Improving lifetime and availability for ad hoc networks to emergency and rescue scenarios

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1 Improving lifetime and availability for ad hoc networks to emergency and rescue scenarios Rommel Torres, Francisco A. Sandoval, Liliana Enciso, and Samanta Cueva Departamento de Ciencias de la Computación y Electrónica Universidad Técnica Particular de Loja {rovitor,fasandoval,lenciso,spcueva}@utpl.edu.ec Abstract. A mobile ad hoc network is a temporary, self-configuring network composed of nodes that communicate through wireless interfaces that can change their geographic location randomly. It is ideal for emergency and rescue scenarios in which an immediate action is necessary and when it may be the first form of communication available. Therefore, it is necessary for the network to be active as long as possible. Additionally, the communication between network nodes must be efficient. The present research demonstrates, E, a proactive, hierarchical routing protocol in which, by using the association and support strategies of the main nodes, the performance of the mobile ad hoc network improves and its lifetime is extended compared with. Keywords: Ad Hoc networks, rescue and emergency scenarios, Ad Hoc routing protocols, hierarchical routing protocols 1 Introduction Ad hoc MANET (Mobile Ad Hoc Networks) mobile networks are temporary and self-configurable networks. The nodes of such a network are its sources, destinations, and bridges for information. It has finite resources (bandwidth, battery, and processing) that must be well-used to improve the performance of the entire network. In this type of network, it is important to maximize the availability, speed, and state of the network as a whole. For example, if a node has insufficient battery or its processor is saturated, to avoid compromising the availability of the entire network, its participation must be limited to acting as a router in the exchange of information [1]. A MANET network is ideal in emergency and rescue scenarios where there is a need for the immediate implementation of a communications network that will be able to promptly support rescue actions and response to emergency incidents. The operation, lifetime, and availability of an ad hoc network depend on the nodes operating time. Using this logic, we propose a routing protocol called the ad hoc Enhanced Backup Cluster Head Protocol (E). This protocol is based on and presents an improvement to [1]. It uses association and hierarchy This work has been supported in part by SENESCYT (Ecuador).

2 2 Rommel Torres, Francisco A. Sandoval, Liliana Enciso, and Samanta Cueva routing techniques and support cluster heads to improve network availability. Unlike its predecessor, E uses a proactive approach as a function of the nodes power to improve the network lifetime in the process of cluster maintenance. This research is organized as follows. Section 2 describes the related work. Section 3 reports the specifications of the proposed protocol (E). Section 4 validates the two objectives of this research (i.e., network lifetime and availability). Finally, the conclusions present details of the advantages of our proposal with respect to its predecessor. 2 Hierarchical Routing The mobile Ad Hoc networks have some challenges that must be taken into account for the information exchange. First, the nodes that make up the network have the ability to autonomously change their geographic position, and each node can move at its own speed and direction. Second, they depend on a power source for their operation. Additionally, network nodes are heterogeneous, and each node has its own software and hardware characteristics and capabilities. Some strategies for dealing with the challenges related to this heterogeneity have been detailed previously [2]. For the purposes of our study, we will assume that all nodes have characteristics and sufficient capacity to support the requirements of E and that they function under similar conditions to initialize their activity in the network. The hierarchical routing protocols divide the network into subsets [3] of nodes called clusters, in which a cluster head (CH) is used to concentrate and distribute the information generated within the cluster. Hierarchical routing enhances network availability because it limits the broadcasting of messages within the cluster. Figure 1 depicts the components of our proposed hierarchical routing protocol. Fig. 1: Model proposed for the MANET routing protocol Cluster head Backup cluster head Gateway node Source node Destination node Managed node C1 BCH CH GN C3 GN BCH CH GN C2 CH BCH GN C4 GN GN BCH CH

3 Enhanced backup cluster head protocol 3 Several studies [4] identify and group hierarchical routing protocols, these center on the choice of CH and the maintenance of the clusters. and others research [5][6] use a reactive approach for cluster maintenance. Here, the backup cluster head (BCH) takes the cluster responsibility when a problem with the CH exists. Therefore, there is a time interval in which the cluster is without the CH; thus, the network loses convergence and, as a result, its availability is reduced. This research focuses on improving the cluster maintenance process of an ad hoc network using a proactive approach to achieve the greatest possible availability for the communication processes. This research also improves the network lifetime by allowing the CH to pass its responsibility to the BCH prior to being without power. We use a residual energy level like a threshold to switch the BCH-CH responsibility. Other authors [7][8][9] use the energy level for CH selection exclusively. A previous study [1] demonstrates that enhances the network s availability compared to CBRP and AODV; however it does not increase its lifetime. To solve this problem, we propose E, which we detail below. 3 Enhanced Backup Cluster Head Protocol The main difference between the E and the BHCP is the improved availability and lifetime of the network via the inclusion of a proactive procedure between the CH and BCH nodes. Both the E and share the same cluster creation strategy, CH selection, and the same components and interfaces. They differ specifically in the process of cluster maintenance. Node initialization At the start of node initialization, the nodes do not belong to any cluster; each node obtains its metrics as a function of velocity, location within the cluster, and battery power status characteristics. This metric is sent in conjunction with the UNDECIDED state by sending HELLO broadcasting messages to its neighbors. Cluster formation In the event that the node is inactive and receives no HELLO message from any neighbor, it will remain in the UNDECIDED state. If a node receives several HELLO messages from its neighbors, it proceeds to update its table of neighbors, including each neighboring node ID, link type, metric, and state. Each node reviews its neighbor table, and the node that has the best metric becomes the CH, the one with the second lowest metric becomes BCH, and the remaining nodes change to the state. Discovery of the adjacent cluster The CH exchanges HELLO cluster messages via the gateway node, GN, to build an adjacent cluster table with the information of the CH and the GN that allows for communication. Cluster maintenance To maintain the cluster hierarchy and improve the network behavior, the CH and the BCH exchange messages periodically, the BCH determines the CH en-

4 4 Rommel Torres, Francisco A. Sandoval, Liliana Enciso, and Samanta Cueva ergy residual level Q when the CH energy residual level is less than the threshold level T defined for the change. The CH becomes the and informs the BCH to become the new CH and proceeded to select the new BCH. This behavior is defined in the respective algorithms. Additional procedures that are displayed are inherited from the and define the behavior of the possible operation scenarios of the E. When the CH is not available, the BCH takes its place and a new BCH is chosen according to the second best metric. When the BCH is not available, the CH broadcasts to the neighbors for the selection of a new BCH. When an ceases to receive updates from the CH, it means that it left the cluster and changed its state to UNDECIDED. When joining multiple clusters, a period of contention is expected. It then proceeds to choose from the CHs of each cluster and its metric, the new CH, the BCH, and the remaining nodes will become the. When a new node with the UNDECIDED state appears, it receives a HELLO message from the CH and depending on its metric decides whether to change state to BCH or. In an Ad hoc network, each node has batteries with different capacities. A node can have battery capacity n times greater than the battery capacity of another node. In this study we assume that all nodes have equal battery capacity. Therefore, we define the threshold T to be expressed as a percentage of use. 4 Validation of the proposal 4.1 Network lifetime validation A network is modeled by a directed graph G = (N, L) where N denotes the set of nodes {1, 2,..., n} (each node represents a wireless device). L (edges) denotes the set of wireless links between the nodes. Therefore, a node j within the broadcasting range of node i is connected to i by a directional link expressed by (i, j). All nodes broadcast at a constant power level. Indeed, each node is assumed to have an initial power E i (E i >, i N), and the energy required by node i to broadcast a unit of information is expressed by e i. For low-power devices that operate in the 2.4 GHz ISM band, the broadcasting and receiving currents are similar [1]. Therefore, it is assumed that energy is consumed when a node broadcasts (alternatively, the power consumed when it receives information can be included in e i. However, because the power necessary to receive a message is not negligible, it can be assumed that even though a few nodes are capable of receiving the same message, only the node that is intended to receive the complete message and resend it is considered. It is assumed that all other nodes are in standby (sleep mode) or communicating with some of its other neighbors. Let F ij be the average flow for a link (i, j) (F ij, (i, j) L), and f ij (flow rate) the relation between F ij and the maximum flow 1 possible in a nodes connection gateway ( f ij < 1). 1 For example, in IEEE the maximum data rate (maximum flow possible) is 2, 4 or 25 Kb/s) is 2, 4 o 25 Kb/s.

5 Enhanced backup cluster head protocol 5 Definition 1: The lifetime of node i for a given flow, represented by T i, is given by [11]: T i = e i E i j Z(i) f ij (1) Where Z(i) denotes the set of neighboring nodes i. Definition 2: The network real lifetime for a given flow is the time until the first battery is discharged (i.e., the minimum lifetime on all nodes). This is expressed by T [11]: T = min T i = min i N i N e i E i j Z(i) f ij (2) The issue is to maximize the network lifetime taking into account an Ad Hoc network that uses both the and E protocols. Basic protocol model (Backup Cluster Head Protocol) A basic protocol model for the is presented under the following assumptions: A single cluster is considered (i.e., all existing nodes are neighbors). All nodes have the same initial energy (E i = K and i {1, 2,..., N}, where K is a constant). All nodes require the same power to broadcast a unit of information (i.e., e i = k and i {1, 2,..., N}, where k is a constant). According to the established assumptions and considering definition 1, we can conclude that the node lifetime i decreases only with the relation between the medium flow rate for a link F ij and the maximum flow possible in a connection, f ij. According to definition 2, the network lifetime depends on the first node to be completely discharged; therefore, it is necessary to evaluate the node that consumes the most battery and, from all of the above mentioned information, that node will also be the one with the highest average flow F ij among N nodes. If we consider a network under the premise of a single cluster, after the network is established there are four types of nodes. This includes the CH, the backup cluster head (BCH), the managed node (), and the gateway node (GN). Therefore, an average lifetime value can be determined for each type of node described above if deemed to have similar characteristics. Additionally, two options are considered with respect to the packet types (control packets and data packets) and three packets routing ways between nodes: If the receiving node is a neighbor (belongs to the same cluster) and reachable for the transmitting node, the packet is sent directly to the receiving node. If the receiving node is a neighbor (belongs to the same cluster) but is not reachable by the transmitting node, the packet is sent to the CH node and this node reroutes the packet to the receiving node.

6 6 Rommel Torres, Francisco A. Sandoval, Liliana Enciso, and Samanta Cueva If the receiving node is not a neighbor (belongs to another cluster) then the packet is sent to the CH node and this node routes the packet to the other cluster through GN. If one considers the number of packets sent per node (direct ratio in relation to the average flow per node) assuming only the period of data packet broadcasting and the possible packet routing ways (assuming an equal number of packets per node), it follows that the node with the greatest number of packets transmitted is the CH node, as the lifetime of the CH node T CH is less than the remaining nodes: T CH < T i CH i, i N. To represent the situation, consider the following example. The model of energy consumption is based on a previous publication [12] that describes an analytical model as a result of ACPI BIOS measurements that evaluate the energy consumption of a wireless network interface examining IEEE It is known that the network interface of a mobile host can assume four states: 1) transmission for communicating data; 2) data reception; 3) idle mode, in which the network interface allows to transmit or receive data (this is the default mode); and 4) sleep mode, in which the power consumption is extremely low, and the interface may not transmit or receive until it is awakened. It is also known that the transmission mode is of greater consumption than the reception mode. One study [12] determined that the power consumption is approximately 25% higher in reception mode than in idle mode, and it is approximately 63% higher in the transmission mode than in the idle mode. Two states are considered in the model of energy consumption: 1) a state of high consumption when the host is in forwarding mode (reception and transmission mode), and 2) low-power state when the host is not sending packets (passive reception or idle mode). Then, according to a previously presented mathematical model [12], in the state of high consumption the percentage of battery charge can be represented by an exponential equation defined as: y = % battery-unit e λ x (3) where λ is a function of the throughput. For the specific case of 2 Mbits/s and.5 Mbits/s of traffic, λ assumes values of.185 and.35, respectively. In the case of low-power state the relation is linear. In view of what was described, the particular model has a single cluster with N = 5(enough to analyze the model trend). It is considered to be in a state of high consumption and to have 1 to 4 managed nodes plus the CH (i.e., for a CH node the traffic is considered to be 2 Mbits/s, and for a managed node the traffic is.5 Mbit/s). Node 1 is considered to be the first CH and after his death in ascending order. Note that the BCH node has intermediate power consumption between the CH and the managed node; however, for this basic model it is ignored and is considered to be a managed node. Battery charge percentage over time is presented in Figure 2a for each node. Basic protocol model E (Enhanced Backup Cluster Head Protocol)

7 Enhanced backup cluster head protocol 7 1 Full discharge model in high consumption mode 1 Full discharge model in high consumption mode E % battery load % battery load Node 1 Node 1 2 Node 2 2 Node 2 Node 3 Node 3 1 Node 4 1 Node 4 Node 5 Node Time in seconds Time in seconds (a) Full discharge model in high(b) Full discharge model in high consumption mode for consumption mode for E Lives curves 6 5 numbers of alive nodes E Time in seconds (c) Lives Curves, protocol and E Fig. 2: Network lifetime validation In this protocol, due to the direct dependence of the network lifetime with respect to the CH node lifetime in the protocol, a limit is established to the minimum residual energy level of the CH. While maintaining the same assumptions for the protocol, it can be induced that the network lifetime for the case of using the E protocol is greater than using the protocol. This means that there are an equal or greater number of active nodes in the network at any point in time when comparing E and protocols, respectively. A replica of the particular example with the protocol is performed using the same parameters considering a minimum allowed battery discharge of 3% for the CH, at which point the current CH becomes managed node. Figure 2b displays the percentage of battery charge as a function of time for the E protocol. Finally, Figure 2c presents a comparison of the network lifetime curves in relation to Figures 2a and 2b. It should be noted that the case evaluated is among the worst-case scenarios, where there is a continuous data transmission, and it has several restrictions compared to real situations. In return, this case allows us to evaluate and confirm an increase of approximately 7% in the nodes lifetime for the E protocol compared to the protocol; moreover,

8 8 Rommel Torres, Francisco A. Sandoval, Liliana Enciso, and Samanta Cueva the value has a tendency to increase due to the nodes low power consumption because of the linear relationship [12]. 4.2 Network availability validation We used an area of Loja City, Ecuador (Figure 3) to simulate an urban area with emergency and rescue services. This same scenario has been used previously in [1]. We used the Network Simulator 2 ( for the simulation. To define the simulation scenarios, the parameters described previously [13] have been used. The values for each of these parameters are listed in Table 1. We used a connection oriented approach with the TCP protocol. To evaluate the E protocol and to measure the network availability, overload, average delay and the packet delay variation indicators have been selected. These indicators have been compared to those obtained for the protocol. Fig. 3: Simulated emergency and rescue scenario Table 1: General parameters for the simulation Parameters Values Simulation area 1m x 5m Mobility model Emergency and rescue Number of nodes 25, 3, 4, 5, 6, 7, 8, 9, 1 Number of connections 2 Simulation time 1 seconds Network layer protocols E, Transport layer protocol TCP MAC layer Propagation model TwoRayGround Type of antenna Omnidirectional Rate of sent packets

9 Enhanced backup cluster head protocol 9 This rate is calculated as the ratio between the number of sent packets and the number of packets received. The formation and maintenance of the cluster requires the exchange of packets to occur by updated routes; E performs better in all cases (Figure 4a). performs greater information exchange in the cluster maintenance phase when there are problems with the CH because, in that case, an exchange of messages occurs while the CH is not available until the BCH takes its place. The proactive approach of E allows for fewer broadcast packets because the cluster maintenance process is reduced relative to the. Routingnpackets/Applicationnpackets SentPpackets/ReceivedPpackets RatePofPsentPpackets 11 1 E Nodes (a) Rate of sent packets OverheadnProtocolnAverage E Nodes (c) Protocol Overhead.14 E.12 SentCpackets/ReceivedCpackets Time SentCpacketsCapplicationCrate E Nodes (b) Sent packets application rate AverageHend to endhdelay 1.9 E Nodes Jitter average (d) End to End delay.1 Time Nodes (e) Average of Packet delay Variation Fig. 4: Network availability validation

10 1 Rommel Torres, Francisco A. Sandoval, Liliana Enciso, and Samanta Cueva Sent packets application rate Figure 4b displays the sent rate of the packets on an application level. The protocol is better because this rate is closer to one. E is equal to or more efficient than the. In general, the E is more stable than the. The proactive E behavior allows a smaller number of not-retransmitted generated application packets compared to the. Protocol overload Protocol overload is defined as the total number of routing packets transmitted during the simulation divided by the number of application packets. This indicator measures ad hoc routing protocol efficiency. The E uses fewer routing packets for the application information transmission (Figure 4c). Average delay For our purpose, average delay is a very significant measurement because it is desirable to send and receive network management information as fast as possible. In figure 4d, the E protocol the average extreme delay is better and more stable than for the. Moreover, the E lag times are similar in all cases. Packet delay variation Packet delay variation is the difference in the delay between extreme-toextreme connections between selected packets. In an ad hoc network it serves to measure the network stability and convergence. The average packet delay variation has been obtained for the entire network (Figure 4e). In the majority of the experiments, the E exhibits more stable behavior than its predecessor. 5 Conclusion and future work A new hierarchical routing protocol, E, has been implemented. E uses a proactive approach to improve network availability. It has been demonstrated that, in general, E is more stable than its predecessor. E uses a residual energy level strategy for change the BCH node status to CH. Improving the availability and lifetime of ad hoc networks is a major challenge that must be solved to meet the needs of emergency and rescue scenarios. E accomplished the objectives of improving the network lifetime and convergence compared to its predecessor,. E improves the network lifetime through the use of a proactive strategy in the maintenance of the cluster. The implementation of the E in real devices is a task that we intend to address in forthcoming research. The study of time adaptive algorithms that enable calculation of the optimal power limit for each node in a network considering the type and traffic on the node will be determined in future studies. References 1. R. Torres, L. Mengual, O. Marban, S. Eibe, E. Menasalvas, B. Maza, A management ad hoc networks model for rescue and emergency scenarios, Expert Systems with Applications 39 (1) (212)

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