Adaptive Joint Lossy Source-Channel Coding for Multihop IoT Networks

Error control coding improves reliability and efficiency of wireless communication systems. This research delves into the latest advancements in error control coding schemes for wireless communication systems, with a specific focus on their application within the domain of Low Power Wide Area Networks (LPWANs) such as Narrowband Internet of Things (NB-IoT) systems, LoRa energy-efficient hardware design, random access control systems, and wireless sensor networks. In the context of LPWANs, particularly NB-IoT systems, we investigate the adaptive coding and modulation (ACM) in NB-IoT systems, which adapts coding rates and modulation techniques to changing channel circumstances. Comprehensive analysis of the literature review shows that the proposed approaches are more energy efficient and less error-prone than fixed coding and modulation schemes. Using Cell Design Methodology (CDM), Single Error Correction (SEC) codes are optimized for energy efficiency. Power and energy efficiency have improved in standard CMOS and CDM logic systems. This impacts IoT devices, memory storage, and security systems. Next, Forward Error Correction (FEC) is examined for ALOHA-based random-access control systems wherein packet loss rate and spectral efficiency metrics are mathematically calculated. FEC is beneficial, especially when time and frequency synchronization diverge. Monte Carlo simulations studied in the literature shows that FEC improves satellite communication systems. This review establishes benefits of FEC, notably in satellite communications systems. In addition, Wireless Sensor Networks (WSNs) error control techniques and network reliability and performance are examined along with Convolutional, Turbo, Low density parity check codes(LDPC), and Polar error correction coding (ECC) methods. We learn about their benefits, drawbacks, and implementation challenges by assessing their efficacy, efficiency, and applicability in wireless sensor networks (WSN) applications. This study further highlights advancements of energy-efficient error control coding when, integrated into network designs alongwith the trade-offs between efficacy and intricacy to help improve wireless communication networks. Finally, this study summarizes current error control coding approaches for wireless communication systems. We provide insights into improving reliability, energy conservation, and efficacy by examining ACM, CDM, FEC, and error correction coding (ECC) across varied domains including LPWAN and IoT technologies with earnest hope to facilitate researchers, engineers, and professionals working in the field of error control coding in wireless communication.

In today’s information technology (IT) world, the multi-hop wireless sensor networks (MHWSNs) are considered the building block for the Internet of Things (IoT) enabled communication systems for controlling everyday tasks of organizations and industry to provide quality of service (QoS) in a stipulated time slot to end-user over the Internet. Smart city (SC) is an example of one such application which can automate a group of civil services like automatic control of traffic lights, weather prediction, surveillance, etc., in our daily life. These IoT-based networks with multi-hop communication and multiple sink nodes provide efficient communication in terms of performance parameters such as throughput, energy efficiency, and end-to-end delay, wherein low latency is considered a challenging issue in next-generation networks (NGN). This paper introduces a single and parallels stable server queuing model with a multi-class of packets and native and coded packet flow to illustrate the simple chain topology and complex multiway relay (MWR) node with specific neighbor topology. Further, for improving data transmission capacity in MHWSNs, an analytical framework for packet transmission using network coding at the MWR node in the network layer with opportunistic listening is performed by considering bi-directional network flow at the MWR node. Finally, the accuracy of the proposed multi-server multi-class queuing model is evaluated with and without network coding at the network layer by transmitting data packets. The results of the proposed analytical framework are validated and proved effective by comparing these analytical results to simulation results.

We study networked control of a dynamical process over a lossy channel with a hybrid automatic repeat request protocol that connects a sensor to an actuator. The dynamical process is modeled by a Gauss-Markov process, and the lossy channel by a packet-erasure channel with ideal feedback. We suppose that data is communicated in the format of packets with negligible quantization error. In such a networked control system, whenever a packet loss occurs, there exists a tradeoff between transmitting new sensory information with a lower success probability and retransmitting previously failed sensory information with a higher success probability. In essence, an inherent tradeoff between freshness and reliability. To address this tradeoff, we consider a linear-quadratic-regulator performance index, which penalizes state deviations and control efforts over a finite horizon, and jointly design optimal encoding and decoding policies for the encoder and the decoder, which are collocated with the sensor and the actuator, respectively. Our emphasis here lies specifically on designing switching and control policies, rather than error-correcting codes. We show that the optimal encoding policy is a threshold switching policy and the optimal decoding policy is a certainty-equivalent control policy. In addition, we determine the equations that the encoder and the decoder need to solve in order to implement the optimal policies. More specifically, we show that the encoder must solve the Kalman filtering equations, a mismatch linear equation, and a Bellman optimality equation, while the decoder must solve a linear filtering equation and an algebraic Riccati equation.

Compressed sensing is well known for its superior compression performance, in existing schemes, in lossy compression. Conventional research aims to reach a larger compression ratio at the encoder, with acceptable quality reconstructed images at the decoder. This implies looking for compression performance with error-free transmission between the encoder and the decoder. Besides looking at compression performance, we applied block compressed sensing to digital images for robust transmission. For transmission over lossy channels, error propagation or data loss can be expected, and protection mechanisms for compressed sensing signals are required for guaranteed quality of the reconstructed images. We propose transmitting compressed sensing signals over multiple independent channels for robust transmission. By introducing correlations with multiple-description coding, which is an effective means for error resilient coding, errors induced in the lossy channels can effectively be alleviated. Simulation results presented the applicability and superiority of performance, depicting the effectiveness of protection of compressed sensing signals.

We consider the problem of real-time surveillance where remote sensors transmit data periodically to a control center through multihop transmissions. All data need to be delivered within a common deadline, which is the time that the control center makes control decisions. We propose a model that jointly considers the end-to-end delay constraints and delivery ratio requirements of flows, the need for multihop transmissions, and the unreliable nature of wireless transmissions. We develop a framework for designing feasibility-optimal policies. We then demonstrate the utility of this framework by considering two types of systems: one where sensors can transmit and receive packets simultaneously, possibly on different channels, and the other where sensors cannot. For the first type of systems, we propose an online distributed scheduling policy and prove that the policy is feasibility optimal. We also provide a heuristic for the second type of systems. We show that this heuristic is still feasibility optimal for some topologies. Simulation results show that both policies outperform state-of-the-art policies by large margins.

In this paper, we propose an adaptive multi-hop communication protocol based on transmission power control and error correction coding to achieve energy efficiency in wireless ad-hoc network. This study is important in extending battery lifetime and consequently the lifetime of nodes and the network. We consider multi-hop communication between pair of nodes where packet needs to be re-transmitted when transmission error or packet loss occurs. Based on the quality of the communication channel (bit error rate and bandwidth) and characteristics of the packet to be transmitted, we adaptively choose the most energy efficient transmission power level and error correction coding. Simulation shows that the proposed algorithm can save significant amount of energy, reduce packet loss rate and potentially the end-to-end packet delay.

Considering the insatiable demand for high-performance computing, on-chip cache capacity increases rapidly. Spin-transfer-torque magnetoresistive random-access memory (STT-MRAM) is a promising cache candidate due to ultralow standby power, high-access speed, and integration density. Unfortunately, when the feature size of magnetic tunnel junction (MTJ) scales down to 1 Xnm, read current approaches write current closely, which may result in read disturbance threatening the reliability of STT-MRAM. Furthermore, the elevating on-chip temperature reduces the thermal stability of STT-MRAM remarkably and aggravates the read disturbance. Error correction code (ECC) is an effective technique to enhance memory reliability. In this paper, we take advantage of the thermal dependence of STT-MRAM and propose a thermally adaptive ECC design, called “Chameleon,” that can adjust the ECC protection strength dynamically to reduce the ECC storage overhead and improve the cache access performance and energy efficiency. Experimental results show that compared to the conservative nonadaptive ECC scheme, our design can improve both cache performance and energy consumption effectively.

Error-bounded lossy compression has been widely adopted in many scientific domains because it can address the challenges in storing, transferring, and analyzing unprecedented amounts of scientific data. However, general error-bounded lossy compressors may fail to meet additional quality requirements for downstream analysis, a.k.a. Quantities of Interest (QoIs). This may lead to uncertainties and even misinterpretations in scientific discoveries, significantly limiting the use of lossy compression in practice. In this paper, we propose QPET, a novel, versatile, and portable framework for QoI-preserving error-bounded lossy compression, which overcomes the challenges of modeling diverse QoIs by leveraging numerical strategies. QPET features (1) high portability to multiple existing lossy compressors, (2) versatile preservation to most differentiable univariate and multivariate QoIs, and (3) significant compression improvements in QoI-preservation tasks. Experiments with six real-world datasets demonstrate that integrating QPET into state-of-the-art error-bounded lossy compressors can gain 2x to 10x compression speedups of existing QoI-preserving error-bounded lossy compression solutions, up to 1000% compression ratio improvements to general-purpose compressors, and up to 133% compression ratio improvements to existing QoI-integrated scientific compressors.

Lossless compression achieves only modest data reduction in noisy medical image sequences such as digital coronary angiograms. Lossy compression achieves greater reductions, but diagnostic performance criteria for use of lossy compression in dynamically displayed image sequences have yet to be established. We performed a quantitative perception study to measure low contrast feature detectability within moving simulated arterial segments inserted frame by frame into actual human cardiac cine radiographs to produce dynamic displays with realistic quantum and structure noise. Thirty-frame angiograms were displayed in loop format at 15 fps. Three subjects made 200 observations of the original images and at each of three levels of JPEG compression in a four-alternative forced-choice experiment. Compressions of 6:1, 12:1, and 18:1 resulted in average relative detectability of 102%, 95%, and 64%, respectively, compared to the original images. Only the detectability loss at 18:1 reached statistical significance at the p<0.05 confidence level in this study. We conclude that observer performance techniques are able to quantify specific changes in actual diagnostic information. Our preliminary data suggest that detection of low contrast objects in coronary angiograms is not degraded for JPEG compression ratios of 12:1 or less.

To achieve reliable packet transmission over a wireless link without feedback, we propose a layered coding approach that uses error-correction coding within each packet and erasure-correction coding across the packets. This layered approach is also applicable to an end-to-end data transport over a network where a wireless link is the performance bottleneck. We investigate how to optimally combine the strengths of error- and erasure-correction coding to optimize the system performance with a given resource constraint, or to maximize the resource utilization efficiency subject to a prescribed performance. Our results determine the optimum tradeoff in splitting redundancy between error-correction coding and erasure-correction codes, which depends on the fading statistics and the average signal to noise ratio (SNR) of the wireless channel. For severe fading channels, such as Rayleigh fading channels, the tradeoff leans towards more redundancy on erasure-correction coding across packets, and less so on error-correction coding within each packet. For channels with better fading conditions, more redundancy can be spent on error-correction coding. The analysis has been extended to a limiting case with a large number of packets, and a scenario where only discrete rates are available via a finite number of transmission modes.

In this paper, we propose a detailed energy survey of the physical, data link, and network layer by analytical techniques. We also show the impact of regular sleep periods on node energy consumption and present a comparison analysis of single-hop vs. multi-hop communications in the energy realm. A detailed energy expenditure analysis of not only the physical layer but also the link and network layer provides a basis for developing new energy efficient wireless sensor networks. Regular, coordinated sleeping extends the lifetime of sensor nodes, but systems can only benefit from sleeping in terms of transmitted packets if the data arrival rate to the system is low. Energy efficiency is the driving motivation for it can be considered the most important factor for wireless sensor networks because of the power constraints set by battery operation. Radio solutions in the lower ISM bands are attractive because of their relatively easy implementation and low power consumption. However, the data rates of these commercial radios are also relatively low, limiting transmittable frame sizes to a few tens of octets along with strict duty cycle requirements. From the analysis we extract key parameters of selected MAC protocols and show that some traditional mechanisms, such as binary exponential backoff, have some inherent problems. We also argue that single-hop communications has up todo% lower energy consumption than multi-hop forwarding within the feasible transmission distances of an ISM radio. Index Terms-Energy efficiency, Wireless sensor net- works, Medium access control (MAC) protocols, multi-hop communications a duty cycIe (Z), (3), which is normally 10% for the 434MHz band and 1% for the 868MHz band. The duty cycle is defined as the ratio, expressed as a percentage, of the maximum transmitter on-time, relative to a one hour period. When a sensor network is expected to work continuously, this duty cycle has to be taken into account and it can affect the energy efficiency of a network. In this paper we perform cross-layer analysis by pre- senting topology, medium access control (MAC) and ra- dio transceiver energy consumption models that work in unison. The hear topology model represents a common network after network layer route discovery has been accomplished. We use an energy consumption model for the transmission and reception of MAC frames originally presented in (4), develop a coordinated sleep group energy consumption model, and analytically investigate the effect of sleep on sensor networks using three MAC protocols. From the analysis we show that although in an ideal scenario multi-hop communications perform better than single-hop communications, realistic energy models and especially the MAC protocol design have a significant impact. We propose a multi-group sleep model for our nanoMAC protocol, and show that reg- ular sleep periods have a significant, energy reducing impact on node energy consumption with low traf!fic rates. The radio transceiver energy model takes into account several important radio parameters and we use the RFM TRlOOO and RFM radio designers guide (5) for realistic transceiver parameters. The main metric used is absolute energy, consumption per useful transmitted bit. This means only the MAC or thc network protocol data unit (PDU) will be considered and all the other communicated bits, headers, control frames, preambles, etc. are considered as overhead. The rest of the paper is organized as follows. Some related work is discussed in Section IT and in Section 111 we look at the radio propagation energy model. Section TV presents the network topology and energy

Energy efficiency is critical in wireless sensor networks (WSN) for system reliability and deployment cost. The power consumption of the communication in multi-hop WSN is primarily decided by three factors: routing distance, signal interference, and computation cost of routing. Several routing algorithms designed for energy efficiency or interference avoidance had been proposed. However, they are either too complex to be useful in practices or specialized for certain WSN architectures. In this paper, we propose two energy efficient geographic routing algorithms (EEGRA) for wireless sensor networks, which are based on existing geographic routing algorithms and take all three factors into account. The first algorithm combines the interference into the routing cost function, and uses it in the routing decision. The second algorithm transforms the problem into a constrained optimization problem, and solves it by searching the optimal discretized interference level. We integrate four geographic routing algorithms: GOAFR+, Face Routing, GPSR, and RandHT, to both EEGRA algorithms and compare them with three other routing methods in terms of power consumption and computation cost for the grid and irregular sensor topologies. The results of our experiments show both algorithms conserve sensor's routing energy 30% ~ 50% comparing to general geographic routing algorithms. In addition, the time complexity of EEGRA algorithms is similar to the geographic greedy routing methods, which is much faster than the optimal SINR-based algorithm.

This paper demonstrates how channel coding can improve the robustness of spatial image watermarks against signal distortion caused by lossy data compression such as the JPEG scheme by taking advantage of the properties of Gray code. Two error-correction coding (ECC) schemes are used here: One scheme, referred to as the vertical ECC (VECC), is to encode information bits in a pixel by error-correction coding where the Gray code is used to improve the performance. The other scheme, referred to as the horizontal ECC (HECC), is to encode information bits in an image plane. In watermarking, HECC generates a codeword representing watermark bits, and each bit of the codeword is encoded by VECC. Simple single-error-correcting block codes are used in VECC and HECC. Several experiments of these schemes were conducted on test images. The result demonstrates that the error-correcting performance of HECC just depends on that of VECC, and accordingly, HECC enhances the capability of VECC. Consequently, HECC with appropriate codes can achieve stronger robustness to JPEG—caused distortions than non-channel-coding watermarking schemes.

In this work, joint optimization of Power Allocation (PA) and Relay Location (RL) is investigated for Amplify-and-Forward (AF) cooperative relaying in order to minimize the Average Symbol Error Rate (ASER). First, a closed-form expression of the ASER is derived for different modulations in Rayleigh fading channel at a high signal-to-noise ratio regime based on moment generating function. Second, we optimize the RL for the AF cooperative relaying with any given power assignment at the source and the relay. Finally, we develop a closed-form explicit formula for joint RL and PA optimization which is a standard fix-point problem. Evaluation of the effect of path-loss exponent demonstrates that in a lossy channel, relay location closer to the source yields better performance. Finally, extensive numerical results and simulation comparisons confirm that the minimum ASER can be achieved via the joint PA-RL optimization.

Wireless networks typically employ some form of forward error correction (FEC) coding and some automatic repeat request (ARQ) protocol to ensure reliable data transmission over lossy channels. We propose to integrate FEC and ARQ in the context of random linear network coding (RLNC). In particular, we develop Caterpillar RLNC with feedback (CRLNC-FB), an RLNC approach with a finite sliding packet transmission window in conjunction with feedback-based selective repeat ARQ. CRLNC-FB employs a novel RLNC decoding method based on a band-form of Gaussian elimination. In response to lost packets, CRLNC-FB retransmits lost packets in systematic (uncoded) form to aid fast in-order packet delivery at the receiver. Extensive performance evaluations indicate that CRLNC-FB gives higher throughput-delay performance than the preceding RLNC approaches with feedback. In particular, CRLNC-FB with its sliding window achieves lower delays than block-based RLNC. Also, the retransmission of uncoded source packets in CRLNC-FB contributes to a significantly higher throughput-delay performance than loss recovery through coded packets interspersed among future source packets at a prescribed code rate.