Prolonged network lifetime, scalability, and load balancing are important requirements for many ad-hoc sensor network applications. Clustering sensor nodes is an effective technique for achieving these goals. In this work, we propose a new energy-efficient approach for clustering nodes in adhoc sensor networks. Based on this approach, we present a protocol, HEED (Hybrid Energy-Efficient Distributed clustering), that periodically selects cluster heads according to a hybrid of their residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED does not make any assumptions about the distribution or density of nodes, or about node capabilities, e.g., location-awareness. The clustering process terminates in O(1) iterations, and does not depend on the network topology or size. The protocol incurs low overhead in terms of processing cycles and messages exchanged. It also achieves fairly uniform cluster head distribution across the network. A careful selection of the secondary clustering parameter can balance load among cluster heads. Our simulation results demonstrate that HEED outperforms weight-based clustering protocols in terms of several cluster characteristics. We also apply our approach to a simple application to demonstrate its effectiveness in prolonging the network lifetime and supporting data aggregation.

Topology control in a sensor network balances load on sensor nodes, and increases network scalability and lifetime. Clustering sensor nodes is an effective topology control approach. In this paper, we propose a novel distributed clustering approach for long-lived ad-hoc sensor networks. Our proposed approach does not make any assumptions about the presence of infrastructure or about node capabilities, other than the availability of multiple power levels in sensor nodes. We present a protocol, HEED (Hybrid Energy-Efficient Distributed clustering), that periodically selects cluster heads according to a hybrid of the node residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED terminates in O(1) iterations, incurs low message overhead, and achieves fairly uniform cluster head distribution across the network. We prove that, with appropriate bounds on node density and intra-cluster and inter-cluster transmission ranges, HEED can asymptotically almost surely guarantee connectivity of clustered networks. Simulation results demonstrate that our proposed approach is effective in prolonging the network lifetime and supporting scalable data aggregation.

Data gathering is a common but critical operation in many applications of wireless sensor networks. Innovative techniques that improve energy efficiency to prolong the network lifetime are highly required. Clustering is an effective topology control approach in wireless sensor networks, which can increase network scalability and lifetime. In this paper, we propose a novel energy efficient clustering schema (EECS) for single-hop wireless sensor networks, which better suits the periodical data gathering applications. Our approach elects cluster heads with more residual energy in a autonomous manner through local radio communication with no iteration while achieving good cluster head distribution; further more, it introduces a novel distance-based method to balance the load among the cluster heads. Simulation results show that EECS prolongs the network lifetime significantly against the other clustering protocols such as LEACH and HEED. Key words: wireless sensor networks(WSN), data gathering, clustering scheme, energy efficient, network lifetime, single-hop.

The large-scale deployment of wireless sensor networks (WSNs) and the need for data aggregation necessitate efficient organization of the network topology for the purpose of balancing the load and prolonging the network lifetime. Clustering has proven to be an effective approach for organizing the network into a connected hierarchy. In this article, we highlight the challenges in clustering a WSN, discuss the design rationale of the different clustering approaches, and classify the proposed approaches based on their objectives and design principles. We further discuss several key issues that affect the practical deployment of clustering techniques in sensor network applications. Recent years have witnessed an increasing interest in using wireless sensor networks (WSNs) in many applications, including environmental monitoring and military field surveillance. In these applications, tiny sensors are deployed and left unattended to continuously

0.1 Introduction Many wireless sensor network applications require information about the geographiclocation of each sensor node. Besides the typical application of correlating sensor readings with physical locations, approximate geographical localization is also neededfor applications such as location-aided routing [2], geographic routing [3], geographic routing with imprecise geographic coordinates [4, 5], geographic hash tables [6], andfor many data aggregation applications.

In wireless sensor networks, clustering sensor nodes into small groups is an effective technique to achieve scalability, self-organization, power saving, channel access, routing, etc. A number of cluster formation protocols have been proposed recently. However, most existing protocols assume benign environments, and are vulnerable to attacks from malicious nodes. In this paper, we propose a secure distributed cluster formation protocol to organize sensor networks into mutually disjoint cliques. Our protocol has the following properties: (1) normal nodes are divided into mutually disjoint cliques; (2) all the normal nodes in each clique agree on the same clique memberships; (3) while external attackers can be prevented from participating in the cluster formation process, inside attackers that do not follow the protocol semantics can be identified and removed from the network; (4) the communication overhead is moderate; (5) the protocol is fully distributed. 1

Abstract — Several sensor network applications, such as environmental monitoring, require data aggregation to an observer. For this purpose, a data aggregation tree, rooted at the observer, is constructed in the network. Node clustering can be employed to further balance load among sensor nodes and prolong the network lifetime. In this paper, we design and implement a system, iHEED, in which node clustering is integrated with multihop routing for TinyOS. We consider simple data aggregation operators, such as AVG or MAX. We use a simple energy consumption model to keep track of the battery consumption of cluster heads and regular nodes. We perform experiments on a sensor network testbed to quantify the advantages of integrating hierarchical routing with data aggregation. Our results indicate that the network lifetime is prolonged by a factor of 2 to 4, and successful transmissions are almost doubled. Clustering plays a dominant role in delaying the first node death, while aggregation plays a dominant role in delaying the last node death. I.

Abstract—Aggregator nodes commonly have the ability to read, corrupt or disrupt the flow of information produced by a Wireless Sensor Network (WSN). Despite this fact, existing aggregator node election schemes do not address an adversary that strives to influence the election process towards candidate nodes that it controls. We discuss the requirements that need to be fulfilled by a non-manipulable aggregator node election protocol. We conclude that these requirements can be satisfied by a distributed random number generator function in which no node is able to determine the output of the function. We provide and compare three protocols that instantiate such function. I.

Clustering sensor nodes increases the scalability and energy efficiency of communications among them. In hostile environments, unexpected failures or attacks on cluster heads (through which communication takes place) may partition the network or degrade application performance. In this work, we propose a new approach, REED (Robust Energy Efficient Distributed clustering), for clustering sensors deployed in hostile environments. Our primary objective is to construct a k-fault- tolerant (i.e., k-connected) network, where k is a constant determined by the application. Fault tolerance can be achieved by selecting k independent sets of cluster heads (i.e., cluster head overlays) on top of the physical network, so that each node can quickly switch to other cluster heads in case of failures or attacks on its current cluster head. The independent cluster head overlays also provide multiple vertex-disjoint routing paths for load balancing and security. Network lifetime is prolonged by selecting cluster heads with high residual energy and low communication cost, and periodically re-clustering the network in order to distribute energy consumption among sensor nodes. We prove that REED can asymptotically achieve k-fault tolerance if certain constraints on node density are satisfied. We also investigate via simulations the clustering properties of REED, and show that building multiple cluster head overlays does not consume significant energy.

In this paper, we introduce PANEL, a position-based aggregator node election protocol for wireless sensor networks. The novelty of PANEL with respect to other aggregator node election protocols is that it supports asynchronous sensor network applications where the sensor readings are fetched by the base stations after some delay. In particular, the motivation for the design of PANEL was to support reliable and persistent data storage applications, such as TinyPEDS [13]. PANEL ensures load balancing, and it supports intraand inter-cluster routing allowing sensor to aggregator, aggregator to aggregator, base station to aggregator, and aggregator to base station communications. We also compare PANEL with HEED [42] in the simulation environment provided by TOSSIM, and show that, on the one hand, PANEL creates more cohesive clusters than HEED, and, on the other hand, that PANEL is more energy efficient than HEED.