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. 1

The constraints of sensor networks, an emerging area of network research, require new approaches in system design. We study the evolution of abstractions and techniques in TinyOS, a popular sensor network operating system. Examining CVS repositories of several research institutions that use TinyOS, we trace three areas of development: single-hop networking, multi-hop networking, and network services. We note common techniques and draw conclusions on the emerging abstractions as well as the novel constraints that have shaped them. 1.

In the paper, we devise and evaluate a fully decentralized, light-weight, dynamic clustering algorithm for target tracking. Instead of assuming the same role for all the sensors, we envision a hierarchical sensor network that is composed of (a) a static backbone of sparsely placed high-capability sensors which will assume the role of a cluster head (CH) upon triggered by certain signal events; and (b) moderately to densely populated low-end sensors whose function is to provide sensor information to CHs upon request. A cluster is formed and a CH becomes active, when the acoustic signal strength detected by the CH exceeds a pre-determined threshold. The active CH then broadcasts an information solicitation packet, asking sensors in its vicinity to join the cluster and provide their sensing information. We address and devise solution approaches (with the use of Voronoi diagram) to realize dynamic clustering: (I1) how CHs cooperate with one another to ensure that only one CH (preferably the CH that is closest to the target) is active with high probability; (I2) when the active CH solicits for sensor information, instead of having all the sensors in its vicinity reply, only a sufficient number of sensors respond with non-redundant, essential information to determine the target location; and and (I3) both the packets that sensors send to their CHs and packets that CHs report to subscribers do not incur significant collision. Through both probabilistic analysis and ns-2 simulation, we show with the use of Voronoi diagram, the CH that is usually closest to the target is (implicitly) selected as the leader and and that the proposed dynamic clustering algorithm effectively eliminates contention among sensors and renders more accurate estimates of target locations as a result of better quality data collected and less collision incurred.

When sensor nodes are organized in clusters, they could use either single hop or multi-hop mode of communication to send their data to their respective cluster heads. We present a systematic cost-based analysis of both the modes, and provide results that could serve as guidelines to decide which mode should be used for given settings. We determine closed form expressions for the required number of cluster heads and the required battery energy of nodes for both the modes. We also propose a hybrid communication mode which is a combination of single hop and multi-hop modes, and which is more cost-effective than either of the two modes. Our problem formulation also allows for the application to be taken into account in the overall design problem through a data aggregation model. Key words: Wireless sensor networks, clustering, single hop vs multi-hop, data aggregation 1

In this paper we study how to reduce energy consumption in large-scale sensor networks which systematically sample a spatio-temporal field. We begin by formulating a distributed compression problem subject to aggregation (energy) costs to a single sink. We show that the optimal solution is greedy and based on ordering sensors according to their aggregation costs-- typically related to proximity-- and, perhaps surprisingly, it is independent of the distribution of data sources. Next we consider a simplified hierarchical model for a sensor network including multiple sinks, compressors/aggregation nodes and sensors. Using a reasonable metric for energy cost, we show that the optimal organization of devices is associated with a Johnson-Mehl tessellation induced by their locations. Drawing on techniques from stochastic geometry, we analyze the energy savings that optimal hierarchies provide relative to previously proposed organizations based on proximity, i.e., associated Voronoi tessellations. Our analysis and simulations show that an optimal organization of aggregation/compression can yield 8-28% energy savings depending on the compression ratio.

Network lifetime has become the key characteristic to be used for evaluating sensor networks in an application specific way. Especially the availability of nodes, the sensor coverage, and the connectivity have been included in discussions on network lifetime. Even quality of service measures can be reduced to lifetime considerations. A great number of algorithms and methods were proposed to increase the lifetime of a sensor network – based on the particularly selected definition of network lifetime. Motivated by the great differences in existing definitions of sensor network lifetime that are used in relevant publications, we reviewed the state of the art in lifetime definitions, their differences, advantages, and limitations. This survey was the starting point for our work towards a generic definition of sensor network lifetime for use in analytic evaluations as well as in simulation models – focusing on a formal and concise definition of accumulated network lifetime and total network lifetime. We also demonstrate the applicability of our definition based on the surveyed lifetime definitions found in the literature as well as using an example to explain the various aspects influencing sensor network lifetime. sensor networks, lifetime, connectivity, coverage, longevity Index Terms I.

Abstract — We present a cost based comparative study of homogeneous and heterogeneous clustered sensor networks. We focus on the case where the base station is remotely located and the sensor nodes are not mobile. Since we are concerned with the overall network dimensioning problem, we take into account the manufacturing cost of the hardware as well as the battery energy of the nodes. A homogeneous sensor network consists of identical nodes, while a heterogeneous sensor network consists of two or more types of nodes (organized into hierarchical clusters). We first consider single hop clustered sensor networks (nodes use single hopping to reach the cluster heads). We use LEACH as the representative single hop homogeneous network, and a sensor network with two types of nodes as a representative single hop heterogeneous network. For multi-hop homogeneous networks (nodes use multi-hopping to reach the cluster head), we propose and analyze a multi-hop variant of LEACH that we call M-LEACH. We show that M-LEACH has better energy efficiency than LEACH in many cases. We then compare the cost of multi-hop clustered sensor networks with M-LEACH as the representative homogeneous network, and a sensor network with two types of nodes (that use in-cluster multi-hopping) as the representative heterogeneous network. I.

Maintaining security of wearable networked health monitoring sensors (Body Sensor Networks (BSN)) is very important for the acceptance and long term viability of the technology. Sensors in BSNs organize themselves into different topologies for efficiency purpose. Securing these topology formation process is of prime importance. In this paper we present two schemes which rely on the novel technique of using physiological values from the wearer’s body for securing a cluster topology formation. Traditional schemes for cluster (one of the most commonly used topology) formation were not designed with security in mind and are susceptible to security flaws. The schemes proposed here not only solve the secure cluster formation problem but also do so efficiently by eliminating all key distribution overheads. We analyzed the security of the protocols and tested their accuracy on a prototype implementation developed using Mica2 motes. 1.

Connectivity-based node clustering has wide-ranging applications in decentralized peer-to-peer (P2P) networks such as P2P file sharing systems, mobile ad-hoc networks, P2P sensor networks, and so forth. This paper describes a Connectivity-based Distributed Node Clustering scheme (CDC). This scheme presents a scalable and efficient solution for discovering connectivity-based clusters in peer networks. In contrast to centralized graph clustering algorithms, the CDC scheme is completely decentralized and it only assumes the knowledge of neighbor nodes instead of requiring a global knowledge of the network (graph) to be available. An important feature of the CDC scheme is its ability to cluster the entire network automatically or to discover clusters around a given set of nodes. To cope with the typical dynamics of P2P networks, we provide mechanisms to allow new nodes to be incorporated into appropriate existing clusters and to gracefully handle the departure of nodes in the clusters. These mechanisms enable the CDC scheme to be extensible and adaptable in the sense that the clustering structure of the network adjusts automatically as nodes join or leave the system. We provide detailed experimental evaluations of the CDC scheme, addressing its effectiveness in discovering good quality clusters and handling the node dynamics. We further study the types of topologies that can benefit best from the connectivitybased distributed clustering algorithms like CDC. Our experiments show that utilizing message-based connectivity structure can considerably reduce the messaging cost and provide better utilization of resources, which in turn improves the quality of service of the applications executing over decentralized peer-to-peer networks.

We study the impact of heterogeneity of nodes, in terms of their energy, in wireless sensor networks that are hierarchically clustered. In these networks some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. We assume that a percentage of the population of sensor nodes is equipped with additional energy resources---this is a source of heterogeneity which may result from the initial setting or as the operation of the network evolves. We also assume that the sensors are randomly (uniformly) distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. We show that the behavior of such sensor networks becomes very unstable once the first node dies, especially in the presence of node heterogeneity. Classical clustering protocols assume that all the nodes are equipped with the same amount of energy and as a result, they can not take full advantage of the presence of node heterogeneity. We propose SEP, a heterogeneous-aware protocol to prolong the time interval before the death of the first node (we refer to as stability period), which is crucial for many applications where the feedback from the sensor network must be reliable. SEP is based on weighted election probabilities of each node to become cluster head according to the remaining energy in each node. We show by simulation that SEP always prolongs the stability period compared to (and that the average throughput is greater than) the one obtained using current clustering protocols. We conclude by studying the sensitivity of our SEP protocol to heterogeneity parameters capturing energy imbalance in the network. We found that SEP yields longer stability region for higher values of extra energy brought by more powerful nodes.