Wireless Sensor Networks (WSNs) consist of small nodes with sensing, computation, and wireless communications capabilities. Many routing, power management, and data dissemination protocols have been specifically designed for WSNs where energy awareness is an essential design issue. The focus, however, has been given to the routing protocols which might differ depending on the application and network architecture. In this paper, we present a survey of the state-of-the-art routing techniques in WSNs. We first outline the design challenges for routing protocols in WSNs followed by a comprehensive survey of different routing techniques. Overall, the routing techniques are classified into three categories based on the underlying network structure: flat, hierarchical, and location-based routing. Furthermore, these protocols can be classified into multipath-based, query-based, negotiation-based, QoS-based, and coherent-based depending on the protocol operation. We study the design tradeoffs between energy and communication overhead savings in every routing paradigm. We also highlight the advantages and performance issues of each routing technique. The paper concludes with possible future research areas.

Wireless Multimedia Sensor Networks (WMSNs) have emerged and shifted the focus from the typical scalar wireless sensor networks to networks with multimedia devices that are capable to retrieve video, audio, images, as well as scalar sensor data. WMSNs are able to deliver multimedia content due to the availability of inexpensive CMOS cameras and microphones coupled with the significant progress in distributed signal processing and multimedia source coding techniques. In this paper, we outline the design challenges of WMSNs, give a comprehensive discussion of the proposed architectures, algorithms and protocols for the different layers of the communication protocol stack for WMSNs, and evaluate the existing WMSN hardware and testbeds. The paper will give the reader a clear view of the state of the art at all aspects of this research area, and shed the light on its main current challenges and future trends. We also hope it will foster discussions and new research ideas among its researchers.

Absbocl-A fundamental challenge in the derlgo of Wireless Sensor Network (WSNs) Is to maximhe their lifetimes. Data aggrrgation has emerged as a basic approach in WSNs lo order to d u c e the number of transmissions of sensor nodes, and hence minimizing the overall power mnsumption in the network. In thh paper, we study optimal data aggregation in WSNs. Data aggregation Is alfrrted by several factors such as the placement of aggrrgalion points, the aggregation function, and the density of the sensors in the network. The determination of an optimal selection of aggregation point b lhw extremely important. We present an exact as well as appmximale algorithms to find the minimum number of aggregation pints in order to maximize the network lifetime. Our algorithms we s fixed virtual wireless backbane that h hilt on top of the physical topdogy. We also study the tradeoffs between enogy ravings and the potmtial delay involved in the data aggregation pmcess. Numerical results show that our approach provides subslanlial energy savings.

Abstract-A fundamental challenge in the design of Wireless Sensor Networks (WSNs) is to maximize their lifetimes especially when they have a limited and nonreplenishable energy supply. To extend the network lifetime, power management and energy-efficient communication techniques at all layers become necessary. In this paper, we present solutions for the data gathering and routing problem with in-network aggregation in WSNs. Our objective is to maximize the network lifetime by utilizing data aggregation and in-network processing techniques. We particularly focus on the joint problem of optimal data routing with data aggregation en route such that the above mentioned objective is achieved. We present Grid-based Routing and Aggregator Selection Scheme (GRASS), a scheme for WSNs that can achieve low energy dissipation and low latency without sacrificing quality. GRASS embodies optimal (exact) as well as heuristic approaches to find the minimum number of aggregation points while routing data to the BaseStation (BS) such that the network lifetime is maximized. Our results show that, when compared to other schemes, GRASS improves system lifetime with acceptable levels of latency in data aggregation and without sacrificing data quality.

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