Shared ride trip planning with geosensor networks

ISPRS Journal of Photogrammetry and Remote Sensing, 2007

Ad-hoc shared-ride trip planning in an urban environment is a complex task within a non-deterministic transportation network. Mobile geosensor networks provide the technical environment for realizing ad-hoc shared-ride trip planning: Network nodes are autonomous agents that interact locally by ad-hoc short-range communication and arrange for shared rides. In a mobile geosensor network, communication costs are critical because of constraints regarding bandwidth, available energy, and memory. This paper introduces spatio-temporal concepts from time geography, which can be employed during the planning process to significantly reduce communication costs. We will integrate network-based algorithms and different wayfinding strategies to assist both shared-ride clients and hosts in finding optimal travel assignments. Multi-agent geosimulation in a real street network is used to demonstrate the applicability of the approach and quantitatively confirm the theoretically foreseen reduction in communication costs.

International Journal of Geographical Information Science, 2006

Recent developments in miniaturization of computing devices, in location-sensing technology and in ubiquitous short-range wireless networks enable new types of social behaviour such as short-term, ad-hoc meetings of people in co-located geographical space. This paper investigates a novel usage type of these technologies, ad-hoc shared-ride trip planning in transportation networks. Shared-ride trips involve transportation clients such as pedestrians travelling with transportation hosts such as private automobiles, buses, taxi cabs or trains. Assigning clients and hosts in an ad-hoc manner challenges current trip planning approaches, in particular for non-scheduled hosts. Thus, in the novel approach we consider the transportation network as an ad-hoc mobile geosensor network using a short-range, self organizing strategy. This approach can be fully scalable if every new transportation request can be solved locally in the geosensor network, a property that we investigate by comparing different communication strategies between nodes in the system. We will demonstrate that local communication strategies save communication costs and still deliver near-to-optimal trips.

We present and discuss a specification for a simulation of shared ride trip planning in ad-hoc mobile geosensor networks. In this scenario, the nodes-clients with transportation demand, and hosts with transportation supply-have to plan routes and manage bookings collaboratively. The specification enables to compare different communication strategies for that purpose, with the goal to find an efficient communication strategy that still guarantees planning of acceptable trips in a continuously changing environment. In particular it makes the route planning strategies and booking mechanisms transparent, and shows their dependence on communication strategies. : If the bus is missed riding with cars becomes an alternative, which requires ad-hoc trip planning.

Lecture Notes in Computer Science, 2004

This paper addresses the issue of how to disseminate relevant information to mobile agents within a geosensor network. Conventional mobile and location-aware systems are founded on a centralized model of information systems, typified by the client-server model used for most location-based services. However, in this paper we argue that a decentralized approach offers several key advantages over a centralized model, including robustness and scalability. We present an environment for simulating information dissemination strategies in mobile ad hoc geosensor networks. We propose several strategies for scalable, peer-to-peer information exchange, and evaluate their performance with regard to their ability to distribute relevant information to agents and minimize redundancy.

Lecture Notes in Computer Science, 2008

Shared ride systems match the travel demand of transport clients with the supply by vehicles, or hosts, such that the clients find rides to their destinations. A peer-to-peer shared ride system allows clients to find rides in an ad-hoc manner, by negotiating directly with nearby hosts via radio-based communication. Such a peer-to-peer shared ride system has to deal with various types of hosts, such as private cars and mass transit vehicles. Their different behaviors affect the negotiation process, and consequently the travel choices. In this paper, we present and discuss a model of a peer-to-peer shared ride system with different types of agents. The behavior of the model is investigated in a simulation of different communication and way-finding strategies. We demonstrate that different types of agents enrich the choices of the clients, and lead to local solutions that are nearly optimal.

2009

Compared to conventional wireless sensor networks (WSNs) that are operated based on the client-server computing model, mobile agent (MA) systems provide new capabilities for energy-efficient data dissemination by flexibly planning its itinerary for facilitating agent based data collection and aggregation. It has been known that finding the optimal itinerary is NP-hard and is still an open area of research. In this paper, we consider the impact of both data aggregation and energyefficiency in sensor networks itinerary selection, We propose an itinerary energy minimum for first-source-selection (IEMF) algorithm, as well as the itinerary energy minimum algorithm (IEMA), the iterative version of IEMF. Our simulation experiments show that IEMF provides higher energy efficiency and lower delay compared to existing solutions, and IEMA outperforms IEMF with some moderate increase in computation complexity.

In geographic routing, not much effort has been given to improving position update mechanism used by nodes in wireless Ad-Hoc networks which is an important issue in order to precisely maintain the geographic position of nodes. Nodes are periodically broadcasting beacon updates that contain geographic location coordinates to the nodes in their vicinity. This is an accepted scheme for maintaining neighbour’s positions. We are solving the problem of high beacon overhead caused by generation of beacons while considering mobility and traffic load of the network. For example, if the beacon interval is too short for nodes that move very slow, i.e. not rapidly changing position, periodic beaconing will create unnecessary amount of beacon updates. In the same way, nodes which are broadcasting lot of data packets will generate the same amount of beacons as the nodes that have fewer data packet to transmit which leads to needless waste of energy. To make progress on these problems, we propose Adaptive Energy Efficient (AEE) mechanism for geographic routing. Based on mobility and traffic load of the network, AEE adapts the beaconing interval to the value that corresponds to the networks demands. We have embedded AEE into well know Greedy Perimeter Stateless Routing protocol (GPSR), and compared it with regular GPSR. We ran extensive simulations in Network Simulator 2 (NS-2) for both networks with high and low initial energy level. Results have shown that AEE reduces beacon overhead up to 90 percent, AEE also reduces energy consumption and the number of packet collision up to 83 percent, without worsening packet delivery fraction of the network. This adaptive method that reduces the number of collision and prolongs the battery usage of each node in the network can be very useful for MANET or VANET where system is completely ad-hoc and no infrastructure is used and nodes are relying on their own power sources.

2009

Agent-based data collection and aggregation have been proved to be efficient in wireless sensor networks (WSNs). While most of existing work focus on designing various single agent based itinerary planning (SIP) algorithms by considering energy-efficiency and/or aggregation efficiency, this paper identifies the drawbacks of this approach in large scale network, and proposes a solution through multi-agent based itinerary planning (MIP). A novel framework is presented to divide our MIP algorithm into four parts: visiting central location (VCL) selection algorithm, source-grouping algorithm, SIP algorithm and its iterative algorithm. Our simulation results have demonstrated that the proposed scheme lowers delay and improves the integrated energy-delay performance compared to the existing solutions with the similar computation complexity.

2001

We introduce a geographical adaptive fidelity (GAF) algorithm that reduces energy consumption in ad hoc wireless networks. GAF conserves energy by identifying nodes that are equivalent from a routing perspective and turning off unnecessary nodes, keeping a constant level of routing fidelity. GAF moderates this policy using application-and system-level information; nodes that source or sink data remain on and intermediate nodes monitor and balance energy use. GAF is independent of the underlying ad hoc routing protocol; we simulate GAF over unmodified AODV and DSR. Analysis and simulation studies of GAF show that it can consume 40% to 60% less energy than an unmodified ad hoc routing protocol. Moreover, simulations of GAF suggest that network lifetime increases proportionally to node density; in one example, a four-fold increase in node density leads to network lifetime increase for 3 to 6 times (depending on the mobility pattern). More generally, GAF is an example of adaptive fidelity, a technique proposed for extending the lifetime of selfconfiguring systems by exploiting redundancy to conserve energy while maintaining application fidelity.

2001

One of the most important problems studied in any sensor network is data fusion. Client/server paradigm has been a commonly used computing model in traditional distributed sensor networks (DSNs). However, the deployment of wireless sensor networks (WSNs) and its ad hoc nature have brought new challenges to the fusion task. For example, the advances in sensor technology allow better, cheaper, and smaller sensors to be used, which results in a much larger number of sensors deployed. On the other hand, sensors communicate through wireless networks where the network bandwidth is much lower than for wired communication. In this paper, we describe the usage of mobile agent for data fusion in WSNs. In this computing model, data stay at the local site, while the fusion process (code) is moved to the data sites. By transmitting the computation engine instead of data, network bandwidth requirement is largely reduced and the performance of the fusion process is more stable. One of the key problems discussed in this mobile-agentbased WSN (MAWSN) is how to plan the itinerary (or route) for a mobile agent in order to achieve progressive fusion accuracy. This paper presents a method to develop an optimal itinerary for mobile agent to fulfill the integration task while consuming minimum amount of resources, including time and power.