Horizontal trajectory based mobile multi-sink routing in underwater sensor networks

Scientific, commercial, exploration, and monitoring applications of underwater sensor networks have drawn the attention of researchers toward the investigation of routing protocols that are robust, scalable, and energy efficient. This has brought significant research in network layer routing protocols. Irrespective of the field of application it is desirable to increase network lifetime by reducing energy consumed by sensor nodes in the network or by balancing energy in the entire network. Energy balancing refers to the uniform distribution of the network’s residual energy such that all nodes remain alive for a long time. It requires uniform energy consumption by each sensor node in the network instead of the same node being involved in every transmission. In this paper, we discuss two routing methods for three-dimensional environments in which the water region under monitor is divided into subregions of equal height and each subregion has a sink. Nodes in the subregion send data to the sink designated for that subregion. The first method called static multi-sink routing uses static sinks and the second method called horizontal trajectory-based mobile multi-sink routing (HT-MMR) uses mobile sinks with a horizontal trajectory. Simulation results show that the proposed HT-MMR reduces average energy consumption and average energy tax by 16.69% and 16.44% respectively. HT-MMR is energy efficient as it enhances network lifetime by 11.11%

A flat routing protocol for sensor networks

The way in which Wireless Sensor Networks (WSN) are designed requires that energy be taken as the most crucial element if WSNs are to be used in the most effective way to serve the purpose for which they have been deployed in the target region. Routing protocols are the main aids that can assist in reducing the energy consumption required by the transmission of data throughout the sensor networks. In this paper we have proposed a flat routing protocol for sensor networks. This approach is one of the simplest protocols in terms of the route determination process and the number of messages through the network. Routes from the source to the destination are determined by use of only the hop count and remaining energy of the neighbor nodes for each node. The proposed protocol is simulated and is compared with the flood routing protocol

Probabilistic Mean Energy Flooding to increase the survivability of MANET

Wireless mobile ad hoc stations have limited battery capacity, hence, Ad Hoc routing protocols ought to be energy conservative. Route discovery is a common operation in routing to resolve many issues relating to energy conservation. In a Mobile Ad Hoc Networks (MANETs) in particular, due to host mobility, such operations are expected to be executed more frequently and a straightforward broadcasting by flooding is usually very costly and will result in serious redundancy, contention, and collision. In this paper, an algorithm is proposed to improve the flooding performance of an Ad Hoc On–Demand Distance Vector (AODV) routing protocol called, Probabilistic–Mean–Energy–Flooding (PMEF) which periodically performs an averaging algorithm Calculate–Average–Energy (CAE) to estimate the average energy Eavg. This algorithm is used in route discovery process to make a rebroadcast decision by the node. Route request message is rebroadcast with a probability that depends on the difference between the residual energy Er and the calculated average energy. Our simulation results show an improvement in the network lifetime and the throughput compared to traditional AODV

Recovery based time synchronization for wireless networks

Time synchronization schemes in Wireless Sensor Net-works have been subjected to various security threats and attacks. In this paper we throw light on some of these at-tacks. Nevertheless we are more concerned with the pulse delay attack which cannot be countered using any of the cryptographic techniques. We propose an algorithm called Resync algorithm which not only detects the delay attack but also aims to rectify the compromised node and intro-duce it back in the network for the synchronization process. In-depth analysis has been done in terms of the rate of suc-cess achieved in detecting multiple outliers ie nodes under attack and the level of accuracy obtained in the offset values after running the Resync algorithm

A flat routing protocol for sensor networks

The way in which Wireless Sensor Networks (WSN) are designed requires that energy be taken as the most crucial element if WSNs are to be used in the most effective way to serve the purpose for which they have been deployed in the target region. Routing protocols are the main aids that can assist in reducing the energy consumption required by the transmission of data throughout the sensor networks. In this paper we have proposed a flat routing protocol for sensor networks. This approach is one of the simplest protocols in terms of the route determination process and the number of messages through the network. Routes from the source to the destination are determined by use of only the hop count and remaining energy of the neighbor nodes for each node. The proposed protocol is simulated and is compared with the flood routing protocol

Detecting extended contexts in tweets using dbpedia

In an era of extensive social media usage, we have enormous amount of user data generated everyday. Finding relevant information from Twitter amongst a constant stream of messages is a herculean task considering the complex sentimental nature of data. Multiple applications across the world now rely on Twitter for various domain sensitive and analytical use-cases. This paper proposes a scalable technique which can be used for a better context modeling of tweets generating primary and extended contexts for a set of tweets. We use ontologies from DBPedia as the resource for generating the contexts and subsequently find relevant extended contexts. DBpedia Spotlight in conjunction with DBpedia Ontology is the backbone for this proposed model. Using tweets from a twitter trend as the input, the framework output is validated against the similarity scores between generated contexts. Assuming the trend is