LNCS 4325 - Adaptive Geographically Bound Mobile ... - Springer Link

Adaptive Geographically Bound Mobile Agents K. Tei1,3 , Ch. Sommer3,5 , Y. Fukazawa1, S. Honiden3,2 , and P.-L. Garoche3,4 1 Waseda University, Japan The University of Tokyo, Japan National Institute of Informatics, Japan 4 IRIT – ENS Cachan, France 5 ETH Zurich, Switzerland 2

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Abstract. With the spread of mobile phones, the use of Mobile Adhoc NETworks (MANETs) for disaster recovery finally becomes feasible. Information retrieval from the catastrophic place is attended in an energy-efficient manner using the Geographically Bound Mobile Agent (GBMA) model. The GBMA, which is a mobile agent on MANETs that retrieves geographically bound data, migrates to remain in a designated region to maintain low energy consumption for data retrieval, and provides location based migration scheme to eliminate needless migration to reduce energy consumption. In the data retrieval using the GBMA model, survivability of the agent is important. In a MANET, a GBMA with retrieved data may be lost due to its host’s death. The lost of the agent causes re-execution of the retrieval process, which depraves energy efficiency. We propose migration strategies of the GBMA to improve its survivability. In the migration strategies, the selection of the next host node is parameterized by node location, speed, connectivity, and battery level. Moreover, in the strategies, multiple migration trigger policies are defined to escape from a dying node. We present the implementation of migration strategies and confirm the achievements with several simulations. This finally leads to the adaptive Geographically Bound Mobile Agent model, which consumes even less energy.

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Introduction

Catastrophes, be they natural, like tsunamis or cyclones, or human, like industrial accidents or terrorism acts, do not only hurt people, they also destroy (often completely) the infrastructure needed by the rescue team. In case of huge fires or cyclones even satellites cannot give any information about the area inside the disaster zone. However, information sources inside the zone might still be intact and could give precious data. Means to access these information sources need to be investigated in order to retrieve their content in crisis situations. Even after a major catastrophe, when communication infrastructure might be down, many communication devices (like cell phones or PDAs) would still be functioning. The authority managing rescue should get access to these devices to be capable of offering communication solutions into the zone. This hypothesis is not unreasonable as rescue operations are often coordinated by government or military J. Cao et al. (Eds.): MSN 2006, LNCS 4325, pp. 353–364, 2006. c Springer-Verlag Berlin Heidelberg 2006

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organizations, which could have the authority to switch communication devices into ad-hoc mode. Since most of them can be programmed to function as hosts of an ad-hoc network, assuming that this switch could be triggered by sending a specific message is reasonable as well. This provides a Mobile Ad-hoc NETwork (MANET) [14], ready to support emergency communication. In mobile hosts, the energy available has to be handled with care, especially in the post-disaster scenario. Tei et al. [11,12,13] proposed to use a mobile agent model that gathers and aggregates location-specific data from the catastrophic zone in an energy-efficient manner instead of querying every resource separately. This paper addresses several improvements of the model to improve energyefficiency. In particular, its major contributions are: – We define a new mobile agent model: the adaptive Geographically Bound Mobile Agent moves according to its geographical location and the dynamics of the MANET topology. – We propose a more realistic approach to deal with post-disaster scenarios, and thus, with specific properties of associated MANETs. – This adaptive mobile agent model is simulated in several scenarios and outperforms the standard GBMA model. A short overview about the GBMA model is given in Section 2. The improvements with implementation are presented in Section 3, affirmed by the simulation results in Section 4, and concluded in Section 5.

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Related Work

In the MANET, hosts are moving around freely and their directions and speeds can hardly be predicted (especially in the post-disaster scenario considered in this work). Research by Aschenbruck et al. [1,2,3] addresses the modeling of real moving habits after a disaster. They build their model by the use of real data from firemen. However, it is still not clear how to simulate such movements appropriately. Johansson et al. [9] propose a mobility model for the post-disaster scenario. Reducing the amount of data transferred in a MANET is one way to improve the energy-efficiency. The in-network aggregation used in [5,10] eliminates redundant data or aggregates data by intermediate nodes between a data source and its data sinks. The in-network aggregation is done according to data reduction code statically deployed in the intermediate nodes. Therefore, the data reduction operators in these works are very simple, such as calculating the maximum, average, or summation. Application-specific data reduction can further improve this approach. The mobile agent model [16] provides the means to deploy application-specific code dynamically. The mobile agent [16] is a software entity that can migrate independently among hosts in a mobile network in order to complete the task assigned by the remote observer. It migrates based on its own needs and choices. Because of the mobility, the new computing model reduces network load, enhances communication efficiency, and adapts dynamically to the changing network environment

Adaptive Geographically Bound Mobile Agents

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in distributed or mobile computing. It migrates with the application-specific code and state to continue its task after migration. In a context of data retrieval of mobile agents in a MANET there are three kinds of cost: data retrieval cost, migration cost, and software execution cost. The data retrieval cost is the total amount of energy consumed for communication to retrieve the data. Intuitively, it depends on the distance between the mobile agent’s host node and data sources. The migration cost is the total energy consumption for transfer of agent program codes and its execution state to migrate the agent. It depends on migration frequency and the amount of data that the agent has collected already. The software execution cost is the total amount of energy consumed during computations of agent execution. The software execution cost is relatively small compared with communication cost such as the data retrieval cost and the migration cost [8]. In this paper, we focus on the data retrieval cost and the migration cost. Using a mobile agent model for data retrieval from a sensor network was used in [7,15,17]. Agilla [7] is a mobile agent middleware for sensor networks and realizes dynamic injection of data aggregation code to reduce data retrieval cost. Wu et al. [17] proposed the computation of the optimal route for a mobile agent in a sensor network. The route computation utilizes signal strengths of nodes to maintain low energy consumption for the agent migration. These works only support proactive migration to retrieve information from various data sources, but they do not support reactive migration to maintain low data retrieval cost for each data source. Without reactive migration, the agents cannot maintain low data retrieval cost while its data retrieval, due to the node mobility. Tseng et al. [15] proposed a location tracking protocol with a mobile agent in a sensor network. The agent migrates among sensors to stay near the moving target. It can adapt its location in response to the change of the target location, but it does not consider the migration cost. As we aim to retrieve information from a distant area, computations performed close to the information host can reduce the amount of data to transfer and the data retrieval cost significantly, allowing a longer life time to mobile nodes of the MANET. Moreover, the computations should stay near the data sources if all nodes move freely. In [11,12,13], Tei et al. introduce the GBMA model that aggregates the data retrieved from different areas and supports reactive migration in response to node-mobility. The GBMA gathers and aggregates location-specific data from the catastrophic zone in an energy-efficient manner instead of querying every resource separately by flooding the network. The observer defines the target zone as the geographic region he is interested in. The GBMA then retrieves the location-specific data from nodes in the target zone. Intuitively, the GBMA migrates to remain near data sources to maintain the low data retrieval cost. Figure 1 shows an example of the behavior of the GBMA. As an important part, they proposed a migration scheme by defining the expected zone to eliminate needless migrations. The expected zone uses a rectangular geographic region as a trigger for migration, which is adjusted dynamically

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Fig. 1. Example of the GBMA behavior

using the host node’s speed. When the node speed is quite low, the migration frequency of the GBMA will hardly increase if the expected zone is narrow, but when the node speed is high, the optimal expected zone will become wide. However, the GBMA model does not consider a possible loss of the agent. Therefore, its cost will increase in a MANET where nodes are down due to battery lost, because its survivability will deprave. In this paper, we propose migration strategies to improve the agent’s survivability.

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The Migration Strategy

In this section we develop the migration policy of the adaptive Geographically Bound Mobile Agent (aGBMA) model to improve its survivability. The survivability is an important factor to reduce the total energy consumption, because the observer has to launch the data retrieval again if an agent is lost. Of course, the relaunch consumes a lot of energy. Therefore, the improvement of the survivability highly improves the energy efficiency. We first introduce the target zone center c, set initially by the observer1. It is assumed that data sources are concentrated at several points not known at first. Such data sources could be part of a sensor network like fire door sensors or smoke detectors, or they could represent a security room with all camera information. Thus, we propose to adapt the virtual center’s position according to the amount of data received from different information sources. The change of the center might result in a migration of the agent (cf. Figure 2). Hosts are able to determine their location s and the agent can compute the distance δ to the target zone’s center c. The movement v of a host can be determined easily using two snapshots of its location for time t and t , namely s and s . Furthermore, factors like connectivity (the number of reachable hosts ν) and power (expected remaining battery β after task) are relevant, because in the 1

This can be done automatically using the coordinates of the target zone.


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Fig. 2. Moving the center towards data sources

worst-case, the agent process has to be restarted and recomputed; this results in high additional energy consumption (considering the post-disaster scenario, loss of time and information might be even more disastrous). The migration policy consists of two main mechanisms: the node selection to define the target of the migration and the migration triggers defining the conditions to migrate. 3.1

Node Selection

In order to choose the host as the best node available, in the aGBMA model, a quality value Q is defined for each mobile host node h. This value depends on four parameters (explained in detail afterwards): the escape speed ρ, the distance δ to the data center, the connectivity ν and the battery power β. ω

Q(h) =

βh β · νhων ω δhωδ · ρh ρ

The weights ωβ , ων , ωδ and ωρ can be chosen according to the scenario. Data center. The aim of the agent is to collect data in the target zone. The initial work relies on the assumption that the center of the target zone is the most important point. Therefore, migration triggers depended on the distance to this center. The new approach takes care of the real position of the data inside the target zone. In the current model, the data can be retrieved by nodes of the MANET at some fixed locations of the target zone. When the aGBMA arrives in the target zone, its knowledge is the geographical center of the zone only. This is taken as the first virtual center. When receiving data messages, the agent discovers data sources and updates its approximation of the virtual center position. The virtual center is defined as the barycenter of the data sources according to the amount of data received . Its position is determined by approximating positions of data sources and by using the amount of real data received from each location. This real data is evaluated by removing redundant data 2 . This 2

Redundant data is defined as data that came from the same source and was given to the first MANET node within some time interval.

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permits to deal with the case when a data source has many neighbors and a large amount of redundant data is received by the agent. The position of each data source itself is evaluated according to the total amount of data received, including (sic!) redundancy. This allows to increase the accuracy of the approximation of the data source positions. The source’s identifier as well as information about the node that first received the message (receiving time and current position) is included as additional information into data messages. Computing the barycenter out of received data allows to give an approximation of the location for each data source. Escape speed. The node movement speeds are important factors influencing its quality. A fast node might soon leave the zone. We do not directly use the node’s speed but rather its escape speed ρ from the center c, because a fast node running around the center is not leaving the zone and thus, remains a good candidate for being a host node. The escape speed is the projection of the speed vector on the axis from the center to the node location (cf. Figure 3). Each node periodically determines its location s and stores it together with its previous one s. The aGBMA retrieves these two location tuples from nodes and calculates their escape speed.

Fig. 3. Escape speed ρ

Connectivity and Battery power. Connectivity ν is the number of one-hop neighbors and indicates the likelihood of isolation. Each node periodically sends “hello” messages to one-hop neighbors and counts the nodes around. Battery β is the remaining amount of node energy and indicates the likelihood of battery exhaustion. The aGBMA retrieves ν and β from surrounding nodes. 3.2

Migration Triggers

We propose three migration triggers that will be evaluated in the simulation section.


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Extension of the Expected Zone evaluation. The migration policy of the earlier GBMA model relies on the distance to the center of the target zone, the threshold was determined by both a coefficient α and the speed of the host. The extension of this trigger uses α · ρ, considering that only the escape speed ρ matters. Quality threshold. This trigger relies on the current quality Qc of the host node. Periodically, the agent checks its quality and stays with a certain probability if Qc ≤ Qi /qt , where Qi denotes the quality of the node when the agent arrived and qt denotes a threshold value, typically 2. Both. This trigger conjugates the two preceding propositions. The agent migrates with a certain probability if its host has a bad quality but it moves as soon as the host leaves the expected zone according to the new criteria.

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Simulation

In this section we compare the performance of the aGBMA model with the earlier GBMA model and a direct P2P approach. Though more sophisticated, the implementation of the aGBMA has almost the same size as the GBMA (about 30 kB). Experiments were computed using the simulator implemented on the Scalable Wireless Ad-hoc Network Simulator (SWANS) [4]. The weights ωβ = 0.6, ων = 0.2, ωδ = 1.5 and ωρ = 1.5 allow to tune the quality threshold and were chosen according to the scenario and to simulation during pre-experiments. We also evaluated the performance of the three different types of migration triggers. In these simulations, the 142 nodes are initially distributed on a grid covering 1000 m × 1000 m described by the coordinates (0, 0) and (1000, 1000). Each node is equipped with both – an IEEE 802.11b wireless device allowing to communicate with other nodes within its range and – a GPS receiver with which the location can be determined. This last assumption could be relaxed considering a relative location as sufficient to determine if the node is going away from the center. This information could be obtained from: – another node equipped with a GPS receiver; – a data source with location information (like a static sensor network node); – other nodes with relative position information. Such solutions are not considered here for the sake of brevity. We implemented, in the SWANS framework, a mobility model slightly extending the mobility model of a disaster area described in [9], to represent a simple but more realistic mobility model according to our post-disaster scenario. We introduce two kinds of nodes: walking nodes (speed: 1-10 m/10 sec) and running nodes (speed: 1-20 m/10 sec). Observe that the fast nodes in our mobility

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Fig. 4. Three scenarios

model are running victims and not rescue staff with cars as in the post-disaster mobility model of [9]. The agent is able to migrate between these groups. The battery level of each node is chosen randomly between 0 and 10 W. 10 W is enough energy to run the complete data retrieval in this simulation. All nodes consume energy according to the energy consumption model described in [6], i.e. when they send or transmit data messages, or when they receive data from information sources. The simulations were run for three different scenarios (cf. Figure 4): – In the first, 36 data sources are placed homogeneously on a grid. They represent a regular fixed sensor network composed of exit doors or movement detectors. Such sensors deliver a small amount of information within a short range but are highly autonomous. This kind of sensor can be used for one week with its own battery. – The second scenario uses the same kind of data sources but placed randomly in the target zone. – The third is built upon the second. We add a special source that delivers a lot of data but has only limited autonomy; for example the security room of the building. It contains a lot of information but is not able deliver it for hours without power supply. In these experiments, the normal sensors provide 10 kB of data to nodes located within 6 m and for an infinite number of times during the simulation. The special source provides 500 kB of data to nodes located within 20 m but at most six times. The target zone is a square region defined by the coordinates (650, 650) and (750, 750). The observer is located at (200, 200) and does not move. Both agents, GBMA and aGBMA, are located at the observer node initially and migrate to a node in the target zone. After one hour, the observer sends result queries to its agent and receives the results. The expected zone parameter α used for the GBMA and the aGBMA with the corresponding triggers is 250, which is optimal, as reported in [11]. Based on these three scenarios, we evaluated the performance of the GBMA model, and that of the aGBMA model with the quality migration trigger (aGBMA-Q), the expected zone migration trigger (aGBMA-E), and with both triggers (aGBMA-B). In the case of the aGBMA model, all nodes periodically


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Table 1. Results of scenario 1 to 3 Energy consumption (W) Scenario 1 2 3 P2P 289 287 351 GBMA 48.4 48.8 118 aGBMA-Q 92.1 88.1 154.1 aGBMA-E 89.9 88.6 145.3 aGBMA-B 93.4 94.3 167.6 aGBMA-Q - ν 49.1 48 127 aGBMA-E - ν 58.2 50.8 115.4 aGBMA-B - ν 53.6 53.5 130.1

Amount of information 1 2 3 5.44 5.22 5.08 4.25 4.58 1.87 4.64 4.51 2.91 4.84 4.44 3.26 4.53 4.94 3.21 4.35 4.82 2.82 4.98 4.51 2.67 3.85 4.34 2.83

Number of migrations 1 2 3 N. A. 2.2 2.15 1.77 5.91 5.78 5.84 2.78 2.56 2.72 6.34 6.43 6 7.2 6.39 6.41 2.44 2.4 2.64 7.6 7.04 7.68

Number of agent loss 1 2 3 N. A. 6.25 10.4 29.8 2.22 2.22 15.6 4.44 2.33 28.3 2.22 2.13 18.8 0 0 6.82 2.08 2.33 16.7 2.13 2.17 12.5

(every 30 sec) send a “hello” message to count their neighbors, but that might be too costly. Therefore, we also evaluated the performance of the aGBMA model without using the connectivity argument ν. We run 50 simulations in each case.

Fig. 5. The rate of agent loss

In these experiments, we compared the aGBMA models and the GBMA model considering energy efficiency and survivability. Table 1 shows the total energy consumption, the total amount of information retrieved and the number of migrations in each scenario. An agent can be lost if its current host node is down due to battery exhaustion. We adopt the percentage of agents lost while data retrieval due to battery exhaustion of its host node as a metric for survivability of agents. Figure 5 shows the percentage of lost agents. Moreover, from the point of energy efficiency, we introduce the amount of energy consumed per retrieved information as a metric for energy-efficiency. Figure 6 shows the amount of energy consumed per retrieved information. In all scenarios, the overhead of using ν is not negligible. Even in the third scenario with one big data source, using ν is relatively expensive. The aGBMA model without ν provides more survivability. This can be explained by the

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Fig. 6. Energy consumption per amount of received information

Fig. 7. Migration rate

“hello” message’s overhead. We therefore consider in the following the migration triggers without ν. Comparison to the GBMA model. The loss rate of the GBMA is bigger than that of the aGBMA, particularly in the third scenario, because the GBMA does not use the battery level neither as node selection parameter nor as migration trigger, whereas the aGBMA uses the battery level at least as node selection parameter. When network load is high, taking care of the battery improves the survivability significantly. From the energy efficiency point of view, considering the third scenario, the aGBMA consumes less energy than the GBMA, because the aGBMA selects a host node to maintain low data retrieval cost using ρ and δ. The GBMA does not care about the location of data sources and the amount of data, it only selects its host node according to its position from the geographical center and cannot maintain low data retrieval cost in a heterogeneous situation. The aGBMA uses the new virtual center for data retrieval determined by the approximated location of data sources and their amount of information and thus adapts well to heterogeneous situations. Compared to the basic P2P approach, the strength of the GBMA and the aGBMA concepts is obvious at first sight. Comparing migration triggers. Among the different aGBMA models, the aGBMA-Q and the aGBMA-B provide better survivability than the aGBMA-E, because they use the battery level as migration trigger. In scenario 1 and 2, the aGBMA-Q provides best energy efficiency because it is hardly lost. It remains to investigate about the characteristics of scenarios 1 and 2 because the simulation results encountered are quite similar. In scenario 3 however, the agent migration cost is more expensive, according to the increased amount of received data. The aGBMA-Q migrates more frequently than the aGBMA-E (cf. Figure 7). Therefore, in scenario 3, the aGBMA-Q provides worse energy efficiency than the aGBMA-E (though, the parameter of the aGBMA-Q is not optimized). The aGBMA-B provides the worst energy efficiency among the three. With its two migration triggers, it migrates quite frequently. This leads to a non-negligible overhead. Thus, using both migration triggers is not efficient.


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Conclusion

The Geographically Bound Mobile Agent was improved by using a far more adaptive migration mechanism. Node speed and movement direction was taken into account, as well as the definition of a virtual data center. Furthermore, a sophisticated node selection strategy prevents from choosing a poor node. Considering the remaining energy in a node’s battery has resulted in a higher survivability of the mobile agent and the overall energy efficiency shows to be better than using the classical GBMA model. The quality value is useful for decisions. Even a not optimal aGBMA model (parameters were selected according to pre-experiments) outperformed the classical but optimized GBMA model considering energy-efficiency. Our future research plans are twofold: in addition to simulations we aim to further improve the aGBMA model as follows. – At the moment, the aGBMA performs many unnecessary migrations. We want to avoid these migrations using optimal parameters, which are to be defined. – We also plan to consider node isolation (in the post-disaster scenario, a node can be separated from the other nodes of the MANET with high probability). The neighborhood value ν has shown to be too costly to compute compared to its use; we aim to use more sophisticated algorithms for this problem. – Furthermore, migration cost depends on the size of the agent, the migration probability should therefore depend on the amount of data retrieved so far. – To address the post-disaster scenario adequately, the mobile agent might send major chunks of data back to its observer (if the position is known) in order to provide valuable information as fast as possible. We have to find the optimal size of such data chunks without loosing the efficiency of the mobile agent approach. – Moreover, if the area is quite large, multiple cooperating agents could be deployed. Besides these optimizations, the post-disaster mobility model used is far from realistic. We slightly extended the random walk model by simulating two kinds of nodes, but the mobility model needs further improvements including but not limited to repulsion points (for example a fire in real world), group interactions and node disappearance.

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