Approximation and heuristic algorithms for delay constrained path selection under inaccurate state information

A fundamental component of many transportation engineering applications is the identification of the route between a given origin and destination. Typically, some type of shortest-path algorithm is used for this task. However, shortest-path algorithms are only applicable when a single criterion, such as minimizing travel time, is used for path selection. When multiple criteria, such as the mean and variance of travel time, are used for path selection, then alternative-path identification methods must be found. The present objective is to develop an algorithm that can identify multiple and reasonable routes in transportation networks so that multiple-criteria decision-making techniques can be used in route selection. First, the definitions of single and multiple routes from a transportation engineering perspective are examined. It is indicated that although the traditional k-shortest-path algorithms can find routes with similar route travel times, the routes may be too similar with respect to the links used and consequently are not appropriate for certain transportation applications. A definition of a reasonable path is developed on the basis of transportation engineering rather than purely mathematical considerations. Two k-reasonable-path algorithms are then illustrated. These algorithms can be used to identify multiple and reasonable routes in transportation networks. Lastly, the two heuristic algorithms were tested on a network from Bryan to College Station, Texas, and the results were compared with the results obtained with a traditional k-shortest-path algorithm. It was found that the reasonable-path algorithms can identify routes that are similar in route travel time but significantly different in terms of the links used.

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A Comprehensive Analysis of ACO for Wireless Sensor Network Routing

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f1628.089620 ◽

2020 ◽

Vol 9 (6) ◽

pp. 547-551

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Greedy Algorithm ◽

Network Lifetime ◽

Heuristic Algorithms ◽

Routing Algorithm ◽

Optimal Path ◽

Path Selection ◽

Wireless Sensor ◽

Route Management

The proliferations of IoT technologies and applications have led to an increased interest in Wireless Sensor Networks (and in particular, multi-hop networks). Wireless sensor networks are composed of small mobile terminals which have limited system resources. Due to this, these networks are vulnerable to changes in network status arising from changes in the network parameters such as, position / layout of sensors, signal strength, environmental conditions, etc. In addition, the network nodes are also constrained in terms of energy provided by the battery. It is an significant consideration to be accounted so as to prolong their operational time, since this adds to the network lifetime. Lot of research has gone into routing and transmission technologies for wireless sensor networks. Conventional routing mechanisms for WSNs still suffer from energy-hole problem caused by difficulties in adaptive route management. Thus, it is imperative that efficient routing mechanisms be developed in order to conserve energy and improve network lifetime. One popular approach is to use meta-heuristic algorithms for optimal path selection in a WSN route management system. A very popular meta-heuristic algorithm used for this objective is the Ant Colony Optimization (ACO) algorithms. ACO has been used as a base for many routing management systems. In this paper an extensive analysis of the performance of ACO based route selection mechanism is reported and also reporting a comparative analysis of efficacy of the ACO routing algorithm over the standard Greedy algorithm in finding routes with different count of sensor nodes and different count of ants. Then find that the ACO routing algorithm outdoes the Greedy algorithm with respect to the number of routes identified.

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