Abstract-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.

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.Abstract-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.

Restoration of disrupted traffic is critical in today's 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 ( 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.

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