Distributed Coding/Decoding Complexity in Video Sensor Networks

Video Sensor Networks (VSNs) are recent communication infrastructures used to capture and transmit dense visual information from an application context. In such large scale environments which include video coding, transmission and display/storage, there are several open problems to overcome in practical implementations. This paper addresses the most relevant challenges posed by VSNs, namely stringent bandwidth usage and processing time/power constraints. In particular, the paper proposes a novel VSN architecture where large sets of visual sensors with embedded processors are used for compression and transmission of coded streams to gateways, which in turn transrate the incoming streams and adapt them to the variable complexity requirements of both the sensor encoders and end-user decoder terminals. Such gateways provide real-time transcoding functionalities for bandwidth adaptation and coding/decoding complexity distribution by transferring the most complex video encoding/decoding tasks to the transcoding gateway at the expense of a limited increase in bit rate. Then, a method to reduce the decoding complexity, suitable for system-on-chip implementation, is proposed to operate at the transcoding gateway whenever decoders with constrained resources are targeted. The results show that the proposed method achieves good performance and its inclusion into the VSN infrastructure provides an additional level of complexity control functionality

An improved energy efficient approach for wsn based tracking applications

Tracking systems using a high number of low cost sensor nodes have been proposed for use in diverse applications including civil, military, and wildlife monitoring applications. In tracking applications, each sensor node attempts to send the target's location information to a sink node. Deploying a tracking system with a high number of sensor nodes results in the following limitations: high packet dropping rate, high congestion, transmission delay, and high power-consumption. Data aggregation schemes can reduce the number of messages transmitted over the network, while prediction schemes can decrease the number of activated beacon nodes in the tracking process. Consequently, data aggregation and prediction approaches can reduce the energy consumed during the tracking process. In this paper, we propose and implement an energy efficient approach for WSN-based tracking applications by integrating both a novel data aggregation method with a simple prediction approach. Three metrics are utilized for the evaluation purposes: total number of messages transmitted in the network, overall power-consumption, and the quality of the tracking accuracy. The proposed system is simulated using the NS2 simulation environment

Distributed Fault-Tolerant Algorithm for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are a set of tiny autonomous and interconnected devices. These nodes are scattered in a region of interest to collect information about the surrounding environment depending on the intended application. In many applications, the network is deployed in harsh environments such as battlefield where the nodes are susceptible to damage. In addition, nodes may fail due to energy depletion and breakdown in the onboard electronics. The failure of nodes may leave some areas uncovered and degrade the fidelity of the collected data. Therefore, establish a fault-tolerant mechanism is very crucial. Given the resource-constrained setup, this mechanism should impose the least overhead and performance impact. This paper focuses on recovery process after a fault detection phase in WSNs. We present an algorithm to recover faulty node called Distributed Fault-Tolerant Algorithm (DFTA).The performance evaluation is tested through simulation to evaluate some factors such as: Packet delivery ratio, control overhead, memory overhead and fault recovery delay. We compared our results with referenced algorithm: Fault Detection in Wireless Sensor Networks (FDWSN), and found that our DFTA performance outperforms that of FDWSN

Performance Evaluation of Various Routing Protocols and quality of service for Wireless Sensor Network

Wireless sensor networks (WSNs) provide great promise for target tracking and environmental monitoring. While many WSN routing protocols have been proposed till date, most of these focus on the mobility of observers and assume that targets are fixed. But, practically, many applications require for sensing data to be propagated from multiple mobile targets to multiple mobile observers. In addition, WSNs often operate under strict energy constraints, and therefore reducing energy dissipation is also an important issue. Clustering in wireless sensor networks (WSNs) is an important technique to ease topology management and routing. Clustering provides an effective method for prolonging lifetime of a WSN. In this paper we discuss the performance of two protocols like Dynamic Source Routing (DSR), Dynamic MANET On-demand Protocol (DYMO) using CBR (Constant Bit Rate) and Traffic-Gen for Multi-Clustering technique and compare various parameters like Average End-to-End Delay (sec.), Residual Battery Capacity (mAhr), No. of packets received at Coordinator, Average End-to-End Delay at PAN Coordinator (sec.) and Throughput at PAN Coordinator (bits/sec.

Energy Modeling of Wireless Sensor Nodes Based on Petri Nets

Energy minimization is of great importance in wireless sensor networks in extending the battery lifetime. Accurately understanding the energy consumption characteristics of each sensor node is a critical step for the design of energy saving strategies. This paper develops a detailed probabilistic model based on Petri nets to evaluate the energy consumption of a wireless sensor node. The model factors critical components of a sensor node, including processors with emerging energy-saving features, wireless communication components, and an open or closed workload generator. Experimental results show that this model is more flexible and accurate than Markov models. The model provides a useful simulation platform to study energy saving strategies in wireless sensor networks

A COMPARATIVE STUDY IN WIRELESS SENSOR NETWORKS

ABSTRAC