Parama Bhaumik

Mobility Models, Traces and Impact of Mobility on Opportunistic Routing Algorithms: A Survey

Mobile Opportunistic Network (MON) is characterized by intermittent connectivity where communication largely depends on the mobility pattern of the participating nodes. In MON, a node can take the custody of a packet for a long time and carry it until a new forwarding path has been established, unlike mobile adhoc network (MANET), where a node must drop the packet otherwise. Therefore, routing in MON depends on the repeated make-and-break of communication links, which again depends on the mobility of the nodes as they encounter and drift away from each other. MONs can simply be formed by humans carrying hand-held devices (like Personal Digital Assistant [PDAs] or cell phones) or on-board devices installed in vehicles. Therefore, with mobility playing a major role in the performance of MON, researchers have repeatedly tried to understand the nature of mobility with respect to humans, vehicles, and wild animals. To study the nature of mobility, researchers have collected mobility traces, proposed mobility models, and analyzed the performance of MON with respect to various mobility parameters. This article provides a detailed survey of different mobility models which have been proposed to date and how mobility largely determines the performance of opportunistic routing. We divide the article into four major sections. First, we provide a detailed survey of all the synthetic mobility models which have been developed to date. Second, we study the various mobility traces which have been collected and analyzed. Third, we study how mobility parameters affect the performance of MON. Finally, we highlight on some of the research areas and open challenges which yet remain unsolved.

Analysing social behaviour and message dissemination in human based delay tolerant network

Abstract Recent advances in mobile communication shows proliferation in networks formed by human carried devices known as the pocket switched network (PSN). Human beings are social animals. They tend to form groups and communities, and have repetitive mobility pattern which can be used to disseminate information in PSNs. In this paper, we give a deeper insight to the nature of community formation and how such information can be used to help opportunistic forwarding in mobile opportunistic networks. Using real world mobility traces, we first derive the adjacency list for each node and form the contact graph. Using tools from social network analysis we then determine various node properties like centrality and clustering coefficient and graph properties like average path length and modularity. Based on the derived graph properties, node encounter process and nature of message dissemination in PSNs, we propose two social based routing, known as the contact based routing and community aware two-hop routing. We compare the proposed routing techniques with generic epidemic and prophet routing and Bubble-Rap, a social based routing. Results show that the proposed algorithms is able to achieve better delivery ratio and lower delay than Bubble Rap, while reducing the high overhead ratio of epidemic and prophet routing.

Improving network performance with affinity based mobility model in opportunistic network

Opportunistic network is a type of Delay Tolerant Network which is characterized by intermittent connectivity amongst the nodes and communication largely depends upon the mobility of the participating nodes. The network being highly dynamic, traditional MANET protocols cannot be applied and the nodes must adhere to store-carry-forward mechanism. Nodes do not have the information about the network topology, number of participating nodes and the location of the destination node. Hence, message transfer reliability largely depends upon the mobility pattern of the nodes. In this paper we have tried to find the impact of RWP (Random Waypoint) mobility on packet delivery ratio. We estimate mobility factors like number of node encounters, contact duration(link time) and inter-contact time which in turn depends upon parameters like playfield area (total network area), number of nodes, node velocity, bit-rate and RF range of the nodes. We also propose a restricted form of RWP mobility model, called the affinity based mobility model. The network scenario consists of a source and a destination node that are located at two extreme corners of the square playfield (to keep a maximum distance between them) and exchange data packets with the aid of mobile ‘helper’ nodes. The source node and the destination node are static. The mobile nodes only help in relaying the message. We prove how affinity based mobility model helps in augmenting the network reliability thereby increasing the message delivery ratio and reduce message delivery latency.

Using self organizing map in wireless sensor network for designing energy efficient topologies

There are many challenges involving WSN design such as the energy resources optimization, the robustness and the network coverage. We address here the problem of energy-efficient topology design. A well designed dynamic topology and efficient routing algorithms may allow a large reduction on the energy consumption. As such type of network, the sensors are connected in an ad-hoc manner, without any deterministic way. This paper is concerned with applying standard routing protocols into wireless sensor network by using topology modified by Neural Network which proves to be energy efficient as compared with unmodified topology. Neural Network has been proved to be a powerful tool in the distributed environment. Here, to capture the true distributed nature of the Wireless Sensor Network (WSN), neural networks Self organizing Feature Map (SOFM) is used.

Estimators for global information in mobile opportunistic network

Lack of global knowledge in a delay tolerant network proves detrimental, for a replication based routing strategy, in terms of resource utilization. It has often been noticed that nodes keep on replicating a message although it has already been delivered to the destination since such information remains unknown to most of the nodes in the network. Moreover recovery mechanisms (like VACCINE for epidemic routing) take time to propagate and incur additional transmission overhead. Global information such as number of nodes in the network, node meeting schedule, and whether a message has already been delivered to the destination is vital for optimal network performance. In this paper we propose model for estimating some of these vital information and show that replication based routing algorithm like epidemic routing, can perform better with minimum resource utilization. First, we use a uniformly minimum variance unbiased estimator (UMVUE) to estimate the number of nodes in the network. Second, we estimate the number of replicas of any given message in the network, at any given instant (t). Third, we show that epidemic routing with a node population of N can perform best with only N/2 message replicas for any given message and that further replication only leads additional overhead without any performance improvement. We show the simulated results using random waypoint mobility model and Shanghai taxi trace.

Analysing topology control protocols in wireless sensor network using network evolution model

In the study of wireless ad hoc and sensor networks, clustering is an important research problem as it aims at maximizing network lifetime and minimizing latency. A large number of algorithms have been devised to compute “good” clusters in a WSN but few papers have tried to characterize these algorithms in an analytical manner. In this paper, we use a local world model to understand and characterize the functioning of three tree based clustering algorithms. In particular, we have chosen simple tree, CDS Rule K, and A3 topology construction protocols. Using our theoretical framework based on a complex network model, we have also tried to quantify some of the observed features of these algorithms such as number of cluster heads and average degree of the resultant graph. The theoretically obtained measures have reasonably matched with measures obtained by simulation studies.

Delay-overhead trade-offs in mobile opportunistic network using TTL based restricted flooding

This paper deals with the delay-overhead trade-offs which can be achieved by restricting packet replication in mobile opportunistic networks, a special class of store-carry-forward network. In this regard we propose a TTL based Restricted Flooding Algorithm where each node is allowed to spread fixed number of message replicas at specified time intervals based on the time-to-live of the message. This overcomes high packet overhead ratio of unrestricted flooding while maintaining a comparable packet delivery ratio. By theoretically analyzing the scheme, we provide an upper bound of delivery delay and provide an estimate for number of message replicas which leads to minimum delay. Using theoretical framework we also show that the scheme is scalable and delay approaches a constant value when relative transmission overhead (i.e., transmissions/message) between restricted TTL scheme and the optimal scheme is kept constant. We discuss the performance of the proposed scheme with respect to delivery ratio, overhead ratio and delay and compare it with some of the well-known opportunistic routing algorithms like epidemic, spray-and-wait and prophet.

Information gradient based content search architecture for mobile opportunistic network

Mobile opportunistic network provides a platform for information dissemination and content search, especially in a disaster management scenario, when there is no end-to-end reliable connection or the entire infrastructure based network is severed. In this paper we propose an architecture for content search in mobile opportunistic network, based on information gradient and query forward with the aim to minimize search time and transmission overhead. We know that in a highly dynamic network, the cost and time required to fetch information increases as the Euclidean distance from the object increases. The concept of information gradient helps in minimizing this cost, even if we are away from the source of information. Our content search protocol has three phases, viz. query submit, query forward, and query response phase. We propose a game theory based query forward strategy to maximize throughput and minimize transmission overhead. Simulation results show the performance improvement of game-theoretic approach against simple flooding.

Designing Energy-Efficient Topologies for Wireless Sensor Network: Neural Approach

Preserving energy or battery power of wireless sensor network is of major concern. As such type of network, the sensors are deployed in an ad hoc manner, without any deterministic way. This paper is concerned with applying standard routing protocols into wireless sensor network by using topology modified by neural network which proves to be energy efficient as compared with unmodified topology. Neural network has been proved to be a powerful tool in the distributed environment. Here, to capture the true distributed nature of the Wireless Sensor Network (WSN), neural networks Self-Organizing Feature Map (SOFM) is used.

A fundamental traffic management strategy for mobile opportunistic network

Mobile opportunistic networks are characterized by intermittent connectivity and practically no end-to-end connection. In these kinds of network, network capacity and throughput largely depends packet replication. As a result buffer space tends to quickly deplete which may result in severe data loss due to packet drop. To the best of our knowledge, the two important phenomenon of traffic management viz., jitter and packet loss have evaded the minds of researchers in this field and hence we study the problem of buffer management and packet scheduling in such networks. We propose a fair queue scheduling policy where packets are sorted based on total waiting time, i.e. sum of waiting times over all the previous hops, before being transferred over a network link. We analytically prove that scheduling packet transmission as above reduces overall waiting time which leads to lower delay. We also propose a source initiated congestion aware buffer management scheme which helps in reducing number of packet loss significantly. The proposed scheduling and buffer management policy is simulated using real world (Cambridge iMote traces) datasets and compared with other existing scheduling policies. Results show that that proposed scheme has lower delay and lower packet drop compared to other scheduling schemes.