Rashmi Ranjan Rout

Enhancement of Lifetime using Duty Cycle and Network Coding in Wireless Sensor Networks

A fundamental challenge in the design of Wireless Sensor Network (WSN) is to enhance the network lifetime. The area around the Sink forms a bottleneck zone due to heavy traffic-flow, which limits the network lifetime in WSN. This work attempts to improve the energy efficiency of the bottleneck zone which leads to overall improvement of the network lifetime by considering a duty cycled WSN. An efficient communication paradigm has been adopted in the bottleneck zone by combining duty cycle and network coding. Studies carried out to estimate the upper bounds of the network lifetime by considering (i) duty cycle, (ii) network coding and (iii) combinations of duty cycle and network coding. The sensor nodes in the bottleneck zone are divided into two groups: simple relay sensors and network coder sensors. The relay nodes simply forward the received data, whereas, the network coder nodes transmit using the proposed network coding based algorithm. Energy efficiency of the bottleneck zone increases because more volume of data will be transmitted to the Sink with the same number of transmissions. This in-turn improves the overall lifetime of the network. Performance metrics, namely, packet delivery ratio and packet latency have also been investigated. A detailed theoretical analysis and simulation results have been provided to show the efficacy of the proposed approach.

On Network Lifetime Expectancy With Realistic Sensing and Traffic Generation Model in Wireless Sensor Networks

Wireless sensor networks (WSNs), which are used in a wide variety of mission-critical applications, are comprised of sensor nodes with limited battery power. The feasibility of such applications is highly influenced by the longevity or sustainability of these networks. Improvement of energy efficiency and estimation of network lifetime are important issues in these energy constrained networks. In this paper, we present a probabilistic model for estimating the network lifetime of a sensor network. The traffic generation model is designed for irregular surfaces while combining the Elfes sensing model and event generation model. A discrete radio model is considered in this paper for better energy efficiency. Two different scenarios, such as, single-hop and multi-hop networks are analyzed with the proposed model. The likelihood of achieving the desired network lifetime with the proposed model is examined. Extensive simulations are done taking the topology from real-sensor network testbeds. We also estimate the packet loss rates and the results also confirm the accuracy and energy efficiency of the proposed model.

Co-operative routing for wireless sensor networks using network coding

Omni-directional two-way traffic flow using flooding leads to broadcast storm problem in a wireless sensor network (WSN). The probabilistic routing protocols help in reducing the broadcast storm but lack reliability. Network coding is a new paradigm that allows an intermediate node to encode incoming packets which improves the bandwidth efficiency and reliability of the WSN. A network coding-based probabilistic routing (NCPR) scheme has been proposed, which is energy-efficient, reliable and alleviates the broadcast storm problem in a clustered WSN. The focus of this work is to design the network coding-based procedures for probabilistic routing protocols. In NCPR, a sensor node initialises a transmission process in a WSN cluster by transmitting a packet from its sensed queue. Each neighbour node encodes its received packet with its sensed packet using XOR network coding and transmits the coded packet with certain probability. Three network coding-based procedures have been proposed for encoding and decoding of packets in intra-cluster and inter-cluster communications by distributing roles among the sensor nodes. It has been shown that the NCPR scheme provides better energy efficiency and reliability compared to the probabilistic routing and pure flooding schemes.

A Network Coding based Probabilistic Routing scheme for Wireless Sensor Network

Network coding scheme improves throughput and reliability in a Wireless Sensor Network. A straight forward broadcasting by flooding in a wireless sensor network is very costly and reduce the network coding benefit. The packet loss probability increases due to contention and collision which is referred as broadcast storm problem. In this paper, we have proposed a Network Coding based Probabilistic Routing scheme (NCPR) for wireless sensor network that alleviate the broadcast storm problem. The NCPR scheme also improves the network coding gain which is defined as the ratio of the native data packets transmitted with network coding to the number of data packets transmitted without network coding using same number of transmissions. Furthermore, for a large scale network the clusters are restricted to some geographical area and they want to share information with each other (battlefield scenario). In NCPR, the decoding operation can be carried at the intermediate nodes rather than only at the sink node; both intra-cluster and inter-cluster information sharing is considered. The protocol also gives protection to the sensor nodes against link failure. Simulation results are presented, which show high degree of coding gain than the flooding scheme.

Network coding-aware data aggregation for a distributed Wireless Sensor Network

Wireless Sensor Networks (WSNs) are immensely deployed for monitoring information like humidity, temperature and soil fertility. As the sensor nodes sense data and floods them in the network, the network traffic increases and may result in congestion which leads to broadcast storm problem. This paper addresses the utility of network coding to optimize data aggregation and to decrease the number of transmitted messages in a WSN. A coding-aware deployment strategy is presented which create opportunity for network coding at aggregate sensor nodes. Our deployment strategy leads to a topology which supports many-to-many network flows (multiple sources and multiple sink network flow). It also gives protection against multiple failures in the network. Furthermore, we propose a heuristic that restricts unnecessary transmissions.

Markov Decision Process-Based Switching Algorithm for Sustainable Rechargeable Wireless Sensor Networks

In a tree-based wireless sensor network (WSN), a tree structure rooted at sink node is usually created for efficient data collection. Recently, the use of solar harvesting technologies for rechargeable sensor nodes is evolving. Moreover, in a tree-based rechargeable WSN, the nodes that belong to different routes will have different energy dissipation due to unequal harvested-energy and utilized-energy. Network sustainability and energy efficiency are important issues in a tree-based rechargeable sensor network. In this paper, a Markov decision process-based switching algorithm has been designed for a sustainable data collection tree while reducing energy consumption in the network. Furthermore, an analysis of energy consumption has been performed using a real-time sensor traffic pattern. A prediction model has been adopted to estimate the harvesting energy (based on solar power) for the rechargeable sensor nodes. In this paper, the state of each node is defined based on different independent energy levels. The state of each node may change with time depending on harvested-energy and utilized-energy. The proposed Markov decision process approach finds the optimal switching policy for sensor nodes, which switch from one parent to another based on energy levels to preserve sustainability. A detailed theoretical analysis has been performed along with simulation results to show the efficacy of the proposed approach.

Markov decision process-based analysis of rechargeable nodes in wireless sensor networks

Wireless Sensor Networks (WSNs) are popular nowadays for applications ranging from environmental monitoring to object tracking and surveillance. But the sensors which are deployed are energy constrained. The nodes in WSNs deplete of their very limited energy over time. In this work, we consider a network of rechargeable sensors deployed in a random sensing environment. Using Markov Decision Processes (MDP), we analyze the sensor nodes having different levels of energies in the batteries, so as to maximize a generalized system performance in terms of recharge delay and the number of sensor nodes recharged. We have formulated a relationship between the residual energy level of a sensor node and the recharge delay. The simulation results establish the effectiveness of our approach in decreasing the number of passive nodes and the recharge delay in WSNs.

Detection of node-misbehavior using overhearing and autonomous agents in wireless Ad-Hoc networks

In Wireless Ad-hoc Networks, nodes co-operate among themselves to forward data packets from a source node to a destination node. Nodes may participate in route discovery or route maintenance process but refuse to forward packets due to presence of faulty hardware or software or to save their resources, such as, battery power and bandwidth. Detection and isolation of misbehavior nodes are important issues to improve the quality of communication service and to save resources of well behaving wireless nodes. In this work, firstly, a neighbor Overhearing based Misbehavior Detection(OMD) scheme is proposed. In OMD, each node overhears the transmissions of its neighbors and calculates packet forwarding ratio of its own as well as its neighbors. Source node uses the calculated information to identify a misbehaving node. Secondly, an Autonomous Agent based Misbehavior Detection( AAMD) technique is proposed. In AAMD, past behavior of nodes in the network is used as a metric to calculate the selection probability of a node. An agent residing at a node is activated using the activation key generated by a trusted third party to verify the misbehavior of the node. The proposed schemes reduce communication overheads and identification delays to detect misbehaving nodes in wireless ad-hoc network. Simulation results are presented to evaluate the performance of the proposed OMD and AAMD schemes.

A Centralized Server Based Cluster Integrated Protocol in Hybrid P2P Systems

P2P systems became popular for attaining high performance price ratio by effectively using several desktop computers. These systems can be used to construct a sharing file system which aggregates the storage capacities of several peers. DHT based approaches combined with clustering mechanisms are playing significant role to build scalable wide area P2P systems. In this paper, two-layer hybrid P2P system is presented, in which top layer combines the clusters as virtual nodes in a Chord network. The cluster layer implements a centralized server approach to maintain the nodes within that cluster. This helps to minimize the influence of frequent peer arrivals and departures on the dynamic system. It also reduces look up latency in the whole system. Focus here is to simplify the maintenance of the cluster.

Learning automata-based trust model for user recommendations in online social networks

Abstract Nowadays, most of the online social media websites provide recommendations as service for selective decision making. Determining a recommended trust path based on the consumer’s non-functional requirements, such as availability of the products, delay for computing recommendations and response time for a good recommendation is one of the challenging issues in online social networks. In this paper, we first design a recommendation-based online social network architecture by incorporating trust information (namely, direct trust and indirect trust), relevance degree and recommended influence value. We propose a high quality of social trust associated model for evaluating a recommended trust path. The proposed model estimates utility values with associated weights based on Shannon entropy information gain. Further, for best recommended trust path selection, we propose a Learning Automata based Recommended Trust Path Selection (LA-RTPS) algorithm to identify multiple recommended trust paths and to determine an aggregate path. The experimentation using real time datasets illustrates the efficacy of the proposed algorithm.