (2006), we have shown that if sensor nodes are equipped with non-adaptive transmitters then the Lex-max-life routing problem can be reduced to a min-cost flow or a convex-cost flow problem using a novel cost scaling technique. Hence, we obtain an integral optimal solution in polynomial time. However, the cost scaling technique is not suitable for large problem instances because the cost numbers grow very fast. In this paper, we propose a novel vectorial representation of link costs. The Lex-max-life routing problem is solved by a vectorial version of min-cost flow or convex-cost flow algorithm which (i) Improves running time complexity and (ii) Circumvents the number explosion phenomenon. Thus, developed algorithm is computationally very efficient, scalable and easy to implement.

Building 2-D stratigraphic and structure models from well log data and control horizons

An algorithm to build a gridded 2-D seismic velocity (or any other physical property) model from well log data and control horizons is developed. Interpolation of well log data onto a 2-D grid uses inverse distance weighting or linear interpolation, guided by the shape of the control horizons that are predefined from seismic or other 2-D constraints. A key feature of the models is that they may contain layers that are truncated by unconformities or at faults, or that lap out smoothly at their tops and bottoms. Abrupt or smooth terminations are controlled by user flags. Applications are illustrated using resistivity and acoustic impedance log data from the Blake Ridge (offshore Carolina) and using a complex structure produced by overthrust tectonics in western Canada. Geologically reasonable models can be produced only if there are sufficient wells to sample every salient element in the model and sufficient control horizons to define the lateral character of the structures at the required level of detail.

Role Assignment for Data Aggregation in Wireless Sensor Networks

One of the key design goals in the design of wireless sensor networks is the efficient utilization of scarce resources such as bandwidth and energy supply. Data aggregation is a powerful technique to reduce the volume of traffic carried by the network. Data aggregation reduces congesting, saves bandwidth, saves energy and improves the lifetime of the network. In this paper we study the integrated routing and role assignment problem for efficient data aggregation in the wireless sensor networks. The objective is to identify an optimal subset of sensor nodes which should play the role of data aggregators so as to maximize the lifetime of the sensor network. Integrated routing and role assignment problem has been formulated as a mixed integer linear program (MILP). Simulations are performed on a sample grid topology to identify the optimal subset of sensor nodes which should operate as aggregators. Results show that input parameters such as transmitter type (adaptive or non-adaptive) and transmission range have significant impact on the outcome of experiments.

A comparative study of restoration schemes and spare capacity assignments in mesh networks

This paper presents the results of a comparative study of spare capacity assignment for original quasipath restoration (OQPR), improved quasipath restoration (IQPR), link restoration (LR), path restoration (PR) and link-disjoint path restoration (LDPR) schemes. Numerical results indicate that the restoration schemes studied can be sorted from most expensive to least expensive (spare capacity assignment cost) in the following order: LR, OQPR, IQPR, LDPR and PR. Since IQPR is computationally very efficient, simpler than PR, scalable, and economical in spare capacity assignment, it provides a good alternative to PR when quick restoration is desired. However, due to the potential difficulty in rapid failure isolation coupled with the increasing importance of restoration speed and simplicity, LDPR is an attractive scheme. A number of networks with different topologies and projected traffic demand patterns are used in the experiments to study the effect of various network parameters on spare capacity assignment cost. The experimental analysis shows that network topology, demand patterns and the average number of hops per primary route have a significant impact on the spare capacity assignment cost savings offered by one scheme over the other.

Improved quasi-path restoration in mesh networks

Restoration of disrupted traffic is critical in todays high-speed self-healing telecommunication networks. A restoration scheme dynamically discovers alternate paths bypassing the failed component. This paper presents an (online) improved quasi-path restoration (IQPR) scheme. IQPR is derived from the two-commodity max-flow algorithm. The running time complexity of IQPR is O(|V|3). Therefore, IQPR is computationally more efficient and more scalable than path restoration (PR). IQPR is faster (in restoration speed) and less complex than PR, and more economical (in spare capacity requirement) than link restoration (LR). Thus, it provides a good alternative to PR when quick restoration of disrupted traffic is desired. The (offline) spare capacity planning problem deals with the allocation of spare capacity to each link in the network, such that the spare capacity requirement is minimized, while guaranteeing the desired level of restoration in the event of a link failure. The spare capacity allocation problems for LR, original quasi-path restoration (OQPR), IQPR, link-disjoint path restoration (LDPR) and PR are formulated as integer linear programming problems. Numerical results illustrate that the restoration schemes studied can be sorted from the least efficient to the most efficient (in the spare capacity requirement) in the following order: LR, OQPR, IQPR, LDPR and PR. The experimental analysis shows that network topology and demand patterns have a significant impact on the spare capacity savings offered by one scheme over the other. Merits and demerits of these schemes are also discussed.

Energy‐efficient capacity‐constrained routing in wireless sensor networks

A critical issue in the design of routing protocols for wireless sensor networks is the efficient utilization of resources such as scarce bandwidth and limited energy supply. Many routing schemes proposed in the literature try to minimize the energy consumed in routing or maximize the lifetime of the sensor network without taking into consideration limited capacities of nodes and wireless links. This can lead to congestion, increased delay, packet losses and ultimately to retransmission of packets, which will waste considerable amount of energy. This paper presents a Minimum‐cost Capacity‐constrained Routing (MCCR) protocol which minimize the total energy consumed in routing while guaranteeing that the total load on each sensor node and on each wireless link does not exceed its capacity. The protocol is derived from polynomial‐time minimum‐cost flow algorithms. Therefore protocol is simple and scalable. The paper improves the routing protocol in (1) to incorporate integrality, node capacity and link capacity constraints. This improved protocol is called Maximum Lifetime Capacity‐constrained Routing (MLCR). The objective of MLCR protocol is to maximize the time until the first battery drains its energy subject to the node capacity and link capacity constraints. A strongly polynomial time algorithm is proposed for a special case of MLCR problem when the energy consumed in transmission by a sensor node is constant. Simulations are performed to analyzed the performance of the proposed protocols.

A distributed algorithm for path restoration in circuit switched communication networks

Path restoration is an important approach for building survivable telecommunication backbone networks. Path restoration is known for high restoration efficiency and its ability to protect against single link, multiple link and node failures. Path restoration can be formulated as the well-known multi-commodity network flow (MCNF) problem. While many centralized algorithms have been proposed for solving the MCNF problem, distributed algorithms have received very little attention. This paper presents an online distributed multi-commodity flow approximation algorithm specifically tailored for path restoration. Our algorithm uses O(|E|diam/sup 2/) messages and O(diam/sup 2/) time in the worst case, and substantially fewer messages and less time in practical networks. When simulated on a sample real-life backbone network similar to those used by the telecommunication service providers, our algorithm finds a solution significantly faster than many published algorithms.