Collaborative Approach for Improving the Scheduling and Providing Advanced Security in Wireless Sensor Network

Recent advances in Wireless Sensor Networks (WSN) have focused towards Geographic forwarding mechanism. It is a promising routing scheme in wireless sensor networks, in which the forwarding decision is determined purely based on the location of each node. Such type of Routing in Geographic domain is also useful for large multi-hop wireless networks where the nodes are not reliable and network topology is frequently changing. This routing requires propagation of single hop topology information that is the best neighbor, to make correct forwarding decisions. The research of Geographic routing has now moved towards duty cycled wireless sensor networks (WSNs). In such type of network, sensors are sleep scheduled which helps in reduction of energy consumption. It works by dynamically putting the nodes to sleep when not in use and reactivate it, when required, by using some sleep scheduling algorithms. Geographic routing is usually based on distance which is considered as its main parameter. This routing uses geographic routing oriented sleep scheduling (GSS) algorithm & geographic-distance-based connected-k neighborhood (GCKN) algorithm. The existing research was done to find out the shortest path from source to destination in Duty-Cycled Mobile sensor networks along with geographic routing, using distance as a parameter. But there may be the case when shortest path is available and the nodes are heavily loaded. Therefore, load balancing also proves to be equally important factor. Hence, this research work proposes the system that will calculate the best optimal path from source node to destination by taking into consideration the load on each node and delay incurred by each node in Duty-Cycled Mobile sensor networks along with geographic routing. The experimental results and performance analysis shows that the newly proposed approach achieves the best results in comparison with the existing system

Algorithmic Foundations of Heuristic Search using Higher-Order Polygon Inequalities

The shortest path problem in graphs is both a classic combinatorial optimization problem and a practical problem that admits many applications. Techniques for preprocessing a graph are useful for reducing shortest path query times. This dissertation studies the foundations of a class of algorithms that use preprocessed landmark information and the triangle inequality to guide A* search in graphs. A new heuristic is presented for solving shortest path queries that enables the use of higher order polygon inequalities. We demonstrate this capability by leveraging distance information from two landmarks when visiting a vertex as opposed to the common single landmark paradigm. The new heuristic’s novel feature is that it computes and stores a reduced amount of preprocessed information (in comparison to previous landmark-based algorithms) while enabling more informed search decisions. We demonstrate that domination of this heuristic over its predecessor depends on landmark selection and that, in general, the denser the landmark set, the better heuristic performs. Due to the reduced memory requirement, this new heuristic admits much denser landmark sets. We conduct experiments to characterize the impact that landmark configurations have on this new heuristic, demonstrating that centrality-based landmark selection has the best tradeoff between preprocessing and runtime. Using a developed graph library and static information from benchmark road map datasets, the algorithm is compared experimentally with previous landmark-based shortest path techniques in a fixed-memory environment to demonstrate a reduction in overall computational time and memory requirements. Experimental results are evaluated to detail the significance of landmark selection and density, the tradeoffs of performing preprocessing, and the practical use cases of the algorithm

Shortest Distances as Enumeration Problem

We investigate the single source shortest distance (SSSD) and all pairs shortest distance (APSD) problems as enumeration problems (on unweighted and integer weighted graphs), meaning that the elements $(u, v, d(u, v))$ -- where $u$ and $v$ are vertices with shortest distance $d(u, v)$ -- are produced and listed one by one without repetition. The performance is measured in the RAM model of computation with respect to preprocessing time and delay, i.e., the maximum time that elapses between two consecutive outputs. This point of view reveals that specific types of output (e.g., excluding the non-reachable pairs $(u, v, \infty)$, or excluding the self-distances $(u, u, 0)$) and the order of enumeration (e.g., sorted by distance, sorted row-wise with respect to the distance matrix) have a huge impact on the complexity of APSD while they appear to have no effect on SSSD. In particular, we show for APSD that enumeration without output restrictions is possible with delay in the order of the average degree. Excluding non-reachable pairs, or requesting the output to be sorted by distance, increases this delay to the order of the maximum degree. Further, for weighted graphs, a delay in the order of the average degree is also not possible without preprocessing or considering self-distances as output. In contrast, for SSSD we find that a delay in the order of the maximum degree without preprocessing is attainable and unavoidable for any of these requirements.Comment: Updated version adds the study of space complexit

Landmark Guided Probabilistic Roadmap Queries

A landmark based heuristic is investigated for reducing query phase run-time of the probabilistic roadmap (\PRM) motion planning method. The heuristic is generated by storing minimum spanning trees from a small number of vertices within the \PRM graph and using these trees to approximate the cost of a shortest path between any two vertices of the graph. The intermediate step of preprocessing the graph increases the time and memory requirements of the classical motion planning technique in exchange for speeding up individual queries making the method advantageous in multi-query applications. This paper investigates these trade-offs on \PRM graphs constructed in randomized environments as well as a practical manipulator simulation.We conclude that the method is preferable to Dijkstra's algorithm or the ${\rm A}^*$ algorithm with conventional heuristics in multi-query applications.Comment: 7 Page