Wireless Sensor Network with MIHOP technique & Mobile Sink

Various radio applications in wireless sensor network where sensor nod es operate on batteries so that energy consumption must be minimized while satisfying given throughput and delay requirements . A major portion of the energy expenditure is when the nodes close to the sensor network gateways used for data collection typical ly suffer a large overhead as these nodes must relay on data from the remaining network. In this paper we discuss various existing energy efficient schemes for WSN and one new method to achieve efficiency , is proposed. The MIHOP (MIMO and Multi - hop) meth od combines cluster - based virtual MIMO and multi - hop technologies. The multi hop mode is employed in transmitting data when the related sensors are located within a specific number of hops from the sink, and the virtual MIMO mode is used in transmitting da ta from the remaining sensor nodes. A controllable mobile sink that reduces the energy consumed in sensor transmission is also adopted fo r data collection

Impact of mobile sink for wireless sensor networks considering goodput and routing efficiency metrics

Sensor networks are a sensing, computing and communication infrastructure that are able to observe and respond to phenomena in the natural environment and in our physical and cyber infrastructure. The sensors themselves can range from small passive micro-sensors to larger scale, controllable weather-sensing platforms. In this work, we investigate how the sensor network performs in the case when the sink node moves. We consider as a metrics for evaluation goodput and Routing Efficiency (RE). We compare the simulation results when the sink node is mobile and stationary considering lattice topology using AODV protocol. The simulation results have shown that for the case of mobile sink, the goodput is better when the number of nodes is 16. However, when the number of nodes are 100 and 256, the goodput of mobile sink is worse than stationary sink when the values Tr is larger than 1pps. For both stationary and mobile sinks, the RE increases with the increase of number of sensor nodes, but the RE of mobile sink is better than the stationary sinkPeer ReviewedPostprint (published version

Path-Constrained Data Gathering Scheme

Several studies in recent years have considered the use of mobile elements for data gathering in wireless sensor networks so as to reduce the need for multi-hop forwarding among the sensor nodes and thereby prolong the network lifetime Since typically practical constraints preclude a mobile element from visiting all nodes in the sensor network the solution must involve a combination of a mobile element visiting a subset of the nodes cache points while other nodes communicate their data to the cache points wirelessly This leads to the optimization problem of minimizing the communication distance of the sensor nodes while keeping the tour length of the mobile element below a given constraint In this paper we investigate the problem of designing the mobile elements tours such that the length of each tour is below a per-determined length and the number of hops between the tours and the nodes not included in the tour is minimized To address this problem we present an algorithmic solution that consider the distribution of the nodes during the process of building the tours We compare the resulting performance of our algorithm with the best known comparable schemes in the literatur

Impact of mobile sink for wireless sensor networks considering different radio models and performance metrics

In this work, we investigate how WSN performs in the case when sink node moves using different radio models and metrics. We consider routing efficiency, delay and number of received packet metrics to evaluate the performance of WSN using AODV routing protocol, lattice topology, and TwoRayGround and Shadowing radio models. We evaluate the performance of WSN by simulations. The performance evaluation results show that for small number of sensor nodes, the RE of Shadowing radio model is better than TwoRayGround model. However, for high node density, the RE of TwoRayGround model is better than Shadowing radio model. When Tr is less 10, the delay of Shadowing model is better than TwoRayGround model. However, for Tr larger than 100, the delay of TwoRayGround model is better than Shadowing model. With increase of the number of nodes, the number of received packets is also increased. For TwoRayGround model, when Tr is less than 10, the number of received packets for 16, 64, and 100 nodes is almost the same. However, comparing TwoRayGround model with Shadowing model, for the same time interval and for the same number of nodes, the number of received packets of TwoRayGround model is better than Shadowing model.Peer ReviewedPostprint (published version

Controlled Mobility in Mobile Sensor Networks: Advantages, Issues and Challenges

International audienceRecently, wireless self-organizing networks are attracting a lot of interest in the research community. Moreover, in the last decade many mobile devices have appeared in the market. Exploiting mobility in a wireless environment, instead of considering it as a kind of disturbance, is a fundamental concept that the research community is beginning to appreciate now. Of course, the advantages obtainable through the use of the mobility imply the knowledge of the different types of mobility and the way to include it in the management architecture of the wireless networks. In this work we claim that mobility and wireless sensor networks can be considered as two synergetic elements of the same reality. For this purpose, we sketch a macro-classification of the different objectives which can be pursued by controlled mobility. Moreover, we identify and highlight the interactions between this specific type of mobility and the layers of the control stack. Lastly, this paper reports a case study in which we show how controlled mobility can be exploited practically