Simulations of the Impact of Controlled Mobility for Routing Protocols

There are many challenges to face while designing wireless networks and protocols, such as obtaining a good throughput, minimizing data delay, and minimizing energy waste. In fact, most of the wireless networks are characterized by battery-equipped devices; thus the minimization of the energy consumption is a key factor. With the miniaturization of computing elements, we have seen many mobile devices appear in the market that can collaborate in an ad hoc fashion without requiring any previous infrastructure control. This gave birth to the concept of self-organization for wireless networks, which is intrinsecally tied to the capability of the nodes to move to different placements. In the last few years, the research community has become interested in the sinergic effect of mobility and wireless networks. Controlled mobility is a new concept for telecommunication research field and can be defined as a kind of mobility where mobile devices are introduced in the network and move to specified destinations with defined mobility patterns for specific objectives. In practice, controlled mobility is a new design dimension of the networks. The use of controlled mobility as a new design dimension to enhance performance of wireless networks represents a recent, innovative, and revolutionary concept. In fact, while opportunistic use of external mobility has been extensively investigated, the use of controlled mobility is largely unexplored. This new design dimension can effectively be used to improve system performance by allowing devices equipped with mobility support to reach favourable locations. Although many communication protocols for wireless networks have been proposed, to the best of our knowledge there is no routing protocol based on controlled mobility. In fact, in [1] authors consider jointly mobility and routing algorithms but the solution they propose is for Wireless Sensor Networks (WSNs) and only the base station is mobile. Among the main routing protocols proposed for Mobile Ad hoc NETworks (MANETs), we have the Ad hoc On Demand Distance Vector (AODV) routing [2, 3] and the Temporally Ordered Routing Algorithm (TORA) [4]. Both are examples of demand-driven algorithms that eliminate most of the overhead associated with table update in high-mobility scenarios. However, the path discovery phase incurs in high energy costs. On the other hand, our system is quasi-static, in the sense that the only mobility we consider is controlled mobility, which is used by nodes to reach specific locations; then, for energy efficiency matters, it is convenient to use a table-driven system. Another interesting routing protocol is [5], where the minimum metric paths are based on two different power metrics:

However, this routing algorithm does not take into account the mobility as a new design dimension. In this paper, we take the controlled mobility into account by investigating the performance of a wireless network, where all the devices are equipped with mobility unit. The idea is to use existing multihop routing protocol; specifically we consider the well-known routing algorithm AODV and achieve further improvements in terms of network performance as throughput, data delay, and energy spent per packet, by explicitly exploiting mobility capabilities of the wireless devices. Previous analytical results formulated in [6‐8] suggests that controlled mobility of nodes helps to improve network performance. Based on these results, we consider jointly controlled mobility and routing strategies. We perform simulations through a well-known simulation tool [9] to quantify the throughput, delay, and energy spent per packet compared with wireless network where AODV is used. The rest of the paper is organized as follows. Section 2 is the related works. We give some practical application example in Section 3. We give details about Routing Protocol based on Controlled Mobility in Section 4. Section 5 is related to simulation, and finally we conclude our work in Section 6.

In the recent past a lot of works studied the effects of mobility in the networks. Often, devices' mobility has been regarded as a negative fact that causes link break, disconnections, and so forth. From a certain moment it has been understood that mobility of nodes can potentially be used to improve perfomance of the network. Grossglauser and Tse [10] showed that mobility increases capacity of a network. Unfortunately, they did not take into account the delay in their work. The research community investigated throughly the delay-throughput trade-off and some interesting results have been obtained. In fact, Gamal et al. [11] determined the throughput-delay trade-off in a fixed and mobile ad hoc network. He showed that, for nodes, the following statement holds , where and are the delay and the throughput, respectively. For a network consisting of mobile nodes, he showed that the delay scales as , where is the velocity of the mobile nodes. Once the trade-off between delay and throughput has been characterized, some algorithms that attain the optimal delay for each throughput value have been proposed. Another model makes it possible to exploit the random waypoint mobility of some nodes, in order to design a routing algorithm that allows high throughput with low delays, where the delay depends on the nodes' mobility, while the throughput is independent of it [12]. De Moraes [13] showed that there is a trade-off among mobility, capacity, and delay in ad hoc networks. A first step in taking advantage of the possibilities that mobility introduces has been made by the research community when predictable mobility became an important research focus. In fact, researchers studied many specific network objectives, under a random mobility-based communication paradigm; nevertheless the mobility of the sinks, for example in military applications, is based on soldier or fire fighter movements, and thus, it is predictable, in substance. Generally, the existing research in wireless sensor networks considers sink movement based on random mobility. However, the trajectories of the sink, in many practical applications, can be determined in advance. Based on these considerations, Lee et al. [14] proposed a predictable mobility-based algorithm, which uses the existing dissemination protocols and it is based on the random mobility-based communication paradigm. He showed the improvements and the various advantages of using the predictable mobility-based communication paradigm as the energy consumption decreases and the network lifetime increases. Predictable mobility of nodes has also been exploited to help in packets delivering [15]. In this work, nodes routing tables are updated with link state and trajectory information, which are received from other nodes. The problem of routing related to the predictable mobility has also been analyzed by [16]. In this work, paths are created by the movements of nodes, which will deliver the message they are carrying when they find other suitable nodes. The space-time routing framework it proposed leverages the predictability of nodes motion. Controlled mobility has been a hot research topic of the robotics community for many years. It concerns the motion coordination of a group of robots for a common objective, typically the coverage of a geographical area. In [17], the authors consider the problem of deploying a mobile sensors network composed of a distributed collection of nodes equipped with locomotion capability. Such mobile nodes use their ability to move in order to maximize the area covered by the network. Their approach is based on a potential-field approach and nodes are treated as virtual particles, subject to virtual forces. The concept of controlled mobility is also used by [18] by considering a hybrid network with both static and mobile nodes, which fully exploits the movement capability of the sensors. In [1] authors consider jointly mobility and routing algorithms, but the solution they proposed is based on the base station as the only controlled mobile device. In this work we are interested to consider the mobility of devices in a controlled fashion along with the routing algorithm. Specifically, we base our proposal on the analytical results obtained in [6, 8] that show the potential advantages obtainable through controlled mobility. In [8], it was not possible to take into account all the constraints of a real routing algorithm and, for this reason, we implemented RPCM in a well-known simulation tool, ns2.

In this section we will give some practical applications of Controlled Mobility. There is an interesting real application of controlled mobility to reduce power consumption realized by Intel, that showed that a few motes equipped with 802.11 wireless capabilities can be added to a sensor network in order to act as wireless hubs [19]. Load balancing through controlled mobility in wireless sensor networks is studied by Luo and Hubaux in [1]. The nodes closest to the base station are the bottleneck in the forwarding of data. A base station, which moves according to an arbitrary trajectory, continuously changes the closest nodes and solves the problem. The authors find the best mobility pattern for the base station in order to ensure an even balance of network load on the nodes. Recently, Intel installed a small sensor network in a vineyard in Oregon and a second one in Northern California to monitor microclimates and Redwood canopies, respectively. In this context, the mobile sensors had to measure, share, and combine the collected data regarding temperature, humidity, and other factors. At the gateway, the data was interpreted and used to help avoid mold, frostbite, and other agricultural problems. The agricultural environment is just an example of how a sensor network can take advantage of mobile robot's capabilities for data gathering and interpretation. Furthermore, sensors often need to be recalibrated and a robot could act as a gateway to the sensor network and perform calibration tasks. An interesting application of sensor devices with mobile robots, related to coverage, is for people with disabilities [20, 21]. In fact, technical devices, such as mobile robots, can aid personal assistance. A mobile robot requires a sensing system in order to control the path of movement and the surrounding environment. The robot can be equipped with sensors for detecting distances and obstacles. Another work worth mentioning has been conducted by Kansal et al. in [22]. The authors do not present an algorithm for the optimization of some parameters; instead they propose Morph as a new vision of sensor networking, where controlled mobility is considered as an additional design dimension of the communication protocols. They argue that, in Morph, controlled mobility can be employed for the sustainability of the network, which consists in both alleviating the lack of resources and improving the network performance.

The network scenario we consider consists of all nodes able to move and control their movements. The communication strategy used in this work considers different paths for each pair source-destination nodes and the best path is selected to be used for data communication. The choice of the best path is based on a metric. Specifically, in this context we consider the path whose nodes have to travel the total minimum distance to reach the evenly spaced positions on the straight line between the source-destination pair. The same metric has been proposed in an optimization model in [8], where the model determines the placement that minimizes the total travelled distance of the sensor nodes. This kind of movement could be useful in all situations where a mobility too high can be dangerous (i.e., military applications such as minefields monitoring) or difficult because of a high presence of obstacles.

As we already said in the previous subsections, the optimization model including the minimization of the nodes' total travelled distances along with other possible metrics has been introduced in [8]. Unfortunately in that analytical work, many practical details could not be taken into consideration. For this reason, we chose to implement one of those possible metrics in a complete routing algorithm and simulate its behaviour in a well-known network simulator: ns2, in order to evaluate the realistic effects of controlled mobility in the routing process in comparison with the AODV protocol.

In this work we focused on both the novel concept of controlled mobility and the routing algorithms. The concept of controlled mobility has been introduced in some previous recent work, but it has only been considered from an analytical point of view or in a marginal fashion, such as only a mobile base station in the network. In this paper we focus on the controlled mobility as a new design dimension and we exploit it by implementing a new routing protocol based on controlled mobility. The most important aspect of this is related to the evaluation performance based on the usage of a well-known simulation tool, ns2. In fact, in previous works the analytical approach limited the use of controlled mobility while in this context, thanks to the simulator, we have been able to consider many realistic aspects of the network, while a routing protocol is implemented. Extensive simulations have been conducted and simulation results have shown how the new routing protocol outperforms a well-known routing algorithm, the AODV. Furthermore, results obtained suggest that other metrics can be easily realized and tested by simulation. In fact, as future works, we intend to study other optimization metrics such as the maximization of the network lifetime or the minimization of the average (or the maximum) distance travelled by nodes belonging to a path.