A Scale-Free Topology Construction Model for Wireless Sensor Networks

A local-area and energy-efficient (LAEE) evolution model for wireless sensor networks is proposed. The process of topology evolution is divided into two phases. In the first phase, nodes are distributed randomly in a fixed region. In the second phase, according to the spatial structure of wireless sensor networks, topology evolution starts from the sink, grows with an energy-efficient preferential attachment rule in the new node's local-area, and stops until all nodes are connected into network. Both analysis and simulation results show that the degree distribution of LAEE follows the power law. This topology construction model has better tolerance against energy depletion or random failure than other non-scale-free WSN topologies.Comment: 13pages, 3 figure

Cross-layer Balanced and Reliable Opportunistic Routing Algorithm for Mobile Ad Hoc Networks

For improving the efficiency and the reliability of the opportunistic routing algorithm, in this paper, we propose the cross-layer and reliable opportunistic routing algorithm (CBRT) for Mobile Ad Hoc Networks, which introduces the improved efficiency fuzzy logic and humoral regulation inspired topology control into the opportunistic routing algorithm. In CBRT, the inputs of the fuzzy logic system are the relative variance (rv) of the metrics rather than the values of the metrics, which reduces the number of fuzzy rules dramatically. Moreover, the number of fuzzy rules does not increase when the number of inputs increases. For reducing the control cost, in CBRT, the node degree in the candidate relays set is a range rather than a constant number. The nodes are divided into different categories based on their node degree in the candidate relays set. The nodes adjust their transmission range based on which categories that they belong to. Additionally, for investigating the effection of the node mobility on routing performance, we propose a link lifetime prediction algorithm which takes both the moving speed and moving direction into account. In CBRT, the source node determines the relaying priorities of the relaying nodes based on their utilities. The relaying node which the utility is large will have high priority to relay the data packet. By these innovations, the network performance in CBRT is much better than that in ExOR, however, the computation complexity is not increased in CBRT.Comment: 14 pages, 17 figures, 31 formulas, IEEE Sensors Journal, 201

Discrete Optimization and Agent-Based Simulation for Regional Evacuation Network Design Problem

Natural disasters and extreme events are often characterized by their violence and unpredictability, resulting in consequences that in severe cases result in devastating physical and ecological damage as well as countless fatalities. In August 2005, Hurricane Katrina hit the Southern coast of the United States wielding serious weather and storm surges. The brunt of Katrina’s force was felt in Louisiana, where the hurricane has been estimated to total more than $108 billion in damage and over 1,800 casualties. Hurricane Rita followed Katrina in September 2005 and further contributed$12 billion in damage and 7 fatalities to the coastal communities of Louisiana and Texas. Prior to making landfall, residents of New Orleans received a voluntary, and then a mandatory, evacuation order in an attempt to encourage people to move themselves out of Hurricane Katrina’s predicted destructive path. Consistent with current practice in nearly all states, this evacuation order did not include or convey any information to individuals regarding route selection, shelter availability and assignment, or evacuation timing. This practice leaves the general population free to determine their own routes, destinations and evacuation times independently. Such freedom often results in inefficient and chaotic utilization of the roadways within an evacuation region, quickly creating bottlenecks along evacuation routes that can slow individual egress and lead to significant and potentially dangerous exposure of the evacuees to the impending storm. One way to assist the over-burdened and over-exposed population during extreme event evacuation is to provide an evacuation strategy that gives specific information on individual route selection, evacuation timing and shelter destination assignment derived from effective, strategic pre-planning. For this purpose, we present a mixed integer linear program to devise effective and controlled evacuation networks to be utilized during extreme event egress. To solve our proposed model, we develop a solution methodology based on Benders Decomposition and test its performance through an experimental design using the Central Texas region as our case study area. We show that our solution methods are efficient for large-scale instances of realistic size and that our methods surpass the size and computational limitations currently imposed by more traditional approaches such as branch-and-cut. To further test our model under conditions of uncertain individual choice/behavior, we create an agent-based simulation capable of modeling varying levels of evacuee compliance to the suggested optimal routes and varying degrees of communication between evacuees and between evacuees and the evacuation authority. By providing evacuees with information on when to evacuate, where to evacuate and how to get to their prescribed destination, we are able to observe significant cost and time increases for our case study evacuation scenarios while reducing the potential exposure of evacuees to the hurricane through more efficient network usage. We provide discussion on scenario performance and show the trade-offs and benefits of alternative batch-time evacuation strategies using global and individual effectiveness measures. Through these experiments and the developed methodology, we are able to further motivate the need for a more coordinated and informative approach to extreme event evacuation

Distributed algorithms for extending the functional lifetime of wireless sensor networks

The functional lifetime of a wireless sensor network (WSN) is among its most important features and serves as an essential metric in the evaluation of its energy-conserving policies. Approaches for extending the lifetime of a wireless sensor node include using an on/off strategy on the sensor nodes and using a topology control algorithm on each node to regulate its transmission power. However, the need to keep the network functional imposes certain additional constraints on strategies for energy conservation. A sensing constraint imposes that the sensing tasks essential to the functionality of the WSN are not compromised. A communication constraint similarly imposes that communications essential to an application on the network remain possible even as battery resources deplete on the nodes. This dissertation presents new distributed algorithms for energy conservation under these two classes of constraints: sensing constraints and communication constraints. One sensing constraint, called the representation constraint in this dissertation, is the requirement that active (on) sensor nodes are evenly distributed in the region of interest covered by the sensor network. This dissertation develops two essential metrics which together allow a rigorous quantitative assessment of the quality of representation achieved by a WSN and presents analytical results which bound these metrics in the common scenario of a planar region of arbitrary shape covered by a sensor network deployment. The dissertation further proposes a new distributed algorithm for energy conservation under the representation constraint. Simulation results show that the proposed algorithm is able to significantly improve the quality of representation compared to other related distributed algorithms. It also shows that improved spatial uniformity has the welcome side-effect of a significant increase in the functional lifetime of a WSN. One communication constraint, called the connectivity constraint, imposes that the network remains connected during its functional life. The connectivity required may be weak (allowing unidirectional communication between nodes) or strong (requiring bidirectional link layer communication between each pair of communicating nodes). This dissertation develops new distributed topology control algorithms for energy conservation under both the strong and the weak connectivity constraint. The proposed algorithm for the more ideal scenario of the weak connectivity constraint uses a game-theoretic approach. The dissertation proves the existence of a Nash equilibrium for the game and computes the associated price of anarchy. Simulation results show that the algorithms extend the network lifetime beyond those achieved by previously known algorithms.Ph.D., Computer engineering -- Drexel University, 201

A Survivability Clustering Algorithm for Ad Hoc Network Based on a Small-World Model

In Ad hoc network, nodes have the characteristics of limited energy, self- organizing and multi-hop. For the purpose of improving the survivability of Ad hoc net- work effectively, this paper proposes a new algorithm named EMDWCA (Based on En- ergy, Mobility and Degrees of the nodes on-demand Weighted Clustering Algorithm). The LEACH algorithm is used to cluster the Ad hoc network in the first election, but the EMDWCA is used in the second election. By considering the appearances, disappearances, and communication link failures of the mobile nodes, this algorithm constructs the topology of Ad hoc network based on a small-world network model. To make sure that nodes can still communicate in the following election cycles, it improves the stability of the network topology and overall network invulnerability. 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