We present a joint scheme that combines both error correction and security at the physical layer. In conventional communication systems, error correction is carried out at the physical layer while data security is performed at an upper layer. As a result, these steps are done as separate steps. However there has been a lot of interest in providing security at the physical layer. As a result, as opposed to the conventional system, we present a scheme that combines error correction and data security as one unit so that both encryption and encoding could be carried out at the physical layer. Hence, in this paper, we present an Error Correction-Based Cipher (ECBC) that combines error correction and encryption/decryption in a single step. Encrypting and encoding or decoding and decrypting in a single step will lead to a faster and more efficient implementation. One of the challenges of using previous joint schemes in a communications channel is that there is a tradeoff between data reliability and security. However, in ECBC, there is no tradeoff between reliability and security. Errors introduced at the transmitter for randomization are removed at the receiver. Hence ECBC can utilize its full capacity to correct channel errors. We show the result of randomization test on ECBC and its security against conventional attacks. We also present the nonpipelined and pipelined hardware architecture of ECBC, and the result of the FPGA implementation of the ECBC encryption. We also compare these results with non-ECBC schemes.

SUMMARYWe consider a structural approach to the consensus building problem in multi‐group multi‐layer (MGML) distributed sensor networks (DSNs) common in many natural and engineering applications. From among the possible network structures, we focus on bipartite graph structure as it represents a typical MGML structure and has a wide applicability in the real world. We establish exact conditions for consensus and derive a precise relationship between the consensus value and the degree distribution of nodes in a bipartite MGML DSN. We also demonstrate that for subclasses of connectivity patterns, convergence time and simple characteristics of network topology can be captured by explicit algebra. Direct inference of the convergence behavior of consensus strategies from MGML DSN structure is the main contribution of this paper. The insights gained from our analysis facilitate the design and development of large‐scale DSNs that meet specific performance criteria. Copyright © 2012 John Wiley & Sons, Ltd.

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Reliable localization is an essential building block of sensor networks. Many techniques have taken advantage of the received signal strength (RSS) measurement for location estimation in wireless sensor networks, since no special hardware implementation is required to measure RSS in almost all kinds of wireless systems. In this paper, two such techniques, MDS method and MLE that are recently proposed for collaborative location estimation, are studied in detail. From the theoretical formulation of the RSS-based location estimation problem, it is seen that MLE is more appropriate than MDS method. However, from simulation studies of both algorithms, which are iterative in nature, it is found that MLE is more sensitive to initial estimate than MDS method. Therefore, in this paper we propose to integrate these two techniques in series so that an estimate is first obtained using MDS method by taking advantage of its better convergence property, then MLE is employed to fine-tune the solution of MDS method to remove modeling errors that are inherent in MDS method. Through extensive simulations it is demonstrated that the new integrated method, named MDS-MLE, consistently outperforms both MDS method and MLE in various simulation scenarios. In this paper, we also address many important issues in collaborative localization, including effects of sensor node density, reference node density, and different deployment strategies of reference nodes.

We consider a structural approach to the consensus building problem in multi-group multi-layer (MGML) distributed sensor networks (DSNs) common in many natural and engineering applications. From among the possible network structures, we focus on bipartite graph structure as it represents a typical MGML structure and has a wide applicability in the real world. We establish exact conditions for consensus and derive a precise relationship between the consensus value and the degree distribution of nodes in a bipartite MGML DSN. We also demonstrate that for subclasses of connectivity patterns, convergence time and simple characteristics of network topology can be captured by explicit algebra. Direct inference of the convergence behavior of consensus strategies from MGML DSN structure is the main contribution of this paper. The insights gained from our analysis facilitate the design and development of large-scale DSNs that meet specific performance criteria. INTRODUCTIONConsensus problem widely appears in natural and engineering applications and has received extensive study in a multitude of fields including sensor networking, distributed computing, and decentralized control [1-5] among others. However, it has not been investigated extensively in networks with multi-group multi-layer (MGML) communication structures; that is, networks composed of multiple groups, in each of which some nodes are designated as leaders and given the responsibility of communicating with other groups through certain layered communication schemes. Such communication structure is commonly observed in nature and has tremendous potential for engineered systems. For instance, this structure is typically observed in bird flocking as studied in, for example, [6,7]. Similarly, in distributed sensor networks (DSNs), properly designed MGML communication structure is envisioned to be more secure and energy efficient [8,9].Network topology has been known to be crucial to the performance of consensus strategies, particularly to convergence time (see e.g., [10,11]), which impacts communication overhead and network lifetime in DSN applications. In designing MGML network structures for consensus building, one question is whether there exists a quantitative relationship between the network structure and its performance. This question is particularly important in large-scale networks, where scalability is of critical concern. We address this issue by studying the network design problem for some common MGML structures. Examples of such structures are illustrated in Figure 1. Bipartite graph structure (Figure 1(a)), which captures a wide arrange of 2-layer lead-follower communication topology, is widely used in coding theory [12,13] and has been recently adopted As is well known and can be found in, for example, [1,3], the convergence time (i.e., the number of iterations such that the difference between sensor value and final consensus value is within ı ‡ Ergodic implies both recurrent and aperiodic. T 1 3 1 3 1 3 /.I 3 3˝0.25 1 4 /xOEk. Explicit results charac...