Energy Saving and Connectivity Tradeoff by Adaptative Transmission Range in g MANETs

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1 Energy Saving and Connectivity Tradeoff by Adaptative Transmission Range in 80.11g MANETs Fatiha Djemili Tolba University of Haute Alsace, Colmar, France Damien Magoni ULP LSIIT Illkirch, France Pascal Lorenz University of Haute Alsace, Colmar, France Abstract Power conservation is a crucial problem in mobile ad hoc wireless networks knowing that, each mobile node has a limited amount of energy concentrated in a battery. The main objective of our paper is to use a variable transmission range in order to save some energy and to keep the connectivity of the network. Our algorithm is implemented at the data link layer of the OSI model and thus can be used by all MANET routing algorithms such as AOVD and DYMO. Simulation experiments were conducted to evaluate the performance of our algorithm in terms of energy used and connectivity. We show that our algorithm has an energy gain between 35% to 85% at reasonable speeds below 0 m/s and a high enough network density. Keyword: mobile ad hoc networks, IEEE 80.11, transmission power, transmission range, connectivity. 1. Introduction Today there is a widespread utilization of Mobile Ad hoc Networks (MANET) in communications. MANET is an autonomous system of mobiles nodes connected by wireless links. The nodes can act as both hosts and routers since they can generate as well as forward packets. These nodes are also free to move and organize themselves into a network. MANET does not require any fixed infrastructure (i.e. a wired or a fixed wireless base station). The principal characteristics of MANET are the dynamic topology and the limited energy of mobiles nodes. The interest in such network architecture is focused on battlefield and military applications such as voice and video communications, and also for disaster relief situations. Ad hoc networks are usually modeled by unit graphs, where two nodes are connected if and only if they are in the same transmission range. This last range determines the range over which the signal can be coherently received, and is therefore crucial in determining the performance of the network such as delay and energy consumption. In such network, each node is characterized by a well defined quantity of energy. The source of this energy is a battery implemented in each node. If the battery is discharged the node can not receive or send any packet. So, it is necessary to control the transmission range for both minimizing energy consumption and extending battery life. To seek the best value of transmission range that preserves connectivity and conserves the energy is an important problem for network functionality. A large value of transmission range cause consumption of increased energy of the node but preserves the connectivity, while smaller value causes preserve of energy but can adversely impact the connectivity of the network by reducing the number of active links and, potentially, partitioning the network [1], []. For this, a value should be found which makes the compromise between the connectivity and the consumption of energy. Several papers treat the minimization of transmission range and power control. The first category of paper aims to find an optimal transmission range in order to control the connectivity [3], [4], [5], [6] or the power consumption [7]. The second category aims to find the shortest path with a power based metric using various parameters such as energy consumed per packet or the energy cost per packet [8][9]. Finally, the third category aims at modifying the MAC layer [10] [11]. In this paper, we propose a new protocol for controlling the transmission range in mobile ad hoc networks. The first objective of this protocol situated in the conservation of energy knowing that the node is powered only by a battery. The second objective situated in the preservation of connectivity between the

2 nodes knowing that the connectivity plays an important role in the route discovery. The novelty in our proposition lies in the possibility to exploit our protocol in all MANET routing algorithms i.e. our protocol is generic and completely distributed. The remainder of this paper is organized as follows. Section reviews some work in this area. Section 3 presents our contribution and details our proposed algorithm. Simulation results presented in section 4 demonstrate that the proposed algorithm is better in terms of energy conservation. Finally, we present our conclusion and we discuss future work of investigation.. Related work In the last decade a lot of researchers have contributed in the controlling of energy in MANET. Consequently, a several algorithms using transmission range have been proposed. An overview of these algorithms is presented as follows: In the first category Dai and Wu [3] proposed three algorithms, Prim s Minimum Spanning Tree, Prim s MST with Fibonacci heap implementation and the area-binary in order to find the minimum uniform transmission range that ensure network connectivity at same time. However, in these algorithms either each node has all information about the network or a specific node has the information about the MST and diffuses it. While, it is more interesting that each node has local information about its neighbors. In [4] Althaus and al. study the problem of transmission range in goal to minimize the power computation for ensure network connectivity. The authors give a minimum spanning tree (MST) based - approximation algorithm for Min-Power Symmetric Connectivity with Asymmetric Power Requirements. In the same problem Santi [5] proves that the Critical Transmission Range (CRT) in the mobile case is at least as large as the CRT in case uniformly distributed points. Narayanaswamy and al. [6] proposed a distributed protocol for power control and provided a conceptualization of this control. This algorithm aims to find the smallest common power (COMmon POWer) level at which the network is connected. In the same category, Elbatt et al. [7] proposed to use the notion of power management where the study of the impact of using different transmission powers on the average power consumption and end-to-end network throughput by controlling the degree of a node. However, this solution is includes in clustering algorithms and it is more suitable for a low mobility (1 to 5 m/s). Whereas our algorithm is more general and it is tested for a high mobility (1 to 80 m/s). In the second category, the power control in the routing in ad hoc networks is used by Kawadia and Kumar [8]. Each node runs several routing layer agents that correspond to different power levels. In this protocol each node along the packet route determines the lowest power routing table in which the destination is reachable. However, this protocol is more suitable for network with lowest mobility and the resultants of simulation are given only for a fixed density of nodes (i.e. number of nodes is invariable). In [9] Spyropoulos and Raghavendra proposed an energy-efficient algorithm for routing and scheduling in ad hoc network with nodes using directional antennas. The first step of this algorithm consists in finding the shortest cost paths, using the metric minimize energy consumed per packet. Next, find the maximum amount of time each link can be up, using the metric maximize network lifetime. In the end scheduled nodes transmissions by executing a series of maximum weight matching. However, since each node is assumed to have a single beam directional antenna, the sender and the receiver must redirect their antenna beam towards each other before transmission and reception can take place []. The idea to change MAC layer is presented in [10]. The authors proposed a power control schemes where the principle is to use two power levels to transmit each data packet: the maximum transmission power for RTS-CTS and the minimum transmit power for DATA-ACT. This work has been implemented using omni-directional antenna. Therefore, the scenario is completely changed when we use directional antenna to transmit and receive signals. Although, Saha and al.[11] propose to use two levels of transmission power using an antenna operating at omni-directional and directional mode. Their work helps to conserve the transmission power when the directional transmission is used. 3. Our contribution In this section, we introduce our contribution in which we give the basic idea. After this, we discuss the details of the algorithm. 3.1 Basic idea The main objective of our protocol is to propose a generic solution that can be used by various routing algorithms such as AOVD, DYMO, etc. Moreover, our solution aims at both preserving the energy and maintaining the connectivity of the mobile nodes. The

3 idea proposed in this paper is that each node uses variable values of transmission range according to the distance between itself and the other nodes. Our protocol is completely distributed and it takes into account some features such as transmission range and position of the node. In the following, we explain the choice of each feature. Transmission range: plays an important role in the communication between two nodes as mentioned previously. However, in the mobility model the nodes are free to move in or out the transmission range that return the precision of its value difficult. Moreover, a large value of transmission range accrues a consumption of the battery energy. In order to prolong the life span of this last it is necessary to choose the better value. In the other hand, the transmission range influenced on the connectivity of the node. For these reasons the transmission range is a paramount feature in our work. Position of the nodes: while we work in an environment where the nodes are mobiles, we must update the coordinates of the nodes every period of time. In our protocol, each node broadcasts its address which is registered by all its neighbors. It is assumed that a node receiving a broadcast from another node can estimate their mutual distance from the power level of the signal received. The Global Position System (GPS) can be another solution, but its disadvantage is the consumption of energy. Initially and for any topology for ad hoc networks, each node has the same value of transmission range (i.e. maximum). Of course, this range gives a maximum connectivity for the nodes (see figure 1). the same time (see figure 3). Note here that a smaller value of transmission range consumes less of energy. 4 Figure. Transmission range according to the connectivity Figure 3. Variation of transmission range 3. Description of the proposed algorithm Application layer Transport layer Network layer Data link layer Our protocol Tr max Physical layer Figure 4. Position of our protocol in the OSI layers. Figure 1.Network topology. After some time we notice that the node number 1 changes its transmission range in order to keep always the same number of neighbors (see figure ). In the same way, the node number 3 changes its transmission range after a time t. Applying the strategy of variation of transmission range these nodes (1 and 3) preserve their connectivity and use the minimum of energy in Initially, we note that our work is focused on level (the Data link layer) of the OSI layers (see figure 4). In the following we describe the proposed algorithm in mobile ad hoc networks. Before proceeding with the presentation of the various steps of the algorithm we describe the system model. We consider a network topology which is represented by a graph G = (V, E) where V is the set of mobile nodes ( V =m) and e = (u, v) E will model wireless link between a pair of node u and v only if they are within wireless range of

4 each other. The procedure consists of seven steps as described below: Step 1 Each node broadcast data packet with some information s about its address, position and time stamp. So, each node will have local information about their neighbors. Initially the transmission range Tr takes the value used by the 80.11g for a throughput of 54Mbps. Step Each node receives this packet, calculates the distance d between itself and its neighbors using the received information, as ( x x ) + ( y ) d = (1) 1 1 y x are the coordinates of sender and receiver node respectively. Step3 Recalculate the distance d 1 taking into account the speed of the node s max for the time Δ t in order to Where ( x 1, y 1 ) and ( ), y envisage the future position of the node. d1 = d + * smax * Δt () Step4 Calculate the necessary time for the packet arrived to the receiver. t = t current t stamp (3) Where t current and t stamp are the time current and the time stamp. Step 5 Compare the time t necessary for sending a packet with the time Δ t. If the time t is inferior or equal to the time-necessary of update all information, so add the sender to the list of neighbors of the receiver when this list is not saturated (inferior to the minimum number of neighbors). Step 6 If the list of neighbors is empty, so set transmission range to maximumtr max in order to have a maximum number of neighbors. Else, set transmission range to the farthest neighbors distance in order to reduce the transmission range by maintaining the sufficient number of neighbors. Step 7 In the final step, set power level P corresponding to the current transmission range. trans Note here that the update of data is carrying out every period Δ t. In the previous steps, we show in first that the transmission range is based on the distance between the receiver and the sender that allow economizing the battery energy. The fact that the node changes its transmission range according to its need (distance) the battery life span can be prolongs. The realization of this last is the heart of our work. In the other hand we find that fixed the same connectivity for all nodes allows both to form a graphs related and do not charge the node. This facilitates the communication between the nodes. 4. Simulation 4.1 Simulation topology The performance evaluation of our algorithm is made via simulation using the Network Simulator (NS- ). We consider a network of n nodes. The nodes are uniformly distributed and moved by using the random waypoint mobility model [1]. The nodes move in all possible directions with speed varying between 1 m/s to a maximum value (see table 1). Parameters Value Number of nodes 10, 0 and 40 Area 1000 x 1000 m Minimum reception power -70 dbm Maximum transmission power 18 dbm Minimum connectivity 16 Pause time 0 s Δ t s Maximum speed of the nodes 5 80 m/s Table 1. Parameters used in simulations. 4. Performance evaluation In these results, each simulation experiment is given for three different node density for networks. Moreover, these results are obtained with a confidence level equal to 0.95 and a maximum error threshold equal to 5%. We measured the following characteristics: - The energy used for various node density and speed. - The connectivity factor for various node density and speed. Figure 5 shows the speed nodes according to the ratio between the quantity of energy used and the maximum energy used. The energy maximum used in our simulation is energy spends if we use a fixed transmission range (that is g). We observe that the quantity of used energy increases with the increases of the speed of the nodes. This is due to the fact, that

5 the moving speed needs more power as distances vary more. We conclude that the lower speeds save more energy. Figure 7 shows the relationship between the energy used and the connectivity factor. We observe that in the mathematic terms it exists a proportional relation i.e. the used energy increases with the increase of the connectivity factor whatever the value of speed or the value of nodes number. Therefore, in reality the relation between these parameters is absolutely the opposite. This is due to the increase of connectivity factor that requires an augmentation of the energy used that must be economized as possible. For this reason, it is necessary to find a compromise between these two features. Figure 5. Energy used vs maximum node speed The figure below shows the node speed according to the ratio between the connectivity factor and the maximum connectivity factor. The connectivity factor is equal to the inverse of the number of connected components in the network (i.e related graph). Concerning the maximum connectivity factor is chosen when used 80.11g. We observe that the connectivity factor increases with the increase of node speed. This due to the fact that when the node moves quickly it risks to loss their neighbors (i.e. the node can leave its transmission range). Moreover, we observe that if the connectivity factor bring closer to 1 the nodes can better communicate in the network. Figure 6. Connectivity factor vs maximum node speed Figure 7. Energy used vs connectivity factor 5. Conclusions and future research In this paper we presented a new approach to controlling the energy used in ad hoc networks. This approach is based on the variation of the transmission range. The transmission range varied according to the position of the node in the network. So, if the node has no neighbors, the transmission range takes the maximum value. Therefore, if the node has a sufficient number of neighbors (i.e. minimum connectivity) the transmission range takes the value of the distance between a sender and a receiver. The proposed algorithm is simulated in NS- environment. Simulation results show that this approach improves the conservation energy between 35% and 85% when the density of the network exceeds 0 nodes and the speed node is below 0m/s. Contrary to the energy conservation when we use a fixed transmission range that is g. In order to continue our investigation in this track, we will exploit this approach in a routing algorithm.

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