Non-optimal Routing Research Articles

Optimization of Charging Stops for Fleet of Electric Vehicles: A Genetic Approach

Electrification of transport is one of the approach to improve transport efficiency and sustainability. The current cost of transport associated with electrical vehicles is mainly related to the cost of acquisition and maintenance of batteries. Finding an efficient way of managing the available energy allows reducing the size of the batteries and thus the cost associated with transport. Recently taxi services and urban delivery companies are introducing electric vehicles in their fleet. Available route planners do not consider properly the characteristics and charging stop requirements of EV fleets in decision making which results in non-optimal routing solution. The proposed work addresses the problem of finding the routes for a fleet of electric vehicles which will not only consider the battery limit of the vehicle, but also the concurrent use of charging stations along the route. The proposed solution computes routes for the fleet of vehicles that minimizes the associated cost which is a combination of travel time, charging time and the energy consumption along the route and is based on an evolutionary genetic algorithm with learning strategy. The results demonstrate that, the proposed algorithm finds a feasible solution in a reasonable amount of time and distributes the vehicles amongst the charging station to minimize the concurrency. The stated problem is non-polynomial and while genetic algorithm allows to efficiently explore large solution spaces, the work also presents some approximations and some strategies that allow to reduce the computational requirements and to find a solution in reasonable time.

Numerosity heuristic in route choice based on the presence of traffic lights

We analyzed how the presence of traffic lights, as a cause of time loss, is taken into account by drivers in planning their route through urban areas. Our hypothesis was that routes with fewer traffic lights are preferred even if the probability of having to stop at those lights is high and the waiting time at the red light is long. We carried out a questionnaire-based study in which car drivers (n=194) chose the route they preferred from pairs of hypothetical itineraries. The binary dependent variable was the type of route chosen: either a route containing fewer lights at which being forced to stop was highly probable, or a route containing more lights at which being forced to stop was far less probable. We found that the number of traffic lights was the preferred criterion, and that this preference could sometimes induce non-optimal route choices. Red- and green-light durations were also used as choice criteria. However, manipulation checks showed that participants did not estimate the probability of being forced to wait vs. being able to go through the light. We concluded that they estimated a threshold of acceptable waiting time at red lights depending on the number of traffic lights along the itinerary.

Comparative analysis of link quality metrics and routing protocols for optimal route construction in wireless mesh networks

We provide a comparative analysis of various routing strategies that affect the end-to-end performance in wireless mesh networks. We first improve well-known link quality metrics and routing algorithms to enhance performance in wireless mesh environments. We then investigate the route optimality, i.e., whether the best end-to-end route with respect to a given link quality metric is established, and its impact on the network performance. Network topologies, number of concurrent flows, and interference types are varied in our evaluation and we find that a non-optimal route is often established because of the routing protocol’s misbehavior, inaccurate link metric design, interflow interference, and their interplay. Through extensive simulation analysis, we present insights on how to design wireless link metrics and routing algorithms to enhance the network capacity and provide reliable connectivity.

Comparative Experimental Studies on Spatial Memory and Learning in Rats and Robots

The study of behavioral and neurophysiological mechanisms involved in rat spatial cognition provides a basis for the development of computational models and robotic experimentation of goal-oriented learning tasks. These models and robotics architectures offer neurobiologists and neuroethologists alternative platforms to study, analyze and predict spatial cognition based behaviors. In this paper we present a comparative analysis of spatial cognition in rats and robots by contrasting similar goal-oriented tasks in a cyclical maze, where studies in rat spatial cognition are used to develop computational system-level models of hippocampus and striatum integrating kinesthetic and visual information to produce a cognitive map of the environment and drive robot experimentation. During training, Hebbian learning and reinforcement learning, in the form of Actor-Critic architecture, enable robots to learn the optimal route leading to a goal from a designated fixed location in the maze. During testing, robots exploit maximum expectations of reward stored within the previously acquired cognitive map to reach the goal from different starting positions. A detailed discussion of comparative experiments in rats and robots is presented contrasting learning latency while characterizing behavioral procedures during navigation such as errors associated with the selection of a non-optimal route, body rotations, normalized length of the traveled path, and hesitations. Additionally, we present results from evaluating neural activity in rats through detection of the immediate early gene Arc to verify the engagement of hippocampus and striatum in information processing while solving the cyclical maze task, such as robots use our corresponding models of those neural structures.

EAMTR: energy aware multi-tree routing for wireless sensor networks

IEEE 802.15.4 is the prevailing standard for low-rate wireless personal area networks. It specifies the physical layer and medium access control sub-layer. Some emerging standards such as ZigBee define the network layer on top of these lower levels to support routing and multi-hop communication. Tree routing is a favourable basis for ZigBee routing because of its simplicity and limited use of resources. However, in data collection systems that are based on spanning trees rooted at a sink node, non-optimal route selection, congestion and uneven distribution of traffic in tree routing can adversely contribute to network performance and lifetime. The imbalance in workload can result in hotspot problems and early energy depletion of specific nodes that are normally the crucial routers of the network. The authors propose a novel light-weight routing protocol, energy aware multi-tree routing (EAMTR) protocol, to balance the workload of data gathering and alleviate the hotspot and single points of failure problems for high-density sink-type networks. In this scheme, multiple trees are formed in the initialisation phase and according to network traffic, each node selects the least congested route to the root node. The results of simulation and performance evaluation of EAMTR show significant improvement in network lifetime and traffic distribution.

Avoid 'void' in geographic routing for data aggregation in sensor networks

Wireless sensor networks have attracted great attention in research and industrial development due to their fast-growing application potentials. New techniques must be developed for sensor networks due to their lack of infrastructure support and the constraints on computation capability, memory space, communication bandwidth and above all, energy supply. To prolong the life time of a battery-powered sensor network, an energy efficient routing algorithm for data collection is essential. We propose a new geographic routing algorithm that forwards packets from sensors to base stations along efficient routes. The algorithm eliminates the voids that cause non-optimal routing paths in geographic routing. It replaces the right-hand rule by distance upgrading. It is fully distributed and responds to topology changes instantly with localised operations. We formally prove the correctness of the algorithm and evaluate its performance by simulations.

Simulation Modeling of Route Guidance Concept

The methodology of a simulation model developed at the University of New South Wales, Australia, for the evaluation of performance of Dynamic Route Guidance Systems (DRGS) is described. The microscopic simulation model adopts the event update simulation method and allows assessment of route guidance performance under different scenarios such as varying levels of participation in guidance technologies and different intensities of travel demand. This research is important for marketing, costing, and introducing route guidance and can assist planners in identifying suitable networks and traffic conditions for such systems. The example application investigates selected operating scenarios of intersection delay conditions under different compositions of participation rates in route guidance. Preliminary investigations indicate that the effectiveness of route guidance is related to the level of intersection delays. An interesting outcome of this application is the identification that in networks with intersection delays which vary with time, those motorists without guidance assistance strive to make better forecasts of travel time in order to assist their route selection. At low levels of intersection delays, some motorists appear to make more than one nonoptimal route selection.

Optimal routing algorithm and the diameter of the cube-connected cycles

Communication between processors is one of the most important issues in parallel and distributed systems. The authors study the communication aspects of a well known multiprocessor structure, the cube-connected cycles (CCC). Only nonoptimal routing algorithms and bounds on the diameter of restricted subclasses of the CCC have been presented in earlier work. The authors present an optimal routing algorithm for the general CCC, with a formal proof of its optimality. Based on this routing algorithm, they derive the exact network diameter for the general CCC. >