Community-aware data replication in sparse vehicular networks

Low density of vehicles in rural areas and during the periods when the vehicle traffic is low in cities (such as at night) makes the classical ad-hoc network algorithms impractical for Vehicular Ad Hoc Networks. End-to-end connectivity requirement of these algorithms causes this deflection. Connections get broken when a suitable network element isn't available for data transfer. This problem which occurs in the low dense Vehicular Networks can be solved by using delay tolerant approaches in routing algorithms. By using the store-carry-forward method, data is carried by network elements until a suitable network element has been found. By this way, network connectivity is provided in a delayed manner between the vehicles which are momentarily not connected. In this paper, the impact of delayed transfer of data to an end-to-end transfer and the impact of data lifetime to the network performance are examined. Considering the speed of the vehicles and the desired throughput, lifetime of data is determined according to the network connectivity. Results contributes to the fulfilment of the Quality of Service (QoS) requirements in Vehicular Network applications.

Vehicle-to-vehicle networks have gained significant interest in recent times from researchers all around the world. V2V/V2I are emerging as an efficient solution for achieving road safety and securely transmitting data from one vehicle to other. However, in such opportunistic environments such as a sparse vehicular network where disruption, dynamic network topology, fast vehicle movement, and environmental conditions are the major concerns, data forwarding is extremely challenging. Traditional ad-hoc routing protocols like Ad hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) fail to deliver in such laborious conditions. Routing protocols of Delay/Disruption Tolerant Networks(DTN) exploit the Store-Carry-Forward Mechanism (SCF) strategy in these cases. Taking advantage of temporary connections to disseminate information is the focus of DTN. DTN helps to increase information propagation coverage in sparse areas where there are very few devices. In this paper, we first compare the performance of AllJoyn framework and Direct Delivery protocol in a single-hop scenario and then draw the attention towards a multi-hop scenario by comparing other three routing protocols that use DTN as the underlying paradigm. These protocols are Flooding, Epidemic, and PRoPHET. The performances are evaluated by transmitting the files of size 1, 4, and 10 MB from a static sender to a mobile receiver in the multi-hop scenario. We also develop an Android application that contains the implementation of these routing protocols along with file sharing functionality. From the outcomes of this experiment, it can be said that Epidemic routing protocol performs the best in our considered multi-hop scenario in terms of transfer delay, coverage, and throughput whereas, Direct Delivery routing protocol performs better than AllJoyn in single-hop communication.

In the study of packet-level random linear network coding (RLNC) in wireless broadcast, RLNC over GF(2<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">^L</i>) is known to asymptotically achieve the optimal completion delay with increasing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i>. Effective utilization of guessing random additive noise decoding (GRAND) at the physical layer can help leverage RLNC packets to generate syndromes so as to reduce packet erasure probabilities and thus further improve the completion delay performance. Prior to this work, only a few studies investigated GRAND-assisted RLNC and they restricted to GF(2)-coding. In this paper, we first provide a general framework to formulate the decoding process of GRAND-assisted RLNC over GF(2<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">^L</i>) for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> ≥ 1. Even for GRAND-assisted GF(2)-RLNC, the formulation is more complete than previous considerations in the sense that it takes the a priori information of which packets have errors into consideration. Moreover, we propose a novel GRAND-assisted GF(2<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">^L</i>)-RLNC scheme whose computational overhead introduced by GRAND is negligible. In particular, a subset of GF(2<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">^L</i>) is carefully designed for random coding coefficient selection. For the novel scheme, we theoretically derive lower bounds on the distribution as well as an upper bound on the expected value of the completion delay. Numerical results demonstrate not only a reduction in average completion delay for the novel scheme, but also the advantage of random coding coefficient selection from the specially designed subset for GRAND-assisted RLNC schemes.

Random linear network coding with ladder-shaped global coding matrix for robust video transmission

Location tracking of mobile entity becomes a research challenge for applications in vehicular networks, mobile sensor networks and mobile adhoc networks. The GPS based tracking in vehicular networks has limitations in certain scenarios such as inside tunnels, tall buildings, dense forests, etc. Tracking of fast-paced mobile entity (say mobile node) finds applications in vehicular networks such as chasing, hit-and-run accidents, etc. Connected dominating set(CDS) provides a virtual backbone in adhoc network. This paper uses connected dominating set(CDS) based virtual backbone in vehicular adhoc networks for tracking of fast-paced mobile node's location. We show that using a simple heuristics that the location updates for mobile node can be reduced to the tune of 40%.

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.

Random linear network coding (RLNC) has the potential to improve the performance of current and future Internet of Things (IoT) communication systems, but is computationally demanding due to matrix multiplications and inversions. Some single-core RLNC implementations achieve already sufficient coding speeds for contemporary multimedia streaming formats. However, advances in multimedia streaming formats and IoT applications will require the exploitation of heterogeneous multicore architectures, which are becoming common for a wide range of IoT nodes, including smartphones. In this paper, we introduce and evaluate efficient RLNC computing strategies for IoT node architectures, including the emerging heterogeneous big.LITTLE multicore architectures with multiple big (fast) cores and multiple LITTLE (slow) cores. In contrast to existing RLNC implementation strategies, we build on and adapt highly optimized dense matrix operations from the high performance computing field to RLNC on heterogeneous multicore IoT nodes. Our approach includes the optimization of RLNC matrix operations through optimized operations on matrix blocks with single instruction multiple data instructions. We schedule block operations on the heterogeneous cores through a directed acyclic graph that avoids artificial synchronization points while ensuring the data dependencies. We examine priority scheduling according to the number of outgoing dependencies of a task and data locality of cached blocks. Our extensive measurements with several heterogeneous big.LITTLE multicore IoT node and smartphone processor boards demonstrate higher RLNC encoding and decoding throughputs than existing approaches. Moreover, our measurements indicate that the utilization of more cores decreases energy consumption, which is an important goal for IoT nodes.

The network coding capacity of a single multicast traffic is characterized by the min-cut/max-flow (mcMF) theorem, which can be achieved by random linear network coding (RLNC). Nonetheless, the graph-theoretic characterization for multiple unicast/multicast traffic remains an open problem. This paper proposes and studies a new class of intersession-network-coding schemes: RLNC with selective cancelling (SC), which inherits the complexity advantage of RLNC once the set of selective cancelling edges is decided. A graph-theoretic characterization is provided for the achievable rates of RLNC with SC for the general multiple multicast setting. The findings contain most existing achievability results as special cases, including the mcMF theorem of the single multicast traffic and the existing characterization of pairwise intersession network coding. One prominent feature of the proposed approach is its focus on the achievability analysis with arbitrary network topology, arbitrary inter-session packet-mixing capability, and arbitrary traffic demands, which distinguishes the results from the special case analysis, capacity outer bound constructions, and the pattern-based (butterfly-based) superposition arguments.

Codes in the projective space and codes in the Grassmannian over a finite field-referred to as subspace codes and constant-dimension codes (CDCs), respectively-have been proposed for error control in random linear network coding. For subspace codes and CDCs, a subspace metric was introduced to correct both errors and erasures, and an injection metric was proposed to correct adversarial errors. In this paper, we investigate the packing and covering properties of subspace codes with both metrics. We first determine some fundamental geometric properties of the projective space with both metrics. Using these properties, we then derive bounds on the cardinalities of packing and covering subspace codes, and determine the asymptotic rates of optimal packing and optimal covering subspace codes with both metrics. Our results not only provide guiding principles for the code design for error control in random linear network coding, but also illustrate the difference between the two metrics from a geometric perspective. In particular, our results show that optimal packing CDCs are optimal packing subspace codes up to a scalar for both metrics if and only if their dimension is half of their length (up to rounding). In this case, CDCs suffer from only limited rate loss as opposed to subspace codes with the same minimum distance. We also show that optimal covering CDCs can be used to construct asymptotically optimal covering subspace codes with the injection metric only.

Basic Safety Messaging plays a crucial role to provide road safety in vehicular ad-hoc networks (VANETs). To avoid potential accidents, vehicles periodically broadcast safety information to neighboring vehicles. However, due to transmission collisions, fading channels and other factors, vehicular networks usually suffer a low packet delivery ratio (PDR) and a large delay, which are intolerant of many safety applications. To tackle these issues, this paper proposes a hybrid medium access control (MAC) protocol for basic safety message (BSM) dissemination based on the framework of Dedicated Short-Range Communication (DSRC). Its partially centralized and partially distributed characteristic not only can effectively suppress the collisions, but keep compatibility with IEEE 802.11p. In addition, the integration of Physical-Layer Network Coding (PNC) and Random Linear Network Coding (RLNC) further strengthens the reliability and efficiency for BSM dissemination. Both the theoretical analysis and comprehensive simulations indicate that, compared with existing schemes, the proposed protocol can significantly improve the PDR by a range of 20% to 300%. Meanwhile, in terms of normalized throughput, it increases by varying percent between 20% and 160% in different scenarios.

A fundamental understanding of the relationship between delay performance and complexity in network coding is instrumental towards its application in practical systems. The main argument against delay-optimal random linear network coding (RLNC) is its decoding complexity, which is O(n 3 ) for n original packets. Fountain codes, such as LT and Raptor codes, reduce the decoding load on the receiver but at the cost of introducing additional, non-negligible delay. The source of this increased delay is the inherent sparsity of the code, which significantly reduces the impact of a new coded packet, i.e., the probability of a packet to be linearly independent from the knowledge at the receiver, as we approach the end of the transmission (when few degrees of freedom are missing). Thus, the additional overhead is mainly due to the transmission of the last data packets. Our key observation is that switching gears to denser codes as the transmission process progresses could considerably reduce the delay overhead while maintaining the complexity advantages of a sparse code. We propose tunable sparse network coding as a dynamic coding mechanism with a code structure with two regions: a sparse region, with various levels of sparsity, and a dense region, where packets are generated as per RLNC. We characterize the problem in multicast sessions on general networks and illustrate trade-offs in especial cases of interest. We also present a novel mechanism to perform efficient Gaussian elimination for sparse matrices that guarantees linear decoding complexity with high probability.

On-demand data retrieval is a crucial routine operation in a vehicular sensor network. However, on-demand data retrieval in a vehicular environment is particularly challenging because of frequent network disruption, large number of data readings and limited transmission opportunities. Real world vehicular datasets usually contain a lot of data redundancy. Motivated by this important observation, we propose an approach called CDR with compressive sensing for on-demand data retrieval in the highly dynamic vehicular environment. The distinctive feature of CDR is that it supports tunable accuracy of data collection. There are two major challenges for the design of CDR. First, the sparsity level of the vehicular dataset is typically unknown beforehand. Second, it is even worse that the sparsity level of the dataset is changing over time. To combat the challenge posed by time-varying data sparsity, CDR can terminate from further collection of measurements, based on an adaptive condition on which only localized measurements and computation are needed. Extensive simulations with real datasets and real vehicular GPS traces show that our approach achieves good performance of data retrieval with user-customized accuracy.

Quadratic permutation polynomial (QPP) has good coding characteristics and parallel processing ability. Random linear network coding (RLNC) is an improved coding algorithm for wireless channel video streaming, which can improve the network throughput and network lifetime of mobile ad hoc network (MANET). In this paper, we study the characteristics of quadratic permutation polynomials (QPP) and RLNC by increasing the amount of available data to the users through the encode nodes. The paper proposes a quadratic permutation polynomials enhancement of a RLNC approach $\mathrm{in}$ MANETs (QPP-RLNC). QPP-RLNC algorithm can better control the complexity of encoding/decoding and restore the original data. The performance of QPP-RLNC algorithm, such as coding overhead, decoding delay, packet loss probability and network throughput, was experimentally studied by using network simulation (NS2) software. Simulation results show that the proposed QPP-RLNC algorithm can improve coding efficiency and network performance.

In a network application, channel coding alone is not sufficient to reliably transmit a message of finite length K from a source to one or more destinations as in, e.g., file transfer. To ensure that no data is lost, it must be combined with rateless erasure correcting schemes on a higher layer, such as a time-division multiple access (TDMA) system paired with automatic repeat request (ARQ) or random linear network coding (RLNC). We consider binary channel coding on a binary symmetric channel (BSC) and q-ary RLNC for erasure correction in a star network, where Y sources send messages to each other with the help of a central relay. In this scenario RLNC has been shown to have a throughput advantage over TDMA schemes as K→∞ and q→∞. In this paper we focus on finite block lengths and compare the expected throughputs of RLNC and TDMA. For a total message length of K bits, which can be subdivided into blocks of smaller size prior to channel coding, we obtain the channel code rate and the number of blocks that maximize the expected throughput of both RLNC and TDMA, and we find that TDMA is more throughput-efficient for small message lengths K and small q.

Network error localizations uses end-to-end observations to localize random or adversarial glitches. For error localization under random linear network codes (RLNCs) the schemes proposed in previous literatures require the priori knowledge of the network topology. Moreover, adversarial error localization is proved computationally intractable under RLNCs. The goal of current paper is designing new network coding schemes to improve the error localization performance of RLNCs while maintaining the key advantages of RLNCs. To be concrete, we introduce Network Reed-Solomon codes (NRSCs), which have the following features: 1. NRSCs are low-complexity distributed linear network codes. 2. NRSCs achieve the network multicast capacity with high probability. 3. In adversarially faulty networks NRSCs enable the receiver to locate a maximum number of adversarial errors in a computationally efficient manner. Moreover, the error localization schemes under NRSCs do not need the priori knowledge of the network topology and thus are robust against dynamic network updating.