Zero-Knowledge Proofs (ZKPs), especially zk-SNARKs, rely heavily on Multi-Scalar Multiplication (MSM), a compute-intensive elliptic curve operation. While prior work targets curves with optimized operations like BLS12-377, MSM on general-purpose curves such as BLS12-381 remains challenging due to imbalanced resource usage, performance gaps between curve operations, and low utilization of point addition. This paper proposes FlexMSM to support scalable MSM cores on a single FPGA for BLS12-381 curve, delivering significant gains over existing works for input sizes from 218 to 226 .

Modular Inversion (MI) is one of the fundamental arithmetic operations in the finite field, which plays an essential role in various cryptographic applications and requires high performance and security. Unfortunately, the simple MI algorithm is vulnerable to side-channel attacks, such as the timing attack, which can compromise the cryptographic system by analyzing the time taken to execute cryptographic algorithms. Attackers may recover the initial data since the time can differ based on the input. Besides, the low complexity and low resource consumption of hardware implementations in MI are also challenging. In this article, we propose two novel modular inversion algorithms, named Constant-Time Integer Modular Inversion (CT-IMI) and Constant-Time Complementary Montgomery Modular Inversion (CT-CMMI). They both consist of constant iteration rounds to resist the timing attack. CT-IMI processes the data in the integer field, which is designed for common scenarios. CT-CMMI is suitable for the cross-domain case, which can directly use data in the Montgomery domain and avoid the conversion steps for some specific applications, e.g., scalar multiplication in Elliptic Curve Cryptography (ECC). In software simulations, we measure the average clock cycles for a single inversion and illustrate the relationship between various bit lengths and the latency. The significant differences between constant and non-constant algorithms demonstrate the vulnerability of modular inversion to timing attacks. In addition, we design two efficient hardware architectures on FPGA. Experimental results show that our CT-IMI can finish a single inversion in 2.56 \(\mu\) s with 4.2k LUTs, 1.8k FFs, and our CT-CMMI requires 2.45 \(\mu\) s with 2.7k LUTs, 1.6k FFs. The product of area and latency of our CT-IMI and CT-CMMI can reach 10.50 and 6.62, respectively, which shows optimal performance compared with all the results in the existing literature.

Time series anomaly detection has been received growing interest in industrial and academic communities due to its substantial theoretical value and practical significance in reality. Recent advanced methods for time series anomaly detection are based on deep learning techniques, since they have shown their superiority in some specific situations. However, most existing deep learning-based anomaly detection methods require predefined, specific tasks of reconstruction or prediction, necessitating task-specific loss functions. Designing such anomaly-aware loss functions poses a significant challenge due to the ambiguity in defining ground-truth anomalies. Moreover, these methods often rely on complex network architectures that tend to lead to over-generalization, resulting in even abnormal data being well reconstructed or fitted. To mitigate this situation, grounded in activation learning theory, we propose a novel unsupervised time series anomaly detection paradigm termed ALAD. ALAD utilizes a straightforward fully connected network architecture, measuring the typicality of input patterns through the sum of the squared output. Despite its simplicity, ALAD achieves competitive performance compared to state-of-the-art models trained using backpropagation. By utilizing various real-world and synthetic datasets, experimental results have confirmed the effectiveness and feasibility of the proposed paradigm. This work also demonstrates that biologically-plausible local learning can sometimes outperform backpropagation in real-world scenarios.

Authenticated Encryption with Associated-Data (AEAD) can ensure both confidentiality and integrity of information in encrypted communication. Distinctive variants are customized from AEAD to satisfy various requirements. In this paper, we take a 128-bit lightweight AEAD stream cipher Fountain as an example. We provide a general cryptographic solution with three Fountain variants. These three variants are for encryption, message authentication code (MAC) generation, and authenticated encryption with associated data, respectively. Besides, we propose area-saved and throughput-improved strategies for the FPGA implementation of Fountain. The conventional paralleled hardware implementation leads to much resource-consuming with higher parallel width. We propose a hybrid architecture with parallel and serial update modes simultaneously. We also analyze the trade-off between area occupation and authentication latency for those two architectures. According to our discussion, hybrid architectures can perform efficiently with higher throughput than most ciphers, including Grain-128 x32. Our Fountain keystream generator occupies 46 slices on Spartan-3 FPGAs, smaller than most ciphers with the same security level, and even smaller than the 80-bit security level cipher Trivium. In summary, the customized Fountain with optimized implementations on FPGA is suitable for various applications in the field of IoT.

View change causes significant differences in the gait appearance. Consequently, recognizing gait in cross-view scenarios is highly challenging. Most recent approaches either convert the gait from the original view to the target view before recognition is carried out or extract the gait feature irrelevant to the camera view through either brute force learning or decouple learning. However, these approaches have many constraints, such as the difficulty of handling unknown camera views. This work treats the view-change issue as a domain-change issue and proposes to tackle this problem through adversarial domain adaptation. This way, gait information from different views is regarded as the data from different sub-domains. The proposed approach focuses on adapting the gait feature differences caused by such sub-domain change and, at the same time, maintaining sufficient discriminability across the different people. For this purpose, a Hierarchical Feature Aggregation (HFA) strategy is proposed for discriminative feature extraction. By incorporating HFA, the feature extractor can well aggregate the spatial-temporal feature across the various stages of the network and thereby comprehensive gait features can be obtained. Then, an Adversarial View-change Elimination (AVE) module equipped with a set of explicit models for recognizing the different gait viewpoints is proposed. Through the adversarial learning process, AVE would not be able to identify the gait viewpoint in the end, given the gait features generated by the feature extractor. That is, the adversarial domain adaptation mitigates the view change factor, and discriminative gait features that are compatible with all sub-domains are effectively extracted. Extensive experiments on three of the most popular public datasets, CASIA-B, OULP, and OUMVLP richly demonstrate the effectiveness of our approach.

Shannon showed that to achieve perfect secrecy in point-to-point communication, the message rate cannot exceed the shared secret key rate giving rise to the simple one-time pad encryption scheme. In this paper, we extend this work from point-to-point to networks. We consider a connected network with pairwise communication between the nodes and assume that each node is provided with a certain amount of secret bits before communication commences. An eavesdropper with unlimited computing power has access to all communication and can hack a subset of the nodes not known to the rest of the nodes. We investigate the limits on information-theoretic secure communication with end-to-end encryption for this network. We establish a tradeoff between the secure channel rate (for a node pair) and the secure network rate (sum over all node pair rates) and show that information-theoretic secrecy can be achieved asymptotically if and only if the sum rate of any subset of unhacked channels does not exceed the shared unhacked-secret-bit rate of these channels. We also propose a practical scheme that achieves a good balance of network and channel rates with information-theoretic secrecy guarantee. This work has a wide range of potential applications for which strong secrecy is desired, such as cyber-physical systems, distributed-control systems, and ad-hoc networks.

Shannon showed that to achieve perfect secrecy in point-to-point communication, the message rate cannot exceed the shared secret key rate giving rise to the simple one-time pad encryption scheme. In this paper, we extend this work from point-to-point to networks. We consider a connected network with pairwise communication between the nodes. We assume that each node is provided with a certain amount of secret bits before communication commences. An eavesdropper with unlimited computing power has access to all communication and can hack a subset of the nodes not known to the rest of the nodes. We investigate the limits on information-theoretic secure communication for this network. We establish a tradeoff between the secure channel rate (for a node pair) and the secure network rate (sum over all node pair rates) and show that perfect secrecy can be achieved if and only if the sum rate of any subset of unhacked channels does not exceed the shared unhacked-secret-bit rate of these channels. We also propose two practical and efficient schemes that achieve a good balance of network and channel rates with perfect secrecy guarantee. This work has a wide range of potential applications for which perfect secrecy is desired, such as cyber-physical systems, distributed-control systems, and ad-hoc networks.

We investigate the fundamental limits of communication over optical on-off-keying channels with crosstalk, where a light pulse may span over multiple time slots or spatial pixels, and the receiver is equipped with single-photon detectors. First, we analyze achievable rates of communication over such channels, and observe that increasing transmission power (expected number of photons emitted per slot or pixel) does not necessarily lead to higher rates. Under simple but reasonable models, the highest rates are often achieved in a low-photon regime, with an average of 3 to 7 photons received in each slot or pixel. We further characterize the tradeoff between information rate and photon efficiency (in terms of the expected number of bits transmitted per photon) in the presence of crosstalk. Finally, we develop guidelines for slot length and pixel size selection for different application scenarios. Our analysis reveals that optimum optical-communication systems do not minimize the level of crosstalk.

The rank-modulation scheme has been recently proposed for efficiently storing data in nonvolatile memories. In this paper, we explore [n, k, d] systematic error-correcting codes for rank modulation. Such codes have length n, k information symbols, and minimum distance d. Systematic codes have the benefits of enabling efficient information retrieval in conjunction with memory-scrubbing schemes. We study systematic codes for rank modulation under Kendall's T-metric as well as under the ℓ∞-metric. In Kendall's T-metric, we present [k + 2, k, 3] systematic codes for correcting a single error, which have optimal rates, unless systematic perfect codes exist. We also study the design of multierror-correcting codes, and provide a construction of [k + t + 1, k, 2t + 1] systematic codes, for large-enough k. We use nonconstructive arguments to show that for rank modulation, systematic codes achieve the same capacity as general error-correcting codes. Finally, in the ℓ∞-metric, we construct two [n, k, d] systematic multierror-correcting codes, the first for the case of d = 0(1) and the second for d = Θ(n). In the latter case, the codes have the same asymptotic rate as the best codes currently known in this metric.

In this paper, we investigate the limits of communication over optical on-off-keying channels with 1-D or 2-D crosstalk, where photons are transferable between adjacent time slots or spatial pixels, and the receiver is equipped with single-photon detectors.We observe that high transmission power (measured by the expected number of photons emitted in each signal slot or pixel) may not lead to high information rate; the maximum capacity is typically achieved in a low-photon regime - with about expected 3 to 8 photons received in each signal slot or pixel. Furthermore, we study the selection of slot length for maximizing the channel bandwidth, as the slot length affects the crosstalk probability and hence the channel capacity. It reveals that optimum optical-communication systems do not minimize the level of crosstalk between slots or pixels.

In this paper, we study the problem of compressing a collection of sequences of variable length that allows us to efficiently add, read, or edit an arbitrary sequence without decompressing the whole data. This problem has important applications in data servers, file-editing systems, and bioinformatics. We propose a novel and practical compression scheme, which shows that, by paying a small price in storage space (3% extra storage space in our examples), we can retrieve or edit a sequence (a few hundred bits) by accessing compressed bits close to the entropy of the sequence.

A stochastic flow network is a directed graph with incoming edges (inputs) and outgoing edges (outputs), tokens enter through the input edges, travel stochastically in the network, and can exit the network through the output edges. Each node in the network is a splitter, namely, a token can enter a node through an incoming edge and exit on one of the output edges according to a predefined probability distribution. Stochastic flow networks can be easily implemented by beam splitters, or by DNA-based chemical reactions, with promising applications in optical computing, molecular computing and stochastic computing. In this paper, we address a fundamental synthesis question: Given a finite set of possible splitters and an arbitrary rational probability distribution, design a stochastic flow network, such that every token that enters the input edge will exit the outputs with the prescribed probability distribution. The problem of probability transformation dates back to von Neumann’s 1951 work and was followed, among others, by Knuth and Yao in 1976. Most existing works have been focusing on the “simulation” of target distributions. In this paper, we design optimal-sized stochastic flow networks for “synthesizing” target distributions. It shows that when each splitter has two outgoing edges and is unbiased, an arbitrary rational probability${{ {a}} \over { {b}}}$with${ {a}} \leq { {b}} \leq {{ 2}^{{n}}}$can be realized by a stochastic flow network of size${ {n}}$that is optimal. Compared to the other stochastic systems, feedback (cycles in networks) strongly improves the expressibility of stochastic flow networks.

Secret key extraction, the task of extracting a secret key from shared information that is partially known by an eavesdropper, has important applications in cryptography. Motivated by the requirements of high-speed quantum key distribution, we study secret-key extraction methods with simple and efficient hardware implementations, in particular, linear transformations based on low-density random matrices. We show that this method can achieve the information-theoretic upper bound (conditional Shannon entropy) on efficiency for a wide range of key-distribution systems. In addition, we introduce a numerical method that allows us to tightly estimate the quality of the generated secret key in the regime of finite block length, and use this method to demonstrate that low-density random matrices achieve very high performance for secret key extraction.

High-dimensional quantum key distribution (QKD) systems that exploit temporal correlation among entangled photons are of growing practical interest. In such systems, the observation time is typically partitioned into frames of fixed duration, with pulse-position modulation (PPM) coding used within each frame, via which a secret key is established between the parties. Such schemes can be very inefficient in their use of photons, since only a fraction of the frames can be used. As an alternative, we describe an efficient class of schemes with adaptive frame size whose performance can converge to the fundamental limit much more quickly. We analyze and compare the performances of both fixed and adaptive PPM schemes, taking into account photon transmission and detection losses. Further numerical results reveal the significant performance gain of adaptive PPM relative to fixed PPM.

The construction of asymmetric error-correcting codes is a topic that was studied extensively, however; the existing approach for code construction assumes that every codeword should toleratetasymmetric errors. Our main observation is that in contrast to symmetric errors, asymmetric errors are content dependent. For example, in Z-channels, the all-1 codeword is prone to have more errors than the all-0 codeword. This motivates us to develop nonuniform codes whose codewords can tolerate different numbers of asymmetric errors depending on their Hamming weights. The idea in a nonuniform codes' construction is to augment the redundancy in a content-dependent way and guarantee the worst case reliability while maximizing the code size. In this paper, we first study nonuniform codes for Z-channels, namely, they only suffer one type of errors, say 1→ 0. Specifically, we derive their upper bounds, analyze their asymptotic performances, and introduce two general constructions. Then, we extend the concept and results of nonuniform codes to general binary asymmetric channels, where the error probability for each bit from 0 to 1 is smaller than that from 1 to 0.

We study the problem of extracting a prescribed number of random bits by reading the smallest possible number of symbols from non-ideal stochastic processes. The related interval algorithm proposed by Han and Hoshi has asymptotically optimal performance; however, it assumes that the distribution of the input stochastic process is known. The motivation for our work is the fact that, in practice, sources of randomness have inherent correlations and are affected by measurement's noise. Namely, it is hard to obtain an accurate estimation of the distribution. This challenge was addressed by the concepts of seeded and seedless extractors that can handle general random sources with unknown distributions. However, known seeded and seedless extractors provide extraction efficiencies that are substantially smaller than Shannon's entropy limit. Our main contribution is the design of extractors that have a variable input-length and a fixed output length, are efficient in the consumption of symbols from the source, are capable of generating random bits from general stochastic processes and approach the information theoretic upper bound on efficiency.

The rank modulation scheme has been proposed recently for efficiently writing and storing data in nonvolatile memories. Error-correcting codes are very important for rank modulation, and they have attracted interest among researchers. In this work, we explore a new approach, systematic error-correcting codes for rank modulation. In an (n, k) systematic code, we use the permutation induced by the levels of n cells to store data, and the permutation induced by the first k cells (k <; n) has a one-to-one mapping to information bits. Systematic codes have the benefits of enabling efficient information retrieval and potentially supporting more efficient encoding and decoding procedures. We study systematic codes for rank modulation equipped with the Kendall's τ-distance. We present (k + 2, k) systematic codes for correcting one error, which have optimal sizes unless perfect codes exist. We also study the design of multi-error-correcting codes, and prove that for any 2 ≤ k <; n, there always exists an (n, k) systematic code of minimum distance n-k. Furthermore, we prove that for rank modulation, systematic codes achieve the same capacity as general error-correcting codes.

The problem of random number generation from an uncorrelated random source (of unknown probability distribution) dates back to von Neumann's 1951 work. Elias (1972) generalized von Neumann's scheme and showed how to achieve optimal efficiency in unbiased random bits generation. Hence, a natural question is what if the sources are correlated? Both Elias and Samuelson proposed methods for generating unbiased random bits in the case of correlated sources (of unknown probability distribution), specifically, they considered finite Markov chains. However, their proposed methods are not efficient or have implementation difficulties. Blum (1986) devised an algorithm for efficiently generating random bits from degree-2 finite Markov chains in expected linear time, however, his beautiful method is still far from optimality on information-efficiency. In this paper, we generalize Blum's algorithm to arbitrary degree finite Markov chains and combine it with Elias's method for efficient generation of unbiased bits. As a result, we provide the first known algorithm that generates unbiased random bits from an arbitrary finite Markov chain, operates in expected linear time and achieves the information-theoretic upper bound on efficiency.

For many nonvolatile memories, - including flash memories, phase-change memories, etc., - maximizing the storage capacity is a key challenge. The existing method is to use multilevel cells (MLC) of more and more levels. The number of levels supported by MLC is seriously constrained by the worst-case performance of cell-programming noise and cell heterogeneity. In this paper, we present variable-level cells (VLC), a new scheme for maximum storage capacity. It adaptively chooses the number of levels and the placement of the levels based on the actual programming performance. We derive its storage capacity, and present an optimal data representation scheme. We also study rewriting schemes for VLC, and present inner and outer bounds to its capacity region.

Phase-change memory (PCM) is an emerging nonvolatile memory technology that promises very high performance. It currently uses discrete cell levels to represent data, controlled by a single amorphous/crystalline domain in a cell. To improve data density, more levels per cell are needed. There exist a number of challenges, including cell programming noise, drifting of cell levels, and the high power requirement for cell programming. In this paper, we present a new cell structure called patterned cell, and explore its data representation schemes. Multiple domains per cell are used, and their connectivity is used to store data. We analyze its storage capacity, and study its error-correction capability and the construction of error-control codes.

Linear transformations have many applications in information theory, like data compression and error-correcting codes design. In this paper, we study the power of linear transformations in randomness extraction, namely linear extractors, as another important application. Comparing to most existing methods for randomness extraction, linear extractors (especially those constructed with sparse matrices) are computationally fast and can be simply implemented with hardware like FPGAs, which makes them very attractive in practical use. We mainly focus on simple, efficient and sparse constructions of linear extractors. Specifically, we demonstrate that random matrices can generate random bits very efficiently from a variety of noisy sources, including noisy coin sources, bit-fixing sources, noisy (hidden) Markov sources, as well as their mixtures. It shows that low-density random matrices have almost the same efficiency as high-density random matrices when the input sequence is long, which provides a way to simplify hardware/software implementation. Note that although we constructed matrices with randomness, they are deterministic (seedless) extractors - once we constructed them, the same construction can be used for any number of times without using any seeds. Another way to construct linear extractors is based on generator matrices of primitive BCH codes. This method is more explicit, but less practical due to its computational complexity and dimensional constraints.

Codes that correct asymmetric errors have important applications in storage systems, including optical disks and Read Only Memories. The construction of asymmetric error correcting codes is a topic that was studied extensively, however, the existing approach for code construction assumes that every codeword could sustain t asymmetric errors. Our main observation is that in contrast to symmetric errors, where the error probability of a codeword is context independent (since the error probability for 1s and 0s is identical), asymmetric errors are context dependent. For example, the all-1 codeword has a higher error probability than the all-0 codeword (since the only errors are 1 → 0). We call the existing codes uniform codes while we focus on the notion of nonuniform codes, namely, codes whose codewords can tolerate different numbers of asymmetric errors depending on their Hamming weights. The goal of nonuniform codes is to guarantee the reliability of every codeword, which is important in data storage to retrieve whatever one wrote in. We prove an almost explicit upper bound on the size of nonuniform asymmetric error correcting codes and present two general constructions. We also study the rate of nonuniform codes compared to uniform codes and show that there is a potential performance gain.

Predetermined fixed thresholds are commonly used in nonvolatile memories for reading binary sequences, but they usually result in significant asymmetric errors after a long duration, due to voltage or resistance drift. This motivates us to construct error-correcting schemes with dynamic reading thresholds, so that the asymmetric component of errors are minimized. In this paper, we discuss how to select dynamic reading thresholds without knowing cell level distributions, and present several error-correcting schemes. Analysis based on Gaussian noise models reveals that bit error probabilities can be significantly reduced by using dynamic thresholds instead of fixed thresholds, hence leading to a higher information rate.

Schemes for probabilistic computation can exploit physical sources to generate random values in the form of bit streams. Generally, each source has a fixed bias and so provides bits with a specific probability of being one. If many different probability values are required, it can be expensive to generate all of these directly from physical sources. This paper demonstrates novel techniques for synthesizing combinational logic that transforms source probabilities into different target probabilities. We consider three scenarios in terms of whether the source probabilities are specified and whether they can be duplicated. In the case that the source probabilities are not specified and can be duplicated, we provide a specific choice, the set {0.4, 0.5} ; we show how to synthesize logic that transforms probabilities from this set into arbitrary decimal probabilities. Further, we show that for any integern≥ 2, there exists a single probability that can be transformed into arbitrary base-nfractional probabilities. In the case that the source probabilities are specified and cannot be duplicated, we provide two methods for synthesizing logic to transform them into target probabilities. In the case that the source probabilities are not specified, but once chosen cannot be duplicated, we provide an optimal choice.

The problem of random number generation from an uncorrelated random source (of unknown probability distribution) dates back to von Neumann's 1951 work. Elias (1972) generalized von Neumann's scheme and showed how to achieve optimal efficiency in unbiased random bits generation. Hence, a natural question is what if the sources are correlated? Both Elias and Samueleson proposed methods for generating unbiased random bits in the case of correlated sources (of unknown probability distribution), specifically, they considered finite Markov chains. However, their proposed methods are not efficient (Samueleson) or have implementation difficulties (Elias). Blum (1986) devised an algorithm for efficiently generating random bits from degree-2 finite Markov chains in expected linear time, however, his beautiful method is still far from optimality. In this paper, we generalize Blum's algorithm to arbitrary degree finite Markov chains and combine it with Elias's method for efficient generation of unbiased bits. As a result, we provide the first known algorithm that generates unbiased random bits from an arbitrary finite Markov chain, operates in expected linear time and achieves the information-theoretic upper bound on efficiency.

A stochastic flow network is a directed graph with incoming edges (inputs) and outgoing edges (outputs), tokens enter through the input edges, travel stochastically in the network and can exit the network through the output edges. Each node in the network is a splitter, namely, a token can enter a node through an incoming edge and exit on one of the output edges according to a predefined probability distribution. We address the following synthesis question: Given a finite set of possible splitters and an arbitrary rational probability distribution, design a stochastic flow network, such that every token that enters the input edge will exit the outputs with the prescribed probability distribution. The problem of probability synthesis dates back to von Neummann's 1951 work and was followed, among others, by Knuth and Yao in 1976, who demonstrated that arbitrary rational probabilities can be generated with tree networks; where minimizing the expected path length, the expected number of coin tosses in their paradigm, is the key consideration. Motivated by the synthesis of stochastic DNA based molecular systems, we focus on designing optimal-sized stochastic flow networks (the size of a network is the number of splitters). We assume that each splitter has two outgoing edges and is unbiased (probability 1/2 per output edge). We show that an arbitrary rational probability a/b with a ≤ b ≤ 2ncan be realized by a stochastic flow network of size n, we also show that this is optimal. We note that our stochastic flow networks have feedback (cycles in the network), in fact, we demonstrate that feedback improves the expressibility of stochastic flow networks, since without feedback only probabilities of the form a/(2n) (a an integer) can be realized.

Many natural systems, including chemical and biological systems, can be modeled using stochastic switching circuits. These circuits consist of stochastic switches, called pswitches, which operate with a fixed probability of being open or closed. We study the effect caused by introducing an error of size. to each pswitch in a stochastic circuit. We analyze two constructions.simple series-parallel and general series-parallel circuits.and prove that simple series-parallel circuits are robust to small error perturbations, while general series-parallel circuits are not. Specifically, the total error introduced by perturbations of size less than isin is bounded by a constant multiple of isin in a simple series-parallel circuit, independent of the size of the circuit. However, the same result does not hold in the case of more general series-parallel circuits. In the case of a general stochastic circuit, we prove that the overall error probability is bounded by a linear function of the number of pswitches.

Stochastic switching circuits are relay circuits that consist of stochastic switches (that we call pswitches). We study the expressive power of these circuits; in particular, we address the following basic question: given an arbitrary integer q, and a pswitch set {1/q, 2/q, ..., q-1/q}, can we realize any rational probability with denominator qn(for arbitrary n) by a simple series-parallel stochastic switching circuit? In this paper, we generalized previous results and prove that when q is a multiple of 2 or 3 the answer is positive. We also show that when q is a prime number the answer is negative. In addition, we prove that any desired probability can be approximated well by a linear in n size circuit, with error less than q-n.

Wireless sensor networks are often composed of resource-constrained sensor nodes with limited memory space, computational capacity and communication range. The links in WSN are often lossy and unreliable. In order to make fluent and reliable data transport on memory-constrained sensor nodes, we propose a new transport layer protocol reliable transport with memory consideration (RTMC), which provides both hop-by-hop retransmission and congestion control. We have implemented RTMC on MICA2 with only 4 K bytes RAM. Experiment, analysis and simulation results show that RTMC can use channel resource effectively and enable all of the segments to be received by the sink with low transport time and low memory cost.

Balancing energy consumption and prolonging network lifetime are open challenges in Wireless Sensor Networks. In this paper, we design a novel load balanced routing protocol to maximize lifetime of Wireless Sensor Networks (WSNs). In order to balance the energy consumption among sensor nodes, we deploy multiple sinks simultaneously which are connected though wired or wireless infrastructure. We introduce a potential model and propose a routing scheme in which sensor nodes construct routes based on local topology information and the state information from sinks' broadcast messages. Sinks monitor their traffic load and adjust their own parameters to balance the traffic load in the network. Theoretical analysis and simulation results show that our protocol can significantly improve the performance of the system in following aspects: robustness, lifetime and reliability.