The limited energy in wireless sensor network has always been a key topic. Most previous studies usually regulate the network loading by periodically directing the data flow to different sinks in order to extend the overall life-time of network and enhance the network availability or data delivery. However, the time and place that an event occurred can not be estimated, thus regular information flow switching may not be able to balance the actual loading of network. Another, multiple paths to sinks for each node are established in the tolerance area defined by a pre-defined difference between the cost, i.e. hop counts away from sinks, of paths to different sinks such that each node in-between multiple sinks can distribute the forwarding burden among the paths to different sinks. Since sending data to a farther sink consumes much more overall energy of network, the gain of data delivery should be evaluated at the sacrifice of energy consumption. In this paper, a quantized model is proposed to evaluate the tradeoff between energy consumption and data delivery under non-uniformly events triggering in network. The tradeoff between network availability and energy consumption would be analyzed and the corresponding simulation results would be included.
Ad-hoc on-demand distance vector routing (AODV) is a well-known routing protocol for mobile ad hoc networks. The original AODV protocol works in a semi-dynamic fashion, by establishing a route on demand and using that route until it breaks. However, to suit the changing network topology of ad hoc networks, more aggressive and adaptable routing strategies are required. A number of researches have proposed improving AODV performance by locally repairing broken links, predicting and replacing potentially vulnerable links, or shortening a link through removing redundant nodes from the transmission path. Although local repair may relieve some problems, it usually results in longer paths and thus a considerable performance drop in heavy traffic conditions. There are also issues regarding packet loss and communication delay due to route rebuilding once the link is broken. Predicting and replacing potentially vulnerable links may require special hardware, additional tables to maintain, or other extra overhead. Finally, path shortening may result in shorter and more efficient routes, but there is no guarantee that the new paths will be robust. This paper proposes integrating preemptive link breaking avoidance and path shortening mechanisms into a modified AODV protocol. However, the difficult issue lies in determining the right timing to initiate the two independent mechanisms so that the two dynamically and complementarily operating mechanisms can work together to improve the routing performance. Through numerical analysis and simulation, we have arranged a simple parameter setting for controlling the activation of each mechanism at the appropriate time. The proposed combination is a highly dynamic ad hoc routing protocol that is capable of adapting itself to the changing network topology and achieving extremely good performance in various routing performance metrics. Extensive simulations show that each of the two schemes alone improves AODV performance. More importantly, the integrated protocol performs even better in terms of data delivery rate, average delay time, and network overhead. To be more specific, in the best cases our protocol can reduce up to 82% in control overhead and 66% in delay time, while achieving 12% more in data delivery rate comparing to AODV.
Reducing energy consumption and prolonging lifetime of network to reduce the amount of packet loss are important issues in wireless sensor networks. Many researches derive the minimum hop path for each sensor to transmit its corresponding data to the sink. The sensors in the path forward the data. However, some common sensors in many forwarding paths will consume much more energy, and then they will die soon. Besides, the establishment and maintenance of the above routing need the whole information of the network, and this will consume more energy in gathering and synchronizing the locations of all sensors. In this paper, each sensor using the information of neighboring sensors derives the minimum hop path to the sink, and with the knowledge of the loading of its up-streaming sensors, it selects the minimum loaded sensor for its first sensor to transmit to. In this way, the loading of each sensor will be balanced. The above routing derives minimum balanced tree (MBT). This data structure will be adjusted locally while some sensors change their statuses in the network such that the control overhead needed to adjust is much less than to reconstruct all over again. Some results of simulated experimentations are shown in this paper.
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