Elsevier

Computer Communications

Volume 31, Issue 5, 25 March 2008, Pages 896-914
Computer Communications

Efficient data propagation strategies in wireless sensor networks using a single mobile sink

https://doi.org/10.1016/j.comcom.2007.12.011Get rights and content

Abstract

Data collection is usually performed in wireless sensor networks by the sensors relaying data towards a static control center (sink). Motivated by important applications (mostly related to ambient intelligence and remote monitoring) and as a first step towards introducing mobility, we propose the basic idea of having a sink moving in the network area and collecting data from sensors. We propose four characteristic mobility patterns for the sink that we combine with different data collection strategies. Through a detailed simulation study, we evaluate several important performance properties of each approach. Our findings demonstrate that by taking advantage of the sinks mobility and shifting work from sensors to the powerful sink, we can significantly reduce the energy spent in relaying traffic and thus greatly extend the lifetime of the network.

Introduction

Wireless Sensor Networks are visioned as very large collections of smart sensor nodes that form ad hoc distributed sensing and data propagation networks that collect quite detailed information about the ambient environment. In a usual scenario, these networks are largely deployed in areas of interest for fine grained monitoring in different classes of applications [1]. The sensor devices are battery powered; energy is the most precious resource of a wireless sensor network, since replacing the battery of the nodes in large scale deployments is infeasible. The collected data is disseminated to a static control point – data sink in the network, using node to node – multi-hop data propagation, [2], [3]. However, in this setting sensor devices consume significant amounts of energy due to the execution of a routing protocol which also increases implementation complexity. Also, a point of failure emerges in the area near the control center where nodes relay the data from other nodes that are farther away.

Additionally, a new category of important sensor networks applications emerges where motion is a fundamental characteristic of the examined system. In such applications sensors are attached to vehicles, animals or people that move around large geographic areas. Data exchange between individual sensors and infrastructure nodes will drive applications such as traffic and wild life monitoring, smart houses and hospitals and pollution control. Clearly, the usual approach of having a statically placed control center is unable to operate efficiently in such scenarios.

Recently, a new approach has been developed that shifts the burden from the sensor nodes to the sink. The main idea is that the sink is mobile, has significant and easily replenishable energy reserves and moves inside the area the sensor network is deployed. When moving inside the network area the sink is constantly in close proximity to a (usually small) subset of the sensor devices and can acquire the data collected by these nodes at very low energy cost. By travelling in the whole network area, the control center is capable of collecting all the available data. The mobility assumption may be especially useful in particular application classes, e.g., in emergency preparedness [4], a set of nodes called actors aggregate and evaluate the collected data in order to detect and react to emergency situations. In this setting mobile elements can move in close proximity to an area and better assess an emergency situation.

Traversing the network area in a timely and efficient way is critical since failure to visit some areas of the network will result in data loss, while infrequently visiting some regions will result in long delivery delays. Also, routing and localization problems in the case of mobile sinks become more difficult to cope with, efficient solutions in the state of the art become inefficient or even inoperable in networks with mobility. Thus new techniques and algorithms need to be developed. Despite the apparent difficulties, this data collection paradigm has many attractive properties (see also [5]). The major advantage of having a mobile sink (or more) is an increase in system lifetime. A mobile agent that moves closer to the nodes can help conserve energy since data is transmitted over fewer hops thus reducing the number of transmitted packets. The extra energy spent for the operation and movement of the sink does not affect overall sensor network lifetime since the mobile sink is considered an external to the network factor, in fact it could be a man navigated vehicle or an unmanned robot that periodically returns to a support center in order to recharge itself.

Another important advantage is that sparse and disconnected networks can be better handled, since full connectivity of the network nodes is not required. A mobile sink allows to monitor a region with fewer sensor devices thus decreasing the operational cost of the network. Also, the sensor devices can reduce their transmission range to the lowest value required to reach the mobile infrastructure, further decreasing the energy consumption. Additionally, the mobile sink can navigate through or bypass problematic regions where sensor devices cannot operate, such as small lakes, or obstacles, e.g., large boulders, that block the propagation path. Conventional multi-hop protocols either fail or spend too many resources in order to overcome such obstacles, thus the monitoring of much harsher environments is possible.

Moreover, increased throughput and data fidelity can be achieved with the use of a mobile sink. By reducing the number of hops, the probability of transmission errors and collisions also reduces. Also, security can be enhanced as the data does not traverse multiple hops across potentially compromised nodes, while an adversary sniffing the data packets from an area will receive only the information regarding that area. A very nice and complete categorization and discussion of several different mobility models and their relevance to different application types and real world scenarios can be found in [6].

In this work, we propose and investigate sink mobility as a method for efficient and robust data delivery in wireless sensor networks. Our work is one of the first few attempts in the relevant state of the art that introduces mobility of the sink. We propose four mobility patterns for the sink, mostly randomized (such as the simple random walk, biased random walks and walks on spanning subgraphs) as well as predictable mobility (moving on a straight line or cycle). These patterns assume and exploit different degrees of freedom, simplicity and network knowledge. To get data from the sensors, the sink movement is combined with three data collection strategies: a passive, a multi-hop and a limited multi-hop.

Each approach has different advantages and disadvantages and achieves different trade-offs, mostly between energy dissipation and time efficiency. We investigate several important performance properties of our protocols through a detailed, large scale simulation evaluation considering non-uniform positioning of sensor nodes. We conduct an experimental rather than theoretical analysis, because of the added complexity due to the number of the proposed protocols and the diversity between them as well as the complications that arise due to the non-uniform evaluation settings.

Our findings demonstrate that having a mobile sink can greatly extend the lifetime of the network. In fact, even by having very limited sink mobility, the overall success rate can be improved by more than 50% and the energy dissipation is reduced by more than 30%. If the sink can be fully mobile (i.e., be able to visit all the areas of the network), we can further reduce the energy consumption and achieve very high success rates, very close to 100%.

Our work differentiates from the state of the art and extends it, in at least the following ways: (a) We assume very weak models, our protocols use only locally obtainable knowledge and impose minimum overhead to the sensor nodes. (b) We present the only work, to the best of our knowledge, that proposes and evaluates comparatively several different methods to exploit mobility. The examined methods span across the gamut of proposed mobility categories and data propagation techniques. (c) We propose elaborate and novel randomized mobility techniques that adapt to network conditions. (d) We perform extensive evaluation of our protocols in realistic settings and present in detail qualitative results in comparison to the static case. Moreover, we feel that our work is a first step towards introducing mobility in wireless sensor networks, e.g., having many/multiple sinks collecting data in a network composed of mobile sensors (or combinations of mobile and static sensors). Though efficient and important in practice, mobility introduces complications and new challenges for protocol design that should be investigated in future research. A preliminary version of this work has been presented in [7].

Several protocols that implement energy efficient information collection, have been proposed for static wireless sensor networks. Probabilistic forwarding schemes (like PFR, [8]) perform redundant optimized multi-path transmissions to combine energy efficiency and fault-tolerance. Such protocols, although well suitable for deployment in sparse networks, tend to spend a lot of energy in the case of high densities. Protocols specifically aimed at achieving energy efficiency at a lower level, have been proposed in [9]. The protocols alternate the operation of the sensors between a low power sleep mode and normal operation. The protocols continually monitor local network conditions, and adapt the length of each period accordingly. Thus, great energy savings are achieved even on highly dynamic networks with incremental and heterogeneous node deployment. Also, in [10] an adaptive randomized algorithm is proposed that balances the energy consumption of the sensor devices. As we will shortly see, mobility of the sink also tends to balance energy consumption.

In the context of mobile ad-hoc networks, mobility and routing have been studied extensively. An introduction in mobile ad-hoc networks as well as detailed descriptions of several routing protocols can be found in [11]. A survey of proposed mobility models can be found in [12]. The results in this area can not be directly applied in wireless sensor networks since, sensor nodes have different, more constrained capabilities. Also, the communication patterns are much different, in wireless sensor networks usually many nodes transmit data to one whereas in mobile ad-hoc networks many nodes communicate with each other. The usual scenario, examined in most research papers in the context of mobile ad-hoc networks, is that nodes move in an uncontrollable fashion and a solution is proposed that establishes and maintains routing paths in a resilient, towards mobility, manner. For example, in [13] an algorithm for constructing a routing backbone out of dedicated powerful or under-utilized nodes is presented. The resulting network uses fewer links and is connected as long as the original network is connected. Another attempt to counter mobility is presented in [14] where the authors propose an algorithm that maintains routes by observing the relative movement between nodes. Here we examine in unison the issues of mobility and routing, proposing joint methods for directly improving network performance exploiting mobility.

Recently, several applications that motivate mobility in wireless sensor networks appeared, [15] presents a case study of applying peer-to-peer techniques in mobile sensor networks designed for wildlife position tracking for biology research. In [16], a data sink was mounted on a public transportation bus. The sensor nodes learn the times at which they have connectivity with the bus, and wake up accordingly to transfer their data.

In [17], a three-tier architecture is proposed that exploits the random motion of mobile entities, such as humans or animals, in order to collect information from the sensors and relay that information to a central control center. In [5], the authors discuss several interesting issues of mobility and implement and evaluate a small sensor network with one mobile entity that moves back and forth on a straight line. This idea is further extended in [18] where the possibility of having multiple mobile sinks is examined. The trajectories of the mobile entities are fixed by the network operator; sinks move in parallel following linear trajectories. An algorithm to load balance the data collection process by assigning sensor nodes to sinks is proposed. Note that the algorithm operates only under the assumption of full coverage of the network by the mobile entities. Here, we examine various types of random movement as well as predefined trajectories of a single sink under weaker assumptions.

Luo et al. [19], nicely investigate how to minimize energy consumption in a wireless sensor network with a mobile sink while also considering multi-hop propagation effects. They conduct an analysis assuming a completely uniform network, both in terms of node distribution and generation of events. They estimate that the overall energy consumption is minimized when the trajectory of the mobile sink is a cycle and calculate its optimal positioning and radius. Optimization of the data propagation process using mobility is examined in [20]. Under the assumption that all sensor nodes can move, the authors propose an algorithm for rearranging the position of nodes in a multi-hop propagation path in order to minimize the energy consumption along this path. In our work, we also focus on increasing network performance by proposing solutions that couple sink movement with efficient data propagation. Additionally, we investigate not only multi-hop but additional propagation methods, like single hop and limited multi-hop. We also examine the behavior of our protocols in more realistic settings assuming networks with non-uniform node distribution.

In [21], the authors investigate the cover time of a graph when performing a random walk that uses deterministic choice. Their proposed scheme selects randomly two nodes and then chooses deterministically to move to the least frequently visited of the two. Note that here, we also propose random walks that perform choices based on various network criteria. We also favor less frequently visited areas, but our method is purely randomized and was developed independently.

In [22], the authors propose a routing scheme suitable for networks with one mobile sink. The sink visits certain anchor points in the network area and remains still while collecting data at each one of them. The sink samples the global power consumption of all nodes while stationed in an anchor point. It uses this data to create power consumption profiles and calculate the optimal resting time at each anchor point. Another approach that optimizes the sink’s trajectory is presented in [23]. The authors assume a mobile sink that initially follows a linear trajectory and collects network information from the sensors. The sink uses this information to break its trajectory into separate line segments that are closer to the network nodes, thus minimizing the data propagation cost. Collecting network knowledge incurs a significant overhead on the sensor nodes. In contrast to these approaches our protocols don’t require global network knowledge and can adapt to changing network conditions using only local observation.

Section snippets

The model

Sensor networks are comprised of a vast number of ultra-small homogeneous sensor devices, which we also refer to as sensors (see also [2]). Each sensor is a fully-autonomous computing and communication device, characterized mainly by its available power supply (battery) and the energy cost of data transmission and by the (limited) processing capabilities and memory. Sensors (in our model here) do not move. The network area A is a flat square region of size D×D; this assumption can be easily

Protocols for data collection with sink mobility

There are many different approaches when considering the mobility pattern that the mobile sink should follow. Depending on the application scenario and the network size and conditions, different approaches may yield diverse results and affect drastically the achieved network performance. A coarse-grained categorization of mobility patterns will identify the following different approaches (see also [6]):

  • Random mobility. The movement of the sink is done in a random manner regarding the position

Experimental evaluation

We implement and evaluate the above discussed protocols in the network simulator platform ns-2 version 2.26. We also included for comparison a well known static multi-hop data propagation paradigm, the Directed Diffusion (see [3]). We considered different simulation setups for various network sizes, number of nodes and mobility parameters. We here present the results for the set of experiments that consider several network topologies. In particular, the size of the network area is 200m×200m and

Conclusions and future work

In this work, we investigate the impact of having a sink moving in the network area and collecting data. We have presented a collection of mobility patterns and data collection strategies that can be employed in applications where the sink is mobile (mostly related to ambient intelligence). Our experimental comparison demonstrates the relative advantages and disadvantages and that different trade-offs can be achieved by each approach. Our results show that for applications where time efficiency

Acknowledgements

This work has been partially supported by the IST Programme of the European Union under contract number IST-2005-15964 (AEOLUS), the Programme PYTHAGORAS under the European Social Fund (ESF) and Operational Program for Educational and Vocational Training II (EPEAEK II). Also, by the Programme PENED under contract number 03ED568, co-funded 75% by European Union – European Social Fund (ESF), 25% by Greek Government – Ministry of Development – General Secretariat of Research and Technology (GSRT),

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