Secure data aggregation without persistent cryptographic operations in wireless sensor networks

In-network data aggregation is an essential operation to reduce energy consumption in large-scale wireless sensor networks. With data aggregation, however, raw data items are invisible to the base station and thus the authenticity of the aggregated data is hard to guarantee. A compromised sensor node may forge an aggregation value and mislead the base station into trusting a false reading. Due to the stringent constraints of energy supply and computing capability on sensor nodes, it is challenging to detect a compromised sensor node and keep it from cheating, since expensive cryptographic operations are unsuitable for tiny sensor devices. This paper proposes a secure aggregation tree (SAT) to detect and prevent cheating. Our method is essentially different from other existing solutions in that it does not require any cryptographic operations when all sensor nodes work honestly. The detection of cheating is based on the topological constraints in the aggregation tree. We also propose a weighted voting scheme to determine a misbehaving node and a secure local recovery scheme to avoid using the misbehaving node.     

Spanning tree based algorithms for low latency and energy efficient data aggregation enhanced convergecast (DAC) in wireless sensor networks

Many wireless sensor networks (WSNs) employ battery-powered sensor nodes. Communication in such networks is very taxing on its scarce energy resources. Convergecast – process of routing data from many sources to a sink – is commonly performed operation in WSNs. Data aggregation is a frequently used energy-conversing technique in WSNs. The rationale is to reduce volume of communicated data by using in-network processing capability at sensor nodes. In this paper, we address the problem of performing the operation of data aggregation enhanced convergecast (DAC) in an energy and latency efficient manner. We assume that all the nodes in the network have a data item and there is an a priori known application dependent data compression factor (or compression factor), γ, that approximates the useful fraction of the total data collected.The paper first presents two DAC tree construction algorithms. One is a variant of the Minimum Spanning Tree (MST) algorithm and the other is a variant of the Single Source Shortest Path Spanning Tree (SPT) algorithm. These two algorithms serve as a motivation for our Combined algorithm (COM) which generalized the SPT and MST based algorithm. The COM algorithm tries to construct an energy optimal DAC tree for any fixed value of α (= 1 − γ), the data growth factor. The nodes of these trees are scheduled for collision-free communication using a channel allocation algorithm. To achieve low latency, these algorithms use the β-constraint, which puts a soft limit on the maximum number of children a node can have in a DAC tree. The DAC tree obtained from energy minimizing phase of tree construction algorithms is re-structured using the β-constraint (in the latency minimizing phase) to reduce latency (at the expense of increasing energy cost). The effectiveness of these algorithms is evaluated by using energy efficiency, latency and network lifetime as metrics. With these metrics, the algorithms’ performance is compared with an existing data aggregation technique. From the experimental results, for a given network density and data compression factor γ at intermediate nodes, one can choose an appropriate algorithm depending upon whether the primary goal is to minimize the latency or the energy consumption.     

Energy balanced data propagation in wireless sensor networks

We study the problem of energy-balanced data propagation in wireless sensor networks. The energy balance property guarantees that the average per sensor energy dissipation is the same for all sensors in the network, during the entire execution of the data propagation protocol. This property is important since it prolongs the network’:s lifetime by avoiding early energy depletion of sensors. We propose a new algorithm that in each step decides whether to propagate data one-hop towards the final destination (the sink), or to send data directly to the sink. This randomized choice balances the (cheap) one-hop transimssions with the direct transimissions to the sink, which are more expensive but “bypass” the sensors lying close to the sink. Note that, in most protocols, these close to the sink sensors tend to be overused and die out early. By a detailed analysis we precisely estimate the probabilities for each propagation choice in order to guarantee energy balance. The needed estimation can easily be performed by current sensors using simple to obtain information. Under some assumptions, we also derive a closed form for these probabilities. The fact (shown by our analysis) that direct (expensive) transmissions to the sink are needed only rarely, shows that our protocol, besides energy-balanced, is also energy efficient. This work has been partially supported by the IST/FET/GC Programme of the European Union under contract numbers IST-2001-33135 (CRESCCO) and 6FP 001907 (DELIS). A perliminary version of the work appeared in WMAN 2004 [11]. Charilaos Efthymiou graduated form the Computer Engineering and Informatics Department (CEID) of the University of Patras, Greece. He received his MSc from the same department with advisor in S. Nikoletseas. He currently continuous his Ph.D studies in CEID with advisor L. Kirousis. His research interest include Probabilistic Techniques and Random Graphs, Randomized Algorithms in Computationally Hard Problems, Stochastic Processes and its Applications to Computer Science. Dr. Sotiris Nikoletseas is currently a Senior Researcher and Managing Director of Research Unit 1 (“Foundations of Computer Science, Relevant Technologies and Applications”) at the Computer Technology Institute (CTI), Patras, Greece and also a Lecturer at the Computer Engineering and Informatics Department of Patras University, Greece. His research interests include Probabilistic Techniques and Random Graphs, Average Case Analysis of Graph Algorithms and Randomized Algorithms, Fundamental Issues in Parallel and Distributed Computing, Approximate Solutions to Computationally Hard Problems. He has published scientific articles in major international conferences and journals and has co-authored (with Paul Spirakis) a book on Probabilistic Techniques. He has been invited speaker in important international scientific events and Universities. He has been a referee for the Theoretical Computer Science (TCS) Journal and important international conferences (ESA, ICALP). He has participated in many EU funded R&D projects (ESPRIT/ALCOM-IT, ESPRIT/GEPPCOM). He currently participates in 6 Fifth Framework projects: ALCOM-FT, ASPIS, UNIVERSAL, EICSTES (IST), ARACNE, AMORE (IMPROVING). Jose Rolim is Full Professor at the Department of Computer Science of the University of Geneva where he leads the Theoretical Computer Science and Sensor Lab (TCSensor Lab). He received his Ph.D. degree in Computer Science at the University of California, Los Angeles working together with Prof. S. Greibach. He has published several articles on the areas of distributed systems, randomization and computational complexity and leads two major projects on the area of Power Aware Computing and Games and Complexity, financed by the Swiss National Science Foundation. Prof. Rolim participates in the editorial board of several journals and conferences and he is the Steering Committee Chair and General Chair of the IEEE Distributed Computing Conference in Sensor Systems.     

An adaptive-weighted two-dimensional data aggregation algorithm for clustered wireless sensor networks

In this paper, an Adaptive-Weighted Time-Dimensional and Space-Dimensional （AWTDSD） data aggregation algorithm for a clustered sensor network is proposed for prolonging the lifetime of the network as well as improving the accuracy of the data gathered in the network. AWTDSD contains three phases： （1） the time-dimensional aggregation phase for eliminating the data redundancy; （2） the adaptive-weighted aggregation phase for further aggregating the data as well as improving the accuracy of the aggregated data; and （3） the space-dimensional aggregation phase for reducing the size and the amount of the data transmission to the base station. AWTDSD utilizes the correlations between the sensed data for reducing the data transmission and increasing the data accuracy as well. Experimental result shows that AWTDSD can not only save almost a half of the total energy consumption but also greatly increase the accuracy of the data monitored by the sensors in the clustered network.     

A dual-mode energy efficient encryption protocol for wireless sensor networks

This paper presents a novel link-layer encryption protocol for wireless sensor networks. The protocol design aims to reduce energy consumption by reducing security related communication overhead. This is done by merging security related data of consecutive packets. The merging (or combining packets) based on simple mathematical operations helps to reduce energy consumption by eliminating the requirement to send security related fields in headers and trailers. We name our protocol as the Compact Security Protocol referred to as C-Sec. In addition to energy savings, the C-Sec protocol also includes a unique security feature of hiding the packet header information. This feature makes it more difficult to trace the flow of wireless communication, and helps to minimize the cost of defending against replay attacks. We performed rigorous testing of the C-Sec protocol and compared it with well-known protocols including TinySec, MiniSec, SNEP and Zigbee. Our performance evaluation demonstrates that the C-Sec protocol outperforms other protocols in terms of energy savings. We also evaluated our protocol with respect to other performance metrics including queuing delay and error probability.     

TinyPEDS: Tiny persistent encrypted data storage in asynchronous wireless sensor networks

In wireless sensor networks there is a need to securely store monitored data in a distributed way whenever it is either not desired or simply not possible to transmit regional volatile information to an authorised recipient in real-time. In particular, for wireless sensor network applications with an asynchronous character, the wireless sensor network itself needs to store the monitored data. Since nodes may disappear over time, a replicated and read-protected, but yet space- and energy-efficient, data storage is mandatory. In this work we provide and analyse an approach for a tiny Persistent Encrypted Data Storage (tinyPEDS) of the environmental fingerprint for asynchronous wireless sensor networks. Even if parts of the network are exhausted, restoring rules ensure that, with a high probability, environmental information from past is still available.     

Design guidelines for wireless sensor networks: communication, clustering and aggregation

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.     

NLDA non-linear regression model for preserving data privacy in wireless sensor networks

Recently, the application of Wireless Sensor Networks (WSNs) has been increasing rapidly. It requires privacy preserving data aggregation protocols to secure the data from compromises. Preserving privacy of the sensor data is a challenging task. This paper presents a non-linear regression-based data aggregation protocol for preserving privacy of the sensor data. The proposed protocol uses non-linear regression functions to represent the sensor data collected from the sensor nodes. Instead of sending the complete data to the cluster head, the sensor nodes only send the coefficients of the non-linear function. This will reduce the communication overhead of the network. The data aggregation is performed on the masked coefficients and the sink node is able to retrieve the approximated results over the aggregated data. The analysis of experiment results shows that the proposed protocol is able to minimize communication overhead, enhance data aggregation accuracy, and preserve data privacy.     

Dynamic proxy tree-based data dissemination schemes for wireless sensor networks

In wireless sensor networks, efficiently disseminating data from a dynamic source to multiple mobile sinks is important for the applications such as mobile target detection and tracking. The tree-based multicasting scheme can be used. However, because of the short communication range of each sensor node and the frequent movement of sources and sinks, a sink may fail to receive data due to broken paths, and the tree should be frequently reconfigured to reconnect sources and sinks. To address the problem, we propose a dynamic proxy tree-based framework in this paper. A big challenge in implementing the framework is how to efficiently reconfigure the proxy tree as sources and sinks change. We model the problem as on-line constructing a minimum Steiner tree in an Euclidean plane, and propose centralized schemes to solve it. Considering the strict energy constraints in wireless sensor networks, we further propose two distributed on-line schemes, the shortest path-based (SP) scheme and the spanning range-based (SR) scheme. Extensive simulations are conducted to evaluate the schemes. The results show that the distributed schemes have similar performance as the centralized ones, and among the distributed schemes, the SR scheme outperforms the SP scheme.     

Topology control of tactical wireless sensor networks using energy efficient zone routing

The US Department of Defense (DoD) routinely uses wireless sensor networks (WSNs) for military tactical communications. Sensor node die-out has a significant impact on the topology of a tactical WSN. This is problematic for military applications where situational data is critical to tactical decision making. To increase the amount of time all sensor nodes remain active within the network and to control the network topology tactically, energy efficient routing mechanisms must be employed. In this paper, we aim to provide realistic insights on the practical advantages and disadvantages of using established routing techniques for tactical WSNs. We investigate the following established routing algorithms: direct routing, minimum transmission energy (MTE), Low Energy Adaptive Cluster Head routing (LEACH), and zone clustering. Based on the node die out statistics observed with these algorithms and the topological impact the node die outs have on the network, we develop a novel, energy efficient zone clustering algorithm called EZone. Via extensive simulations using MATLAB, we analyze the effectiveness of these algorithms on network performance for single and multiple gateway scenarios and show that the EZone algorithm tactically controls the topology of the network, thereby maintaining significant service area coverage when compared to the other routing algorithms.     

Energy aware efficient geographic routing in lossy wireless sensor networks with environmental energy supply

Wireless sensor networks are characterized by multihop wireless lossy links and resource constrained nodes. Energy efficiency is a major concern in such networks. In this paper, we study Geographic Routing with Environmental Energy Supply (GREES) and propose two protocols, GREES-L and GREES-M, which combine geographic routing and energy efficient routing techniques and take into account the realistic lossy wireless channel condition and the renewal capability of environmental energy supply when making routing decisions. Simulation results show that GREESs are more energy efficient than the corresponding residual energy based protocols and geographic routing protocols without energy awareness. GREESs can maintain higher mean residual energy on nodes, and achieve better load balancing in terms of having smaller standard deviation of residual energy on nodes. Both GREES-L and GREES-M exhibit graceful degradation on end-to-end delay, but do not compromise the end-to-end throughput performance. Kai Zeng received his B.E. degree in Communication Engineering and M.E. degree in Communication and Information System both from Huazhong University of Science and Technology, China, in 2001 and 2004, respectively. He is currently a Ph.D. student in the Electrical and Computer Engineering department at Worcester Polytechnic Institute. His research interests are in the areas of wireless ad hoc and sensor networks with emphases on energy-efficient protocol, cross-layer design, routing, and network security. Kui Ren received his B. Eng. and M. Eng. both from Zhejiang University, China, in 1998 and 2001, respectively. He worked as a research assistant at Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences from March 2001 to January 2003, at Institute for Infocomm Research, Singapore from January 2003 to August 2003, and at Information and Communications University, South Korea from September 2003 to June 2004. Currently he is a PhD candidate in the ECE department at Worcester Polytechnic Institute. His research interests include ad hoc/sensor network security, wireless mesh network security, Internet security, and security and privacy in ubiquitous computing environments. Wenjing Lou is an assistant professor in the Electrical and Computer Engineering department at Worcester Polytechnic Institute. She obtained her Ph.D. degree in Electrical and Computer Engineering from University of Florida in 2003. She received the M.A.Sc. degree from Nanyang Technological University, Singapore, in 1998, the M.E. degree and the B.E. degree in Computer Science and Engineering from Xi’an Jiaotong University, China, in 1996 and 1993 respectively. From December 1997 to July 1999, she worked as a Research Engineer in Network Technology Research Center, Nanyang Technological University. Her current research interests are in the areas of ad hoc and sensor networks, with emphases on network and system security and routing. Patrick J. Moran received his MSEE from Carnegie Mellon University, 1993. He is currently the CTO and Founder of AirSprite Technologies Inc, and is driving the company to utilize advanced networking protocols for low-power wireless network systems. His interests include architecture, protocols and high performance implementation of emerging communication technologies. Patrick has been involved in deployment of communication and signal processing technologies since graduating from the University of Minn. in 1986. He holds several patents and publications relating to storage, medical and data processing information systems. He is a member of the IEEE.     

A probabilistic algorithm for efficient and robust data propagation in wireless sensor networks

We study the problem of data propagation in sensor networks, comprised of a large number of very small and low-cost nodes, capable of sensing, communicating and computing. The distributed co-operation of such nodes may lead to the accomplishment of large sensing tasks, having useful applications in practice. We present a new protocol for data propagation towards a control center (“sink”) that avoids flooding by probabilistically favoring certain (“close to optimal”) data transmissions. Motivated by certain applications (see [I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless sensor networks: a survey, Journal of Computer Networks 38 (2002) 393–422], [C. Intanagonwiwat, R. Govindan, D. Estrin, Directed diffusion: a scalable and robust communication paradigm for sensor networks, in: 6th ACM/IEEE Annual International Conference on Mobile Computing (MOBICOM 2000), 2000, pp. 56–67]) and also as a starting point for a rigorous analysis, we study here lattice-shaped sensor networks. We however show that this lattice shape emerges even in randomly deployed sensor networks of sufficient sensor density. Our work is inspired and builds upon the directed diffusion paradigm of [C. Intanagonwiwat, R. Govindan, D. Estrin, Directed diffusion: a scalable and robust communication paradigm for sensor networks, in: 6th ACM/IEEE Annual International Conference on Mobile Computing (MOBICOM 2000), 2000, pp. 56–67].This protocol is very simple to implement in sensor devices, uses only local information and operates under total absence of co-ordination between sensors. We consider a network model of randomly deployed sensors of sufficient density. As shown by a geometry analysis, the protocol is correct, since it always propagates data to the sink, under ideal network conditions (no failures). Using stochastic processes, we show that the protocol is very energy efficient. Also, when part of the network is inoperative, the protocol manages to propagate data very close to the sink, thus in this sense it is robust. We finally present and discuss large-scale simulation findings validating the analytical results.     

State-of-the-art survey of privacy-preserving data aggregation in wireless sensor networks

A state-of-the-art survey of privacy-preserving data aggregation techniques in wireless sensor networks was reviewed.Firstly,preliminaries were introduced,including network models,adversary models,and performance evaluation metrics.Secondly,existing related work was classified into several types according to privacy preservation techniques,such as homomorphic encryption,data perturbation,slicing-mixing technique,generalization,secure multiparty computation,and the key mechanisms of typical protocols were elaborated and analyzed.Finally,the promising future research directions were discussed.     

Pareto-optimal clustering scheme using data aggregation for wireless sensor networks

The presence of cluster heads (CHs) in a clustered wireless sensor network (WSN) leads to improved data aggregation and enhanced network lifetime. Thus, the selection of appropriate CHs in WSNs is a challenging task, which needs to be addressed. A multicriterion decision-making approach for the selection of CHs is presented using Pareto-optimal theory and technique for order preference by similarity to ideal solution (TOPSIS) methods. CHs are selected using three criteria including energy, cluster density and distance from the sink. The overall network lifetime in this method with 50% data aggregation after simulations is 81% higher than that of distributed hierarchical agglomerative clustering in similar environment and with same set of parameters. Optimum number of clusters is estimated using TOPSIS technique and found to be 9–11 for effective energy usage in WSNs.     

Energy efficient node-to-node authentication and communication confidentiality in wireless sensor networks

A distributed Wireless Sensor Network (WSN) is a collection of low-end devices with wireless message exchange capabilities. Due to the scarcity of hardware resources, the lack of network infrastructures, and the threats to security, implementing secure pair-wise communications among any pair of sensors is a challenging problem in distributed WSNs. In particular, memory and energy consumption as well as resilience to sensor physical compromise are the most stringent requirements. In this paper, we introduce a new threat model to communications confidentiality in WSNs, the smart attacker model. Under this new, more realistic model, the security features of previously proposed schemes decrease drastically. We then describe a novel pseudo-random key pre-deployment strategy ESP that combines all the following properties: (a) it supports an energy-efficient key discovery phase requiring no communications; (b) it provides node to node authentication; (c) it is highly resistant to the smart attacker.We provide both asymptotic results and extensive simulations of the schemes that are beingproposed. This work was partially funded by the WEB-MINDS project supported by the Italian MIUR under the FIRB program, and by the PRIN 2003 “Web-based Management and Representation of Spatial and Geographic Data” program from the Italian MIUR. Roberto Di Pietro is partially funded by ISTI-CNR, WNLab, Pisa, with a Post-doc grant under the IS-MANET program. Roberto Di Pietro received the Ph.D. in Computer Science from the University of Roma “La Sapienza”, Italy, in 2004. He received the Bs. and Ms. degree in Computer Science from the University of Pisa, Italy, in 1994. Since 1995 he has been working for the technical branch of the Italian Army and the Internal Affairs Ministry. His main research interests include: security for mobile ad hoc and wireless networks, security for distributed systems, secure multicast, applied cryptography and computer forensics. Luigi V. Mancini received the PhD degre in Computer Science from the University of Newcastle upon Tyne, UK, in 1989, and the Laurea degree in Computer Science from the University of Pisa, Italy, in 1983. From 2000, he is a full professor of Computer Science at the Dipartimento di Informatica of the University of Rome “La Sapienza”. Since 1994, he is a visiting research professor of the Center for Secure Information Systems, GMU, Virginia, USA. Currently he is the advisor of six Ph.D students. His current research interests include: computer network and information security, wireless network security, fault-tolerant distributed systems, large-scale peer-to-peer systems, and hard-real-time distributed systems. He published more than 60 scientific papers in international conferences and journals such as: ACM TISSEC, IEEE TKDE, IEEE TPDS, and IEEE TSE. He served in the program committees of several international conferences which include: ACM Conference on Computer and Communication Security, ACM Conference on Conceptual Modeling, ACM Symposium on Access Control Models and Technology, ACM Workshop of Security of Ad-hoc and Sensor Networks, IEEE Securecomm, IEEE Conference on Cluster Computing. He is also the program chair of the first two editions of the IEEE Workshop on Hot Topics in Peer-to-Peer Systems held in 2004 (Volendam, Holand) and in 2005 (San Diego, California). Currently, he is a member of the Scientific Board of the Italian Communication Police force, and the director of the Master degree program in Computer and Network Security of the University of Rome “La Sapienza”, Italy. Alessandro Mei received the Laurea degree in computer science from the University of Pisa, Italy, in 1994, and the PhD degree in mathematics from the University of Trento, Italy, in 1999. In 1998, he was at the Department of EE-Systems of the University of Southern California, Los Angeles, as a visiting scholar for one year. After holding a postdoctoral position at the University of Trento, in 2001 he joined the Faculty of Science of the University of Rome "La Sapienza", Italy, as an assistant professor of computer science. His main research interests include security of distributed systems and networks, algorithms for parallel, distributed, and optical systems and reconfigurable computing. He was presented with the Best Paper Award of the 16th IEEE International Parallel and Distributed Processing Symposium in 2002, the EE-Systems Outstanding Research Paper Award of the University of Southern California for 2000, and the Outstanding Paper Award of the Fifth IEEE/ACM International Conference on High Performance Computing in 1998. He is a member of the ACM and the IEEE and, from 2005, he is an Associate Editor of IEEE Transactions on Computers.     

The impact of data aggregation on the performance of wireless sensor networks

With the increasing need for different energy saving mechanisms in Wireless Sensor Networks (WSNs), data aggregation techniques for reducing the number of data transmissions by eliminating redundant information have been studied as a significant research problem. These studies have shown that data aggregation in WSNs may produce various trade‐offs among some network related performance metrics such as energy, latency, accuracy, fault‐tolerance and security. In this paper, we investigate the impact of data aggregation on these networking metrics by surveying the existing data aggregation protocols in WSNs. Our aim is twofold: First, providing a comprehensive summary and comparison of the existing data aggregation techniques with respect to different networking metrics. Second, pointing out both the possible future research issues and the need for collaboration between data management and networking research communities working on data aggregation in WSNs. Copyright © 2006 John Wiley & Sons, Ltd.     

An efficient energy consumption model using data aggregation for wireless sensor network

Wireless sensor networks (WSNs) have become increasingly important in recent years. Small and low-power sensor nodes make up these sensor networks. A random distribution of nodes is made throughout an unmanaged target region. One of WSN's key challenges is its limited and irreplaceable energy supply. In most situations, sensor nodes cannot be replaced since they operate in a hostile physical environment. The act of gathering and aggregating usable data from different sensor nodes situated to perceive almost the same attribute of the occurrence is known as data aggregation. The mathematical model is used in this research study to generate cluster-based data aggregation, which is an effective technique to increase energy usage by minimising the number of data transfers. The proposed mathematical model-based data aggregation (MM-DA) attains a 97% packet delivery ratio with minimal energy consumption. The MM-DA outperforms other existing approaches in terms of packet delivery ratio (PDR), energy consumption (EC), network lifetime and control overhead.     

Secure data aggregation in wireless sensor networks using homomorphic encryption

In a Wireless Sensor Network (WSN), aggregation exploits the correlation between spatially and temporally proximate sensor data to reduce the total data volume to be transmitted to the sink. Mobile agents (MAs) fit into this paradigm, and data can be aggregated and collected by an MA from different sensor nodes using context specific codes. The MA-based data collection suffers due to large size of a typical WSN and is prone to security problems. In this article, homomorphic encryption in a clustered WSN has been proposed for secure and efficient data collection using MAs. The nodes keep encrypted data that are given to an MA for data aggregation tasks. The MA performs all the data aggregation operations upon encrypted data as it migrates between nodes in a tree-like structure in which the nodes are leafs and the cluster head is the root of the tree. It returns and deposits the encrypted aggregated data to the cluster head after traversing through all the intra cluster nodes over a shortest path route. The homomorphic encryption and aggregation processing in encrypted domain makes the data collection process secure. Simulation results confirm the effectiveness of the proposed secure data aggregation mechanism. In addition to security, MA-based mechanism leads to lesser delay and bandwidth requirements.     

Energy efficient path selection for mobile sink and data gathering in wireless sensor networks

Mobile sink (MS) has drawn significant attention for solving hot spot problem (also known as energy hole problem) that results from multihop data collection using static sink in wireless sensor networks (WSNs). MS is regarded as a potential solution towards this problem as it significantly reduces energy consumption of the sensor nodes and thus enhances network lifetime. In this paper, we first propose an algorithm for designing efficient trajectory for MS, based on rendezvous points (RPs). We next propose another algorithm for the same problem which considers delay bound path formation of the MS. Both the algorithms use k-means clustering and a weight function by considering several network parameters for efficient selection of the RPs by ensuring the coverage of the entire network. We also propose an MS scheduling technique for effective data gathering. The effectiveness of the proposed algorithms is demonstrated through rigorous simulations and comparisons with some of the existing algorithms over several performance metrics.     

Energy‐efficient and localized lossy data aggregation in asynchronous sensor networks

In wireless sensor networks, most data aggregation scheduling methods let all nodes aggregate data in every time instance. It is not energy efficient and practical because of link unreliability and data redundancy. This paper proposes a lossy data aggregation (LDA) scheme to reduce traffic and save energy. LDA selects partial child nodes to sample data at partial time slots and allows estimated aggregation at parent nodes or a root in a network. We firstly consider that all nodes sample data synchronously and find that the error between the real value of a physical parameter and that measured by LDA is bounded respectively with and without link unreliability. Detailed analysis is given on error bound when a confidence level is previously assigned to the root by a newly designed algorithm. Thus, each parent can determine the minimum number of child nodes needed to achieve its assigned confidence level. We then analyze a probability to bound the error with a confidence level previously assigned to the root when all nodes sample data asynchronously. An algorithm then is designed to implement our data aggregation under asynchronization. Finally, we implement our experiment on the basis of real test‐beds to prove that the scheme can save more energy than an existing algorithm for node selection, Distributive Online Greedy (DOG). Copyright © 2012 John Wiley & Sons, Ltd.