Multiple sinks routing is envisioned as a possible solution to the bottleneck research problem in Wireless Sensor Networks (WSN). In addition to focusing on minimizing the energy consumption in a WSN, it is also equally important to design routing protocols that fairly and evenly distribute the network traffic; in order to prolong the network life time and improve its scalability. In this paper we present an enhancement to the GRPW algorithm for wireless sensor networks. Performance of GRPW algorithm algorithm depends heavily on single sink position , we propose a protocol called GRPW-MuS( Geographic Routing to Multiple Sinks in connected wireless sensor networks) based on Multiple Static Sinks, we modified the existing sink location privacy protection scheme by dividing nodes in the network containing multiple sink into different levels in which real packets are forwarded to sink belong to corresponding logical levels and the intermediate node generating fake packets and sending it to fake sinks. Using OMNET++ simulation and the MiXiM framework, it is shown that proposed protocol significantly improves the robustness and adapts to rapid topological changes with multiple mobile sinks, while efficiently reducing the communication overhead and the energy consumption.

Abstract-There has been recently a trend of exploiting the heterogeneity in WSNs and the mobility of either the sensor nodes or the sink nodes to facilitate data dissemination in WSNs. Recently, there has been much focus on mobile sensor networks, and we have even seen the development of small-profile sensing devices that are able to control their own movement. Although it has been shown that mobility alleviates several issues relating to sensor network coverage and connectivity, many challenges remain. Among these, the need for position estimation is perhaps the most important. Not only is localization required to understand sensor data in a spatial context, but also for navigation, a key feature of mobile sensors. This paper concerns the localization problem in the case where all nodes in the network (anchors and others sensors) are mobile. We propose the technique following the capabilities of nodes. Thus, each node obtains either an exact position or an approximate position with the knowledge of the maximal error born. Also, we adapt the periods where nodes invoke their localization. Simulation results show the performances of our method in term of accuracy and determinate the technique the more adapted related to the network configurations.

GRPW-MuS (Geographic Routing to Multiple Sinks in connected wireless sensor networks based on Multiple Sinks) is one of the basic routing protocols used for Supporting Mobile Sinks in Wireless Sensor Networks . GRPW-MuS, a geographical routing protocol for wireless sensor networks , is based on an architecture partitioned by logical levels, on the other hand based on a multipoint relaying flooding technique to reduce the number of topology broadcast. GRPW-MuS uses periodic HELLO packets to neighbor detection. As introduced in Reference [9,17], the wormhole attack can form a serious threat in wireless sensor networks, especially against many wireless sensor networks routing protocols and location-based wireless security systems. Here, a trust model to handle this attack in GRPW-MuS is provided called GRPW-MuS-s . Using OMNET++ simulation and the MiXiM framework, results show that GRPW-MuS-s protocol only has very small false positives for wormhole detection during the neighbor discovery process (less than GRPW-MuS). The average energy usage at each node for GRPW-MuS-s protocol during the neighbor discovery and route discovery is very low than GRPW-MuS, which is much lower than the available energy at each node. The cost analysis shows that GRPW-MuS-s protocol only needs small memory usage at each node , which is suitable for the sensor network.

Location information is essential in many applications of WSNs,it is natural to use this information for routing as well. Location-based protocols or geographical routing protocols to exploit the location information of each node to provide efficient and scalable routing. Various routing algorithms that feat geographic information (e.g., GPSR) have been aimed to attain this goal. These algorithms refer to all nodes by their location, not address, and use those coordinates to route greedily, when possible, towards the destination. However, there are dozen's situations where location information is not available at the node. This paper presents a new geographical routing protocol for Wireless Sensor Networks (WSN) energyefficient data forwarding, called GRPW(geographic routing protocol washbasin).Protocol GRPW ensures a load balancing, minimizing energy consumption and the rate of message delivery using a routing policy with logical levels, inspired from the water flow in a washbasin, without making the assumption that all sensors are localized. GRPW protocol performance compared to the protocol GPSR show that maximizes the lifetime of the network, provides quality service parameterizable, and is appropriate for dense sensor networks confronting our method to an optimal algorithm. General Terms:Routing, wireless sensor network (WSN)

This paper addresses target localization problem in a cooperative 3-D wireless sensor network (WSN). We employ a hybrid system that fuses distance and angle measurements, extracted from the received signal strength (RSS) and angle-of-arrival (AoA) information, respectively. Based on range measurement model and simple geometry, we derive a novel convex estimator based on Jarv's scan . The network is said to be uniquely localizable if there is a unique set of locations consistent with the given data.This paper presents an improved localization algorithm with high accuracy in large-scale Sensor networks with a large number of sensor nodes based on the Jarvis' March ,called SLSNJ. the Jarvis' March adapted here for our approximation technique to determining the convex hull of a set of sensors used instead of the Grid-Scan method,to take into account the requirements in memory, to make it scalable and rapidly convergent with small location estimation error.We verify our algorithm in various scenarios and compare it with AT-Dist method. Our simulation results show that the new estimators have excellent performance in terms of the estimation accuracy and convergence, and they confirm the effectiveness of combining two radio measurements in large-scale.