Mobile Mules Research Articles

WITHDRAWN: Reduction in information loss due to isolated and dumb nodes using ant colony optimization for WSN

A Wireless Sensor Network is a collection of miniature devices known as Sensors that are intended for the purpose of sensing and sending data and information to the sink. They are used in various applications and in areas that require continuous monitoring such as military fields and medical cabins. The most critical issue or the important criteria regarding this WSN is energy conservation and lifetime maintenance. Although various clustering and routing mechanisms are proposed for WSNs, the problem remains unsolved and still it remains as a popular research topic. One such idea to improve the lifetime improvement is the effective usage of the isolated and dumb nodes in the network. Dumb nodes are different from the isolated ones. The nodes that continue sensing but unable to send the data is categorized as dumb nodes while the nodes that aren’t included into clusters are called isolated nodes. We have proposed a method known as RILoID-ACO (Ant Colony Optimization enriched scheme for Reduction in information loss in the occurrence of Dumb and isolated nodes) that enables the practice of mobile mule in collecting the data from isolated and dumb nodes by enhancing a trust parameter. Success probability of 100% and data loss protection over 19% is obtained as the result of simulation that shows the success of the proposed system.

Improved Solution to Data Gathering with Mobile Mule

In this work we study the problem of collecting protected data in ad-hoc sensor network using a mobile entity called MULE. The objective is to increase information survivability in the network. Sensors from all over the network, route their sensing data through a data gathering tree, towards a particular node, called the sink. In case of a failed sensor, all the aggregated data from the sensor and from its children is lost. In order to retrieve the lost data, the MULE is required to travel among all the children of the failed sensor and to re-collect the data. There is a cost to travel between two points in the plane. We aim to minimize the MULE traveling cost, given that any sensor can fail. In order to reduce the traveling cost, it is necessary to find the optimal data gathering tree and the MULE location. We are considering the problem for the unit disk graphs and Euclidean distance cost function. We propose a primal–dual algorithm that produces a $$\left( 20+\varepsilon \right) $$ -approximate solution for the problem, where $$\varepsilon \rightarrow 0$$ as the sensor network spreads over a larger area. The algorithm requires $$O\left( n^{3}\cdot \varDelta \left( G\right) \right) $$ time to construct a gathering tree and to place the MULE, where $$\varDelta \left( G\right) $$ is the maximum degree in the graph and n is the number of nodes.

Sensor Network Topology Design and Analysis for Efficient Data Gathering by a Mobile Mule

In this paper, we study the problem of data gathering in ad-hoc sensor networks using a mobile entity called mule. The mule traverses the children of failed sensors, to prevent loss of data. Our objective is to define the optimal communication tree, and the mule’s placement such that the mule’s overall traveling distance is minimized. We explore this problem in several network topologies including: unit disc graph on a line (UDL), general unit disc graph (UDG), and a complete graph with failing probabilities on the nodes (CGFP). We provide an optimal solution for the UDL problem and three approximation algorithms for the UDG problem. For the CGFP problem we outline the two possible structures of an optimal solution and provide near optimal approximation algorithms.

A load balancing virtual level routing (LBVLR) using mobile mule for large sensor networks

In a large sensor network, the data are usually routed back to the static base station (BS) using multi-hop communication. The sensor nodes near the BS suffer from an energy hole problem owing to the heavier data traffic load on them. Hence, the nodes near to the BS die early and thereby minimize the network lifetime. The mobile carriers have been considered as a better approach to balance the energy consumption by minimizing the number of transmissions. In spite of several benefits, the mobile carriers have a lot of challenges in data collection from sensor nodes. This paper presents a load balancing virtual level-based routing (LBVLR) using a data mule as a mobile carrier to deal with energy holes and ensure reliable data transfer. Our scheme has attempted to palliate the energy hole problem by applying horizontal level routing in the left and right directions. Fixed virtual rectangular grids of equal size are formed with the help of a mobile data mule and the BS that minimizes the network overheads, which saves a significant amount of energy. This approach uses three different data collection schemes based on the mule speed during trajectory, and we name them uniform speed of mule, variable speed of mule and sojourn point of mule. To compare the performance with the other works, LBVLR is evaluated mathematically and extensive simulations are conducted. The simulation results show a significant improvement in terms of average energy usage, data delivery delay and data delivery ratio compared with the other two similar kinds of well-known routing protocols.

RILoD: Reduction of Information Loss in a WSN System in the Presence of Dumb Nodes

A sensor node behaves as “dumb,” when it continues to sense its vicinity, but fail to communicate with its neighbors due to the shrinkage in the communication range attributed to adverse environmental conditions such as rainfall, fog, and high temperature. However, the node behaves normally with the resumption of favorable environmental conditions. The nodes get isolated from the network only temporarily because of the temporal nature of adverse environmental conditions. Therefore, the dumb behavior of sensor nodes is temporary in nature. During the period of isolation, a sensor node continues its sensing, but it cannot send the sensed information to the sink, which increases the possibility of information loss in the network. Connectivity reestablishment of an isolated node with the network by activating the intermediate sleep nodes or adjusting the communication range gives the opportunity to send data to the sink. However, all the isolated nodes cannot reestablish connectivity due to the unavailability of intermediate nodes in the reduced or adjusted communication range. In this context, we propose a scheme, named RILoD , for a self-adaptive wireless sensor network, in which we use mobile mules to collect data from these isolated nodes and deliver them to the parent node, which can, in turn, forward the data to the sink. Using this technique, every isolated node can send their sensed information to the sink. Simulation results show that the proposed scheme reduces information loss by up to 32%. Furthermore, the proposed scheme has improved success probability of data delivery over the existing state of the art.

Quality-of-information-aware data collection for mobile sensor networks

Quality of information (QoI) in sensor networks measures information attributes such as precision, timeliness, completeness, and relevance of data ultimately delivered to users. It is a challenge to provide the required QoI in mobile sensor networks given the large scale and complexity of the networks with heterogeneous mobile and sensing devices. In this paper, we provide a comprehensive study on major QoI metrics for mobile sensor networks and discuss how QoI-aware data collection can be achieved. The cases with mobile sensors, mobile sinks, and mobile mules carrying data and their impact on QoI are discussed in detail. Mobility creates challenges in terms of timeliness but also opportunities in increased coverage and relevance.In a case study, we design a QoI-aware publish/subscribe system for mobile sensor networks. Users can subscribe to obtain information about events of interest by specifying the target area, sensing context, etc. The subscriptions and the sensing data are delivered to relevant sensors and users by location-based routing. We also discuss techniques that can be applied to further enhance the QoI in our publish/subscribe system. Simulation results demonstrate that the users can receive their subscribed data successfully with low communication overhead. Our publish/subscribe system can also handle mobility of clients smoothly without causing any data loss.

Opportunistic data collection for disconnected wireless sensor networks by mobile mules

This paper considers a field with a number of isolated wireless sensor networks served by some mobile mules and base stations (BSs). Sensing data needs to be carried by mobile mules to BSs via opportunistic contact between them. Also, such contact may not be frequent. Thus there are four types of communications in this environment: (i) inter-node communications within a WSN, (ii) opportunistic WSN-to-mule communications, (iii) opportunistic mule-to-mule communications, and (iv) opportunistic mule-to-BS communications. In such disconnected WSNs, since sensors’ memory spaces are limited and data collection from isolated WSNs to mules and then to BSs relies on opportunistic communications in the sense that contact between these entities is occasional, storing and collecting higher-priority data is necessary. Therefore, there are two critical issues to be addressed: the data storage management in each isolated WSN and opportunistic data collection between these entities. We address the storage management problem by modeling the limited memory spaces of a WSN’s sensor nodes as a distributed storage system. Assuming that there is a sink in the WSN that will be visited by mobile mules occasionally, we address three issues: (i) how to buffer sensory data to reduce data loss due to a shortage of storage spaces, (ii) if dropping of data is inevitable, how to avoid higher-priority data from being dropped, and (iii) how to manage the data nearby the sink to facilitate the downloading jobs of mules when the downloading time is unpredictable. We propose a Distributed Storage Management (DSM) strategy based on a novel shuffling mechanism similar to heap sort. It allows nodes to exchange sensory data with neighbors efficiently in a distributed manner. For the opportunistic data collection problem, based on a utility model, we then develop an Opportunistic Data Exchange (ODE) strategy to guide two mules to exchange data that would lead to a higher reward. To the best of our knowledge, this is the first work addressing distributed storage strategy for isolated WSNs with opportunistic communications using mobile mules. We conduct extensive simulations to investigate the merit of DSM and ODE. The simulation results indicate that the level of data importance collected by our DSM is very close to a global optimization and our ODE could facilitate delivery of important data to BSs through mules. We also implement these strategies in a real sensor platform, which demonstrates that the simple and lightweight protocols can achieve our goals.

Energy‐conserving data gathering by mobile mules in a spatially separated wireless sensor network

ABSTRACTThis paper considers a spatially separated wireless sensor network, which consists of a number of isolated subnetworks that could be far away from each other in distance. We address the issue of using mobile mules to collect data from these sensor nodes. In such an environment, both data‐collection latency and network lifetime are critical issues. We model this problem as a bi‐objective problem, called energy‐constrained mule traveling salesman problem (EM‐TSP), which aims at minimizing the traversal paths of mobile mules such that at least one node in each subnetwork is visited by a mule and the maximum energy consumption among all sensor nodes does not exceed a pre‐defined threshold. Interestingly, the traversal problem turns out to be a generalization of the classical traveling salesman problem (TSP), an NP‐complete problem. With some geometrical properties of the network, we propose some efficient heuristics for EM‐TSP. We then extend our heuristics to multiple mobile mules. Extensive simulation results have been conducted, which show that our proposed solutions usually give much better solutions than most TSP‐like approximations. Copyright © 2011 John Wiley & Sons, Ltd.