- Open Access
Stable geographic forwarding with link lifetime prediction in mobile adhoc networks for battlefield environment
© The Author(s) 2016
Received: 7 October 2014
Accepted: 18 September 2016
Published: 10 December 2016
Mobile adhoc network (MANET) is one of the most relevant areas of research in wireless communication that has gained prevalence due to its diversity over large-scale highly mobile networks to small-scale networks having low mobility and power constraints. Adhoc networks are inherently envisioned to be highly vulnerable to dynamic changes because of mobile sensor nodes. Node mobility often results in breakage of communication links, thereby introducing additional overhead for establishing new routes and transmitting table updates, further causing rapid exhaustion of energy reserve. Especially in military applications, preventing communication disruptions is an important security concern for defence applications engaged in safeguarding national boundaries. This necessitates the need for efficient routing strategy for battlefield environments susceptible to frequent link failures due to random mobility of groups/individuals. In this regard, we have proposed an efficient stable geographic forwarding with link-lifetime prediction (SGFL) that utilizes the broadcast nature of wireless channel and multicasts with node mobility. During the next hop selection process, a node preferably selects the neighbours which are at the least distance from the destination with low mobility. Unlike position based opportunistic routing, our scheme allows selection of backup node that lies within the transmission range of selected neighbours. Link lifetime prediction with backup nodes enhances efficiency and reliability of routing in highly mobile and congested adhoc networks. Simulation results show that our proposed SGFL achieves better performance than existing counterparts in terms of packet delivery ratio, packet loss and end-to-end delay under high node density as well as increased traffic flow.
Adhoc network consists of mobile sensor nodes distributed across a geographical region, performing wireless communication to interact with each other. In case the host sensor is not in the direct range of its destined location, it preferably forms multi-hop network via intermediate nodes in absence of any central coordination or infrastructure. These networks are mostly predominant in applications where rapid deployment is required, for instance, battlefield surveillance, disaster relief services, medical consultation in remote areas, etc.
On the basis of the type of information used in packet routing, Mobile adhoc network (MANET) routing protocols are categorized as topology-based and position-based protocols [1, 2]. The topology-based protocols utilize link information from the network to forward packet in a proactive or reactive manner. In proactive routing, all paths are computed before packet forwarding initiates, hence increasing network overhead. Reactive routing allows computation of the single path required for communication. This reduces route computation overhead but increases the delay incurred in performing route discovery. Second category of protocols belonging to position-based routing relies on the knowledge of geographical position of the nodes instead of network addresses . In this scheme, each node receives its position information through location service techniques (or global positioning devices) for forwarding packets efficiently towards its intended destination. Therefore, geographic routing alleviates the need for maintaining route information. This further reduces message exchanges resulting from table updates, thereby automatically mitigating network overhead as compared to topology-based routing. Greedy forwarding is another basic scheme that is used in geographic routing to relay packets . This strategy allows source nodes to select next-hop from neighbour list that lies in the progress area towards destination node. In greedy routing, communication link is available only when node lies in the progress area.
Problem definition and motivation
Due to high node mobility the neighbour list of a node might change more often, thereby raising the need for predicting link lifetime for improving reliability in communication. The cases when network size is large or traffic rate is very high, often leads to frequent congestion in the network. Moreover, when nodes attempt to update their location information, then there is a higher probability for loss of beacon messages due to congested network. In such situation, the node fails to receive acknowledgement for its beacon messages and assumes that its current neighbour list has got invalidated. This initiates search for new neighbours to forward data which ultimately causes tremendous increase in delays involved in delivering network services.
These circumstances are mostly prevalent in battlefield operations, where each person uses wireless device having limited battery power and each missile vehicle are wireless enabled with higher power reserve. Therefore, energy-aware routing becomes an issue of prime importance for the armed individuals relying on restricted powered wireless devices. Wastage of radio resources and battery lifetime leads to degradation of network performance. This further encourages design of efficient routing protocols that can provide reliable and efficient data delivery against node mobility and dynamic topology.
There are several routing schemes based on greedy forwarding, can be employed in this regard. However, greedy forwarding applies only distance metric to select next forwarder which is not enough for high mobility and congested network scenario. For this purpose, an additional metric along with distance parameter is required for improving efficiency of routing by maximizing packet delivery with reduced delay and energy consumption for both the scenarios.
In our research, we have proposed link stability metric with distance metric to forward packet from between a pair of communicating nodes. This helps the source node to identify links that are more stable than others during next forwarder selection. Communication over stable links reduces packet loss because of link failure. The proposed scheme employs backup node along with the next forwarder node that prevents interruption in communication even if the link fails. Our method considers the position, speed and distance of sensor node (i.e. the armed person carrying the sensor).
The rest of the paper is organized as follows: “Background” section presents an overview of the existing link lifetime estimation methods and stability based routing protocols. “Related work” section highlights the network model assumptions for battlefield environment. Our proposed routing protocol with link stability prediction is proposed in “Proposed backup routing protocol based on link lifetime prediction” section. In “Performance evaluation” section, the performance of proposed SGFL is validated through simulation results. Finally, “Conclusion” section concludes with our research with future directions.
In MANET, researchers have developed numerous methods for estimating link lifetime, some of them are lifetime based or mobility based mechanism to calculate the stability of link between pair of node. Link stability refers to the duration for which connectivity between a pair of nodes exists. Several mobility models for adhoc networks have been described, for instance, random walk mobility, random waypoint mobility and random group mobility models . In battlefield environment, individuals (sensors) move independently or in group. Such military applications require continued connectivity with link stability [6–12]. In Greedy based forwarding schemes, link is considered to be stable when pair of nodes lies in the progress area of source node. A neighbouring node lies in the progress area if distance from source to destination is greater than distance between neighbour nodes to destination node. Power boosting geographic routing (PBGR)  is greedy forwarding technique that performs link lifetime estimation to select next forwarder node. When a dead node problem occurs then PBGR increase its node transmission range to find neighbour node. It involves complex relation between power consumption and node interference range. Another scheme illustrated in , greedy perimeter stateless routing with lifetime (GPSR-L) is a combination of greedy routing and face routing, which further analyses the lifetime between source and neighbour node, and accordingly set the lifetime timer for each of its neighbour. The neighbour node having least distance from destination and largest timer value is selected as next forwarder node to forward packet. The results show that GPSR-L improves network performance for both high mobility and congested scenario. However in case of void area or dead node, absence of neighbours in the progress area of source node towards destination makes it necessary to switch from greedy mode to perimeter mode. As a result, this increases the hop count and introduces additional overhead and wastage of sensor energy.
In  authors have proposed stability aware greedy (SAG) routing that calculates link lifetime metric using node position, speed and direction for each node and finally selects highest link lifetime stable node as next forwarder node. When sending node detects link failure using beacon message then source node searches for new node to designate it as the next forwarder. Another scheme presented in , greedy forwarding with more reliability (MFR) that two other metrics besides distance metric, first is velocity similarity metric which select neighbour which has similar speed with sending node and second is power similarity metric that selects neighbour based on power similarity with sending node. However, in battlefield MFR is not suitable because it requires time sensitive application. The authors of  proposed link stability and path stability based routing to select path with minimum hop count. The distance between two adjacent nodes is computed using log distance path loss model. When the distance between two neighbour nodes is more than stable zone of the communication range then re-routing is initiated before the route fails.
In order to improve the reliability of a communication system in military application, the most effective concept is to increase the degree of redundancy. There are several existing robust routing protocol and can be categorized as end-to-end redundancy (e.g. multipath routing) and hop-by-hop redundancy (e.g. opportunistic forwarding), which takes the advantage of wireless channel and multicasting to forward packets. Position based opportunistic routing (POR)  is an instance of opportunistic forwarding. POR follows greedy forwarding scheme to transmit packets and make the forwarding list that includes next forwarder node along with several backup nodes. When next forwarder fails to transmit the packet then backup node replaces to continue transmission. POR performs better in highly mobile scenario where network size is small or traffic rate is slow. Link lifetime based backup routing (LBR)  uses multipath scheme with adhoc on demand distance vector (AODV) routing to improve route stability. LBR determines the shortest path between source and destination through flooding and creates backup path locally for each link with primary path. Every link in the primary path is selected with respect to the link lifetime estimation. When primary path fails then local backup path takes place to continue the communication. In yet another research conducted in , authors developed link lifetime-based segment-by-segment routing protocol (LL-SSR). The strategy allows the selection of a stable route segment-wise to reduce the overhead of routing protocol with each node maintaining a routing table for its k-hop region.
The research aims to build a reliable scheme to route communication for critical applications, including defence and battlefield services that demands reliability in both, connectivity and continuity.
The scheme involves building protocol to establish geographic forwarding with link prediction and stability.
It uses link stability metric with respect to least distance and low mobility of nodes lying in the progress area of source node, for selecting next forwarder.
In addition, a stable backup node is designated along with the next forwarder, so that in case of link breakage, the backup node can forward transmission without further delays.
The performance of our proposed scheme nomenclatured as Stable Geographic Forwarding with Link-lifetime prediction (SGFL) was established on the basis of network performance parameters, involving packet delivery ratio, end-to-end delay, routing overhead and energy consumption.
Proposed backup routing protocol based on link lifetime prediction
We propose a next hop selection metric that is based on the stability of links among mobile nodes. Stability of link is denoted as a function of the least distance from destination and lower mobility than neighbour nodes with respect to the source node. The selected next-forwarder will be responsible for choosing its backup node and forwarding the packet to its next-forwarder. Only the nodes located in the progress area and half of communication range of next- forwarder would get the chance to be elected as backup node. A neighbour node lies in the progress area if and only if it satisfies the two conditions: (i) firstly, distance between each node from source should not exceed the communication range R of source node and (ii) secondly, distance between neighbour nodes to destination node should not exceed from distance between source nodes to destination node. Three algorithms are proposed, (1) Packet Forwarding Strategy, (2) Next-hop forwarder selection and (3) backup node selection approach. Source node initially executes algorithm 1 if there is no neighbour between source and destination node to forward packet, otherwise directly executes algorithm 2 and 3.
Source or sender node
Destination or receiving node
List of neighbours
List of sorted neighbour based on distance form destination
List of all backup nodes of next forwarder which is initially zero
∆(N i , N j )
Function that return distance between N i and N j
δ(N i )
Function that return speed of node N i
Next-hop forwarder node obtained from NextForwarder()
Backup node used when next hop fails to transmit packet, acquired as outcome from BackupNode()
Random group mobility model (RGM), random way point model (RWP)
Number of nodes
Speed of node
1, 2 s
Radio energy model
Impact of network traffic on individual and group mobility scenarios
Figures 6c and 7c show the variation of packet rate with respect to routing overhead in terms of total number of sent and forwarded packets in routing process with the increase of CBR packet frequency in scenario 1 and 2. As the number of CBR packet frequency increases, routing overhead is increased for both protocols. In case of SGFL routing overhead is lower than POR because our SGFL protocol allows only one backup node with each selected next hop that decrease the possibility of nodes out of range which reduces packet loss during the transmission. In Figs. 6(d, e) and 7d variation of network traffic rate with respect to average energy consumption is highlighted. As number of CBR packet frequency increases, energy consumption is increased slowly for both protocols. In group mobility, all nodes belonging to a particular group are often found closely connected to all other members of that group. This compels POR to choose more nodes in backup as compared to proposed mechanism employed with SGFL.
Impact of node speed on individual and group mobility scenarios
Figures 8a and 9a depicts the relationship of packet delivery ratio with respect to node speed for both the protocols. Decrease in the trend is observed because of the fact that link fails easily as node speed increases. However, the rate of decrease of packet delivery ratio for SGFL is obviously higher than that of POR. That is because POR is distance based routing protocol, link failure often occurs due to node mobility, and data packets are dropped upon experiencing a link failure. Figures 8b and 9b highlights the comparison of delays for SGFL and POR protocols. It is evident from the experimental outcome that, delays vary as node speed increases. That is because as node speed increases, topology changes faster, which more easily leads to link failure and triggers route rediscovery process, thereby raising an increase in transmission delay. However, the routing protocols indicate different performances. SGFL has smaller variation in end to end delay than POR, which indicates an improvement in transmission delay as node speed increases. Figures 8c and 9c shows the routing overhead with the increase of node mobility. Routing overhead is increased for both protocols as the node speed increases. However routing overhead of SGFL slower than POR. The reason behind this is the novel methodology employed by proposed SGFL in selection of next hop forwarder and backup node. This imparts stability to inter-node communication by decreasing the possibility of packet loss during transmission. The results in Figs. 8(d, e) and 9d show that our protocol SGFL dominates over POR with respect to average energy consumption, even when the node speed increases.
Comparative analysis over multiple routing protocols for random network scenario
POR is another existing counterpart that performs comparatively better than HARP and LBR in performance. This mechanism allows sender node to maintain a set of backup nodes, instead of building entire backup paths. Though, the routing overhead is comparatively reduced to some extent, but determining the optimal number of backup nodes plays an important trade-off between performance and routing (and buffer) overhead. Also, this approach does not takes into account the impact of network parameters in case of group mobility. Our proposed SGFL scheme addresses the above challenges and trade-offs while imparting performance to dynamic scenarios, involving individual and group mobility. SGFL is equipped with link stability metric that selects the next forwarder node having least distance and low mobility with respect to nodes lying in the progress area of source node. In addition, single backup node is associated with the forwarder to ensure continuation of network services even in case of link failure without inducing further delays. This perfectly balances the trade-off among performance in transmission and associated overheads in routing and resource utilization.
Research in this paper proposes a novel SGFL mechanism that performs geographic routing and link quality analysis with backup node selection. The usage of link lifetime prediction computed in terms of least distance and low mobility while choosing next hop forwarder; greatly contributed to increase communication reliability. Also, we adopt a backup node scheme to avoid dead node problem as well as link breakage and failure, thereby increasing lifetime of the communication route. The results obtained from performance analysis confirmed the dominance of our proposed SGFL over POR in both scenarios, involving group mobility and individual random mobility. Our research showed 30–40 % increase in the packet delivery ratio for congested networks and 10–20 % for high mobility networks. Moreover, the results indicated significant improvement in packet delivery ratio for different CBR intervals and node speed when compared to POR. Similarly, constructive outcomes were reflected from the simulation of the proposed methodology in terms of reduced routing overhead, end-to-end delay, and energy consumption. Furthermore, it can be concluded that our proposed SGFL protocol would deliver better performance when implemented on battlefield environment. As future enhancements, improved mechanisms embedded in battlefield heavy vehicles can be constructed for sensor nodes, which can support power generator modules to harvest energy from natural sources. Also, impact of different realistic mobility models can be taken into consideration for high and low traffic scenario. Moreover, establishing secured communication for achieving reliability in battlefield services, could be another promising extension.
ADI participated in the design of models and their conceptual working, eventually leading to developing algorithms. PJ designed scenarios, implemented algorithms and generated output, along with preparing initial drafts. ADI upgraded results with comparative study, laid foundation for interpretive conclusion from outcomes and outlined manuscript with critical revision. Both, ADI and PJ participated in debugging and code refinement process while implementation. Both authors read and approved the final manuscript.
Both authors declare that they have no competing interests.
Priyanka Jaiswal received her Master of Technology in Computer Science in 2014 from Jaypee Institute of Information Technology, Noida, India. Her major research interest lies in wireless network research including, energy-aware routing in mobile adhoc network, network protocol development, mobility modelling and management.
Dr. Adwitiya Sinha was awarded with philosophy of doctorate in computer science from Jawaharlal Nehru University (JNU), New Delhi, India in 2015. During her research in wireless sensor networks, she was honoured with Senior Research Fellowship (SRF) from Council of Scientific and Industrial Research (CSIR), New Delhi. She received Master of Technology in Computer Science in 2010 from the same university. She also completed Master and Bachelor programmes in Computer Applications in 2008 and 2006, respectively. Presently she is working as Assistant Professor (Senior Grade) in Jaypee Institute of Information Technology, Noida, India. She is actively involved in wireless network research, especially in areas including, energy-efficient wireless routing protocols, data aggregation, multi-layer fusion techniques, intelligent traffic pattern recognition and other new generation computing technologies with social network integration.
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