In this paper, we address the uplink radio resource allocation problem in a 5G massive Machine Type Communication (mMTC) scenario, characterized by a large number of battery constrained MTC devices generating small-sized bursty traffic. The current cellular network is unsuitable for this scenario, due to the limited uplink resources allocated to the Physical Random Access Channel (PRACH) and to the Physical Uplink Shared Channel (PUSCH). For this reason, we adopt in the PUSCH the Sparse Code Multiple Access (SCMA) technique, which is suitable for the multiplexing of a huge amount of small-sized data, and propose a dynamic load-aware PRACH and PUSCH resource allocation. In addition, in order to make our solution viable, we propose a predictive estimate of expected traffic based only on information available at the gNodeB. By simulations, we compare our dynamic control with static resource allocations. The results show that the proposed control significantly improves the number of succeeded communications, while guaranteeing lower energy consumption.
The dissemination of vehicle position data all over the network is a fundamental task in Vehicular Ad Hoc Network (VANET) operations, as applications often need to know the position of other vehicles over a large area. In such cases, intervehicular communications should be exploited to satisfy application requirements, although congestion control mechanisms are required to minimize the packet collision probability. In this work, we face the issue of achieving accurate vehicle position estimation and prediction in a VANET scenario. State of the art solutions to the problem try to broadcast the positioning information periodically, so that vehicles can ensure that the information their neighbors have about them is never older than the inter-transmission period. However, the rate of decay of the information is not deterministic in complex urban scenarios: the movements and maneuvers of vehicles can often be erratic and unpredictable, making old positioning information inaccurate or downright misleading. To address this problem, we propose to use the Quality of Information (QoI) as the decision factor for broadcasting. We implement a threshold-based strategy to distribute position information whenever the positioning error passes a reference value, thereby shifting the objective of the network to limiting the actual positioning error and guaranteeing quality across the VANET. The threshold-based strategy can reduce the network load by avoiding the transmission of redundant messages, as well as improving the overall positioning accuracy by more than 20% in realistic urban scenarios.
We consider a multipoint-to-point network in which sensors periodically send measurements to a gateway. The system uses Long Range (LoRa) communications in a frequency band with duty-cycle limits. Our aim is to enhance the reliability of the measurement transmissions. In this setting, retransmission protocols do not scale well with the number of sensors as the duty cycle limit prevents a gateway from acknowledging all receptions if there are many sensors. We thus intend to improve the reliability without acknowledgments by transmitting multiple copies of a measurement, so that the gateway is able to obtain this measurement as long as it receives at least one copy. Each frame includes the current and a few past measurements. We propose a strategy for redundancy allocation that takes into account the effects of fading and interference to determine the number of measurements to be included in a frame. Numerical results obtained using the simulation tool LoRaSim show that the allocation of redundancy provides up to six orders of magnitude decrease in the outage probability. Compared to a system that blindly allocates the maximum redundancy possible under duty-cycle and delay constraints of the gateway and memory constraints of the sensors, our technique provides up to 30% reduction in the average energy spent to successfully deliver a measurement to the gateway.
This paper proposes the optimal protection scheme for the reliable distributed storage in the Internet of Things (IoT) scenarios, where the system loses the random number of devices per failure event. We investigate the protection problem with the random number of failures, and model the protection strategies based on a path graph. Furthermore, we devise an algorithm to identify the optimal strategy (OP) that achieves the minimum bandwidth use with low complexity. The numerical results show that in a system with 10 nodes, our proposed optimal strategy reduces the protection bandwidth up to more than five times the original data size, and the number of connections as much as 74 %, with respect to a strategy generalized from the case of one by one failure.
Next generation use-cases of wireless networks require a great deal of flexibility in order to adopt to the constantly changing state of innovation and to accommodate future application requirements. Header compression has been an ever present solution since the advent of wireless networks and the most current version of it, Robust Header Compression (RoHC), has seen a widespread adoption in Long Term Evolution (LTE) cellular networks. Recent research has mostly focused on the integration and enhancement of RoHC, instead of advancing the core concept of the compression. In this paper we present for the first time a novel design that can tackle the compression of arbitrary packet streams regardless of the employed protocols and the transmitted data. We present our initial findings for an error-free scenario with various simulated and real-life packet streams and show that even with the absence of design time knowledge about the packet structures, one can compress a significant part of the streams, yielding compression gains for IP packets up to 90 %, as an example.
We consider a two-way relay channel (TWRC), in which two source nodes exchange their data with the help of a relay node using network coding (NC). The sources, to be exchanged are double-layer bit-streams, can be partitioned into a high-priority (HP) bit-stream and a low-priority (LP) bit-stream. The HP bit-stream should be generally more protected than the LP bit-stream because it is assumed that the HP bit-stream includes more important data. Such an assumption is applicable to video communications using a base and enhancement layers of scalable video coding (SVC). In this paper, we propose an NC mapping rule that can continuously improve bit error rate (BER) performance even when the relay node is located anywhere between source nodes. We define the target and the minimum requirements for high-quality video communications and confirm that our proposed NC mapping rule remarkably improves the BER performance, regardless of the mobility.
Blockchain systems are on the rise, especially after the introduction and popularization of Bitcoin. The potential of the technology has been expanded and rebooted by Ethereum to support a broader range of applications through smart contracts. Transactions and contracts are activated every day in the Ethereum network and blocks are created at a very high rate. Often blocks are mined at conflicting times, which causes a split on the chain or a fork. Forks pose an inconsistency problem on the network until they are solved and the network agrees on the same instance of the blockchain. Transactions included on side forks, once the fork is resolved, are discarded and have to be mined again. Fork rates also impact the overall performance of the blockchain and the security of the system. This work studies how one of the main networking factors, namely communication delay, impacts forking of the blockchain. An analytical model for forking probability is presented. Furthermore, the impact of block distribution delays on the amount of forks is quantified using a private Ethereum blockchain in controlled experiments in a lab.
Over the past few years, the Dynamic Adaptive Streaming over HTTP (DASH) standard has been widely adopted by video streaming services; this has led to a considerable amount of research on efficient adaptation algorithms to maximize users' Quality of Experience (QoE), but the comparison between algorithms is often flawed and unrealistic. Trace-based simulations or experiments in wired testbeds often fail to capture the full complexity and variability of a live network environment, and the comparison between them is often biased by the simplistic assumptions made about the simulation/testing setup. In this work, we implement four of the most representative adaptation algorithms and compare their performance in a real campus network. This allows us to have a fair comparison of the various algorithms' strengths and weaknesses in a representative scenario, improving the understanding of the dynamics of video streaming adaptation and highlighting the open problems in the field. The test results make a clear trade-off emerge, as algorithms designed for high fairness in a static scenario cannot deal with a dynamic channel and cross-traffic effectively and result in very low QoE levels, while more aggressive algorithms face rebuffering events in fast-varying scenarios.
Fulcrum combines two encoding stages, first using one high Galois field, such as GF(2(Exp 8)) and then with the binary Galois field GF(2). This allows intermediate and end devices, depending their computation power, to select either field to recode and decode coded packets. To ensure a high decoding probability, Fulcrum codes introduce r expansion packets at the outer coding stage, to increase the chance of producing linearly independent packets. However, the fixed number r hinder the chance of obtaining both high decoding probability and efficient transmission at a reduced computation complexity. We propose tunable expansion packet (TEP) protocols, which adjust the extra coefficients in the outer coding stage based on feedback from receivers. Comprehensive evaluations show that TEP protocols significantly increase goodput and reduce computation complexity while maintaining a low overhead.
Virtualisation and virtual network slicing represent the main paradigms to enable efficient and effective end-toend service provisioning in future fifth generation and beyond networks. While practical aspects and implementation have been extensively investigated, the development of theoretic models to enable the design and analysis of advanced slicing algorithms has only recently started. However, even if existing models are useful to analyse specific aspects of network slicing performance in specific topologies, they still outline limitations and drawbacks for providing an actual theoretical basis for network slicing. This article proposes a novel general model for network slicing based on multilayer graphs, linear algebra and algebraic graph theory. The proposed framework generalises specific legacy models by allowing a more comprehensive study of different perspectives of network slicing in future generation networks.
Cloud and distributed storage applications require processing of large fragments of data. This poses memory, delay, and processing speed challenges for systems using erasure codes to reduce the cost of storage and/or increase the reliability of the system. To address these, this paper proposes and deploys designs that exploit current multi-threading capabilities of microprocessors to accelerate the encoding and decoding process of erasure codes, focusing on the case of Random Linear Network Coding (RLNC). More specifically, we propose a strategy for parallel computation based on splitting symbols into significantly smaller fragments and reordering data to speed up computation and decreasing the potential for thread blocking. We implement the strategy in C++ and carry out benchmark experiments and compare them with the single threaded state-of-the-art block RLNC encoders. We show a reduction of processing time by a factor of three by using our strategies for files of 32 MB or more as well as reducing memory usage drastically in the system. We also show that our approach provides a better scaling than the single-threaded option when increasing the number of fragments that a file is broken into. In other words, distributed storage systems can split files or data into a larger number of fragments without experiencing a speed penalty.
LoRa has established itself as one of the leading technologies within evolving Low Power Wide Area Networks. LoRa is primarily pillared on its patented LoRa modulation scheme that features linearly increasing chirp signals that span the LoRa bandwidth. The patented implementation of LoRa signals relies on the accurate generation of stable frequency modulated chirps using a fractional-N phase-locked loop. On the other hand, very limited research have explored alternatives for the DSP realization of LoRa-based modulation. In this paper, we focus on mathematical representations that enable the DSP generation of LoRa signals. Furthermore, we emphasize on the ability to guarantee inter-symbol phase continuity with the perspective of supporting coherent detection of LoRa signals. In conjunction with such coherent realizations, we investigate utilization of the quadrature axis with respect to each nominal LoRa signal in constructing additional orthogonal dimensions for LoRa signaling. Consequently, each transmitted extended LoRa (E-LoRa) symbol may include one extra bit when compared to nominal LoRa symbols. We build on our previous analysis for the non-coherent detection of LoRa signals in order to derive approximation formulas for BER performance of nominal LoRa under coherent detection as well as of E-LoRa. Analytical and simulation results show that coherent detection of LoRa renders approximately 0.7 dB of performance gain. On the other hand, E-LoRa supports a capacity increase that could reach up to 14% with a minimal penalty on BER performance at the scale of only 0.3 dB.
The Age of information (AoI) was proposed in the literature to quantify the freshness of information. The majority of the work done in this area has theoretically evaluated AoI and its Peak (PAoI). In this paper, a method for obtaining the value of AoI and PAoI from experiments is proposed. We conducted an experiment emulating an M/M/1 queue and used the proposed method to evaluate AoI and PAoI. The values were compared to the expressions presented previously in the literature. Our results show that the proposed method is accurate for the M/M/1 queue. A statistical test was conducted to confirm the reliability of this conclusion.
The interplay between Software Defined Networks (SDN) and Network Function Virtualization (NFV) provides a new networking paradigm where Virtual Network Functions (VNFs) run on physical devices. However, despite the fact that SDN and NFV enable VNFs in the optimal physical device, the optimal physical node may change over time, and the VNFs need to be moved in a seamless and reliable way. Live service migration provides the necessary tools to move virtual resources between physical hosts. The two main virtualization technologies today are virtual machines (VMs) and containers. Containers are known to boot faster than VMs, and thus, lower service downtime in the application. Despite the previous works studying the performance of the live migration process, there are still questions on how the system would behave in a real scenario where conditions are not ideal. To answer those questions, we present in this paper a testbed that performs a container live migration process under tunable conditions, namely image size, network capacity, network load, container's CPU load and container's RAM load. We evaluate both migration time and downtime for different configurations of the aforementioned parameters. We use Docker containers and a Python application in the migration process. The results showthat overloading the network can degrade the migration performance remarkably. In contrast, the impact of stressing a container inside a host is marginal.
Mobile edge computing (MEC) is one of the promising solutions to enable augment reality (AR) by processing computational-intensive tasks within short latency constraint. However, the quality of user experience of MEC-enabled AR will significantly degrade in mobile scenarios, as it is difficult to obtain accurate channel state information (CSI) to make optimal offloading decision. In this paper, we propose an online offloading algorithm based on Lyapunov optimization to dynamically optimize the selections of the transmission rate and edge server without prior CSI. The algorithm makes a tradeoff between the reliability and latency in the MEC-enabled AR system. The numerical results show that the proposed algorithm outperforms the scheme with outdated CSI and is particularly applicable to the mobile scenarios for AR applications.
Internet-based networks face technical challenges establishing efficient communications infrastructure while keeping inter-application message exchange secure. Group communications based on transport or network layer security is a way to reduce the number of required transmissions for common message exchanges. In this work, we study secure multicast protocols for group communications in IP wireless networks for the Internet of Things (IoT) with focus on two protocols: Datagram Transport Layer Security (DTLS) and Internet Protocol Security (IPsec). We review the performance of Elliptic Curve Cryptography (ECC) in wireless sensor nodes with the TelosB mote to address the feasibility of the underlying cryptosystem. Then, we review the performance of unicast and multicast as transmission modes. Later, we introduce our implementation of a DTLS-based multicast security that shows 3x to 4x reductions in application payload transmissions for the largest group sizes. Finally, a comparison between the DTLS-based multicast security and IPsec for application payload is presented, where we argue for DTLS.
This paper introduces FileTribe, a secure decentralized application for sharing files in closed groups. It employs IPFS, a distributed file system, as its data storage layer, avoiding the pitfalls of centralized storage solutions. Changes to files and group membership are recorded separately on a blockchain, making them irrefutable. A Decentralized Application (Dapp) on top of Ethereum is responsible for user authentication and reaching consensus among group members regarding the state of the group and shared files. File access control is supervised by the members themselves. These features combine to make FileTribe unique in its ability to serve a wide range of safetycritical scenarios, including ones where cloud-based or pure peerto- peer solutions fall short.
Routing in MANETs is challenging due to the time-variance of the communication channel. The reliability of the end-to-end link in such channels is improved with Opportunistic Routing (OpR) protocols, which endeavor the broadcasting nature of wireless channels using several nondisjoint propagation paths for the same traffic flow. Network coding is used together with OpR to provide multi-path coding redundancy. This combination allows to achieve optimal throughput in stationary wireless channels for mesh technologies with contention-based channel access. This paper focuses on the end-to-end throughput stability in time-variant channels. It proposes enhancements to OpR that achieve stable throughput in case of edge failures due to nodes mobility. Traces from real channel measurements are used in the evaluations. The resultant throughput is much more stable in comparison to the state-of-the-art OpR protocols.
We show that a generalization of deduplication can enable compressed storage of sensor data. The method uses error-correcting codes in a non-traditional manner to identify similar elements, and then leverages this similarity for compression. Using Reed Solomon codes, our method has a theoretical potential to reduce the cost of storing chunks of 16 bytes to as much as 5 times less, and up to 65 times less for chunks of 255 bytes. We define a simple model for sensor data, and show how our approach is able to compress data from the model, realizing its compression potential with much smaller data sets than classic deduplication requires. This demonstrates that generalized deduplication can be a viable solution for practical lossless compression of small IoT data in scenarios where classic deduplication is ineffective.
This paper analyzes the performance of cooperative caching for cellular networks by modeling the locations of base stations as a homogeneous Poisson point process, where each base station is equipped with a storage to cache the content and has cooperation with its neighboring base stations. With the proposed probabilistic cooperative caching strategy, the analytical expressions of system performance are derived and simulation results are presented to show that our analytical results closely match the numerical performance observed from the simulations. The study shows that with cooperative caching, the performance in terms of caching content delivery outage, caching content delivery delay and network congestion can indeed be improved. The paper further proposes an approximated solution for optimal probabilistic cooperative caching.
