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Decentralized cryptocurrency systems, known as blockchains, have shown promise as infrastructure for mutually distrustful parties to agree on transactions safely. However, Bitcoin-derived blockchains and their variants suffer from the limitations of the CAP Trilemma, which is difficult to be solved through optimizing consensus protocols. Moreover, the P2P network of blockchains have problems in efficiency and reliability without considering the matching of physical and logical topologies. For the CAP Trilemma in consortium blockchains, we propose a physical topology based on the multi-dimensional hypercube with excellent partition tolerance in probability. On the other hand, the general hypercube has advantages in solving the mismatch problems in P2P networks. The general topology is further extended to a hierarchical recursive topology with more medium links or short links to balance the reliability requirements and the costs of physical network. We prove that the hypercube topology has better partition tolerance than the regular rooted tree topology and the ring lattice topology, and effectively fits the upper-layer protocols. As a result, blockchains constructed by the proposed topology can reach the CAP guarantee bound through adopting the proper transmission and the consensus protocols protocols that have strong consistency and availability., Comment: 13 pages, 11 figures

The 5G vehicle-to-everything (V2X) communication for autonomous and semi-autonomous driving utilizes the wireless technology for communication and the Millimeter Wave bands are widely implemented in this kind of vehicular network application. The main purpose of this paper is to broadcast the messages from the mmWave Base Station to vehicles at LOS (Line-of-sight) and NLOS (Non-LOS). Relay using Machine Learning (RML) algorithm is formulated to train the mmBS for identifying the blockages within its coverage area and broadcast the messages to the vehicles at NLOS using a LOS nodes as a relay. The transmission of information is faster with higher throughput and it covers a wider bandwidth which is reused, therefore when performing machine learning within the coverage area of mmBS most of the vehicles in NLOS can be benefited. A unique method of relay mechanism combined with machine learning is proposed to communicate with mobile nodes at NLOS., Comment: 14 pages, 9 figures, conference 7th International conference on Computer Networks and Communications (CCNET 2020), AIRCC Publishing Corporation

Investigating and classifying sentiments of social media users towards an item, situation, and system are very popular among the researchers. However, they rarely discuss the underlying socioeconomic factor associations for such sentiments. This study attempts to explore the factors associated with positive and negative sentiments of the people about reopening the economy, in the United States (US) amidst the COVID-19 global crisis. It takes into consideration the situational uncertainties (i.e., changes in work and travel pattern due to lockdown policies), economic downturn and associated trauma, and emotional factors such as depression. To understand the sentiment of the people about the reopening economy, Twitter data was collected, representing the 51 states including Washington DC of the US. State-wide socioeconomic characteristics of the people, built environment data, and the number of COVID-19 related cases were collected and integrated with Twitter data to perform the analysis. A binary logit model was used to identify the factors that influence people toward a positive or negative sentiment. The results from the logit model demonstrate that family households, people with low education levels, people in the labor force, low-income people, and people with higher house rent are more interested in reopening the economy. In contrast, households with a high number of members and high income are less interested to reopen the economy. The model can correctly classify 56.18% of the sentiments. The Pearson chi2 test indicates that overall this model has high goodness-of-fit. This study provides a clear indication to the policymakers where to allocate resources and what policy options they can undertake to improve the socioeconomic situations of the people and mitigate the impacts of pandemics in the current situation and as well as in the future.

Trust models that rely on recommendation trusts are vulnerable to badmouthing and ballot-stuffing attacks. To cope with these attacks, existing trust models use different trust aggregation techniques to process the recommendation trusts and combine them with the direct trust values to form a combined trust value. However, these trust models are biased as recommendation trusts that deviate too much from one's own opinion are discarded. In this paper, we propose a non-biased trust model that considers every recommendation trusts available regardless they are good or bad. Our trust model is based on a combination of 2 techniques: the dissimilarity test and the Dempster-Shafer Theory. The dissimilarity test determines the amount of conflict between 2 trust records, whereas the Dempster-Shafer Theory assigns belief functions based on the results of the dissimilarity test. Numerical results show that our trust model is robust against reputation-based attacks when compared to trust aggregation techniques such as the linear opinion pooling, subjective logic model, entropy-based probability model, and regression analysis. In addition, our model has been extensively tested using network simulator NS-3 in an Infrastructure-based wireless mesh networks and a Hybrid-based wireless mesh networks to demonstrate that it can mitigate blackhole and grayhole attacks.

Wireless Mesh Networks (WMNs) have emerged as a key technology for the next generation of wireless networking due to its self-forming, self-organizing and self-healing properties. However, due to the multi-hop nature of communications in WMN, we cannot assume that all nodes in the network are cooperative. Nodes may drop all of the data packets they received to mount a Denial of Service (DoS) attack. In this paper, we proposed a lightweight trust detection mechanism called Trusted Entropy and Dempster Shafer (TEDS) to mitigate the effects of blackhole attacks. This novel idea combines entropy function and Dempster Shafer belief theory to derive a trust rating for a node. If the trust rating of a node is less than a threshold, it will be blacklisted and isolated from the network. In this way, the network can be assured of a secure end to end path free of malicious nodes for data forwarding. Our proposed idea has been extensively tested in simulation using network simulator NS-3 and simulation results show that we are able to improve the packet delivery ratio with slight increase in normalized routing overhead.

Public key infrastructure (PKI) is the most widely used security mechanism for securing communications over the network. However, there are known performance issues, making it unsuitable for use in vehicular networks. In this paper, we propose a secure and authenticated key management protocol (SA-KMP) to overcome the shortcomings of the PKI. The SA-KMP scheme distributes repository containing the bindings of the en-tity's identity and its corresponding public key to each vehicle and road side unit. By doing so, certificate exchanges and certificate revocation lists are eliminated. Furthermore, the SA-KMP scheme uses symmetric keys derived based on a 3-D-matrix-based key agreement scheme to reduce the high computational costs of using asymmetric cryptography. We demonstrate the efficiency of the SA-KMP through performance evaluations in terms of transmission and storage overhead, network latency, and key generation time. Analytical results show that the SA-KMP is more scalable and outperforms the certificate-based PKI. Simulation results indicate that the key generation time of the SA-KMP scheme is less than that of the existing Elliptic Curve Diffie--Hellman and Diffie--Hellman protocols. In addition, we use Proverif to prove that the SA-KMP scheme is secure against an active attacker under the Dolev and Yao model and further show that the SA-KMP scheme is secure against denial of service, collusion attacks, and a wide range of other malicious attacks.

Content distribution is an application in intelligent transportation system to assist vehicles in acquiring information such as digital maps and entertainment materials. In this paper, we consider content distribution from a single roadside infrastructure unit to a group of vehicles passing by it. To combat the short connection time and the lossy channel quality, the downloaded contents need to be further shared among vehicles after the initial broadcasting phase. To this end, we propose a joint infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communication scheme based on batched sparse (BATS) coding to minimize the traffic overhead and reduce the total transmission delay. In the I2V phase, the roadside unit (RSU) encodes the original large-size file into a number of batches in a rateless manner, each containing a fixed number of coded packets, and sequentially broadcasts them during the I2V connection time. In the V2V phase, vehicles perform the network coded cooperative sharing by re-encoding the received packets. We propose a utility-based distributed algorithm to efficiently schedule the V2V cooperative transmissions, hence reducing the transmission delay. A closed-form expression for the expected rank distribution of the proposed content distribution scheme is derived, which is used to design the optimal BATS code. The performance of the proposed content distribution scheme is evaluated by extensive simulations that consider multi-lane road and realistic vehicular traffic settings, and shown to significantly outperform the existing content distribution protocols., Comment: 12 pages and 9 figures

Heterogeneous cellular networks (HCNs) usually exhibit spatial separation amongst base stations (BSs) of different types (termed tiers in this paper). For instance, operators will usually not deploy a picocell in close proximity to a macrocell, thus inducing separation amongst the locations of pico and macrocells. This separation has recently been captured by modeling the small cell locations by a Poisson Hole Process (PHP) with the hole centers being the locations of the macrocells. Due to the presence of exclusion zones, the analysis of the resulting model is significantly more complex compared to the more popular Poisson Point Process (PPP) based models. In this paper, we derive a tight bound on the distribution of the distance of a typical user to the closest point of a PHP. Since the exact distribution of this distance is not known, it is often approximated in the literature. For this model, we then provide tight characterization of the downlink coverage probability for a typical user in a two-tier closed-access HCN under two cases: (i) typical user is served by the closest macrocell, and (ii) typical user is served by its closest small cell. The proposed approach can be extended to analyze other relevant cases of interest, e.g., coverage in a PHP-based open access HCN., Comment: Proc., Biennial Symposium on Communications (BSC), 2016

Interference field in wireless networks is often modeled by a homogeneous Poisson Point Process (PPP). While it is realistic in modeling the inherent node irregularity and provides meaningful first-order results, it falls short in modeling the effect of interference management techniques, which typically introduce some form of spatial interaction among active transmitters. In some applications, such as cognitive radio and device-to-device networks, this interaction may result in the formation of holes in an otherwise homogeneous interference field. The resulting interference field can be accurately modeled as a Poisson Hole Process (PHP). Despite the importance of PHP in many applications, the exact characterization of interference experienced by a typical node in a PHP is not known. In this paper, we derive several tight upper and lower bounds on the Laplace transform of this interference. Numerical comparisons reveal that the new bounds outperform all known bounds and approximations, and are remarkably tight in all operational regimes of interest. The key in deriving these tight and yet simple bounds is to capture the local neighborhood around the typical point accurately while simplifying the far field to attain tractability. Ideas for tightening these bounds further by incorporating the effect of overlaps in the holes are also discussed. These results immediately lead to an accurate characterization of the coverage probability of the typical node in a PHP under Rayleigh fading., Comment: 15 pages. Submitted to IEEE Transactions on Wireless Communications

This paper develops a comprehensive analytical framework with foundations in stochastic geometry to characterize the performance of cluster-centric content placement in a cache-enabled device-to-device (D2D) network. Different from device-centric content placement, cluster-centric placement focuses on placing content in each cluster such that the collective performance of all the devices in each cluster is optimized. Modeling the locations of the devices by a Poisson cluster process, we define and analyze the performance for three general cases: (i)$k$-Tx case: receiver of interest is chosen uniformly at random in a cluster and its content of interest is available at the $k^{th}$ closest device to the cluster center, (ii) $\ell$-Rx case: receiver of interest is the $\ell^{th}$ closest device to the cluster center and its content of interest is available at a device chosen uniformly at random from the same cluster, and (iii) baseline case: the receiver of interest is chosen uniformly at random in a cluster and its content of interest is available at a device chosen independently and uniformly at random from the same cluster. Easy-to-use expressions for the key performance metrics, such as coverage probability and area spectral efficiency (ASE) of the whole network, are derived for all three cases. Our analysis concretely demonstrates significant improvement in the network performance when the device on which content is cached or device requesting content from cache is biased to lie closer to the cluster center compared to baseline case. Based on this insight, we develop and analyze a new generative model for cluster-centric D2D networks that allows to study the effect of intra-cluster interfering devices that are more likely to lie closer to the cluster center., Comment: 16 pages, 10 figures. Submitted to IEEE Transactions on Communications

Device-to-device (D2D) communication enables direct communication between proximate devices thereby improving the overall spectrum utilization and offloading traffic from cellular networks. This paper develops a new spatial model for D2D networks in which the device locations are modeled as a Poisson cluster process. Using this model, we study the performance of a typical D2D receiver in terms of coverage probability under two realistic content availability setups: (i) content of interest for a typical device is available at a device chosen uniformly at random from the same cluster, which we term uniform content availability, and (ii) content of interest is available at the $k^{th}$ closest device from the typical device inside the same cluster, which we term $k$-closest content availability. Using these coverage probability results, we also characterize the area spectral efficiency (ASE) of the whole network for the two setups. A key intermediate step in this analysis is the derivation of the distributions of distances from a typical device to both the intra- and inter-cluster devices. Our analysis reveals that an optimum number of D2D transmitters must be simultaneously activated per cluster in order to maximize ASE. This can be interpreted as the classical tradeoff between more aggressive frequency reuse and higher interference power. The optimum number of simultaneously transmitting devices and the resulting ASE increase as the content is made available closer to the receivers. Our analysis also quantifies the best and worst case performance of clustered D2D networks both in terms of coverage and ASE., Comment: 34 double-spaced pages, 10 figures. Submitted to IEEE Transactions on Wireless Communications

In this paper, we consider the wireless broadcasting scenario with a source node sending some common information to a group of closely located users, where each link is subject to certain packet erasures. To ensure reliable information reception by all users, the conventional approach generally requires repeated transmission by the source until all the users are able to decode the information, which is inefficient in many practical scenarios. In this paper, by exploiting the close proximity among the users, we propose a novel two-phase wireless broadcasting protocol with user cooperations based on an efficient batched network code, known as batched sparse (BATS) code. In the first phase, the information packets are encoded into batches with BATS encoder and sequentially broadcasted by the source node until certain terminating criterion is met. In the second phase, the users cooperate with each other by exchanging the network-coded information via peer-to-peer (P2P) communications based on their respective received packets. A fully distributed and light-weight scheduling algorithm is proposed to improve the efficiency of the P2P communication in the second phase. The performance of the proposed two-phase protocol is analyzed and the channel rank distribution at the instance of decoding is derived, based on which the optimal BATS code is designed. Simulation results demonstrate that the proposed protocol significantly outperforms the existing schemes. Lastly, the performance of the proposed scheme is further verified via testbed experiments.