In Wireless Nanoscale Sensor Networks (WNSN), collision problems are unlikely to happen, and therefore data aggregation should be encouraged to save power consumption. On the other hand, molecular absorption is non-negligible in Terahertz band so that signal impairment should consider free space path loss and molecular absorption simultaneously. In this paper, we consider energy efficient data aggregation routing problem in WNSN with addressing physical layer's signal impairments from free space path loss and molecular absorption. We first propose mathematical formulation to capture this cross-layer (layer 1 and layer 3) design problem. By intelligently setting the power aware and signal quality aware arc weight in a WNSN, data aggregation routing decisions can be determined by the proposed PESAWNSN algorithm. According to the computational experiments, the PESAWNSN algorithm can successfully identify low power data aggregation routing paths in meeting signal quality requirements, especially for low range transmission.

Molecular Nanoscale Sensor Networks (MNSNs) introduce a new molecular routing paradigm where the molecular communication is enabled by activating the nanosensors on the routing path to release the molecules. This new molecular activation mechanism is a new many-to-many scheme where the transmitting nanosensors transmits molecules to multiple receiving nanosensors and the receiving nanosensors receives the molecules from multiple transmitting nanosensors. Molecular activation mechanism poses two new capacity constraints where the received molecules must be above a threshold to activate the receiving node and the molecules released from the transmitter should not exceed its molecular capacity. These two new criteria (many-to-many communication and capacity constraint) make the molecular activation and communication scheme a very challenging issue in an MNSN, which is totally different from the communication and routing scheme in existing wireless IP networks. In this paper, for the first time, we propose a sound mathematical model to capture the many-to-many communication scheme, activation capacity constraint and molecular capacity constraint in the MNSN. We then propose a novel QoS (cost and capacity) aware algorithm, CACAMA, to identify the cost efficient molecular activation and communication path in the MNSN. From the computational experiments, it shows that the CACAMA algorithm is superior to other two heuristics, SP and MCST, in all network settings.

Traditional Wireless Sensor Networks (WSN) transmits the scalar data (e.g., temperature, irradiation) to the sink node. A new Wireless Visual Sensor Network (WVSN) that can transmit images is a more promising solution than the WSN on sensing, detecting and monitoring the environment to enhance awareness of the cyber, physical, and social contexts of our daily activities. Sensor coverage in WVSN is more challenging than in WSN due to besides the sensing range coverage, the Field of View (FoV) should also be considered in deploying the sensors. In this paper, we study the sensor coverage problem in WVSN. We first propose the mathematical model to formulate the sensor coverage problem in WVSN. We devise a heuristic algorithm (FoVIC) algorithm to tackle this sensor coverage problem in WVSN. The basic idea of FoVIC algorithm is to deploy a sensor one at a time that can cover the largest number of uncovered nodes and then the algorithm checks for any sensor deployed in the earlier stage that could be removed. From the computational experiments, they show that larger span angle could help the sensors to cover more nodes in bigger grid size and fewer sensors will be need in smaller grid size when in fixed sensing range and span angle.

In wireless sensor networks, data aggregation routing could reduce the number of data transmission so as to achieve efficient total energy consumption. However, this kind of data aggregation introduces data retransmission that is caused by co-channel interference from neighbouring sensor nodes. Hence, more data aggregation leads to more extra energy consumption and significant retransmission delay from retransmission. This could jeopardise the benefits of data aggregation. One possible solution to circumvent retransmission caused by co-channel interference is to assign different channel to every sensor node that is within each other's interference range on the data aggregation tree. As the number of non-overlapping channels is limited in wireless networks, it is unlikely that we could assign a different channel to every sensor node on the data aggregation tree. Then, an interesting problem is to perform data aggregation routing in conjunction with channel assignment to minimise total transmission power under limited number of non-overlapping channels. This problem is an NP-complete problem. We devise heuristic algorithm, Iterative Channel Adjustment Data Aggregation Routing algorithm (ICADAR), and other three heuristics, to tackle this problem. From the simulation results, the ICADAR algorithm outperforms the other three algorithms under all experimental cases.

In this paper, we investigate delay constrained multicast routing for supporting QoS guaranteed point to multi-point communications in IP over WDM networks. To achieve high bandwidth utilization, packets coming from different multicast connections are groomed and carried together over a single wavelength. Lightpath scheme is adopted in this paper that unicast lightpath is provisioned to support the multicast traffic in the IP network. Hop count constraint is introduced to deal with and queueing delay from traffic grooming. The challenge of the problem comes not only from considering delay constrained multicast routing but also WDM lightpath routing and wavelength assignment (RWA). We formulated the problem as an integer optimization problem in which the revenue from admitting multicast groups is to be maximized. The problem constraints include hop count constraint for end-to-end QoS requirements, tree constraint for multicast routing, IP link capacity and WDM fiber link capacity constraints, and wavelength continuity constraint. We apply Lagrangean relaxation technique to perform constraint relaxation and propose optimization-based heuristics (LGR) to tackle this problem. We draw performance comparisons between the LGR and the minimum hop (MH) heuristics. Numerical results demonstrate that LGR outperforms MH algorithm under all experimental cases.

In a WOBAN, the back end is a wired optical network, the front end is managed by wireless connectivity, and, in between, the tail ends of the optical part [known as optical network unit (ONU)] communicate directly with wireless access points (AP). We study a WOBAN deployment scenario and investigate an algorithm to optimize the placement of multiple ONUs. To obtain some representative data on locations of typical wireless users, we have conducted a survey on the distribution and types of wireless routers in the Wildhorse residential neighborhood of North Davis, CA. We also formulate the multiple-ONU deployment problem using a combinatorial optimizer, viz., simulated annealing. Having found the suitable locations for ONUs, we compare the expenditures of a WOBAN vs. a wired access solution, namely Passive Optical Network (PON). To capture the challenges behind a complete WOBAN setup, we propose and investigate a joint optimization algorithm, which considers design aspects of both the wireless front end, such as avoiding interference among neighboring APs, and the optical back end, such as minimizing expensive fiber layout.

Hybrid wireless-optical broadband access network (WOBAN) is a promising architecture for future network operations. Recently, the wireless part of WOBAN has been gaining increasing attention and early versions are being deployed as a municipal access solution to eliminate the wired backhaul to every wireless router. This architecture saves on network deployment costs because fiber (or wiring) does not need to extend to the end user, and it extends the reach of emerging optical access solutions, e.g., passive optical network (PON)-based access solutions. However, a major research opportunity exists in developing an efficient routing algorithm for the wireless front end of WOBAN. We propose and investigate the characteristics of "delay- aware routing algorithm (DARA)" that minimizes the average packet delay in the wireless front end of a WOBAN. We model wireless routers as queues and predict wireless link states periodically. Our simulation experiments show that DARA achieves better load balancing and less congestion compared to tradional approaches such as minimum-hop routing algorithm (MHRA) and shortest- path routing algorithm (SPRA). In addition to minimizing the delay, DARA also improves the average hop count compared to the predictive throughput routing algorithm (PTRA), a popular protocol used in several deployments for the wireless front end of a WOBAN.

Multigranular optical WDM network aims to reduce network cost by grouping multiple wavelengths and then switching those wavelengths together at waveband or fiber levels. To configure such multi-granular network, we have two choices-homogeneous network and heterogeneous network. The former applies only a single type of optical cross connect (OXC) while the latter allows different types of OXCs. Due to the demands varies and change asymmetrically geographically, networks with heterogeneous OXC nodes is especially suitable for placing best switching types at different location. In this paper, we aim at the design of an algorithm to solve the network planning problem in optical network with heterogeneous multi- granularity OXCs. The planning program determines not only the switching granularity for each node but also determine the routing and wavelength assignments to satisfy the given demands. The contributions of the paper are four folded. First, we propose a graph model to represent the OXC node. The transformed graph simplifies the representation of node structure and helps to model the problem. Secondly, we propose a mathematical formulation to model the network planning problem as an ILP problem. Thirdly, a Lagrangean relaxation based heuristic algorithm is proposed to obtain a near optimal solution. Fourthly, we studied the impact of waveband size on network cost. This work reveals that the waveband size plays a crucial factor. The proposed algorithms can determine the optimal number of waveband size.

The concept of a hybrid wireless-optical broadband access network (referred to as WOBAN here) is a very attractive one. This is because it may be costly in several situations to run fiber to every home from the telecom central office (CO); also, providing wireless access from the CO to every end user may not be possible because of limited spectrum. Thus, running fiber as far as possible from the CO towards the end user and then having wireless access technologies take over may be an excellent compromise. How far should fiber penetrate before wireless takes over is an interesting engineering design and optimization problem, which our study is focussing on. We propose and investigate the characteristics of a "mixed integer programming (MIP)" model for optimum placements of base stations (BS) and optical network units (ONU) in a WOBAN (primal problem). We develop several constraints to be satisfied: BS and ONU installation, user and channel assignment, and signal-quality and interference constraints. To solve this MIP with reasonable accuracy, we use "Lagrangean relaxation" to obtain the corresponding "Lagrangean dual" problem. Via simulation experiments, we verify how sensitive is the placement problem with respect to a set of chosen metrics.

Incorporating sensor nodes with data aggregation capability to transmit less data flow in wireless sensor networks could reduce the total energy consumption. However, the penalty from data retransmissions due to collision could jeopardize the advantages from data aggregation. In this paper, for the first time, we consider the energy consumption tradeoffs between the data aggregation and retransmission in wireless sensor network. By using the CSMA-CA MAC protocol, the retransmission energy consumption function is well formulated. We propose a rigorous non-linear mathematical formulation, where the objective function is to minimize the total energy consumption of data transmission subject to data aggregation tree and data retransmission. The solution approach is based on Lagrangean relaxation in conjunction with the optimization-based heuristics. From the computational experiments, it is shown that the proposed algorithms could construct more energy efficient data aggregation tree with MAC layer retransmission mechanism than existing data centric algorithms up to 93%.

Incorporating sensor nodes with data aggregation capability to transmit less data flow in wireless sensor networks could reduce the total energy consumption. This calls for the efficient and effective data-centric routing algorithm to facilitate this advantage. In this paper, we model the data-centric routing problem by a rigorous mixed integer and linear mathematical formulation where the objective function is to minimize the total transmission cost subject to multicast tree constraints. The solution approach is based on Lagrangean relaxation in conjunction with the optimization-based heuristics. From the computational experiments, it is shown that the proposed algorithm calculates a better solution than other existing heuristics with improvement ratio up to 169% for a network with 300 random generated nodes in five minutes of computational time.

An essential issue in designing, operating and managing a modern network is to assure end-to-end quality-of-service (QoS) from users' perspective, and in the meantime to optimize a certain "average" performance objective from the system's perspective. We consider the problem of minimizing the average cross-network packet delay in virtual circuit networks subject to an end-to-end delay constraint for each origin-destination user pair. The problem is formulated as a multicommodity network flow problem with integer routing decision variables, where additional end-to-end delay constraints are considered. The difficulties of this problem result from the integrality nature and particularly the nonconvexity associated with the end-to-end delay constraints. The basic approach to the algorithm development is Lagrangean relaxation in conjunction with number of optimization-based heuristics. In the computational experiments, it is shown that the proposed algorithm calculates solutions which are within 1% and 3% of optimal solutions under lightly and heavily loaded conditions, respectively, in minutes of CPU time for networks with up to 26 nodes.